Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Capital-Asset Constraints on Commercial Bank Lending from 1990 to 1992 June 3 , 2002

The U.S. experienced a sharp decline in commercial bank lending from 1990 to 1992. This particular event has caught the attention of many scholars because it is often blamed as one of the factors that led to a failure of a speedy recovery from the ongoing recession. This issue remains controversial because debate exists on whether the credit decline was due to a reduction in supply or to a reduction in demand. Both categories are investigated in this paper by dealing with potential causes ranging from recessionary effects to capital-asset constraints. The analytical component focuses on involves single and multiple regressions employing data sets from aggregate state-by-state totals and individual banks in California. The results from the analysis suggest that low capitalasset levels and strict regulatory standards during 1990 resulted in a supply-induced credit decline.
Sex differences in pacing during ‘Ultraman Hawaii’

Background To date, little is known for pacing in ultra-endurance athletes competing in a non-stop event and in a multi-stage event, and especially, about pacing in a multi-stage event with different disciplines during the stages. Therefore, the aim of the present study was to examine the effect of age, sex and calendar year on triathlon performance and variation of performance by events (i.e., swimming, cycling 1, cycling 2 and running) in ‘Ultraman Hawaii’ held between 1983 and 2015. Methods Within each sex, participants were grouped in quartiles (i.e., Q1, Q2, Q3 and Q4) with Q1 being the fastest (i.e., lowest overall time) and Q4 the slowest (i.e., highest overall time). To compare performance among events (i.e., swimming, cycling 1, cycling 2 and running), race time in each event was converted in z score and this value was used for further analysis. Results A between-within subjects ANOVA showed a large sex × event (p = 0.015, η2 = 0.014) and a medium performance group × event interaction (p = 0.001, η2 = 0.012). No main effect of event on performance was observed (p = 0.174, η2 = 0.007). With regard to the sex × event interaction, three female performance groups (i.e., Q2, Q3 and Q4) increased race time from swimming to cycling 1, whereas only one male performance group (Q4) revealed a similar trend. From cycling 1 to cycling 2, the two slower female groups (Q3 and Q4) and the slowest male group (Q4) increased raced time. In women, the fastest group decreased (i.e., improved) race time from swimming to cycling 1 and thereafter, maintained performance, whereas in men, the fastest group decreased race time till cycling 2 and increased it in the running. Conclusion In summary, women pace differently than men during ‘Ultraman Hawaii’ where the fastest women decreased performance on day 1 and could then maintain on day 2 and 3, whereas the fastest men worsened performance on day 1 and 2 but improved on day 3.
New Constraints on the Energetics, Progenitor Mass, and Age of the Supernova Remnant G292.0+1.8 Containing PSR J1124–5916

We present spatially resolved spectroscopy of the supernova remnant (SNR) G292.0+1.8 with the Chandra X-Ray Observatory. This composite-type SNR contains the 135 ms pulsar, J1124-5916, recently discovered by Camilo and coworkers. We apply nonequilibrium ionization models to fit the ejecta-dominated regions and to identify the blast wave of the supernova explosion. By comparing the derived abundances with those predicted from nucleosynthesis models, we estimate a progenitor mass of 30-40 M☉. We derive the intrinsic parameters of the supernova explosion such as its energy, the age of the SNR, the blast wave velocity, and the swept-up mass. In the Sedov interpretation, our estimated SNR age of 2600 yr is close to the pulsar's characteristic age of 2900 yr. This confirms the pulsar/SNR association and relaxes the need for the pulsar to have a noncanonical value for the braking index, a large period at birth, or a large transverse velocity. We discuss the properties of the pulsar wind nebula (PWN) in the light of the Kennel & Coroniti model and estimate the pulsar wind magnetization parameter. We also report the first evidence for steepening of the power-law spectral index with increasing radius from the pulsar, a result that is expected from synchrotron losses and is reminiscent of Crab-like PWNe.
Artificial Neural Network Inference (ANNI): A Study on Gene-Gene Interaction for Biomarkers in Childhood Sarcomas

Objective To model the potential interaction between previously identified biomarkers in children sarcomas using artificial neural network inference (ANNI). Method To concisely demonstrate the biological interactions between correlated genes in an interaction network map, only 2 types of sarcomas in the children small round blue cell tumors (SRBCTs) dataset are discussed in this paper. A backpropagation neural network was used to model the potential interaction between genes. The prediction weights and signal directions were used to model the strengths of the interaction signals and the direction of the interaction link between genes. The ANN model was validated using Monte Carlo cross-validation to minimize the risk of over-fitting and to optimize generalization ability of the model. Results Strong connection links on certain genes (TNNT1 and FNDC5 in rhabdomyosarcoma (RMS); FCGRT and OLFM1 in Ewing’s sarcoma (EWS)) suggested their potency as central hubs in the interconnection of genes with different functionalities. The results showed that the RMS patients in this dataset are likely to be congenital and at low risk of cardiomyopathy development. The EWS patients are likely to be complicated by EWS-FLI fusion and deficiency in various signaling pathways, including Wnt, Fas/Rho and intracellular oxygen. Conclusions The ANN network inference approach and the examination of identified genes in the published literature within the context of the disease highlights the substantial influence of certain genes in sarcomas.
Once in Orange Always in Orange? The Cognitive, Emotional and Material Elements of De-Identification and Logic Resilience

ii Dedication . iii Acknowledgements . iv List of Tables . viii List of Figures . ix Introduction . 1 Literature Review. 6 Institutional Logic Perspective . 6 Microfoundations: Individuals and Institutional Logics . 10 Identity . 14 Identity Work, Agency and Control . 17 Identity Work, Transitions, and De-identification . 20 Research Design and Methodology . 24 Data Sources and Collection . 25 Macro: Institutional logics . 25 Micro: Individuals in transition . 26 Ethics . 27 Table 1: Data Sources for Actors Within the Field of Canadian Corrections . 28 Data Analysis . 28 Macro-level analysis: field logics. 29 Table 2: Prison Logics and Associated Institutional Defined Convict Identities . 30 Table 3: Coding Dictionary for Institutional Logics . 31 Coding: Stage one. 31 Coding: Stage two. 32 Coding: Stage three . 32 Frequency checks and pattern documenting. 33 Figure 1 : Overview of Data Coding Process . 34 Trustworthiness . 35 The Field of Corrections: Prison Logics, Defining the Incarcerated . 36 Figure 2: Actors in the Field of Corrections . 38 Figure 3: Field Actor and Relative Use of Prison Logics . 39 Life Behind Bars: Becoming a Convict . 41 Table 4: Interviewee Demographic Information and Identity Work Pattern . 43 Transition and De-identification . 49 Figure 4: Frequency of Identity Work Types by Pattern . 51 Defensive Patterns of Identity Work: Blaming and Differentiating . 52 Blaming. 52 Differentiating . 54 Patterns of Reflexive Identity Work: Opening up, Getting Critical and Self-regulating . 55 Opening up . 55 Getting critical . 56 Self-regulating. 58 De-identification and Persistent Identification . 61 Figure 5: Frequency of Prison Logic Coded by Identity Work Pattern . 62
Long-Term Effects, Pathophysiological Mechanisms, and Risk Factors of Chemotherapy-Induced Peripheral Neuropathies: A Comprehensive Literature Review

Neurotoxic anticancer drugs, such as platinum-based anticancer drugs, taxanes, vinca alkaloids, and proteasome/angiogenesis inhibitors are responsible for chemotherapy-induced peripheral neuropathy (CIPN). The health consequences of CIPN remain worrying as it is associated with several comorbidities and affects a specific population of patients already impacted by cancer, a strong driver for declines in older adults. The purpose of this review is to present a comprehensive overview of the long-term effects of CIPN in cancer patients and survivors. Pathophysiological mechanisms and risk factors are also presented. Neurotoxic mechanisms leading to CIPNs are not yet fully understood but involve neuronopathy and/or axonopathy, mainly associated with DNA damage, oxidative stress, mitochondria toxicity, and ion channel remodeling in the neurons of the peripheral nervous system. Classical symptoms of CIPNs are peripheral neuropathy with a “stocking and glove” distribution characterized by sensory loss, paresthesia, dysesthesia and numbness, sometimes associated with neuropathic pain in the most serious cases. Several risk factors can promote CIPN as a function of the anticancer drug considered, such as cumulative dose, treatment duration, history of neuropathy, combination of therapies and genetic polymorphisms. CIPNs are frequent in cancer patients with an overall incidence of approximately 38% (possibly up to 90% of patients treated with oxaliplatin). Finally, the long-term reversibility of these CIPNs remain questionable, notably in the case of platinum-based anticancer drugs and taxanes, for which CIPN may last several years after the end of anticancer chemotherapies. These long-term effects are associated with comorbidities such as depression, insomnia, falls and decreases of health-related quality of life in cancer patients and survivors. However, it is noteworthy that these long-term effects remain poorly studied, and only limited data are available such as in the case of bortezomib and thalidomide-induced peripheral neuropathy.
Diagenesis and clay mineral formation at Gale Crater, Mars

The Mars Science Laboratory rover Curiosity found host rocks of basaltic composition and alteration assemblages containing clay minerals at Yellowknife Bay, Gale Crater. On the basis of the observed host rock and alteration minerals, we present results of equilibrium thermochemical modeling of the Sheepbed mudstones of Yellowknife Bay in order to constrain the formation conditions of its secondary mineral assemblage. Building on conclusions from sedimentary observations by the Mars Science Laboratory team, we assume diagenetic, in situ alteration. The modeling shows that the mineral assemblage formed by the reaction of a CO2‐poor and oxidizing, dilute aqueous solution (Gale Portage Water) in an open system with the Fe‐rich basaltic‐composition sedimentary rocks at 10–50°C and water/rock ratio (mass of rock reacted with the starting fluid) of 100–1000, pH of ~7.5–12. Model alteration assemblages predominantly contain phyllosilicates (Fe‐smectite, chlorite), the bulk composition of a mixture of which is close to that of saponite inferred from Chemistry and Mineralogy data and to that of saponite observed in the nakhlite Martian meteorites and terrestrial analogues. To match the observed clay mineral chemistry, inhomogeneous dissolution dominated by the amorphous phase and olivine is required. We therefore deduce a dissolving composition of approximately 70% amorphous material, with 20% olivine, and 10% whole rock component.
Program Development Tools : Debugging by Reverse Computing

More and more program development tools have turned to Web-based. It has an advantage on being a multiplatform tool. This work proposes a debugging tool based on web interface. The main idea of the tool is that the execution of the program can be regarded as a flow. This flow can be captured and played back later. Therefore, any state of the executed program can be analyzed and errors can be pinpointed. We propose a time-reversal computing as a way to capture the flow of execution so that the time step can be reversed without starting from time-zero. Keywords— debugging, web-based tools, reverse computing
Aperiodic Neurodynamics Using a Simplified K-Set Neural Population Model

Mesoscopic level neurodynamics study the collective dynamical behavior of neural populations. Such models are becoming increasingly important in understanding large-scale brain processes. Brains exhibit aperiodic oscillations with a much more rich dynamical behavior than fixed-point and limitcycle approximation allow. Here we present a discretized model inspired by Freeman’s K-set mesoscopic level population model [1], [2]. We compare this discrete time, difference equation model to the original K-sets and to standard artificial neural network (ANN) models. The comparison with ANNs illustrates how the K-sets differ from standard ANN models. We then show how the simplified K-set models can replicate the important principles of aperiodic neurodynamics, and how these processes might be important in the emergence of collective behavior among neural populations for developing cognitive mechanisms.
Atmospheric NLTE-models for the spectroscopic analysis of blue stars with winds. II. Line-blanketed models

Received; accepted Abstract. We present new or improved methods for calculating NLTE, line-blanketed model atmospheres for hot stars with winds (spectral types A to O), with particular emphasis on a fast performance. These methods have been implemented into a previous, more simple version of the model atmosphere code Fastwind (Santolaya-Rey et al. 1997) and allow to spectroscop- ically analyze rather large samples of massive stars in a rea sonable time-scale, using state-of-the-art physics. Alth ough this updated version of the code has already been used in a number of recent investigations, the corresponding methods have not been explained in detail so far, and no rigorous comparison with results from alternative codes has been performed. This paper intends to address both topics. In particular, we describe our (partly approximate) approach to solve the equations of statistical equilibrium for tho se elements which are primarily responsible for line-blocking and blanketing, as well as an approximate treatment of the line-blocking itself, which is based on a simple statistical approach usin g suitable means for line opacities and emissivities. Both methods are validated by specific tests. Furthermore, we comment on our i mplementation of a consistent temperature structure. In the second part, we concentrate on a detailed comparison with results from those two codes which have been used in alternative spectroscopical investigations, namely cmfgen (Hillier & Miller 1998) andwm-Basic (Pauldrach et al. 2001). All three codes predict almost identical temperature structur es and fluxes for λ > 400 A, whereas at lower wavelengths a number of discrepancies are found. Particularly in the Heii continua, where fluxes and corresponding numbers of ionizin g photons react extremely sensitively to subtle differences in the models, we consider any uncritical use of these quantities (e.g., in the context of nebula diagnostics) as being dangerous. Optical H/He lines as synthesized byfastwind are compared with results from cmfgen, obtaining a remarkable coincidence, except for the Hei singlets in the temperature range between 36,000 to 41,000 K for dwarfs and between 31,000 to 35,000 K for supergiants, wherecmfgen predicts much weaker lines. Consequences due to these discrepancies are discussed. Finally, suggestions are presented how to adequately parameterize model-grids for hot stars with winds, with only one additional parameter compared to standard grids from plane-parallel, hydrostatic models.
Underestimation of Species Richness in Neotropical Frogs Revealed by mtDNA Analyses

Background Amphibians are rapidly vanishing. At the same time, it is most likely that the number of amphibian species is highly underestimated. Recent DNA barcoding work has attempted to define a threshold between intra- and inter-specific genetic distances to help identify candidate species. In groups with high extinction rates and poorly known species boundaries, like amphibians, such tools may provide a way to rapidly evaluate species richness. Methodology Here we analyse published and new 16S rDNA sequences from 60 frog species of Amazonia-Guianas to obtain a minimum estimate of the number of undescribed species in this region. We combined isolation by distance, phylogenetic analyses, and comparison of molecular distances to evaluate threshold values for the identification of candidate species among these frogs. Principal Findings In most cases, geographically distant populations belong to genetically highly distinct lineages that could be considered as candidate new species. This was not universal among the taxa studied and thus widespread species of Neotropical frogs really do exist, contrary to previous assumptions. Moreover, the many instances of paraphyly and the wide overlap between distributions of inter- and intra-specific distances reinforce the hypothesis that many cryptic species remain to be described. In our data set, pairwise genetic distances below 0.02 are strongly correlated with geographical distances. This correlation remains statistically significant until genetic distance is 0.05, with no such relation thereafter. This suggests that for higher distances allopatric and sympatric cryptic species prevail. Based on our analyses, we propose a more inclusive pairwise genetic distance of 0.03 between taxa to target lineages that could correspond to candidate species. Conclusions Using this approach, we identify 129 candidate species, two-fold greater than the 60 species included in the current study. This leads to estimates of around 170 to 460 frog taxa unrecognized in Amazonia-Guianas. Significance As a consequence the global amphibian decline detected especially in the Neotropics may be worse than realised.
Advanced voice activity detection on mobile phones by using microphone array and phoneme-specific Gaussian mixture models

This paper presents an advanced voice activity detection (VAD) system, developed for mobile Android OS platforms with limited hardware capabilities. The system uses a dual microphone array for noise suppression and a decoder with a constrained grammar for speech detection, where Gaussian mixture models (GMMs) are used together with their acoustic weights and energy in order to increase the robustness of the proposed system. The system is presented as part of the Voice Assistant application for mobile phones, and the results are given on a database that was especially designed for that purpose. The results presented in this paper show a high accuracy even when a large amount of background noise is present.
Guided Synthesis of Control Programs for a Batch Plant using UPPAAL

In this paper we address the problem of scheduling and synthesizing distributed control programs for a batch production plant. We use a timed automata model of the batch plant and the verification tool Uppaal to solve the scheduling problem. In modeling the plant, we aim at a level of abstraction which is sufficiently accurate in order that synthesis of control programs from generated timed traces is possible. Consequently, the models quickly become too detailed and complicated for immediate automatic synthesis. In fact, only models of plants producing two batches can be analyzed directly! To overcome this problem, we present a general method allowing the user to guide the model-checker according to heuristically chosen strategies. The guidance is specified by augmenting the model with additional guidance variables and by decorating transitions with extra guards on these. Applying this method has made synthesis of control programs feasible for a plant producing as many as 60 batches. The synthesized control programs have been executed in a physical plant. Besides proving useful in validating the plant model and in finding some modeling errors, we view this final step as the ultimate litmus test of our methodology's ability to generate executable (and executing) code from basic plant models.
Personality and Politics: Values, Traits, and Political Choice

Voters' political choices have presumably come to depend more on their personal preferences and less on their social characteristics in Western democracies. We examine two aspects of personality that may influence political choice, traits and personal values, using the Five Factor Model of personality traits and the Schwartz (1992) theory of basic personal values. Data from 3044 voters for the major coalitions in the Italian national election of 2001 showed that supporters of the two coalitions differed in traits and values, largely as hypothesized. Center-left voters were higher than center-right voters in the traits of friendliness and openness and lower in energy and conscientiousness. Regarding values, center-left voters were higher than center-right voters in universalism, benevolence, and self-direction and lower in security, power, achievement, conformity, and tradition. Logistic regressions revealed that values explained substantial variance in past and future voting and in change of political choice, trumping personality traits. We discuss explanations for the primacy of values and implications for the social cognitive view of personality.
A fast eye state computing algorithm

Nowadays, the driver drowsiness has become a main cause of many traffic accidents, while the state of eye is a significant factor which has a close relationship with sleep. This article presents an algorithm which can estimate drivers' eye states, aiming at reducing the rates of traffic accidents by monitoring drivers' drowsiness. Firstly, a driver's face images are grabbed from a digital camera and the Haar-like algorithm is implemented to extract the face area from these images. Then, the algorithm that we proposed can find the exact location of eyes and this step uses the technique called Integral Projection and several morphological operations, which makes the computing very fast. Finally, the eye states can be estimated by comparing the parameters of eye's contour. Also, a prototype system is conducted to test the performance of the algorithm. It turns out that this algorithm is very efficient and can reach a high accuracy in different light conditions.
Distributed dynamic feedback control for smart power networks with tree topology

In this paper, we design a distributed dynamic feedback controller that implements real-time economic optimization in power networks with tree topology. We consider the coupling between the dynamics of the power network and the appropriately formulated optimization problem in the real-time market, i.e., when the time-scale separation between markets and network dynamics fades. The design methodology is motivated by optimization decomposition methods in that the controller is derived from a primal-dual decomposition approach. We then prove the asymptotic stability of the overall (closed-loop) system in a scalable fashion. The performance of the controller is illustrated by numerical investigations.