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The coronene molecules have been bombarded by protons of energy by 75 to 300 keV. The time of flight mass spectrum has been recorded using a two stage Wiley McLaren type spectrometer. A large enhancement in the doubly and triply ionized recoil ion is observed compared to the singly ionized one. The single, double and triple ionization yields have also been calculated using the continuum distorted wave eikonal initial state (CDW EIS) theoretical model and are compared with the experimental results. Experimental double to single ionization yield ratios and triple to single ionization yield ratios have been compared with the theoretical ratios which are found to be much higher w.r. t. the gas atoms. Evaporation peaks due to the loss of several neutral C2H2 and C3H3 are observed corresponding to their parent singly, doubly and triply charge coronene ions. Small fragmentation peaks CnHx+ (n = 3 to 7) are present in the spectra due to higher energy transfer by the projectile to the molecule. The hydrogen losses are observed in the cation, di-cation and tri-cation coronene peak structures. A maximum of the 7 H losses are detected which depends on the beam energy., Comment: After careful consideration, I have decided to withdraw this paper due to significant improvements needed in the paper and require extensive revisions. I plan to address these issues and resubmit a corrected version in the future."

To avoid failures on out-of-distribution data, recent works have sought to extract features that have an invariant or stable relationship with the label across domains, discarding "spurious" or unstable features whose relationship with the label changes across domains. However, unstable features often carry complementary information that could boost performance if used correctly in the test domain. In this work, we show how this can be done without test-domain labels. In particular, we prove that pseudo-labels based on stable features provide sufficient guidance for doing so, provided that stable and unstable features are conditionally independent given the label. Based on this theoretical insight, we propose Stable Feature Boosting (SFB), an algorithm for: (i) learning a predictor that separates stable and conditionally-independent unstable features; and (ii) using the stable-feature predictions to adapt the unstable-feature predictions in the test domain. Theoretically, we prove that SFB can learn an asymptotically-optimal predictor without test-domain labels. Empirically, we demonstrate the effectiveness of SFB on real and synthetic data., Comment: NeurIPS 2023 Camera-Ready

The dynamical aspect of accelerating cosmological model has been studied in this paper in the context of modified symmetric teleparallel gravity, the $f(Q)$ gravity. Initially, we have derived the dynamical parameters for two well known forms of $f(Q)$ such as: (i) log-square-root form and (ii) exponential form. The equation of state (EoS) parameter for the dark energy in the $f(Q)$ gravity in both the models emerges into a dynamical quantity. At present model-I shows the quintessence behavior and behave like the $\Lambda$CDM at the late time whereas model-II shows phantom behaviour. Further, the dynamical system analysis has been performed to determine the cosmological behaviour of the models along with its stability behaviour. For both the models the critical points are obtained and analysed the stability at each critical points with phase portraits. The evolutionary behaviour of density parameters for the matter-dominated, radiation-dominated, and dark energy phases are also shown for both the models., Comment: 13 pages, 9 figures

Eye-tracking has potential to provide rich behavioral data about human cognition in ecologically valid environments. However, analyzing this rich data is often challenging. Most automated analyses are specific to simplistic artificial visual stimuli with well-separated, static regions of interest, while most analyses in the context of complex visual stimuli, such as most natural scenes, rely on laborious and time-consuming manual annotation. This paper studies using computer vision tools for "attention decoding", the task of assessing the locus of a participant's overt visual attention over time. We provide a publicly available Multiple Object Eye-Tracking (MOET) dataset, consisting of gaze data from participants tracking specific objects, annotated with labels and bounding boxes, in crowded real-world videos, for training and evaluating attention decoding algorithms. We also propose two end-to-end deep learning models for attention decoding and compare these to state-of-the-art heuristic methods., Comment: To be published in Proceedings of the NeurIPS 2022 Gaze Meets ML Workshop

Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To achieve this, DG is commonly formulated as an average- or worst-case problem over the set of possible domains. However, predictors that perform well on average lack robustness while predictors that perform well in the worst case tend to be overly-conservative. To address this, we propose a new probabilistic framework for DG where the goal is to learn predictors that perform well with high probability. Our key idea is that distribution shifts seen during training should inform us of probable shifts at test time, which we realize by explicitly relating training and test domains as draws from the same underlying meta-distribution. To achieve probable DG, we propose a new optimization problem called Quantile Risk Minimization (QRM). By minimizing the $\alpha$-quantile of predictor's risk distribution over domains, QRM seeks predictors that perform well with probability $\alpha$. To solve QRM in practice, we propose the Empirical QRM (EQRM) algorithm and provide: (i) a generalization bound for EQRM; and (ii) the conditions under which EQRM recovers the causal predictor as $\alpha \to 1$. In our experiments, we introduce a more holistic quantile-focused evaluation protocol for DG and demonstrate that EQRM outperforms state-of-the-art baselines on datasets from WILDS and DomainBed., Comment: NeurIPS 2022 camera-ready (+ minor corrections)

We show that the hypergeometric groups corresponding to the seven pairs of the parameters $\alpha$, $\beta$ where $\alpha$ = (0, 0, 0, 0, 0, 0) and $\beta$ is any of the parameters (1/2, 1/2, 1/2, 1/2, 1/2, 1/2), (1/2, 1/2, 1/2, 1/2, 1/3, 2/3), (1/2, 1/2, 1/2, 1/2, 1/4, 3/4), (1/2, 1/2, 1/2, 1/2, 1/6, 5/6), (1/2, 1/2, 1/3, 2/3, 1/3, 2/3), (1/2, 1/2, 1/3, 2/3, 1/4, 3/4), (1/2, 1/2, 1/5, 2/5, 3/5, 4/5) are thin., Comment: Final version; accepted for publication in Glasgow Mathematical Journal

Traditional models of active learning assume a learner can directly manipulate or query a covariate $X$ in order to study its relationship with a response $Y$. However, if $X$ is a feature of a complex system, it may be possible only to indirectly influence $X$ by manipulating a control variable $Z$, a scenario we refer to as Indirect Active Learning. Under a nonparametric model of Indirect Active Learning with a fixed budget, we study minimax convergence rates for estimating the relationship between $X$ and $Y$ locally at a point, obtaining different rates depending on the complexities and noise levels of the relationships between $Z$ and $X$ and between $X$ and $Y$. We also identify minimax rates for passive learning under comparable assumptions. In many cases, our results show that, while there is an asymptotic benefit to active learning, this benefit is fully realized by a simple two-stage learner that runs two passive experiments in sequence., Comment: To appear in proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS)

We show that the hypergeometric groups associated to the pairs of the parameters $\left(0,0,\frac{1}{3}, \frac{2}{3}\right)$, $\left(\frac{1}{2},\frac{1}{2},\frac{1}{4},\frac{3}{4}\right)$; and $\left(0,\frac{1}{12}, \frac{5}{12},\frac{7}{12},\frac{11}{12}\right), \left(\frac{1}{2},\frac{1}{3},\frac{1}{3},\frac{2}{3},\frac{2}{3}\right)$ are arithmetic., Comment: 9 pages

Two-electron one-photon transitions have been successfully observed for the Ne projectile and Al target at low energy regime. Experimental energy values of two-electron one-photon transitions are compared with previously reported theoretical and experimental values. Ionization cross-section of two-electron one-photon transition is reported.

The vast majority of statistical theory on binary classification characterizes performance in terms of accuracy. However, accuracy is known in many cases to poorly reflect the practical consequences of classification error, most famously in imbalanced binary classification, where data are dominated by samples from one of two classes. The first part of this paper derives a novel generalization of the Bayes-optimal classifier from accuracy to any performance metric computed from the confusion matrix. Specifically, this result (a) demonstrates that stochastic classifiers sometimes outperform the best possible deterministic classifier and (b) removes an empirically unverifiable absolute continuity assumption that is poorly understood but pervades existing results. We then demonstrate how to use this generalized Bayes classifier to obtain regret bounds in terms of the error of estimating regression functions under uniform loss. Finally, we use these results to develop some of the first finite-sample statistical guarantees specific to imbalanced binary classification. Specifically, we demonstrate that optimal classification performance depends on properties of class imbalance, such as a novel notion called Uniform Class Imbalance, that have not previously been formalized. We further illustrate these contributions numerically in the case of $k$-nearest neighbor classification, Comment: Parts of this paper have been revised from arXiv:2004.04715v2 [math.ST]

The K x-ray spectra of different targets (Cu, Zn, and Ge) induced by 3 to 5 MeV/u Si projectile ions have been measured to determine the K-shell ionization cross-section. A significant difference is observed between the measurements and theoretical estimates, where the theoretical ones are about 28-35% of the experimental results. Such difference is reduced to a good extent 51-56% if multiple ionization effects are taken into account. Remaining discrepancy may be attributed to the electron capture contribution., Comment: 7 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2103.08299

To have a better understanding of a physical process, a comparison of ex-perimental data with theoretical values is mandatory. The comparison ismeaningful if the uncertainty in the experiment is accounted well. However,it is seldom seen, especially for a complex phenomenon. We take a test casethrough L subshell ionization of atoms by particle impact. Experimentally,x-ray production cross-sections are measured, but ionization cross-sectionsare calculated theoretically. Furthermore, the uncertainty of the x-ray pro-duction cross-section is mainly statistics and detector-efficiency driven. Butionization cross-section involves many other factors because of the relation-ship between the production and ionization cross section, having wide uncer-tainty spectrum. Consequently, determining the measurement uncertainty inL subshell ionization cross-section is always difficult. We have studied thisissue in the simplest way, where the rule of weighted propagation of rela-tive uncertainty is utilised. We notice that larger uncertainties are involvedin atomic parameters relevant to L1(2s1/2) subshell than those associatedwith the other two L2(2p1/2) and L3(2p3/2) subshells. Hence, comparisonbetween theory and experiment would give higher emphasis onL2andL3subshell ionization cross sections. We believe this work aware us that theappropriate uncertainty evaluation is extremely important for providing theright judgment on the data., Comment: 31 pages, 2 figures