Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth Volatility and Inequality in the U.S.: A Wavelet Analysis

This study applies wavelet coherency analysis to explore the relationship between the U.S. economic growth volatility, and income and wealth inequality measures over the period 1917 to 2015 and 1962 to 2014. We consider the relationship between output volatility during positive and negative growth scenarios. Wavelet analysis simultaneously examines the correlation and causality between two series in both the time and frequency domains. Our findings provide evidence of positive correlation between the volatility and inequality across high (short-run)- and low-frequencies (long-run). The direction of causality varies across frequencies and time. Strong evidence exists that volatilities lead inequality at low-frequencies across income inequality measures from 1917 to 1997. After 1997, however, the direction of causality changes. In the time-domain, the time-varying nature of long-run causalities implies structural changes in the two series. These findings provide a more thorough picture of the relationship between the U.S. growth volatility and inequality measures over time and frequency domains, suggesting important implications for policy makers.
"I'm here to fight for ground truth": HTR-United, a solution towards a common for HTR training data

The growth of computation power and rise of artificial intelligence (in particular Deep Learning) allowed for the development of automatic text recognition, both on printed texts (OCR) and handwritten ones (HTR). Such technologies can now make millions of images of texts from various periods of time, held in patrimonial institutions, available for further search and processing. HTR became more accessible when user friendly interfaces started to be developed: namely Transkribus from 2015 [6] and eScriptorium from 2019[5]. In the case of HTR and old prints though, one of the hurdles remaining to be overcome is the access to robust models capable of recognizing coherent texts in spite of the multiple variations in handwriting or fonts. Such models usually necessitate users to produce large amounts of manual transcriptions considered as perfect -called ground truth, which is a costly task. It requires a good understanding of the way deep learning functions, skills in paleography, and time. An easy way to reduce the costs of creating the training data to obtain a model is to rely on the data produced by other projects. Unfortunately, they are hard to find and not always published, because there is no incentive to put in this extra effort, neither for their publication nor for their documentation. HTR-United is
Clinical experience with the NIR stent: Retrospective observation on acute results and follow-up in 709 patients

The NIR stent is an expandable cellular coronary prosthesis with high flexibility and excellent trackability. The very low crossing profile, associated to the high flexibility, makes this stent suitable for complex and distal lesions. The aim of this study was to evaluate the feasibility, safety, and long-term (clinical follow-up) efficacy of elective and/or urgent deployment of the NIR stent in a broad patient group with coronary artery disease. Between June 1996 and May 1997, 986 NIR stents were implanted in 781 vessels (de novo or restenotic lesions in native vessels, saphenous vein grafts and internal mammary conduits) of 709 consecutive patients (498 men, 211 women) undergoing coronary angioplasty in two Villa Maria Group Catheterization Laboratories. Procedural angiographic success was achieved in 98.4% of all the lesions treated, ranging from 100% success rate in type A lesion to 97.6% in type C lesions (overall procedural success, 98.4%). Major adverse cardiac events (MACE) were considered from PTCA/ stenting time to the patient discharge. The in-hospital MACE were limited and occurred in 15 patients: urgent surgical revascutarization 0.8%, death 0.7%, sub-acute stent thrombosis 0.5% (overall in-hospital MACE rate 2.1%). Clinical follow-up data were obtained at 8.6 + 2.8 months following the stenting procedure. The event-free survival rate was 87.9%. The late MACE rate evaluation showed death, target lesion revascularization 9.9% (over-all late MACE rate 12.1%). The NIR stent performances in this broad patient population were excellent, showing very high procedural success rate both in normal and complex coronary anatomy. On the basis of the previous data, we can define NIR stent a safe multifunctional device suitable both for easy and complex situations. The clinical late results are Correspondence to: Alberto Cremonesi, M.D,, F.I. C.A, Interventional Cath. Lab. Director, Department of Medical and Surgical Cardiology, Villa Maria Cecilia Hospital, Via Corriera 1, 48010 Cotignola (RA), Italy very promising in term of event-free survivals and Iate
Radar‐ID: human identification based on radar micro‐Doppler signatures using deep convolutional neural networks

Human identification is crucial in various applications, including terrorist attack preventing, criminal seeking, defence and so on. Traditional human identification methods are usually based on vision, biological features, radio-frequency identification cards and so on. In this study, the authors propose an identification method based on radar micro-Doppler signatures using deep convolutional neural networks (DCNNs) for the first time, which can identify human in non-contact, remote and no lighting status. They employ a K-band Doppler radar to acquire the raw signals due to its stationary clutter rejection and movement detection ability as well as its short wavelength which can generate larger Doppler shift. Then short-time Fourier transform is applied to the raw signals to characterise micro-Doppler signatures. They adopt the DCNNs to deal with the spectrograms for human identification problem. The DCNNs can learn the necessary features and classification conditions from raw micro-Doppler spectrograms without employing any explicit features. While the traditional supervised learning techniques relying on the extracted features require domain knowledge of each problem. It is shown that this method can achieve average accuracy ~97.1% for 4 people, 90.9% for 6 people, 89.1% for 8 people, 85.6% for 10 people, 77.4% for 12 people, 72.6% for 16 people and 68.9% for 20 people.
Adaptive Plenoptic Sampling: Theory and Applications

Image-Based Rendering (IBR) is an effective technique for rendering novel views of a scene from multi-view images. The plenoptic function enables IBR to be formulated in terms of sampling and reconstruction. In this thesis, we combine the theoretical results from uniform plenoptic sampling with non-uniform camera placement. The central concept is that geometry of the scene can be modelled with a sequence of slanted planes. The positions of the cameras are then derived from the plenoptic spectral analysis of a slanted plane. To this end, we present novel results for the plenoptic spectral analysis of a slanted plane and an algorithm for adaptive plenoptic sampling. The novelty of our spectral analysis lies in the inclusion of two realistic conditions when calculating the plenoptic spectrum: finite scene width and cameras with finite field of view. Using these conditions, we derive an exact closed-form expression for the plenoptic spectrum of a slanted plane with bandlimited texture. From this spectrum, we determine an expression for the maximum spacing between adjacent cameras. Using synthetic and real scenes, we show that this expression is a more accurate gauge of the Nyquist sampling density than the current state-of-the-art. Based on these results, we design an adaptive plenoptic sampling algorithm for a scene with a smoothly varying surface and bandlimited texture. The algorithm operates by determining the best sequence of slanted planes to model the scene given its geometry and a limited number of cameras. Once this sequence of planes is obtained, the algorithm then positions the cameras using our sampling analysis of a slanted plane. Using synthetic and real scenes, we show that this algorithm outperforms uniform sampling. Finally, we also present a novel reconstruction filter for plenoptic sampling that
I-MAC: an incorporation MAC for wireless sensor networks

ABSTRACT This paper proposes an innovative MAC protocol called I-MAC. Protocol for wireless sensor networks, which combines the advantages of collision tolerance and collision cancellation. The protocol increases the number of antenna in wireless sensor nodes. The purpose is to monitor the occurrence of packet collisions by increasing the number of antenna in real time. The built-in identity structure is used in the frame structure in order to help the sending node to identify the location of the receiving node after a data packet collision is detected. Packets can be recovered from where the conflict occurred. In this way, we can monitor the conflict for a fixed period of time. It can improve the channel utilisation through changing the transmission probability of collision nodes and solve the problem of hidden terminal through collision feedback mechanism. We have evaluated our protocol. Our results show that the throughput of I-MAC is 5 percentage points higher than that of carrier sense multiple access/collision notification. The network utilisation of I-MAC is more than 92%.
Spectrum Analysis with Adaptive Precision for Space Vector Pulse Width Modulation

Aiming at the problem that the theoretical spectrum expression of the output voltage waveform in the space vector pulse width modulation strategy is often extremely complicated, most tedious and quite error-prone, the study develops a universal procedure and algorithm with adaptive precision to analyze the harmonic spectrum. The key steps and technique of the procedure and algorithm are given firstly. The algorithm steps include storing the switching states and the vector sequences, computing the basic space vector duration time, storing the duration time, sampling output voltage values and computing the spectrum using the fast Fourier transform algorithm. The sampling number doubles in each iteration for the next iteration. The adaptive precision is realized through controlling the amplitude or magnitude error between the current result and the previous result. The realization method and the key codes are presented secondly. Finally, three numerical experiments are presented to verify the developed algorithm, and the results verify its correctness, reliability and convenience.
Word Sense Frequency Estimation for Russian: Verbs, Adjectives, and Different Dictionaries

In this paper we investigate several extensions to our prior work on sense frequency estimation for Russian. Our method is based on semantic vectors and is able to achieve good accuracy for sense frequency estimation traine d on dictionary entries from the Active Dictionary of Russian and unannotated corpora. We apply our method to verbs and adjectives to obtain sense frequencies for 329 verbs and 256 adjectives in an academic corpus and a web-based corpus. We compare frequency distributions against dictionary sense ordering and between two corpora and find that the first dictionary sense is not the most frequent for almost half of the words we studied. Evaluation of verbs and adjectives shows that frequency estimation error is lower than 15%. We investigate the effect of sense granularity, evaluating how the accuracy of our method changes when applied to more coarse-grained senses. We also investigate if our method can be applied to other dictionaries with less elaborate sense descriptions, by evaluating its accuracy when training on dictionary entries from two other dictionaries.
Joint denoising, demosaicing, and chromatic aberration correction for UHD video

High-resolution video capture is crucial for numerous applications such as surveillance, security, industrial inspection, medical imaging and digital entertainment. In the last two decades, we are witnessing a dramatic increase of the spatial resolution and the maximal frame rate of video capturing devices. In order to achieve further resolution increase, numerous challenges will be facing us. Due to the reduced size of the pixel, the amount of light also reduces, leading to the increased noise level. Moreover, the reduced pixel size makes the lens imprecisions more pronounced, which especially applies to chromatic aberrations. Even in the case when high quality lenses are used some chromatic aberration artefacts will remain. Next, noise level additionally increases due to the higher frame rates. To reduce the complexity and the price of the camera, one sensor captures all three colors, by relying on Color Filter Arrays. In order to obtain full resolution color image, missing color components have to be interpolated, i.e. demosaicked, which is more challenging than in the case of lower resolution, due to the increased noise and aberrations. In this paper, we propose a new method, which jointly performs chromatic aberration correction, denoising and demosaicking. By jointly performing the reduction of all artefacts, we are reducing the overall complexity of the system and the introduction of new artefacts. In order to reduce possible flicker we also perform temporal video enhancement. We evaluate the proposed method on a number of publicly available UHD sequences and on sequences recorded in our studio.
Development of GelSight: A High-resolution Tactile Sensor for Measuring Geometry and Force

Tactile sensing is an important perception mode for robots, but the existing tactile 1 technologies have multiple limitations. What kind of tactile information robots need, and how 2 to use the information, remain open questions. We believe a soft sensor surface and high-resolution 3 sensing of geometry should be important components of a competent tactile sensor. In this paper, 4 we introduce the development of a vision-based optic tactile sensor, GelSight. Unlike the traditional 5 tactile sensors which measure contact force, GelSight basically measures geometry, with a high spatial 6 resolution from 1 to 30 microns. The sensor has a contact surface of soft elastomer, and it directly 7 measure its deformation, both vertical and lateral, which corresponds to the exact object shape and 8 the tension on the contact surface. The contact force, and slip could be inferred from the sensor’s 9 deformation as well. Particularly, we focus on the hardware and software that support GelSight’s 10 application on robot hands. This paper reviews the development of GelSight, with the emphasis 11 in the sensing principle and sensor design. We introduce the design of sensor’s optical system, 12 the algorithm for shape, force and slip measurement, and the hardware designs and fabrication of 13 different sensor versions. We also show the experimental evaluation on the GelSight’s performance on 14 geometry and force measurement. With the high-resolution measurement of shape and contact force, 15 the sensor has successfully assist multiple robotic tasks, including material perception or recognition, 16 in-hand localization for robot manipulation. 17
From Sketching to Coding: Visualization as a Thinking Process

Discussions of data visualization or visual analytics in the (digital) humanities often focus on relevant software tools. When it comes to teaching approaches to creating and interpreting visualizations, however, we need to stress the theory and decision-making processes that feed into the resulting visual representations. This workshop will explore and discuss strategies for teaching visualization literacy - from sketching by hand to creating visualizations using computational tools. Led by instructors from the humanities, visual analytics, design, and computer science, sessions will cover different methodologies and challenges of teaching visualization to audiences with varied technical and disciplinary expertise and goals.
A Reexamination of the Formula Expressing the Equilibrium Water Vapor Pressure over an Aqueous Solution Droplet

where pr' and p0 are the equilibrium water vapor pressures over the solution droplet and over a plane surface of pure water, respectively; Mm is the molecular weight of water, * the surface tension of the droplet, r the droplet radius, R the universal gas constant, T the absolute temperature and v the quantity to be mentioned later; * is the osmotic coefficient, m the molality of the solution and * the number of moles of ions formed by the dissociation of one mole of solute (e.g., * = 2 for NaCl and * = 3 for (NH4)2SO4). In the above expression a slight change in Byers's original notation was made for convenience of the following discussion. In Eq. (1) the first term on the right-hand side expresses the well-known curvature effect and the second term the solution effect. However, as Herbert (1975) and Tang (1976) pointed out, some confusion exists in the literature regarding the quantity v appearing in the first term. In the monographs of Byers (1965) and Mason (1971) 1/*L' is used in place of v, where* L' is the density of the solution: in this case v stands for the specific volume o f the solution. On the other hand, Dufour and Defay (1963) and Herbert (1975) identify v with the specific volume of pure water. More recently Tang (1976) claimed that v should be the partial specific volume o f water in the solution. Needless to say, these are different quantities. Therefore, the following questions arise: What is the correct quantity for v in Eq. (1)? How much error would result from using incorrect quantities? In the following sections we shall deal with these problems.
Nonlinear interaction of intense left- and right-hand polarized laser pulse with hot magnetized plasma

In this article, self-focusing of an intense circularly polarized laser pulse in the presence of an external oblique magnetic field in hot magnetized plasma, using Maxwell’s equations and the relativistic fluid momentum equation, is studied. An envelope equation governing the spot size of the laser beam for both of left- and right-hand polarizations has been derived and the effects of the plasma temperature and oblique magnetic field on the electron density distribution of hot plasma with respect to variation of the normalized laser spot size has been investigated. Numerical results depict that in right-hand polarization, self-focusing of the laser pulse along the propagation direction in hot magnetized plasma becomes better and more compressed with increasing $\unicode[STIX]{x1D703}$ . Inversely, in left-hand polarization, increase of $\unicode[STIX]{x1D703}$ in an oblique magnetic field leads to enhancement of the spot size and reduction self-focusing. Besides, in the plasma density profile, self-focusing of the laser pulse improves in comparison with no oblique magnetic field. Also it is shown that plasma temperature has a key role in the laser spot size, normalized laser output power and the variation of plasma density.
Types for the Scott Numerals

where case(n)(a)(f) returns a if n is 0 and f(x) if n is the successor of x (see, e.g., [2]). The Scott numerals are distinguished from the Church numerals by their \linearity": the bound variables of 0, succ, and case occur at most once in the bodies of these functions, and the predecessor function n:n(0)( x:x) can be computed trivially. The Scott numerals can be typed in an extension of System F with covariant recursive types. We can take the type of the Scott numerals to be the solution to the equation S = 8R: (R! (S ! R)! R):
Dynamical Systems and Models of Continuous Time

Historically it has been easier to focus on measuring and describing differences between groups of people rather than try to describe the dynamic ways that individuals change. Dynamical systems are mathematical models that aim to describe how constructs change over time. Frequently these models are continuous time models; models that try to capture the function that underlies a set of observations. This chapter introduces the concept of a dynamical system and of continuous time models. Two methods are introduced for the fitting of continuous time models to observed data: one using the approximate discrete model for a first-order autoregressive model and the second using a method of estimating latent derivatives for a second-order autoregressive model.
Reverse spontaneous laser line sweeping in ytterbium fiber laser

Self-induced laser line sweeping of various regimes of sweep direction is reported for an experimental ytterbium fiber laser. The regimes involve sweeping from shorter to longer wavelengths (1076 nm→1083 nm)—so-called normal self-sweeping; from longer to shorter wavelengths (1079 nm→1073 nm)—so-called reverse self-sweeping; and a mixed regime in which a precarious balance of the normal and reverse sweeping exists and the sweep direction can change between consecutive sweeps. The regimes of sweeping were selected by changing the pump wavelength only. A detailed explanation of this sweep direction dynamics is presented based on a semi-empirical model. This model also provides a way to predict the sweep direction of fiber lasers based on other rare-earth-doped laser media.
Using convolutional neural network to identify irregular segmentation objects from very high-resolution remote sensing imagery

Abstract. Convolutional neural network (CNN) has shown great success in computer vision tasks, but their application in land-use type classifications within the context of object-based image analysis has been rarely explored, especially in terms of the identification of irregular segmentation objects. Thus, a blocks-based object-based image classification (BOBIC) method was proposed to carry out end-to-end classification for segmentation objects using CNN. Specifically, BOBIC takes advantage of CNN to automatically extract complex features from the original image data, thereby avoiding the uncertainty caused by the manual extraction of features in OBIC. Additionally, OBIC compensates for the shortcomings of CNN whereby it is difficult to delineate a clear right boundary for ground objects at the pixel level. Using three high-resolution test images, the proposed BOBIC was compared with support vector machine (SVM) and random forest (RF) classifiers, and then, the effect of image blocks and mixed objects on classification accuracy was evaluated for the proposed BOBIC. Compared with conventional SVM and RF classifiers, the inclusion of CNN improved the OBIC classification performance substantially (5% to 10% increases in overall accuracy), and it also alleviated the effect derived from mixed objects.