Miracle-dz/UniFAR-FTUs huggingface dataset:
Gasoline Prices and the Fuel Economy Discount Puzzle

It is often asserted that consumers purchasing automobiles and other durable goods un- derweight gasoline or other future add-on costs. We test this hypothesis in the US automobile market by examining the equilibrium eects of time series variation in gasoline price expectations on the market shares and relative prices of vehicles with dierent fuel economy ratings. When gas prices rise, gas guzzlers become less desirable while higher fuel economy vehicles become more attractive. We should therefore expect the relative price of the former to go up. However, market share changes - increased production of high fuel economy vehicles and scrappage of low fuel economy vehicles - can attenuate this eect. We identify the causal impact of gas prices on willingness-to-pay for fuel e¢ ciency using a discrete choice framework that captures substi- tution patterns between new and used vehicles. This framework suggests a natural instrument for the market share of a used vehicle: the interaction of fuel economy with gasoline prices in the year the vehicle was produced. We construct an unprecedentedly rich dataset that includes the prices, quantities, and characteristics of all new and used vehicles on the road monthly from 2003 to 2008. We show that the American auto consumer is willing to pay just $0.25 to reduce future gas expenditures by $1 (in present discounted value). Our …nding provides support for a paternalistic justi…cation of interventions such as energy e¢ ciency standards: they force con- sumers to purchase energy-e¢ cient goods, regardless of whether their demand patterns indicate that they want them. — — — — — — — — — — — — — — — — — — — — — — — — — — We thank Alberto Abadie, Orley Ashenfelter, Lucas Davis, Kelly Gallagher, Penny Goldberg, Michael Greenstone, Mark Jacobsen, James Kahn, Lutz Kilian, David Laibson, Henry Lee, Robin Lee, Lars Lefgren, Alessandro Lizzeri, Erich Muehlegger, Sendhil Mullainathan, Richard Newell, Ariel Pakes, Jesse Rothstein, Jim Sallee, Rob Stavins, Chris Timmins, Roger von Haefen, Matt White, and seminar participants at Harvard, MIT, Princeton, and Stanford for their feedback. Funding for this project was provided by Princeton's Industrial Relations Section, Harvard's Energy Technology Innovation Policy group and the Harvard University Center for the Environment. We thank Carmen Collyns and Jordon Ricks for research assistance on this and related projects. We especially thank Sounman Hong for meticulous and tireless research assistance.
Saliency-Guided Feature Matching for Self-Driving Systems

This paper presents a novel method for increasing the reliability of feature matching based on visual saliency. The key idea of the proposed method is to suppress outliers frequently occurring in the background clutter by adopting visually attractable (i.e., salient) regions as a filtering weight, which are highly relevant to underlying structures (e.g., signs, buildings, etc.) in a given scene. This significantly improves the accuracy of feature matching while reducing the memory usage. Experimental results on the KlTTI dataset demonstrate that the proposed method is effective for increasing the accuracy of feature matching under diverse outdoor environments.
A Search Method of Large-Scale Resources for Providing Efficient Computing on a Participating Fine-Granular Cloud Computing Platform

Currently, most of cloud computing services based on IaaS are provided by one specific provider for many users. In contrast, we have advocated a “participating fine-granular cloud computing platform” that a large number of personal or small company level resource suppliers participates in, configures and provides cloud computing, as an ultimately evolved form of cloud computing environment. When large-scale resources are requested by users in environments where small amount of resources are provided by many suppliers, appropriate resources for a large-scale request should be selected. Therefore, in this paper, we propose a resource search method for large-scale computing, in which resources that are close to each other are selected and used by each user to support quick computation and communication response, and low traffic load in the network.
Learning inequalities between primary and secondary school. Difference-in-difference with international assessments

Evaluating the effect of institutional features by exploiting cross-country variability with crosssectional data is difficult. Difference-in-difference strategies are sometimes employed to reach identification. In this paper, we discuss the difference-in-difference strategies adopted in the literature to evaluate the effect of early tracking on learning inequalities using surveys administered to children of different grades. In their seminal paper: “Does educational tracking affect performance and inequality? Differences-in-differences evidence across countries” Economic Journal (2006), Hanushek, and Woessmann analyze the effect of early tracking on inequalities with two-step analysis. Other scholars, instead, focus on the social background regression coefficient, using individual-level models applied to pooled data from all countries. We demonstrate that since test scores are measured on different scales at different surveys, pooled data strategies may yield to completely uninformative results. Against this background, we use data on reading literacy in PIRLS 2006 and PISA 2012 and carry out two-step difference-in-difference analyses on the effect of early tracking on social background learning inequalities.
Determinants of recognition of TRACES certification as valuable opportunity at the farm level in São Paulo, Brazil

TRACES (Trade Control and Expert System) is a veterinary health network created by the European Union to certify and monitor trade of animal products. This research aims to identify the factors that explain why few Brazilian farmers recognized adoption of the TRACES certification as a valuable opportunity. A farm is required to adopt traceability and a set of associated technologies to obtain certification. A sample of 84 beef cattle farms provided data for the testing of the hypotheses by means of a bivariate logit model. The recognition of the opportunity is positively correlated with previous experience with other quality programs, access to up-to-date information, a more diversified life experience, and active participation in relationship networks.
Choice of Pension Discount Rate in Financial Accounting and Stock Prices

Under present financial accounting standards, in Japan and in U.S., firms can choose pension discount rates, which they use for earnings measurement, at their discretion. First, this paper investigates, what factors affect their choice of pension discount rates. The sample firms in this paper reduced their discount rates, when market interest rate declined in 1990s, more slowly than the tendency in the market. We analyze the causes of this delay by logit model. Regression results show that, given declining interest rate, the significant factor affecting the firms' choice is not leverage, but profitability (return on equity: ROE). Second, this research investigates empirically how stock prices reflect the size of pension discount rate. Both unamortized pension obligations and pension expenses in the year are positively associated with stock prices. However, the coefficients for the firms, whose discount rates are higher than median, are smaller than those for the firms choosing lower rates. Those coefficients are not significantly different from zero. These results indicate that the firms, which chose lower pension discount rates, are valued higher. This asymmetric result is consistent with the first point in this paper, concerning the firms' motives for pension discount rate choice. Key Words: Pension accounting; Discount rate; Earnings response coefficients; Capital market; Japan
DeepTP: An End-to-End Neural Network for Mobile Cellular Traffic Prediction

The past 10 years have witnessed the rapid growth of global mobile cellular traffic demands due to the popularity of mobile devices. While accurate traffic prediction becomes extremely important for stable and high-quality Internet service, the performance of existing methods is still poor due to three challenges: complicated temporal variations including burstiness and long periods, multi-variant impact factors such as the point of interest and day of the week, and potential spatial dependencies introduced by the movement of population. While existing traditional methods fail in characterizing these features, especially the latter two, deep learning models with powerful representation ability give us a chance to consider these from a new perspective. In this article, we propose Deep Traffic Predictor (DeepTP), a deep-learning-based end-toend model, which forecasts traffic demands from spatial-dependent and long-period cellular traffic. DeepTP consists of two components: a general feature extractor for modeling spatial dependencies and encoding the external information, and a sequential module for modeling complicated temporal variations. In the general feature extractor, we introduce a correlation selection mechanism for a spatial modeling and embedding mechanism to encode external information. Moreover, we apply a seq2seq model with attention mechanism to build the sequential model. Extensive experiments based on large-scale mobile cellular traffic data demonstrate that our model outperforms the state-of-the-art traffic prediction models by more than 12.31 percent.
Measured Valued Solutions to Conservation Laws Motivated by Traffic Modeling

A well known problem in several areas of applied science is the connection between collective and individual behavior. A famous example is provided by gas dynamics, where the rigorous proof on the convergence of the Boltzmann equations to the Euler equations (i.e. the kinetic limit) is a long standing open question, see [3, 15]. Similar problems arise also in traffic flow modeling, where many continuum flow models currently available [7, 6, 11, 14] are still waiting for their deduction from rules on the individual behavior of drivers, see however [1].
Indirect bonding of Ni-electroless plated AlN and Cu by hot pressing method

The electroless Ni (EN) plating method was employed to metallize the AlN ceramic substrates. The EN-plated AlN substrate was bonded with the Cu foil to form a sandwich-like AlN-EN/Cu/EN-AlN assembly by hot pressing in vacuum with a pressure of 6.5 MPa for 30 min. For the bonding temperature below the Ni-P eutectic temperature of EN at 880/spl deg/C, the samples were bonded through solid state diffusion. On the other hand, the samples were bonded via a liquid phase media through both wetting and diffusion if the bonding temperature was above 880/spl deg/C. An optimum adhesion strength around 10 MPa occurred within bonding temperature range 600-700/spl deg/C. The fracture took place in the EN/Cu interface for samples bonded below 600/spl deg/C, while fracture took place in the AlN/EN interface above 700/spl deg/C, The increasing temperature enhanced interdiffusion of Cu and EN to form a strong bond, yet resulted in a large residual thermal stress in the AlN/EN interface. The bonded samples with as-received AM exhibited higher adhesion strength than those with polished AlN because there existed a residual compression perpendicular to the AlN/EN interface at surface irregularities in the as-received AlN, which resulted in an additional shear strength offset in the adhesion test. The adhesion strength of samples with etched AlN was the highest as compared to those of as-received and polished AlN, although the surface roughness of the etched AlN was the same as that of the as-received one. It is argued that the etched surface of AlN with micro-etched holes provides the anchor sites for interlocking with the EN film, which results in a good mechanical bonding in the joint. However, a mechanical trimming on the edges of bonded samples would damage the joint, and a low adhesion strength is instead observed. >
An Additive Gaussian Process Approximation for Large Spatio-Temporal Data

We propose a new additive spatio-temporal Gaussian process approximation to model complex dependence structures for large spatio-temporal data. The proposed approximation method incorporates a computational-complexity-reduction method and a separable covariant function, which can capture different scales of variation and spatio-temporal interactions. The first component is able to capture nonseparable variation while the second component captures the separable variation of all scales. The Bayesian inference is carried out through a Markov chain Monte Carlo algorithm based on the hierarchical representation of the model. Through this representation we are able to utilize the computational advantages of both components. To demonstrate the inferential and computational benefits of the proposed method, we carry out four different simulation studies as well as a real application that concerns the spatio-temporal measurements of ground-level ozone in the Eastern United States.
Joint Identification-Verification Model for Visual Tracking

Similarity algorithms determine the location of the target by the similarity between the template and the candidate, the most similar candidate to the template is considered as the target in visual tracking. Similarity algorithms search the most similar candidate to the template as the current estimation for visual object. In practice, most trackers only take usage of the intra-class similarity, yet the inter-class semantic separability is ignored. In this paper, a joint identification-verification model is proposed to learn the similarity with the category attribute for visual tracking. This approach constructs the cost function both on the inter-class semantic separability and intra-class similarity, firstly. Then, the training dataset is fed into the network. To the end, the discriminative features are learned in the embedding space. During tracking phase, the template and candidates are fed into the network simultaneously. Thereforce, the target will be located correctly by the similarity metric between the template and candidates in the learned embedding space. We evaluate the proposed approach on the open benchmark: OTB50 and UAV123 dataset. A large number of experimental results show that the inter-class semantic separability can increase the discrimination for the similar distractors effectively, and bootstrap the tracking performances of the trackers based on the similarity learning.
Genetic perspective on stress response and disease resistance in aquaculture.

Despite continuous progress and improvements in aquaculture technologies and husbandry techniques, fish diseases remain a major limiting factor in the fish culture industry. The aquaculture environment exposes fish to repeated acute stress, which leads to physiological responses that have suppressive effects on growth, reproduction, and immune capacity. The strong link between stress and susceptibility to diseases in farm animals has long been acknowledged, and parameters of high and low stress response are associated with disease resistance in fish. Few studies on genetic aspects of immune response to stress have been conducted. The estimated heritability of several parameters of the innate immune response is mostly moderate. Quantitative trait loci have been found for different responses to stress conditions. Gene expression studies show that hundreds of genes can be involved in fishes’ physiological and immunological response to stress. This general response to stress is controlled by a few major genes at the top of the pathway, which activate a cascade of reactions, having a significant effect on the overall health of the fish. Selective breeding for disease resistant fish is an attractive strategy for disease prevention and several studies have reported progress in this field. However, the biological pathways of stress response and disease resistance are not well characterized, and their genetic basis and control are still poorly understood. Extensive research is still needed for a better understanding of these pathways, and this should be a collaborative effort of researchers from different fields: genetics, immunology, pathology, physiology, and endocrinology. Introduction It is widely accepted that stress is an important factor to be considered in successful fish husbandry. Indeed, stress has various effects on important traits in cultured fish, including metabolism, growth, social interactions, reproduction, and immunocompetence. Only a limited amount of research has been carried out on the genetic basis of stress response in fish. However, it is clear that stress response has a distinct genetic component and therefore may be modified by selective breeding. The objective of this paper is to review the current * Tel. : +1-603-862-1652; fax: +1-603-862-2940; e-mail: acnaani@unh.edu
Hatching Asynchrony Decreases the Magnitude of Parental Care in Domesticated Zebra Finches: Empirical Support for the Peak Load Reduction Hypothesis

Parent-offspring conflict over the supply of parental care results in offspring attempting to exert control using begging behaviours and parents attempting to exert control by manipulating brood sizes and hatching patterns. The peak load reduction hypothesis proposes that parents can exert control via hatching asynchrony, as the level of competition amongst siblings is determined by their age differences and not by their growth rates. Theoretically, this benefits the parents by reducing both the peak load of the offspring's demand and their overall demand for food and benefits the offspring by reducing the amplification of their competition. However, the peak load reduction hypothesis has only received mixed support. Here, we describe an experiment where we manipulated the hatching patterns of domesticated zebra finch Taeniopygia guttata broods and quantified patterns of nestling begging and parental feeding effort. There was no difference in the begging intensity of nestlings raised in asynchronous or experimentally synchronous broods, yet parental feeding effort was lower when provisioning asynchronous broods and particularly so when levels of nestling begging were low. Further, both parents acted in unison, as there was no evidence of parentally biased favouritism in relation to hatching pattern. Therefore, our study provided empirical support for the prediction that hatching asynchrony reduces the feeding effort of parents, thereby providing empirical support for the peak load reduction hypothesis.
Comparison among Various Emerging PV Cells with History, Current Status and Future Challenges

Due to energy crises in the future, much effort is being directed towards alternate sources. Solar energy is accepted as a novel substitute for conventional sources of energy. A solar cell is an electrical device that converts the energy of light (solar energy) directly into electricity by the photovoltaic effect. This article, briefly describes about the evaluation of various emerging photovoltaic solar cells out of the long list of various types of solar cells available on the market. Since it is widely accepted that solar cells can be broadly categorized into one of three technology "generations" and the "third generation" usually reserved for so-called emerging technologies. Emerging PV is one of the fastest growing of all the renewable energy technologies in fact, it is one of the fastest growing industries at present. Because there is a strong need for the development of photovoltaic cells with low cost, high efficiency, and good stability. The article further discusses in detail the constructions and the operation principles of various emerging PV cells. It presents applications, latest conversion efficiencies and comparable information about strength and weakness of these. It reviews general approaches, challenges and solutions, as well as latest research progress and future prospects in the field of emerging photovoltaic solar cells.
Analysis optical contribution of rear passivation layer to photo- generated current of silicon solar cell with numerical simulation

For high efficiency solar cell, both side of passivation layer is necessary for achieving high open circuit voltage. Generally, passivation layer reduces recombination site of silicon surface, thereby increasing lifetime of carriers. Also, in case of front passivation layer, it reduces reflectance so light could be more absorbed to Si substrate than without passivation layer. On the other side, rear side of solar cell, rear metal electrode is located which is highly reflective. So, it is difficult to experimentally demonstrate optical contributions of rear passivation layer. In this paper, using numerical optic simulation which based on transfer matrix method, we calculate reflectance of solar cell which have rear passivation layers. For improving light trapping effects in solar cell. We design two different solar cell structures which have different type of rear passivation layer. Single layer structure consists only two rear passivation layers in rear side of solar cell. Multiple layer structure consists of repeated two different thin oxide layer 12 times. We calculate reflectance of two structures with simulation tool and compare the optical effect of rear passivation layers. Through this, we can optimize the condition of rear passivation layer for highest efficiency.
A COMPLETE ATLAS OF H i ABSORPTION TOWARD H ii REGIONS IN THE SOUTHERN GALACTIC PLANE SURVEY (SGPS I)

We present a complete catalog of H i emission and absorption spectrum pairs, toward H ii regions, detectable within the boundaries of the Southern Galactic Plane Survey (SGPS I), a total of 252 regions. The catalog is presented in graphical, numerical, and summary formats. We demonstrate an application of this new data set through an investigation of the locus of the Near 3 kpc Arm.
Phenol removal from industrial wastewater using chitosan- immobilized Pseudomonas putida

* CorrespondingAuthor: Email: mkhavarpoor@yahoo.com, Fax: (+98)-1143217212 commonly present in various industrial waste streams such as oil refining, resin and plastic, coal gasification plants, coke oven, pulp and paper plants, pharmaceutics and agroindustrial units [5]. The ingestion of phenol in the human body resulted in serious health risk such as protein degeneration, paralysis of the central nervous system and liver and kidney damage [6]. The presence of phenol in Article history: Received: 8/Apr/2014 Received in revised form:11/May/2014 Accepted: 5/Jun/2014 Abstra c t The present study deals with degradation of phenol in industrial wastewater using Pseudomonas putida. Biodegradation process at various initial phenol concentrations ranging from 50 to 200 mg/l was evaluated at different conditions. Phenol removal as single source of carbon at initial phenol concentration of 200 mg/l took place within 22 days. Phenol/ glucose mixture used as dual system to improve phenol degradation. The presence of glucose as supplementary substrate degraded phenol at initial concentration of 200 mg/l within 19 days. Acclimated Pseudomonas putida was able to degrade phenol at initial phenol concentration of 200 mg/l within 15 days. It was also revealed that phenol degradation using acclimated Pseudomonas putida immobilized on chitosan was carried out at the shortest period of time in contrast to the other conditions. The obtained results represented that microorganism was able to consume phenol as a substrate.