Generalized Analogs of the Heisenberg Uncertainty Inequality

We investigate locally compact topological groups for which a generalized analogue of Heisenberg uncertainty inequality hold. In particular, it is shown that this inequality holds for $\mathbb{R}^n \times K$ (where $K$ is a separable unimodular locally compact group of type I), Euclidean Motion group and several general classes of nilpotent Lie groups which include thread-like nilpotent Lie groups, $2$-NPC nilpotent Lie groups and several low-dimensional nilpotent Lie groups

Feature Selection for Text and Image Data Using Differential Evolution with SVM and Naïve Bayes Classifiers

Classification problems are increasing in various important applications such as text categorization, images, medical imaging diagnosis and bimolecular analysis etc. due to large amount of attribute set. Feature extraction methods in case of large dataset play an important role to reduce the irrelevant feature and thereby increases the performance of classifier algorithm. There exist various methods based on machine learning for text and image classification. These approaches are utilized for dimensionality reduction which aims to filter less informative and outlier data. Therefore, these approaches provide compact representation and computationally better tractable accuracy. At the same time, these methods can be challenging if the search space is doubled multiple time. To optimize such challenges, a hybrid approach is suggested in this paper. The proposed approach uses differential evolution (DE) for feature selection with naïve bayes (NB) and support vector machine (SVM) classifiers to enhance the performance of selected classifier. The results are verified using text and image data which reflects improved accuracy compared with other conventional techniques. A 25 benchmark datasets (UCI) from different domains are considered to test the proposed algorithms.  A comparative study between proposed hybrid classification algorithms are presented in this work. Finally, the experimental result shows that the differential evolution with NB classifier outperforms and produces better estimation of probability terms. The proposed technique in terms of computational time is also feasible

PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOMAIN USING FULL COUNTER PROPAGATION NEURAL NETWORK

ABSTRAC

Prosthetic Rehabilitation of Congenitally Missing Digits

Complete or partial fingers are the most commonly encountered forms of partial hand losses. Though finger amputations are commonly due to traumatic injuries, digit loss may also be attributed to congenital malformations and diseases. Irrespective of the etiology, digit loss has a considerable functional, psychological and social impact on an individual. This clinical report describes the fabrication of silicone glove type finger prosthesis for a 36 years old male patient with congenitallymissing index and middle finger in the left hand. The prosthesis offered psychological, functional and rehabilitative advantages for the patient

Utilization of Augmented Reality for Human Organ Analysis

This research paper investigates the utilization of augmented reality (AR) technology for human organ analysis in medical education. The study aims to develop and evaluate an AR application that provides an immersive and interactive learning experience for medical students. The research follows a quantitative methodology, to develop and test the effectiveness of the AR application in improving learning outcomes. The research examines the impact of the AR application on student engagement, retention of information, and performance on assessments. The results show that the AR application has a significant positive impact on learning outcomes. The use of AR technology improves student engagement, retention of information, and performance on assessments. The application's design and functionality were found to be intuitive and user-friendly, making it accessible for both students and educators. The research highlights the potential of AR technology in medical education and provides insights into its effectiveness in improving learning outcomes. The findings suggest that AR technology can be a valuable tool in medical education, enhancing the way students learn about human anatomy. This research can contribute to the existing literature on the use of AR technology in education, paving the way for future research and innovation in the field. Ultimately, the study shows that the integration of AR technology in medical education can significantly enhance the learning experience for students, providing them with an immersive and interactive approach to learning about human anatomy

Spectroscopic Studies of the Modification of Crystalline Si(111) Surfaces with Covalently-Attached Alkyl Chains Using a Chlorination/Alkylation Method

A two-step procedure, involving radical-initiated chlorination of the Si surface with PCl_5 followed by reaction of the chlorinated surface with alkyl-Grignard or alkyl-lithium reagents, has been developed to functionalize crystalline (111)-oriented H-terminated Si surfaces. The surface chemistry that accompanies these reaction steps has been investigated using X-ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES), temperature programmed desorption spectroscopy (TPDS), high-resolution electron energy loss spectroscopy (HREELS), infrared (IR) spectroscopy in both glancing transmission (TIR) and attenuated total multiple internal reflection (ATR) modes, ellipsometry, and contact angle goniometry. The XPS data show the appearance of the Cl signal after exposure to PCl_5 and show its removal, and concomitant appearance of a C 1s signal, after the alkylation step. Auger electron spectra, in combination with TPD spectroscopy, demonstrate the presence of Cl after the chlorination process and its subsequent loss after thermal desorption of Si−Cl fragments due to heating the Si surface to 1200 K. High-resolution XP spectra of the Si 2p region show a peak corresponding to Si−Cl bond formation after the chlorination step, and show the subsequent disappearance of this peak after the alkylation step. IR spectra show the loss of the perpendicularly polarized silicon monohydride (Si−H) vibration at 2083 cm^(-1) after the chlorination step, whereas HREELS data show the appearance of vibrations due to Si−Cl stretches upon chlorination of the Si surface. The HREELS data furthermore show the disappearance of the Si−Cl stretch and the appearance of a Si−C vibration at 650 cm^(-1) after alkylation of the Si surface. Ellipsometric measurements indicate that the thickness of the alkyl overlayer varies monotonically with the length of the alkyl group used in the reactant. Contact angle and IR measurements indicate that the packing of alkyl groups in the monolayers produced by this method is less dense than that found in alkylthiol monolayers on Au. As determined by XPS, the alkylated surfaces show enhanced resistance to oxidation by various wet chemical treatments, compared to the H-terminated Si surface. The two-step reaction sequence thus provides a simple approach to functionalization of (111)-oriented, H-terminated silicon surfaces using wet chemical methods

ADaPT: As-Needed Decomposition and Planning with Language Models

Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative executors) or generating plans and executing sub-tasks using LLMs (plan-and-execute). However, these methods struggle with task complexity, as the inability to execute any sub-task may lead to task failure. To address these shortcomings, we introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT), an approach that explicitly plans and decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity and LLM capability. Our results demonstrate that ADaPT substantially outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel compositional dataset that we introduce. Through extensive analysis, we illustrate the importance of multilevel decomposition and establish that ADaPT dynamically adjusts to the capabilities of the executor LLM as well as to task complexity.Comment: Project Page: https://allenai.github.io/adaptll