Abstract

We study a family of subcodes of the $m$-dimensional product code $mathscr{C}^{otimes m}$ (`subproduct codes') that have a recursive Plotkin-like structure, and which include Reed-Muller (RM) codes and Dual Berman codes as special cases. We denote the codes in this family as $mathscr{C}^{otimes [r,m]}$, where mbox{$0 leq r leq m$} is the `order' of the code. These codes allow a `projection' operation that can be exploited in iterative decoding, viz., the sum of two carefully chosen subvectors of any codeword in $mathscr{C}^{otimes [r,m]}$ belongs to $mathscr{C}^{otimes [r-1,m-1]}$. Recursive subproduct codes provide a wide range of rates and block lengths compared to RM codes while possessing several of their structural properties, such as the Plotkin-like design, the projection property, and fast ML decoding of first-order codes. Our simulation results for first-order and second-order codes, that are based on a belief propagation decoder and a local graph search algorithm, show instances of subproduct codes that perform either better than or within $0.5$~dB of comparable RM codes and CRC-aided Polar codes.

Abstract

Conversation is the most natural form of human communication, where each utterance can range over a variety of possible emotions. While significant work has been done towards the detection of emotions in text, relatively little work has been done towards finding the cause of the said emotions, especially in multimodal settings. SemEval 2024 introduces the task of Multimodal Emotion Cause Analysis in Conversations, which aims to extract emotions reflected in individual utterances in a conversation involving multiple modalities (textual, audio, and visual modalities) along with the corresponding utterances that were the cause for the emotion. In this paper, we propose models that tackle this task as an utterance labeling and a sequence labeling problem and perform a comparative study of these models, involving baselines using different encoders, using BiLSTM for adding contextual information of the conversation, and finally adding a CRF layer to try to model the inter-dependencies between adjacent utterances more effectively. In the official leaderboard for the task, our architecture was ranked 8th, achieving an F1-score of 0.1759 on the leaderboard.

Abstract

While significant work has been done in the field of NLP on vertical thinking, which involves primarily logical thinking, little work has been done towards lateral thinking, which involves looking at problems from an unconventional perspective and defying existing conceptions and notions. Towards this direction, SemEval 2024 introduces the task of BRAINTEASER, which involves two types of questions -- Sentence Puzzles and Word Puzzles that defy conventional common-sense reasoning and constraints. In this paper, we tackle both types of questions using few-shot prompting on GPT-3.5 and gain insights regarding the difference in the nature of the two types. Our prompting strategy placed us 26th on the leaderboard for the Sentence Puzzle and 15th on the Word Puzzle task.

Abstract

Large Language Models (LLMs) have showcased impressive abilities in generating fluent responses to diverse user queries. However, concerns regarding the potential misuse of such texts in journalism, educational, and academic contexts have surfaced. SemEval 2024 introduces the task of Multigenerator, Multidomain, and Multilingual Black-Box MachineGenerated Text Detection, aiming to develop automated systems for identifying machinegenerated text and detecting potential misuse. In this paper, we i) propose a RoBERTaBiLSTM based classifier designed to classify text into two categories: AI-generated or human ii) conduct a comparative study of our model with baseline approaches to evaluate its effectiveness. This paper contributes to the advancement of automatic text detection systems in addressing the challenges posed by machinegenerated text misuse. In the official leaderboard for the task, our architecture was ranked 46th, achieving an accuracy of 0.8083 on the leaderboard.

Abstract

Statistical analysis of high-frequency stock market order transaction data is conducted to understand order transition dynamics. We employ a first-order time-homogeneous discrete-time Markov chain model to the sequence of orders of stocks belonging to six different sectors during the US–China trade war of 2018. The Markov property of the order sequence is validated by the Chi-square test. We estimate the transition probability matrix of the sequence using maximum likelihood estimation. From the heatmap of these matrices, we found the presence of active participation by different types of traders during high volatility days. On such days, these traders place limit orders primarily with the intention of deleting the majority of them to influence the market. These findings are supported by high stationary distribution and low mean recurrence values of add and delete orders. Further, we found similar spectral gap and entropy rate values, which indicates that similar trading strategies are employed on both high and low volatility days during the trade war. Among all the sectors considered in this study, we observe that there is a recurring pattern of full execution orders in the Finance & Banking sector. This shows that the banking stocks are resilient during the trade war. Hence, this study may be useful in understanding stock market order dynamics and devise trading strategies accordingly on high and low volatility days during extreme macroeconomic events.

Abstract

The concept of sum labelling was introduced in 1990 by Harary. A graph is a sum graph if its vertices can be labelled by distinct positive integers in such a way that two vertices are connected by an edge if and only if the sum of their labels is the label of another vertex in the graph. It is easy to see that every sum graph has at least one isolated vertex, and every graph can be made a sum graph by adding at most n2 isolated vertices to it. The minimum number of isolated vertices that need to be added to a graph to make it a sum graph is called the sum number of the graph. The sum number of several prominent graph classes (e.g., cycles, trees, complete graphs) is already well known. We examine the effect of taking the disjoint union of graphs on the sum number. In particular, we provide a complete characterisation of the sum number of graphs of maximum degree two, since every such graph is the disjoint union of paths and cycles

Abstract

In this paper, we determine the computational complexity of recognizing two graph classes, grounded 𝖫-graphs and stabbable grid intersection graphs. An 𝖫-shape is made by joining the bottom end-point of a vertical (|) segment to the left end-point of a horizontal (−) segment. The top end- point of the vertical segment is known as the anchor of the 𝖫-shape. Grounded 𝖫-graphs are the intersection graphs of 𝖫-shapes such that all the 𝖫-shapes’ anchors lie on the same horizontal line. We show that recognizing grounded 𝖫-graphs is 𝖭𝖯-complete. This answers an open question asked by Jelínek & Töpfer (Electron. J. Comb., 2019). Grid intersection graphs are the intersection graphs of axis-parallel line segments in which two vertical (similarly, two horizontal) segments cannot intersect. We say that a (not necessarily axis-parallel) straight line 𝓁 stabs a segment 𝑠, if 𝑠 intersects 𝓁. A graph 𝐺 is a stabbable grid intersection graph (S TAB GIG) if there is a grid intersection representation of 𝐺 in which the same line stabs all its segments. We show that recognizing S TAB GIG graphs is 𝖭𝖯-complete, even on a restricted class of graphs. This answers an open question asked by Chaplick et al. (Order, 2018).

Abstract

Empirical investigations demonstrate similar cognitive processing patterns for objects and sexualized women. However, sexual objectification (SO) extends beyond sexualized women. To explore SO, we apply eye-tracking technique in conjunction with local/global and body-inversion paradigms. Ninety-four college students participated in the study. The visual gaze on non-sexualized South-Asian wo(men) images and the response time in Navon task post- priming with upright and inverted images is analyzed. Results indicate that participants of both genders gaze objectify females. Interestingly, male images are also processed as objects and body-inversion effect is observed. A comparison of attention allocation to face versus sexual body parts in upright versus inverted female images shows a reduced face- to-body ratio for the latter orientation, indicating a gender- specific attention shift. Combining the two SO theories, the study objectively substantiates the claim that women undergo objectification in even in non-sexual attire.

Abstract

Investors rely on judgmental heuristics and comparative analysis for future stock price prediction based on specific components of information in hand. Information components are used as anchors for price estimation. Through an eye-tracking experiment, we aim to understand the perceived significance of various formats of information, particularly focusing on graphical and numerical components, and to explore the influence of complex time-varying patterns in stock price line plots. Results show that graphical components capture higher visual attention. Participants are not always loss-averse and prominently exhibit disposition effects for investment decisions in profitable scenarios. The 52-week high is allotted the highest fixation duration, signifying its perception as a strong reference point. Investment choices were found to be varying based on levels of prior knowledge and experience. The visual gaze analysis provides behavioural insights into complex decision-making processes.

Abstract

Various studies have shown the advantages of using Machine Learning (ML) techniques for analog and digital IC design automation and optimization. Data scarcity is still an issue for electronic designs, while training highly accurate ML models. This work proposes generating and evaluating artificial data using generative adversarial networks (GANs) for circuit data to aid and improve the accuracy of ML models trained with a small training data set. The training data is obtained by various simulations in the Cadence Virtuoso, HSPICE, and Microcap design environment with TSMC 180 nm and 22 nm CMOS technology nodes. The artificial data is generated and tested for an appropriate set of analog circuits and digital cells. The experimental results show that the proposed artificial data generation significantly improves ML models and reduces the percentage error by more than 50% of the original percentage error, which were previously trained with insufficient data. Furthermore, this research aims to contribute to the extensive application of AI/ML in the field of VLSI design and technology by relieving the training data availability-related challenges.