Abstract

This paper presents a simple and effective framework to combine various data-driven machine learning (ML) algorithms for short-range reservoir inflow forecasting, including the large-scale climate phenomenon indices addressing forecasting uncertainty. Random Forest (RF), Gradient Boosting Regressor (GBR), K-Nearest Neighbors Regressor (KNN), and Long Short-Term Memory (LSTM) were employed for predicting daily reservoir inflows considering various climate phenomenon indices (e.g., Arctic Oscillation, North Atlantic Oscillation, and Southern Oscillation Index) and hydroclimatic variables (e.g., precipitation), accounting for time-lag effects. After training the individual ML algorithm, a framework was developed to create an ensemble model using a robust weighted voting regressor (VR) method to quantify forecasting uncertainty and to improve model performances. The results of the study reveal that, for 2-day forecasts, the LSTM approach has the greatest influence on prediction accuracy, followed comparably by each model. However, none of the four models seem to be noticeably superior to the VR method, regardless of the prediction lead time. The developed framework was examined on a tropical reservoir, Bhadra reservoir Tunga-Bhadra River, located in India

Abstract

Background: Association of visceral adipose tissue (VAT) with high molecular weight (HMW) adiponectin and insulin resistance (IR) is known, whereas impact of subcutaneous adipose tissue (SCAT) is unclear. Our objective was to find out association of VAT and SCAT-derived HMW adiponectin with serum HMW adiponectin and explore their impact on HOMA-IR and type 2 diabetes (T2DM). Methods: Among 51 subjects (Age-30 to 57 years), 33 subjects (women= 20, men= 13) without T2DM and 18 subjects (women= 11, men= 7) with new onset diabetes (less than 1 year) were categorized as control and T2DM respectively according to American Diabetic Association (ADA) guideline, 2010. Adipocytes were isolated and cultured from adipose tissue biopsies obtained from these subjects during their abdominal surgery. Adiponectin mRNA expression was analysed by Real-Time PCR, and HMW adiponectin …

Abstract

A face morph is created by strategically combining two or more face images corresponding to multiple identities. The intention is for the morphed image to match with multiple identities. Current morph attack detection strategies can detect morphs but cannot recover the images or identities used in creating them. The task of deducing the individual face images from a morphed face image is known as de-morphing. Existing work in de-morphing assume the availability of a reference image pertaining to one identity in order to recover the image of the accomplice - i.e., the other identity. In this work, we propose a novel demorphing method that can recover images of both identities simultaneously from a single morphed face image without needing a reference image or prior information about the morphing process. We propose a generative adversarial network that achieves single image-based de-morphing with a surprisingly high degree of visual realism and biometric similarity with the original face images. We demonstrate the performance of our method on landmark-based morphs and generative model-based morphs with promising results.

Abstract

Impact due to demographic factors such as age, sex, race, etc., has been studied extensively in automated face recognition systems. However, the impact of digitally modified demographic and facial attributes on face recognition is relatively under-explored. In this work, we study the effect of attribute manipulations induced via generative adversarial networks (GANs) on face recognition performance. We conduct experiments on the CelebA dataset by intentionally modifying thirteen attributes using AttGAN and STGAN and evaluating their impact on two deep learning-based face verification methods, ArcFace and VGGFace. Our findings indicate that some attribute manipulations involving eyeglasses and digital alteration of sex cues can significantly impair face recognition by up to 73% and need further analysis. Keywords: Face recognition · Generative adversarial network (GAN) · Attribute manipulation.

Abstract

We investigate the possible sizes of all the CP-violating asymmetries offered by the angular distribution of rare decay Λb → Λ(→ pπ−)` +` − in the Standard Model and new physics scenarios motivated by the recent b → s`+` − anomalies. We work in a model-independent effective theory framework and discuss the sensitivity of CP asymmetries to new O9,10 operators and their chirality flipped counterparts. We find that the size of many of the CP asymmetries can be at the level of few percent in new physics scenarios consistent with current b → s`+` − data at level of 1σ. We emphasize that measurements of these CP asymmetries can be used to discriminate different new physics scenarios in b → s`+` −.

Abstract

We determine the strange quark mass ( � � ) and quark mixing element | � � � | , and their joint determination from the Cabibbo suppressed hadronic � decays in various perturbative schemes. We improve this analysis compared to the previous analysis based on the optimal renormalization or the renormalization group summed perturbation theory (RGSPT) scheme by replacing the theoretical longitudinal contributions with phenomenological parametrization; the RGSPT coefficients are used for the dimension-4 Adler functions. The improved analysis results in the extraction of � � ( 2     GeV ) = 98 ± 19   MeV and | � � � | = 0.2191 ± 0.0043 from the RGSPT scheme

Abstract

An electrocardiogram (ECG) monitors the electrical activity generated by the heart and is used to detect fatal cardiovascular diseases (CVDs). Conventionally, to capture the precise electrical activity, clinical experts use multiple-lead ECGs (typically 12 leads). Recently, large-scale deep learning models have been used to detect these diseases, however, they require large memory and long inference time. We propose a low-parameter model, Low Resource Heart-Network (LRH-Net), that detects ECG anomalies in a resource-constrained environment. On top, multi-level knowledge distillation (MLKD) is employed to improve model generalization. MLKD distils the dark-knowledge from higher parameter models (teachers) trained on different lead configurations to LRH-Net. The LRH-Net has 106× fewer parameters and 76% faster inference than the teacher model for detecting CVDs. Using MLKD, the performance of LRH-Net on reduced lead data was scaled up to 3.25%, making it suitable for edge devices.

Abstract

Functional connectivity derived from functional MRI reflects the neuronal synchronization between brain regions anchored through an anatomical infrastructure of white matter pathways that can be computed from diffusion MRI. The inter-relation between the structure-function topography is of high interest in

Abstract

Predicting Individual Differences from Brain Responses to Music using Functional Network Centrality

Abstract

Music sharing trends have been shown to change during times of socio-economic crises. Studies have also shown that music can act as a social surrogate, helping to significantly reduce loneliness by acting as an empathetic friend. We explored these phenomena through a novel study of online music sharing during the Covid-19 pandemic in India. We collected tweets from the popular social media platform Twitter during India’s first and second wave of the pandemic (n = 1,364). We examined the different ways in which music was able to accomplish the role of a social surrogate via analyzing tweet text using Natural Language Processing techniques. Additionally, we analyzed the emotional connotations of the music shared through the acoustic features and lyrical content and compared the results between pandemic and pre-pandemic times. It was observed that the role of music shifted to a more community focused function rather than tending to a more self-serving utility. Results demonstrated that people shared music during the Covid-19 pandemic which had lower valence and shared songs with topics that reflected turbulent times such as Hardship and Exclusion when compared to songs shared during pre-Covid times. The results are further discussed in the context of individualistic versus collectivistic cultures.