Miracle-dz/UniFAR-FTUs huggingface dataset:
Zoomers and Boomers: Asset Prices and Intergenerational Inequality

We construct a perpetual youth DSGE model with aggregate uncertainty in which there are dynamically complete markets and agents have Epstein-Zin preferences. We prove that, when en-dowments have a realistic hump-shaped age-proﬁle, our model has three steady-state equilibria. One of these equilibria is dynamically ineﬃcient and displays real price indeterminacy. We estimate the parameters of our model and we ﬁnd that a fourth-order approximation around the indeterminate steady-state provides the best ﬁt to U.S. data. Our work interprets the large and persistent generational inequality that has been observed in western economies over the past century as the result of uninsurable income shocks to birth cohorts.
Detailed rupture imaging of the 25 April 2015 Nepal earthquake using teleseismic P waves

We analyze the rupture process of the 25 April 2015 Nepal earthquake with globally recorded teleseismic P waves. The rupture propagated east‐southeast from the hypocenter for about 160 km with a duration of ∼55 s. Backprojection of both high‐frequency (HF, 0.2 to 3 Hz) and low‐frequency (LF, 0.05 to 0.2 Hz) P waves suggest a multistage rupture process. From the low‐frequency images, we resolve an initial slow downdip (northward) rupture near the nucleation area for the first 20 s (Stage 1), followed by two faster updip ruptures (20 to 40 s for Stage 2 and 40 to 55 s for Stage 3), which released most of the radiated energy northeast of Kathmandu. The centroid rupture power from LF backprojection agrees well with the Global Centroid Moment Tensor solution. The spatial resolution of the backprojection images is validated by applying similar analysis to nearby aftershocks. The overall rupture pattern agrees well with the aftershock distribution. A multiple‐asperity model could explain the observed multistage rupture and aftershock distribution.
Detection of 13C17O and Observations of Rare CO Isotopomers toward the ρ Ophiuchi Molecular Cloud

We report the detection of 13C17O and present observations of three additional rare CO isotopomers made toward core C of the ρ Ophiuchi molecular cloud. The data encompass single-pointed observations of the J = 1 → 0 and J = 2 → 1 rotational transitions for C18O, C17O, 13C17O, and the J = 1 → 0 transition for 13C18O. Emission is detected for all isotopes and transitions observed. For the two 17O-containing species, the interstellar spectra display a partially resolved hyperfine spectrum due to the I = 5/2 spin of the 17O nucleus. The isotope abundance ratios derived from these measurements are [12C]/[13C] = 65.0 ± 6.3(stat) ± 9.2(syst) and [18O]/[17O] = 4.15 ± 0.52(stat) ± 0.59(syst), consistent with recent measurements for nearby molecular clouds. Thus, our measurements support the idea that both ratios are smaller in the local interstellar medium (ISM) than their terrestrial values of 89 and 5.5.
Adaptive Decentralized NN Control of Nonlinear Interconnected Time‐Delay Systems with Input Saturation

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of interconnected large‐scale uncertain nonlinear time‐delay systems with input saturation. Radial basis function (RBF) neural networks (NNs) are used to tackle unknown nonlinear functions. Then, the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique, along with the minimal‐learning‐parameters (MLP) algorithm. The stability analysis subject to the effect of input saturation constraints are conducted with the help of an auxiliary design system based on the Lyapunov–Krasovskii method. The proposed controller guarantees uniform ultimate boundedness (UUB) of all of the signals in the closed‐loop large‐scale system, while the tracking errors converge to a small neighborhood around the origin. An advantage of the proposed control scheme lies in the number of adaptive parameters of the whole system being reduced to one and in the solution of the three problems of “computational explosion,” “dimension curse,” and “controller singularity”. Finally, simulation results along with comparisons are presented to demonstrate the advantages, effectiveness, and performance of the proposed scheme.
The thermoelectric power of MnAs1-xPx compounds

The thermoelectric power S(T) of MnAs1-xPx compounds has been measured in the temperature range from 5K to 350K and for compositions between x=0 and x=0.1. In addition to diffusion thermopower several anomalies are observed. Phonon drag effects may be responsible for a maximum below 30K while other anomalies are related to magnetic phase transitions or to particular scattering mechanisms which have been inferred from resistivity measurements.
Application of the Satellite Triaxial Accelerometer Experiment to Atmospheric Density and Wind Studies.

Abstract : A new satellite triaxial accelerometer system has been developed. This instrument has been flight-tested on three-axis stabilized satellites and has demonstrated the capability to accurately measure accelerations in the satellite's in-track, cross-track, and radial directions. The in-track data provide direct determination of atmospheric density. These data, obtained during a period of high solar flux, supplement the extensive set of measurements obtained by other accelerometer experiments during low solar flux conditions. The cross-track and radial results permit, for the first time, large-scale measurements of the zonal and, possibly, radial components of neutral atmospheric winds. These simultaneous wind and density data provide an extremely valuable input for the understanding of dynamic processes in the atmosphere and for the improvement of atmospheric models. (Author)
How Can We Cultivate Senn's Abilirty

Stephen Senn points out how, in practice, people who claim to have carried out a (subjective) Bayesian analysis have actually done something very different— but that then they heap credit on the Bayesian ideal. “A very standard form of argument I do object to is the one frequently encountered in many applied Bayesian papers where the first paragraphs laud the Bayesian approach on various grounds, in particular its ability to synthesize all sources of information, and in the rest of the paper the authors assume that because they have used the Bayesian machinery of prior distributions and Bayes theorem they have therefore done a good analysis. It is this sort of author who believes that he or she is Bayesian but in practice is wrong. ” (58)1 Why in practice is this wrong? For starters, Senn points out, the analysis seems to violate such strictures as temporal coherence: “Attempts to explain away the requirement of temporal coherence always seem to require an appeal to a deeper order of things—a level at which inference really takes place that absolves one of the necessity of doing it properly at the level of Bayesian calculation. ” (ibid.) So even if they come out with sensible analyses, Senn is saying, it is despite rather than because they followed strict Bayesian rules and requirements. It is thanks to certain unconscious interventions, never made explicit, and perhaps not even noticed by the Bayesian reasoner. “This is problematic”, Senn thinks, “because it means that the informal has to come to the rescue of the formal”. Not that he thinks there is anything wrong with informality. “Indeed, I think it is inescapable. I am criticizing claims to have found the perfect system of inference as some form of higher logic because the claim looks rather foolish if the only thing that can rescue it from producing silly results is the operation of the subconscious. ” (59) Now, many Bayesians would concede to Senn that in arriving at their outputs they violate strict norms laid down by De Finetti or other subjective Bayesians.
Improve Content-based Image Retrieval using Deep learning model

The complexity of multimedia has expanded dramatically as a result of recent technology breakthroughs, and retrieval of similar multimedia material remains an ongoing research topic. Content-based image retrieval (CBIR) systems search huge databases for pictures that are related to the query image (QI). Existing CBIR algorithms extract just a subset of feature sets, limiting retrieval efficacy. The sorting of photos with a high degree of visual similarity is a necessary step in any image retrieval technique. Because a single feature is not resilient to image datasets modifications, feature combining, also known as feature fusion, is employed in CBIR to increase performance. This work describes a CBIR system in which combining DarkNet-19 and DarkNet-53 information to retrieve images. Experiments on the Wang (Corel 1K) database reveal a considerable improvement in precision over state-of-the-art classic techniques as well as Deep Convolutional Neural Network(DCNN).
Comparison of Power Efficient Inverter Circuit using adiabatic technique

The power decadence in conventional CMOS (Complementary Metal oxide Semiconductor) circuit can be diminished by utilizing adiabatic method. Working of Adiabatic logic depends on switching activities, which diminishes the power by giving energy dissipated as heat back to supply. Adiabatic word has come from thermodynamic framework which says that no heat transfer take place from framework to climate as well as the other way around. Adiabatic logic is well known as energy recovery logic because its main objective is to reuse energy. It reuses the energy back to the power supply along the lines diminishing the power dissipation. Adiabatic procedures depend on adiabatic logic guideline. Anu Priya has propose the technique in 2014 [3], of adiabatic procedure where energy dissipation in PMOS organization can be limited and some of energy dissipated at load capacitance can be reused rather than dispersed as heat. Yet, the adiabatic method is exceptionally reliant upon parameter variation. Expecting completely adiabatic activity from a circuit is practically not possible as it is an ideal condition, also adiabatic technology is highly dependent on parameter variations so it’s quite difficult to achieve and ideal adiabatic circuit. It could be just accomplished with extremely sluggish exchanging speed. In examination, three logic families, ECRL (Efficient Charge Recovery Logic), PFAL (Positive Feedback Adiabatic Logic) and CPAL (Complementary Pass Transistor Adiabatic Logic) are contrasted and conventional CMOS logic for inverter circuit. MICROWIND technology is used while investigating different adiabatic circuits; energy utilization is broke down by variety of parameter. Result shows that adiabatic strategy is better decision in case of underpowered application in explicit frequency range.
Privacy Preservation of Digital Health Records in Cloud

The variety and amount of patient healthcare digital data is rapidly expanding. The field of artificial intelligence has fast proven revolutionary for healthcare, allowing for unprecedented speed and precision in data analysis. Many hospitals have already transitioned to electronic health records (EHRs), a digital version of paper medical information. Information Technology has already aided in simplifying operations in this area, making the process far more efficient and patient-centered than in the past. A patient’s trust may disintegrate in the face of recurrent incidents if there is no precise control and safety in place. As a result, it limits the potential for digital health to promote an age of more accessible, connected, and individualized treatment. When it comes to picking a cloud solution, healthcare providers are most concerned about security and privacy. In this research, we seek to demonstrate various ways that aid patient data privacy while simultaneously allowing cloud services to be used.
Detection of Security Attacks on Edge Computing of IoT Devices through NS2 Simulation

The limited resource and computation capacity of an IoT device always being a target vector for intruders to use to steal sensitive data from the device. Computational problems later solved by the engineers by introducing an edge server near to IoT device network boundary, but still it is not secure from the hands of intruders. IoT device vulnerabilities still can allow Edge server to give the control of the device to the intruders. Hence both the Edge server and the connected IoT device may fall under the control of an intruder for a misuse. In this paper we study how IoT devices in case of Edge computing falls under security threats. We have used NS2 simulator to identify a malicious node that behaves inconsistent in terms of Packet Delivery Ratio, End-to-End Delay, and Throughput.
Reordering adaptive directed acyclic graphs: an improved algorithm for multiclass support vector machines

The problem of extending binary support vector machines (SVMs) for multiclass classification is still an ongoing research issue. Ussivakul and Kijsirikul proposed the adaptive directed acyclic graph (ADAG) approach that provides accuracy comparable to that of the standard algorithm - Max Wins and requires low computation. However, different sequences of nodes in the ADAG may provide different accuracy. In this paper we present a new method for multiclass classification, reordering ADAG, which is the modification of the original ADAG method. We show examples to exemplify that the margin (or 2/|w| value) between two classes of each binary SVM classifier affects the accuracy of classification, and this margin indicates the magnitude of confusion between the two classes. In this paper, we propose an algorithm to choose an optimal sequence of nodes in the ADAG by considering the |w| values of all classifiers to be used in data classification. We apply minimum-weight perfect matching with the reordering algorithm in order to select the best sequence of nodes in polynomial time. We then compare the performance of our method with previous methods including the ADAG and the Max Wins algorithm. Experiments denote that our method gives the higher accuracy, and runs faster than Max Wins.
Metal-enriched Neutral Gas Reservoir around a Strongly Lensed Low-mass Galaxy at z = 4 Identified by JWST/NIRISS and VLT/MUSE

Direct observations of low-mass, low-metallicity galaxies at z ≳ 4 provide an indispensable opportunity for detailed inspection of the ionization radiation, gas flow, and metal enrichment in sources similar to those that reionized the universe. Combining the James Webb Space Telescope (JWST), Very Large Telescope/MUSE, and Atacama Large Millimeter/submillimeter Array, we present detailed observations of a strongly lensed, low-mass (≈107.6 M ⊙) galaxy at z = 3.98 (also see Vanzella et al.). We identify strong narrow nebular emission, including C iv λ λ1548, 1550, He ii λ1640, O iii] λ λ1661, 1666, [Ne iii] λ3868, [O ii] λ3727, and the Balmer series of hydrogen from this galaxy, indicating a metal-poor H ii region (≲0.12 Z ⊙) powered by massive stars. Further, we detect a metal-enriched damped Lyα system (DLA) associated with the galaxy with the H i column density of N H I ≈ 1021.8 cm−2. The metallicity of the associated DLA may reach the supersolar metallicity (≳Z ⊙). Moreover, thanks to JWST and gravitational lensing, we present the resolved UV slope (β) map at the spatial resolution of ≈100 pc at z = 4, with steep UV slopes reaching β ≈ −2.5 around three star-forming clumps. Combining with low-redshift analogs, our observations suggest that low-mass, low-metallicity galaxies, which dominate reionization, could be surrounded by a high covering fraction of the metal-enriched, neutral-gaseous clouds. This implies that the metal enrichment of low-mass galaxies is highly efficient, and further supports that in low-mass galaxies, only a small fraction of ionizing radiation can escape through the interstellar or circumgalactic channels with low-column-density neutral gas.
Histogram Layers for Synthetic Aperture Sonar Imagery

Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much success in SAS analysis; however, the features extracted by these approaches may not be suitable for capturing certain textural information. To address this problem, we present a novel application of histogram layers on SAS imagery. The addition of histogram layer(s) within the deep learning models improved performance by incorporating statistical texture information on both synthetic and real-world datasets.
T2LR-Net: An Unrolling Reconstruction Network Learning Transformed Tensor Low-Rank prior for Dynamic MR Imaging

While the methods exploiting the tensor low-rank prior are booming in high-dimensional data processing and have obtained satisfying performance, their applications in dynamic magnetic resonance (MR) image reconstruction are limited. In this paper, we concentrate on the tensor singular value decomposition (t-SVD), which is based on the Fast Fourier Transform (FFT) and only provides the definite and limited tensor low-rank prior in the FFT domain, heavily reliant upon how closely the data and the FFT domain match up. By generalizing the FFT into an arbitrary unitary transformation of the transformed t-SVD and proposing the transformed tensor nuclear norm (TTNN), we introduce a flexible model based on TTNN with the ability to exploit the tensor low-rank prior of a transformed domain in a larger transformation space and elaborately design an iterative optimization algorithm based on the alternating direction method of multipliers (ADMM), which is further unrolled into a model-based deep unrolling reconstruction network to learn the transformed tensor low-rank prior (T$^2$LR-Net). The convolutional neural network (CNN) is incorporated within the T$^2$LR-Net to learn the best-matched transform from the dynamic MR image dataset. The unrolling reconstruction network also provides a new perspective on the low-rank prior utilization by exploiting the low-rank prior in the CNN-extracted feature domain. Experimental results on two cardiac cine MR datasets demonstrate that the proposed framework can provide improved recovery results compared with the state-of-the-art optimization-based and unrolling network-based methods.
Opponent Indifference in Rating Systems: A Theoretical Case for Sonas

In competitive games, it is common to assign each player a real number rating signifying their skill level. A rating system is a procedure by which player ratings are adjusted upwards each time they win, or downwards each time they lose. Many matchmaking systems give players some control over their opponent's rating; for example, a player might be able to selectively initiate matches against opponents whose ratings are publicly visible, or abort a match without penalty before it begins but after glimpsing their opponent's rating. It is natural to ask whether one can design a rating system that does not incentivize a rating-maximizing player to act strategically, seeking matches against opponents of one rating over another. We show the following: - The full version of this"opponent indifference"property is unfortunately too strong to be feasible. Although it is satisfied by some rating systems, these systems lack certain desirable expressiveness properties, suggesting that they are not suitable to capture most games of interest. - However, there is a natural relaxation, roughly requiring indifference between any two opponents who are ``reasonably evenly matched'' with the choosing player. We prove that this relaxed variant of opponent indifference, which we call $P$ opponent indifference, is viable. In fact, a certain strong version of $P$ opponent indifference precisely characterizes the rating system Sonas, which was originally proposed for its empirical predictive accuracy on the outcomes of high-level chess matches.
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency

Discourse-aware techniques, including entity-aware approaches, play a crucial role in summarization. In this paper, we propose an entity-based SpanCopy mechanism to tackle the entity-level factual inconsistency problem in abstractive summarization, i.e. reducing the mismatched entities between the generated summaries and the source documents. Complemented by a Global Relevance component to identify summary-worthy entities, our approach demonstrates improved factual consistency while preserving saliency on four summarization datasets, contributing to the effective application of discourse-aware methods summarization tasks.
Deep Learning for Hierarchical Beam Alignment in mmWave Communication Systems

Fast and precise beam alignment is crucial to support high-quality data transmission in millimeter wave (mmWave) communication systems. In this work, we propose a novel deep learning based hierarchical beam alignment method that learns two tiers of probing codebooks (PCs) and uses their measurements to predict the optimal beam in a coarse-to-fine searching manner. Specifically, the proposed method first performs coarse channel measurement using the tier-1 PC, then selects a tier-2 PC for fine channel measurement, and finally predicts the optimal beam based on both coarse and fine measurements. The proposed deep neural network (DNN) architecture is trained in two steps. First, the tier-1 PC and the tier-2 PC selector are trained jointly. After that, all the tier-2 PCs together with the optimal beam predictors are trained jointly. The learned hierarchical PCs can capture the features of propagation environment. Numerical results based on realistic ray-tracing datasets demonstrate that the proposed method is superior to the state-of-art beam alignment methods in both alignment accuracy and sweeping overhead.
Leading quantum correction to the 
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 kink form factor

Recently, Jarah has constructed the kink form factor relevant to the scattering of an ultrarelativistic meson with an arbitrary nonrelativistic scalar kink. However the formula was only applied to the Sine-Gordon model, where the form factor was long ago determined by Weisz using integrability. In this paper, using various known analytic results for the (1+1)-dimensional real scalar $\Phi^4$ model and a kink wave packet construction, we analytically calculate the leading quantum correction to the form factor of the $\Phi^4$ kink. We discuss its properties in general and also in the ultra-relativistic meson case and provide a numerical check of our results.
Black Hole Statistical Mechanics and The Angular Velocity Ensemble

An new ensemble - the angular velocity ensemble - is derived using Jaynes’ method of maximising entropy subject to prior information constraints. The relevance of the ensemble to black holes is motivated by a discussion of external parameters in statistical mechanics and their absence from the Hamiltonian of general relativity. It is shown how this leads to difficulty in deriving entropy as a function of state and recovering the first law of thermodynamics from the microcanonical and canonical ensembles applied to black holes.