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How do voters respond to welfare vis-à-vis public good programs? Theory and evidence of political clientelism
No full textUsing rural household survey data from West Bengal, we find that voters respond positively to excludable government welfare benefits but not to local public good programs, while reporting having benefited from both. Consistent with these voting patterns, shocks to electoral competition induced by exogenous redistricting of villages resulted in upper-tier governments manipulating allocations across local governments only for excludable benefit programs. Using a hierarchical budgeting model, we argue these results provide credible evidence of the presence of clientelism rather than programmatic politics
Imprints of dark matter–massive neutrino interaction in upcoming post-reionization and galaxy surveys
No full textWe explore possible signatures of the interaction between dark matter (DM) and massive neutrinos during the post-reionization epoch. Using both Fisher matrix forecast analysis and Markov chain Monte Carlo simulation, we conduct a thorough investigation of the constraints and imprints of the scenario on the upcoming post-reionization and galaxy surveys. Our investigation focuses on two key parameters: the strength of the DM–massive neutrino interaction (u) and the total neutrino mass (Mtot), on top of the usual six cosmological parameters. We utilize future 21-cm intensity mapping, galaxy clustering, and cosmic shear observations in order to investigate the possible constraints of these parameters in the future observations: Square Kilometre Array (SKA1 and SKA2) and Euclid, taking both conservative and realistic approaches. All these missions show promise in constraining both the parameters u and Mtot by few orders compared to the current constraints from Planck18 (SKA2 performing the best among them). Although we do not find much improvement in H0 and σ8 tensions from our forecast analysis, SKA2 constrains them better in conservative approach. We further perform a brief investigation of the prospects of some of the next-generation cosmic microwave background (CMB) missions in combinations with large-scale structure experiments in improving the constraints. Our analysis reveals that both SKA2 and CMB-S4 (Cosmic Microwave Background Stage-4) + Euclid + SKA1 IM2 (Square Kilometre Array1 Intensity Mapping Band2) combination will put the strongest bounds on the model parameters
Inertia of Kwong matrices
No full textLet r be any real number and for any n let p1, ..., pn be distinct positive numbers. A Kwong matrix is the n × n matrix whose (i, j) entry is (pir + pjr)/(pi + pj). We determine the signatures of eigenvalues of all such matrices. The corresponding problem for the family of Loewner matrices [(pir - pjr)/(pi - pj)] has been solved earlier
Integrated Spatio-Temporal Deep Clustering (ISTDC) for cognitive workload assessment
No full textTraditional high-dimensional electroencephalography (EEG) features (spectral or temporal) may not always attain satisfactory results in cognitive workload estimation. In contrast, deep representation learning (DRL) transforms high-dimensional data into cluster-friendly low-dimensional feature space. Therefore, this paper proposes an Integrated Spatio-Temporal Deep Clustering (ISTDC) model that uses DRL followed by a clustering method to achieve better clustering performance. The proposed model is illustrated using four Algorithms and Variational Bayesian Gaussian Mixture Model (VBGMM) clustering method. Temporal and spatial Variational Auto Encoder (VAE) models (mentioned in Algorithm 2 and Algorithm 3) learn temporal and spatial latent features from sequence-wise EEG signals and scalp topographical maps using the Long short-term memory and Convolutional Neural Network models. The concatenated spatio-temporal latent feature (mentioned in Algorithm 4) is passed to the VBGMM clustering method to efficiently estimate workload levels of n-back task. For the 0-back vs. 2-back task, the proposed model achieves the maximum mean clustering accuracy of 98.0%, and it improves by 11.0% over the state-of-the-art method. The results also indicate that the proposed multimodal approach outperforms temporal and spatial latent feature-based unimodal models in workload assessment
Join of affine semigroups
No full textIn this paper, we study the class of affine semigroups generated by integral vectors, whose components are in generalized arithmetic progression and we observe that the defining ideal is determinantal. We also give a sufficient condition on the defining ideal of the semigroup ring for the equality of the Betti numbers of the defining ideal and those of its initial ideal. We introduce the notion of an affine semigroup generated by join of two affine semigroups and show that it preserves some nice properties, including Cohen-Macaulayness, when the constituent semigroups have those properties
Learning-Based Microservice Placement and Migration for Multi-Access Edge Computing
No full textIn Multi-Access Edge Computing (MEC), a number of mechanisms exist to determine the optimal placement of monolithic service workflows. For applications designed as microservice workflow architectures, service placement schemes need to be revisited owing to the inherent interdependencies which exist between microservices. The dynamic environment, with stochastic user movement and service invocations, along with a large placement configuration space makes microservice placement in MEC a challenging task. Additionally, owing to user mobility, a placement scheme may need to be recalibrated, triggering service migrations to maintain the advantages offered by MEC. Existing microservice placement and migration schemes consider on-demand strategies. In this work, we take a different route and propose a Reinforcement Learning (RL) based proactive mechanism using a Learning Automata (LA) for microservice placement and migration that on one hand, keeps track of user mobility and resorts to migration when necessary, while on the other hand, keeps track of server residual capacities so that no server is overloaded. We use the San Francisco Taxi dataset to validate our approach. Experimental results show the effectiveness of our approach in comparison to other methods
LOWER BOUNDS FOR THE MAHLER MEASURE AND INERTIA DEGREES OF PRIMES
No full textWe investigate the relationship between lower bounds for the Mahler measure and splitting of primes, and prove various lower bounds for the Mahler measure of algebraic integers in terms of the least common multiples of all inertia degrees of primes. The results generalise work of the second author and Kumar [\u27Lehmer’s problem and splitting of rational primes in number fields\u27, Acta Math. Hungar. 169(2) (2023), 349–358]
Mental health problems raise the odds of cognitive impairment in COVID-19 survivors
No full textBackground: COVID-19 survivors around the globe are suffering from mental health issues. While mental health problems can be an early warning sign of dementia, they may also increase the chances of developing the disease. In this study, we examined the mental health of COVID-19 survivors and mapped its associations with cognitive and demographic variables. Method: COVID-19 survivors listed in the databases of three tertiary care hospitals in Kolkata were contacted sequentially. 376 willing patients were interviewed over the telephone. 99 COVID-19 patients and 31 matched controls participated in the in-person interviews that were arranged for a more detailed investigation. The participants were administered standardized tests that are widely used for the assessment of cognitive functioning and mental health status. Result: 64.89% of COVID-19 survivors reported a deterioration in physical functioning. 44.95% reported a decline in mental health, whereas 41.49% reported a drop in cognitive performance. Detailed investigations revealed that they had an increased risk of having depression, anxiety, and poor sleep quality by 91%, 68%, and 140%, respectively. 6.1% of the patients had mild cognitive impairment, and 4% had dementia. COVID-19 patients who had depression and anxiety were 8.6 and 19.4 times more likely to have cognitive decline, respectively. Compared to the matched controls, COVID-19 patients had greater depression (p\u3c.001), anxiety (p\u3c.001), stress (p =.003), and insomnia (p \u3c.001). They also scored significantly lower on Addenbrooke’s Cognitive Examination-III (p =.009) and Picture Naming Test (p =.005) and took significantly longer to complete Trail Making Test-A (p =.002). Conclusion: COVID-19 survivors in this study had major mental health issues even one year after contracting the virus. They had significant cognitive deficits that might progress into dementia. Strict monitoring and systematic treatment plans should be implemented as soon as possible
Multi-Task Learning and Sparse Discriminant Canonical Correlation Analysis for Identification of Diagnosis-Specific Genotype-Phenotype Association
No full textThe primary objective of imaging genetics research is to investigate the complex genotype-phenotype association for the disease under study. For example, to understand the impact of genetic variations over the brain functions and structure, the genotypic data such as single nucleotide polymorphism (SNP) is integrated with the phenotypic data such as imaging quantitative traits. The sparse models, based on canonical correlation analysis (CCA), are popular in this area to find the complex bi-multivariate genotype-phenotype association, as the number of features in genotypic and/or phenotypic data is significantly higher as compared to the number of samples. However, the sparse CCA based methods are, in general, unsupervised in nature, and fail to identify the diagnose-specific features those play an important role for the diagnosis and prognosis of the disease under study. In this regard, a new supervised model is proposed to study the complex genotype-phenotype association, by judiciously integrating the merits of CCA, linear discriminant analysis (LDA) and multi-task learning. The proposed model can identify the diagnose-specific as well as the diagnose-consistent features with significantly lower computational complexity. The performance of the proposed method, along with a comparison with the state-of-the-art methods, is evaluated on several synthetic data sets and one real imaging genetics data collected from Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) cohort. In the current study, the SNP as genetic data and resting state functional MRI (fMRI) as imaging data are integrated to find the complex genotype-phenotype association. An important finding is that the proposed method has better correlation value, improved noise resistance and stability, and also has better feature selection ability. All the results illustrate the power and capability of the proposed method to find the diagnostic group-specific imaging genetic association, which may help to understand the neurodegenerative disorder in a more comprehensive way
On estimators of the mean of infinite dimensional data in finite populations
No full textThe Horvitz-Thompson (HT), the Rao-Hartley-Cochran (RHC) and the generalized regression (GREG) estimators of the finite population mean are considered, when the observations are from an infinite dimensional space. We compare these estimators based on their asymptotic distributions under some commonly used sampling designs and some superpopulations satisfying linear regression models. We show that the GREG estimator is asymptotically at least as efficient as any of the other two estimators under different sampling designs considered in this paper. Further, we show that the use of some well known sampling designs utilizing auxiliary information may have an adverse effect on the performance of the GREG estimator, when the degree of heteroscedasticity present in linear regression models is not very large. On the other hand, the use of those sampling designs improves the performance of this estimator, when the degree of heteroscedasticity present in linear regression models is large. We develop methods for determining the degree of heteroscedasticity, which in turn determines the choice of appropriate sampling design to be used with the GREG estimator. We also investigate the consistency of the covariance operators of the above estimators. We carry out some numerical studies using real and synthetic data, and our theoretical results are supported by the results obtained from those numerical studies