Miracle-dz/UniFAR-FTUs huggingface dataset:
Contextual Aware Joint Probability Model Towards Question Answering System

In this paper, we address the question answering challenge with the SQuAD 2.0 dataset. We design a model architecture which leverages BERT's capability of context-aware word embeddings and BiDAF's context interactive exploration mechanism. By integrating these two state-of-the-art architectures, our system tries to extract the contextual word representation at word and character levels, for better comprehension of both question and context and their correlations. We also propose our original joint posterior probability predictor module and its associated loss functions. Our best model so far obtains F1 score of 75.842% and EM score of 72.24% on the test PCE leaderboad.
FAMILIES OF MINIMAX ESTIMATORS OF MATRIX OF NORMAL MEANS WITH UNKNOWN COVARIANCE MATRIX

The risk is denoted by R (B;B,E’). In the following sections we obtain classes of minimax estimators with respect to the loss(1.2)for the case m>p+1. For the case.8=ID with m>p十1, Stein[8]considered a family of estimators which utilize the eigenvalues of X’X. Using integration by parts and calculus on eigenstructure he gave a condition under which the corresponding estimator is minimax. By Stein’s method Zheng[9]obtained a class of minimax estimators which include the Efron-Morris
DLADC: Deep Learning Based Semiconductor Wafer Surface Defects Recognition

— In semiconductor manufacturing, surface defects on wafers must be classified accurately for better yield management. To manage the increasing chip demand in speed and scale, automatic defect classification (ADC) system has been introduced. Most existing ADC systems utilize machine learning-based algorithms that require manual feature extractions and manual intervention such as human-based classification for accuracy and consistency. These methods are labour-intensive, unreliable, and highly prone to human error. Therefore, by leveraging on deep learning technologies, this paper proposes DLADC - an ADC system using a deep convolutional neural network (CNN) architecture for detecting and classifying semiconductor wafer surface defects. The proposed system takes Scanning Electron Microscope (SEM) images as input and outputs the defect’s class and location. The proposed system also sub-classifies particle-type defects into various sizing groups. Identification of defect types that occurred on wafer surfaces allows for better defect root cause analysis, and the additional information of defect size further serves as an essential indication of the origin of machine failure. The proposed DLADC promotes 2x time saving while achieving an improved accuracy of 93.69% based on experimental results with a real semiconductor defect dataset. Not only does DLADC outperforms the 70% classification performance of trained operators, but it also surpasses the 90% classification performance of industrially pragmatic defect classification.
Derivation of exact master equation with stochastic description: Models in quantum optics

The methodology of stochastic description for dissipation, a generic scheme to decouple the interaction between two subsystems, is applied to the study of dissipative dynamics in quantum optics. It is shown that the influence of the coupled thermal or vacuum field on the quantum mode can be exactly represented by the induced stochastic fields. The quantum mode thereby satisfies a stochastic differential equation and dissipation effect due to the coupling with the environment is obtained through statistical averaging. Within the framework of stochastic description, it is demonstrated how to derive the master equation for a single optical mode interacting with the bosonic bath. A numerical algorithm for solving the master equation in which the coefficients are determined by a set of integral equations is discussed and a comparison with the known results is displayed. The derivation of the master equation for the spontaneous decay of two-state atoms in the vacuum is also presented.
Online Learning in Non-Cooperative Conﬁgurable Markov Decision Process

In the Conﬁgurable Markov Decision Processes there are two entities, a Reinforcement Learning agent and a conﬁgurator which can modify some parameters of the environment to improve the performance of the agent. What if the conﬁgurator does not have the same intentions as the agent? In this paper, we introduce the Non-Cooperative Conﬁgurable Markov Decision Process, a framework that allows having two (possibly different) reward functions for the conﬁgurator and for the agent. In this setting, we consider an online learning problem, where the conﬁgurator has to ﬁnd the best among a ﬁnite set of possible conﬁgurations. We propose a learning algorithm to minimize the conﬁgurator expected regret, which exploits the structure of the problem. While a naïve application of the UCB algorithm yields a regret that grows indeﬁnitely over time, we show that our approach suffers only bounded regret. Furthermore, we empirically show the performance of our algorithm in simulated domains.
Bouncing cosmology with future singularity from modified gravity

We investigate which Jordan frame $F(R)$ gravity can describe a Type IV singular bouncing cosmological evolution, with special emphasis given near the point at which the Type IV singularity occurs. The cosmological bounce is chosen in such a way so that the bouncing point coincides exactly with Type IV singularity point. The stability of the resulting $F(R)$ gravity is examined and in addition, we study the Einstein frame scalar-tensor theory counterpart of the resulting Jordan frame $F(R)$ gravity. Also, by assuming that the Jordan frame metric is chosen in such a way so that, when conformally transformed in the Einstein frame, it yields a quasi de Sitter or de Sitter Friedmann-Robertson-Walker metric, we study the observational indexes which turn out to be consistent with Planck 2015 data in the case of the Einstein frame scalar theory. Finally, we study the behavior of the effective equation of state corresponding to the Type IV singular bounce and after we compare the resulting picture with other bouncing cosmologies, we critically discuss the implications of our analysis.
Influence of aerosols from biomass burning on the spectral analysis of Cherenkov telescopes

During the last decade, imaging atmospheric Cherenkov telescopes (IACTs) have proven themselves as astronomical detectors in the very-high-energy (VHE; E>0.1 TeV) regime. The IACT technique observes the VHE photons indirectly, using the Earth's atmosphere as a calorimeter. Much of the calibration of Cherenkov telescope experiments is done using Monte Carlo simulations of the air shower development, Cherenkov radiation and detector, assuming certain models for the atmospheric conditions. Any deviation of the real conditions during observations from the assumed atmospheric model will result in a wrong reconstruction of the primary gamma-ray energy and the resulting source spectra. During eight years of observations, the High Energy Stereoscopic System (H.E.S.S.) has experienced periodic natural as well as anthropogenic variations of the atmospheric transparency due to aerosols created by biomass burning. In order to identify data that have been taken under such long-term reductions in atmospheric transparency, a new monitoring quantity, the Cherenkov transparency coefficient, has been developed and will be presented here. This quantity is independent of hardware changes in the detector and, therefore, isolates atmospheric factors that can impact the performance of the instrument, and in particular the spectral results. Its positive correlation with independent measurements of the atmospheric optical depth (AOD) retrieved from data of the Multi-angle Imaging SpectroRadiometer (MISR) on board of the Terra NASA's satellite is also presented here.
Cavity optomechanics and optical frequency comb generation with silica whispering-gallery-mode microresonators

In this thesis, I report on the exploration of non-linear optical phenomena in silica whispering gallery mode (WGM) microcavities. In particular, optomechanical interactions, and the generation of optical frequency combs due to the Kerr non-linearity of silica are investigated. Radiation pressure couples the optical field stored in a microcavity to mechanical degrees of freedom of its boundary. Our systematic survey of the mechanical modes present in silica WGM microcavities has enabled engineering devices which exhibit strong optomechanical coupling between highquality, radio-frequency (30–120 MHz) mechanical radial-breathing modes and ultra-high finesse optical WGMs. The finite build-up time of the intracavity field leads to complex dynamics in the optomechanical interaction. We observe and analyze the effect of dynamical backaction, in which the radiation-pressure force modifies the dynamics of the mechanical mode. In particular, we demonstrate for the first time, how this effect can be exploited to optically cool a mechanical mode. Quantum-noise limited optical interferometry is employed for the measurement of mechanical displacement fluctuations, from which the effective temperature of the mode is inferred. An imprecision at the level of 1 · 10 m/ √ Hz is reached, below the expected quantum mechanical zeropoint position fluctuations of the mechanical mode. For the first time, we demonstrate laser cooling also in the “resolvedsideband regime”, in which the optical photon storage time exceeds the mechanical oscillation period, as required for ground-state cooling. Technical sources of heating are eliminated by using low-noise lasers, a He-cryogenic environment for the experiment and suppressing laser absorption. Occupations down to 〈n〉 = 63 ± 20 mechanical excitation quanta are achieved in this manner. Simultaneously, we are able to assess the perturbation of the mechanical mode due to the process of measurement (measurement backaction). The optical techniques employed here are shown to operate in a near-ideal manner according to the principles of Quantum Measurement, displaying a backaction-imprecision product close to the quantum limit. In an independent set of experiments, we show that the high intensities circulating in silica WGM microcavities give rise to strong optical four-wave mixing due to the material’s Kerr nonlinearity. Starting from a continuouswave pump laser, broad, discrete optical spectra are formed by a cascade of optical sidebands to the pump. These “comb” spectra span more than 500 nm, and consist of lines spaced roughly by the cavity’s free spectral range. An optical frequency comb based on a mode-locked laser is used as a reference to determine the homogeneity of the microcavity comb lines’ spacing in the frequency domain. The microcavity comb lines are found to be equidistant at a relative level of 7 · 10, in spite of the presence of cavity dispersion. We have therefore demonstrated a novel, ultracompact source of optical frequency combs with applications in astronomy and telecommunication technology.
Electro-optic sampling for time resolving relativistic ultrafast electron diffraction

The Pegasus laboratory at UCLA features a state‐of‐the‐art electron photoinjector capable of producing ultrashort (<100 fs) high‐brightness electron bunches at energies of 3.75 MeV. These beams recently have been used to produce static diffraction patterns from scattering off thin metal foils, and it is foreseen to take advantage of the ultrashort nature of these bunches in future pump‐probe time‐resolved diffraction studies. In this paper, single shot 2‐d electro‐optic sampling is presented as a potential technique for time of arrival stamping of electron bunches used for diffraction. Effects of relatively low bunch charge (a few 10’s of pC) and modestly relativistic beams are discussed and background compensation techniques to obtain high signal‐to‐noise ratio are explored. From these preliminary tests, electro‐optic sampling is suitable to be a reliable nondestructive time stamping method for relativistic ultrafast electron diffraction at the Pegasus lab.
The Role of Brain-Derived Neurotrophic Factor in Immune-Related Diseases: A Narrative Review

Brain-derived neurotrophic factor (BDNF) is a neurotrophin regulating synaptic plasticity, neuronal excitability, and nociception. It seems to be one of the key molecules in interactions between the central nervous system and immune-related diseases, i.e., diseases with an inflammatory background of unknown etiology, such as inflammatory bowel diseases or rheumatoid arthritis. Studies show that BDNF levels might change in the tissues and serum of patients during the course of these conditions, e.g., affecting cell survival and modulating pain severity and signaling pathways involving different neurotransmitters. Immune-related conditions often feature psychiatric comorbidities, such as sleep disorders (e.g., insomnia) and symptoms of depression/anxiety; BDNF may be related as well to them as it seems to exert an influence on sleep structure; studies also show that patients with psychiatric disorders have decreased BDNF levels, which increase after treatment. BDNF also has a vital role in nociception, particularly in chronic pain, hyperalgesia, and allodynia, participating in the formation of central hypersensitization. In this review, we summarize the current knowledge on BDNF’s function in immune-related diseases, sleep, and pain. We also discuss how BDNF is affected by treatment and what consequences these changes might have beyond the nervous system.
THREE-DIMENSIONAL SIMULATIONS OF MAGNETOHYDRODYNAMIC TURBULENCE BEHIND RELATIVISTIC SHOCK WAVES AND THEIR IMPLICATIONS FOR GAMMA-RAY BURSTS

Relativistic astrophysical phenomena such as gamma-ray bursts (GRBs) and active galactic nuclei often require long-lived strong magnetic fields that cannot be achieved by shock compression alone. Here, we report on three-dimensional special-relativistic magnetohydrodynamic (MHD) simulations that we performed using a second-order Godunov-type conservative code to explore the amplification and decay of macroscopic turbulence dynamo excited by the so-called Richtmyer–Meshkov instability (RMI; a Rayleigh–Taylor-type instability). This instability is an inevitable outcome of interactions between shock and ambient density fluctuations. We find that the magnetic energy grows exponentially in a few eddy-turnover times because of field-line stretching and then, following the decay of kinetic turbulence, decays with a temporal power-law exponent of −0.7. The magnetic energy fraction can reach ϵB ∼ 0.1 but depends on the initial magnetic field strength, which can diversify the observed phenomena. We find that the magnetic energy grows by at least two orders of magnitude compared to the magnetic energy immediately behind the shock, provided the kinetic energy of turbulence injected by the RMI is greater than the magnetic energy. This minimum degree of amplification does not depend on the amplitude of the initial density fluctuations, while the growth timescale and the maximum magnetic energy depend on the degree of inhomogeneity in the density. The transition from Kolmogorov cascade to MHD critical balance cascade occurs at ∼1/10th the initial inhomogeneity scale, which limits the maximum synchrotron polarization to less than ∼2%. We derive analytical formulas for these numerical results and apply them to GRBs. New results include the avoidance of electron cooling with RMI turbulence, the turbulent photosphere model via RMI, and the shallow decay of the early afterglow from RMI. We also perform a simulation of freely decaying turbulence with relativistic velocity dispersion. We find that relativistic turbulence begins to decay much more quickly than one eddy-turnover time because of rapid shock dissipation, which does not support the relativistic turbulence model by Narayan & Kumar.
UV studies of the local interstellar medium

We present new measurements of the spatial distribution of cool and hot gas in the local interstellar medium as a result of a search for O VI absorption in the FUV spectra of 100 white dwarfs at distances between 50 and 300 pc. We have carried out a detailed velocity analysis on the detected O VI lines to ascertain the degree of contamination from the background star. The position of the stars relative to the local cavity is calculated through cold gas phase column density measurements. Through curve of growth analysis we note that the local cavity is depleted in metals relative to hydrogen by a factor of 5 -10 compared to the space beyond. Whereas the O VI producing hot gas was originally thought to be observed in the interface regions between hot and cold gas, i.e. the cavity wall, our investigation finds no unambiguously hot gas in lines of sight that span such regions. We do, however, find hot gas at high galactic latitudes where there are no such walls. We discuss the wider context of this result by comparing the position of these detections to the soft X-ray background. This work ultimately changes our picture of the local interstellar environment and its processes by removing the ubiquitous million degree gas that was once thought to surround the solar system. One possible explanation worthy of further investigation is that the local cavity has recently begun to be inundated by hot gas spilling out of the loop 1 supernova remnant. In support of this work there is an enclosed CD that contains the data and models used in this thesis. Examples of the information found on this CD can be found in the appendix. Explanations of the data, charts and graphs can be found in the body of the text when required.
Heart Failure Pathogenesis Elucidation and New Treatment Method Development

Heart failure (HF) is a leading cause of death worldwide. In Japan, the number of HF patients has increased with its aging population, resulting in “HF pandemic. ” HF is the final stage of various cardiovascular diseases, including valvular heart disease, ischemic heart disease, atrial fibrillation, and hypertension. Cardiac hypertrophy is a compensatory response to increased workload and maintains cardiac function. Pressure overload due to mechanical stress causes cardiac hypertrophy, whereas continuous cardiac stress reduces wall thickness and consequently causes HF. Understanding the molecular mechanisms underlying this process is crucial to elucidate HF pathophysiology. We demonstrated that ischemia and DNA damage are important in the progression of hypertrophy to HF. Genetic mutations associated with cardiomyopathy and prognosis has been identified. To realize precision medicines for HF, the underlying molecular mechanisms need to be elucidated. In this review, we introduce new paradigms for understanding HF pathophysiology discovered through basic research.
Adaptive control for coupled lagrangian systems with structured uncertainty in the interconnections

In this paper, an adaptive control scheme for coupled lagrangian systems is proposed. The interconnection terms among subsystems are assumed to have known structure and depend nonlinearly on uncertain parameters. Also, these coupling functions are restricted to those which are concave or convex with respect to the parameters. Two parameter adaptation laws are used to estimate local and interconnection unknown parameters. Both centralized and decentralized controls are designed. Ultimately bounded tracking with arbitrary precision is achieved regardless of ability to share information among subsystem. A numerical simulation, consisting of two pendulums interconnected by a nonlinear spring, is included to illustrate the effectiveness of the proposed schemes. Copyright © 2013 John Wiley & Sons, Ltd.
Accounting Quality and Audit Attributes on the Stock Price Crashes in an Emerging Market

This study addresses the relationship between accounting quality and audit attributes (i.e., audit quality, auditor industry specialization, audit concentration, and audit fees) with companies’ SPCRs listed on the Iraqi Stock Exchange. A multivariate regression model was used to test the hypotheses. The research hypotheses were tested using a sample of 210 observations of the listed Iraqi firms from 2013 to 2018 and a multiple regression model based on the random-effects model’s panel data technique. The findings indicate a negative and significant relationship between the accounting quality, audit, auditor industry specialization and SPCRs. Results also conveyed a meaningful and positive association between stock price crash risk (SPCR) and audit fees. The results did not confirm the relationship between corporate governance and audit concentration with SPCR. The primary research model was tested with additional methods (t + 1, fixed effects, ordinary least squares). Since this is the first study addressing this issue in the emerging markets, it provides users, analysts, and legal entities with helpful information about audit attributes that significantly affect SPCR. These results also contribute to developing science and knowledge in this field and fill the literature gap.
A QUASI-STELLAR OBJECT PLUS HOST SYSTEM LENSED INTO A 6″ EINSTEIN RING BY A LOW-REDSHIFT GALAXY

We report the serendipitous discovery of an Einstein Ring in the optical band from the Sloan Digital Sky Survey (SDSS) data and four associated images of a background source. The lens galaxy appears to be a nearby dwarf spheroid at a redshift of 0.0375 ± 0.002. The lensed quasar is at a redshift of 0.6842 ± 0.0014, and its multiple images are distributed almost 360° around the lens nearly along a ring of radius ∼6. ″0. Single-component lens models require a mass of the galaxy of almost 1012 M☉ within 6. ″0 from the lens center. With the available data, we are unable to determine the exact positions, orientations, and fluxes of the quasar and the galaxy, though there appears to be evidence for a double- or multiple-merging image of the quasar. We have also detected strong radio and X-ray emissions from this system. It is indicative that this ring system may be embedded in a group or cluster of galaxies. This unique ring, by virtue of the closeness of the lens galaxy, offers a possible probe of some key issues such as the mass-to-light ratio of intrinsically faint galaxies and the existence of large-scale magnetic fields in elliptical galaxies.
$\mathcal{l}_{1}$ Filtering for Positive Takagi–Sugeno Fuzzy Systems via Successive Linear Programming

This article studies the <inline-formula><tex-math notation="LaTeX">$\mathcal{l}_{1}$</tex-math></inline-formula> filtering problem for positive Takagi–Sugeno fuzzy systems. A pair of filters is sought to assure the stability and positivity of filtering error systems with guaranteed <inline-formula><tex-math notation="LaTeX">$\mathcal{l}_{1}$</tex-math></inline-formula> performance indices. For this purpose, a pair of linear copositive Lyapunov functions that capture the positivity characteristics of filtering error systems is adopted to derive sufficient conditions for the existence of filters, which boils down to the bilinear programming problem. To solve this dilemma, we propose a convex approximation strategy, and thus, a sequence of convex surrogates is developed to reasonably replace the nonconvex constraints, based on which a successive linear programming algorithm is constructed. Therefore, the filters can be achieved by solving a battery of linear programming. Finally, the efficiency of the designed filtering scheme is justified by simulation validations.
Quantum discord in bipartite systems based on projection measurements

We propose a method to detect an exact quantum discord for some general bipartite quantum systems analytically. Here we show that for some density matrices, after the orthogonal projective measurement the state of the matrix will be diagonal and classic. So we can obtain the maximum amount of classical information in this case. Consequently, for these density matrices instead of using the positive operator valued measures (POVMs), projective operators can be used. Also we obtain the relation between quantum discord with relative entropy of discord for these states and show that for various examples of X-states such as on two qubit and qubit-qutrit density matrices, the relative entropy of discord is replaced by quantum discord.