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The COVID-19 Infodemic had an unprecedented impact on health behaviors and outcomes at a global scale. While many studies have focused on a qualitative and quantitative understanding of misinformation, including sentiment analysis, there is a gap in understanding the emotion-carriers of misinformation and their differences across geographies. In this study, we characterized emotion carriers and their impact on vaccination rates in India and the United States. A manually labelled dataset was created from 2.3 million tweets and collated with three publicly available datasets (CoAID, AntiVax, CMU) to train deep learning models for misinformation classification. Misinformation labelled tweets were further analyzed for behavioral aspects by leveraging Plutchik Transformers to determine the emotion for each tweet. Time series analysis was conducted to study the impact of misinformation on spatial and temporal characteristics. Further, categorical classification was performed using transformer models to assign categories for the misinformation tweets. Word2Vec+BiLSTM was the best model for misinformation classification, with an F1-score of 0.92. The US had the highest proportion of misinformation tweets (58.02%), followed by the UK (10.38%) and India (7.33%). Disgust, anticipation, and anger were associated with an increased prevalence of misinformation tweets. Disgust was the predominant emotion associated with misinformation tweets in the US, while anticipation was the predominant emotion in India. For India, the misinformation rate exhibited a lead relationship with vaccination, while in the US it lagged behind vaccination. Our study deciphered that emotions acted as differential carriers of misinformation across geography and time. These carriers can be monitored to develop strategic interventions for countering misinformation, leading to improved public health.

Vision Transformers (ViTs) have demonstrated state-of-the-art performance on many Computer Vision Tasks. Unfortunately, deploying these large-scale ViTs is resource-consuming and impossible for many mobile devices. While most in the community are building for larger and larger ViTs, we ask a completely opposite question: How small can a ViT be within the tradeoffs of accuracy and inference latency that make it suitable for mobile deployment? We look into a few ViTs specifically designed for mobile applications and observe that they modify the transformer's architecture or are built around the combination of CNN and transformer. Recent work has also attempted to create sparse ViT networks and proposed alternatives to the attention module. In this paper, we study these architectures, identify the challenges and analyze what really makes a vision transformer suitable for mobile applications. We aim to serve as a baseline for future research direction and hopefully lay the foundation to choose the exemplary vision transformer architecture for your application running on mobile devices.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods.

This paper investigates a poorly studied open cluster, NGC 5288, using 2MASS JHKS and the recently released Gaia DR3 astrometric and photometric data. The mean proper motions in Right Ascension and Declination are estimated as (-3.840 +/- 0.230) and (-1.934 +/- 0.162) mas/yr, respectively. We also derive the age and distance of the cluster as 510 +/- 190 Myr and 2.64 +/- 0.11 kpc, using colour-magnitude diagrams (CMDs). We have also obtained distance as 2.77 +/- 0.42 kpc using the parallax method. Interstellar reddening E(B-V) in the direction of the cluster is determined as 0.45 mag using the ((J - H), (J - K)) colour-colour diagram. We have found the mass function slope for main-sequence stars as 1.39 +/- 0.29 within the mass range 1.0 - 2.7 solar mass, which agrees with Salpeter's value within uncertainty. Galactic orbits are derived using the Galactic potential model, indicating that NGC 5288 follows a circular path around the Galactic center., Comment: This article has been accepted for the publication in Revista Mexicana de Astronom\'ia y Astrof\'isica and contain total 23 pages, 14 figures and 4 tables

The Weakly Interacting Massive Particles(WIMPs) have long been the favored CDM candidate in the standard $\Lambda$CDM model. However, owing to great improvement in the experimental sensitivity in the past decade, some parameter space of the SUSY-based WIMP model is ruled out. In addition, WIMP as the CDM particle is also at variance with other astrophysical observables at small scales. We consider a model that addresses both these issues. In the model, the WIMP decays into a massive particle and radiation. We study the background evolution and the first order perturbation theory (coupled Einstein-Boltzmann equations) for this model and show that the dynamics can be captured by a single parameter $r=m_L/q$, which is the ratio of the lighter mass and the comoving momentum of the decay particle. We incorporate the relevant equations in the existing Boltzmann code CLASS to compute the matter power spectra and CMB angular power spectra. The decaying WIMP model is akin to a non-thermal Warm Dark Matter(WDM) model and suppresses matter power at small scales, which could alleviate several issues that plague the CDM model. We compare the predictions of the model with CMB, galaxy clustering, and high-z HI data. Both these data sets yield $r\gtrsim 10^6$, which can be translated into the bounds on other parameters. In particular, we obtain the following lower bounds on the thermally-averaged self-annihilation cross-section of WIMPs $\langle \sigma v \rangle$, and the lighter mass $m_L$: $\langle \sigma v \rangle \gtrsim 4.9\times 10^{-34} \, \rm cm^3 \, sec^{-1}$ and $m_L \gtrsim 2.4 \, \rm keV$. The lower limit on $m_L$ is comparable to constraints on the mass of thermally-produced WDM particle. The limit on the self-annihilation cross-section greatly expands the available parameter space as compared to the stable WIMP scenario., Comment: 26 pages, 7 figures, accepted for publication in JCAP

Load-Dependent Branches (LDB) often do not exhibit regular patterns in their local or global history and thus are inherently hard to predict correctly by conventional branch predictors. We propose a software-to-hardware branch pre-resolution mechanism that allows software to pass branch outcomes to the processor frontend ahead of fetching the branch instruction. A compiler pass identifies the instruction chain leading to the branch (the branch backslice) and generates the pre-execute code that produces the branch outcomes ahead of the frontend observing them. The loop structure helps to unambiguously map the branch outcomes to their corresponding dynamic instances of the branch instruction. Our approach also allows for covering the loop iteration space selectively, with arbitrarily complex patterns. Our method for pre-execution enables important optimizations such as unrolling and vectorization, in order to substantially reduce the pre-execution overhead. Experimental results on select workloads from SPEC CPU 2017 and graph analytics workloads show up to 95% reduction of MPKI (21% on average), up to 39% speedup (7% on average), and up to 3x improvement on IPC (23% on average) compared to a core with TAGE-SC-L-64KB branch predictor.

Health experts assert that hope plays a crucial role in enhancing individuals' physical and mental well-being, facilitating their recovery, and promoting restoration. Hope speech refers to comments, posts and other social media messages that offer support, reassurance, suggestions, inspiration, and insight. The detection of hope speech involves the analysis of such textual content, with the aim of identifying messages that invoke positive emotions in people. Our study aims to find computationally efficient yet comparable/superior methods for hope speech detection. We also make our codebase public at https://github.com/aflah02/Hope_Speech_Detection, Comment: Published as a Tiny Paper at ICLR 2023, 7 Pages

This foundational research provides additional support for using the Fuzzy ARTMAP neural network as a classification algorithm in the TAR domain. While research opportunities exist to improve recall performance and explanation, the robust recall results from this study and the proof-of-concept demonstration of If-Then rules for tf-idf vectorization strongly substantiate that a Fuzzy ARTMAP-based TAR system is a potentially viable explainable alternative to "black box" TAR systems., Comment: ACM ECIR 2023 in Legal Information Retrieval

We propose a new technique called CHATTY: Coupled Holistic Adversarial Transport Terms with Yield for Unsupervised Domain Adaptation. Adversarial training is commonly used for learning domain-invariant representations by reversing the gradients from a domain discriminator head to train the feature extractor layers of a neural network. We propose significant modifications to the adversarial head, its training objective, and the classifier head. With the aim of reducing class confusion, we introduce a sub-network which displaces the classifier outputs of the source and target domain samples in a learnable manner. We control this movement using a novel transport loss that spreads class clusters away from each other and makes it easier for the classifier to find the decision boundaries for both the source and target domains. The results of adding this new loss to a careful selection of previously proposed losses leads to improvement in UDA results compared to the previous state-of-the-art methods on benchmark datasets. We show the importance of the proposed loss term using ablation studies and visualization of the movement of target domain sample in representation space., Comment: 10 pages, 4 figures

The scale and terms of aggregate borrowing in an economy depend on the manner in which wealth is distributed across potential creditors with heterogeneous beliefs about the future. This distribution evolves over time as uncertainty is resolved, in favour of optimists if loans are repaid in full, and in favour of pessimists if there is widespread default. We model this process in an economy with two assets - risky bonds and risk-free cash. Within periods, given the inherited distribution of wealth across belief types, the scale and terms of borrowing are endogenously determined. Following good states, aggregate borrowing and the face value of debt both rise, and the interest rate falls. In the absence of noise, wealth converges to beliefs that differ systematically from the objective probability governing state realisations, with greater risk-aversion associated with greater optimism. In the presence of noise, the economy exhibits periods of high performance, punctuated by periods of crisis and stagnation.