Miracle-dz/UniFAR-FTUs huggingface dataset:
Indole-3-carbaldehyde Semicarbazone Derivatives: Synthesis, Characterization, and Antibacterial Activities

Four indole-3-carbaldehyde semicarbazone derivatives, 2-((5-bromo-1H-indol-3-yl)methylene)hydrazinecarboxamide (1), 2-((5-chloro-1H-indol-3-yl)methylene)hydrazinecarboxamide (2), 2-((5-methoxy-1H-indol-3-yl)methylene)hydrazinecarboxamide (3), and 2-((4-nitro-1H-indol-3-yl)methylene)hydrazinecarboxamide (4) were synthesized and characterized by ESI-MS and spectroscopic (FT-IR, 1H NMR, and 13C NMR) techniques. The two-dimensional NMR (in acetone-d6) spectral data revealed that the molecules 1 and 2 in solution are in the cisE isomeric form. This evidence is supported by DFT calculations at the B3LYP/6-311++G(d,p) level of theory where it was shown that the corresponding most stable conformers of the synthesized compounds have a cisE geometrical configuration, in both the gas and liquid (acetone and DMSO) phases. The in vitro antibacterial activity of compounds 1–4 was determined against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Pseudomonas aeruginosa and Escherichia coli) bacteria. Among all the tested semicarbazones, 1 and 2 exhibited similar inhibitory activities against Staphylococcus aureus (MIC = 100 and 150 μg/mL, respectively) and Bacillus subtilis (MIC = 100 and 150 μg/mL, respectively). On the other hand, 3 and 4 were relatively less active against the tested bacterial strains compared with 1, 2, and tetracycline.
The Cost of Fraud Prediction Errors

We compare seven fraud prediction models with a cost-based measure that nets the benefits of correctly anticipating instances of fraud, against the costs borne by incorrectly flagging non-fraud firms. We find that even the best models trade off false to true positives at rates exceeding 100:1. Indeed, the high number of false positives makes all seven models considered too costly for auditors to implement, even in subsamples where misreporting is more likely. For investors, M-Score and at higher cut-offs the F-Score, are the only models providing a net benefit. For regulators, several models are economically viable as false positive costs are limited by the number of investigations regulators can initiate, and by the relatively low market value loss a "falsely accused" firm would bear in denials of requests under the Freedom of Information Act (FOIA). Our results are similar whether we consider fraud or two alternative restatement samples.
Safety of dorsolateral talonavicular joint fixation in modified double arthrodesis: an anatomic study

Background: Augmentation of talonavicular fusion with a retrograde percutaneous naviculocalcaneal screw has been shown to increase fixation stiffness of double (subtalar and talonavicular joint) arthrodesis. However, previous authors have speculated that neurovascular structures could be at risk in a percutaneous procedure. We investigated the safety of this procedure. We hypothesized that a high risk of neurovascular injury would be found. Methods: Dorsolateral talonaviculocalcaneal screw fixation was performed in 17 fresh-frozen cadaver limbs. A 1.5-cm incision was created over the lateral third of the navicular. Using percutaneous technique, a 4.5-mm cannulated screw was inserted using a guide wire. Blunt dissection and careful soft-tissue retraction were performed. The incision was extended, and a caliper was used to measure the distance from the guide wire to the superficial peroneal nerve (SPN), extensor hallucis longus, extensor digitorum longus, deep peroneal nerve (DPN), and deep peroneal artery (DPA). All injuries were recorded. Results: Injury to the deep neurovascular bundle occurred in five of 17 specimens. These injuries involved four insults to the DPN, two to the DPA, and one to branches of the SPN. Conclusions: Use of a retrograde naviculocalcaneal screw to augment talonavicular fusion in the setting of double arthrodesis was associated with high risk of injury to anatomic structures, including the deep neurovascular bundle. Clinical Relevance: An open procedure with careful dissection along the neurovascular bundle should be considered with use of a retrograde naviculocalcaneal screw to augment talonavicular fusion to avoid risk to nearby anatomic structures.
Effects of M&A in the Renewable Energy Sector: An Event Study Analysis

In this paper, we examine the effects of mergers and acquisitions (M&A) on the value of renewable energy companies, considering the types of M&A among the renewable energy, conventional energy, and other sectors. The event study methodology is used based on the ordinary least squares (OLS) and the general autoregressive conditional heteroscedasticity (GARCH) models, the latter of which allows for conditional variances. We classify the data into four groups by the type of M&A and test the significance of the cumulative abnormal return (CAR). The results show that the effect of M&A differs depending on the homogeneity of the involved firms’ sectors and in the different industries within the renewable energy sector. Homogeneous M&A within the renewable energy sector has the most significant positive effect on firms in the renewable energy industries as well as on all firms. Consequently, the financial synergy and risk diversification effects have the most significant, positive impact in M&A in the renewable energy sector, regardless of the specific industry.
Multiple primary malignant neoplasms: Results from a 5-year retrospective analysis in a Metropolitan Hospital

Background: The high effectiveness of cancer screening and therapies resulted in the increased diagnosis of multiple primary malignancies (MPMNs) in Taiwan. The aim of the present study was to investigate the clinical data of patients and to determine the frequency and clinical features of MPMNs. Methods: Between January 2010 and December 2014, a total of 2518 patients were screened and obtained retrospectively from the Tumor Registry Center of a metropolitan hospital. We quantified the clinical features and the most common cancer pairs of MPMNs using statistical and epidemiological indicators. Results: Two hundred and eleven patients with MPMNs were evaluated. The median age at initial cancer diagnosis was 63 years old (range 12–100 years). The median age of diagnosis of secondary cancer was 67 years old (range 35–95 years). The median time between initial and secondary cancer diagnoses was 5 months (range 0–57.1). The overall incidence of MPMNs was 8.38%, and the male: female ratio was 2.01:1. The most frequent types of cancer at secondary diagnosis were digestive (79 patients, 3.05%), breast (22 patients, 0.87%), liver (22 patients, 0.87%), head-neck (18 patients, 0.71%), and bladder cancer (14 patients, 0.56%). In women, the most frequent types of cancer at secondary diagnosis were digestive (25 cases, 2.21%), breast (22 cases, 1.95%), liver (11 cases, 0.97%), lung (8 cases, 0.71%), and gynecologic cancer (7 cases, 0.62%). In men, the most frequent types of cancer at secondary diagnosis were digestive (54 cases, 3.89%), head-neck (16 cases, 1.15%), liver (11 cases, 0.79%), bladder (10 cases, 0.72%), and prostate cancer (10 cases, 0.72%). The most common cancer pairs in males were digestive/digestive (22 cases, 1.59%), head-neck/head-neck (4 cases, 0.29%), lung/digestive (3 cases, 0.22%), and prostate/bladder (3 cases, 0.22%). In addition, tobacco smoking, alcohol consumption, and betel-chewing were observed to be important risk factors for the development of MPMNs. Conclusion: Indeed, the cancer registries can help us understand the disease better and use our resources to the best effect in prevention and treatment of MPMNs. In the present study, we discovered that patients with MPMNs tend to be older than those with a single primary malignant neoplasm. In conclusion, the clinical features and the most common cancer pairs of MPMNs are demonstrated in the present study. Based on these data, further analysis of the relationship between them is worthwhile.
Partition Regularity for Systems of Diagonal Equations

We consider systems of $n$ diagonal equations in $k$th powers. Our main result shows that if the coefficient matrix of such a system is sufficiently non-singular, then the system is partition regular if and only if it satisfies Rado's columns condition. Furthermore, if the system also admits constant solutions, then we prove that the system has non-trivial solutions over every set of integers of positive upper density.
Swift Spectroscopy of the Accretion Disk Wind in the Black Hole GRO J1655–40

Chandra obtained two High Energy Transmission Grating spectra of the stellar-mass black hole GRO J1655−40 during its 2005 outburst, revealing a rich and complex disk wind. Soon after its launch, the Neil Gehrels Swift Observatory began monitoring the same outburst. Some X-ray Telescope (XRT) observations were obtained in a mode that makes it impossible to remove strong Mn calibration lines, so the Fe Kα line region in the spectra was previously neglected. However, these lines enable a precise calibration of the energy scale, facilitating studies of the absorption-dominated disk wind and its velocity shifts. Here we present fits to 15 Swift/XRT spectra, revealing variability and evolution in the outflow. The data strongly point to a magnetically driven disk wind: both the higher-velocity (e.g., ) and lower-velocity (e.g., ) wind components are typically much faster than is possible for thermally driven outflows ( ), and photoionization modeling yields absorption radii that are two orders of magnitude below the Compton radius that defines the typical inner extent of thermal winds. Moreover, correlations between key wind parameters yield an average absorption measure distribution that is consistent with magnetohydrodynamic wind models. We discuss our results in terms of recent observational and theoretical studies of black hole accretion disks and outflows, as well as future prospects.
The effect of surface gravity on line-depth ratios in the wavelength range 0.97–1.32 µm

A line-depth ratio (LDR) of two spectral lines with different excitation potentials is expected to be correlated with the effective temperature (Teff). It is possible to determine Teff of a star with a precision of tens of Kelvin if dozens or hundreds of tight LDR–Teff relations can be used. Most of the previous studies on the LDR method were limited to optical wavelengths, but Taniguchi and collaborators reported 81 LDR relations in the YJ band, 0.97–1.32 µm, in 2018. However, with their sample of only 10 giants, it was impossible to account for the effects of surface gravity and metallicity on the LDRs well. Here, we investigate the gravity effect based on YJ-band spectra of 63 stars including dwarfs, giants, and supergiants observed with the WINERED spectrograph. We found that some LDR–Teff relations show clear offsets between the sequence of dwarfs and those of giants/supergiants. The difference between the ionization potentials of the elements considered in each line pair and the corresponding difference in the depths can, at least partly, explain the dependency of the LDR on the surface gravity. In order to expand the stellar parameter ranges that the LDR method can cover with high precision, we obtained new sets of LDR–Teff relations for solar-metal G0–K4 dwarfs and F7–K5 supergiants, respectively. The typical precision that can be achieved with our relations is 10–30 K for both dwarfs and supergiants.
The impact of spectroscopic incompleteness in direct calibration of redshift distributions for weak lensing surveys

Obtaining accurate distributions of galaxy redshifts is a critical aspect of weak lensing cosmology experiments. One of the methods used to estimate and validate redshift distributions is to apply weights to a spectroscopic sample, so that their weighted photometry distribution matches the target sample. In this work, we estimate the selection bias in redshift that is introduced in this procedure. We do so by simulating the process of assembling a spectroscopic sample (including observer-assigned confidence flags) and highlight the impacts of spectroscopic target selection and redshift failures. We use the first year (Y1) weak lensing analysis in Dark Energy Survey (DES) as an example data set but the implications generalize to all similar weak lensing surveys. We find that using colour cuts that are not available to the weak lensing galaxies can introduce biases of up to Δz ∼ 0.04 in the weighted mean redshift of different redshift intervals (Δz ∼ 0.015 in the case most relevant to DES). To assess the impact of incompleteness in spectroscopic samples, we select only objects with high observer-defined confidence flags and compare the weighted mean redshift with the true mean. We find that the mean redshift of the DES Y1 weak lensing sample is typically biased at the Δz = 0.005−0.05 level after the weighting is applied. The bias we uncover can have either sign, depending on the samples and redshift interval considered. For the highest redshift bin, the bias is larger than the uncertainties in the other DES Y1 redshift calibration methods, justifying the decision of not using this method for the redshift estimations. We discuss several methods to mitigate this bias.
Systematic Evaluation of Privacy Risks of Machine Learning Models

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the model. In this paper, we show that prior work on membership inference attacks may severely underestimate the privacy risks by relying solely on training custom neural network classifiers to perform attacks and focusing only on the aggregate results over data samples, such as the attack accuracy. To overcome these limitations, we first propose to benchmark membership inference privacy risks by improving existing non-neural network based inference attacks and proposing a new inference attack method based on a modification of prediction entropy. We also propose benchmarks for defense mechanisms by accounting for adaptive adversaries with knowledge of the defense and also accounting for the trade-off between model accuracy and privacy risks. Using our benchmark attacks, we demonstrate that existing defense approaches are not as effective as previously reported. Next, we introduce a new approach for fine-grained privacy analysis by formulating and deriving a new metric called the privacy risk score. Our privacy risk score metric measures an individual sample's likelihood of being a training member, which allows an adversary to perform membership inference attacks with high confidence. We experimentally validate the effectiveness of the privacy risk score metric and demonstrate that the distribution of the privacy risk score across individual samples is heterogeneous. Finally, we perform an in-depth investigation for understanding why certain samples have high privacy risk scores, including correlations with model sensitivity, generalization error, and feature embeddings. Our work emphasizes the importance of a systematic and rigorous evaluation of privacy risks of machine learning models.
Assessment of mechanical, thermal and surface properties of monoclinic M$_1$ and M$_3$ C$_3$S by molecular dynamics

Monoclinic M$_1$ and M$_3$, which are the two main forms of industrial alite, were characterized at the molecular scale. Several methods were used to compute their mechanical and thermal properties. These methods were discussed and results in good agreement with experimental measurements were found. The cleavage energies were computed and the (100) and (001) showed the lowest energies for both polymorphs. Using the Wulff construction method, equilibrium shapes were proposed, and discussed with base on previous crystallographic studies.
Theoretical analysis and simulation methods for Hawkes processes and their diffusion approximation

Oscillatory systems of interacting Hawkes processes with Erlang memory kernels were introduced in Ditlevsen and Locherbach (2017). They are piecewise deterministic Markov processes (PDMP) and can be approximated by a stochastic diffusion. First, a strong error bound between the PDMP and the diffusion is proved. Second, moment bounds for the resulting diffusion are derived. Third, approximation schemes for the diffusion , based on the numerical splitting approach, are proposed. These schemes are proved to converge with mean-square order 1 and to preserve the properties of the diffusion, in particular the hypoellipticity, the ergod-icity and the moment bounds. Finally, the PDMP and the diffusion are compared through numerical experiments, where the PDMP is simulated with an adapted thinning procedure.
Disformal invariance of cosmological observables

We study the frame dependence/independence of cosmological observables under disformal transformations, extending the previous results regarding conformal transformations, and provide the correspondence between Jordan-frame and Einstein-frame variables. We consider quantities such as the gravitational constant in the Newtonian limit, redshift, luminosity and angular diameter distances, as well as the distance-duality relation. Also, the Boltzmann equation, the observed specific intensity, and the adiabaticity condition are discussed. Since the electromagnetic action changes under disformal transformations, photons in the Einstein frame no longer propagate along null geodesics. As a result, several quantities of cosmological interest are modified. Nevertheless, we show that the redshift is invariant and the distance-duality relation (the relation between the luminosity distance and the angular diameter distance) still holds in general spacetimes even though the reciprocity relation (the relation between two geometrical distances) is modified.
Runaway electron synchrotron radiation in a vertically translated plasma

Synchrotron radiation observed from runaway electrons (REs) in tokamaks depends upon the position and size of the RE beam, the RE energy and pitch distributions, as well as the location of the observer. We show experimental synchrotron images of a vertically moving RE beam sweeping past the detector in the Tokamak à Configuration Variable (TCV) tokamak and compare it with predictions from the synthetic synchrotron diagnostic Soft. This experimental validation lends confidence to the theory underlying the synthetic diagnostics which are used for benchmarking theoretical models of and probing runaway dynamics. We present a comparison of synchrotron measurements in TCV with predictions of kinetic theory for runaway dynamics in uniform magnetic fields. We find that to explain the detected synchrotron emission, significant non-collisional pitch angle scattering as well as radial transport of REs would be needed. Such effects could be caused by the presence of magnetic perturbations, which should be further investigated in future TCV experiments.
Evolving grain-size distributions embedded in gas flows

We present a numerical approach for accurately evolving a dust grain-size distribution undergoing number-conserving (such as sputtering) and/or mass-conserving (such as shattering) processes. As typically observed interstellar dust distributions follow a power law, our method adopts a power-law discretization and uses both the grain mass and number densities in each bin to determine the power-law parameters. This power-law method is complementary to piecewise-constant and linear methods in the literature. We find that the power-law method surpasses the other two approaches, especially for small bin numbers. In the sputtering tests, the relative error in the total grain mass remains below 0.01 per cent independent of the number of bins N, while the other methods only achieve this for N > 50 or higher. Likewise, the shattering test shows that the method also produces small relative errors in the total grain numbers while conserving mass. Not only does the power-law method conserve the global distribution properties, it also preserves the inter-bin characteristics so that the shape of the distribution is recovered to a high degree. This does not always happen for the constant and linear methods, especially not for small bin numbers. Implementing the power-law method in a hydrodynamical code thus minimizes the numerical cost while maintaining high accuracy. The method is not limited to dust grain distributions, but can also be applied to the evolution of any distribution function, such as a cosmic ray distribution affected by synchrotron radiation or inverse-Compton scattering.
Exact equivalences and phase discrepancies between random matrix ensembles

We study two types of random matrix ensembles that emerge when considering the same probability measure on partitions. One is the Meixner ensemble with a hard wall and the other are two families of unitary matrix models, with weight functions that can be interpreted as characteristic polynomial insertions. We show that the models, while having the same exact evaluation for fixed values of the parameter, may present a different phase structure. We find phase transitions of the second and third order, depending on the model. Other relationships, via direct mapping, between the unitary matrix models and continuous random matrix ensembles on the real line, of Cauchy–Romanovski type, are presented and studied both exactly and asymptotically. The case of orthogonal and symplectic groups is studied as well and related to Wronskians of Chebyshev polynomials, that we evaluate at large N.
Equation of State of Four- and Five-Dimensional Hard-Hypersphere Mixtures

New proposals for the equation of state of four- and five-dimensional hard-hypersphere mixtures in terms of the equation of state of the corresponding monocomponent hard-hypersphere fluid are introduced. Such proposals (which are constructed in such a way so as to yield the exact third virial coefficient) extend, on the one hand, recent similar formulations for hard-disk and (three-dimensional) hard-sphere mixtures and, on the other hand, two of our previous proposals also linking the mixture equation of state and the one of the monocomponent fluid but unable to reproduce the exact third virial coefficient. The old and new proposals are tested by comparison with published molecular dynamics and Monte Carlo simulation results and their relative merit is evaluated.
Mirrored Autoencoders with Simplex Interpolation for Unsupervised Anomaly Detection

Use of deep generative models for unsupervised anomaly detection has shown great promise partially owing to their ability to learn proper representations of complex input data distributions. Current methods, however, lack a strong latent representation of the data, thereby resulting in sub-optimal unsupervised anomaly detection results. In this work, we propose a novel representation learning technique using deep autoencoders to tackle the problem of unsupervised anomaly detection. Our approach replaces the $L_{p}$ reconstruction loss in the autoencoder optimization objective with a novel adversarial loss to enforce semantic-level reconstruction. In addition, we propose a novel simplex interpolation loss to improve the structure of the latent space representation in the autoencoder. Our technique improves the state-of-the-art unsupervised anomaly detection performance by a large margin on several image datasets including MNIST, fashion MNIST, CIFAR and Coil-100 as well as on several non-image datasets including KDD99, Arrhythmia and Thyroid. For example, On the CIFAR-10 dataset, using a standard leave-one-out evaluation protocol, our method achieves a substantial performance gain of 0.23 AUC points compared to the state-of-the-art.