cssi/SciSciGPT-SciSciNet huggingface dataset:
On Consensus-Optimality Trade-offs in Collaborative Deep Learning

In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper, we build on recent algorithmic progresses in distributed deep learning to explore various consensus-optimality trade-offs over a fixed communication topology. First, we propose the incremental consensus-based distributed stochastic gradient descent (i-CDSGD) algorithm, which involves multiple consensus steps (where each agent communicates information with its neighbors) within each SGD iteration. Second, we propose the generalized consensus-based distributed SGD (g-CDSGD) algorithm that enables us to navigate the full spectrum from complete consensus (all agents agree) to complete disagreement (each agent converges to individual model parameters). We analytically establish convergence of the proposed algorithms for strongly convex and nonconvex objective functions; we also analyze the momentum variants of the algorithms for the strongly convex case. We support our algorithms via numerical experiments, and demonstrate significant improvements over existing methods for collaborative deep learning.
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization

We introduce four new real-world distribution shift datasets consisting of changes in image style, image blurriness, geographic location, camera operation, and more. With our new datasets, we take stock of previously proposed methods for improving out-of-distribution robustness and put them to the test. We find that using larger models and artificial data augmentations can improve robustness on real-world distribution shifts, contrary to claims in prior work. We find improvements in artificial robustness benchmarks can transfer to real-world distribution shifts, contrary to claims in prior work. Motivated by our observation that data augmentations can help with real-world distribution shifts, we also introduce a new data augmentation method which advances the state-of-the-art and outperforms models pretrained with 1000 times more labeled data. Overall we find that some methods consistently help with distribution shifts in texture and local image statistics, but these methods do not help with some other distribution shifts like geographic changes. Our results show that future research must study multiple distribution shifts simultaneously, as we demonstrate that no evaluated method consistently improves robustness.
A Systematic Benchmarking Analysis of Transfer Learning for Medical Image Analysis

Transfer learning from supervised ImageNet models has been frequently used in medical image analysis. Yet, no large-scale evaluation has been conducted to benchmark the efficacy of newly-developed pre-training techniques for medical image analysis, leaving several important questions unanswered. As the first step in this direction, we conduct a systematic study on the transferability of models pre-trained on iNat2021, the most recent large-scale fine-grained dataset, and 14 top self-supervised ImageNet models on 7 diverse medical tasks in comparison with the supervised ImageNet model. Furthermore, we present a practical approach to bridge the domain gap between natural and medical images by continually (pre-)training supervised ImageNet models on medical images. Our comprehensive evaluation yields new insights: (1) pre-trained models on fine-grained data yield distinctive local representations that are more suitable for medical segmentation tasks, (2) self-supervised ImageNet models learn holistic features more effectively than supervised ImageNet models, and (3) continual pre-training can bridge the domain gap between natural and medical images. We hope that this large-scale open evaluation of transfer learning can direct the future research of deep learning for medical imaging. As open science, all codes and pre-trained models are available on our GitHub page https://github.com/JLiangLab/BenchmarkTransferLearning.
“Choose your opponent”: A new knockout design for hybrid tournaments †

We present a new, simple knockout format for sports tournaments, that we call ``Choose Your Opponent'', where the teams that have performed best during a preliminary group stage can choose their opponents during the subsequent knockout stage. The main benefit of this format is that it essentially solves a recently identified incentive compatibility problem when more than one teams from a group advance to the knockout stage, by effectively canceling the risk of tanking. This new design also makes the group stage more exciting, by giving teams a strong incentive to perform at their best level, and more fair, by limiting the risk of collusion and making sure that the best group winners are fairly rewarded in the knockout round. The choosing procedure would add a new, exciting strategic component to the competition. Advancing teams would choose their opponent during new, much anticipated TV shows which would attract a lot of media attention. We illustrate how this new format would work for the round of 16 of the UEFA Champions League, the most popular soccer club competition in the world.
A deep learning semiparametric regression for adjusting complex confounding structures

Deep Treatment Learning (deepTL), a robust yet efficient deep learning-based semiparametric regression approach, is proposed to adjust the complex confounding structures in comparative effectiveness analysis of observational data, for example, electronic health record (EHR) data in which complex confounding structures are often embedded. Specifically, we develop a deep learning neural network with a score-based ensembling scheme for flexible function approximation. An improved semiparametric procedure is further developed to enhance the performance of the proposed method under finite sample settings. Comprehensive numerical studies have demonstrated the superior performance of the proposed methods, as compared with existing methods, with a remarkably reduced bias and mean squared error in parameter estimates. The proposed research is motivated by a postsurgery pain study, which is also used to illustrate the practical application of deepTL. Finally, an R package, “deepTL,” is developed to implement the proposed method.
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learning *

State-of-the-art human-in-the-loop robot grasping is hugely suffered by Electromyography (EMG) inference robustness issues. As a workaround, researchers have been looking into integrating EMG with other signals, often in an ad hoc manner. In this paper, we are presenting a method for end-to-end training of a policy for human-in-the-loop robot grasping on real reaching trajectories. For this purpose we use Reinforcement Learning (RL) and Imitation Learning (IL) in DEXTRON (DEXTerity enviRONment), a stochastic simulation environment with real human trajectories that are augmented and selected using a Monte Carlo (MC) simulation method. We also offer a success model which once trained on the expert policy data and the RL policy roll-out transitions, can provide transparency to how the deep policy works and when it is probably going to fail.
A Brief Introduction to Observational Entropy

In the past several years, observational entropy has been developed as both a (time-dependent) quantum generalization of Boltzmann entropy, and as a rather general framework to encompass classical and quantum equilibrium and non-equilibrium coarse-grained entropy. In this paper we review the construction, interpretation, most important properties, and some applications of this framework. The treatment is self-contained and relatively pedagogical, aimed at a broad class of researchers.
Sliding on Manifolds: Geometric Attitude Control with Quaternions

This work proposes a quaternion-based sliding variable that describes exponentially convergent error dynamics for any forward complete desired attitude trajectory. The proposed sliding variable directly operates on the non-Euclidean space formed by quaternions and explicitly handles the double covering property to enable global attitude tracking when used in feedback. In-depth analysis of the sliding variable is provided and compared to others in the literature. Several feedback controllers including nonlinear PD, robust, and adaptive sliding control are then derived. Simulation results of a rigid body with uncertain dynamics demonstrate the effectiveness and superiority of the approach.
A Survey on Common Threats in npm and PyPi Registries.

Software engineers regularly use JavaScript and Python for both front-end and back-end automation tasks. On top of JavaScript and Python, there are several frameworks to facilitate automation tasks further. Some of these frameworks are Node Manager Package (npm) and Python Package Index (PyPi), which are open source (OS) package libraries. The public registries npm and PyPi use to host packages allow any user with a verified email to publish code. The lack of a comprehensive scanning tool when publishing to the registry creates security concerns. Users can report malicious code on the registry; however, attackers can still cause damage until they remove their tool from the platform. Furthermore, several packages depend on each other, making them more vulnerable to a bad package in the dependency tree. The heavy code reuse creates security artifacts developers have to consider, such as the package reach. This project will illustrate a high-level overview of common risks associated with OS registries and the package dependency structure. There are several attack types, such as typosquatting and combosquatting, in the OS package registries. Outdated packages pose a security risk, and we will examine the extent of technical lag present in the npm environment. In this paper, our main contribution consists of a survey of common threats in OS registries. Afterward, we will offer countermeasures to mitigate the risks presented. These remedies will heavily focus on the applications of Machine Learning (ML) to detect suspicious activities. To the best of our knowledge, the ML-focused countermeasures are the first proposed possible solutions to the security problems listed. In addition, this project is the first survey of threats in npm and PyPi, although several studies focus on a subset of threats.
An index for two-dimensional SPT states

We define an index for 2D G-invariant invertible states of bosonic lattice systems in the thermodynamic limit for a finite symmetry group G with a unitary action. We show that this index is an invariant of the symmetry protected phase.
Spacetime duality between localization transitions and measurement-induced transitions

Space-time rotation of quantum circuits relates the phenomena of many-body localization to the phase transitions induced by measurements in monitored quantum systems.
Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance

This paper reviews and advocates against the use of permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures proposed for random forests, partial dependence plots, and individual conditional expectation plots remain popular because they are both model-agnostic and depend only on the pre-trained model output, making them computationally efficient and widely available in software. However, numerous studies have found that these tools can produce diagnostics that are highly misleading, particularly when there is strong dependence among features. The purpose of our work here is to (i) review this growing body of literature, (ii) provide further demonstrations of these drawbacks along with a detailed explanation as to why they occur, and (iii) advocate for alternative measures that involve additional modeling. In particular, we describe how breaking dependencies between features in hold-out data places undue emphasis on sparse regions of the feature space by forcing the original model to extrapolate to regions where there is little to no data. We explore these effects across various model setups and find support for previous claims in the literature that PaP metrics can vastly over-emphasize correlated features in both variable importance measures and partial dependence plots. As an alternative, we discuss and recommend more direct approaches that involve measuring the change in model performance after muting the effects of the features under investigation.
Euphemistic Phrase Detection by Masked Language Model

It is a well-known approach for fringe groups and organizations to use euphemisms—ordinary-sounding and innocent-looking words with a secret meaning—to conceal what they are discussing. For instance, drug dealers often use “pot” for marijuana and “avocado” for heroin. From a social media content moderation perspective, though recent advances in NLP have enabled the automatic detection of such single-word euphemisms, no existing work is capable of automatically detecting multi-word euphemisms, such as “blue dream” (marijuana) and “black tar” (heroin). Our paper tackles the problem of euphemistic phrase detection without human effort for the first time, as far as we are aware. We first perform phrase mining on a raw text corpus (e.g., social media posts) to extract quality phrases. Then, we utilize word embedding similarities to select a set of euphemistic phrase candidates. Finally, we rank those candidates by a masked language model—SpanBERT. Compared to strong baselines, we report 20-50% higher detection accuracies using our algorithm for detecting euphemistic phrases.
Pricing climate-related risks of energy investments

Abstract This paper presents a framework for pricing the climate resilience of an energy infrastructure project through assessing the value of its required debt and equity investments. Integrating climate scenarios into an asset valuation model provides useful and specific insights for risk management, but there is a lack of academic and market tools that effectively address this need. The critical barrier is that climate-related risks (physical and transition) are typically indirect variables in the cash flow calculation, and they should be computed based on the direct variables such as revenue, capital expenditures, operating expenses, and financing costs. The implementation of this framework shows how to delineate climate-related risks that are asset-specific and transforms them into financial risks. Using cash flow simulation and scenario analysis, it estimates an energy infrastructure asset's probability of default due to climate-related risks and the size and timing of the losses for any given default. To demonstrate the framework's application, we simulate the price climate-related risks of a utility-scale electricity generation facility powered by natural gas.
Data and its (dis)contents: A survey of dataset development and use in machine learning research.

Summary In this work, we survey a breadth of literature that has revealed the limitations of predominant practices for dataset collection and use in the field of machine learning. We cover studies that critically review the design and development of datasets with a focus on negative societal impacts and poor outcomes for system performance. We also cover approaches to filtering and augmenting data and modeling techniques aimed at mitigating the impact of bias in datasets. Finally, we discuss works that have studied data practices, cultures, and disciplinary norms and discuss implications for the legal, ethical, and functional challenges the field continues to face. Based on these findings, we advocate for the use of both qualitative and quantitative approaches to more carefully document and analyze datasets during the creation and usage phases.
On the Power of Edge Independent Graph Models

Why do many modern neural-network-based graph generative models fail to reproduce typical real-world network characteristics, such as high triangle density? In this work we study the limitations of edge independent random graph models, in which each edge is added to the graph independently with some probability. Such models include both the classic Erd\"{o}s-R\'{e}nyi and stochastic block models, as well as modern generative models such as NetGAN, variational graph autoencoders, and CELL. We prove that subject to a bounded overlap condition, which ensures that the model does not simply memorize a single graph, edge independent models are inherently limited in their ability to generate graphs with high triangle and other subgraph densities. Notably, such high densities are known to appear in real-world social networks and other graphs. We complement our negative results with a simple generative model that balances overlap and accuracy, performing comparably to more complex models in reconstructing many graph statistics.
On the Symmetry of Blast Waves

This article presents a brief historical review of G. I. Taylor's solution of the point blast wave problem which was applied to the Trinity test of the first atomic bomb. Lie group symmetry techniques are used to derive Taylor's famous two-fifths law that relates the position of a blast wave to the time after the explosion and the total energy released. The theory of exterior differential systems is combined with the method of characteristics to demonstrate that the solution of the blast wave problem is directly related to the basic relationships that exist between the geometry (or symmetry) and the physics of wave propagation through the equations of motion. The point blast wave model is cast in terms of two exterior differential systems and both systems are shown to be integrable with local solutions for the velocity, pressure, and density along curves in space and time behind the blast wave.
Disruption of Brachypodium Lichenase Alters Metabolism of Mixed-linkage Glucan and Starch.

Mixed-linkage glucan (MLG), which is widely distributed in grasses, is a polysaccharide highly abundant in cell walls of grass endosperm and young vegetative tissues. Lichenases are enzymes that hydrolyze mixed-linkage glucan first identified in mixed-linkage glucan rich lichens. In this study, we identify a gene encoding a lichenase we name Brachypodium distachyon LICHENASE 1 (BdLCH1), which is highly expressed in the endosperm of germinating seeds and coleoptiles and at lower amounts in mature shoots. RNA in situ hybridization showed that BdLCH1 is primarily expressed in chlorenchyma cells of mature leaves and internodes. Disruption of BdLCH1 resulted in an eight-fold increase in mixed-linkage glucan content in senesced leaves. Consistent with the in situ hybridization data, immunolocalization results showed that mixed-linkage glucan was not removed in chlorenchyma cells of lch1 mutants as it was in wild type and implicate the BdLCH1 enzyme in removing mixed-linkage glucan in chlorenchyma cells in mature vegetative tissues. We also show that mixed-linkage glucan accumulation in lch1 mutants was resistant to dark induced degradation, and eight-week-old lch1 plants showed a faster rate of starch breakdown than wild type in darkness. Our results suggest a role for BdLCH1 in modifying the cell wall to support highly metabolically active cells.
Computational design of thermoelectric alloys through optimization of transport and dopability.

Alloying is a common technique to optimize the functional properties of materials for thermoelectrics, photovoltaics, energy storage etc. Designing thermoelectric (TE) alloys is especially challenging because it is a multi-property optimization problem, where the properties that contribute to high TE performance are interdependent. In this work, we develop a computational framework that combines first-principles calculations with alloy and point defect modeling to identify alloy compositions that optimize the electronic, thermal, and defect properties. We apply this framework to design n-type Ba2(1-x)Sr2xCdP2 Zintl thermoelectric alloys. Our predictions of the crystallographic properties such as lattice parameters and site disorder are validated with experiments. To optimize the conduction band electronic structure, we perform band unfolding to sketch the effective band structures of alloys and find a range of compositions that facilitate band convergence and minimize alloy scattering of electrons. We assess the n-type dopability of the alloys by extending the standard approach for computing point defect energetics in ordered structures. Through the application of this framework, we identify an optimal alloy composition range with the desired electronic and thermal transport properties, and n-type dopability. Such a computational framework can also be used to design alloys for other functional applications beyond TE.
Attention to dividends, inattention to earnings?

We examine whether dividends serve as substitutes or complements to accounting information in firm valuation. Consistent with dividends substituting for earnings information, we find that dividend paying firms have 11%–15% lower earnings response coefficients (ERCs) than non-payers. We find more substitution when the dividend provides a stronger signal of permanent earnings: when the firm is less likely to cut the dividend, when the firm is likely to fund the dividend out of earnings rather than cash reserves, or when the dividend is larger. We then show that dividend payers have lower absolute returns, less trading volume, and fewer analyst forecasts at the earnings announcement (EA), suggesting that dividend payers attract less attention to their less informative EAs. Finally, we show that the lower EA attention translates into less earnings management and fewer earnings-related disclosures for dividend payers relative to non-payers. Collectively, this evidence suggests that dividends supply information about permanent earnings and, although costly, could be an efficient way for some firms to satisfy investors’ demand for earnings information.
Dynamic Grasping with Reachability and Motion Awareness

Grasping in dynamic environments presents a unique set of challenges. A stable and reachable grasp can become unreachable and unstable as the target object moves, motion planning needs to be adaptive and in real time, the delay in computation makes prediction necessary. In this paper, we present a dynamic grasping framework that is reachabilityaware and motion-aware. Specifically, we model the reachability space of the robot using a signed distance field which enables us to quickly screen unreachable grasps. Also, we train a neural network to predict the grasp quality conditioned on the current motion of the target. Using these as ranking functions, we quickly filter a large grasp database to a few grasps in real time. In addition, we present a seeding approach for arm motion generation that utilizes solution from previous time step. This quickly generates a new arm trajectory that is close to the previous plan and prevents fluctuation. We implement a recurrent neural network (RNN) for modelling and predicting the object motion. Our extensive experiments demonstrate the importance of each of these components and we validate our pipeline on a real robot.
Exact higher-order bulk-boundary correspondence of corner-localized states

We demonstrate that the presence of a localized state at the corner of an insulating domain is not always a predictor of a certain non-trivial higher-order topological invariant, even though they appear to co-exist in the same Hamiltonian parameter space. Our analysis of $C^n$-symmetric crystalline insulators and their multi-layer stacks reveals that topological corner states are not necessarily correlated with other well-established higher-order boundary observables, such as fractional corner charge or filling anomaly. In a $C^3$-symmetric breathing Kagome lattice, for example, we show that the bulk polarization, which successfully predicts the fractional corner anomaly, fails to be the relevant topological invariant for zero-energy corner states; instead, these corner states can be exactly explained by the decoration of topological edges. Also, while the zero-energy corner states in $C^4$-symmetric topological crystalline insulators have long been conjectured to be the result of the bulk polarization at quarter-filling, we correct this misconception by introducing a proper bulk invariant at half-filling and establishing a precise bulk-corner correspondence. By refining several bulk-corner correspondences in two-dimensional topological crystalline insulators, our work motivates further development of rigorous theoretical grounds for associating the existence of corner states with higher-order topology of host materials.
Tier 3 of the RTI model : problem solving through a case study approach

Preface Acknowledgments About the Authors 1. Introduction to RTI and the Case Study Model Context and History The Response to Intervention Process The Case Study Model and Prereferral Intervention Summary 2. Assessment Principles and Practices Assessment Methods Assessment at Tier 1 Assessment at Tier 2 Assessing Behavior Summary 3. Facilitating Response to Intervention in Schools School Psychologist as Systems Change Agent Evaluating the System Goal Setting RTI Implementation Summary 4. Problem Identification Tier 1 Tier 2 Tier 3 Behavioral Definition of the Problem Problem Certification: Establishing the Severity of the Discrepancy Between Actual and Expected Performance Formulating Intervention Goals Planning Data-Collection Activities for Baseline Measurement and Progress Monitoring Assessment of Factors Related to the Problem Summary 5. Problem Analysis Generating Hypotheses Primary and Secondary Dependent Variables Types of Hypotheses Intervention Selection Hypothesis Testing Summary 6. Single-Case Design Single-Case Design and Hypothesis Testing Single-Case Design and Progress Monitoring Choosing a Single-Case Design Summary 7. Intervention Selecting Research-Based Interventions Intervention Integrity Linking Interventions to Hypotheses Characteristics of Effective Academic Interventions Characteristics of Effective Behavioral Interventions Targets of Intervention Summary 8. Evaluating Case Study Outcomes Visual Analysis Conducting Visual Analyses Magnitude of Change Choosing, Calculating, and Interpreting Effect Sizes Quality of the Outcome: Goal-Attainment Scaling Choosing Evaluation Methods and Judging Outcomes Summary 9. Using the Case Study to Determine Special Education Eligibility Disability Versus Eligibility Determination Responsiveness Versus Resistance Using Case Study Information for Special Education Eligibility Determination RTI and Specific Learning Disabilities Decision Making in Eligibility Determination Technical Adequacy of Decision-Making Practices Eligibility Decisions Summary 10. Program Evaluation Case Study Structure Case Study Rubric Case Study: Morgan Case Study: Reggie Evaluation of School Psychology Services and Programs Case Study Fidelity Intervention Integrity Magnitude of Change Goal-Attainment Scaling Summary References Index
ICT as a Corruption Deterrent: A Theoretical Perspective

Investigations of white collar crimes such as corruption are often hindered by the lack of information or physical evidence. However, vast amount of information recorded, stored, analyzed, and shared using information and communication technologies (ICT) by businesses, governments, and citizens may help in investigating and prosecuting these crimes, and in deterring future crimes. This paper investigates the relationship between ICT and corruption at the country level using the theoretical lens of general deterrence theory. Using time-lagged regression and multilevel analysis of country level data from 97 countries for the years 2011-2013, we demonstrate that countries with higher ICT penetration and higher rule of law tend to have lesser corruption after accounting for social, economic, and political controls.
Asymptotic Stability of 2-Switched Systems based on Lyapunov Functions

This paper examines a 2-switched system case study as an application of the ideas of global asymptotic stability developed in a number of previous papers. The idea here is to construct a globally attractive n-1-dimensional hyperspace having the property that the "average" system restricted on the hyperspace is stable. Definite quadratic Lyapunov functions are then constructed having energy-reducing regions whose intersection creates an "envelope" around the hyperspace for the 2-switched system to evolve in a stable fashion. Care is taken for the union of the energy-reducing regions to cover the state-space as well as satisfy certain theorems and conditions set forth in previous work.
The Indian Child Welfare Act: A Case Update (August 2008-August 2009)

This paper, prepared for the Michigan State Bar's American Indian Law Section annual program, provides an update of major cases decided nationally on Indian Child Welfare Act questions.
Development of a Protocol for Capillary Blood Glucose Testing in Nursing Home and Rehabilitation Settings

OBJECTIVES: To develop an algorithm to standardize capillary blood glucose (CBG) testing in nursing home and rehabilitation patients. DESIGN: Descriptive study in which an interdisciplinary team from a nursing home, a rehabilitation center, and a diabetes mellitus care program developed and tested a protocol to standardize diabetes management parameters and CBG testing frequency. SETTING: Department of Veterans Affairs nursing home and rehabilitation unit. PARTICIPANTS: One hundred one patients admitted to the units during the 6-month study period who had orders for CBG testing. INTERVENTION: Use of a standardized CBG testing protocol. MEASUREMENTS: Use of management goal, use of CBG testing protocol, total CBG tests/month. RESULTS: One hundred one subjects received orders for CBG testing; 72 (72%) received orders for a management goal, and 69 (69%) received orders to use the CBG protocol. Of these 69 patients, 22 met their CBG goals and were advanced to less-frequent CBG testing using the protocol, and 15 did not meet their CBG goals and were not advanced. An additional 15 patients were advanced to less-frequent CBG testing but not using the protocol. In all, 54 of 69 patients (78%) were advanced or could have been advanced by protocol to less-frequent CBG testing. Total CBG testing per month did not change before, during, or after the study period. CONCLUSION: This protocol would be useful in long-term care facilities and in other congregate living settings where patients with diabetes mellitus have staff assisting with their diabetes management. Barriers to successful implementation are discussed.
The Los Alamos foil implosion project

The goal of the Los Alamos foil implosion project is to produce an intense (>100 TW), multimegajoule, laboratory soft x-ray source for material studies and fusion experiments. The concept involves the implosion of annular, current-carrying, cylindrical metallic plasmas via their self-magnetic forces. The project features inductive storage systems using both capacitor banks and high explosive-driven flux compression generators as prime energy sources. Fast opening switches are employed to shorten the electrical pulses. The program will be described and activities to date will be summarized.
Transmission through a simple frequency selective surface (FSS) with random manufacturing errors

A single thin conducting plane with an array of thin linear slots in free space is investigated. Using a finite-by-infinite array analysis, random errors are introduced in the slot geometries along the finite direction, while perfect periodicity is maintained in the infinite direction, allowing Floquet's theorem to be used. Fields transmitted from an incident plane wave are examined in the principal plane, normal to the infinite axis. A study is made by varying the standard deviation of the geometric errors, to determine manufacturing tolerance requirements for maintaining peak transmission levels and for keeping sidelobes down. >
Induced work participation and the returns to experience for welfare women: Evidence from a social experiment

Abstract In the late 1970's, a welfare reform experiment with 'workfare'-like provisions was conducted in Minnesota. The experiment provided education and training to AFDC recipients in an attempt to improve client long-term labor-market outcomes. An evaluation of the experiment applying simultaneous-equations modeling of outcomes, attrition, and endogenously missing data is presented. We find imprecise evidence that the program increased the probability of post-program work participation, and we measure a high rate of return in wages to work experience. Although we find no evidence of attrition bias, there is considerable influence of selectivity in the wage equations.
Aestivation and diapause syndromes reduce the water balance requirements for pupae of the Hessian fly, Mayetiola destructor

We report the water balance of aestivating (summer), diapausing (winter), and non-diapausing pupae of Hessian fly, Mayetiola destructor (Say) (Diptera: Cecidomyiidae). Maintaining water requirements during pupal dormancy is particularly important because water cannot be replenished actively by drinking. Dehydration tolerance (25% loss before succumbing to dehydration) and water content (63–65%) were not different for the three types of pupae. Differences were noted in the net transpiration rates (NTRs, % body water per hour at 0% r.h.) between dormant (0.24–0.28% per hour) and non-diapausing (0.47% per hour) pupae 10 days after pupariation, but not between aestivating (0.28% per hour) and diapausing (0.24% per hour) pupae. These reduced NTRs result in extended pupal survival, indicated by adult eclosion, during exposure to dehydrating conditions. Net transpiration rates for aestivating and diapausing pupae were further reduced as dormancy progressed (up to 130 days) until individuals were moved to conditions that break dormancy. Pupae could not take up water from the atmosphere below vapor saturation (100% r.h. or 1.00 av), and rely upon contact with liquid water or moist plant tissue to replenish their water stores. The critical transition temperatures (CTT) of the aestivating and diapausing pupae were significantly higher than those of non-diapausing pupae, suggesting that modified cuticular lipids are present on aestivating and diapausing pupae. Thus, aestivation and diapause trigger a dormancy specific water balance profile characterized by reduced NTRs and increased CTTs.
Near-field effects in radio-frequency emission from particle showers in a dense medium

The SLAC T-510 Experiment studied the effects of a magnetic field upon the radiofrequency emission from particle showers in high-density polyethylene. Ultra-High Energy (UHF) and Very High Frequency (VHF) antennas were used to measure the radio frequency (RF) radiation from particle showers in the target. Special near field data runs were performed with the UHF antenna array positioned closer to the target (along the beam axis). The signal was split into two linearly polarized components, vertical and horizontal, arising from two different mechanisms: the Askaryan and Magnetic effects, respectively. Peak voltage data from the near-field runs was used to determine the behavior of the radiation amplitude as a function of distance from the target and angle made with respect to the beam line. Power spectral densities were produced using near field runs to analyze how various frequency ranges behaved as a function of
Ancient High-Strength Ceramics: Fritted Faience Bracelet Manufacture at Harappa (Pakistan), Ca. 2300-1800 B.C.

Unlike most ancient faience, which consists of a quite porous and weak body coated with a glassy glaze, much Harappan faience has a quite dense body with a continuous glassy phase and a relatively thin skin of glaze. A multiple-step grinding and fritting process of manufacture was used to make the finely ground, uniformly blue-green and other colored bodies. Intentional additions of glass frit and clay are postulated. The Harappan faience bodies are particularly strong, compared with contemporaneous faience from Southwest Asia and Egypt, because Harappan faience is more dense, because differential thermal contraction during cooling places the continuous glassy phase in compression, and perhaps also due to crack deflection toughening. A similar technology is not found in Egypt until much later during the New Kingdom about 1450 B.C., and then only glass is added. The socio-cultural reasons for the development of such a specialized technology probably resides in the properties required of the particular type of artifact in which this technology is most often found, that is, bracelets. Bracelets, or bangles, were worn on arms and legs; most often several were worn at a time. Such use required the bracelets to meet criteria of increased abrasion resistance and impact strength, and thus an innovative technology was developed to meet these needs.
How best to assure patient co-operation during a pediatric echocardiography examination?

One of the important challenges inherent in using echocardiography (and other diagnostic testing methods) in young children is how best to ensure patient cooperation during testing. This is particularly important because increasingly, decisions about treatment, including complex cardiac surgical procedures and interventional cardiac catheterization, are based on noninvasive test results. Accordingly, it is increasingly important that echocardiographic studies in children be of high quality and as complete as necessary to answer the relevant questions. Every pediatric echocardiography laboratory has developed its own approach, and no single best strategy has been established. The practices range from distraction by video viewing to the sedation of nearly all infants and toddlers presenting for echocardiography. Although indications for sedation differ among the pediatric echocardiography labs, there are times when sedation clearly is required for an uncooperative infant or toddler. Different medications and combinations of medications have been used for this purpose. Safety, rates of side effects and complications, time from administration to beginning echocardiography, length of sedation, ease of administration and patient monitoring, cost, and the effects of sedation on the measured parameters are all important considerations in this decision. Because an ideal medication does not exist, almost every pediatric echocardiography laboratory has developed expertise with a ‘‘favorite’’ drug that it prefers to use. Two studies published in the present issue describe the sedation experiences of two high-volume pediatric echocardiography laboratories. Warden et al evaluated Cincinnati Children’s Hospital echocardiography laboratory’s 12-year experience using pentobarbital in 9,796 patients. They found that nearly 99% of patients were successfully sedated. Of those, 61% were sedated for 30 to 60 minutes. Nearly 20% were sedated for >1 hour. Fifty-one patients had adverse effects; these were more common in children aged 1 to 4 years. The authors conclude that pentobarbital is an effective and safe sedative but acknowledge that alternative sedation strategies may be beneficial in children aged 1 to 4 years. A second study from Children’s Hospital of Philadelphia by Nicolson et al compared the efficacy and safety of chloral hydrate with inhaled anesthesia using a mixture of oxygen and sevoflurane, and on occasion nitrous oxide, for transthoracic echocardiography. From July 2006 to August 2008, a total of 538 patients were sedated for echocardiography; 507 were included in the study. They were divided into two statistically comparable groups. Patients sedated with chloral hydrate required longer times from sedation to imaging and longer image acquisition times. Nearly one third of those sedated
A red cell deformation model for hemolysis in cross flow membrane plasmapheresis

Hemolysis is a serious potential problem in continuous flow membrane plasmapheresis, which is increasingly used for plasma collection and for therapeutic treatment of a variety of immunologic and metabolic disorders. Previously developed theory, which is based upon the notion that red cells hemolyze upon contact with the membrane, does not correctly predict experimental observations of the effects of transmembrane pressure drop, membrane pore size, and wall shear rate on hemolysis in this process. We propose that hemolysis occurs by deformation of red blood cells as they are forced into membrane pores. The cells hemolyze if the strain in the cell membrane caused by this deformation is sufficient to rupture the cell membrane. This strain is a function not only of the transmembrane pressure drop which forces the cells into the pores but also of the residence time for the cells in the pores, which is assumed to be a function of wall shear rate. Predictions of the model agree well with data. The model provide...
Desipramine blocks alcohol-induced anxiety- and depressive-like behaviors in two rat strains.

Epidemiological studies indicate significant co-morbid expression of alcoholism, anxiety, and depression. These symptoms are often under-diagnosed and under-treated and can worsen prognostic and treatment outcome for alcoholism. Nonetheless, a causal relationship between alcoholism and these conditions is yet to be established. In this study we sought to determine the effects of daily alcohol administration on the indices of anxiety and depression in two rat strains, one of which exhibits inherent depressive-like characteristics. Moreover, it was of relevance to examine the effects of a clinically useful antidepressant on alcohol-induced behavioral changes. Wistar-Kyoto (WKY) rats derived from Wistar stock show low levels of locomotor activity in an open field and high levels of immobility in the forced swim test (FST) which is considered a measure of their helplessness and hence are considered a putative animal model of depression. Adult female WKY and Wistar rats were exposed for 3 hrs daily to 95% ethanol vapor to achieve a mean blood alcohol level (BAL) of approximately 150 mg/dL. Controls were exposed to air in similar inhalation chambers. Sixteen to 18 hrs following 7 or 14 days of exposure to alcohol, locomotor activity (LCA) in open field, duration of time spent in the open arm of the elevated plus-maze (EPM), reflective of anxiety-like behavior and immobility in FST were evaluated. Alcohol exposure for 7 or 14 days reduced LCA only in Wistar rats but enhanced FST immobility in both strains at both time points. Only 14 day alcohol exposure reduced EPM open arm time in both WKY and Wistar rats. Daily treatment with desipramine (8 mg/kg) blocked all the changes induced by alcohol in both strains. Thus, subchronic (7 day) exposure to alcohol induces depressive-like characteristics in Wistar rats and exacerbates that of WKY rats. Chronic (14 day) exposure, however, also induces an anxiety-like effect in both strains. The depressive-and anxiety-like behaviors induced by alcohol were blocked by daily treatment with a tricyclic antidepressant. It may be suggested that prophylactic treatment of alcoholics with an antidepressant prior to detoxification may improve treatment outcome for alcoholism.
Adolescent Schoolboys in SED Classes: Implications for Child Psychiatry

Abstract Objective The primary purpose of this study was to determine basic psychosocial characteristics for adolescent socially and emotionally disturbed (SED) boys. Method Boys aged 12 to 16 years who were recommended for SED placement were compared with same-aged boys recommended for other educational intervention. The main measures were the Kiddie SADS-E interview and Achenbach's parent Child Behavior Checklist and Teacher Report Form scales. Results The adolescent SED boys were found to have normal intelligence, high rates of family stressors, predominantly DSM-III externalizing disorders, serious dysfunction according to checklist ratings by both teachers and parents, and little current community mental health intervention. They were significantly different from the comparison group on several variables: lower socioeconomic status, more abuse experience, greater diagnostic comorbidity, higher Axis V clnician ratings, and higher teacher ratings on externalizing scales. Conclusions Findings are similar to those for other age-gender groups of SED students. One implication is the need for more private child psychiatrists to become involved in the collaborative treatment of this complex and seriously ill group of special education students. Consequently, a second implication is to increase the experience of child psychiatry trainees in dealing with the many consultative needs of SED students and staff. J. Am. Acad. Child Adolesc. Psychiatry , 1993,32,6:1223–1228.
A User-Oriented Model of Factors that Affect Information Requirements Determination Process Quality

This paper discusses a study by the author to identify factors that affect the process quality of the information requirements determination IRD process from a user perspective. A nominal group process was used with three groups of users that have had experience with the IRD process. The results indicate there is a set of factors that users agree impact the quality of the IRD process. A total of 33 factors were identified as critical to IRD process quality. This study should benefit IT users, IT professionals, project managers, and IT researchers. The factors identified may be used to develop metrics to be used to monitor the IRD process or measure its success or quality. For IT researchers, this study offers two primary contributions: 1 identification of the critical factors suggests that there are many variables that have not received attention, and 2 an example of an approach to generate potential variables for further study.
Design procedures for fiber composite box beams

Step-by-step procedures are described which can be used for the preliminary design of fiber composite box beams subjected to combined loadings. These procedures include a collection of approximate closed-form equations so that all the required calculations can be performed using pocket calculators. Included is an illustrated example of a tapered cantilever box beam subjected to combined loads. The box beam is designed to satisfy strength, displacement, buckling, and frequency requirements.
Low risk of bacteremia in children with febrile seizures.

Objective To evaluate the risk of bacteremia in children with febrile seizures treated as outpatients. Methods A retrospective cohort study was performed involving 379 children aged 2 to 24 months presenting to an urban tertiary care children's hospital emergency department with a febrile seizure between February 1, 1993, and May 31, 1996. Results The mean patient age was 15.9 months, and 217 (57%) were male. In 40 patients (10.6%), the use of oral antibiotics before initial emergency department evaluation was reported. Bacteremia occurred in 8 (2.1%) of 379 children studied. None of the children with bacteremia had received previous antibiotics. The causative organisms were Streptococcus pneumoniae in 7 cases and group A Streptococcus in 1 case. There were 5 contaminated cultures (1.3%). Although 2 of the 8 children with bacteremia ultimately required admission, there were no serious adverse outcomes. Six of 7 episodes of S pneumoniae bacteremia were caused by serotypes included in the pneumococcal conjugate vaccine, which was not available at the time of this study. Conclusions Children 2 to 24 months of age with febrile seizures are at similar risk for occult bacteremia as those with fever alone. Widespread use of the pneumococcal conjugate vaccine may further decrease the incidence of bacteremia in this population.