Training and Placement department is one of the important area to any educational Institute, even in this era, we are doing most of the work by using human interventions. The main aim of this paper is to automate the Training and Placement cell of Reva University. The main feature of this project is the generation, verification, authentication and easy analysis with maintenance of relevant data. This is achieved by means of modern Technology like Android and database servers. This will provide the facility to maintaining student data along with placement records of the college. This will serve as a medium of free communication and feedback between the students and the placement department. The Planner in the application will help all the users to select what they want to study and, plan their day accordingly. All the syllabus and faculties will be one touch away from the users. Users can post their queries and can be in direct touch with the Training and placement cell. The project aims to provide maximum optimization and security along with minimal manual work. This will be helpful in efficient and better management of all placement and Training activities on campus. With the development of this project, the University can maintain computerized records without redundant entries.
This work introduces LAB (Large-scale Alignment for chatBots), a novel methodology designed to overcome the scalability challenges in the instruction-tuning phase of large language model (LLM) training. Leveraging a taxonomy-guided synthetic data generation process and a multi-phase tuning framework, LAB significantly reduces reliance on expensive human annotations and proprietary models like GPT-4. We demonstrate that LAB-trained models can achieve competitive performance across several benchmarks compared to models trained with traditional human-annotated or GPT-4 generated synthetic data. Thus offering a scalable, cost-effective solution for enhancing LLM capabilities and instruction-following behaviors without the drawbacks of catastrophic forgetting, marking a step forward in the efficient training of LLMs for a wide range of applications.
Threats targeting cyberspace are becoming more prominent and intelligent day by day. This inherently leads to a dire demand for continuous security validation and testing. Using this paper, we aim to provide a holistic and precise security analysis rating framework for organizations that increases the overall coherency of the outcomes of such testing. This scorecard is based on the security assessment performed following the globally accessible knowledge base of adversary tactics and techniques called the MITRE ATTACK matrix. The scorecard for an evaluation is generated by ingesting the security testing results into our framework, which provides an organizations overall risk assessment rating and the risk related to each of the different tactics from the ATTACK matrix.
Context: The human tongue, a unique organ with complex architecture, exhibits significant morphological variations in the human body consistent with its complex role. Its morphological characteristic features and varieties can go about as proof of life and can be used for personal identification. The morphology and surface highlights of the tongue are qualities of each person, and these characteristics can be utilized as legal distinguishing proof of each person.Aims: This study is aimed to analyze varieties in morphological qualities of the tongue and to find out gender differences.Settings and Design: This study was conducted on 206 (Group I consist of 105 females and Group II consist of 101 male) participants with an age range of 21–30 years for 4 months who had visited the Department of Oral Medicine and Radiology.Subjects and Methods: The tongue was exposed to visual assessment following which alginate impression of the dorsal surface of the tongue was taken to make tongue cast and to assess distinct morphological features of the tongue and its variations in males and females.Statistical Analysis Used: The discrete (categorical) data were summarized in number (n) and percentage (%) and compared using the Chi-square (χ2) test.Results: A total of 206 participants in both groups, the U-shaped tongue, was the most common findings in males, as well as in females followed by a V-shaped tongue with a sharp tip that was observed more in females compared to males. Scalloped borders and multiple fissures were more common in males as compared to females.Conclusions: Variations of tongue shape and surface properties can be used as personal human identification in forensic odontology, and tongue prints can also be used as a standard method for the collection of data. The collected data of tongue prints may be used as a professional recognition database to ease human identification and to avoid future scams.
Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempted to promote further diversity in DE via hyperparameters or regularizing loss functions, these methods primarily still rely on a stochastic approach to explore the hypothesis space. In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters. We leverage recent advances in contrastive representation learning to create models that separately capture opposing hypotheses of invariant and equivariant functional classes and present a simple ensembling approach to efficiently combine appropriate hypotheses for a given task. We show that MSE effectively captures the multiplicity of conflicting hypotheses that is often required in large, diverse datasets like ImageNet. As a result of their inherent diversity, MSE improves classification performance, uncertainty quantification, and generalization across a series of transfer tasks.
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of applications, including image and speech synthesis, natural language processing, and drug discovery. However, when applied to engineering design problems, evaluating the performance of these models can be challenging, as traditional statistical metrics based on likelihood may not fully capture the requirements of engineering applications. This paper doubles as a review and practical guide to evaluation metrics for deep generative models (DGMs) in engineering design. We first summarize the well-accepted `classic' evaluation metrics for deep generative models grounded in machine learning theory. Using case studies, we then highlight why these metrics seldom translate well to design problems but see frequent use due to the lack of established alternatives. Next, we curate a set of design-specific metrics which have been proposed across different research communities and can be used for evaluating deep generative models. These metrics focus on unique requirements in design and engineering, such as constraint satisfaction, functional performance, novelty, and conditioning. Throughout our discussion, we apply the metrics to models trained on simple-to-visualize 2-dimensional example problems. Finally, we evaluate four deep generative models on a bicycle frame design problem and structural topology generation problem. In particular, we showcase the use of proposed metrics to quantify performance target achievement, design novelty, and geometric constraints. We publicly release the code for the datasets, models, and metrics used throughout the paper at https://decode.mit.edu/projects/metrics/.
Functions of the ratio of the densities $p/q$ are widely used in machine learning to quantify the discrepancy between the two distributions $p$ and $q$. For high-dimensional distributions, binary classification-based density ratio estimators have shown great promise. However, when densities are well separated, estimating the density ratio with a binary classifier is challenging. In this work, we show that the state-of-the-art density ratio estimators perform poorly on well-separated cases and demonstrate that this is due to distribution shifts between training and evaluation time. We present an alternative method that leverages multi-class classification for density ratio estimation and does not suffer from distribution shift issues. The method uses a set of auxiliary densities $\{m_k\}_{k=1}^K$ and trains a multi-class logistic regression to classify the samples from $p, q$, and $\{m_k\}_{k=1}^K$ into $K+2$ classes. We show that if these auxiliary densities are constructed such that they overlap with $p$ and $q$, then a multi-class logistic regression allows for estimating $\log p/q$ on the domain of any of the $K+2$ distributions and resolves the distribution shift problems of the current state-of-the-art methods. We compare our method to state-of-the-art density ratio estimators on both synthetic and real datasets and demonstrate its superior performance on the tasks of density ratio estimation, mutual information estimation, and representation learning. Code: https://www.blackswhan.com/mdre/
Aging is a complex process that can be characterized by functional and cognitive decline in an individual. Aging can be assessed based on the functional capacity of vital organs and their intricate interactions with one another. Thus, the nature of aging can be described by focusing on a specific organ and an individual itself. However, to fully understand the complexity of aging, one must investigate not only a single tissue or biological process but also its complex interplay and interdependencies with other biological processes. Here, using RNA-seq, we monitored changes in the transcriptome during aging in four tissues (including brain, blood, skin and liver) in mice at 9 months, 15 months, and 24 months, with a final evaluation at the very old age of 30 months. We identified several genes and processes that were differentially regulated during aging in both tissue-dependent and tissue-independent manners. Most importantly, we found that the electron transport chain (ETC) of mitochondria was similarly affected at the transcriptome level in the four tissues during the aging process. We also identified the liver as the tissue showing the largest variety of differentially expressed genes (DEGs) over time. Lcn2 (Lipocalin-2) was found to be similarly regulated among all tissues, and its effect on longevity and survival was validated using its orthologue in Caenorhabditis elegans. Our study demonstrated that the molecular processes of aging are relatively subtle in their progress, and the aging process of every tissue depends on the tissue's specialized function and environment. Hence, individual gene or process alone cannot be described as the key of aging in the whole organism.
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