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Understanding the interplay among different magnetic exchange interactions and its physical consequences, especially in the presence of itinerant electrons and disorders, remains one of the central themes in condensed matter physics. In this vein, the coupling between antiferromagnetic and spin glass orders may lead to large exchange bias, a property of potential broad technological applications. In this article, we report the coexistence of antiferromagnetic order and spin glass behaviors in a quasi-one-dimensional iron telluride TaFe$_{1+x}$Te$_3$ ($x$=0.25). Its antiferromagnetism is believed to arise from the antiferromagnetic interchain coupling between the ferromagnetically aligned FeTe chains along the $b$-axis, while the spin glassy state stems from the disordered Fe interstitials. This dichotomic role of chain and interstitial sublattices is responsible for the large exchange bias observed at low temperatures, with the interstitial Fe acting as the uncompensated moment and its neighboring Fe chain providing the source for its pinning. This iron-based telluride may thereby represent a new paradigm to study the large family of transition metal chalcogenides whose magnetic order or even the dimensionality can be tuned to a large extent, forming a fertile playground to manipulate or switch the spin degrees of freedom thereof.

We consider zero-sum stochastic games for continuous time Markov decision processes with risk-sensitive average cost criterion. Here the transition and cost rates may be unbounded. We prove the existence of the value of the game and a saddle-point equilibrium in the class of all stationary strategies under a Lyapunov stability condition. This is accomplished by establishing the existence of a principal eigenpair for the corresponding Hamilton-Jacobi-Isaacs (HJI) equation. This in turn is established by using the nonlinear version of Krein-Rutman theorem. We then obtain a characterization of the saddle-point equilibrium in terms of the corresponding HJI equation. Finally, we use a controlled population system to illustrate results., Comment: 28 pages

Bayesian statistical inference for Generalized Linear Models (GLMs) with parameters lying on a constrained space is of general interest (e.g., in monotonic or convex regression), but often constructing valid prior distributions supported on a subspace spanned by a set of linear inequality constraints can be challenging, especially when some of the constraints might be binding leading to a lower dimensional subspace. For the general case with canonical link, it is shown that a generalized truncated multivariate normal supported on a desired subspace can be used. Moreover, it is shown that such prior distribution facilitates the construction of a general purpose product slice sampling method to obtain (approximate) samples from corresponding posterior distribution, making the inferential method computationally efficient for a wide class of GLMs with an arbitrary set of linear inequality constraints. The proposed product slice sampler is shown to be uniformly ergodic, having a geometric convergence rate under a set of mild regularity conditions satisfied by many popular GLMs (e.g., logistic and Poisson regressions with constrained coefficients). One of the primary advantages of the proposed Bayesian estimation method over classical methods is that uncertainty of parameter estimates is easily quantified by using the samples simulated from the path of the Markov Chain of the slice sampler. Numerical illustrations using simulated data sets are presented to illustrate the superiority of the proposed methods compared to some existing methods in terms of sampling bias and variances. In addition, real case studies are presented using data sets for fertilizer-crop production and estimating the SCRAM rate in nuclear power plants.

Single gap Resistive Plate Chamber (RPC) is one of the very popular gaseous detectors used in high-energy physics experiments nowadays. It is a very fast detector having low cost of fabrication. The RPCs are usually built using glass or bakelite plates having high resistivity $\sim~10^{10}-10^{11}$ $\Omega$~cm. Bakelite RPCs are generally fabricated with a linseed oil coating inside to make the inner electrode surface smoother which helps to reduce the micro discharge probability. Linseed oil coating also reduces the surface UV sensitivity dramatically and effectively protect the bakelite surfaces from the Hydrofluoric Acid (HF), produced by the interaction of fluorine with the water vapour. There is a conventional way to do this linseed oil coating after making the gas gap as done in experiments $e.g.$ ALICE, CMS etc. A new technique is introduced here to do the linseed oil coating on the bakelite plate before making the gas gap. 100\% Tetrafluoroethane (C$_2$H$_2$F$_4$) gas is used to test the RPC module in the avalanche mode with cosmic rays. Conventional NIM electronics is used for this study. The efficiency and noise rate are measured. In this article, the detailed method of fabrication and the first test results are presented., Comment: 9 pages, 4 figures

The growing popularity of data mining catalyses the researchers to explore various exciting aspects of education. Early prediction of student performance is an emerging area among them. The researchers have used various predictors in performance modelling studies. Although prior cognition can affect student performance, establishing their relationship is still an open research challenge. Quantifying the knowledge from readily available data is the major challenge here. We have proposed a semantic approach for this purpose. Association mining on nearly 0.35 million observations establishes that prior cognition impacts the student performance. The proposed approach of measuring domain knowledge can help the early performance modelling studies to use it as a predictor., Comment: Published in 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). 8 pages, 5 figures (includes 16 plots). Accessible at https://ieeexplore.ieee.org/document/9368439

With the advancement of the accelerator systems and the requirements of high luminosity particle beams to reach different physics goals, detectors with good position resolution and high rate handling capability have become essential for designing any High Energy Physics (HEP) experiments. The Gas Electron Multiplier (GEM) detectors are widely used in many HEP experiments as a tracking device because of their good spatial resolution and rate handling capability. The presence of the dielectric medium inside the active volume of the GEM detector changes its behaviour when exposed to external radiation. This mechanism is commonly referred as the charging-up effect. In this article, the effect of the charging-up phenomenon and the initial polarisation effect of the dielectric on the gain of the chamber are reported for a single mask triple GEM chamber with Ar/CO2 gas mixture., Comment: 11 pages, 9 figures

The long-term stability in terms of gain and energy resolution of a prototype triple Gas Electron Multiplier (GEM) detector has been investigated with high rate X-ray irradiation. Premixed Ar/CO2 (80:20) and (90:10) gases have been used for this stability study. A strong Fe55 X-ray source is used to irradiate the chamber. The uniqueness of this work is that the same source has been used to irradiate the GEM prototype and also to monitor the spectra. This arrangement is important since it reduces the mechanical complexity of using an X-ray generator as well as the cost of the setup. A small area of the chamber is exposed continuously to the X-ray for the entire duration of the operation. The effect of temperature and pressure on the gain and energy resolution is monitored. The result of the long-term stability test for a triple GEM detector using Ar/CO2 (70:30) gas mixture has been reported earlier [1]. The results with Ar/CO2 (80:20) and (90:10) gas mixtures for the same chamber are presented in this article., Comment: 10 pages, 8 figures

The presence of missing values within high-dimensional data is an ubiquitous problem for many applied sciences. A serious limitation of many available data mining and machine learning methods is their inability to handle partially missing values and so an integrated approach that combines imputation and model estimation is vital for down-stream analysis. A computationally fast algorithm, called EMFlow, is introduced that performs imputation in a latent space via an online version of Expectation-Maximization (EM) algorithm by using a normalizing flow (NF) model which maps the data space to a latent space. The proposed EMFlow algorithm is iterative, involving updating the parameters of online EM and NF alternatively. Extensive experimental results for high-dimensional multivariate and image datasets are presented to illustrate the superior performance of the EMFlow compared to a couple of recently available methods in terms of both predictive accuracy and speed of algorithmic convergence. We provide code for all our experiments.

The relationship between mass and radius (M-R relation) is the key for inferring the planetary compositions and thus valuable for the studies of formation and migration models. However, the M-R relation alone is not enough for planetary characterization due to the dependence of it on other confounding variables. This paper provides a non-trivial extension of the M-R relation by including the incident flux as an additional variable. By using Bayesian hierarchical modeling (BHM) that leverages the flexibility of finite mixture models, a probabilistic mass-radius-flux relationship (M-R-F relation) is obtained based on a sample of 319 exoplanets. We find that the flux has nonnegligible impact on the M-R relation, while such impact is strongest for hot-Jupiters. On the population level, the planets with higher level of flux tend to be denser, and high flux could trigger significant mass loss for plants with radii larger than $13R_{\oplus}$. As a result, failing to account for the flux in mass prediction would cause systematic over or under-estimation. With the recent advent of computing power, although a lot of complex statistical models can be fitted using Monte Carlo methods, it has largely remain illusive how to validate these complex models when the data are observed with large measurement errors. We present two novel methods to examine model assumptions, which can be used not only for the models we present in this paper but can also be adapted for other statistical models., Comment: Accepted for publication in MNRAS

We study nonzero-sum stochastic games for continuous time Markov decision processes on a denumerable state space with risk-sensitive ergodic cost criterion. Transition rates and cost rates are allowed to be unbounded. Under a Lyapunov type stability assumption, we show that the corresponding system of coupled HJB equations admits a solution which leads to the existence of a Nash equilibrium in stationary strategies. We establish this using an approach involving principal eigenvalues associated with the HJB equations. Furthermore, exploiting appropriate stochastic representation of principal eigenfunctions, we completely characterize Nash equilibria in the space of stationary Markov strategies.

It depends: While we find within holography that the lifetime of the magnetic field for collider energies like the ones achieved at RHIC is long enough to build up the chiral magnetic current, the lifetime of the magnetic field at LHC seems to be too short. We study the real time evolution of the chiral magnetic effect out-of-equilibrium in strongly coupled holographic gauge theories. We consider the backreaction of the magnetic field onto the geometry and monitor pressure and chiral magnetic current. Our findings show that generically at small magnetic field the pressure builds up faster than the chiral magnetic current whereas at strong magnetic field the opposite is true. At large charge we also find that equilibration is delayed significantly due to long lived oscillations. We also match the parameters of our model to QCD parameters and draw lessons of possible relevance to the realization of the chiral magnetic effect in heavy ion collisions. In particular, we find an equilibration time of about $\sim0.35$ fm/c in presence of the chiral anomaly for plasma temperatures of order $T\sim300-400$ MeV., Comment: v2 matches version published in PRD; 11 pages + appendix, 9 + 3 figures

Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to mitigate this challenge. However, the effectiveness of such automation may depend on the richness of the training dataset and model efficiency. We have recently developed a novel classifier named random wheel which provides a more interpretable output. In this work, we have used an enhanced version of random wheel to facilitate a trustworthy recommendation for credit card approval process. It not only produces more accurate and precise recommendation but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit card application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which in turn can enhance the efficiency of the credit card approval process., Comment: 14 pages, 8 figures, 2 tables