cssi/SciSciGPT-SciSciNet huggingface dataset:
Multi-Scale Games: Representing and Solving Games on Networks with Group Structure.

Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage this multi-scale structure, and derive sufficient conditions for convergence of these to a Nash equilibrium. Our numerical experiments demonstrate that the proposed approaches enable orders of magnitude improvements in scalability when computing Nash equilibria in such games. For example, we can solve previously intractable instances involving up to 1 million agents in under 15 minutes.
Is Framing More Effective Than Regulating Disclosures? The Effects of Risk Disclosure Frame and Regime on Managers' Disclosure Choices†

I conduct an experiment with senior executives (CEOs, CFOs, controllers) to examine how their risk disclosure quality, with respect to disclosure volume and specificity, is influenced by three factors: first, whether the disclosure behavior is framed internally by the firm as obtaining a gain or avoiding a loss from disclosure; second, whether the external disclosure regime mandates risk mitigation disclosures that explain how a risk is handled; and third, whether the risk under consideration for disclosure is weakly or strongly mitigated. This research question is important because high-quality risk disclosures are challenging to regulate and changing how disclosure behavior is framed could substitute for costly disclosure regulations. I predict and find that a gain frame prompts managers to make more detailed risk disclosures than a loss frame, regardless of the disclosure regime. I also predict and find that a loss frame leads to less detailed and more boilerplate disclosure of weakly mitigated risks when risk mitigation plans are mandated. Given that the SEC (2016) is considering mandating risk mitigation disclosures similar to the practice in other regimes, my findings provide insights on the limitations of mandating these disclosures. My results suggest that changing managers’ disclosure frame internally through firm initiatives could be more effective in prompting higher quality risk disclosures.
The impact of climate change on the cost of bank loans

Abstract We find robust empirical evidence that firms in locations with higher exposure to climate change pay significantly higher spreads on their bank loans. To alleviate the concerns related to using firms' headquarters in determining climate risk exposure, we exploit the economic link between a firm and its customers and find that the exposure of a firm's customers to climate risk also adversely affects that firm's cost of borrowing. In the cross-section, we find that the long-term loans of poorly rated firms drive the effect. Overall, our evidence suggests that lenders increasingly view climate change as a relevant risk factor.
Steady and Unsteady Transonic Flow

An investigation of the transonic flow over a circular arc airfoil was conducted to obtain basic information for turbulence modeling of shock-induced separated flows and to verify numerical computer codes which are being developed to simulate such flows. The investigation included the employment of a laser velocimeter to obtain data concerning the mean velocity, the shear stress, and the turbulent kinetic energy profiles in the flowfield downstream of the airfoil midchord where the flow features are more complex. Depending on the free-stream Mach number, the flowfield developed was either steady with shock-wave-induced separation extending from the foot of the shock wave to beyond the trailing edge or unsteady with a periodic motion also undergoing shock-induced separation. The experimental data were compared with the results of numerical simulations in which a computer code was employed that solved the time-dependent Reynolds' averaged compressible Navier-Stokes equations.
Semantic Audio-Visual Navigation

Recent work on audio-visual navigation assumes a constantly-sounding target and restricts the role of audio to signaling the target’s position. We introduce semantic audio-visual navigation, where objects in the environment make sounds consistent with their semantic meaning (e.g., toilet flushing, door creaking) and acoustic events are sporadic or short in duration. We propose a transformer-based model to tackle this new semantic AudioGoal task, incorporating an inferred goal descriptor that captures both spatial and semantic properties of the target. Our model’s persistent multimodal memory enables it to reach the goal even long after the acoustic event stops. In support of the new task, we also expand the SoundSpaces audio simulations to provide semantically grounded sounds for an array of objects in Matterport3D. Our method strongly outperforms existing audio-visual navigation methods by learning to associate semantic, acoustic, and visual cues.1
Multiple-quantum 2D spectroscopy of many-body correlations in GaAs quantum wells

Multiple-quantum two-dimensional Fourier transform optical (2D FTOPT) spectroscopy was developed and conducted on GaAs quantum wells. Spatiotemporal femtosecond pulse shaping was used to control the optical phases and time delays of ultrashort pulses in multiple non-collinear beams so that fully coherent four-wave and higher-order mixing measurements could be conducted without delay stages, multiple interferometers, or any active phase control. Coherences of biexcitons, unbound but correlated exciton pairs, and excitons undergoing rephasing were observed directly.
Minimal reduction type and the Kazhdan–Lusztig map

Abstract We introduce the notion of minimal reduction type of an affine Springer fiber, and use it to define a map from the set of conjugacy classes in the Weyl group to the set of nilpotent orbits. We show that this map is the same as the one defined by Lusztig in Lfromto, (2011) and that the Kazhdan–Lusztig map in Kazhdan and Lusztig, (1998) is a section of our map. This settles several conjectures in the literature. For classical groups, we prove more refined results by introducing and studying the “skeleta” of affine Springer fibers.
Scaling Local Self-Attention for Parameter Efficient Visual Backbones

Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to parameter-dependent scaling and content-independent interactions of convolutions. Self-attention models have recently been shown to have encouraging improvements on accuracy-parameter trade-offs compared to baseline convolutional models such as ResNet-50. In this work, we develop self-attention models that can outperform not just the canonical baseline models, but even the high-performing convolutional models. We propose two extensions to self-attention that, in conjunction with a more efficient implementation of self-attention, improve the speed, memory usage, and accuracy of these models. We leverage these improvements to develop a new self-attention model family, HaloNets, which reach state-of-the-art accuracies on the parameter-limited setting of the ImageNet classification benchmark. In preliminary transfer learning experiments, we find that HaloNet models outperform much larger models and have better inference performance. On harder tasks such as object detection and instance segmentation, our simple local self-attention and convolutional hybrids show improvements over very strong baselines. These results mark another step in demonstrating the efficacy of self-attention models on settings traditionally dominated by convolutions. 1
Acorn: adaptive coordinate networks for neural scene representation

Neural representations have emerged as a new paradigm for applications in rendering, imaging, geometric modeling, and simulation. Compared to traditional representations such as meshes, point clouds, or volumes they can be flexibly incorporated into differentiable learning-based pipelines. While recent improvements to neural representations now make it possible to represent signals with fine details at moderate resolutions (e.g., for images and 3D shapes), adequately representing large-scale or complex scenes has proven a challenge. Current neural representations fail to accurately represent images at resolutions greater than a megapixel or 3D scenes with more than a few hundred thousand polygons. Here, we introduce a new hybrid implicit-explicit network architecture and training strategy that adaptively allocates resources during training and inference based on the local complexity of a signal of interest. Our approach uses a multiscale block-coordinate decomposition, similar to a quadtree or octree, that is optimized during training. The network architecture operates in two stages: using the bulk of the network parameters, a coordinate encoder generates a feature grid in a single forward pass. Then, hundreds or thousands of samples within each block can be efficiently evaluated using a lightweight feature decoder. With this hybrid implicit-explicit network architecture, we demonstrate the first experiments that fit gigapixel images to nearly 40 dB peak signal-to-noise ratio. Notably this represents an increase in scale of over 1000X compared to the resolution of previously demonstrated image-fitting experiments. Moreover, our approach is able to represent 3D shapes significantly faster and better than previous techniques; it reduces training times from days to hours or minutes and memory requirements by over an order of magnitude.
Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data

We present a new non-negative matrix factorization model for $(0,1)$ bounded-support data based on the doubly non-central beta (DNCB) distribution, a generalization of the beta distribution. The expressiveness of the DNCB distribution is particularly useful for modeling DNA methylation datasets, which are typically highly dispersed and multi-modal; however, the model structure is sufficiently general that it can be adapted to many other domains where latent representations of $(0,1)$ bounded-support data are of interest. Although the DNCB distribution lacks a closed-form conjugate prior, several augmentations let us derive an efficient posterior inference algorithm composed entirely of analytic updates. Our model improves out-of-sample predictive performance on both real and synthetic DNA methylation datasets over state-of-the-art methods in bioinformatics. In addition, our model yields meaningful latent representations that~accord with existing biological knowledge.
Dynamics and Control of a Flapping Wing UAV with Abdomen Undulation Inspired by Monarch Butterfly

This paper presents a dynamic model and a control system for a flapping-wing unmanned aerial vehicle. Inspired by flight characteristics captured from live Monarch butterflies, a new dynamic model is presented to account the effects of low-frequency flapping and abdomen undulation. It is developed according to Lagrangian mechanics on a Lie group to obtain an elegant, global formulation of dynamics. Then, a feedback control system is presented to asymptotically stabilize periodic motions with active motion of abdomen, and its stability is verified according to Floquet theory. In particular, it is illustrated that the abdomen undulation has the desirable effects of reducing the variation of the total energy and also improving the stability of the proposed control system.
The effect of venture capitalists straying from their industry comfort zones

Abstract I empirically test and find evidence that venture capital industry experience is more informative and impactful in venture capital investment when measured at the low non-aggregated industry level. Additional evidence shows that venture capital investments made outside of a venture capitalist's preferred investment industry suffer from significant decreases in exit success likelihood. This effect becomes stronger the more distant the investment industry is from the venture capitalist's preferred investment industry.
Sex differences and similarities in the neuroimmune response to central administration of poly I:C.

BACKGROUND: The neuroimmune system is required for normal neural processes, including modulation of cognition, emotion, and adaptive behaviors. Aberrant neuroimmune activation is associated with dysregulation of memory and emotion, though the precise mechanisms at play are complex and highly context dependent. Sex differences in neuroimmune activation and function further complicate our understanding of its roles in cognitive and affective regulation. METHODS: Here, we characterized the physiological sickness and inflammatory response of the hippocampus following intracerebroventricular (ICV) administration of a synthetic viral mimic, polyinosinic:polycytidylic acid (poly I:C), in both male and female C57Bl/6N mice. RESULTS: We observed that poly I:C induced weight loss, fever, and elevations of cytokine and chemokines in the hippocampus of both sexes. Specifically, we found transient increases in gene expression and protein levels of IL-1α, IL-1s, IL-4, IL-6, TNFα, CCL2, and CXCL10, where males showed a greater magnitude of response compared with females. Only males showed increased IFNα and IFNI³ in response to poly I:C, whereas both males and females exhibited elevations of IFNs, demonstrating a specific sex difference in the anti-viral response in the hippocampus. CONCLUSION: Our data suggest that type I interferons are one potential node mediating sex-specific cytokine responses and neuroimmune effects on cognition. Together, these findings highlight the importance of using both males and females and analyzing a broad set of inflammatory markers in order to identify the precise, sex-specific roles for neuroimmune dysregulation in neurological diseases and disorders.
Statistical detection of Josephson, Andreev, and single quasiparticle currents in scanning tunneling microscopy

We present a method to identify distinct tunneling modes in a tunable superconducting tunnel junction composed of a superconducting tip and a sample in a scanning tunneling microscope. Combining the relative decay constant of tunneling current extracted from $I\ensuremath{-}V\ensuremath{-}z$ spectroscopy with its statistical analysis over the atomic disorders in the sample surface, we identified the crossover of dominant tunneling modes between single charge tunneling, Andreev reflection (AR), and Josephson tunneling with respect to the bias voltage at a measurement temperature nearly half of the critical temperature. The method enables one to determine the specific tunneling regime independently of the spectral shapes and to reveal intrinsic modulation of AR and Josephson current by disorder that will be crucial for superconducting quantum information processing.
Deep2Lead: A distributed deep learning application for small molecule lead optimization

Lead optimization is a key step in drug discovery to produce potent and selective compounds. Historically, in silico screening and structure-based small molecule designing facilitated the processes. Although the recent application of deep learning to drug discovery piloted the possibility of their in silico application lead optimization steps, the real-world application is lacking due to the tool availability. Here, we developed a single user interface application, called Deep2Lead. Our web-based application integrates VAE and DeepPurpose DTI and allows a user to quickly perform a lead optimization task with no prior programming experience.
Systematics in the XENON1T data: The 15-keV anti-axion

Abstract The XENON1T collaboration Aprile et al. [2020] has found an excess of electron recoil events in their Science Run 1 data below ∼ 7 keV with a spectral shape consistent with that expected from a solar-axion-induced signal. The claimed statistical significance of the solar-axion model over the null hypothesis is 3.5 σ . In this work we provide suggestive evidence for mismodeling in the electron recoil data that may decrease the local significance of the axion model. To reach this conclusion, we search for a signal with the spectral template of the solar axion model, but shifted to higher (unphysical) energies above ∼ 7 keV. We find that the distribution of significances found from this side-band analysis does not follow the expected chi-square distribution. For example, we find a high-significance feature in the data, with a global p -value p ≈ 0 . 026 , when the solar axion model is shifted upwards in energy by ∼ 15 keV and allowed to have a negative normalization. We argue that such statistical tests, if performed a priori, provide a data-driven way to test and potentially account for systematic uncertainties on the background model in low-threshold dark matter experiments.
Flavor SU(3) in Cabibbo-favored D-meson decays

Model-independent description of nonleptonic decays of charmed mesons is a challenging task due to large nonperturbative effects of strong interactions on the transition amplitudes. We discuss the equivalence of two different flavor-SU(3)-based descriptions of Cabibbo-favored non-leptonic decays of charmed mesons to two-pseudoscalars final states including the η and η′ mesons.
MicroRNA function can be reversed by altering target gene expression levels.

Summary Paradoxically, many microRNAs appear to exhibit entirely opposite functions when placed in different contexts. For example, miR-125b has been shown to be pro-apoptotic in some studies, but anti-apoptotic in others. To investigate this phenomenon, we combine computational modeling with experimental approaches to examine how the function of miR-125b in apoptosis varies with respect to the expression levels of its pro-apoptotic and anti-apoptotic targets. In doing so, we elucidate a general trend that miR-125b is more pro-apoptotic when its anti-apoptotic targets are overexpressed, whereas it is more anti-apoptotic when its pro-apoptotic targets are overexpressed. We show that it is possible to completely reverse miR-125b′s function in apoptosis by modifying the expression levels of its target genes. Furthermore, miR-125b′s function may also be altered by the presence of anticancer drugs. These results suggest that the function of a microRNA can vary substantially and is dependent on its target gene expression levels.
Generalized Carleson embeddings into weighted outer measure spaces

Abstract We prove generalized Carleson embeddings for the continuous wave packet transform from L p ( R , w ) into an outer L p space over R × R × ( 0 , ∞ ) for 2 p ∞ and weight w ∈ A p / 2 . This work is a weighted extension of the corresponding Lebesgue result in [13] and generalizes a similar result in [10] . The proof in this article relies on L 2 restriction estimates for the wave packet transform which are geometric and may be of independent interest.
Existence of nonnegative solutions of nonlinear fractional parabolic inequalities

We study the existence of nontrivial nonlocal nonnegative solutions u(x, t) of the nonlinear initial value problems $$\begin{aligned} \left\{ \begin{array}{ll} (\partial _t -\Delta )^\alpha u\ge u^\lambda &{}\quad \mathrm{in}\,\, {\mathbb {R}}^n \times {\mathbb {R}},\,n\ge 1 \\ u=0 &{}\quad \mathrm{in}\,\, {\mathbb {R}}^n \times (-\infty ,0) \end{array}\right. \end{aligned}$$ and $$\begin{aligned} \left\{ \begin{array}{ll} C_1 u^\lambda \le (\partial _t -\Delta )^\alpha u\le C_2 u^\lambda &{}\quad \mathrm{in}\,{\mathbb {R}}^n \times {\mathbb {R}},\,n\ge 1 \\ u=0 &{}\quad \mathrm{in}\, {\mathbb {R}}^n \times (-\infty ,0), \end{array}\right. \end{aligned}$$ where $$\lambda ,\alpha ,C_1$$ , and $$C_2$$ are positive constants with $$C_1 <C_2$$ . We use the definition of the fractional heat operator $$(\partial _t -\Delta )^\alpha $$ given in Taliaferro (J Math Pures Appl 133:287–328, 2020) and compare our results in the classical case $$\alpha =1$$ to known results.
Duplicity Games for Deception Design With an Application to Insider Threat Mitigation

Recent incidents such as the Colonial Pipeline ransomware attack and the SolarWinds hack have shown that traditional defense techniques are becoming insufficient to deter adversaries of growing sophistication. Proactive and deceptive defenses are an emerging class of methods to defend against zero-day and advanced attacks. This work develops a new game-theoretic framework called the duplicity game to design deception mechanisms that consist of a generator, an incentive modulator, and a trust manipulator, referred to as the GMM mechanism. We formulate a mathematical programming problem to compute the optimal GMM mechanism, quantify the upper limit of enforceable security policies, and characterize conditions on user’s identifiability and manageability for cyber attribution and user management. We develop a separation principle that decouples the design of the modulator from the GMM mechanism and an equivalence principle that turns the joint design of the generator and the manipulator into the single design of the manipulator. A case study of dynamic honeypot configurations is presented to mitigate insider threats. The numerical experiments corroborate the results that the optimal GMM mechanism can elicit desirable actions from both selfish and adversarial insiders and consequently improve the security posture of the insider network. In particular, a proper modulator can reduce the incentive misalignment between the players and achieve win-win situations for the selfish insider and the defender. Meanwhile, we observe that the defender always benefits from faking the percentage of honeypots when the optimal generator is presented.
Evolving hierarchical memory-prediction machines in multi-task reinforcement learning

A fundamental aspect of intelligent agent behaviour is the ability to encode salient features of experience in memory and use these memories, in combination with current sensory information, to predict the best action for each situation such that long-term objectives are maximized. The world is highly dynamic, and behavioural agents must generalize across a variety of environments and objectives over time. This scenario can be modeled as a partially-observable multi-task reinforcement learning problem. We use genetic programming to evolve highly-generalized agents capable of operating in six unique environments from the control literature, including OpenAI’s entire Classic Control suite. This requires the agent to support discrete and continuous actions simultaneously. No task-identification sensor inputs are provided, thus agents must identify tasks from the dynamics of state variables alone and define control policies for each task. We show that emergent hierarchical structure in the evolving programs leads to multi-task agents that succeed by performing a temporal decomposition and encoding of the problem environments in memory. The resulting agents are competitive with task-specific agents in all six environments. Furthermore, the hierarchical structure of programs allows for dynamic run-time complexity, which results in relatively efficient operation.
Embedding closed hyperbolic 3–manifolds in small volume hyperbolic 4–manifolds

In this paper we study existence and lack thereof of closed embedded orientable co-dimension one totally geodesic submanifolds of minimal volume cusped orientable hyperbolic manifolds.
Turbulent field fluctuations in gyrokinetic and fluid plasmas

A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modeling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.
Entanglement minimization in hadronic scattering with pions

Recent work [S. R. Beane, D. B. Kaplan, N. Klco and M. J. Savage, Phys. Rev. Lett. 122, 102001 (2019), arXiv:1812.03138.] conjectured that entanglement is minimized in low-energy hadronic scatterin...
Mis)alignment Between Stance Expressed in Social Media Data and Public Opinion Surveys.

Stance detection, which aims to determine whether an individual is for or against a target concept, promises to uncover public opinion from large streams of social media data. Yet even human annotation of social media content does not always capture “stance” as measured by public opinion polls. We demonstrate this by directly comparing an individual’s self-reported stance to the stance inferred from their social media data. Leveraging a longitudinal public opinion survey with respondent Twitter handles, we conducted this comparison for 1,129 individuals across four salient targets. We find that recall is high for both “Pro’’ and “Anti’’ stance classifications but precision is variable in a number of cases. We identify three factors leading to the disconnect between text and author stance: temporal inconsistencies, differences in constructs, and measurement errors from both survey respondents and annotators. By presenting a framework for assessing the limitations of stance detection models, this work provides important insight into what stance detection truly measures.
Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation

We study reinforcement learning (RL) with linear function approximation. Existing algorithms for this problem only have high-probability regret and/or Probably Approximately Correct (PAC) sample complexity guarantees, which cannot guarantee the convergence to the optimal policy. In this paper, in order to overcome the limitation of existing algorithms, we propose a new algorithm called FLUTE, which enjoys uniform-PAC convergence to the optimal policy with high probability. The uniform-PAC guarantee is the strongest possible guarantee for reinforcement learning in the literature, which can directly imply both PAC and high probability regret bounds, making our algorithm superior to all existing algorithms with linear function approximation. At the core of our algorithm is a novel minimax value function estimator and a multi-level partition scheme to select the training samples from historical observations. Both of these techniques are new and of independent interest.
Geometric network analysis provides prognostic information in patients with high grade serous carcinoma of the ovary treated with immune checkpoint inhibitors.

Network analysis methods can potentially quantify cancer aberrations in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high-grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs) and to rank key genes associated with prognosis. Copy number alterations (CNAs) from targeted and whole-exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. The network curvature analysis stratified patients at high risk of mortality with p = 0.00047 in Kaplan-Meier analysis in HGSOC patients receiving ICI. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. Network curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.
Composing networks of automated market makers

Automated market makers (AMMs) are automata that trade electronic assets at rates set by mathematical formulas. AMMs are usually implemented by smart contracts on blockchains. In practice, AMMs are often composed: trades can be split across AMMs, and outputs from one AMM can be directed to another. This paper proposes a mathematical model for AMM composition. We define sequential and parallel composition operators for AMMs in a way that ensures that AMMs are closed under composition, in a way that works for "higher-dimensional" AMMs that manage more than two asset classes, and so the composition of AMMs in "stable" states remains stable.
This Is the Way the World Ends, Not With a Bang but Bonds and Bullets

This article explores instinctive frames of human decision-making in environmental and resource economics. Higher-moment asset pricing combines rational, mathematically informed economic reasoning with psychological and biological insights. Leptokurtic blindness and skewness preference combine in particularly challenging ways for carbon mitigation. At their worst, human heuristics may generate perverse decisions. Information uncertainty and the innate preference for bonds-and-bullets portfolios may impair responses to catastrophic climate change.
The Pelvis and Hip: Acquired Disorders

This chapter first presents the general pathophysiology of avascular necrosis (AVN) of the femoral head. Extensive discussion of different manifestations of AVN follows, including Legg-Perthes disease, Gaucher disease, sickle cell disease and other hemoglobinopathies, corticosteroid-induced osteonecrosis, avascular necrosis in treated developmental hip dislocation, posttraumatic femoral head necrosis, avascular necrosis in osteomyelitis and septic arthritis, and femoral head dysplasia. Femoral acetabular impingement is then reviewed. Sections on slipped capital femoral epiphysis, traumatic hip fracture and dislocation, and apophyseal injuries of the adolescent hip follow. The chapter concludes with brief discussions of transient synovitis, septic arthritis, juvenile idiopathic arthritis, and synovial chondromatosis.
Teff Grass Response to Nitrogen Fertilization

Teff, a warm season annual grass native to Africa, is gaining popularity as a forage crop in the United States. Little information regarding nitrogen fertilization is available for teff grass production. This field experiment was conducted to evaluate teff grass response to varying nitrogen fertilization rates under dryland conditions. There was no yield response to increasing nitrogen rate or nitrogen source applied.
1 Root-Branches Structuralism in the Huainanzi

In both China and the world at large, the size, scope, and complexity of the Huainanzi have hampered its scholarly treatment as an integral text. The most influential global assessment of the Huainanzi was rendered by the Hanshu 'Yiwen zhi', which classified it as a 'za', or 'mixed', text for the purpose of bibliographical organization. Even twentieth-century scholars inclined to grant the Huainanzi some overall coherence, such as Xu Fuguan and Dai Junren, have used the za category as their point of departure. The metaphor of 'root and branches' is familiar to any student of early Chinese letters. It is a mechanism deployed in numerous early texts to establish normative priorities. Griet Vankeerberghen and Charles Le Blanc have noted the particular prominence of 'root-branches' imagery in the Huainanzi . Even on a subdivisional level root-branches structuralism informs the organization of the text. Keywords: Charles Le Blanc; Dai Junren; Early China; Griet Vankeerberghen; Huainanzi ; root-branches structuralism; Xu Fuguan; Yiwen zhi
Examples of Measures of Subjective Well Being

Readers who are not interested in research issues pertaining to subjective well being may skip this chapter. This chapter is written to help those not familiar with subjective well-being measures become familiar with a variety of methodological and research issues. Readers who are reading this book exclusively to help them understand what strategies people use to enhance their subjective well being can skip this chapter and the next chapter too. Those personal growth strategies are discussed starting Chapter 4.
Functional Magnetic Resonance Imaging in the Investigation of Brain Recovery and Reorganization after Ischemic Infarct

This chapter discusses the application of functional magnetic resonance imaging (fMRI) in the investigation of brain recovery and reorganization after ischemic infarct. The fMRI techniques have been developed to detect functional changes in the brain. The most versatile of these fMRI techniques is based on detecting local changes in blood deoxyhemoglobin concentration in active regions of the brain. Many positron emission tomography (PET) experiments have demonstrated that during brain activation, the regional cerebral blood flow (CBF) increases about 29–50%, and the regional oxygen consumption increases only about 5%, resulting in increases in regional cerebral blood oxygenation level and reduced concentrations of deoxyhemoglobin in active regions of the brain. The main advantage of the fMRI technique is that it is noninvasive and can be used routinely and repeatedly. MRI methodology can provide both anatomy and function information and allows for direct spatial correlation with many other diagnostic images. MRI can produce images with higher temporal and spatial resolutions than PET.
Anti-HIV-1 activity of cellulose acetate phthalate: Synergy with soluble CD4 and induction of "dead-end" gp41 six-helix bundles

Cellulose acetate phthalate (CAP), a promising candidate microbicide for prevention of sexual transmission of the human immunodeficiency virus type 1 (HIV-1) and other sexually transmitted disease (STD) pathogens, was shown to inactivate HIV-1 and to block the coreceptor binding site on the virus envelope glycoprotein gp120. It did not interfere with virus binding to CD4. Since CD4 is the primary cellular receptor for HIV-1, it was of interest to study CAP binding to HIV-1 complexes with soluble CD4 (sCD4) and its consequences, including changes in the conformation of the envelope glycoprotein gp41 within virus particles. Enzyme-linked immunosorbent assays (ELISA) were used to study CAP binding to HIV-1-sCD4 complexes and to detect gp41 six-helix bundles accessible on virus particles using antibodies specific for the α-helical core domain of gp41. 1) Pretreatment of HIV-1 with sCD4 augments subsequent binding of CAP; 2) there is synergism between CAP and sCD4 for inhibition of HIV-1 infection; 3) treatment of HIV-1 with CAP induced the formation of gp41 six-helix bundles. CAP and sCD4 bind to distinct sites on HIV-1 IIIB and BaL virions and their simultaneous binding has profound effects on virus structure and infectivity. The formation of gp41 six-helical bundles, induced by CAP, is known to render the virus incompetent for fusion with target cells thus preventing infection.
Favorable effect of VEGF gene transfer on ischemic peripheral neuropathy

Ischemic peripheral neuropathy is a frequent, irreversible complication of lower extremity vascular insufficiency. We investigated whether ischemic peripheral neuropathy could be prevented and/or reversed by gene transfer of an endothelial cell mitogen designed to promote therapeutic angiogenesis. Intramuscular gene transfer of naked DNA encoding vascular endothelial growth factor (VEGF) simultaneously with induction of hindlimb ischemia in rabbits abrogated the substantial decrease in motor and sensory nerve parameters, and nerve function recovered promptly. When gene transfer was administered 10 days after induction of ischemia, nerve function was restored earlier and/or recovered faster than in untreated rabbits. These findings are due in part to enhanced hindlimb perfusion. In addition, however, the demonstration of functional VEGF receptor expression by Schwann cells indicates a direct effect of VEGF on neural integrity as well. These findings thus constitute a new paradigm for the treatment of ischemic peripheral neuropathy.
COX3 mRNA-specific translational activator proteins are associated with the inner mitochondrial membrane in Saccharomyces cerevisiae

Three yeast proteins coded by nuclear genes, PET54, PET122, and PET494, are specifically required to activate translation of the mitochondrially coded COX3 mRNA, and one of them, PET122, has been shown to interact functionally with both the mitochondrial ribosomal small subunit and the COX3 mRNA leader. To determine the location of these proteins within mitochondria, we have used antisera directed against them to assay for their presence in submitochondrial fractions. Approximately half of the PET54 protein present in mitochondria from wild-type cells pelleted with membranes, while half was released from disrupted mitochondria in soluble form. The membrane-bound PET54 floated with the inner membrane during buoyant density gradient centrifugation, but was efficiently solubilized by extraction with alkaline carbonate. The PET122 and PET494 proteins could only be detected in cells overproducing these proteins. PET122 was completely membrane-bound and could not be extracted with alkaline carbonate. Most of the PET494 protein pelleted with membranes and very little could be solubilized by alkaline carbonate. Both PET122 and PET494 floated with membranes during buoyant density gradient centrifugation. These data suggest the possibility that synthesis of the highly hydrophobic subunit III of cytochrome c oxidase is activated by PET54, PET122, and PET494 at the inner mitochondrial membrane. Amino-terminal sequencing of proteins isolated from mitochondria revealed that PET54 is not processed during import to mitochondria while the PET122 precursor is cleaved between the 8th and 9th residues.
Affinity through Mathematical Activity: Cultivating Democratic Learning Communities.

In this article, the author demonstrates how a broader view of what shapes affinity is ideologically and practically linked to creating democratic learning communities. Specifically, the author explores how a teacher employed complex instruction (an equity pedagogy) with her ethnically and racially diverse students in the “lowest track” Algebra I course. Sociometric network analyses used to model peer relationships revealed an affinity among three students that could not be explained by shared attributes or history (e.g., race or gender). Through field note analyses, the author argues these students’ affinity was forged through shared mathematical activity—what she terms a workship. This workship reflected equitable relationships born of diverse youth learning to work together by working together. The author discusses implications of the workship for teachers and researchers, as well as the constraints that stratified mathematics programs can place on classroom-based efforts to advance equity.
Honoring Expectations About Taxes: Are Roth IRAs Different?

Beginning in January 2010, taxpayers may convert balances in traditional IRAs into tax prepaid Roth IRAs. Those who decide to do so will be relying on current law that allows the payment of a tax now to replace any future income tax on the earnings within the Roth IRA and on withdrawals therefrom. In this paper, I explore whether these taxpayers should be afforded any exception from the generally accepted idea that changes in tax laws that affect pre-existing investments raise no special concerns. I conclude that although the Roth IRA holder, especially of the Roth IRA holder who has converted a traditional IRA into a Roth IRA through the prepayment of income taxes on the account, is qualitatively different from most investors facing tax changes, this difference gives rise only to political, and not constitutional, concerns. I further conclude that the current Congress would be wise to remove the ability of current IRA holders to convert their accounts into Roth IRAs, and thus to minimize the political challenge future Congresses will face given the expectations of the holders of such accounts.
Policy-based security management for ad hoc wireless systems

Ad hoc network nodes work cooperatively to route data packets and maintain network functions. However, this cooperative behavior can be a potential vulnerability for security breaches. Moreover, network node capacity differences may result in an asymmetrical defense capability. Even network nodes that are capable of running a strong security mechanism may choose not to use it because of impacts on the system performance. On the other hand, there are several sub-systems, standards, and techniques to solve the security problems at different levels of a network paradigm. Therefore, it is necessary to integrate these techniques in a coherent manner to provide both end-to-end and multi-layer security management. This paper proposes a policy-based network security management mechanism called the "Ripple Effect" to activate different levels of security defense schemes in an ad hoc network. This mechanism works in responsive and preemptive modes. The fundamental idea of this proposal is to deploy proper security protections at the right place and at the right time
Performance of valve-regulated lead acid batteries in EV1 extended series strings

Delphi Automotive began commercial production of a valve-regulated lead acid (VRLA) battery in 1996 that was specifically designed to deliver the power and energy demands required of electric vehicles such as the EV1. Field results support the need for thermal control of the battery pack in order to maintain capacity, state-of-charge balance, and overall performance. The influences of manufacturing variance, self-discharge rate, monoblock age, and charge acceptance are quantified and discussed.
Who Will Protect the Children? The Untold Story of Unaccompanied Minors in Witness Protection Programs

The juvenile justice system was designed with the special needs of minors in mind. However, on every given day, children and youths interact with the criminal justice system under countless different circumstances, beyond the boundaries of the juvenile justice system. The inclusion of unaccompanied juveniles in witness protection programs is only one of the less commonly explored such interactions. This article is the first scholarly piece to peek into the secretive world of witness protection, to identify the unique challenges associated with the protection of unaccompanied minor witnesses. Using authentic case-studies and invaluable insights of practitioners in the field, it provides an in-depth interdisciplinary analysis of the legal, social, psychological and developmental factors that affect the protection of adolescents. Thus, the article provides a comprehensive basis for policy decisions that facilitate the effective protection of minor witnesses, who are a vital element in the prosecution of serious felonies threatening public safety. More broadly, it can inform policy makers about the unique characteristics of juveniles, and the urgent importance of adapting criminal justice policies, beyond the criminal justice system, to their special needs, in order to increase efficacy.
Multiple-granularity interleaving for piggyback query processing

Piggyback query processing is a new technique, described in [24], intended to perform additional useful computation (e.g., database statistics collection) during normal query processing, taking full advantage of data resident in main memory. Different types of benecial piggybacking have been identifed and studied, but how to efficiently integrate piggyback operations with a given user query is still an open issue. In this paper, we propose a technique of multiple-granularity interleaving to efficiently integrate multiple piggyback operations with a given query at different levels of data granularity. We introduce an algebraic notation to capture the main characteristics of data flows in a database management system (DBMS), facilitating the study of piggybacking and enabling the automated integration of piggyback operations and user queries in a DBMS supporting the piggyback method. Various integration techniques are introduced to facilitate multiple-granularity interleaving including merging shared work, augmenting user queries, and downgrading piggyback operations. A set of transformations and heuristics are suggested that preserve the semantics of a user query, while efficiently interleaving the operations. Our preliminary experiments indicate that interleaving at proper levels of data granularity is key to the efficient implementation of the piggyback method.
Engagement, Exploration, Empowerment

Engagement, exploration, and empowerment are significant practice strategies used by occupational therapy practitioners as a means of getting to know what matters to clients and how to facilitate their participation in everyday life. Applied to the American Occupational Therapy Association (AOTA) as an organization, professional engagement, exploration of new service contexts, and empowerment of members to take an active role in shaping the profession's future are examined. This address, given at the 2015 AOTA Annual Convention & Expo, looks to the future in terms of engaging greater numbers of members; participating in Vision 2025, a strategic planning initiative that will be unveiled at the 2016 AOTA Annual Conference & Expo; and empowering members to achieve excellence in occupational therapy.
Electronic conferencing in undergraduate engineering classes

Two different electronic conferencing programs have been used in undergraduate engineering classes at Arizona State University, USA. The capabilities of these programs are compared to faculty-generated "wants" and "needs" for electronic conferencing. Classroom experience with small and large, lower and upper division engineering classes are described, and student feedback is used to highlight specific strengths and weaknesses of the conferencing programs described. The authors have found that faculty and student satisfaction with electronic conferencing depends very strongly on the organization of topics within the conference, on ease of learning of the conferencing program, on the appearance of the program interface, on simplicity of program use and on the quality of instructional materials, whether online or offline. Specific examples are cited. Potential future applications of electronic conferencing in an academic environment go well beyond conferencing within a course, and include departmental, programmatic and student organization bulletin boards, cross-department, cross-college and even inter-university topical bulletin boards, and there is strong potential for such programs to facilitate direct contact between industry and students within the context of course topics.
Sheath combustion of sprays

Most fuel sprays do not burn or evaporate as individual droplets but as a group. Based on previous studies it is shown that the transition from single droplet to group combustion depends on a single parameter ∈. In the limiting case ∈≪1, which corresponds to most spray parameters of practical interest, evaporation of the fuel occurs in a thin sheath or vaporization wave at the edge of the cloud and combustion occurs at a diffusion flame outside the cloud. In this case ∈ is the ratio of vaporization wave thickness to cloud radius. For this regime, known as sheath combustion, it is shown that the cloud lifetime and cloud ignition and extinction are simply related to analogous results for individual droplet burning.