Miracle-dz/UniFAR-FTUs huggingface dataset:
A semiparametric type II Tobit model for time-to-event data with application to a merger and acquisition study

ABSTRACT Merger and acquisition is an important corporate strategy. We collect recent merger and acquisition data for companies on the China A-share stock market to explore the relationship between corporate ownership structure and speed of merger success. When studying merger success, selection bias occurs if only completed mergers are analyzed. There is also a censoring problem when duration time is used to measure the speed. In this article, for time-to-event outcomes, we propose a semiparametric version of the type II Tobit model that can simultaneously handle selection bias and right censoring. The proposed model can also easily incorporate time-dependent covariates. A nonparametric maximum likelihood estimator is proposed. The resulting estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Some Monte Carlo studies are carried out to assess the finite-sample performance of the proposed approach. Using the proposed model, we find that higher power balance of a company is associated with faster merger success.
Feature and Model Level Fusion of Pretrained CNN for Remote Sensing Scene Classification

Convolutional neural networks (CNN) have attracted tremendous attention in the remote sensing community due to its excellent performance in different domains. Especially for remote sensing scene classification, the CNN-based methods have brought a great breakthrough. However, it is not feasible to fully design and train a new CNN model for remote sensing scene classification, as this usually requires a large number of training samples and high computational costs. To alleviate these limitations of fully training a new model, some work attempts to use the pretrained CNN models as feature extractors to build feature representation of scene images for classification and has achieved impressive results. In this scheme, how to construct feature representation of scene image via the pretrained CNN model becomes the key process. Existing studies paid a little attention to build more discriminative feature representation by exploring the potential benefits of multilayer features from a single CNN model and different feature representations from multiple CNN models. To this end, this paper presents a fusion strategy to build the feature representation of the scene images by integrating multilayer features of a single pretrained CNN model, and extends it to a framework of multiple CNN models. For these purposes, a multiscale improved Fisher kernel coding method is used to build feature representation of the scene images on convolutional layers, and a feature fusion approach based on two feature subspace learning methods [principal component analysis (PCA)/spectral regression kernel discriminant analysis and PCA/spectral regression kernel locality preserving projection] is proposed to construct final fused features for scene classification. For validation and comparison purposes, the proposed approaches are evaluated with two challenging high-resolution remote sensing datasets and shows the competitive performance compared with existing state-of-the-art baselines such as fully trained CNN models, fine tuning CNN models, and other related works.
Commonsense Reasoning

For long time ago, researchers have been study the theoretical parts of how to represent commonsense knowledge. However, nobody has compared different opinions of what is in fact the commonsense knowledge. This research area is new and it needs our attention. The commonsense reasoning has been studied in different areas, such as philosophy, psychology, and computer science. As part of our research, we studies different views of what is commonsense knowledge and reasoning. At the end of this chapter, we suggest four important definitions for our research. We start with the notions of common sense as part of " folk psychology ". According to W ikipedia Online Dictionary, " folk psychology " (sometimes called naive psychology or common sense psychology) is the set of background assumptions, socially-conditioned prejudices and convictions that are implicit in our everyday descriptions of others' behavior and in our ascriptions of their mental states. We also discuss how some of the knowledge representation researchers in different Computer Science fields describe commonsense reasoning, as well as default reasoning and non-monotonic reasoning.
The heroic age, the creation of quantum mechanics, 1925–1940

Many readers will be aware that the answer to each of these questions was both a theory, and post/prediction of a new particle. The photon, W-boson, Z-boson, antimatter, and the Higgs boson, respectively. In Theories of everything the prolific Frank Close makes a typically clear and readily understood description of much of this. We have now our Standard Model, which has the annoying property of explaining almost all measurements to the limits of experimental precision, and yet fails to include critical physics – what is dark matter? Why is there matter at all? How do we integrate gravity with quantum physics? Our standard model is clearly a theory of much, but not all, of what we can measure in expensive laboratories, and fails to even start to explain most of the Universe the astronomer’s measure. It is very far indeed from a theory of everything. This history of science tells us that pushing experiment to ever more extreme conditions will discover new phenomena, which, in turn, will lead to more fundamentals and unifications. That has certainly been the history of physics so far, as superbly explained by Heilbron and competently Close in these two studies. It is the basis on which many of us continue to devote our lives and efforts. Certainly, the rate of discovery, expected and unexpected – gravitational waves, darkmatter, dark energy – has not slackened. Physics remains an exciting challenge, irrespective of such vague notions as a Theory of Everything, which will be perhaps nomore than an improved StandardModel, describing fundamental physics. Perhaps full understanding will remain elusive as we have available for study only one part of what may be one among many Universes. Everything may be too big a challenge.
ESTIMATES FOR MULTILINEAR COMMUTATOR OF SINGULAR INTEGRAL OPERATORS WITH NON – SMOOTH KERNELS ∗

Let T be the Calderón-Zygmund singular integral operator, a well known result of Coifman, Rochberg and Weiss states that the commutator [b, T ](f) = bT (f) − T (bf) (where b ∈ BMO(R)) is bounded on L(R) for 1 < p < ∞. In [2][4][7][1114], the sharp estimates for some multilinear commutators of the Calderón-Zygmund singular integral operators are obtained. In [3] and [10], the boundedness of the singular integral operators with non-smooth kernels and their commutators are obtained. The main purpose of this paper is to study the properties of multilinear commutator of singular integral operators with non-smooth kernels by means of the establishment of their sharp maximal function estimates.
Performance Measurements for Robotic Actuators

The first part of this paper present an argument on the importance of quantitative methods in robotic research. The remaining sections propose detailed performance metrics for actuator evaluation and example specifications. Several measurements which are new to actuator research are proposed: Impedance (“Backdrivability”), Force Dynamic Range and Force Fidelity. These specifications have emerged from research in robotics and haptics and address the need to obtain a more thorough understanding of machine performance.
A MODIS-Derived Value-Added Climatology of Maritime Cloud Liquid Water Path That Conserves Solar Reflectance

AbstractA dataset is generated from a method to retrieve distributions of cloud liquid water path over partially cloudy scenes. The method was introduced in a 2011 paper by Foster and coauthors that described the theory and provided test cases. Here it has been applied to Moderate Resolution Imaging Spectroradiometer (MODIS) collection-5 and collection-6 cloud products, resulting in a value-added dataset that contains adjusted distributions of cloud liquid water path for more than 10 years for marine liquid cloud for both Aqua and Terra. This method adjusts horizontal distributions of cloud optical properties to be more consistent with observed visible reflectance and is especially useful in areas where cloud optical retrievals fail or are considered to be of low quality. Potential uses of this dataset include validation of climate and radiative transfer models and facilitation of studies that intercompare satellite records. Results show that the fit method is able to reduce bias between observed visible .
ON THE SOLUTION OF THE 3 RD CLAY MILLENNIUM PROBLEM. A SHORT AND ELEGANT PROOF THAT P ≠ NP IN THE CONTEXT OF DETERMINISTIC TURING MACHINES AND ZERMELO- FRANKEL SET THEORY

In this paper we try to handle the confusing situation in the literature of having plenty many proofs published in respectable journals with referees, some of them of 100 or more pages, that prove contradictory things like P ≠ NP, P = NP, or suggestions that both are non-provable. Obviously not all of them can be correct. We try to clear the situation by providing a very short but decisive proof that P ≠ NP, so short that anyone familiar with the area, would discover any flaw or error if it existed. We provide a simple proof in the context of the ZF set theory and deterministic Turing machines. We discuss also the subtle implications of considering the P versus NP problem, in different axiomatic theories! The results of the current paper definitely solve the 3 rd Clay Millennium problem P versus NP, in a simple and transparent away that the general scientific community, but also the experts of the area, can follow, understand and therefore becoming able to accept.
Interference Level Detector with PCB HDI Rogowski coil for PLC Narrow-Band Applications

This article presents and discusses the structure, principle of operation, and operational properties of a newly developed interference level detector (ILD) designed to measure conducted supraharmonic disturbances (1–150 kHz) in the power grid and to assess the effectiveness of narrow-band Power Line Communication (PLC) transmission, especially in the PRIME technology. The usability assessment was made on the basis of the validation and the results of tests carried out in a low-voltage network with non-linear loads. Appropriate practical conclusions were then formulated.
Neural Recruitment, Information Processing and the Equation for Subjective Time

Retrospective and Prospective subjective duration estimate experiments reveal that the perceived time duration is in general different from the external objective interval depending on factors such as attention and psychopathological conditions etc. We derive an equation for subjective time to explain its variation with objective time incorporating the role of neural recruitment and the rate of information processing in the brain. The role of attention is included via an attention function defined to have a maximum value one and a minimum zero. A centralized timing resulting from integration of distributed timings in different neural assemblies comes out as a possibility.
Improved frequency‐domain design method for the fractional order proportional–integral–derivative controller optimal design: a case study of permanent magnet synchronous motor speed control

An improved frequency-domain method for the fractional order PI λ D μ controller design is proposed in this study. A proportional relation between the integral gain and derivative gain of the controller is built, while the derivative order is set to be equal to the integral order. Applying the improved method, the PI λ D μ controller parameters can be calculated analytically according to different design requirements. The proportional coefficient between the integral and derivative gains is studied and then the model of the optimal proportional coefficient for the commonly used permanent magnet synchronous motor speed control system is built. Based on the established model, the optimal PI λ D μ controllers are obtained analytically applying the proposed method. Motor speed control simulations and experiments are performed, comparing the performance of the controllers obtained using the proposed method and those obtained using the current frequency-domain method and other design methods. Simulation and experimental results show that the control system obtained using the proposed method achieves the specified stability, robustness to gain variations and the optimal dynamic performance.
Muscle–Brain crosstalk in cognitive impairment

Sarcopenia is an age-related, involuntary loss of skeletal muscle mass and strength. Alzheimer's disease (AD) is the most common cause of dementia in elderly adults. To date, no effective cures for sarcopenia and AD are available. Physical and cognitive impairments are two major causes of disability in the elderly population, which severely decrease their quality of life and increase their economic burden. Clinically, sarcopenia is strongly associated with AD. However, the underlying factors for this association remain unknown. Mechanistic studies on muscle–brain crosstalk during cognitive impairment might shed light on new insights and novel therapeutic approaches for combating cognitive decline and AD. In this review, we summarize the latest studies emphasizing the association between sarcopenia and cognitive impairment. The underlying mechanisms involved in muscle–brain crosstalk and the potential implications of such crosstalk are discussed. Finally, future directions for drug development to improve age-related cognitive impairment and AD-related cognitive dysfunction are also explored.
Modularity of networks

Modularity is a quality function on partitions of a network which may be used to identify highly clustered components. It is commonly used to analyse large real networks, for example in social networks and protein discovery to find communities and related proteins respectively. Given a graph G, the modularity of a partition of the vertex set measures the extent to which edge density is higher within parts than between parts, and the maximum modularity qa(G) of G (where 0 ≤ qa(G) L 1) is the maximum modularity of a partition of V(G). In essence, modularity allows us to feed in large sets of data, and output a vertex partition with an associated score. Knowledge of the maximum modularity of random graphs is important to determine the statistical significance of the maximum modularity found on a real network. This thesis establishes numerical bounds on the likely maximum modularity for random regular graphs. The modularity of a random cubic network is shown to be whp in the interval (0.66, 0.81). This result has practical applications. It establishes that a large cubic network with modularity greater than 0.81 has a statistically significant clustering structure. The evolution of the maximum modularity of Erdos-Renyi random graphs as the edge probability increases is investigated. Three different phases of the likely maximum modularity are found. For np=1+o(1) the maximum modularity is 1 + o(1) whp and for np → ∞ the maximum modularity is o(1) whp. For np = c with c G 1 a constant, functions are constructed with 0 L a(c) L b(c) L 1 and b(c) → 0 as c → ∞ such that whp the maximum modularity is bounded between these functions. Concentration of the maximum modularity about its expectation and structural properties of any optimal partition are also established. Finding the maximum modularity of graph classes helps us understand the behaviour of the modularity function. We study trees, lattices and related graphs. The maximum modularity of a large k-ary tree was shown to be near 1 by Bagrow 2012. This was extended to trees with maximal degree o(n1/5) in Montgolfier et al. 2011. This thesis further extends the result to any tree with maximal degree o(n). Indeed it is shown that the maximum modularity will tend to 1 for any graph where the product of the treewidth and the maximal degree is much less than the number of edges. This shows random planar graphs typically have modularity 1 + o(1). Lower bounds were given in Guimera et al. 2004 for the maximum modularity of complete sections of the integer lattice and for lattices with extra axis aligned edges included. The maximum modularity of any subgraph of this lattice is shown to be at least the same order of magnitude in the number of edges. This generalises to any graph which can be embedded in Euclidean space such that no edge is too long and no two vertices fall too close together.
Versatile power-efficient octave-spanning soliton crystals with high conversion in a Si3N4 microresonator

We demonstrate various octave-spanning soliton crystals in a Si<inf>3</inf>N<inf>4</inf> microresonator. The conversion efficiency can reach above 50% and the repetition rate (f<inf>rep</inf>) can be tuned from 6 to 8 THz.
Geometrically Convergent Simulation of the Extrema of Lévy Processes

We develop a novel approximate simulation algorithm for the joint law of the position, the running supremum, and the time of the supremum of a general Lévy process at an arbitrary finite time. We identify the law of the error in simple terms. We prove that the error decays geometrically in Lp (for any [Formula: see text]) as a function of the computational cost, in contrast with the polynomial decay for the approximations available in the literature. We establish a central limit theorem and construct nonasymptotic and asymptotic confidence intervals for the corresponding Monte Carlo estimator. We prove that the multilevel Monte Carlo estimator has optimal computational complexity (i.e., of order [Formula: see text] if the mean squared error is at most [Formula: see text]) for locally Lipschitz and barrier-type functions of the triplet and develop an unbiased version of the estimator. We illustrate the performance of the algorithm with numerical examples.
A Machine Learning Model for Flaw Identification in Fibre-Reinforced Composites

A Haar cascade classifier is a machine learning (ML) algorithm used for object detection. In this paper, the Haar algorithm is introduced in the context of a non-destructive evaluation of fibrereinforced composite (FRC) structures. The Haar learning model is used for flaw identification from thermal images. Thermal images are created from cross-ply (CP) carbon fibre-reinforced laminates with flat-bottomed holes (6–10 mm) of different depths from the surface (0.5–1.5 mm). After training is complete, the model successfully detects similar artificial flaws in previously unseen thermal images. In doing so, the feasibility of Haar classifiers for automatic evaluation of FRCs is established.
Applications of manpower levels for business using six and eight point state space in the stochastic models

The present study aim is to compare the steady rate of crisis and steady state of probabilities under varying conditions which are manpower, under irregular conditions of full availability and nil availability in the case of business and manpower. Six and eight point state space has been compared under the different assumption that the transition from one state to another in both business and manpower occur in exponential time with different parameters.
New Thermonuclear 10B(α,p)13C Rate and Its Astrophysical Implication in the νp-process

We have derived a new thermonuclear rate with an associated uncertainty for the 10B(α,p)13C reaction by evaluating the available experimental data for the first time. We provide this rate with a much smaller uncertainty than that estimated in the literature. Our rate differs significantly from the theoretical rates adopted in the current reaction rate libraries. Utilizing this new rate, we have investigated its astrophysical implications on the heavy-element (especially, p-nuclei) production in the νp-process in a stellar model of the neutrino-driven wind of type II core-collapse supernova. It shows that our rate with a much smaller uncertainty strongly constrains the nucleosynthetic results of the light p-nulcei with A ∼ 80–100. In addition, it shows that the difference between observed and predicted abundances for light p-nuclei is quite large, implying either that the present stellar model still needs modification or that additional astrophysical sources are required to account for the origin of some p-nuclei, such as 92Mo and 94Mo.
Millimeter wave control using a plasma filled photonic crystal resonator

The manipulation of millimeter-scale microwaves is studied using plasma discharges formed within a vacancy defect of a two-dimensional photonic crystal consisting of a hexagonal lattice of sapphire rods. When an externally driven discharge pulse is generated within the defect, the photonic crystal response is to transmit microwave pulses with variable properties, such as pulse widths and delays as small as 300 ns. Without external drive of the discharge, the device is also shown to behave as a nonlinear power limiter and attenuates the incoming power by 10–15 dB in less than one microsecond when the input power surpasses the threshold of microwave breakdown within the cavity, even at modest incident power of order 1 W. Lastly, by combining external pulse drive with high incident field power but below self-ignition limits, we show using a simple model analysis of the resonator response that the plasma formed spans a wide range of plasma densities, dependent upon the sustaining microwave frequency.
A phase-change memory model for neuromorphic computing

Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels in a controllable way makes PCM devices particularly well-suited for synaptic realizations in neuromorphic computing. A key attribute that enables this application is the progressive crystallization of the phase-change material and subsequent increase in device conductance by the successive application of appropriate electrical pulses. There is significant inter- and intra-device randomness associated with this cumulative conductance evolution, and it is essential to develop a statistical model to capture this. PCM also exhibits a temporal evolution of the conductance values (drift), which could also influence applications in neuromorphic computing. In this paper, we have developed a statistical model that describes both the cumulative conductance evolution and conductance drift. This model is based on extensive characterization work on 10 000 memory devices. Finally, the model is used to simulate the supervised training of both spiking and non-spiking artificial neuronal networks.Phase-change memory (PCM) is an emerging non-volatile memory technology that is based on the reversible and rapid phase transition between the amorphous and crystalline phases of certain phase-change materials. The ability to alter the conductance levels in a controllable way makes PCM devices particularly well-suited for synaptic realizations in neuromorphic computing. A key attribute that enables this application is the progressive crystallization of the phase-change material and subsequent increase in device conductance by the successive application of appropriate electrical pulses. There is significant inter- and intra-device randomness associated with this cumulative conductance evolution, and it is essential to develop a statistical model to capture this. PCM also exhibits a temporal evolution of the conductance values (drift), which could also influence applications in neuromorphic computing. In this paper, we have developed a statistical model that describes both the cumulative conductance evoluti.