Miracle-dz/UniFAR-FTUs huggingface dataset:
Labor Unions and the Labor Wedge: A Macroeconomic Perspective

The measured labor wedge, defined as the difference between marginal product of labor and marginal rate of substitution, is relatively stable from 1940s to 70s, and declines secularly from 1980s onwards. This paper aims to investigate the effect of a particular labor market institution, labor union, on labor wedge. Labor unions command a wage premium, which invites job application queues and job rationing in the unionized sector. This waiting values of unionized jobs creates a wedge between wages and households’ willing to work. We provide sectoral evidence that supports a union-wedge connection in the manufacturing sector. A quantitative model which features two labor market, one competitive and the other unionized, is developed to estimate the effect of union power on labor wedge. Based on our quantitative results, approximately 20% of the decline in labor wedge from 1970s to 2000s is accounted for by the decrease in union densities.
Testing Distributions of Stochastically Generated Yield Curves AFIR Colloquium 2003 Topic: Asset Valuation

A method is introduced for testing the distribution of yield curves that are produced by asset scenario generators. The method is based on historical relationships in the conditional distributions of yield spreads given the short-term rate. As an illustration, this method is used to test a few selected models. To provide background, stochastic modeling for interest rates and fitting methods are briefly discussed.
deepRGBXYZ: Dense Pixel Description Utilizing RGB and Depth with Stacked Dilated Convolutions

In this paper, we propose deepRGBXYZ – a feature descriptor to represent pixels for robust dense pixel matching. To this end, we concatenate RGB image (appearance) information with the depth (geometric) information represented as XYZ in order to build a robust descriptor which is more invariant to photometric and geometric changes. Both information (RGB and depth) are embedded as an early fusion into one neural network which is based on stacked dilated convolutions for enlarging the receptive field. We alleviate the limitations of image-only descriptors especially within ill-conditioned light regions or textureless objects. Additionally, we overcome the difficulty of using depth-only information which show less descriptive details compared to image-only. We demonstrate the superior accuracy of our deepRGBXYZ descriptor against the state-of-the-art image-only descriptors and we verify our design decision. In addition, we investigate the superior robustness of our deepRGBXYZ descriptor by bringing it into the application of optical flow and scene flow estimation on the established data sets KITTI and FlyingThings3D.
Improved Topic Representations of Medical Documents to Assist COVID-19 Literature Exploration

Efﬁcient discovery and exploration of biomedical literature has grown in importance in the context of the COVID-19 pandemic, and topic-based methods such as latent Dirichlet allocation (LDA) are a useful tool for this purpose. In this study we compare traditional topic models based on word tokens with topic models based on medical concepts, and pro-pose several ways to improve topic coherence and speciﬁcity.
Portfolio Optimization with Drift Uncertainty

We study the utility maximization problem of a portfolio of one risky asset, a stock, and one riskless asset, a bond, under Knightian uncertainty on the drift term representing the long term growth rate of the risky asset. We further assume that the investor has a prior estimate about the drift term, so that we incorporate into the model a penalty term for deviating from the prior about the mean. We provide explicit solutions, when the investor has logarithmic, power and exponential utility functions. A numerical case study with respect to different utilities has also been presented.
Attosecond light science and its application for probing quantum materials

In this paper, we review the development and application of coherent short wavelength light sources implemented using the high harmonic generation (HHG) process. The physics underlying HHG brought quantum physics into the domain of attosecond time-scales for the first time. The observation and manipulation of electron dynamics on such short time-scales—a capability not conceived—of just a few decades ago—is becoming both more-and-more sophisticated and useful as a route to achieve exquisite control over short wavelength light. New experimental techniques are enabling HHG light sources to provide new insights into fundamental quantum interactions in materials, making it possible for the first time to capture the fastest charge, spin, photon and phonon interactions and to achieve diffraction-limited imaging at short wavelengths.
Inequality and Inclusive Growth: Evidence from the Selected East European and CIS Countries

The rise of protectionism, expansionary fiscal policies and antiglobalization leading to Brexit and similar right-wing sentiments in few other countries has perhaps been the expression of dissatisfaction towards existing growth strategies being adopted across different countries. The questions are being raised as to how the growth strategies have impacted poverty and inequalities; the two banes of the economies. The voices of discontentment have become stronger in recent years when the growth is not creating enough jobs. In view of the importance of the issue of inequality and the increasing discontentment against the rising inequality and demand for a more inclusive growth; the paper has dig deeper into the available evidence of inequality in the East European (EE) and Commonwealth of Independent States (CIS) countries to understand the status of inclusive growth.
Learning Large Electrical Loads via Flexible Contracts With Commitment

Large electricity customers (e.g., large data centers) can exhibit huge and variable electricity demands, which poses significant challenges for the electricity suppliers to plan for sufficient capacity. Thus, it is desirable to design incentive and coordination mechanisms between the customers and the supplier to lower the capacity cost. This paper proposes a novel scheme based on flexible contracts. Unlike existing demand-side management schemes in the literature, a flexible contract leads to information revelation. That is, a customer committing to a flexible contract reveals valuable information about its future demand to the supplier. Such information revelation allows the customers and the supplier to share the risk of future demand uncertainty. On the other hand, the customer will still retain its autonomy in operation. We address two key challenges for the design of optimal flexible contracts: i) the contract design is a non-convex optimization problem and is intractable for a large number of customer types, and ii) the design should be robust to unexpected or adverse responses of the customers, i.e., a customer facing more than one contract yielding the same benefit may choose the contract less favorable to the supplier. We address these challenges by proposing sub-optimal contracts of low computational complexity that can achieve a provable fraction of the performance gain under the global optimum.
My title

Difference-in-Differences (DID) research designs usually rely on variation of treatment timing such that, after making an appropriate parallel trends assumption, one can identify, estimate, and make inference about causal effects. In practice, however, different DID procedures rely on different parallel trends assumptions (PTA), and recover different causal parameters. In this paper, we focus on staggered DID (also referred as event-studies) and discuss the role played by the PTA in terms of identification and estimation of causal parameters. We document a “robustness” vs. “efficiency” trade-off in terms of the strength of the underlying PTA, and argue that practitioners should be explicit about these trade-offs whenever using DID procedures. We propose new DID estimators that reflect these tradeoffs and derived their large sample properties. We illustrate the practical relevance of these results by assessing whether the transition from federal to state management of the Clean Water Act affects compliance rates.
A Regionalized or Unified Oil Market: The Price Spread Between Brent and WTI

Over the last several years, I have published papers that show arbitrage opportunities largely unify the world oil market and that they can account for much of the price differences among crude oils. Exceptions include crude oils that are especially heavy or originate in nations with poor governance. But my confidence in these results was limited by the sudden appearance of a large spread in the prices for Brent and WTI, which are two of the world’s most important benchmark crude oils. As stated by the Morpheus character in the film The Matrix, “You don’t know what it is, but it’s there, like a splinter in your mind, driving you mad. ” My splinter, I had to understand why to crude oils from nations with relatively transparent governments and similar physical characteristics (both light and sweet), suddenly seemed to be priced by regionalized markets. This sudden separation in price also perplexed other researchers. They posited several possible explanations; the shale oil boom, imports from Canada, building inventories, the lifting of the US ban on crude oil exports, changes in transportation infrastructure, declining production of crude oils that make-up Brent, the collapse of Libyan production, and even exchange rates. But these efforts did not satisfy my curiosity. Often, authors examined only a subset of possible explanations, which raised the specter of omitted variable bias. In others, authors ignored the nonstationary nature of the data, which raised the possibility that the statistical results were spurious. Finally, many analyses implicitly assumed that the spread was caused by a sudden change in an explanatory variable and so looked for a change-point in the price spread, rather than focusing on the factors that influenced the price of Brent and WTI.
Knowledge Intensive Learning of Generative Adversarial Networks

While Generative Adversarial Networks (GANs) have accelerated the use of generative modelling within the machine learning community, most of the applications of GANs are restricted to images. The use of GANs to generate clinical data has been rare due to the inability of GANs to faithfully capture the intrinsic relationships between features. We hypothesize and verify that this challenge can be mitigated by incorporating domain knowledge in the generative process. Specifically, we propose human-allied GANs that using correlation advice from humans to create synthetic clinical data. Our empirical evaluation demonstrates the superiority of our approach over other GAN models.
Cognitive Indexical Usage of Demonstrative Ku in Korean and a Split DP Analysis

Recent typological research has shown that when two lexical N modifiers co-occur modifying the same nominal, the heavier one occurs more in the periphery of the DP than the lighter one does; that is, a ‘heavy N modifier > light N modifier’ surface order obtains, where ‘>’ means ‘structurally higher’ (see Cinque 2010 and the references there). In general, this adjective ordering restriction (AOR) holds for Korean. This can be seen by comparing (1a) and (1b): (1b) is judged bad and this follows from said AOR since here, a lighter N modifier, i.e., an adjective phrase (AP), occurs preceding a heavier one, i.e., a relative clause (RC). (Here and below, e represents the gap inside a RC and the subscripts indicates co-indexation.)
Asking the Right Questions: Interpretable Action Model Learning using Query-Answering

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a minimal query interface with the agent, and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a vocabulary provided by the user. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images.
Parturition site selection in moose (Alces alces): evidence for social structure

Female natal philopatry has often been implicated as an important factor in moose (Alces alces) home range formation, with many populations showing behavioral evidence of sympatric home ranges among related individuals. However, previous genetic studies have failed to detect genetic subpopulation structure, leading to questions as to whether philopatry is a significant factor contributing to intra- and inter-population genetic structure. Here, we examine calving location data from radiomarked individuals (n = 110) collected in 2 separate populations in Berners Bay and Gustavus, Alaska, to examine the extent to which genetic structure originating from philopatry is evident at fine spatial scales. When populations were combined, their overall relatedness correlogram was significant (P < 0.001), with mean r = 0.079 between 0 km and 1.5 km (P = 0.079). Additionally, 13% of the population shows significantly positive relatedness to their 4 nearest neighbors, with an overall average r = 0.19 of those focal individuals to their neighbors. We suggest that habitat structure, especially linear habitats (i.e., river valleys), or habitat bounded with barriers to dispersal, may be a factor in promoting the development of this structure. This study presents the 1st known evidence for fine-scale social genetic structure in moose and natal philopatry to calving locations in moose. In the context of natal philopatry within cervids, these findings identify several cervid-wide commonalities.
The use of digital traces: a promising basis for the design of adapted information systems?

Designing information systems with adaptation capacities is a challenge of great importance to improve the appropriation of the system by the users. In this paper we view visualized digital traces as a means to support reflexive type coupling between the system and the user. We examine the way users make use of digital traces with the objective of identifying possible use invariants. This is an indispensable stage in the enrichment of a trace system. We present a case study of an instrumented collaborative situation that answers this question. This case study uses theoretical and methodological tools from cognitive ergonomics that enabled us to integrate the subjects' point of view in the activity analysis. This study allows us to formalize four schemas of trace usage. We discuss these results with regard to the appropriation and adaptation of information systems. This leads us to argue that a trace-based approach is promising for the design of adaptive information systems.
Audio Signal Performance Analysis using Integer MDCT Algorithm

In Audio coding Algorithms Integer Modified Discrete Cosine Transform is mostly used. In this paper, we propose Analysis and synthesis method for perfect reconstruction of audio signal and Integer modified discrete cosine transform is mainly used to overcome Time domain aliasing cancellation problem. This works mainly depends on Integer MDCT, which is an integer approximation of a lapped transform. It inherits the properties of MDCT and it provides critical sampling, perfect reconstruction property. In both the (Analysis/Synthesis) stages Kaiser Bessel window functions are used to ensure perfect reconstruction property. The purpose of this work is to analysis and synthesis IntMDCT algorithm in terms of Lifting Scheme and to ensure its perfect reconstruction without any error. If any noise is generated in analysis stage, it will be removed by means of RLS algorithm then the signal is rewindowed and deframed in it for perfect reconstruction. Performance is measured by Mean square error and Peak Signal to noise ratio.
Exponential Signal Reconstruction With Deep Hankel Matrix Factorization

Exponential function is a basic form of temporal signals, and how to fast acquire this signal is one of the fundamental problems and frontiers in signal processing. To achieve this goal, partial data may be acquired but result in severe artifacts in its spectrum, which is the Fourier transform of exponentials. Thus, reliable spectrum reconstruction is highly expected in the fast data acquisition in many applications, such as chemistry, biology, and medical imaging. In this work, we propose a deep learning method whose neural network structure is designed by imitating the iterative process in the model-based state-of-the-art exponentials’ reconstruction method with the low-rank Hankel matrix factorization. With the experiments on synthetic data and realistic biological magnetic resonance signals, we demonstrate that the new method yields much lower reconstruction errors and preserves the low-intensity signals much better than compared methods.
Application of Formal Contexts in the Analysis of Heterogeneous Biomedical Data

The paper proposes a method of conceptual modeling based on the use of formal contexts. Formal context is the main notion in the Formal Concept Analysis (FCA), the lattice-based data analysis approach. Biomedical data and the tasks of their analysis under Biomedical Natural Language Processing are discussed. Two variants of formal contexts constructed on natural language texts are considered. They are the contexts constructed with the use of keywords and n -grams. Textual n -grams are acquired by using conceptual graphs and Abstract Meaning Representation (AMR) schemata. Both contexts are used in the text clustering task. It is shown that the classical FCA clustering on keyword based context entails appearing bag-of-words in clusters. It is proposed the clustering approach for n -grams based multidimensional contexts which avoid appearance of a bag-of-words in clusters. The method was tested on the texts of annotations of scientific articles from PubMed databases.
Word Sense Disambiguation using Word2Vec

자연어 문서에 출현하는 단어는 중의성을 가질 수 있 으며 단어의 정확한 의미 파악은 정보검색이나 기계번 , 역 등의 자연어 처리 시스템의 성능을 높일 수 있다 예 . 를 들어 어절 배다 에서 배 는 품사가 명사 동사 ‘ ’ ‘ ’ , 등이 될 수 있으며 품사가 명사일 경우에는 먹는 배 , , 운송수단 배 신체부위 배 등으로 여러 가지 의미가 있 , 으며 동사일 경우는 냄새가 스며드는 배다 배속에 , ‘ ’, 새끼를 가지는 배다 등의 의미를 가진다 따라서 이 ‘ ’ . 단어의 뜻에 맞는 문서를 찾아주거나 번역을 하기 위해 서는 단어의 의미 구분이 선행되어야 한다 일반적으로 . 단어 중의성 해소는 품사 태깅이 된 단어에 대해 의미 모호성을 해소하는 것이며 본 논문에서도 이런 가정을 , 따른다. 한국어 단어 의미 중의성 해소는 다양한 방법으로 많 은 연구가 진행되어 왔다 이 중 단어 공간 모델에 [1-6]. 기반을 둔 방법은 대상 단어의 문맥에 나온 또 다른 단 어들을 벡터의 각 요소로 표현하여 대상 단어들의 의미 를 비교하는 방법이다 에서는 세종 형태 의미 분석 . [1] 말뭉치를 학습하여 중의성 있는 단어들에 대한 한국어 단어 공간 모델 을 만들고 이를 이용하여 의미 모호 [7] , 성을 해소하여 우수한 성능을 보였다. 하지만 이 방법은 의미 단어 벡터의 차원이 너무 커져 공간이 많이 필요하 고 처리 속도가 느려지는 단점이 있다 이러한 단점을 . 보완하기 위해 본 연구에서는 을 활용하여 Word2Vec[8] 성능을 유지하면서도 단어 벡터의 차원을 효과적으로 줄 이는 방법을 제안한다.
Analyse de la repartition géographique des Malphigiaceae d’apres les caractêres du pollen et de la pollinisation

ANALYSIS OF THE GEOGRAPHICAL DISTRIBUTION OF MALPHIGIACEAE USING POLLEN ANDPOLLINATION CHARACTERS A comparative study o f pollen and pollination in the family Malphigiaceae shows that the genera o f the Old World are derived from those in South America. They have existed on the continents involved since the Cretaceous. In South America there is coadaptation oetween the pollen characters and the pollinating insects present only on this continent and subsequently diversified after the enlargement o f the Atlantic in the Tertiary. Pollen characters o f the Malphigiaceae show affinities with those o f other families o f the Polygalales. Palynologically, Malphigiaceae approaches Euphorbiaceae on the one hand, and Leguminosae and Rutales-Sapindales on the other.
Evaluating the environmental impact from injection of treated wastewater in a coastal aquifer

The data from a comprehensive baseline water quality survey was used in conjunction with a numerical groundwater model to evaluate the impact of reclaimed water injection into a coastal limestone aquifer located in the Ewa Plain region of the island of Oahu, Hawaii. The water quality data identified the chemical and microbial constituents of concern and constrained the hydrologic parameters used in the model. The finite element, densitydependent, coupled flow and transport model FEMWATER was used to simulate the movement of nutrients and microbes from an injection point near the Honouliuli Wastewater Treatment Plant (HWTP) to the shoreline. Numerical transport simulations conducted for a 18.9-million liters (MLit) per day injection scenario indicate that a non-reactive constituent will take between 100 and 200 days to reach the shoreline and will achieve steady-state concentrations in roughly four to five and one half years. The mass flux rate of nitrogen entering the near-shore environment is estimated to be roughly 150% higher than current nitrogen flux rates but 75% lower than historic mass fluxes during the previous era of large-scale agricultural development in the Ewa area. The concentration of the recalcitrant microbe Clostridium perfringens entering coastal waters is estimated to be approximately 0.008% of this microbes concentration in the effluent source.
REAL TIME FACIAL RECOGNITION USING OPENCV PYTHON TECHNIQUES

: The biometric is an analysis of the human behavior and characteristics. Face-recognition is biometric technique in which Different approaches are used for this. In this article, a survey is included for all these strategies to evaluate different algorithms and processes. Face recognition is the new biometric division for health, since no faces can be overcome as a security strategy. So, how can we recognize a face using face detection method with the help of computer is given in this paper.
Research and Ground Verification of the Force Compliance Control Method for Space Station Manipulator

The space station manipulator does lots of tasks with contact force/torque on orbit. To ensure the safety of the space station and the manipulator, the contact force/torque of manipulator must be controlled. Based on analyzing typical tasks’ working flows and force control requirements, such as ORU (orbit replacement unit) changeout and dual arm collaborative payload transport, an impedance control method based on wrist 6 axis force/torque feedback is designed. For engineering implementation of the impedance control algorithm, the discretization method and impedance control parameters selection principle are also studied. To verify the compliance control algorithm, a ground experiment platform adopting industrial manipulators is developed. In order to eliminate the influence of gravity, a real-time gravity compensation algorithm is proposed. Then, the correctness of real-time gravity compensation and force compliance control algorithm is verified on the experiment platform. Finally, the ORU replacement and dual arm collaborative payload transport experiments are done. Experimental results show that the force compliance control method proposed in this paper can control the contact force and torque at the end of the manipulator when executing typical tasks.