Abstract

Portrait sketching involves capturing identity specific attributes of a real face with abstract lines and shades. Unlike photo-realistic images, a good portrait sketch generation method needs selective attention to detail, making the problem challenging. This paper introduces textbf{Portrait Sketching StyleGAN (PS-StyleGAN)}, a style transfer approach tailored for portrait sketch synthesis. We leverage the semantic $W+$ latent space of StyleGAN to generate portrait sketches, allowing us to make meaningful edits, like pose and expression alterations, without compromising identity. To achieve this, we propose the use of Attentive Affine transform blocks in our architecture, and a training strategy that allows us to change StyleGAN's output without finetuning it. These blocks learn to modify style latent code by paying attention to both content and style latent features, allowing us to adapt the outputs of StyleGAN in an inversion-consistent manner. Our approach uses only a few paired examples ($sim 100$) to model a style and has a short training time. We demonstrate PS-StyleGAN's superiority over the current state-of-the-art methods on various datasets, qualitatively and quantitatively.

Abstract

Edge computing allows for reduced latency and operational costs compared to centralized cloud systems. In this context, serverless functions are emerging as a lightweight and effective paradigm for managing computational tasks on edge infrastructures. However, the placement of such functions in constrained edge nodes remains an open challenge. On one hand, it is key to minimize network delays and optimize resource consumption; on the other hand, decisions must be made in a timely manner due to the highly dynamic nature of edge environments. In this paper, we propose POSEIDON, a solution based on Deep Reinforcement Learning for the efficient placement of functions at the edge. POSEIDON leverages Proximal Policy Optimization (PPO) to place functions across a distributed network of nodes under highly dynamic workloads. A comprehensive empirical evaluation demonstrates that POSEIDON significantly reduces execution time, network delay, and resource consumption compared to state-of-the-art methods.

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by challenges in social communication, repetitive behaviors, and restricted interests. Emerging research has emphasized the significance of metabolic dysfunctions in the pathophysiology of ASD, including abnormalities in energy production, mitochondrial dysfunction, and oxidative stress. This study explores metabolic alterations specific to brain tissues, cell types and blood in individuals with ASD by integrating omics data with the human metabolic network. We identified significant brain-specific metabolic changes in ASD, underscoring hypometabolism and reduced glucose utilization. Additionally, alterations in pathways related to fatty acid metabolism, amino acid metabolism, and vitamin metabolism were observed. The analysis of cell-specific metabolic activities in the ASD brain showed alterations in pathways in astrocytes, microglia, and oligodendrocytes. Metabolic gene expression in peripheral blood mononuclear cells (PBMC) samples also showed potential as diagnostic biomarkers. These findings offer insights into metabolic interventions in ASD, as well as potential biomarkers and therapeutic targets for ASD.

Abstract

In the circuit model of quantum computing, amplitude amplification techniques can be used to find solutions to NP-hard problems defined on n-bits in time poly(n)2^{n/2}. In this work, we investigate whether such general statements can be made for adiabatic quantum optimization, as provable results regarding its performance are mostly unknown. Although a lower bound of Ω(2^{n/2}) has existed in such a setting for over a decade, a purely adiabatic algorithm with this running time has been absent. We show that adiabatic quantum optimization using an unstructured search approach results in a running time that matches this lower bound (up to a polylogarithmic factor) for a broad class of classical local spin Hamiltonians. For this, it is necessary to bound the spectral gap throughout the adiabatic evolution and compute beforehand the position of the avoided crossing with sufficient precision so as to adapt the adiabatic schedule accordingly. However, we show that the position of the avoided crossing is approximately given by a quantity that depends on the degeneracies and inverse gaps of the problem Hamiltonian and is NP-hard to compute even within a low additive precision. Furthermore, computing it exactly (or nearly exactly) is #P-hard. Our work indicates a possible limitation of adiabatic quantum optimization algorithms, leaving open the question of whether provable Grover-like speed-ups can be obtained for any optimization problem using this approach.

Abstract

Cyber-physical systems (CPS), robotics, the Internet of Things (IoT), and automotive systems are integral to modern technology. They are characterized by their safety criticality, accuracy, and real-time control requirements. Control software plays a crucial role in achieving these objectives by managing and coordinating the operations of various sub-systems. This paper presents a novel systematic mapping study (SMS) for control software engineering, analyzing 115 peer-reviewed papers. The study identifies, classifies, and maps existing solutions, providing a comprehensive and structured overview for practitioners and researchers. Our contributions include (i) a unique classification of literature into six research themes—engineering phases, engineering approaches, engineering paradigms, engineering artefacts, target application domains, and engineering concerns; (ii) insights into the specificity of approaches to target technologies and phases; (iii) the prominence of model-driven approaches for design and testing; (iv) the lack of end-to-end engineering support in existing approaches; and (v) the emerging role of agile-based methods versus the dominance of waterfallbased methods. This paper’s significance lies in its thorough analysis and the high-level mapping of the solution space, offering new perspectives and a detailed roadmap for future research and innovation in control software engineering. The findings will guide advancements and best practices in the field, underscoring the paper’s impact.

Abstract

Lane marker identification is crucial for developing Intelligent Transportation Systems and Autonomous Vehicles. While deep learning models excel in accuracy, their high com- putational requirements make them unsuitable for low-power edge devices. While various image-processing-based algorithms are used to pre-process images at different stages, there is a lack of an efficient algorithm specifically designed to enhance the lane-modeling stage. This paper proposes a two-stage pre- processing algorithm, consisting of a static phase and a dy- namic phase, to implement a divide-and-conquer approach for enhancing existing lane-modeling algorithms. The static phase generates an initial pixel label map and seed locations, while the dynamic phase uses the pre-processed input image and seed locations to generate a dynamic pixel map for localizing lane marking zones. Focusing on the center two lanes, the proposed method improves overall accuracy and recall, which is critical for capturing smaller lane details. Experimental results demonstrate the superior performance of the proposed two-stage algorithm compared to traditional HT and PHT methods, with the two- stage algorithm outperforming the standard HT(0.88 vs 0.92) and PHT(0.89 vs 0.95) in terms of Average Accuracy and Recall. Additionally, the two-stage PHT method significantly reduces power consumption compared to the Ultra Fast Lane Detection (UFLD) model, making it ideal for low-power edge devices like the Raspberry Pi 4B and Jetson Nano.

Abstract

In the legal domain, research efforts are being made to enhance precedent retrieval by exploiting features based on meta-data, catchphrases, citations, sentences, paragraphs, etc. It is reported in the legal domain that the text surrounding a citation provides information to identify the referenced judgment and supplies additional information about the referenced judgment and its connection to the formulated argument. In this paper, we have exploited the resourcefulness of the text surrounding the citation to improve the document representation of the referenced judgment. Experiments conducted on Indian court judgments show that the proposed Preceding citation Anchor Text (PAT)-based approach captures certain nuances that are not captured by the text present in the referenced judgment, indicating that there is a scope to exploit PAT to improve the performance of precedent retrieval systems.

Abstract

Smart home technology integrates various devices and systems that upgrade the convenience, security, and efficiency of a home. Due to these benefits, this technology is gaining global attention. In this paper, we have explored the perspectives of the residents of Mumbai on smart home technology adoption. A survey of 425 respondents was conducted, over a period of 3 months, covering demographics, awareness, preferences, concerns, and willingness to adopt. The key factors influencing their willingness to adopt such technology have been identified. The findings reveal insights into the perceived benefits, barriers, and demographic influences on smart home technology adoption in Mumbai. Most respondents show a high level of willing-ness to install smart home technology. More than 40% of respondents con-sider security their top priority when thinking about smart home features. Data privacy and security are the top concerns among the respondents, as they are worried about their personal data management and device vulnerabilities. The results are valuable for policymakers, advertisers, and developers working to promote smart home technology in the urban areas of India.

Abstract

The limited generalization of coreference resolution (CR) models has been a major bottleneck in the task’s broad application. Prior work has identified annotation differences, especially for mention detection, as one of the main reasons for the generalization gap and proposed using additional annotated target domain data. Rather than relying on this additional annotation, we propose an alternative referential task, Major Entity Identification (MEI), where we: (a) assume the target entities to be specified in the input, and (b) limit the task to only the frequent entities. Through extensive experiments, we demonstrate that MEI models generalize well across domains on multiple datasets with supervised models and LLM-based few-shot prompting. Additionally, MEI fits the classification framework, which enables the use of robust and intuitive classification-based metrics. Finally, MEI is also of practical use as it allows a user to search for all mentions of a particular entity or a group of entities of interest.

Abstract

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.