Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Chromospheric Activity in the Solar-Type Stars in the Open Cluster M67

We present the results of a spectroscopic survey of the Ca II H and K core strengths in a sample of 60 solar-type stars that are members of the solar-age and solar-metallicity open cluster M67. We adopt the HK index, defined as the summed H+K core strengths in 1 Å bandpasses centered on the H and K lines, respectively, as a measure of the chromospheric activity that is present. We compare the distribution of mean HK index values for the M67 solar-type stars with the variation of this index as measured for the Sun during the contemporary solar cycle. We find that the stellar distribution in our HK index is broader than that for the solar cycle. Approximately 17% of the M67 Sun-like stars exhibit average HK indices that are less than solar minimum. About 7%-12% are characterized by relatively high activity in excess of solar maximum values, while 72%-80% of the solar analogs exhibit Ca II H+K strengths within the range of the modern solar cycle. The ranges given reflect uncertainties in the most representative value of the maximum in the HK index to adopt for the solar cycle variations observed during the period AD 1976-2004. Thus, ~20%-30% of our homogeneous sample of Sun-like stars have mean chromospheric H+K strengths that are outside the range of the contemporary solar cycle. Any cycle-like variability that is present in the M67 solar-type stars appears to be characterized by periods greater than ~6 yr. Finally, we estimate a mean chromospheric age for M67 in the range of 3.8-4.3 Gyr.
The Rodney L . White Center for Financial Research Risks for the Long Run : Estimation and Inference

In this paper we empirically evaluate the ability of the long-run risks model to explain asset returns. Exploiting asset pricing Euler equations we develop methods for estimating the long-run risks model, and show that it can successfully account for the market, value, and size sorted returns at reasonable values of risk aversion and the intertemporal elasticity of substitution. Our empirical evidence highlights the importance of low-frequency movements and time-varying uncertainty in economic growth for understanding risk-return tradeoffs in financial markets. ∗We thank Andy Abel, George Constantinides, Lars Hansen, John Heaton, Tom Sargent, Martin Schneider, Annette Vissing-Jorgensen, seminar participants at University of California-Berkeley, Copenhagen School of Business, Duke University, Norwegian School of Management, University of Chicago, Wharton and conference participants at the Summer Econometric Society Meetings, the NBER Asset-Pricing meeting, and CIRANO Financial Econometrics Conference in Montreal for useful comments. Yaron thanks support from the Rodney White Center at the Wharton School. †Fuqua School of Business, Duke University and NBER, ravi.bansal@duke.edu. ‡The Wharton School, University of Pennsylvania, kiku@wharton.upenn. edu. §The Wharton School, University of Pennsylvania and NBER, yaron@wharton.upenn. edu.
Earlier Development of the Accumbens Relative to Orbitofrontal Cortex Might Underlie Risk-Taking Behavior in Adolescents

Adolescence has been characterized by risk-taking behaviors that can lead to fatal outcomes. This study examined the neurobiological development of neural systems implicated in reward-seeking behaviors. Thirty-seven participants (7–29 years of age) were scanned using event-related functional magnetic resonance imaging and a paradigm that parametrically manipulated reward values. The results show exaggerated accumbens activity, relative to prefrontal activity in adolescents, compared with children and adults, which appeared to be driven by different time courses of development for these regions. Accumbens activity in adolescents looked like that of adults in both extent of activity and sensitivity to reward values, although the magnitude of activity was exaggerated. In contrast, the extent of orbital frontal cortex activity in adolescents looked more like that of children than adults, with less focal patterns of activity. These findings suggest that maturing subcortical systems become disproportionately activated relative to later maturing top–down control systems, biasing the adolescent's action toward immediate over long-term gains.
Application of neural networks to turbulence control for drag reduction

A new adaptive controller based on a neural network was constructed and applied to turbulent channel flow for drag reduction. A simple control network, which employs blowing and suction at the wall based only on the wall-shear stresses in the spanwise direction, was shown to reduce the skin friction by as much as 20% in direct numerical simulations of a low-Reynolds number turbulent channel flow. Also, a stable pattern was observed in the distribution of weights associated with the neural network. This allowed us to derive a simple control scheme that produced the same amount of drag reduction. This simple control scheme generates optimum wall blowing and suction proportional to a local sum of the wall-shear stress in the spanwise direction. The distribution of corresponding weights is simple and localized, thus making real implementation relatively easy. Turbulence characteristics and relevant practical issues are also discussed.
Analysis of Snowboarding Performance At The Burton Open Australian Half-Pipe Championships

Half-pipe snowboarding performance is assessed by subjective measures. Knowledge of the relative importance of objective performance indicators however can improve training and competition performance assessment. We analysed previously developed key performance indicators at the Burton Open Australian Half-Pipe Championships over three years (2006, 2007, 2008). Linear regression showed the two individual key performance indicators most strongly correlated to competition success and multiple linear regression (enter method) showed the shared variance in scores explained by these objective variables. The two objective performance variables most highly correlated with competition success were average air time (AAT) and average degree of rotation (ADR). When combined (multiple linear regression), AAT and ADR objectively explain 71 – 94% of shared variance in subjective competition scores. We compared magnitudes of differences in AAT and ADR between athletes achieving top three (podium) final rankings and those achieving final rankings outside the top three. Magnitude of difference between athletic performances was established with a standardised (Cohen’s) effect size (ES) with 95% confidence limits. Differences in AAT and ADR between athletes placing in the top three and those finishing outside the top three routinely showed moderate (ES = 0.6 – 1.2 95% CL) to very large (ES >2.0 95% CL) effects.
Strong Proxy Signature and its Applications

Proxy signature is a signature scheme that an original signer delegates his/her signing capability to a proxy signer, and then the proxy signer creates a signature on behalf of the original signer. In this paper we show various attack scenarios against previous proxy signature schemes, which shows that proxy signature schemes should be designed very carefully. Based on these weaknesses, we provide new classifications of proxy signatures; strong vs. weak proxy signatures, designated vs. non-designated proxy signatures, and self-proxy signatures. We construct a simple and efficient strong non-designated proxy signature scheme and apply it to multi-proxy signature when plural delegations of multiple original signers exist. We also show that self-proxy signature can be applied to partially blind signatures.
Evaluating a telemedicine system to assist in the management of dermatology referrals

Background Teledermatology systems fall into two categories: live video or store‐and‐forward. In the former, video‐conferencing equipment is used to connect a patient with a remote consultant. This method has been evaluated as an aid to dermatology, but it is expensive both in terms of capital and running costs. Video consultations are generally longer than conventional ones and harder to schedule. Some authors have considered store‐and‐forward as an alternative to live video: instead of a consultation, specialists could make a rapid inspection of a transmitted still image.
Epitope Specificity of Anti-Citrullinated Protein Antibodies

Anti-citrullinated protein antibodies are primarily associated with a progressive course in the autoimmune disease rheumatoid arthritis, a disease with a chronic and inflammatory nature. These antibodies do not appear to have any strict dependency for reactivity except from the presence of the non-genetically encoded amino acid citrulline, which is the result of a posttranslational modification, catalyzed by calcium-dependent peptidylarginine deiminase enzymes. Nevertheless, several amino acids surrounding the citrulline residue notably influence antibody reactivity, especially with a central-Cit-Gly-motif being essential for antibody reactivity. Most importantly, these antibodies have been proposed to be divided into two groups, based on their ability to recognize multiple citrullinated peptides. Thus, an "overlapping" antibody group, which appears to recognize several citrullinated peptides, and a "non-overlapping" antibody group, which only recognizes a limited number of citrullinated peptides, have been proposed. Based on these findings, we suggest that antibodies recognizing several citrullinated targets, also referred to as cross-reactive antibodies, primarily are backbone-dependent, whereas less cross-reactive antibodies primarily depend on the side chains of the amino acids comprising the epitopes for stable antibody-antigen interactions, which reduces the degree of cross-reactivity significantly. Clarifying the reactivity pattern of anti-citrullinated protein antibodies may contribute to determining their true nature of origin.
RNA editing by ADARs is important for normal behavior in Caenorhabditis elegans

Here we take advantage of the well‐characterized and simple nervous system of Caenorhabditis elegans to further our understanding of the functions of RNA editing. We describe the two C.elegans ADAR genes, adr‐1 and adr‐2, and characterize strains containing homozygous deletions in each, or both, of these genes. We find that adr‐1 is expressed in most, if not all, cells of the C.elegans nervous system and also in the developing vulva. Using chemotaxis assays, we show that both ADARs are important for normal behavior. Biochemical, molecular and phenotypic analyses indicate that ADR‐1 and ADR‐2 have distinct roles in C.elegans, but sometimes act together.
Garch forecasting performance under different distribution assumptions

This paper investigates the forecasting performance of the Garch (1, 1) model when estimated with NINE different error distributions on Standard and Poor's 500 Index Future returns. By utilizing the theory of realized variance to construct an appropriate ex post measure of volatility from intra-day data it is shown that allowing for a leptokurtic error distribution leads to significant improvements in variance forecasts compared to using the normal distribution. This result holds for daily, weekly as well as monthly forecast horizons. It is also found that allowing for skewness and time variation in the higher moments of the distribution does not further improve forecasts. Copyright © 2006 John Wiley & Sons, Ltd.
An Unusual Case of Systemic Inflammatory Myofibroblastic Tumor with Successful Treatment with ALK-Inhibitor

Systemic inflammatory myofibroblastic tumor is an exceedingly rare entity. A 45-year-old Hispanic female presented with a 6-month history of left-sided thigh pain, low back pain, and generalized weakness. PET/CT scan revealed abnormal activity in the liver, adrenal gland, and pancreas. MRI of the abdomen demonstrated two 6-7 cm masses in the liver. MRI of the lumbar spine demonstrated lesions in the L2 to L4 spinous processes, paraspinal muscles, and subcutaneous tissues, as well as an 8 mm enhancing intradural lesion at T11, all thought to be metastatic disease. A biopsy of the liver showed portal tract expansion by a spindle cell proliferation rich in inflammation. Tumor cells showed immunoreactivity for smooth muscle actin and anaplastic lymphoma kinase 1 (ALK1). Tissue from the L5 vertebra showed a process histologically identical to that seen in the liver. FISH analysis of these lesions demonstrated an ALK (2p23) gene rearrangement. The patient was successfully treated with an ALK-inhibitor, Crizotinib, and is now in complete remission. We present the first reported case, to our knowledge, of inflammatory myofibroblastic tumor with systemic manifestations and ALK translocation. This case is a prime example of how personalized medicine has vastly improved patient care through the use of molecular-targeted therapy.
The more we know, the less we agree: Higher-order expectations and public announcements

Polarization of opinions after public announcement is widely observed, but often considered to be inconsistent with Bayesian learning. I show that this is not the case in environments where higher-order expectations play a role. I characterize informational structures where public announcement leads to polarization in all higher-order expectations, but not in first-order expectations. To illustrate the economic consequences, I modify two workhorse models of asymmetric information. I show in a Morris-Shin(1998) model that more disclosure of public information can increase the chance of successful currency attacks. I show in a dynamic Grossman-Stiglitz(1980) model that hectic trading around public announcements is consistent with common priors and Bayesian learning.
Hypnotizability is modifiable: an alternative approach.

Abstract 2 major ways of construing increases in hypnotic responsivity are suggested. Both the trait viewpoint and the skill-learning model are discussed as are a number of issues differentiating the two approaches. The controversy concerning the roles played by both aptitude and attitude is highlighted. It is argued that the skilllearning position is plausible and the nature of skill-based approaches to training is considered. Empirical findings consistent with the skill viewpoint are summarized. Methodological deficiencies and suggestions for future directions in modification research are discussed. The concluding section specifies 3 probable core components in successful modification procedures including optimal learning factors, attitudinal and set factors, and cognitive strategy factors.
Expert Systems in the Steel Industry

EEL PRODUCTION INVOLVES A number of stages, such as melting, casting, rolling, and forging, that entail complex chemical and thermic reactions as well as intricate mechanical operations. Because these processes do not lend themselves to exact mathematical modeling, steel manufacturers must turn to techniques for reasoning with incomplete and uncertain data. Their decisions often rely on the experience of individual experts. Nearly all steelmakers worldwide now use expert systems, fuzzy logic, and neural nets to improve quality assurance and production efficiency. This special track of IEEE Expert looks at several typical, successfully fielded systems. For many years, the steel industry’s main objective has been to maximize production by automating processes and streamlining plant organization. As with the Republic of Korea’s Kwangyang Works, steel manufacturers have been erecting new plants from scratch, locating them near the sea to make the delivery of steel from blast furnace to final shipment as direct as possible. Because they restricted the diversity of their products, such new plants have become very competitive. Asia’s steel industry, in particular, used these approaches in the 1980s to produce high-quality steel cheaper than its Western competitors. (See the sidebar for historical overview of steel production.) However, the continuing improvement of substitutes for steel has raised the demand for even higher-quality steel with dedicated characteristics. By using different alloying metals and various heat and surface treatments, steelmakers now can offer a manifold of products. Ongoing research into new steel qualities has produced a broad range of products, which present many new’control problems. Although other industries reflect the same tendency toward processing in smaller lot sizes, the steelmaking environment shows more diversity than most because of the particular characteristics of its matenal and manufactunng technology Furthermore, the capital-intensive nature of the industry can make unanticipated violakons of technological constraints extremely costly A look at several typical factors will illustrate these considerations Most steelmaking processes are temperature-sensitive For each process, the steel must have aprescribed temperature, and any time it spends waiting on the next processing step will incur a costly reheating. Moreover, because chemical reactions depend on temperature, any loss of heat during processing may degrade the steel’s quality Iithe prescribed sohkfication temperature profile is violated, an incorrect internal structure of the steel might result Although process ames are difficult to predict precisely, steelmakers do exercise some degree of control. Treatment time in furnaces depends on the temperature, which can be controlled through heat input, generally sub-
Content Area Learning in Middle School Social Studies Classrooms and Students with Emotional or Behavioral Disorders: A Comparison of Strategies

This study investigated the use of integrated explicit strategy instruction in social studies classes using a peer tutoring format. Thirty students attending social studies classes in a middle school for students with emotional or behavioral disorders studied social studies content in a crossover design, in which all students received each of two instructional conditions. In one treatment condition, students served as peer tutors using a paragraph summary strategy. In the other condition, students received traditional instruction. After 4 weeks of tutoring, results indicated that students scored higher on content tests and on-task behavior while in the tutoring condition. Qualitative data suggested that students enjoyed peer tutoring relative to traditional instruction and reported that they would like to use it in other classes. Teachers agreed that peer tutoring and strategy instruction had exerted a facilitative effect on student performance. The authors discuss implications for practice.
Learning From An Optimization Viewpoint

In this dissertation we study statistical and online learning problems from an optimization viewpoint.The dissertation is divided into two parts : I. We first consider the question of learnability for statistical learning problems in the general learning setting. The question of learnability is well studied and fully characterized for binary classification and for real valued supervised learning problems using the theory of uniform convergence. However we show that for the general learning setting uniform convergence theory fails to characterize learnability. To fill this void we use stability of learning algorithms to fully characterize statistical learnability in the general setting. Next we consider the problem of online learning. Unlike the statistical learning framework there is a dearth of generic tools that can be used to establish learnability and rates for online learning problems in general. We provide online analogs to classical tools from statistical learning theory like Rademacher complexity, covering numbers, etc. We further use these tools to fully characterize learnability for online supervised learning problems. II. In the second part, for general classes of convex learning problems, we provide appropriate mirror descent (MD) updates for online and statistical learning of these problems. Further, we show that the the MD is near optimal for online convex learning and for most cases, is also near optimal for statistical convex learning. We next consider the problem of convex optimization and show that oracle complexity can be lower bounded by the so called fat-shattering dimension of the associated linear class. Thus we establish a strong connection between offline convex optimization problems and statistical learning problems. We also show that for a large class of high dimensional optimization problems, MD is in fact near optimal even for convex optimization.
Apilimod Inhibits the Production of IL-12 and IL-23 and Reduces Dendritic Cell Infiltration in Psoriasis

Psoriasis is characterized by hyperplasia of the epidermis and infiltration of leukocytes into both the dermis and epidermis. IL-23, a key cytokine that induces TH17 cells, has been found to play a critical role in the pathogenesis of psoriasis. Apilimod is a small-molecule compound that selectively suppresses synthesis of IL-12 and IL-23. An open-label clinical study of oral administration of apilimod was conducted in patients with psoriasis. Substantial improvements in histology and clinical measurements were observed in patients receiving 70mg QD. The expression of IL-23p19 and IL-12/IL-23p40 in skin lesions was significantly reduced in this dose group, with a simultaneous increase in IL-10 observed. A decrease in the levels of TH1 and TH17 cytokines/chemokines in skin lesions followed these p19 and p40 changes. In parallel, a reduction in skin-infiltrating CD11c+ dendritic cells and CD3+ T cells was seen, with a greater decrease in the CD11c+ population. This was accompanied by increases in T and B cells, and decreases in neutrophils and eosinophils in the periphery. This study demonstrates the immunomodulatory activity of apilimod and provides clinical evidence supporting the inhibition of IL-12/IL-23 synthesis for the treatment of TH1- and TH17-mediated inflammatory diseases.
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations

In our work, we bridge deep neural network design with numerical differential equations. We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations. This finding brings us a brand new perspective on the design of effective deep architectures. We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks. As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations. The LM-architecture is an effective structure that can be used on any ResNet-like networks. In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the networks obtained by applying the LM-architecture on ResNet and ResNeXt respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on both CIFAR and ImageNet with comparable numbers of trainable parameters. In particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly compress ($>50$\%) the original networks while maintaining a similar performance. This can be explained mathematically using the concept of modified equation from numerical analysis. Last but not least, we also establish a connection between stochastic control and noise injection in the training process which helps to improve generalization of the networks. Furthermore, by relating stochastic training strategy with stochastic dynamic system, we can easily apply stochastic training to the networks with the LM-architecture. As an example, we introduced stochastic depth to LM-ResNet and achieve significant improvement over the original LM-ResNet on CIFAR10.