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The growing interest in accessible software reflects in computing educators' and education researchers' efforts to include accessibility in core computing education. We integrated accessibility in a junior/senior-level Android app development course at a large private university in India. The course introduced three accessibility-related topics using various interventions: Accessibility Awareness (a guest lecture by a legal expert), Technical Knowledge (lectures on Android accessibility guidelines and testing practices and graded components for implementing accessibility in programming assignments), and Empathy (an activity that required students to blindfold themselves and interact with their phones using a screen-reader). We evaluated their impact on student learning using three instruments: (A) A pre/post-course questionnaire, (B) Reflective questions on each of the four programming assignments, and (C) Midterm and Final exam questions. Our findings demonstrate that: (A) significantly more ($p<.05$) students considered disabilities when designing an app after taking this course, (B) many students developed empathy towards the challenges persons with disabilities face while using inaccessible apps, and (C) all students could correctly identify at least one accessibility issue in the user interface of a real-world app given its screenshot, and 90% of them could provide a correct solution to fix it., Comment: 7 pages, 1 figure, submitted to ACM SIGCSE 2023

We report the most sensitive upper limits to date on the 21 cm epoch of reionization power spectrum using 94 nights of observing with Phase I of the Hydrogen Epoch of Reionization Array (HERA). Using similar analysis techniques as in previously reported limits (HERA Collaboration 2022a), we find at 95% confidence that $\Delta^2(k = 0.34$ $h$ Mpc$^{-1}$) $\leq 457$ mK$^2$ at $z = 7.9$ and that $\Delta^2 (k = 0.36$ $h$ Mpc$^{-1}) \leq 3,496$ mK$^2$ at $z = 10.4$, an improvement by a factor of 2.1 and 2.6 respectively. These limits are mostly consistent with thermal noise over a wide range of $k$ after our data quality cuts, despite performing a relatively conservative analysis designed to minimize signal loss. Our results are validated with both statistical tests on the data and end-to-end pipeline simulations. We also report updated constraints on the astrophysics of reionization and the cosmic dawn. Using multiple independent modeling and inference techniques previously employed by HERA Collaboration (2022b), we find that the intergalactic medium must have been heated above the adiabatic cooling limit at least as early as $z = 10.4$, ruling out a broad set of so-called "cold reionization" scenarios. If this heating is due to high-mass X-ray binaries during the cosmic dawn, as is generally believed, our result's 99% credible interval excludes the local relationship between soft X-ray luminosity and star formation and thus requires heating driven by evolved low-metallicity stars., Comment: 57 pages, 37 figures. Updated to match the accepted ApJ version. Corresponding author: Joshua S. Dillon

Fe3GeTe2 is a 2-dimensional van der Waals material exhibiting itinerant ferromagnetism upto 230 K. Here, we study aspects of scattering mechanism in Fe3Ge2Te2 single crystals via resistivity, magneto-transport and Hall effect measurements. The quadratic temperature dependence of electrical resistivity below the Curie temperature hints towards the dominance of electron-magnon scattering. A non-saturating positive magnetoresistance (MR) is observed at low temperatures when the magnetic field is applied parallel to the sample plane. The linear negative MR at high fields for T < TC corroborates to the suppression in magnon population due to the damping of spin waves. In the high temperature regime T > TC,MR can be described by the scattering from spin fluctuations using the model described by Khosla and Fischer. Isothermal Hall resistivity curves unveil the presence of anomalous Hall resistivity. Correlation between MR and side jump mechanism further reveals that the electron-magnon scattering is responsible for the side jump contribution to the anomalous Hall effect. Our results provide a clear understanding of the role of electron-magnon scattering on anomalous Hall effect that rules out its origin to be the topological band structure.

One of the keys to flying quadrotors is to optimize their trajectories within the set of collision-free corridors. These corridors impose nonconvex constraints on the trajectories, making real-time trajectory optimization challenging. We introduce a novel numerical method that approximates the nonconvex corridor constraints with time-triggered convex corridor constraints. This method combines bisection search and repeated infeasibility detection. We further develop a customized C++ implementation of the proposed method, based on a first-order conic optimization method that detects infeasibility and exploits problem structure. We demonstrate the efficiency and effectiveness of the proposed method using numerical simulation on randomly generated problem instances as well as indoor flight experiments with hoop obstacles. Compared with mixed integer programming, the proposed method is about 50--200 times faster.

Fault injection (FI) is a powerful attack methodology allowing an adversary to entirely break the security of a target device. As finite-state machines (FSMs) are fundamental hardware building blocks responsible for controlling systems, inducing faults into these controllers enables an adversary to hijack the execution of the integrated circuit. A common defense strategy mitigating these attacks is to manually instantiate FSMs multiple times and detect faults using a majority voting logic. However, as each additional FSM instance only provides security against one additional induced fault, this approach scales poorly in a multi-fault attack scenario. In this paper, we present SCFI: a strong, probabilistic FSM protection mechanism ensuring that control-flow deviations from the intended control-flow are detected even in the presence of multiple faults. At its core, SCFI consists of a hardened next-state function absorbing the execution history as well as the FSM's control signals to derive the next state. When either the absorbed inputs, the state registers, or the function itself are affected by faults, SCFI triggers an error with no detection latency. We integrate SCFI into a synthesis tool capable of automatically hardening arbitrary unprotected FSMs without user interaction and open-source the tool. Our evaluation shows that SCFI provides strong protection guarantees with a better area-time product than FSMs protected using classical redundancy-based approaches. Finally, we formally verify the resilience of the protected state machines using a pre-silicon fault analysis tool.

Cobalt based sulphides of compositional formula Co$ _{3} $A$ _{2} $S$ _{2} $ (A = Sn and In) are endowed with frustrated kagome lattice structure and a plethora of novel phenomena determined from the topological band structure. Here-in, we report on the detailed exploration of anisotropic magnetic properties of single crystals of ferromagnetic compound Co$ _{3} $Sn$ _{2} $S$ _{2} $ under magnetic field $H$ applied along the c-axis $(H\parallel c)$ and the ab-plane $(H\parallel ab)$. A low temperature clustered-glassy magnetic behaviour is revealed in field-cooled and zero field-cooled magnetization and memory effect measurement protocols. The sharp downturn and non-linearity observed in the inverse susceptibility above the critical temperature $T _{\text{C}} $ in the paramagnetic region corroborates to the presence of short-range ferromagnetic clusters above $ T _{\text{C}} $ in Co$ _{3} $Sn$ _{2} $S$ _{2} $. The deviation from linear Curie-Weiss behaviour in the paramagnetic state signifies the strong Griffiths singularity in the material. The slow spin dynamics behaviour and zero spontaneous magnetization above $ T _{\text{C}} $ give an evidence of Griffiths phase owing to the ferromagnetic clusters. The Arrott plots derived from magnetization reveal convex type curvature at low fields and linear positive behavior in the high field region, confirming the second order magnetic phase transition in Co$ _{3} $Sn$ _{2} $S$ _{2} $. The Takahashi spin fluctuation theory analysis provides sufficient evidence for itinerant ferromagnetism in Co$ _{3} $Sn$ _{2} $S$ _{2} $. A large magneto-crystalline anisotropy concomitant with a high anisotropy field suggests the dominance of strong spin orbit coupling phenomenon. Our experimental results emphasize an intuitive understanding of the complex nature of magnetism present in Co-based shandite systems.

Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes. In this paper, we present auton-survival, an open-source repository of tools to streamline working with censored time-to-event or survival data. auton-survival includes tools for survival regression, adjustment in the presence of domain shift, counterfactual estimation, phenotyping for risk stratification, evaluation, as well as estimation of treatment effects. Through real world case studies employing a large subset of the SEER oncology incidence data, we demonstrate the ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions.

The current paper provides a comprehensive examination of a dark energy cosmological model in the classical regime, in which a generic scalar field is regarded as a dark energy source. Einstein's field equations are solved in model independent way i.e. using a scheme of cosmological parametrization. A parametrization of the density parameter as a function of the cosmic scale factor has been investigated in this line. The result is noteworthy because it shows a smooth transition from a decelerating to an accelerating phase in the recent past. The model parameters involved in the functional form of the parametrization approach utilized here were constrained using certain external datasets. The updated Hubble datasets containing 57 datapoints, 1048 points of recently compiled Pantheon datasets, and also the Baryon Acoustic Oscillation (BAO) datasets are used here to determine the best-fitting model parameter values. The expressions of several significant cosmological parameters are represented as a function of redshift `$z$' and illustrated visually for the best fit values of the model parameters to better comprehend cosmic evolution. The obtained model is also compared with the $\Lambda CDM$ model. Our model has a distinct behavior in future and shown a big crunch type collapse. The best fit values of the model parameters are also used to compute the current values of several physical and geometrical parameters, as well as phase transition redshift. To examine the nature of dark energy, certain cosmological tests and diagnostic analyses are done on the derived model., Comment: 19 pages, 12 figures with sub figures

Estimation of treatment efficacy of real-world clinical interventions involves working with continuous outcomes such as time-to-death, re-hospitalization, or a composite event that may be subject to censoring. Counterfactual reasoning in such scenarios requires decoupling the effects of confounding physiological characteristics that affect baseline survival rates from the effects of the interventions being assessed. In this paper, we present a latent variable approach to model heterogeneous treatment effects by proposing that an individual can belong to one of latent clusters with distinct response characteristics. We show that this latent structure can mediate the base survival rates and helps determine the effects of an intervention. We demonstrate the ability of our approach to discover actionable phenotypes of individuals based on their treatment response on multiple large randomized clinical trials originally conducted to assess appropriate treatments to reduce cardiovascular risk., Comment: KDD 2022 Applied Data Science Paper. Note this version includes a correction of the published version in the definition of Restricted Mean Survival Time

Topological semimetals with superconducting properties provide an emergent platform to explore the properties of topological superconductors. We report magnetization, and magneto-transport measurements on high quality single crystals of transition metal dichalcogenide SnTaS2. It is a nodal line semimetal with superconducting transition below Tc = 2.9 K. Moderate anisotropy (3.1) is observed in upper critical fields along H||c and H||ab plane. In the normal state we observe large magneto-resistance and weak anti-localization effect that provide unambiguous confirmation of topological features in SnTaS2. Therefore, genuine topological characteristics can be studied in this material, particularly with regard to microscopic origin of order parameter symmetry.

In this paper, we present how Bell's Palsy, a neurological disorder, can be detected just from a subject's eyes in a video. We notice that Bell's Palsy patients often struggle to blink their eyes on the affected side. As a result, we can observe a clear contrast between the blinking patterns of the two eyes. Although previous works did utilize images/videos to detect this disorder, none have explicitly focused on the eyes. Most of them require the entire face. One obvious advantage of having an eye-focused detection system is that subjects' anonymity is not at risk. Also, our AI decisions based on simple blinking patterns make them explainable and straightforward. Specifically, we develop a novel feature called blink similarity, which measures the similarity between the two blinking patterns. Our extensive experiments demonstrate that the proposed feature is quite robust, for it helps in Bell's Palsy detection even with very few labels. Our proposed eye-focused detection system is not only cheaper but also more convenient than several existing methods., Comment: Published in the Proceedings of the 34th Canadian Conference on Artificial Intelligence. Please cite this paper in the following manner: S. A. Ansari, K. R. Jerripothula, P. Nagpal, and A. Mittal. "Eye-focused Detection of Bell's Palsy in Videos". In: Proceedings of the 34th Canadian Conference on Artificial Intelligence (June 8, 2021). doi: 10.21428/594757db.d2f8342b

We propose a mathematical framework for designing robust networks of coupled phase-oscillators by leveraging a vulnerability measure proposed by Tyloo et. al that quantifies how much a small perturbation to a phase-oscillator's natural frequency impacts the system's global synchronized frequencies. Given a fixed complex network topology with specific governing dynamics, the proposed framework finds an optimal allocation of edge weights that minimizes such vulnerability measure(s) at the node(s) for which we expect perturbations to occur by solving a tractable semidefinite programming problem. We specify the mathematical model to high voltage electric grids where each node corresponds to a voltage phase angle associated with a bus and edges correspond to transmission lines. Edge weights are determined by the susceptance values along the transmission lines. In this application, frequency synchronization is increasingly challenged by the integration of renewable energy, yet is imperative to the grid's health and functionality. Our framework helps to alleviate this challenge by optimizing the placement of renewable generation and the susceptance values along the transmission lines., Comment: 9 pages, 4 figures

Free-breathing cardiac MRI schemes are emerging as competitive alternatives to breath-held cine MRI protocols, enabling applicability to pediatric and other population groups that cannot hold their breath. Because the data from the slices are acquired sequentially, the cardiac/respiratory motion patterns may be different for each slice; current free-breathing approaches perform independent recovery of each slice. In addition to not being able to exploit the inter-slice redundancies, manual intervention or sophisticated post-processing methods are needed to align the images post-recovery for quantification. To overcome these challenges, we propose an unsupervised variational deep manifold learning scheme for the joint alignment and reconstruction of multislice dynamic MRI. The proposed scheme jointly learns the parameters of the deep network as well as the latent vectors for each slice, which capture the motion-induced dynamic variations, from the k-t space data of the specific subject. The variational framework minimizes the non-uniqueness in the representation, thus offering improved alignment and reconstructions.

The severity of the coronavirus pandemic necessitates the need of effective administrative decisions. Over 4 lakh people in India succumbed to COVID-19, with over 3 crore confirmed cases, and still counting. The threat of a plausible third wave continues to haunt millions. In this ever changing dynamic of the virus, predictive modeling methods can serve as an integral tool. The pandemic has further triggered an unprecedented usage of social media. This paper aims to propose a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. In this study, we seek to understand how the surges in COVID-19 related tweets can indicate rise in the cases. This prospective analysis can be utilised to aid administrators about timely resource allocation to lessen the severity of the damage. Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs).Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0.80 and 0.62 respectively. Finally, we explain the thematic utility of the SDs.

Topological materials with extremely large magnetoresistance exhibit a prognostic feature of resistivity turn-on behaviour. This occurs when the temperature dependence of resistivity changes from metallic to semiconducting characteristics on application of external magnetic field above a threshold value. Here, we study the magneto-transport properties of type-II Weyl Semimetal WP2. We find that semi-classical theories of magnetoresistance are consistent with our data without the need to invoke topological surface states. Our findings in this work provides an alternative basis to understand the temperature dependence of magnetoresistance in topological materials.

In this paper, we have considered a quadratic variation of the deceleration parameter ($q$) as a function of cosmic time ($t$) which describes a smooth transition from the decelerating phase of the Universe to an accelerating one and also show some distinctive feature from the standard model. The logical move of this article is against the behavior of the future Universe, \textit{i.e.} whether the Universe expands forever or ends with a Big Rip, and we observe that the outcome of the considered parametrization comes in favor of Big Rip future of the Universe. The whole set up of the parametrization and solution is taken in $f(R,T)$ theory of gravity for a spatially flat Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) geometry. Furthermore, we have considered the functional form of $f(R,T)$ function as $% f(R)+f(T)$, where a quadratic correction of the geometric term $R$ is adopted as the function $f(R)$, and a linear matter term $f(T)$. We have investigated some features of the model by examining the behavior of physical parameters. Our primary goal here is to discuss the physical dynamics of the model in $f(R,T)$ gravity. We have found, the EoS parameter also has the same singularity as that of the Hubble parameter \textit{i.e.} at the initial phase and at the Big Rip. The EoS parameter is explored in some detail for our choice of $f(R,T)$ function considered here. Different cases for $f(R,T)$ functional form for different values of the coupling parameters are discussed, and the evolution of the physical parameters is shown graphically., Comment: 14 pages, 3 figures with sub-figures

Let $R=\mathbf{C}[\xi_1,\xi_2,\ldots]$ be the infinite variable polynomial ring, equipped with the natural action of the infinite symmetric group $\mathfrak{S}$. We classify the $\mathfrak{S}$-primes of $R$, determine the containments among these ideals, and describe the equivariant spectrum of $R$. We emphasize that $\mathfrak{S}$-prime ideals need not be radical, which is a primary source of difficulty. Our results yield a classification of $\mathfrak{S}$-ideals of $R$ up to copotency. Our work is motivated by the interest and applications of $\mathfrak{S}$-ideals seen in recent years.

Bilinear models that decompose dynamic data to spatial and temporal factors are powerful and memory-efficient tools for the recovery of dynamic MRI data. These methods rely on sparsity and energy compaction priors on the factors to regularize the recovery. The quality of the recovered images depend on the specific priors. Motivated by deep image prior, we introduce a novel bilinear model whose factors are represented using convolutional neural networks (CNNs). The CNN parameters are learned from the undersampled data off the same subject. To reduce the run time and to improve performance, we initialize the CNN parameters. We use sparsity regularization of the network parameters to minimize the overfitting of the network to measurement noise. Our experiments on free breathing and ungated cardiac cine data acquired using a navigated golden-angle gradient-echo radial sequence show the ability of our method to provide reduced spatial blurring as compared to low-rank and SToRM reconstructions.

Low resolution fine-grained classification has widespread applicability for applications where data is captured at a distance such as surveillance and mobile photography. While fine-grained classification with high resolution images has received significant attention, limited attention has been given to low resolution images. These images suffer from the inherent challenge of limited information content and the absence of fine details useful for sub-category classification. This results in low inter-class variations across samples of visually similar classes. In order to address these challenges, this research proposes a novel attribute-assisted loss, which utilizes ancillary information to learn discriminative features for classification. The proposed loss function enables a model to learn class-specific discriminative features, while incorporating attribute-level separability. Evaluation is performed on multiple datasets with different models, for four resolutions varying from 32x32 to 224x224. Different experiments demonstrate the efficacy of the proposed attributeassisted loss for low resolution fine-grained classification.

For $n\geq 4$ (even), the function $\varphi_{n\mathcal{L}}(z)=1+nz/(n+1)+z^n/(n+1)$ maps the unit disk $\mathbb{D}$ onto a domain bounded by an epicycloid with $n-1$ cusps. In this paper, the class $\mathcal{S}^*_{n\mathcal{L}} = \mathcal{S}^*(\varphi_{n\mathcal{L}})$ is studied and various inclusion relations are established with other subclasses of starlike functions. The bounds on initial coefficients is also computed. Various radii problems are also solved for the class $\mathcal{S}^*_{n\mathcal{L}}$., Comment: 23 pages Comments are welcome

Some differential implications of classical Marx-Strohh\"acker theorem are extended for multivalent functions. These results are also generalized for functions with fixed second coefficient by using the theory of first order differential subordination which in turn, corrects the results of Selvaraj and Stelin [On multivalent functions associated with fixed second coefficient and the principle of subordination, Int. J. Math. Anal. {\bf 9} (2015), no.~18, 883--895].

We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.

We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well as the latent vectors are jointly estimated from the undersampled measurements using stochastic gradient descent. We penalize the norm of the gradient of the generator to encourage the learning of a smooth surface/manifold, while temporal gradients of the latent vectors are penalized to encourage the time series to be smooth. The main benefits of the proposed scheme are (a) the quite significant reduction in memory demand compared to the analysis based SToRM model, and (b) the spatial regularization brought in by the CNN model. We also introduce efficient progressive approaches to minimize the computational complexity of the algorithm.