pwc-archive/papers-with-abstracts huggingface dataset:
Channel Estimation via Loss Field: Accurate Site-Trained Modeling for Shadowing Prediction
Authors: Jie Wang, Meles G. Weldegebriel, Neal Patwari
Date: 2023-10-18 00:00:00

Future mobile ad hoc networks will share spectrum between many users. Channels will be assigned on the fly to guarantee signal and interference power requirements for requested links. Channel losses must be re-estimated between many pairs of users as they move and as environmental conditions change. Computational complexity must be low, precluding the use of some accurate but computationally intensive site-specific channel models. Channel model errors must be low, precluding the use of standard statistical channel models. We propose a new channel model, CELF, which uses channel loss measurements from a deployed network in the area and a Bayesian linear regression method to estimate a site-specific loss field for the area. The loss field is explainable as the site's 'shadowing' of the radio propagation across the area of interest, but it requires no site-specific terrain or building information. Then, for any arbitrary pair of transmitter and receiver positions, CELF sums the loss field near the link line to estimate its channel loss. We use extensive measurements to show that CELF lowers the variance of channel estimates by up to 56%. It outperforms 3 popular machine learning methods in variance reduction and training efficiency.
Measuring Thermal Profiles in High Explosives using Neural Networks
Authors: John Greenhall, David K. Zerkle, Eric S. Davis, Robert Broilo, Cristian Pantea
Date: 2023-10-18 00:00:00

We present a new method for calculating the temperature profile in high explosive (HE) material using a Convolutional Neural Network (CNN). To train/test the CNN, we have developed a hybrid experiment/simulation method for collecting acoustic and temperature data. We experimentally heat cylindrical containers of HE material until detonation/deflagration, where we continuously measure the acoustic bursts through the HE using multiple acoustic transducers lined around the exterior container circumference. However, measuring the temperature profile in the HE in experiment would require inserting a high number of thermal probes, which would disrupt the heating process. Thus, we use two thermal probes, one at the HE center and one at the wall. We then use finite element simulation of the heating process to calculate the temperature distribution, and correct the simulated temperatures based on the experimental center and wall temperatures. We calculate temperature errors on the order of 15{\deg}C, which is approximately 12% of the range of temperatures in the experiment. We also investigate how the algorithm accuracy is affected by the number of acoustic receivers used to collect each measurement and the resolution of the temperature prediction. This work provides a means of assessing the safety status of HE material, which cannot be achieved using existing temperature measurement methods. Additionally, it has implications for range of other applications where internal temperature profile measurements would provide critical information. These applications include detecting chemical reactions, observing thermodynamic processes like combustion, monitoring metal or plastic casting, determining the energy density in thermal storage capsules, and identifying abnormal battery operation.
Identification of a DAGLB Mutation in a Non-Chinese Patient with Parkinson's Disease
Authors: Christelle Tesson, Mohamed Sofiane Bouchetara, Mélanie Ferrien, Suzanne Lesage, Alexis Brice
Date: 2023-10-19 00:00:00

Liu et al. recently reported that biallelic mutations in DAGLB are responsible for autosomal recessive early-onset Parkinson's disease. They identified six patients carrying DAGLB mutations, all of Chinese origin and presenting with typical Parkinson disease. No additional cases outside China have been reported so far.To assess the causality of DAGLB in our cohort, we used data mining in the exomes of 684 index cases with either autosomal recessive or sporadic early onset Parkinson disease (< 50 years). We identified a homozygous p.Pro357Leu missense variant in a single consanguineous PD case. This mutation predicted deleterious, affects a conserved amino acid localized in the catalytic domain of the protein nearby the pathological p.Asp363Gly mutation described in the previous paper. As the most frequent genes involved in AR-PD (PRKN, PINK1), the DAGLB-associated disease presents and evolves like typical PD.This work reinforces the fact that DAGLB is involved in early onset Parkinson disease, but given the fact that we identified a single patient among 684 index cases screened, we conclude that DAGLB is a very rare cause of early onset autosomal recessive Parkinson disease. However, we demonstrate that, mutations in DAGLB are not limited to the Chinese population but can also account for PD in North Africa.We feel that these new data indicate that DAGLB variants should be considered in non-Chinese cases with early-onset typical Parkinson disease.
Role of Cold Storage in Rooting of Stem Cuttings: A Review
Authors: Aram Akram Mohammed
Date: 2023-10-18 00:00:00

Cold storage is a strategy mainly used for ornamental cuttings for the purpose of prolonging production duration that provide the cuttings with the best condition at the time of planting. The results have been obtained from cold storage of carnations and chrysanthemums confirmed the fact that the herbaceous cuttings could be stored for about two months at not lower than 0 C and more than 4 C, with similar results of the cuttings directly planted without storage. On the other hand, hardwood cuttings are stored at low temperature in order to improve rooting ability via reducing rooting inhibitors that occur in dormant cuttings which were taken during dormant seasons (late fall or early winter). So, declining in rooting inhibitors has been observed in the hardwood cuttings of deciduous trees and Vitis spp which were subjected to low temperature for around 2-6 months. Consequently, rooting was improved in these species. Also, cold storage of cuttings of some coniferous trees increased rooting at a high rate. Other factors like cultivar and storage method may interact with cold storage duration and temperature degree as well. This review article outlines the information of the studies that have been obtained as a result of cold storage of the cuttings of different species.
AI-Based Decadal Predictive Analysis of Twenty Infectious Diseases in China with an Improved BSTS-MCMC Model
Authors: Peiwen Tan
Date: 2023-10-06 00:00:00
Tasks: Management, Time Series

This study embarks on a comprehensive exploration of the decadal trends and future trajectories of twenty distinct infectious diseases in China from 1998 to 2021. A refined Hybrid Bayesian Structural Time Series (BSTS)-Markov Chain Monte Carlo (MCMC) model is employed, intertwining with Long Short-Term Memory (LSTM) networks to dissect intricate relationships amidst population demographics, economic indices, and the evolution of infectious diseases. The findings reveal the persistent prevalence of high incidence diseases in future 10 years, like AIDS, Gonorrhea, and Syphilis, and stable occurrences of middle incidence rate diseases such as Brucellosis and Scarlet Fever, while also foretelling the potential disappearance of lower incidence rate diseases like Cholera, Encephalitis B, and Measles. The study particularly underscores the transformative impact of the COVID-19 pandemic, showcasing its extensive implications on the incidences and management of a plethora of diseases, urging a deeper probe into the nuanced alterations in disease transmission, testing, and reporting modalities amidst global health crises. This research accentuates the critical role of advanced predictive analytics in fostering global preparedness and response mechanisms, and in fortifying the resilience and adaptability of China public health framework against burgeoning infectious disease threats.
Interpreting Sequence-Levenshtein distance for determining error type and frequency between two embedded sequences of equal length
Authors: Robert Logan, Amy W. Wehe, Dori C. Woods, Jon Tilly, Konstantin Khrapko
Date: 2023-10-19 00:00:00

Levenshtein distance is a commonly used edit distance metric, typically applied in language processing, and to a lesser extent, in molecular biology analysis. Biological nucleic acid sequences are often embedded in longer sequences and are subject to insertion and deletion errors that introduce frameshift during sequencing. These frameshift errors are due to string context and should not be counted as true biological errors. Sequence-Levenshtein distance is a modification to Levenshtein distance that is permissive of frameshift error without additional penalty. However, in a biological context Levenshtein distance needs to accommodate both frameshift and weighted errors, which Sequence-Levenshtein distance cannot do. Errors are weighted when they are associated with a numerical cost that corresponds to their frequency of appearance. Here, we describe a modification that allows the use of Levenshtein distance and Sequence-Levenshtein distance to appropriately accommodate penalty-free frameshift between embedded sequences and correctly weight specific error types.
Black-Litterman Asset Allocation under Hidden Truncation Distribution
Authors: Jungjun Park, Andrew L. Nguyen
Date: 2023-10-18 00:00:00

In this paper, we study the Black-Litterman (BL) asset allocation model (Black and Litterman, 1990) under the hidden truncation skew-normal distribution (Arnold and Beaver, 2000). In particular, when returns are assumed to follow this skew normal distribution, we show that the posterior returns, after incorporating views, are also skew normal. By using Simaan three moments risk model (Simaan, 1993), we could then obtain the optimal portfolio. Empirical data show that the optimal portfolio obtained this way has less risk compared to an optimal portfolio of the classical BL model and that they become more negatively skewed as the expected returns of portfolios increase, which suggests that the investors trade a negative skewness for a higher expected return. We also observe a negative relation between portfolio volatility and portfolio skewness. This observation suggests that investors may be making a trade-off, opting for lower volatility in exchange for higher skewness, or vice versa. This trade-off indicates that stocks with significant price declines tend to exhibit increased volatility.
RK-core: An Established Methodology for Exploring the Hierarchical Structure within Datasets
Authors: Yao Lu, Yutian Huang, Jiaqi Nie, Zuohui Chen, Qi Xuan
Date: 2023-10-10 00:00:00
Tasks: Diversity

Recently, the field of machine learning has undergone a transition from model-centric to data-centric. The advancements in diverse learning tasks have been propelled by the accumulation of more extensive datasets, subsequently facilitating the training of larger models on these datasets. However, these datasets remain relatively under-explored. To this end, we introduce a pioneering approach known as RK-core, to empower gaining a deeper understanding of the intricate hierarchical structure within datasets. Across several benchmark datasets, we find that samples with low coreness values appear less representative of their respective categories, and conversely, those with high coreness values exhibit greater representativeness. Correspondingly, samples with high coreness values make a more substantial contribution to the performance in comparison to those with low coreness values. Building upon this, we further employ RK-core to analyze the hierarchical structure of samples with different coreset selection methods. Remarkably, we find that a high-quality coreset should exhibit hierarchical diversity instead of solely opting for representative samples. The code is available at https://github.com/yaolu-zjut/Kcore.
Stochastic Average Gradient : A Simple Empirical Investigation
Authors: Pascal Junior Tikeng Notsawo
Date: 2023-07-27 00:00:00

Despite the recent growth of theoretical studies and empirical successes of neural networks, gradient backpropagation is still the most widely used algorithm for training such networks. On the one hand, we have deterministic or full gradient (FG) approaches that have a cost proportional to the amount of training data used but have a linear convergence rate, and on the other hand, stochastic gradient (SG) methods that have a cost independent of the size of the dataset, but have a less optimal convergence rate than the determinist approaches. To combine the cost of the stochastic approach with the convergence rate of the deterministic approach, a stochastic average gradient (SAG) has been proposed. SAG is a method for optimizing the sum of a finite number of smooth convex functions. Like SG methods, the SAG method's iteration cost is independent of the number of terms in the sum. In this work, we propose to compare SAG to some standard optimizers used in machine learning. SAG converges faster than other optimizers on simple toy problems and performs better than many other optimizers on simple machine learning problems. We also propose a combination of SAG with the momentum algorithm and Adam. These combinations allow empirically higher speed and obtain better performance than the other methods, especially when the landscape of the function to optimize presents obstacles or is ill-conditioned.
AI Potentiality and Awareness: A Position Paper from the Perspective of Human-AI Teaming in Cybersecurity
Authors: Iqbal H. Sarker, Helge Janicke, Nazeeruddin Mohammad, Paul Watters, Surya Nepal
Date: 2023-09-28 00:00:00
Tasks: Position

This position paper explores the broad landscape of AI potentiality in the context of cybersecurity, with a particular emphasis on its possible risk factors with awareness, which can be managed by incorporating human experts in the loop, i.e., "Human-AI" teaming. As artificial intelligence (AI) technologies advance, they will provide unparalleled opportunities for attack identification, incident response, and recovery. However, the successful deployment of AI into cybersecurity measures necessitates an in-depth understanding of its capabilities, challenges, and ethical and legal implications to handle associated risk factors in real-world application areas. Towards this, we emphasize the importance of a balanced approach that incorporates AI's computational power with human expertise. AI systems may proactively discover vulnerabilities and detect anomalies through pattern recognition, and predictive modeling, significantly enhancing speed and accuracy. Human experts can explain AI-generated decisions to stakeholders, regulators, and end-users in critical situations, ensuring responsibility and accountability, which helps establish trust in AI-driven security solutions. Therefore, in this position paper, we argue that human-AI teaming is worthwhile in cybersecurity, in which human expertise such as intuition, critical thinking, or contextual understanding is combined with AI's computational power to improve overall cyber defenses.
Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining
Authors: Zhexiong Liu, Mohamed Elaraby, Yang Zhong, Diane Litman
Date: 2023-10-15 00:00:00
Tasks: Argument Mining, Classification, Persuasiveness, Stance Classification

This paper presents an overview of the ImageArg shared task, the first multimodal Argument Mining shared task co-located with the 10th Workshop on Argument Mining at EMNLP 2023. The shared task comprises two classification subtasks - (1) Subtask-A: Argument Stance Classification; (2) Subtask-B: Image Persuasiveness Classification. The former determines the stance of a tweet containing an image and a piece of text toward a controversial topic (e.g., gun control and abortion). The latter determines whether the image makes the tweet text more persuasive. The shared task received 31 submissions for Subtask-A and 21 submissions for Subtask-B from 9 different teams across 6 countries. The top submission in Subtask-A achieved an F1-score of 0.8647 while the best submission in Subtask-B achieved an F1-score of 0.5561.
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Authors: Katikapalli Subramanyam Kalyan
Date: 2023-10-04 00:00:00
Tasks: Data Augmentation, Self-Supervised Learning, Survey, Transfer Learning

Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation. LLMs, because of their large size and pretraining on large volumes of text data, exhibit special abilities which allow them to achieve remarkable performances without any task-specific training in many of the natural language processing tasks. The era of LLMs started with OpenAI GPT-3 model, and the popularity of LLMs is increasing exponentially after the introduction of models like ChatGPT and GPT4. We refer to GPT-3 and its successor OpenAI models, including ChatGPT and GPT4, as GPT-3 family large language models (GLLMs). With the ever-rising popularity of GLLMs, especially in the research community, there is a strong need for a comprehensive survey which summarizes the recent research progress in multiple dimensions and can guide the research community with insightful future research directions. We start the survey paper with foundation concepts like transformers, transfer learning, self-supervised learning, pretrained language models and large language models. We then present a brief overview of GLLMs and discuss the performances of GLLMs in various downstream tasks, specific domains and multiple languages. We also discuss the data labelling and data augmentation abilities of GLLMs, the robustness of GLLMs, the effectiveness of GLLMs as evaluators, and finally, conclude with multiple insightful future research directions. To summarize, this comprehensive survey paper will serve as a good resource for both academic and industry people to stay updated with the latest research related to GPT-3 family large language models.
Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis
Authors: Aram M. Ahmed, Tarik A. Rashid, Bryar A. Hassan, Jaffer Majidpour, Kaniaw A. Noori, Chnoor Maheadeen Rahman, Mohmad Hussein Abdalla, Shko M. Qader, Noor Tayfor, Naufel B Mohammed
Date: 2023-09-03 00:00:00
Tasks: Diversity

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity.
Fine-Tuning Self-Supervised Learning Models for End-to-End Pronunciation Scoring
Authors: Ahmed I. Zahran, Aly A. Fahmy, Khaled T. Wassif, Hanaa Bayomi
Date: 2023-09-19 00:00:00
Proceeding: IEEE Access 2023 9
Tasks: Feature Engineering, Phone-level pronunciation scoring, Phoneme Recognition, Self-Supervised Learning, speech-recognition, Speech Recognition, Transfer Learning

Automatic pronunciation assessment models are regularly used in language learning applications. Common methodologies for pronunciation assessment use feature-based approaches, such as the Goodness-of-Pronunciation (GOP) approach, or deep learning speech recognition models to perform speech assessment. With the rise of transformers, pre-trained self-supervised learning (SSL) models have been utilized to extract contextual speech representations, showing improvements in various downstream tasks. In this study, we propose the end-to-end regressor (E2E-R) model for pronunciation scoring. E2E-R is trained using a two-step training process. In the first step, the pre-trained SSL model is fine-tuned on a phoneme recognition task to obtain better representations for the pronounced phonemes. In the second step, transfer learning is used to build a pronunciation scoring model that uses a Siamese neural network to compare the pronounced phoneme representations to embeddings of the canonical phonemes and produce the final pronunciation scores. E2E-R achieves a Pearson correlation coefficient (PCC) of 0.68, which is almost similar to the state-of-the-art GOPT-PAII model while eliminating the need for training on additional native speech data, feature engineering, or external forced alignment modules. To our knowledge, this work presents the first utilization of a pre-trained SSL model for end-to-end phoneme-level pronunciation scoring on raw speech waveforms.
Optimized self-adaptive PID speed control for autonomous vehicles
Authors: Yassine Kebbati, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichalal, Dominique Gruyer
Date: 2021-09-02 00:00:00
Proceeding: International Conference on Automation and Computing (ICAC) 2021 9
Tasks: Autonomous Vehicles

The main control tasks in autonomous vehicles are steering (lateral) and speed (longitudinal) control. PID controllers are widely used in the industry because of their simplicity and good performance, but they are difficult to tune and need additional adaptation to control nonlinear systems with varying parameters. In this paper, the longitudinal control task is addressed by implementing adaptive PID control using two different approaches: Genetic Algorithms (GA-PID) and then Neural Networks (NN-PID) respectively. The vehicle nonlinear longitudinal dynamics are modeled using Powertrain blockset library. Finally, simulations are performed to assess and compare the performance of the two controllers subject to external disturbances.
Optimized adaptive MPC for lateral control of autonomous vehicles
Authors: Yassine Kebbati, Vicenç Puig, Naima Ait-Oufroukh, Vincent Vigneron, Dalil Ichalal
Date: 2021-11-11 00:00:00
Proceeding: International Conference on Control, Mechatronics and Automation (ICCMA) 2021 11
Tasks: Autonomous Driving, Autonomous Vehicles, Model Predictive Control, Steering Control

Autonomous vehicles are the upcoming solution to most transportation problems such as safety, comfort, and efficiency. The steering control is one of the main important tasks in achieving autonomous driving. Model predictive control (MPC) is among the fittest controllers for this task due to its optimal performance and ability to handle constraints. This paper proposes an adaptive MPC controller (AMPC) for the path-tracking task and an improved PSO algorithm for optimizing the AMPC parameters. Parameter adaption is realized online using a lookup table approach. The proposed AMPC performance is assessed and compared with the classic MPC and the Pure Pursuit controller through simulations.