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Abstract

Monuments were built for thousands of years as the most enduring and well-known representations of past civilizations. It is crucial to preserve these because they are national symbols with unique cultural and historical significance. The deterioration of the structures happens as a result of weathering, fire, natural disasters like earthquakes, construction flaws, and numerous other factors. In general, the issue with repairing and reinforcing regular buildings is very different from the issue with restoring monument buildings. The goal of keeping a monumental structure in use may be viewed as secondary in importance and any event as a result of the effort made to fulfil the main task. In the case of monumental buildings, emphasis is placed on the preservation of their aesthetic and historical aspects. Therefore, the restoration strives to protect and highlight the monument’s or building’s aesthetic and historical aspects. Choosing the appropriate material for the restoration depends on how the original structure looks. New materials must be compatible with those already used in the building. The goal of the present study is to provide an overview of the materials used to restore the structural integrity of historic structures and monuments that have been damaged by seismic activity. All of these buildings need extra attention because of their specific historical or architectural value, or because they are significant because they are still standing examples of a former way of life.

Abstract

Masonry structures cover a very wide range of monumental works in space and time, covering almost 6,000 years. The structural assessment of the load-carrying capacity of such old structures depends on several parameters such as: (a) material strength; (b) texture of the structure; (c) geometrical form and dimensions; (d) existing loading and constraints; and (e) expected loadings at failure. Detailed calculations are also required for the structural evaluation using most of the above parameters firstly by considering the gravity loads and secondly by the combination of gravity load and lateral action in areas of high seismicity. However, at the same time, it is equally significant for the structural engineer, who is involved in structural restoration design, to know in depth qualitatively the basic forms and their structural behaviour of monumental buildings. The structural engineer in charge must understand from the beginning, qualitatively, the structural behaviour of a building under consideration to various basic actions so that one can make a preliminary diagnosis of the causes that are responsible for the existing damages (cracks, drifts, settlements, splitting etc.). In this way, one will be in a position to focus on the detailed research, in situ and laboratory, to the critical points of the building, and can conduct the analysis and design using suitable models for a credible safety evaluation. Therefore, an effort is made in the present study to give a qualitative approach to the structural behaviour of the basic structural forms of masonry monumental and historical buildings, where the consequences of the seismic action are traumatic now and then. This article gives an outline and creates background information for further structural evaluation of the monuments.

Abstract

Today’s open-source chip development ecosystem relies on various Electronic Design Automation (EDA) tools, accessible through permissive licenses on GitHub [1]–[3]. As the open-source chip design ecosystem ma- tures, the EDA tool maintainers gradually incorporate customized features to boost the user’s experience. Such is the case of OpenFASoC, an Open-Source, Fully Autonomous SoC Synthesis tool that uses customiz- able Cell-Based Synthesizable Analog Circuits. This process-agnostic tool delivers, through different alter- natives, a framework of integrated open-source tools capable of synthesizing highly optimized, verifiable analog blocks ready for tape-out with reproducible results [4]. The tool also boasts a cloud-based Contin- uous Integration (CI) framework that allows developers to continually collaborate and improve the tool while staying on the latest version. Keywords: Open-Source, Analog Automation, EDA, Cloud, Continuous Integration, IC.

Abstract

Current approaches to Analog Layout Automation apply ML techniques such as Graph Convolutional Neural Networks (GCN) to translate netlist to layout. While these ML approaches have proven to be effective, they lack the powerful reasoning capabilities, an intuitive human interface, and standard evaluation benchmarks that have been improving at a rapid development pace in Large Language Models (LLMs). The GLayout framework introduced in this work translates analog layout into an expressive, technology generic, compact text representation. Then, an LLM is taught to understand analog layout through fine-tuning and in-context learning using Retrieval Augmented Generation (RAG). The LLM is able to successfully layout unseen circuits based on new information provided in-context. We train 3.8, 7, and 22 Billion parameter quantized LLMs on a dataset of less than 50 unique circuits, and text documents providing layout knowledge. The 22B parameter model is tuned in 2 hours on a single NVIDIA A100 GPU. The open-source evaluation set is proposed as an automation benchmark for LLM layout automation tasks, and ranges from 2-transistor circuits to a ΔΣ ADC. The 22B model completes 70% of the tasks in the evaluation set, and passes DRC and LVS verification on 44% of evaluations with verified correct blocks up to 4 transistors in size.

Abstract

Data visualization is key to representing data effectively, especially in large multidimensional datasets. In this paper, we extend the BEADS i.e. breaking cluster into chunks of points and represent each chunk as bead. We do this to study the underlying pattern i.e. shape, size and location of the clusters and visualize them in a manner in which the users understand the distribution of the data points. BEADS cluster visualization system is implemented to represent the high-dimensional shape of a cluster by using standard two-dimensional structures like squares and circles as metaphors. This is achieved by subdividing clusters into smaller chunks, or "beads," using the K-means algorithm and optimizing the lp-norm value to determine the most suitable shape for each bead. The visualization helps comprehend the shape, size and overall placement of clusters in high dimensions. Future work involves finding the best number of beads for each cluster individually and improving the efficiency to speed up the process for large datasets. Instead of K-mean, any other appropriate partitioning algorithm could be used at the user's discretion.

Abstract

The Śabdabrahman exercise platform (SBE), at sabdabrahman.org, is an on-line interactive Sanskrit instructional platform that offers immediate feedback and focused help to students at every step of analyzing and translating the Sanskrit sentences in a first-year Sanskrit textbook. Steps include transliteration from Devanagari to standard Romanization and vice versa, analysis of sandhi, identification of nominal and verbal inflection, syntax, compound analysis, and translation. Student submission at each step is evaluated, errors highlighted, and links supplied to relevant help. Source documents are prepared in XML in accordance with the Text-Encoding Initiative guidelines. The platform is coded in Flask and ReactJS and hosted at Amazon Web Services (AWS). The platform has been successfully used in first-year Sanskrit courses offered through The Sanskrit Library (sanskritlibrary.org).

Abstract

Today, in the post-industrial society, the data created by computers and the internet are becoming a new ‘means of production’. With this, there is a new- found realisation on the question of its ownership, distribution and protection. The question of accessibility in this regard is also becoming a crucial issue in democratic political systems. Internet and data have led to epistemological changes such that new rights for citizens are required with an overhaul of democratic processes founded in digital methods. It will definitely have its own challenges, limitations and possibilities. As data protection bills are now being introduced in many countries of the world, it is important to analyse the key theoretical nuances in the data ownership discourse. In this regard, this article attempts to highlight these nuances along with the scope for new public policy frameworks required in the domain of data ownership, distribution and protection.

Abstract

This paper considers the contextual multi-armed bandit (CMAB) problem with fairness and privacy guarantees in a federated environment. We consider merit-based exposure as the desired fair outcome, which provides exposure to each action in proportion to the reward associated. We model the algorithm's effectiveness using fairness regret, which captures the difference between fair optimal policy and the policy output by the algorithm. Applying fair CMAB algorithm to each agent individually leads to fairness regret linear in the number of agents. We propose that collaborative -- federated learning can be more effective and provide the algorithm Fed-FairX-LinUCB that also ensures differential privacy. The primary challenge in extending the existing privacy framework is designing the communication protocol for communicating required information across agents. A naive protocol can either lead to weaker privacy guarantees or higher regret. We design a novel communication protocol that allows for (i) Sub-linear theoretical bounds on fairness regret for Fed-FairX-LinUCB and comparable bounds for the private counterpart, Priv-FairX-LinUCB (relative to single-agent learning), (ii) Effective use of privacy budget in Priv-FairX-LinUCB. We demonstrate the efficacy of our proposed algorithm with extensive simulations-based experiments. We show that both Fed-FairX-LinUCB and Priv-FairX-LinUCB achieve near-optimal fairness regret.

Abstract

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as vehicle routing, radio frequency assignments, and distributed scheduling of meetings. However, optimization scenarios may involve multiple agents wanting to protect their preferences’ privacy. Researchers propose privacy-preserving algorithms for DCOPs that provide improved privacy protection through cryptographic primitives such as partial homomorphic encryption, secret-sharing, and secure multiparty computation. These privacy benefits come at the expense of high computational complexity. Moreover, such an approach does not constitute a rigorous privacy guarantee for optimization outcomes, as the result of the computation may compromise agents’ preferences. In this work, we show how to achieve privacy, specifically Differential Privacy, by randomizing the solving process. In particular, we present P-Gibbs, which adapts the current state-of-the-art algorithm for DCOPs, namely SD-Gibbs, to obtain differential privacy guarantees with much higher computational efficiency. Experiments on benchmark problems such as Ising, graph-coloring, and meeting-scheduling show P-Gibbs’ privacy and performance trade-off for varying privacy budgets and the SD-Gibbs algorithm. More concretely, we empirically show that P-Gibbs provides fair solutions for competitive privacy budgets.