Indian Statistical Institute

ABSTRACT The redshifted 21-cm signal from the dark ages offers a powerful probe of cosmological models and the underlying dark matter (DM) microphysics. We investigate deviations from the standard $\Lambda$ cold dark matter ($\Lambda$CDM) prediction, an absorption trough of approximately $-40.6\, \mathrm{mK}$ at redshift $z\simeq 85.6$, in the context of co-SIMP (strongly interacting massive particle) DM. The co-SIMP interaction strength is encoded by the parameter $C_{\rm int}$, incorporating the masses of DM and standard model particles, the interaction cross-section, and the amount of heat exchange between the two sectors. Increasing $C_{\rm int}$ deepens the absorption feature and shifts the trough to higher redshifts in the global signal. For $C_{\rm int}=1.0$, the minimum brightness temperature reaches $-50.6\,\mathrm{mK}$ at $z\simeq 86.2$. The 21-cm power spectrum increases with $C_{\rm int}$ in addition to the global signal. We assess the detectability of these signatures using signal-to-noise ratio (SNR) and Fisher forecasts. The maximum SNR reaches ${\sim} 15.7$ for $C_{\rm int}=1.0$ for the global signal. Fisher forecasts for 1000 h of integration time show that this model can be distinguished from a null-signal at $4.3\sigma$ and a mild $1.6\sigma$ from $\Lambda$CDM, improving by an order of magnitude for 100 000 h. For the 21-cm power spectrum, a $5\,\mathrm{km}^2$ array with 1000 h yields a $4.63\sigma$ detection and is mildly separated from the standard scenario at $1.78\sigma$. These findings highlight the potential of the 21-cm cosmology to probe the properties of DM and demonstrate that upcoming dark ages experiments, particularly space-based and lunar observations, can offer a promising avenue to test co-SIMP models.

A bstract Lung cancer is characterized by profound intratumoral and inter-patient heterogeneity, spanning histological subtypes, molecular landscapes, and the tumor microenvironment. While multi-omics integration is essential for capturing this complexity, leveraging these data to explicitly define survival-associated subpopulations remains a significant challenge. In this study, we developed NeuroMDAVIS-FS, an unsupervised deep learning framework designed to stratify lung cancer patients by survival risk, and identify molecular determinants underlying improved clinical outcomes. Using the CPTAC cohort, we integrated genomic (CNV), transcriptomic (RNA-seq), and proteomic profiles to extract modality-specific features. Candidate biomarkers were validated through Kaplan- Meier (KM) survival analysis and univariate Cox proportional hazards (CoxPH) regression. A final multivariate CoxPH model effectively stratified patients into high-risk and low-risk cohorts (Kaplan Meier p -value < 0.001). Notably, the integration of these molecular features with baseline clinical models significantly enhanced prognostic accuracy, improving the concordance index by 43.79% in LUAD, 31.05% in LSCC, and 23.76% across the pan-lung cancer cohort. These results demonstrate that NeuroMDAVIS-FS identifies robust, biologically relevant features that surpass traditional clinical variables in predicting patient outcomes, offering a scalable path for precision oncology.

Hyperspectral remote sensing (HRS) image classification is time-consuming and complex due to its wide spectral range and large number of samples. Factors such as heterogeneous behaviour, unbalanced class distributions, complex boundaries, multiple spectral bands, inconsistent samples, significant spatial variability, and interclass similarities further challenge image classification. The design of a robust classification model that addresses these challenges demands efficient pre-processing of input information, e.g., dimensionality reduction and representative feature extraction, with the ability to handle information uncertainty and to reduce computational complexity. In line with these objectives, we have proposed a classification model called FRELM-SAE, which utilises fuzzy granulation of input features, a rule-based extreme learning machine (ELM), and a stacked autoencoder (SAE). Fuzzy granulation and rule-based ELM address the generalisation aspects and the complexity of the decision-making process. The SAE reduces the noise from the input feature space and performs representative feature extraction. Various experiments demonstrate the proposed model’s performance in classifying two HRS images. The experimental results show its supremacy over related work across performance metrics, including overall accuracy, precision, recall, etc.