Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of Supernova Gamma-Rays to the Gamma-Ray Background Emission

Diffuse γ-ray background emission has been observed by a number of satellites and balloons. The spectrum spreads over a wide range of photon energies, and the emission is almost isotropic. Although the origin of diffuse γ-ray background emission is still unclear, some candidates are suggested, such as Seyfert galaxies, blazars, and type-Ia supernovae. The type-Ia supernovae synthesize a large amount of 56 Ni, which emit γ-ray photons while decaying into 56 Co and then to 56 Fe, and thus emit strong γ-rays of energy around 1MeV. We estimate the intensity of the diffuse γ-ray background emission from supernovae, and compare the result with the recent observation of COMPTEL. The resultant spectrum is too small to explain the observation, because it is only 10% of the observed
Specific heat anomaly of BaPb1-xBixO3 at the superconducting transition

A specific heat anomaly in BaPb1-xBixO3 single crystals at the superconducting transition has been found by AC calorimetry, in contrast to the previous result of Methfessel et al. (1980) that no specific heat anomaly was observed. The present experimental result seems to support the idea that the superconductivity of the compounds can be described by the simple BCS theory.
Quantum Resistant Authentication Methods for Quantum Key Distribution

Quantum Key Distribution (QKD) can distribute keys securely, even in the era of quantum computers, only if the classical channel has been authenticated. This master thesis investigates several methods and optimal parameters for authenticating the classical channel as quickly as possible in the QKD protocol BB84. We utilized quantum-resistant signature algorithms for authentication as they withstand attacks from quantum adversaries. We introduce a novel authentication approach, mono-authentication, which comprises authenticating only at the end rather than the traditional multi-authentication after each stage of communication. We first simulated a simile of what would be performed in classical cryptography to distribute a key, where we ask the QKD for a determined number of security bits. Next, we studied how four different signature algorithms performed in a noisy quantum channel and found the optimal cases for implementing these algorithms. Then, we obtained a frequency of authentications for three payloads. Finally, we used the previous results to calculate the minimum period for each post-quantum algorithm needs for authentication in terms of the key rate. Results show that the mono-authentication style is at least twice faster than the multi-authentication case. We conclude that in noisy channels, the mono-case reduces its cost significantly. Regarding the performance of the signatures, CRYSTALS-DILITHIUM is shown to be the fastest overall, and in contrast to the other algorithms, its number of signatures per second fluctuates with the key rate while being consistently low for the others.
Recent Advances in Cancer Vaccines: Challenges, Achievements, and Futuristic Prospects

Cancer is a chronic disease, and it can be lethal due to limited therapeutic options. The conventional treatment options for cancer have numerous challenges, such as a low blood circulation time as well as poor solubility of anticancer drugs. Therapeutic cancer vaccines emerged to try to improve anticancer drugs’ efficiency and to deliver them to the target site. Cancer vaccines are considered a viable therapeutic technique for most solid tumors. Vaccines boost antitumor immunity by delivering tumor antigens, nucleic acids, entire cells, and peptides. Cancer vaccines are designed to induce long-term antitumor memory, causing tumor regression, eradicate minimal residual illness, and prevent non-specific or unpleasant effects. These vaccines can assist in the elimination of cancer cells from various organs or organ systems in the body, with minimal risk of tumor recurrence or metastasis. Vaccines and antigens for anticancer therapy are discussed in this review, including current vaccine adjuvants and mechanisms of action for various types of vaccines, such as DNA- or mRNA-based cancer vaccines. Potential applications of these vaccines focusing on their clinical use for better therapeutic efficacy are also discussed along with the latest research available in this field.
A case for using A-weighted equivalent energy as a damage risk criterion for impulse noise exposure

Damage risk criteria (DRCs) for continuous noise rely upon epidemiologic analyses of populations of persons exposed over several years to noise in occupational environments. In 2006, the U.S. Army proposed to update the MIL-STD 1474D to use the Auditory Hazard Assessment Algorithm for Humans (AHAAH) and discontinue using the peak sound pressure level, envelope duration and number of impulses. The National Institute for Occupational Safety and Health has conducted two separate evaluations of the data used to justify the AHAAH methodology and found that the use of the A-weighted equivalent energy LAeq8 was more suitable for the purposes of predicting the effects of temporary threshold shifts (TTS) both in humans and in chinchillas. The LAeq8 method provided best fit for the TTS outcomes and demonstrated the greatest discrimination (ability to predict TTS) when compared to AHAAH, MIL-STD 1474D and two other proposed DRCs. Similarly, LAeq8 was found to give the best-fit and greatest discrimination for the chi.
Norges Teknisk-naturvitenskapelige Universitet Quadrature Methods Based on the Cayley Transform Quadrature Methods Based on the Cayley Transform

Integration of Lie type equations on matrix Lie groups using Magnus series methods has recently been proposed by Iserles and NNrsett. The methods use the exponential mapping, whose computation may b e v ery costly. In this paper a smaller class of Lie groups is considered , for which the exponential mapping can be replaced by e.g. the Cayley transform or the diagonal Pad e approximants. Particular methods are being derived, and numerical experiments that illustrate and verify properties of the new methods are included.
Modeling the Progression of HIV Infection

Abstract Statistical modeling of the progression of markers of HIV infection is complicated by three problems: (1) the times of infection are generally unknown; (2) the follow-up of infected patients is generally much shorter than the average time from infection to AIDS; and (3) the repeated measures of the markers are correlated. The marker we study in this article is T-helper cell count. As a consequence of these three problems, it is difficult to distinguish between different models for the decline in T-helper cell count over time for HIV-infected individuals. Some investigators have proposed that the decline is gradual until shortly before the onset of AIDS, yet available data do not reject models of a fairly constant decline over the entire latency period between HIV infection and onset of AIDS. The ability to discriminate between models can be enhanced by incorporating information about the distributions of the times of seroconversion (development of measurable antibodies) among HIV seropositive ind.
Socio-epidemiological Insights From a Yearlong COVID-19 Twitter Stream

—We analyze Twitter data from the last two years of the COVID-19 pandemic. We use two different approaches to enrich the current topic modelling approach, which is based on clustering hashtags based on their Word2Vec representation, by applying Latent Dirichlet Allocation and BERTopic on the whole content of a tweet. In this process, we compare the topic trends with various epidemiological factors to gather insights into people’s interests. We collect our findings into an interactive website: https://topics.derinbay. com/.
Classifying astronomical sources with machine learning

: More than 4 million astronomical sources extracted from the Sloan Digital Sky Survey catalog have been used to train a set of machine learning models, selected with a benchmarking program, in order to identify the best basic classifier of astronomical sources for future observations. We have also applied different filters to our dataset that modify its selection function, measuring the accuracy of the selected model to evaluate under which observational constraints this model performs better.
The Formation of Novae Spectra

The early post-outburst spectra of novae can be divided into two classes based on the stronger of two groups of emission lines, either Fe II lines or lines of He and N. Novae with prominent Fe II lines evolve more slowly, have a lower level of ionization, and show P Cygni absorption components. Novae with stronger lines of He and N have larger expansion velocities and a higher level of ionization, and the lines are more flat-topped with little absorption. The spectral evolution of the «He/N» novae is very rapid, and usually results in coronal lines, very strong neon lines, or no forbidden lines at all, whereas over half of the «Fe II» novae develop a standard forbidden line spectrum
Implementation and Analysis of Image Contrast Enhancement using Gaussian Mixture Model

In this paper, we propose a new global contrast enhancement algorithm using the histogram color and depth images. On the basis of the histogram-modiﬁcation framework, the color and depth image histograms are ﬁrst partitioned into sub- intervals using the Gaussian mixture model. The positions partitioning the color histogram are then adjusted such that spatially neighboring pixels with the similar intensity and depth values can be grouped into the same sub-interval. By estimating the mapping curve of the contrast enhancement for each sub-interval, the global image contrast can be improved without over-enhancing the local image contrast. Experimental results demonstrate the effectiveness of the proposed algorithm. The current major project in contrast enhancement is to partition the input histogram into multiple sub histograms before final equalization of each sub-histogram is performed. This paper presents a novel contrast enhancement method based on Gaussian mixture modeling of image histograms, which provides a sound theoretical underpinning of the partitioning process . By estimating the mapping curve of the contrast enhancement for each sub-interval, the global image contrast can be improved without over-enhancing the local image contrast .
Prediction of Variables Via Their Order Statistics in Bivariate Elliptical Distributions with Application in the Financial Markets

Assuming that (X1, X2) has a bivariate elliptical distribution, we obtain an exact expression for the joint probability density function (pdf) as well as the corresponding conditional pdfs of X1 and X(2) ≔ max {X1, X2}. The problem is motivated by an application in financial markets. Exchangeable random variables are discussed in more detail. Two special cases of the elliptical distributions that is the normal and the student’s t models are investigated. For illustrative purposes, a real data set on the total personal income in California and New York is analyzed using the results obtained. Finally, some concluding remarks and further works are discussed.
Itinerant multipolar order in URu2Si2 and its signature in magnetic and lattice properties

The high degeneracy of partly filled f shells leads to exotic type of multipolar hidden order (HO). In URuSi, it has been particularly difficult to establish its symmetry due to dominating itinerant character of 5f electrons. There are now, however, magnetic torque measurements, cyclotron resonance experiments and high precision x-ray investigations that advocate a breaking of the fourfold rotational symmetry in the HO phase. We show that these observations, especially the magnetic torque oscillations, are only compatible with a doubly degenerate E-type symmetry of the HO and in particular exclude the hexadecapole HO. These experiments, however, cannot determine the even or odd character of HO under time reversal. We argue that within the context of an itinerant 5f model, the HO is due to a commensurate nesting with Q = (0,0,1) of electron-hole pockets with predominantly character. This leads to an instability of the multipolar susceptibility corresponding to the odd rank-5 dotriakontapole E HO. We also discuss the consequences of prominent HO candidates on elastic constant anomalies.
Two new lithium-deficient population II dwarfs

G122-69 and G139-8 are otherwise ordinary halo dwarfs with [Fe/H] = -2.5 and T eff = 5900 K. High signal-to-noise echelle spectra of the two stars show no discernible Li I features at 6707 A, corresponding to abundance upper limits of N(Li) = 12 + log (Li/H) ≤ 1.02 and 1.26, respectively. In contrast, almost all other extremely metal poor subdwarfs in the temperature interval 5700 ≤ T eff ≤ 6300 K uniformly show N(Li) = 2.10 ± 0.10. G122-69 and G139-8 are therefore lithium deficient by about one order of magnitude relative to the «Spite plateau»
Characterization and identification of aquifer heterogeneity with generalized parameterization and Bayesian estimation

This research proposes a generalized parameterization method (referred to as GP) and Bayesian estimation for parameter heterogeneity characterization and identification in groundwater modeling. GP unifies zonation and interpolation through a set of weighting coefficients and is capable of creating a zonation structure, a continuous distribution, or a mixed structure. GP shows greater flexibility not only in manipulating the highly complex spatial distribution but also in identifying the parameter structure. With GP, parameter structure identification seeks to identify the parameter dimension, parameter pattern, parameter values, as well as the values of the weighting coefficients simultaneously through a set of basis points. Additionally, this study develops an embedded genetic algorithm (GA) for solving the structure identification problem. A Bayesian estimator that estimates the basis point values as well as the values of the weighing coefficients is embedded in the GA, which searches for the best basis point locations. We demonstrate the inverse methodology by a numerical example in which the distributed transmissivity in a two‐dimensional confined aquifer is identified. We calculate the Jacobian matrix by the adjoint state method. With GP we have successfully identified the transmissivity structure with four basis points that results in a good fitting in groundwater heads and captures the nonsmooth characteristic as well as the trend of the true transmissivity field. We compare GP with Voronoi tessellation (zonation) and natural neighbor interpolation. Results show that GP outperforms the other two parameterization methods in that GP identified the transmissivity field with a smaller parameter uncertainty along with a sufficiently small fitting residual and without over‐parameterization.
Nonresonant third‐order optical nonlinearity of quantum‐confined CdS clusters: A third harmonic generation study

We report the first study, to the best of our knowledge, of the nonresonant third‐order optical nonlinearity of quantum‐confined CdS clusters using third harmonic generation from 1.91 to 0.64 μm. We observe that the nonlinearity increases with the increase in the cluster size. The intrinsic, nonresonant χ(3)CdS for ∼30, ∼15 A thiophenolate‐capped CdS clusters and [Cd10S4(SPh)16]−4 molecular clusters are 3.3×10−10, 2.5×10−11, 4.7×10−12 esu, respectively. To utilize this large nonresonant nonlinearity it is necessary to incorporate these clusters into a sample in high concentrations.We report the first study, to the best of our knowledge, of the nonresonant third‐order optical nonlinearity of quantum‐confined CdS clusters using third harmonic generation from 1.91 to 0.64 μm. We observe that the nonlinearity increases with the increase in the cluster size. The intrinsic, nonresonant χ(3)CdS for ∼30, ∼15 A thiophenolate‐capped CdS clusters and [Cd10S4(SPh)16]−4 molecular clusters are 3.3×10−10, 2.5×10−11, 4.7×10−12 esu, respectively. To utilize this large nonresonant nonlinearity it is necessary to incorporate these clusters into a sample in high concentrations.
Nonlinear dislocation dynamics at ultrasonic frequencies

An analytical model of the effects of the Peierls–Nabarro barrier stress on the nonlinear dynamics of dislocation motion in crystalline solids resulting from a perturbative ultrasonic wave is derived. The nonlinearity is quantified by a material nonlinearity parameter β extracted from measurements of the amplitudes of the fundamental and harmonically generated ultrasonic waveforms. The β parameter is found to be functionally dependent on the magnitude of the Peierls–Nabarro barrier stress, the dislocation loop length, the shear modulus, and the Burgers vector of the crystal. The parameter is shown to exhibit a Bessel function oscillatory dependence on the stress amplitude of the fundamental ultrasonic wave resulting directly from the Peierls–Nabarro barrier stress. A sharp increase in the magnitude of β is shown to occur at low ultrasonic amplitudes where the dislocation motion is confined between adjacent lattice planes bounding the unperturbed dislocation. The generalization of the model to polycrystall.
Lidar signal depolarization by solid targets and application of polarimetric lidar to terrain mapping and 3D imagery

Ladar technology has long since established its advantages as a reliable method for automated Terrain Mapping. One still remaining important problem of this methodology though happens at data processing time. Ladars generate huge amounts of data referred to as 'point clouds'. The very first task in data processing consists of segmenting the terrain image between ground and non-ground data points. The standard processing methods all rely on some slope analysis technique. At the present moment, all these techniques still require interactive evaluation and manual editing of the results. In this work, Ladar polarization is used to discriminate between solid targets by using their polarization signatures. The addition of this feature, over and above range and intensity, could greatly help in the process wherein ground and nonground points are to be separated. Linear and circular polarizations measurements were performed on different specimens in various conditions and at various wavelengths. The results presented herein are a validation of the fact that typical solid targets show a response to the Ladar sensor which conforms to the behavior predicted by the most recent polarimetric BDRF theories. Hence, their polarization signature is expected to be repeatable. The results presented herein also show that, to the extent that more than one wavelength is used, solid targets can be discriminated against each other by the use of their polarization signatures.
A Technique for Melodic Motivic Analysis in the Music of Charles Ives

A complete melodic motivic analysis involves several interdependent stages: first, identifying the melodic motives within the musical work, second, describing how the motives are varied or developed throughout the work, and finally, determining the function of motivic development within the structure of the work as a whole. The process of analysis may not follow these stages in strictly chronological order. The conclusions reached at one stage will influence the decisions made in the following stage, but may also, in retrospect, lead to revisions of the conclusions reached at an earlier stage of analysis. This article will focus on the second stage of motivic analysis, presenting a formal analytic system which can be used to describe precisely how melodic motives are related.
Hypersonic Magneto-Fluid-Dynamic Compression in Cylindrical Inlet

Hypersonic magneto-fluid-dynamic interaction has been successfully performed as a virtual leading-edge strake and a virtual cowl of a cylindrical inlet. In a side-by-side experimental and computational study, the magnitude of the induced compression was found to be depended on configuration and electrode placement. To better understand the interacting phenomenon the present investigation is focused on a direct current discharge at the leading edge of a cylindrical inlet for which validating experimental data is available. The present computational result is obtained by solving the magneto-fluid-dynamics equations at the low magnetic Reynolds number limit and using a nonequilibrium weakly ionized gas model based on the drift-diffusion theory. The numerical simulation provides a detailed description of the intriguing physics. After validation with experimental measurements, the computed results further quantify the effectiveness of a magnet-fluid-dynamic compression for a hypersonic cylindrical inlet. At a minuscule power input to a direct current surface discharge of 8.14 watts per square centimeter of electrode area produces an additional compression of 6.7 percent for a constant cross-section cylindrical inlet. Nomenclature
Depression Severity Estimation Using Learned Vocal Biomarkers

Human speech contains a rich set of acoustic biomarkers. When properly leveraged, these biomarkers can give powerful insights into the physical and mental health of the speaker. By exploiting these vocal biomarkers, machine learning models can be trained to detect altered speech patterns caused by depression or other mental health disorders. These speech based models serve as powerful, accurate, and non-invasive diagnostic tools. Prior works have explored the potential of these models and proven the feasibility of such systems on toy datasets. To see if these models have potential as a medical device, I re-implement some of these works on a dataset two orders of magnitude larger. Additionally, I introduce a new model that dramatically outperforms the current standard of care. I end with an investigation into this model’s behaviour and a discussion of potentially relevant biomarkers.
A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images

Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the ability of deep image prior to reconstruct remote sensing scenes with different levels of corruption in them. Additionally, we compare the performance of our approach with the performance of classical single-image gap-filling methods. We demonstrate a quantitative advantage of the proposed approach compared with classical gap-filling methods. The lowest-performing restoration made by the deep image prior approach reaches 0.812 in r2, while the best value for the classical approaches is 0.685. We also present the robustness of deep image prior in comparing the influence of the number of corrupted pixels on the restoration results. The usage of this approach could expand the possibilities for a wide variety of agricultural studies and applications.
Generation of large scale potential difference by currentless plasma jets along the mirror field

A new mechanism of producing auroral electrons is proposed. Field-aligned plasma jets, presumably generated by magnetic reconnection in the magnetotail, give rise to charge separation along the mirror field whereby electrons are accelerated by the parallel electric field. The analysis shows that the potential difference can be expressed by eΦ=W∥ i (1-1/γ)R/(1+R) where W∥ i is the ion parallel flow energy, γ is the mirror ratio and R is the ratio of the electron to ion temperature. In the magnetotail, W∥ i≃10keV, R≃0.2∽0.5 and γ≃10³, so that the potential drop can reach several kV which is reasonable to make auroral electrons.