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We explore the velocity distributions in a vibrated binary granular gas system, focusing on how these distributions are influenced by the coefficient of restitution (CoR) and the inelasticity of particle collisions. Through molecular dynamics simulations, we examine the system's behavior for a range of CoR values below unity: specifically, 0.80, 0.85, 0.90, and 0.95. We track the evolution of velocity distributions as the system approaches a non-equilibrium steady state. Our findings reveal a clear departure from the classical Maxwell-Boltzmann distribution, with increasing deviations as CoR decreases, indicating non-equipartition of energy between the two particle types. This behavior underscores the intricate dynamics inherent in inelastic collisions and highlights the significance of particle inelasticity in determining the system's velocity distributions. These results provide insight into non-equilibrium statistical mechanics and have implications for real-world applications where granular materials are subject to external vibrations.

The advent of Large Language Models has revolutionized information retrieval, ushering in a new era of expansive knowledge accessibility. While these models excel in providing open-world knowledge, effectively extracting answers in diverse linguistic environments with varying levels of literacy remains a formidable challenge. Retrieval Augmented Generation (RAG) emerges as a promising solution, bridging the gap between information availability and multilingual comprehension. However, deploying RAG models in real-world scenarios demands careful consideration of various factors. This paper addresses the critical challenges associated with implementing RAG models in multicultural environments. We delve into essential considerations, including data feeding strategies, timely updates, mitigation of hallucinations, prevention of erroneous responses, and optimization of delivery speed. Our work involves the integration of a diverse array of tools, meticulously combined to facilitate the seamless adoption of RAG models across languages and literacy levels within a multicultural organizational context. Through strategic tweaks in our approaches, we achieve not only effectiveness but also efficiency, ensuring the accelerated and accurate delivery of information in a manner that is tailored to the unique requirements of multilingual and multicultural settings.

Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-technical users. In this paper we tested an unmodified version of GPT 3.5, a fine-tuned version, and the same unmodified model when given access to a vectorised RAG database, both in isolation and in combination with a basic, non-algorithmic soft prompt. In each case we tested the model's ability to answer a set of 100 questions relating primarily to events that occurred after September 2021 (the point at which GPT 3.5's training data set ends). We found that if commercial platforms are used and default settings are applied with no iteration in order to establish a baseline set of outputs, a fine-tuned model outperforms GPT 3.5 Turbo, while the RAG approach out-performed both. The application of a soft prompt significantly improved the performance of each approach., Comment: 10 pages, LaTeX; typos corrected, using the correct term 'system prompting' instead of 'soft prompting'

Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) are both known for their susceptibility to adversarial attacks. Therefore, researchers in the recent past have extensively studied the robustness and defense of DNNs and SNNs under adversarial attacks. Compared to accurate SNNs (AccSNN), approximate SNNs (AxSNNs) are known to be up to 4X more energy-efficient for ultra-low power applications. Unfortunately, the robustness of AxSNNs under adversarial attacks is yet unexplored. In this paper, we first extensively analyze the robustness of AxSNNs with different structural parameters and approximation levels under two gradient-based and two neuromorphic attacks. Then, we propose two novel defense methods, i.e., precision scaling and approximate quantization-aware filtering (AQF), for securing AxSNNs. We evaluated the effectiveness of these two defense methods using both static and neuromorphic datasets. Our results demonstrate that AxSNNs are more prone to adversarial attacks than AccSNNs, but precision scaling and AQF significantly improve the robustness of AxSNNs. For instance, a PGD attack on AxSNN results in a 72\% accuracy loss compared to AccSNN without any attack, whereas the same attack on the precision-scaled AxSNN leads to only a 17\% accuracy loss in the static MNIST dataset (4X robustness improvement). Similarly, a Sparse Attack on AxSNN leads to a 77\% accuracy loss when compared to AccSNN without any attack, whereas the same attack on an AxSNN with AQF leads to only a 2\% accuracy loss in the neuromorphic DVS128 Gesture dataset (38X robustness improvement)., Comment: Accepted full paper in DATE 2023

Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping. In this paper, we demonstrate that pre-processing not only speeds up the algorithms but also improves accuracy in both supervised and unsupervised learning. In pre-processing of DDR, first PCA based DDR is used for supervised learning, then we explore AE based DDR for unsupervised learning. In PCA based DDR, we first compare supervised learning algorithms accuracy and time before and after applying PCA. Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning. Supervised learning algorithms including support-vector machines (SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic Gradient Descent classifier (SGDC) and unsupervised learning algorithm including K-means clustering, are used for classification purpose. We used two datasets MNIST and FashionMNIST Our experiment shows that there is massive improvement in accuracy and time reduction after pre-processing in both supervised and unsupervised learning., Comment: Our paper is accepted at International Conference On Emerging Technologies In Electronics, Computing And Communication (ICETECC) 2022