Abstract

In shotgun sequencing, the input string (typically, a long DNA sequence composed of nucleotide bases) is sequenced as multiple overlapping fragments of much shorter lengths (called textit{reads}). Modelling the shotgun sequencing pipeline as a communication channel for DNA data storage, the capacity of this channel was identified in a recent work, assuming that the reads themselves are noiseless substrings of the original sequence. Modern shotgun sequencers however also output quality scores for each base read, indicating the confidence in its identification. Bases with low quality scores can be considered to be erased. Motivated by this, we consider the textit{shotgun sequencing channel with erasures}, where each symbol in any read can be independently erased with some probability $delta$. We identify achievable rates for this channel, using a random code construction and a decoder that uses typicality-like arguments to merge the reads.

Abstract

This paper describes our approach for SemEval- 2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sense. The BRAIN- TEASER task comprises multiple-choice Ques- tion Answering designed to evaluate the mod- els’ lateral thinking capabilities. It consists of Sentence Puzzle and Word Puzzle subtasks that require models to defy default common- sense associations and exhibit unconventional thinking. We propose a unique strategy to im- prove the performance of pre-trained language models, notably the Gemini 1.0 Pro Model, in both subtasks. We employ static and dy- namic few-shot prompting techniques and in- troduce a model-generated reasoning strategy that utilizes the LLM’s reasoning capabilities to improve performance. Our approach demon- strated significant improvements, showing that it performed better than the baseline models by a considerable margin but fell short of per- forming as well as the human annotators, thus highlighting the efficacy of the proposed strate- gies. We have made our code open-sourced for the replicability of our methods.

Abstract

Anti-discrimination represents a critical area of study for researchers, policymakers, and designers of technical systems. Drawing on AI fairness and inclusion studies research, we analyse algorithmic bias and their propensity to reproduce bias [Selbst et al., 2019] on two types of platforms- a gaming environment and a dating platform. On the gaming platform we conduct research on toxicity to comprehend its manifestations, diverse interpretations of toxicity, the conditions fostering toxic gaming, specifically affecting gendered identities. We also investigate if toxic behaviours are normalised within gaming domains, adversely impacting gendered perceptions and experiences. For the second platform, we offer results from an experiment on the Bumble dating platform to identify and address the presence of discrimination in the matching algorithms the platform pushes to its users in the form of profiles for potential dates in the real world. Forms of Interactions of user discovery pose a significant risk of sexual violence and other forms of harm for diverse communities of gender on dating platforms. By reporting experiences of non-binary individuals using dating platforms we contribute to a largely unexplored research field. This study not only highlights the discrepancies and potential biases in dating algorithms but also serves as a catalyst to foster more inclusive design principles, thereby acknowledging the diverse needs across genders and communities.

Abstract

Sugarcane holds a critical position in global agriculture, serving as a basis for the sugar and bioenergy sectors. The integration of remote sensing technologies and sophisticated machine learning approaches and related models has revolutionized sugarcane research. These tools ofer efcient, noninvasive, and large-scale assessment methods. This review highlights the utilization of satellite imagery and sensor data, encompassing RGB, multispectral, hyperspectral, and unmanned aerial vehicles (UAVs) in sugarcane agriculture. It addresses crop identifcation, pest and disease management, yield and acreage estimation, modeling, phenotypic measurement, and their impact on empowering farmers with insights for optimal irrigation, fertilizer application, and overall crop management. These advancements signifcantly increase productivity and foster environmental sustainability. The review had dual aims: (1) consolidate RS data applications in India’s sugarcane research and development, and (2) examine the pros and cons of RS and AI methods in sugarcane farming. The review employed prominent bibliographic databases—google scholar, scopus, researchgate, and web of science—along with pertinent research articles on RS and AI applications in sugarcane, and comprehensive data on sensors and UAVs retrieved from these databases. The study concludes that AI-driven crop RS stands as an efective method for monitoring and managing sugarcane, contributing signifcantly to improving yield and quality, while simultaneously ofering substantial benefts in social, economic, and environmental realms. However, challenges in the sugar industry, such as adapting technology, high initial costs, climate impact, communication, policy, and regulation, must be addressed.

Abstract

Architectural Knowledge Management (AKM) involves the organized handling of information related to architectural decisions and design within a project or organization. An essential artefact of AKM is the Architecture Decision Records (ADR), which documents key design decisions. ADRs are documents that capture decision context, decision made and various aspects related to a design decision, thereby promoting transparency, collaboration, and understanding. Despite their benefits, ADR adoption in software development has been slow due to challenges like time constraints and inconsistent uptake. Recent advancements in Large Language Models (LLMs) may help bridge this adoption gap by facilitating ADR generation. However, the effectiveness of LLM for ADR generation or understanding is something that has not been explored. To this end, in this work, we perform an exploratory study which aims to investigate the feasibility of using LLM for the generation of ADRs given the decision context. In our exploratory study, we utilize GPT and T5-based models with 0-shot, few-shot, and fine-tuning approaches to generate the Decision of an ADR given its Context. Our results indicate that in a 0-shot setting, state-of-the-art models such as GPT-4 generate relevant and accurate Design Decisions, although they fall short of human-level performance. Additionally, we observe that more cost-effective models like GPT-3.5 can achieve similar outcomes in a few-shot setting, and smaller models such as Flan-T5 can yield comparable results after fine-tuning. To conclude, this exploratory study suggests that LLM can generate Design Decisions, but further research is required to attain human-level generation and establish standardized widespread adoption.

Abstract

This paper presents the design and implementation of a hardware system for real-time object detection and range estimation utilizing Frequency-Modulated Continuous Wave (FMCW) millimeter wave radar signals, which are commonly used in Advance Driver Assistance Systems (ADAS) and robotic applications. The proposed system utilizes the Fast Fourier Transform (FFT) algorithm to process the FMCW radar signal for estimating the range of an object. For developing a resource-efficient and low latency range-estimation hardware, this paper also presents a comparative analysis for the implementation of three popular FFT architectures (iterative Radix-2, iterative Radix-4, and pipelined Radix-2) on FPGA. Based on the presented analysis the lowest latency range-estimation architecture has been chosen for FFT implementation and a system prototype is developed as a proof-of-concept by integrating the commercially available Texas Instruments' 77 GHz AWR1642BOOST FMCW radar module with a FPGA to host the chosen FFT architecture. Measurement results of the proposed system hardware are also presented in this paper, which shows >99.21% range-estimation accuracy.

Abstract

The number of individuals having identical names on the internet is increasing. Thus making the task of searching for a specific individual tedious. The user must vet through many profiles with identical names to get to the actual individual of interest. The online presence of an individual forms the profile of the individual. We need a solution that helps users by consolidating the profiles of such individuals by retrieving factual information available on the web and providing the same as a single result. We present a novel solution that retrieves web profiles belonging to those bearing identical Full Names through an end-to-end pipeline. Our solution involves information retrieval from the web (extraction), LLM-driven Named Entity Extraction (retrieval), and standardization of facts using Wikipedia, which returns profiles with fourteen multi-valued attributes. After that, profiles that correspond to the same real-world individuals are determined. We accomplish this by identifying similarities among profiles based on the extracted facts using a Prefix Tree inspired data structure (validation) and utilizing ChatGPT's contextual comprehension (revalidation). The system offers varied levels of strictness while consolidating these profiles, namely strict, relaxed, and loose matching. The novelty of our solution lies in the innovative use of GPT -- a highly powerful yet an unpredictable tool, for such a nuanced task. A study involving twenty participants, along with other results, found that one could effectively retrieve information for a specific individual.

Abstract

Human posture analysis has gained a significant research interest in the recent times. It helps in many applications such as gait analysis for detecting neurological disorders, fall detection of elderly people, and continuous monitoring of severely ill patients. Camera-based vision systems are commonly employed for detecting human postures; however, they cause concerns over the subjects' privacy. To address this challenge, we present a millimeter wave (mmWave) radar-based, truly non-contact, non-intrusive, and privacy-conscious posture detection and classification system in this research. The proposed system utilizes three-dimensional point cloud data of the subject to comprehensively classify body postures, capturing intricate real-time details. In this work, we also present a custom-designed Convolutional Neural Network (CNN) and its comparison with other models, which are conventionally used for posture classification. We also demonstrate the hardware implementation of the proposed system and present the measurement results using Texas Instruments' IWR1843BOOST radar module. The proposed CNN model achieves an accuracy of 97.10% while classifying standing, sitting, lying and bending postures.

Abstract

In this letter, we have modeled and analyzed the impact of alignment errors in fiber lasers and collimating lens arrays on incoherent beam combination systems. The combined intensity expression is calculated considering different errors, and efficiency comparisons are made between 7 and 61 laser beam arrays. The results show that the misalignment error also increases with an increase in the number of beam arrays, leading to a decrease in the combination efficiency. We observed that to achieve more than 80% combination efficiency, maximum error tolerances should be 3 µm, 0.3 mm, and 4 mrad for displacement, focal and tilt errors, respectively. The findings presented in this letter have practical relevance for enhancing the design, operation, and optimization of beam combination systems.

Abstract

In this paper, a cost-effective and easy-to- implement auto-trim technique is introduced for PTAT or CTAT resistance based current references. The resistance with a process-insensitive temperature coefficient requires only a single point trim at room temperature, achieving process and temperature-insensitive current. The approach utilizes a data comparison method where on-chip current data is compared with off-chip reference data to trim the resistance of the current reference. The off-chip reference data is generated using a low-cost external resistor that is used only during the trimming operation. The proposed trimming sensor effectively calibrates the current, offering precision close to manual trimming, leading to cost, time, and resource savings. To validate the working of the proposed technique, the auto trim sensor with on-chip current reference is designed in TSMC 180nm technology. The auto trim sensor calibrates the on-chip current from ±25% (due to voltage and resistance process variation) to ±1.5% across the process and 3σ mismatch