pwc-archive/papers-with-abstracts huggingface dataset:
Segmentation of waterbodies in remote sensing images using deep stacked ensemble model
Authors: Kaveh Moradkhani, Abdolhossein Fathi
Date: 2022-07-01 00:00:00
Proceeding: Applied Soft Computing 2022 7
Tasks: Body Detection, Image Segmentation, Segmentation Of Remote Sensing Imagery, Semantic Segmentation

Identifying surface water resources is considered as one of the principal applications of remote sensing image analysis that plays a crucial role in controlling optimal use of these resources, and preventing floods and crises such as drought. Traditional machine learning methods for extracting waterbodies require complex spectral analysis and selection of features based on previous knowledge. Although applying deep learning-based approaches, which has been considered in recent years, has eliminated the necessity of extracting manual features, they require too many training data and computational resources to achieve high performance. Consequently, each presented deep architecture can detect some of the existing patterns in the predefined conditions. This paper trains and optimizes three robust deep architectures, presented in various fields, using surface water data, and combines their results to achieve a robust model for detecting surface water. To this end, a deep hybrid architecture called ”deep stacked ensemble model” is employed on the outputs of three independent deep sub-models and extracts the final segmentation mask of the water areas more accurately. We evaluated our proposed model on a water body detection dataset provided by artificial intelligence crowd landsat (AIcrowd1 LNDST) challenge. The proposed technique improves the semantic segmentation performance and surpasses state-of-the-art results.
Grsnet: gated residual supervision network for pixel-wise building segmentation in remote sensing imagery
Authors: Samaneh Molavi Vardanjani, Abdolhossein Fathi, Kaveh Moradkhani
Date: 2022-09-23 00:00:00
Proceeding: International Journal of Remote Sensing 2022 9
Tasks: Image Segmentation, Segmentation Of Remote Sensing Imagery

The increasing development of imaging technology has made aerial image analysis one of the most widely used fields in image processing. Building extraction is the basic step in analysing urban structures, detecting construction violations, updating urban geographical divisions, and forecasting natural disasters. In this study, the aim is to automatically segment buildings in high-resolution satellite images using a new hybrid deep learning model, named Gated Residual Supervision Network (GRSNet). GRSNet extends the UNet framework by including three important components, i.e. attention gates (AG), residual units, and deep supervision part, with the main focus on transferring and reusing features. At first, the AG mechanism utilizes channel and spatial fine features to merge them effectively and deep supervision transfers feature details from the depths of the network. Then, residual units retrieve the information at different levels to train the model quickly. Finally, a fully connected classifier recovers features from the input image. GRSNet is evaluated on Massachusetts buildings public dataset and Inria aerial image labeling benchmark. The obtained results show the superiority of the proposed method compared to other deep learning-based state-of-the-art building segmentation methods with an intersection over union (IOU) of 89.86% and an F1-score of 94.53%.
SoK: Memorization in General-Purpose Large Language Models
Authors: Valentin Hartmann, Anshuman Suri, Vincent Bindschaedler, David Evans, Shruti Tople, Robert West
Date: 2023-10-24 00:00:00
Tasks: Memorization, Question Answering

Large Language Models (LLMs) are advancing at a remarkable pace, with myriad applications under development. Unlike most earlier machine learning models, they are no longer built for one specific application but are designed to excel in a wide range of tasks. A major part of this success is due to their huge training datasets and the unprecedented number of model parameters, which allow them to memorize large amounts of information contained in the training data. This memorization goes beyond mere language, and encompasses information only present in a few documents. This is often desirable since it is necessary for performing tasks such as question answering, and therefore an important part of learning, but also brings a whole array of issues, from privacy and security to copyright and beyond. LLMs can memorize short secrets in the training data, but can also memorize concepts like facts or writing styles that can be expressed in text in many different ways. We propose a taxonomy for memorization in LLMs that covers verbatim text, facts, ideas and algorithms, writing styles, distributional properties, and alignment goals. We describe the implications of each type of memorization - both positive and negative - for model performance, privacy, security and confidentiality, copyright, and auditing, and ways to detect and prevent memorization. We further highlight the challenges that arise from the predominant way of defining memorization with respect to model behavior instead of model weights, due to LLM-specific phenomena such as reasoning capabilities or differences between decoding algorithms. Throughout the paper, we describe potential risks and opportunities arising from memorization in LLMs that we hope will motivate new research directions.
Systematic Analysis of COVID-19 Ontologies
Authors: Debanjali Bain, Biswanath Dutta
Date: 2023-09-15 00:00:00

This comprehensive study conducts an in-depth analysis of existing COVID-19 ontologies, scrutinizing their objectives, classifications, design methodologies, and domain focal points. The study is conducted through a dual-stage approach, commencing with a systematic review of relevant literature and followed by an ontological assessment utilizing a parametric methodology. Through this meticulous process, twenty-four COVID-19 Ontologies (CovOs) are selected and examined. The findings highlight the scope, intended purpose, granularity of ontology, modularity, formalism, vocabulary reuse, and extent of domain coverage. The analysis reveals varying levels of formality in ontology development, a prevalent preference for utilizing OWL as the representational language, and diverse approaches to constructing class hierarchies within the models. Noteworthy is the recurrent reuse of ontologies like OBO models (CIDO, GO, etc.) alongside CODO. The METHONTOLOGY approach emerges as a favored design methodology, often coupled with application-based or data-centric evaluation methods. Our study provides valuable insights for the scientific community and COVID-19 ontology developers, supplemented by comprehensive ontology metrics. By meticulously evaluating and documenting COVID-19 information-driven ontological models, this research offers a comparative cross-domain perspective, shedding light on knowledge representation variations. The present study significantly enhances understanding of CovOs, serving as a consolidated resource for comparative analysis and future development, while also pinpointing research gaps and domain emphases, thereby guiding the trajectory of future ontological advancements.
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models
Authors: Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie, Jingren Zhou
Date: 2023-10-20 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

This paper introduces WordArt Designer, a user-driven framework for artistic typography synthesis, relying on the Large Language Model (LLM). The system incorporates four key modules: the LLM Engine, SemTypo, StyTypo, and TexTypo modules. 1) The LLM Engine, empowered by the LLM (e.g., GPT-3.5), interprets user inputs and generates actionable prompts for the other modules, thereby transforming abstract concepts into tangible designs. 2) The SemTypo module optimizes font designs using semantic concepts, striking a balance between artistic transformation and readability. 3) Building on the semantic layout provided by the SemTypo module, the StyTypo module creates smooth, refined images. 4) The TexTypo module further enhances the design's aesthetics through texture rendering, enabling the generation of inventive textured fonts. Notably, WordArt Designer highlights the fusion of generative AI with artistic typography. Experience its capabilities on ModelScope: https://www.modelscope.cn/studios/WordArt/WordArt.
Incentivizing Forecasters to Learn: Summarized vs. Unrestricted Advice
Authors: Yingkai Li, Jonathan Libgober
Date: 2023-10-29 00:00:00

How should forecasters be incentivized to acquire the most information when learning takes place over time? We address this question in the context of a novel dynamic mechanism design problem where a designer can incentivize learning by conditioning a reward on an event's outcome and expert reports. Eliciting summarized advice at a terminal date maximizes information acquisition if an informative signal fully reveals the outcome or has predictable content. Otherwise, richer reporting capabilities may be required. Our findings shed light on incentive design for consultation and forecasting by illustrating how learning dynamics shape qualitative properties of effort-maximizing contracts.
From Generative AI to Generative Internet of Things: Fundamentals, Framework, and Outlooks
Authors: Jinbo Wen, Jiangtian Nie, Jiawen Kang, Dusit Niyato, Hongyang Du, Yang Zhang, Mohsen Guizani
Date: 2023-10-27 00:00:00
Tasks: Decision Making, Management

Generative Artificial Intelligence (GAI) possesses the capabilities of generating realistic data and facilitating advanced decision-making. By integrating GAI into modern Internet of Things (IoT), Generative Internet of Things (GIoT) is emerging and holds immense potential to revolutionize various aspects of society, enabling more efficient and intelligent IoT applications, such as smart surveillance and voice assistants. In this article, we present the concept of GIoT and conduct an exploration of its potential prospects. Specifically, we first overview four GAI techniques and investigate promising GIoT applications. Then, we elaborate on the main challenges in enabling GIoT and propose a general GAI-based secure incentive mechanism framework to address them, in which we adopt Generative Diffusion Models (GDMs) for incentive mechanism designs and apply blockchain technologies for secure GIoT management. Moreover, we conduct a case study on modern Internet of Vehicle traffic monitoring, which utilizes GDMs to generate effective contracts for incentivizing users to contribute sensing data with high quality. Finally, we suggest several open directions worth investigating for the future popularity of GIoT.
Improved Lossless Coding for Storage and Transmission of Multichannel Immersive Audio
Authors: Toni Hirvonen, Mahmoud Namazi
Date: 2023-10-27 00:00:00

In this paper, techniques for improving multichannel lossless coding are examined. A method is proposed for the simultaneous coding of two or more different renderings (mixes) of the same content. The signal model uses both past samples of the upmix, and the current time samples of downmix samples to predict the upmix. Model parameters are optimized via a general linear solver, and the prediction residual is Rice coded. Additionally, the use of an SVD projection prior to residual coding is proposed. A comparison is made against various baselines, including FLAC. The proposed methods show improved compression ratios for the storage and transmission of immersive audio.
Unveil Sleep Spindles with Concentration of Frequency and Time
Authors: Riki Shimizu, Hau-Tieng Wu
Date: 2023-10-27 00:00:00
Tasks: Deep Learning, EEG, Spindle Detection

Objective: Sleep spindles contain crucial brain dynamics information. We introduce the novel non-linear time-frequency analysis tool 'Concentration of Frequency and Time' (ConceFT) to create an interpretable automated algorithm for sleep spindle annotation in EEG data and to measure spindle instantaneous frequencies (IFs). Methods: ConceFT effectively reduces stochastic EEG influence, enhancing spindle visibility in the time-frequency representation. Our automated spindle detection algorithm, ConceFT-Spindle (ConceFT-S), is compared to A7 (non-deep learning) and SUMO (deep learning) using Dream and MASS benchmark databases. We also quantify spindle IF dynamics. Results: ConceFT-S achieves F1 scores of 0.749 in Dream and 0.786 in MASS, which is equivalent to or surpass A7 and SUMO with statistical significance. We reveal that spindle IF is generally nonlinear. Conclusion: ConceFT offers an accurate, interpretable EEG-based sleep spindle detection algorithm and enables spindle IF quantification.
Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices
Authors: Xiaolong Tu, Anik Mallik, Dawei Chen, Kyungtae Han, Onur Altintas, Haoxin Wang, Jiang Xie
Date: 2023-10-19 00:00:00
Tasks: Deep Learning, Edge-computing

Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023.
A Stochastic Nonlinear Model Predictive Control with an Uncertainty Propagation Horizon for Autonomous Vehicle Motion Control
Authors: Baha Zarrouki, Chenyang Wang, Johannes Betz
Date: 2023-10-28 00:00:00
Tasks: Autonomous Vehicles, Model Predictive Control, State Estimation

Employing Stochastic Nonlinear Model Predictive Control (SNMPC) for real-time applications is challenging due to the complex task of propagating uncertainties through nonlinear systems. This difficulty becomes more pronounced in high-dimensional systems with extended prediction horizons, such as autonomous vehicles. To enhance closed-loop performance in and feasibility in SNMPCs, we introduce the concept of the Uncertainty Propagation Horizon (UPH). The UPH limits the time for uncertainty propagation through system dynamics, preventing trajectory divergence, optimizing feedback loop advantages, and reducing computational overhead. Our SNMPC approach utilizes Polynomial Chaos Expansion (PCE) to propagate uncertainties and incorporates nonlinear hard constraints on state expectations and nonlinear probabilistic constraints. We transform the probabilistic constraints into deterministic constraints by estimating the nonlinear constraints' expectation and variance. We then showcase our algorithm's effectiveness in real-time control of a high-dimensional, highly nonlinear system-the trajectory following of an autonomous passenger vehicle, modeled with a dynamic nonlinear single-track model. Experimental results demonstrate our approach's robust capability to follow an optimal racetrack trajectory at speeds of up to 37.5m/s while dealing with state estimation disturbances, achieving a minimum solving frequency of 97Hz. Additionally, our experiments illustrate that limiting the UPH renders previously infeasible SNMPC problems feasible, even when incorrect uncertainty assumptions or strong disturbances are present.
Estimation of Semiconductor Power Losses Through Automatic Thermal Modeling
Authors: Jose Miguel Sanz-Alcaine, Eduardo Sebastian, Francisco Jose Perez-Cebolla, Asier Arruti, Carlos Bernal-Ruiz, Iosu Aizpuru
Date: 2023-10-29 00:00:00

The optimal design of power converters requires accurate knowledge of the dissipation elements of its system to achieve the desired performance and security requirements. Calorimetric methods have surpassed classical electrical methods for the estimation of semiconductor power losses but have mechanical limitations and resort to analytical electrothermal equivalent circuits for this task. These electrothermal models are highly dependent on the topology and technology used on the power converter leading to either simplifications that underestimate the thermal effects or intractable sets of differential equations. To overcome these issues, we propose a novel data-driven identification method to characterize the thermal dynamics of power converters allowing the designer to obtain semiconductor total power losses only by means of temperature measurements without the need of a calorimeter. Given a set of power vs.temperature profiles, our solution identifies the linear model that best fits the data. The solution is based on an optimization problem that allows not only accurate identification but also coding of the desired modeling requirements, such as dynamics' invertibility to allow the estimation of power losses from the temperature profiles. The proposed methodology can be applied to any power converter topology. Furthermore, by obtaining a linear model, standard control theory techniques can be exploited to analyze and control the thermal dynamics. Real experiments validate the generality and accuracy of the proposal.
RIS-aided Real-time Beam Tracking for a Mobile User via Bayesian Optimization
Authors: Junshuo Liu, Rujing Xiong, Jialong Lu, Tiebin Mi, Robert C. Qiu
Date: 2023-10-29 00:00:00
Tasks: Bayesian Optimization, Management

The conventional beam management procedure mandates that the user equipment (UE) periodically measure the received signal reference power (RSRP) and transmit these measurements to the base station (BS). The challenge lies in balancing the number of beams used: it should be large enough to identify high-RSRP beams but small enough to minimize reporting overhead. This paper investigates this essential performance-versus-overhead trade-off using Bayesian optimization. The proposed approach represents the first application of real-time beam tracking via Bayesian optimization in RIS-assisted communication systems. Simulation results validate the effectiveness of this scheme.
A 0.21-ps FOM Capacitor-Less Analog LDO with Dual-Range Load Current for Biomedical Applications
Authors: Yasemin Engur, Mahsa Shoaran
Date: 2023-10-29 00:00:00

This paper presents an output capacitor-less low-dropout regulator (LDO) with a bias switching scheme for biomedical applications with dual-range load currents. Power optimization is crucial for systems with multiple activation modes such as neural interfaces, IoT and edge devices with varying load currents. To enable rapid switching between low and high current states, a flipped voltage follower (FVF) configuration is utilized, along with a super source follower buffer to drive the power transistor. Two feedback loops and an on-chip compensation capacitor Cc maintain the stability of the regulator under various load conditions. The LDO was implemented in a 65nm CMOS process with 1.5V input and 1.2V output voltages. The measured quiescent current is as low as 3uA and 50uA for the 0-500uA and 5-15mA load current ranges, respectively. An undershoot voltage of 100mV is observed when the load current switches from 0 to 15mA within 80ns, with a maximum current efficiency of 99.98%. Our design achieved a low Figure-of-Merit of 0.21ps, outperforming state-of-the-art analog LDOs.
Model-based Control of the Scanning Tunneling Microscope: Enabling New Modes of Imaging, Spectroscopy, and Lithography
Authors: Hamed Alemansour, S. O. Reza Moheimani
Date: 2023-10-28 00:00:00

The invention of scanning tunneling microscope (STM) dates back to the work of Binnig and Rohrer in the early 1980s, whose seminal contribution was rewarded by the 1986 Nobel Prize in Physics for the design of the scanning tunneling microscope. Forty years later, the STM remains the best existing tool for studying electronic, chemical, and physical properties of conducting and semiconducting surfaces with atomic precision. It has opened entirely new fields of research, enabling scientists to gain invaluable insight into properties and structure of matter at the atomic scale. Recent breakthroughs in STM-based automated hydrogen depassivation lithography (HDL) on silicon have resulted in the STM being considered a viable tool for fabrication of error-free silicon-based quantum-electronic devices. Despite the STM's unique ability to interrogate and manipulate matter with atomic precision, it remains a challenging tool to use. It turns out that many issues can be traced back to the STM's feedback control system, which has remained essentially unchanged since its invention about 40 years ago. This article explains the role of feedback control system of the STM and reviews some of the recent progress made possible in imaging, spectroscopy, and lithography by making appropriate changes to the STM's feedback control loop. We believe that the full potential of the STM is yet to be realized, and the key to new innovations will be the application of advanced model-based control and estimation techniques to this system.
Integrated Relative-Measurement-Based Network Localization and Formation Maneuver Control (Extended Version)
Authors: Xu Fang, Lihua Xie, Xiaolei Li
Date: 2023-10-28 00:00:00
Tasks: Translation

This paper studies the problem of integrated distributed network localization and formation maneuver control. We develop an integrated relative-measurement-based scheme, which only uses relative positions, distances, bearings, angles, ratio-of-distances, or their combination to achieve distributed network localization and formation maneuver control in $\mathbb{R}^d (d \ge 2)$. By exploring the localizability and invariance of the target formation, the scale, rotation, and translation of the formation can be controlled simultaneously by only tuning the leaders' positions, i.e., the followers do not need to know parameters of the scale, rotation, and translation of the target formation. The proposed method can globally drive the formation errors to zero in finite time over multi-layer $d\!+\!1$-rooted graphs. A simulation example is given to illustrate the theoretical results.