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Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages. Our study evaluated the effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT) for predicting autism-related symptoms. The results show that real-time quality control improves data quality by 19 percent compared to pre-quality control. Fine-tuning Bio-BERT using crowdsourced data generally increased recall compared to the Bio-BERT baseline but lowered precision. Our findings highlighted the potential of crowdsourcing and quality control in resource-constrained environments and offered insights into optimizing healthcare LLMs for informed decision-making and improved patient care., Comment: Published in AMIA Summit, Boston, 2024. https://knowledge.amia.org/Info2024/pdf/Info2024a022/Info2024fl021

A fundamental question regarding the evolution of dwarf spheroidal galaxies is the identification of the key physical mechanisms responsible for gas depletion. Here, we focus on the study of stellar feedback in isolated dwarf spheroidal galaxies, by performing numerical simulations using a modified version of the SPH code GADGET-3. The Milky Way satellite Leo II (PGC 34176) in the Local Group was considered as our default model dwarf galaxy. The parameter space for the stellar feedback models was explored to match observational constraints of Leo II, such as residual gas mass, total mass within the tidal radius, star formation history, final stellar mass, stellar ages and metallicity. Additionally, we examined the impact of the binary fraction of stars, initial mass function, dark matter halo mass and initial gas reservoir. Many simulations revealed recent star formation quenching due to stellar feedback. In general, the gas depletion, expected star formation history, total mass of stars and total mass within the tidal radius were adequately reproduced in the simulations when compared to observational estimates. However, there were discrepancies in the distribution of stellar ages and metallicities, which suggested that the cosmic gas infall would play a more complex role in our dwarf spheroidal galaxy than captured by a monolithic infall scenario. Our results suggest that currently quenched dwarf galaxies may not necessarily need to evolve within clusters or groups, and that stellar feedback alone could be a sufficient factor in shaping at least some of these galaxies as we observe them today., Comment: To be published in The Astrophysical Journal. 33 pages, 20 figures

Health literacy is crucial to supporting good health and is a major national goal. Audio delivery of information is becoming more popular for informing oneself. In this study, we evaluate the effect of audio enhancements in the form of information emphasis and pauses with health texts of varying difficulty and we measure health information comprehension and retention. We produced audio snippets from difficult and easy text and conducted the study on Amazon Mechanical Turk (AMT). Our findings suggest that emphasis matters for both information comprehension and retention. When there is no added pause, emphasizing significant information can lower the perceived difficulty for difficult and easy texts. Comprehension is higher (54%) with correctly placed emphasis for the difficult texts compared to not adding emphasis (50%). Adding a pause lowers perceived difficulty and can improve retention but adversely affects information comprehension., Comment: This manuscript is accepted to American Medical Informatics Association summit, 2024

CMOS VLSI technology is the most dominant integration methodology prevailing in the world today. Various signal-processing blocks are made using analog or digital design techniques in MOS VLSI. An important component is the Memory unit used to store data. In the project a memory cell has been built up using analog design method. A capacitor is used as the basic storage device. The main idea behind analog memory is that the analog value of the charge or voltage stored in the capacitor is the data stored. So the dielectric quality of the capacitor becomes important here to determine how effectively it can store some charge. Analog memory is a trade off between hardware cost, chip area and accuracy or quality of storage. The circuit of analog memory cell was developed starting from the idea that required voltage will be stored in a capacitor and MOS transistors were used as switches. A given technology of integration was used and hence the dielectric property of the capacitor was fixed. By suitable circuit configuration the analog voltage value was written to the capacitor, read out when required and the charge loss was also refreshed. The results obtained are as given in the thesis., Comment: Bachelors Thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Technology (Honours) in Electronics and Electrical Communication Engineering at the Indian Institute of Technology - Kharagpur (year 2002), by Paramita Barai under the guidance of Prof. Dr. A. S. Dhar

Profiling various application characteristics, including the number of different arithmetic operations performed, memory footprint, etc., dynamically is time- and space-consuming. On the other hand, static analysis methods, although fast, can be less accurate. This paper presents an LLVM-based probabilistic static analysis method that accurately predicts different program characteristics and estimates the reuse distance profile of a program by analyzing the LLVM IR file in constant time, regardless of program input size. We generate the basic-block-level control flow graph of the target application kernel and determine basic-block execution counts by solving the linear balance equation involving the adjacent basic blocks' transition probabilities. Finally, we represent the kernel memory accesses in a bracketed format and employ a recursive algorithm to calculate the reuse distance profile. The results show that our approach can predict application characteristics accurately compared to another LLVM-based dynamic code analysis tool, Byfl., Comment: This paper was accepted at the MEMSYS '23 conference, The International Symposium on Memory Systems, October 02, 2023 - October 05, 2023, Alexandria, VA

The global need for effective disease diagnosis remains substantial, given the complexities of various disease mechanisms and diverse patient symptoms. To tackle these challenges, researchers, physicians, and patients are turning to machine learning (ML), an artificial intelligence (AI) discipline, to develop solutions. By leveraging sophisticated ML and AI methods, healthcare stakeholders gain enhanced diagnostic and treatment capabilities. However, there is a scarcity of research focused on ML algorithms for enhancing the accuracy and computational efficiency. This research investigates the capacity of machine learning algorithms to improve the transmission of heart rate data in time series healthcare metrics, concentrating particularly on optimizing accuracy and efficiency. By exploring various ML algorithms used in healthcare applications, the review presents the latest trends and approaches in ML-based disease diagnosis (MLBDD). The factors under consideration include the algorithm utilized, the types of diseases targeted, the data types employed, the applications, and the evaluation metrics. This review aims to shed light on the prospects of ML in healthcare, particularly in disease diagnosis. By analyzing the current literature, the study provides insights into state-of-the-art methodologies and their performance metrics., Comment: 8 pages

We investigate the coalescence of massive black hole ($M_{\rm BH}\gtrsim 10^{6}~\rm M_{\odot}$) binaries (MBHBs) at $63 \sigma$) overdense regions ($M_h\sim 10^{12}~\rm M_{\odot}$ dark matter halos at $z = 6$) of the Universe. We first analyse the impact of different resolutions and AGN feedback prescriptions on the merger rate, assuming instantaneous mergers. Then, we compute the halo bias correction factor due to the overdense simulated region. Our simulations predict merger rates that range between 3 - 15 $\rm yr^{-1}$ at $z\sim 6$, depending on the run considered, and after correcting for a bias factor of $\sim 20-30$. For our fiducial model, we further consider the effect of delay in the MBHB coalescence due to dynamical friction. We find that 83 per cent of MBHBs will merge within the Hubble time, and 21 per cent within 1 Gyr, namely the age of the Universe at $z > 6$. We finally compute the expected properties of the gravitational wave (GW) signals and find the fraction of LISA detectable events with high signal-to-noise ratio (SNR $>$ 5) to range between 66-69 per cent. However, identifying the electro-magnetic counterpart of these events remains challenging due to the poor LISA sky localization that, for the loudest signals ($\mathcal M_c\sim 10^6~\rm M_{\odot}$ at $z=6$), is around 10 $\rm deg^2$., Comment: 17 pages, 11 figures, 3 tables. Accepted for publication in MNRAS

The lithium metal-solid-state electrolyte interface plays a critical role in the performance of solid-state batteries. However, operando characterization of the buried interface morphology in solid-state cells is particularly difficult because of the lack of direct optical access. Destructive techniques that require isolating the interface inadvertently modify the interface and cannot be used for operando monitoring. In this work, we introduce the concept of thermal wave sensing using modified 3-omega sensors that is attached to the outside of the lithium metal-solid state cells to non-invasively probe the morphology of the lithium metal-electrolyte interface. We show that the thermal interface resistance measured by the 3-omega sensors relates directly to the physical morphology of the interface and demonstrate that 3-omega thermal wave sensing can be used for non-invasive operando monitoring the morphology evolution of the lithium metal-solid state electrolyte interface.