Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Regressors

Selection of regressors is an old and important problem in econometrics as well as in any other field that uses regression analysis. Many criteria of selection have been proposed in the econometric and other literature, but I have not found a good unified treatment of the subject. This paper is such an attempt. Selection of regressors should be based on economic-theoretic considerations as well as on statistical evidence. Economic theory can ofteni indicate what regressors should be included in any equation, and sometimes, but less often, a class of functional forms that should be considered. In addition, it canl often tell the researcher the likely sign of a coefficient and sometimes even a shorter range within which the coefficient is likely to lie. These types of a priori information should be utilized either formally by means of the Bayesian method or informally by means of the classical method. Hence, mechanical reliance on goodness of fit should be avoided as much as possible. However, other things being equal, it would be convenient to be able to choose a regression equation on the basis of a single statistic such as the sum of squared residuals or its monotonic transformation, R2. However, R2 has an obvious weakness; i.e. it can be maximized by maximizing the number of regressors. Therefore, some kind of correction that accounts for the degrees of freedom is necessary. In the econometric literature, Theil [1961, p. 213] was the first to propose such a correction, and since then his R2 has become an important index with which an econometrician judges the merit of a regression equation. However, a theoretical weakness of Theil's R2 is that it is not based on an explicit consideration of a loss fuLnction. For a criticism of K2 on an empirical ground, see Mayer [1975]. In this paper I reiterate my suggestion (Amemiya [1966] and [1972]) to develop a selection criterion based on minimizing the unconditional mean square prediction error (UMSPE) as the risk functioni, which results in a modification of R2 that corrects for the degrees of freedom to a greater extent than Theil's R2. I call this Prediction Criterion (PC) and show that it can be used as a selection criterion in the nonlinear regression model and/or in the case where the error term has a general variancecovariance matrix. Thus it has a wide applicability.
Si-wire grating couplers for integrated optical transceivers based on single-mode fiber connection

Recently, grating couplers (GCs) have been extensively reported as a promising device for optical coupling between Siwire waveguides and standard single mode optical fibers. In this paper, we report the feasibility study of Si-wire type GCs fabricated by the 248-nm KrF lithography process on a 200-mm silicon-on-insulator (SOI) wafer with a 220-nmthick Si layer. We report a 1D-GC for a transmitter that operates only with TE mode and a 2D-GC for a receiver that splits arbitrary polarization from a single mode fiber operating at a wavelength of 1550 nm. We investigated the fundamental feasibility with the simple design. Especially, the wafer uniformity in inter-dies of 1D and 2D-GC is mainly examined on a 200-mm SOI wafer. Additionally, we experimentally clarify the fundamental characteristics of the 1DGC from the viewpoint of an insertion loss and a 1-dB fiber misalignment tolerance. As for the 2D-GC, in addition to the same aspects with the 1D-GC, an optical crosstalk and a polarization dependent loss will also be discussed around 1550 nm wavelength. Also, the center wavelength against a grating period of the 1D-GC and a circular hole diameter of the 2D-GC will be characterized.
Estimation of Dynamic Link Flows and Origin-Destination Matrices from Lower Polling Frequency Probe Vehicle Data

The authors present a methodology of estimating dynamic link flows and origin-destination matrices using lower polling frequency probe vehicle data (e.g. one point every 30-60s). Link travel time is first obtained from map-matched probe points using a method of proportional allocation. A derived speed-density function is then fitted for different types of roads. A Bayesian method that carefully incorporates prior information is used to estimate dynamic link flows from link travel speed. A bi-level generalized least-square (GLS) estimator is formulated so as to estimate dynamic OD matrices from estimated link flows. A traffic simulator in VISSIM is developed for a median size urban network using an open data set. The results validate the advantages of the proposed method for lower polling frequency probe vehicle data.
Watson-Crick ω-automata

Watson-Crick ω-automata are finite automata working with infinite double-stranded words, where the two strands relate to each other through a complementary relation inspired by the DNA complementarity. There are several natural possibilities to define such automata, in particular to define the acceptance condition for such infinit double-stranded words. We present here several results on the equivalence of Buchi, Rabin, Streett, and Muller-like acceptance conditions. We prove that these acceptance conditions are equivalent for nondeterministic Watson-Crick ω-automata. For deterministic automata, we prove that the four conditions are equivalent for nonempty stateless automata, while the general case remains open. We also investigate the relationship between the classes of languages accepted by several types of Watson-Crick ω-automata.
Weighted norm inequalities for fractional integrals

The principal problem considered is the determination of all nonnegative functions, V(x), such that IIhTf(x)V(x)14 < ClIf(x)V(x)lIp where the functions are defined on R', 0 < y < n, 1 < p < n/y, l/q = l/p -yn, C is a constant independent of f and Tyf (x) = S f(x y) Ly|f dy. The main result is that V(x) is such a function if and only if (-fQ [V(X)]qdx (jff [vx) P'd)1 (IQIJQVX]^ (QI J [V(X)]-p, ) where Q is any n dimensional cube, IQI denotes the measure of Q, p' = p/(p 1) and K is a constant independent of Q. Substitute results for the cases p = 1 and q = oo and a weighted version of the Sobolev imbedding theorem are also proved.
Estimation Strategies of Four Groups

Selected groups of 44 academic “pure” mathematicians, 44 accountants, 44 psychology students, and 44 English students were given Levine's (1982) computational estimation task, which involved mentally estimating the products and quotients of 20 multiplication and division problems and describing their strategies. The mathematicians were the most accurate estimators, and the English students the least accurate, with psychology students and accountants obtaining similar scores intermediate between the other groups. All groups demonstrated an impressively versatile use of appropriate strategies. The mathematicians and accountants used significantly larger numbers of appropriate strategies than the other groups and strongly resembled one another in this respect, despite the significantly greater accuracy of the mathematicians. All the non-mathematician groups used significantly larger numbers of inappropriate strategies than did the mathematicians. We discuss (1) the implications for cognitive psychology of th.
Protean: An Energy-Efficient and Heterogeneous Platform for Adaptive and Hardware-Accelerated Battery-Free Computing

Battery-free and intermittently powered devices offer long lifetimes and enable deployment in new applications and environments. Unfortunately, developing sophisticated inference-capable applications is still challenging due to the lack of platform support for more advanced (32-bit) microprocessors and specialized accelerators---which can execute data-intensive machine learning tasks, but add complexity across the stack when dealing with intermittent power. We present Protean to bridge the platform gap for inference-capable battery-free sensors. Designed for runtime scalability, meeting the dynamic range of energy harvesters with matching heterogeneous processing elements like neural network accelerators. We develop a modular "plug-and-play" hardware platform, SuperSensor, with a reconfigurable energy storage circuit that powers a 32-bit ARM-based microcontroller with a convolutional neural network accelerator. An adaptive task-based runtime system, Chameleon, provides intermittency-proof execution of machine learning tasks across heterogeneous processing elements. The runtime automatically scales and dispatches these tasks based on incoming energy, current state, and programmer annotations. A code generator, Metamorph, automates conversion of ML models to intermittent safe execution across heterogeneous compute elements. We evaluate Protean with audio and image workloads and demonstrate up to 666x improvement in inference energy efficiency by enabling usage of modern computational elements within intermittent computing. Further, Protean provides up to 166% higher throughput compared to non-adaptive baselines.
Finding all solutions of piecewise-linear resistive circuits using simple sign tests

This paper presents an efficient algorithm for finding all solutions of piecewise-linear resistive circuits. The algorithm uses two types of sign tests; one is a new test that is proposed in this paper, and the other is the test proposed by Yamamura and Ochiai (1992). The computational complexity of the new test is much smaller than that of Yamamura and Ochiai's test. These tests eliminate many linear regions that do not contain a solution. Therefore, the number of simultaneous linear equations to be solved is substantially reduced. The proposed algorithm is very simple and efficient. >
Reading assignment 5

We propose a new deterministic approach to coreference resolution that combines the global information and precise features of modern machine-learning models with the transparency and modularity of deterministic, rule-based systems. Our sieve architecture applies a battery of deterministic coreference models one at a time from highest to lowest precision, where each model builds on the previous model’s cluster output. The two stages of our sieve-based architecture, a mention detection stage that heavily favors recall, followed by coreference sieves that are precision-oriented, offer a powerful way to achieve both high precision and high recall. Further, our approach makes use of global information through an entity-centric model that encourages the sharing of features across all mentions that point to the same real-world entity. Despite its simplicity, our approach gives state-of-the-art performance on several corpora and genres, and has also been incorporated into hybrid state-of-the-art coreference systems for Chinese and
Enabling Transparent Acceleration of Big Data Frameworks using Heterogeneous Hardware

The ever-increasing demand for high performance Big Data analytics and data processing, has paved the way for heterogeneous hardware accelerators, such as Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), to be integrated into modern Big Data platforms. Currently, this integration comes at the cost of programmability since the end-user Application Programming Interface (APIs) must be altered to access the underlying heterogeneous hardware. For example, current Big Data frameworks, such as Apache Spark, provide a new API that combines the existing Spark programming model with GPUs. For other Big Data frameworks, such as Flink, the integration of GPUs and FPGAs is achieved via external API calls that bypass their execution models completely. In this paper, we rethink current Big Data frameworks from a systems and programming language perspective, and introduce a novel co-designed approach for integrating hardware acceleration into their execution models. The novelty of our approach is attributed to two key design decisions: a) support for arbitrary User Defined Functions (UDFs), and b) no modifications to the user level API. The proposed approach has been prototyped in the context of Apache Flink, and enables unmodified applications written in Java to run on heterogeneous hardware, such as GPU and FPGAs, transparently to the users. The performance evaluation of the proposed solution has shown performance speedups of up to 65x on GPUs and 184x on FPGAs for suitable workloads of standard benchmarks and industrial use cases against vanilla Flink running on traditional multi-core CPUs.
AUTOMATED CLASSIFICATION OF DEPRESSION EEG SIGNALS USING WAVELET ENTROPIES AND ENERGIES

Depression is a mental disorder that relates to a state of sadness and dejection. It also affects the emotional and physical state of a person. Currently, there are no standard diagnostic tests for depression that are able to produce conclusive results and more over the symptoms of depression are hard to diagnose. A lot of people who are suffering from depression are unaware of their illness. The electroencephalographic (EEG) signals can be used to detect the alterations in the brain's electrochemical potential. The present work is based on the automated classification of the normal and depression EEG signals. Thus, signal processing methods are used to extract hidden information from the EEG signals. In this work, normal and depression EEG signals are used and discrete wavelet transform (DWT) is performed up to two levels. The features (skewness, energy, kurtosis, standard deviation (SD), mean and entropy) are extracted at the various detailed coefficients levels of the DWT. The extracted features then undergo a statistical analysis method, which is the Student's t-test that determines the significance of differences in the features. Support Vector Machine classifier with Radial Basis Kernel Function (SVM RBF) was used and the classification accuracy results of 88.9237% was obtained. Hence, this proposed automatic classification system can serve as a useful diagnostic and monitoring tool for detection of depression.
A Note on 'Bayesian analysis of the random coefficient model using aggregate data', an alternative approach

In this note on the paper from (Jiang, Manchanda & Rossi 2009) I want to discuss a simple alternative estimation method of the multinomial logit model for aggregated data, the so called BLP model, named after (Berry, Levinsohn & Pakes 1995). The estimation is conducted through a bayesian estimation similar to (Jiang et al. 2009). But in difference to them here the time intensive contraction mapping for assessing the mean utility in every iteration step of the estimation procedure is not needed. This is because the likelihood function is computed via a special case of the control function method ((Petrin & Train 2002) and (Park & Gupta 2009)) and hence a full random walk MCMC algorithm is applied. In difference to (Park & Gupta 2009) the uncorrelated error, which is explicitly introduced through the control function procedure, is not integrated out, but sampled with a random walk MCMC. The introduced proceeding enables to use the whole information from the data set in the estimation and beyond that accelerates the computation.
Reggeization of Elementary Particles in Renormalizable Gauge Theories: Vectors and Spinors

The question of the Reggeization of the elementary particles in renormalizable gauge theories with the Higgs-Kibble mechanism is examined. It is concluded that the massive non-Abelian gauge vector mesons and J = 1/2 fermions of these theories lie on Regge trajectories in every case in which a counting criterion due to Mandelstam is satisfied. Several explicit examples are presented which verify that the necessary factorization condition of the Born helicity matrices is satisfied at J = 1 and J = 1/2, supporting this conclusion. (auth)
Influence of ridge geometry on lateral mode stability of (Al,In)GaN laser diodes

Height and width of the ridge forming the laser diode waveguide determine threshold current density and lateral mode stability. We measure the optical near‐field of the laser mode and simulate the two–dimensional mode distribution including waveguide losses and optical gain. The simulations show that weak guiding and not current spreading is the major cause for increased threshold current densities in weakly guided laser diodes. The near‐field measurements show fundamental and higher order modes for nominally identical ridge laser diodes. We demonstrate that asymmetric losses in the waveguide bias the lateral mode competition towards higher order modes for an intermediate range of index guiding. This asymmetric damping is specific for (Al,In)GaN laser diodes due to absorption introduced in the p‐waveguide by magnesium doping. This competition of lateral modes can also be seen spectrally as two longitudinal mode combs of different optical gain. (© 2005 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Ab initio studies of disorder in the full Heusler alloy Co2FexMn1−xSi

Co-based full Heusler alloys have been predicted to be half-metals, with 100% spin polarisation at the Fermi level, yet this has yet to be realised in practice. Heusler thin-films often exhibit a degree of atomic disorder, and this is believed to be one cause of the low magnetoresistance in Heusler-based spin valves' devices. We present an ab initio density functional theory + U investigation into the effect of disorder on the electronic properties of Co2Fe0.5Mn0. 5Si. It is found that the half-metallicity depends strongly on the kind of disorder present, with low or even reversed spin-polarisation when the Co sublattice mixes with either the Fe/Mn or Si sublattice, but keeps the high spin polarisation when the Mn/Fe and Si sublattices are mixed. Calculations of the formation energy show that this latter kind of disorder is by far the most likely to occur, an encouraging result which means that it may not be necessary to produce perfectly ordered Heusler alloys in order to achieve 100% spin polarisation.
Effects of Resolution on the Simulation of Boundary‐layer Clouds and the Partition of Kinetic Energy to Subgrid Scales

Seven boundary‐layer cloud cases are simulated with UCLA‐LES (The University of California, Los Angeles – large eddy simulation) model with different horizontal and vertical gridspacing to investigate how the results depend on gridspacing. Some variables are more sensitive to horizontal gridspacing, while others are more sensitive to vertical gridspacing, and still others are sensitive to both horizontal and vertical gridspacings with similar or opposite trends. For cloud‐related variables having the opposite dependence on horizontal and vertical gridspacings, changing the gridspacing proportionally in both directions gives the appearance of convergence. In this study, we mainly discuss the impact of subgrid‐scale (SGS) kinetic energy (KE) on the simulations with coarsening of horizontal and vertical gridspacings. A running‐mean operator is used to separate the KE of the high‐resolution benchmark simulations into that of resolved scales of coarse‐resolution simulations and that of SGSs. The diagnosed SGS KE is compared with that parameterized by the Smagorinsky‐Lilly SGS scheme at various gridspacings. It is found that the parameterized SGS KE for the coarse‐resolution simulations is usually underestimated but the resolved KE is unrealistically large, compared to benchmark simulations. However, the sum of resolved and SGS KEs is about the same for simulations with various gridspacings. The partitioning of SGS and resolved heat and moisture transports is consistent with that of SGS and resolved KE, which means that the parameterized transports are underestimated but resolved‐scale transports are overestimated. On the whole, energy shifts to large‐scales as the horizontal gridspacing becomes coarse, hence the size of clouds and the resolved circulation increase, the clouds become more stratiform‐like with an increase in cloud fraction, cloud liquid‐water path and surface precipitation; when coarse vertical gridspacing is used, cloud sizes do not change, but clouds are produced less frequently. Cloud fraction and liquid water path decrease.
Periodic orbits and a correlation function for the semiclassical density of states

A principle of uniform density of periodic orbits in the phase space of a Hamiltonian system with bound classical motion is proposed and used to obtain information about the semiclassical quantum eigenvalue spectrum. It supplies a more refined statistic than the 'one state per Planck cell' rule for the average semiclassical density of states, namely the limiting behaviour of a certain correlation function of the density of states. Unlike the average, this correlation shows markedly different behaviour for systems with integrable and ergodic classical motion.
Estimation of Time Varying Skewness and Kurtosis with an Application to Value at Risk

This paper generalizes the Hyperbolic Asymmetric Power ARCH (HYAPARCH) model to allow for time varying skewness and kurtosis in the conditional distribution. This is done by modeling the conditional skewness and degrees of freedom of the skewed Student's t distribution of Lambert and Laurent (2001) as a function of the conditioning information. The proposed specification nests a large number of models in the literature and represents the first attempt to jointly model long memory in volatility and time variation in the third and fourth moments. The finite sample properties of MLE for this class of model are examined. The results indicate that the ARCH class of processes with time varying skewness can be reliably estimated with realistic sample sizes. Simulations and empirical evidence are unable to replicate the findings of Harvey and Siddique (1999), that accounting for time varying skewness reduces the persistence and asymmetry properties of the conditional variance. Simulations also suggest that time varying kurtosis estimation must be viewed with caution, because it can be difficult to identify in the presence of ARCH effects. Application of the HYAPARCH model with time varying skewness and degrees of freedom illustrates the usefulness of the proposed approach. Out of sample forecasts of the value at risk (VaR) however, generally support parsimonious models that assume conditional normality. When forecasting VaR, skewness and leptokurtosis in the unconditional return distribution is generally better captured via an asymmetric conditional variance model with Gaussian innovations.
Ray-tracing-based prediction and subtraction of water-layer multiples

Many methods of multiple suppression break down when the structure that produces the reverberation possesses significant lateral variation; a common example of this situation occurs in marine data with the multiple reflections that are generated by seafloor topography. Such multiples may be suppressed by techniques based upon wave-equation extrapolation; the recorded seismic data are mathematically propagated through a simulated water layer to generate a set of multiple arrivals which may, after data matching, be subtracted. However, the computational effort required to propagate prestack data to a laterally varying datum is very large. In this paper, a method of suppressing selected multiples with arbitrary moveout is presented. In order to reduce the computational cost, prediction of the multiple arrival times is performed by ray tracing through a model of the laterally varying water layer and, possibly, the subsurface. An estimate of the multiple waveform on each trace is obtained by stacking a window of data about the calculated arrival times. The multiple arrival can then be attenuated by subtracting this wavelet from each trace in the prestack gather from which the estimate is derived. In practice, calculations of the variation in multiple amplitude and of any errors in the moveout correction require the multiple reflections to be of comparable, or higher, amplitude than contemporary primary events, a situation that is often the case where multiple contamination is a problem.