10,660 research outputs found
Monetary policy with heterogeneous households and financial frictions
This paper presents and estimates a sticky-price model with heterogenous households and financial frictions. Frictions in state-contingent asset markets lead to imperfect risksharing among households with idiosyncratic labor incomes. I study the impacts of the introduced financial frictions on optimal monetary policy by documenting implications for the central bank's objective function, the equation that characterizes inflation-output gap trade-offers, targeting rules, interest rate rules, and welfare of the economy. Employing the estimated model, the paper argues that the central bank should place a stronger emphasis on stabilizing inflation than it has, and failing to do so can generate nontrivial welfare costs
Heterogeneous households in a sticky-price model
This paper introduces heterogeneous households into an otherwise standard sticky-price model with industry-specific labor markets. Households differ in labor incomes and asset markets are incomplete. I show that household heterogeneity affects equilibrium dynamics nontrivially by amplifying price stickiness endogenously through wealth effects on labor supply. To quantify the importance of household heterogeneity in amplifying stickiness, I estimate and compare representative and heterogeneous household models. The quantitative exercise shows the heterogenous household model performs better than its representative counterpart in accounting for aggregate and sectoral dynamics in the U.S., while being more consistent with empirical evidence on nominal rigidity at the aggregate and sectoral levels, thanks to the stickiness endogenously generated by the model
The effects of financial, human, and social capital on the perception of financial well-being
A considerable amount of research has focused on several effects of social capital such as economic benefits, forming human capital, and crime rate. However, less attention has been paid to the mediating effect of social capital on perception of financial well-being. Community social capital\u27s effect on individual perception of financial well-being has also received little attention. This paper seeks the answer to these questions: (1) What are social capital\u27s mediating effects on the perception of financial well-being and household financial capital? (2) What are community social capital, financial capital, and human capital\u27s effects on individual perception of financial well-being? Data for this study came from the Northwest Area Foundation Horizons Cluster Social Capital Survey conducted from 2004 to 2005. The survey covered 36 communities participating in the Northwest Area Foundation Horizons Program. The results of the study approved the mediating effects of social capital on the perception of financial well-being and on the forming of household financial capital. Among three community capitals, only community social capital shows a significant effect on perception of financial well-being. Community social capital also shows a greater effect on perception of financial well-being than that of individual social capital. It illustrates social capital\u27s characteristic as a public good rather than a private property. Distribution of three capitals indicated that social capital is more equally distributed than those of household financial capital and human capital
Stability tests for identifying structural changes in the key performance measures
http://deepblue.lib.umich.edu/bitstream/2027.42/96909/1/MBA_Lee_F_1998_final.pd
Maximizing Charge Density of Triboelectric Nanogenerator based on Design of Dielectric Layer
Department of Materials Science and EngineeringMost recently, a newly designed energy generating device named the triboelectric nanogenerator (TENG) was reported and various types of TENGs have been demonstrated, proven as a highly efficient, simple, robust and cost-effective technique for efficiently converting various mechanical energies around us to electricity. In principle, the electrical energy is generated as two different materials are brought into contact with each other, in conjunction with the triboelectric electrification and electrostatic induction. The sources of mechanical energy such as winds and moving things are available anywhere and anytime in our surroundings, thus, TENG will be appropriate as a power-supply unit for portable devices although the energy is not big as expected. Since the first demonstration of the TENG on 2012, various TENG structures and new functional materials brought about the significant increase of the instantaneous power density up to several tens of mW/cm2. However, further enhancement is required for faster commercialization, which may be possible by developing functional triboelectric materials, such as dielectrics. First, we set out to design and synthesize Polyvinylidene fluoride (PVDF) graft copolymers to incorporate poly(tert-butyl acrylate) (PtBA) through an atom-transfer radical polymerization (ATRP) technique as an efficient dielectric to enhance the output performance of the TENGs. This increase in the dielectric constant significantly increased the density of the charges that can be accumulated on the copolymer during physical contact. The markedly enhanced output performance is quite stable and reliable in harsh mechanical environments due to the high flexibility of the films. Second, we demonstrate high-output TENGs based on polyimide (PI)-based polymers by introducing functionalities (e.g., electron-withdrawing and electron-donating groups) into the backbone. The TENG based on 6FDA-APS PI, possessing the most negative electrostatic potential and the low-lying lowest unoccupied molecular orbital level, produces the highest effective charge density in practical working conditions without the ion injection process. This may be ascribed to the excellent charge-retention characteristics as well as the enhanced charge transfer capability. This article provides a comprehensive review of effective dielectrics used so far in TENG, as well as the fundamental issues regarding the materials. Finally, we show some strategies for obtaining the properties that the materials should have as effective dielectrics.clos
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Όλ¬Έ (μμ¬) -- μμΈλνκ΅ λνμ : 곡과λν μ»΄ν¨ν°κ³΅νλΆ, 2021. 2. κ°μ .Given an imbalanced dataset, how can we create high fidelity synthetic minority instances for training robust and unbiased classifiers? Data imbalance is common in mission-critical fields where costs associated with procuring minority instances are prohibitively expensive. Training classifiers on imbalanced datasets result in unreliable predictions and low performance. Oversampling techniques are employed to restore balance to the dataset, allowing the classifier to learn a more accurate representation of the true data distribution. Thus, generating a set of synthetic samples that are i) realistic, ii) containing varying degrees of class confidence, and iii) diverse is essential. Existing methods create samples that do not satisfy all the desired properties.
We propose Synthetic Minority Sampler (SMS), an oversampling framework designed for highly imbalanced datasets. SMS employs two generators to create a balanced ratio of normal and borderline samples that teach classifiers a robust and unbiased class representation. SMS accounts for the scarce minority instances via a class-conditional diversity loss to ensure that generated minority samples are diverse. Additionally, SMS stabilizes the training process by introducing a weighted random sampler to balance the class proportion of mini-batches, and data augmentation to prevent the discriminator from overfitting. Experimental results show that models trained on an imbalanced dataset augmented with synthetic data sampled from SMS outperform competitors in the binary classification task, achieving up to 10.06% higher F1-score than the competitors.ν΄λμ€κ° λΆκ· νν λ°μ΄ν°κ° μ£Όμ΄μ‘μ λ μ΄λ»κ² μμ ν΄λμ€μ λν λ°μ΄ν°λ₯Ό μΈκ³΅μ μΌλ‘ μ¦λνμ¬ ν΄λμ€ λΆλ₯ μ±λ₯μ λμΌ μ μμκΉ? λ°μ΄ν° λΆκ· ν λ¬Έμ λ κ³ μ₯ μ§λ¨ λ° μ§λ³ λΆλ₯μ κ°μ΄ νμͺ½μ ν΄λμ€ μκ° λ€λ₯Έ νμͺ½μ μλ³΄λ€ κ·Ήλ¨μ μΌλ‘ μ μ λ λ°μνλ λ¬Έμ λ₯Ό μΌμ»«λλ€. μ΄λ¬ν λΆκ· νν λ°μ΄ν°λ₯Ό ν΅ν΄ νμ΅λ λͺ¨λΈμ μλͺ»λ μμΈ‘ κ²°κ³Ό μ’μ§ λͺ»ν λΆλ₯ μ±λ₯μ 보μΈλ€. μ΄λ₯Ό ν΄κ²°νκΈ° μν΄ μΌλ°μ μΌλ‘ μμ ν΄λμ€μ λν΄ μΈκ³΅μ μΌλ‘ μνμ μ¦λνμ¬ κ° ν΄λμ€μ μνμ μλ₯Ό λμΌνκ² νλ λ°©μμ μ¬μ©νλ€. μΈκ³΅μ μΌλ‘ μ¦λκ° λ μνμ μ¬μ€μ μ΄κ³ κΈ°μ‘΄μ μνκ³Ό λμΌνμ§ μμμΌ νλ©° λ€μν μ±μ§μ ν¬ν¨νμ¬μΌ νλλ° μ ν μ°κ΅¬λ€μ μ΄λ¬ν μμλ₯Ό μΆ©μ‘±νμ§ λͺ»νκ³ μλ€.
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μμ λμ νμ§μ μΈκ³΅ λ°μ΄ν°λ₯Ό μ€λ²μνλ§ (oversampling) νλ νλ μμν¬μΈ Synthetic Minority Sampler (SMS)λ₯Ό μ μνλ€. SMSλ λ κ°μ μμ±κΈ°λ₯Ό μ¬μ©νμ¬ κ΅¬λΆμ΄ λͺ
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ννμ§ μμ μνμ μ μ ν λΉμ¨λ‘ μμ±νκ³ μ΄λ₯Ό ν΅ν΄ λΆλ₯κΈ°λ₯Ό λμ± κ²¬κ³ νκ³ μΌλ°νλ λ°©ν₯μΌλ‘ νμ΅μν¨λ€. SMSλ ν΄λΉ λ
Όλ¬Έμμ κ³ μλ μμ€ ν¨μ (class-conditional diversity loss)λ₯Ό μ¬μ©νμ¬ μΈκ³΅μ μΌλ‘ μμ±λ μμ ν΄λμ€ μνμ λ€μμ±μ 보μ₯νλ€. λν λ―Έλ λ°°μΉμ ν΄λμ€ λΉμ¨μ μ μ νκ² λ°°λΆνλ μμ μνλ¬μ ꡬλΆκΈ° (discriminator)μ μ€λ²νΌν
λ°©μ§λ₯Ό μν λ°μ΄ν° μ¦κ° κΈ°λ²μ μ¬μ©νμ¬ SMSμ νμ΅μ μμ ννλ€. μ€ν κ²°κ³Όμμλ SMSλ₯Ό ν΅ν΄ μμ±λ μΈκ³΅ λ°μ΄ν°λ₯Ό κΈ°μ‘΄μ λ°μ΄ν°μ
μ μΆκ°νμ¬ νμ΅ν λͺ¨λΈμ΄ μ΄μ§ λΆλ₯ (binary classification) λ¬Έμ μμ νμν μ±λ₯μ 보μμΌλ©°, κ²½μ λ©μλλ³΄λ€ 10.06% λμ F1 μ€μ½μ΄λ₯Ό κΈ°λ‘νμλ€.I. Introduction 1
II. Related Works 4
III. Proposed Method 6
3.1 Overview 6
3.2 Normal and Borderline Sample Generation 8
3.3 Class-conditional DiversityLoss 10
3.4 Generators, Discriminator, and Classifier Training 11
3.5 Stabilizing Training 12
IV. Experiments 14
4.1 Experimental Settings 14
4.2 Performance 19
4.3 Synthetic Image Quality and Diversity 22
4.4 Ablation Study 23
V. Conclusion 26
References 27
Abstract in Korean 29Maste
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