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.

Abstract

Restless multi-armed bandits (RMABs) generalize the multi-armed bandits where each arm exhibits Markovian behavior and transitions according to their transition dynamics. Solutions to RMAB exist for both offline and online cases. However, they do not consider the distribution of pulls among the arms. Studies have shown that optimal policies lead to unfairness, where some arms are not exposed enough. Existing works in fairness in RMABs focus heavily on the offline case, which diminishes their application in real-world scenarios where the environment is largely unknown. In the online scenario, we propose the first fair RMAB framework, where each arm receives pulls in proportion to its merit. We define the merit of an arm as a function of its stationary reward distribution. We prove that our algorithm achieves sublinear fairness regret in the single pull case , with being the total number of episodes. Empirically, we show that our algorithm performs well in the multi-pull scenario as well.

Abstract

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, which considers two levels of fairness. At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group. To address the unbalanced allocation of pulls to individual arms within a group, we consider meritocratic fairness at the second level, which ensures that each arm is pulled according to its merit within the group. Our work shows that we can adapt a UCB-based algorithm to achieve a Bi-Level Fairness by providing (i) anytime Group Exposure Fairness guarantees and (ii) ensuring individual-level Meritocratic Fairness within each group. We first show that one can decompose regret bounds into two components: (a) regret due to anytime group exposure fairness and (b) regret due to meritocratic fairness within each group. Our proposed algorithm BF-UCB balances these two regrets optimally to achieve the upper bound of on regret; being the stopping time. With the help of simulated experiments, we further show that BF-UCB achieves sub-linear regret; provides better group and individual exposure guarantees compared to existing algorithms; and does not result in a significant drop in reward with respect to UCB algorithm, which does not impose any fairness constraint.

Abstract

Dysarthria due to Amyotrophic Lateral Sclerosis (ALS) affects the acoustic characteristics of different speech sounds. The effects intensify with increasing severity leading to the collapse of the acoustic space of the affected individuals. With an aim to characterize such changes in the acoustic space, this paper studies the variations in band-specific and full-band spectral properties of 4 sustained vowels (/a/, /i/, /o/, /u/) and 3 sustained fricatives (/s/, /sh/, /f/) at different dysarthria severity levels. Effect of dysarthria on spectral features of these phonemes are not well explored. Statistical comparison of these features among different severities for the phonemes considered and among different vowels/fricatives for every severity level using speech data from 119 ALS and 40 healthy subjects indicate the followings. Though all band-specific and full-band features of the three fricatives and most of those features for the four vowels become statistically similar at high severity levels, certain features remain distinguishable. Spectral differences in 0-2 kHz band between /a/ and the other vowels and in the 2-6 kHz band between /a/ and /o/, /u/ persist through all severity levels. Moreover, properties of /f/ remain mostly unchanged with increasing dysarthria severity levels.

Abstract

: Gathering requirements for developing virtual reality (VR) software products is a labor-intensive process. It requires detailed elicitation of scene flow, articles in the scene, action responses, custom behaviors, and timeline of events. The slightest change in requirements will escalate the design and development costs. While most VR practitioners depend on conventional software engineering (SE) requirement-gathering techniques, there is a need for novel methods to streamline VR software development. With severe software platform fragmentation and hardware volatility, VR practitioners need assistance specifying non-volatile requirements for a minimum viable VR software product.