Diagnosis of systematic defects based on design-for-manufacturability guidelines

All products in the Very-Large-Scale-Integrated-Circuit (VLSIC) industry go through three major stages of production - Design, Verification and Manufacturing. Unfortunately, neither of these stages are truly perfect, hence we need two more sub-stages of manufacturing, namely Testing and Defect Diagnosis to prevent imperfections in ICs. Testing is used to generate test vectors to validate the functionality of the Device-under-Test (DUT), and Defect Diagnosis is the process of identifying the root-cause of a failing chip, i.e., the location and nature of defect. Systematic defects are unintended structural and material changes at specific locations with a higher probability of failure due to repeating manufacturing imperfections. While Design-For-Manufacturability (DFM) guidelines are not always applied due to limited resources like circuit area and design time, enforcing these guidelines helps in ensuring sufficient product yields by preventing systematic defects. However, even if the DFM guidelines are strictly enforced, systematic defects may still occur as complete information about the process and manufacturing is not available due to reducing available time-to-market for chips. ^ An earlier work used DFM guidelines as a basis for modeling of defects, and diagnostic test generation. Under this framework, a circuit is processed to identify layout locations that violate DFM rules. Next, these coordinates are mapped and translated to faults based on different fault models including stuck-at-faults, bridging faults and transition faults. ^ The goal of this thesis is to perform systematic defect diagnosis and analyze the accuracy of diagnosis under the same DFM framework. Thus, systematic defect candidates are generated from DFM guidelines and the generated faultlist is used to perform diagnosis. Because defects may not always be systematic, a new heuristic to dynamically switch between DFM and non-DFM faultlists has also been implemented. This presents us with the best option to follow to further optimize the accuracy of diagnosis. The results demonstrate that the DFM framework can be used to improve the accuracy of diagnosis with minimal resource requirements

Neural Network Algorithms for using Radon Emanations as an Earthquake Precursor

The investigation throughout the world in past two decades provides evidence which indicates that significance variation of radon and other soil gases may occur in association with major geophysical events such as earthquake events. The traditional statistical algorithm which included regression to remove the effect of the meteorological parameters from the as is measured radon along with additional variation that periodicity in seasonal variations is computed using Fast Fourier Transform has shown to improve reliability of prediction of earthquake The present paper deals with the use of neural network algorithms which can learn the behavior of radon with respect to known meteorological parameters. This method has potential of tracking 201C;changing patterns201D; in dependence of radon on meteorological parameters and it may adapt to such changes on its own in due course of time. Another neural network algorithm using Probabilistic Neural Networks that requires neither an explicit step of regression nor use of any specific period is also presented

Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHF

During the last stage of RLHF, a large language model is aligned to human intents via PPO training, a process that generally requires large-scale computational resources. In this technical report, we empirically investigate an efficient implementation of RLHF using low-rank adaptation (LoRA), which allows us to align the LLaMA 7B checkpoint on the Alpaca dataset using only two A100 GPUs instead of the eight required for full model fine-tuning. Despite tuning only 0.2% of LLaMA 7B's parameters, our implementation achieves better performance than the publicly-released AlpacaFarm checkpoint with full model fine-tuning. Next, we analyze several configurations of our LoRA-based PPO implementation, varying the form of the KL regularization term in the training objective. We find that (1) removing this penalty term does not harm performance on the AlpacaFarm evaluation set under our LoRA setup; (2) other regularizers, such as Jensen-Shannon divergence, lead to improved performance; and (3) while PPO training negatively impacts the factuality of model-generated responses, training with LoRA largely mitigates this effect. We release our code and pretrained checkpoints to facilitate future research on more efficient RLHF

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management

Reinforcement learning (RL) has shown great promise for developing dialogue management (DM) agents that are non-myopic, conduct rich conversations, and maximize overall user satisfaction. Despite recent developments in RL and language models (LMs), using RL to power conversational chatbots remains challenging, in part because RL requires online exploration to learn effectively, whereas collecting novel human-bot interactions can be expensive and unsafe. This issue is exacerbated by the combinatorial action spaces facing these algorithms, as most LM agents generate responses at the word level. We develop a variety of RL algorithms, specialized to dialogue planning, that leverage recent Mixture-of-Expert Language Models (MoE-LMs) -- models that capture diverse semantics, generate utterances reflecting different intents, and are amenable for multi-turn DM. By exploiting MoE-LM structure, our methods significantly reduce the size of the action space and improve the efficacy of RL-based DM. We evaluate our methods in open-domain dialogue to demonstrate their effectiveness w.r.t.\ the diversity of intent in generated utterances and overall DM performance.Comment: Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Behavior Alignment via Reward Function Optimization

Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identification of reward structures that are not sparse and that avoid inadvertently inducing undesirable behaviors. Naively modifying the reward structure to offer denser and more frequent feedback can lead to unintended outcomes and promote behaviors that are not aligned with the designer's intended goal. Although potential-based reward shaping is often suggested as a remedy, we systematically investigate settings where deploying it often significantly impairs performance. To address these issues, we introduce a new framework that uses a bi-level objective to learn \emph{behavior alignment reward functions}. These functions integrate auxiliary rewards reflecting a designer's heuristics and domain knowledge with the environment's primary rewards. Our approach automatically determines the most effective way to blend these types of feedback, thereby enhancing robustness against heuristic reward misspecification. Remarkably, it can also adapt an agent's policy optimization process to mitigate suboptimalities resulting from limitations and biases inherent in the underlying RL algorithms. We evaluate our method's efficacy on a diverse set of tasks, from small-scale experiments to high-dimensional control challenges. We investigate heuristic auxiliary rewards of varying quality -- some of which are beneficial and others detrimental to the learning process. Our results show that our framework offers a robust and principled way to integrate designer-specified heuristics. It not only addresses key shortcomings of existing approaches but also consistently leads to high-performing solutions, even when given misaligned or poorly-specified auxiliary reward functions.Comment: (Spotlight) Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

Pituitary apoplexy in setting of Dengue Hemorrhagic Fever with thrombocytopenia: Case report and review of literature

Pituitary apoplexy is an acute clinical syndrome. It may occur spontaneously or as a result of several precipitating factors; one such factor being thrombocytopenia. Acute febrile illness accompanying with bleeding tendency is the main clinical feature of dengue. If the diagnosis is made in time, urgent treatment in the form of decompression of optic nerves may help to save vision. According to literature, only seven cases have been reported with pituitary apoplexy in setting of Dengue hemorrhagic fever. We report eighth case of Pituitary apoplexy in patient having Dengue hemorrhagic fever with its management and review of literature

Herança de ativos digitais: Analisando o conceito de herança digital em plataformas de mídia social

We are moving toward a technologically driven world where the traditional way of looking at assets merely from a physical perspective is slowing evolving to include digital assets. With our extreme indulgence in internet activities, we end up creating a heap of digital assets without realizing the risk associated with its privacy and ownership after death. We share our personal pictures, videos, thoughts, and beliefs through daily posts on social media platforms. All these files are a type of digital assets with social and emotional value, but neither the law nor the social media platforms take strong steps for preserving them and ensuring their inheritance after the user’s death. There is no universally accepted definition of digital assets and hence the concept of digital asset inheritance is in vain. This paper analyzes the posthumous rights of a social media user. It also explores the laws available in the US and India that protect the digital assets and examines the rules and regulations adopted by some of the most important social media platforms for the inheritance of the digital assets. Recommendations and suggestions for the practical implementation of digital inheritance are provided.Nos estamos moviendo hacia un mundo impulsado por la tecnología donde la forma tradicional de ver los activos meramente desde una perspectiva física, está evolucionando lentamente para incluir activos digitales. con nuestro extremo indulgencia en actividades en Internet, terminamos creando un montón de activos digitales sin darnos cuenta de la riesgo asociado con su privacidad y propiedad después de la muerte. Compartimos nuestras fotos personales, videos, pensamientos y creencias a través de publicaciones diarias en las plataformas de redes sociales. Todos estos archivos son un tipo de archivo digital. activos con valor social y emocional, pero ni la ley ni las plataformas de redes sociales son fuertes medidas para conservarlos y asegurar su herencia después de la muerte del usuario. No hay universalmente definición aceptada de activos digitales, y por lo tanto el concepto de herencia de activos digitales es en vano. Este artículo analiza los derechos póstumos de un usuario de redes sociales. También explora las leyes disponibles en el Estados Unidos e India que protegen los activos digitales y examina las reglas y regulaciones adoptadas por algunos de los las plataformas de redes sociales más importantes para la herencia de activos digitales. Recomendaciones y se brindan sugerencias para la implementación práctica del patrimonio digital.Estamos nos movendo em direção a um mundo impulsionado pela tecnologia, onde a maneira  radicional de olhar para os ativos meramente de uma perspectiva física está evoluindo lentamente para incluir ativos digitais. Com nosso extremo indulgência em atividades na internet, acabamos criando um monte de ativos digitais sem perceber o risco associado à sua privacidade e propriedade após a morte. Compartilhamos nossas fotos pessoais, vídeos, pensamentos e crenças por meio de postagens diárias em plataformas de mídia social. Todos esses arquivos são um tipo de arquivo digital ativos com valor social e emocional, mas nem a lei nem as plataformas de mídia social são fortes passos para preservá-los e garantir sua herança após a morte do usuário. Não há universalmente definição aceita de ativos digitais e, portanto, o conceito de herança de ativos digitais é em vão. Este artigo analisa os direitos póstumos de um usuário de mídia social. Também explora as leis disponíveis no EUA e Índia que protegem os ativos digitais e examina as regras e regulamentos adotados por alguns dos as plataformas de mídia social mais importantes para a herança dos ativos digitais. Recomendações e sugestões para a implementação prática da herança digital são fornecida

Bharati Vidyapeeth's Institute of Computer Applications and Management

ABSTRAC