pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Power-optimized CMOS sEMG Device with Ultra Low-noise integrated with ConvNet (VGG16) for Biomedical Applications
Authors: Ahmed Ayman - Mohamed Sabry
Date: 2023-01-04 00:00:00
Tasks: EEG

The needle bio-potential sensors for measuring muscle and brain activity need invasive surgical targeted muscle reinnervation (TMR) and a demanding process to maintain, but surface bio-potential sensors lack clear bio-signal reading (Signal-Interference). In this research, a novel power-optimized complementary metal-oxide-semiconductor (CMOS) Surface Electromyography (sEMG) is developed to improve the efficiency and quality of captured bio-signal for biomedical application: The early diagnosis of neurological disorders (Dystonia) and a novel compatible mind-controlled prosthetic leg with human daily activities. A novel sEMG composed of CMOS Op-Amp based PIC16F877A 8-bit CMOS Flash-based Microcontroller is utilized to minimize power consumption and data processing time. sEMG Circuit is implemented with developed analog filter along with infinite impulse response (IIR) digital filter via Fast Fourier Transform (FFT), Z-transform, and difference equations. The analysis shows a significant improvement of 169.2% noise-reduction in recorded EMG signal using developed digital filter compared to analog one according to numerical root mean square error (RMSE). Moreover, digital IIR was tested in two stages: algorithmic and real-world. As a result, IIR's algorithmic (MATLAB) and real-world RMSEs were 0.03616 and 0.05224, respectively. A notable advancement of 20.8% in data processing duration in EMG signal analysis. Optimizing VGG, AlexNet, and ResNet ConvNet as trained and tested on 15 public EEG (62-electrode) and 18 subjects' observed EMG data. The results indicate that VGG16-1D is 98.43% higher. During real testing, the accuracy was 95.8 +/- 4.6% for 16 subjects (6 Amputees-10 Dystonia). This study demonstrates the potential for sEMG, paving the way for biomedical applications.
Hatchery-produced sandfish (Holothuria scabra) show altered genetic diversity in New Caledonia
Authors: Florentine Riquet, Cécile Fauvelot, Pauline Fey, Daphné Grulois, Marc Leopold
Date: 2023-01-23 00:00:00
Tasks: Diversity, Management

Facing an alarming continuing decline of wild sea cucumber resources, management strategies were developed over the past three decades to sustainably promote development, maintenance, or regeneration of wild sea cucumber fisheries. In New Caledonia (South Pacific), dedicated management efforts via restocking and sea ranching programs were implemented to cope with the overharvesting of the sandfish Holothuria scabra and the recent loss of known populations. In order to investigate genetic implications of a major H. scabra restocking program, we assessed the genetic diversity and structure of wild stocks and hatchery-produced sandfish and compared the genetic outcomes of consecutive spawning and juvenile production events. For this, 1358 sandfish collected at four sites along the northwestern coasts of New Caledonia, as well as during five different restocking events in the Tiabet Bay, were genotyped using nine polymorphic microsatellite markers. We found that wild H. scabra populations from the northwestern coast of New Caledonia likely belonged to one panmictic population with high level of gene flow observed along the study scale. Further, this panmictic population displayed an effective size of breeders large enough to ensure the feasibility of appropriate breeding programs for restocking. In contrast, hatchery-produced samples did suffer from an important reduction in the effective population size: the effective population size were so small that genetic drift was detectable over one generation, with the presence of inbred individuals, as well as more related dyads than in wild populations. All these results suggest that dedicated efforts in hatcheries are further needed to maintain genetic diversity of hatchery-produced individuals in order to unbalance any negative impact during this artificial selection.
Mathematical modelling of zika virus in Brazil
Authors: Saúl E. Buitrago Boret, René Escalante, Minaya Villasana
Date: 2017-08-03 00:00:00

In this paper we study some deterministic mathematical models that seek to explain the expansion of zika virus, as a viral epidemic, using published data for Brazil. SIR type models are proposed and validated using the epidemic data found, considering several aspects in the spread of the disease. Finally, we confirmed that the crucial epidemic parameter such as $R_0$ is consistent with those previously reported in the literature for other areas. We also explored variations of the parameters within Brazil for different federal entities. We concluded that a parsimonious model that includes both human and vector populations best describe the epidemic parameters.
Modeling COVID-19 pandemic with financial markets models: The case of Jaén (Spain)
Authors: Julio Guerrero, Maria del Carmen Galiano, Giuseppe Orlando
Date: 2023-01-20 00:00:00
Tasks: valid

The main objective of this work is to test whether some stochastic models typically used in financial markets could be applied to the COVID-19 pandemic. To this end we have implemented the ARIMAX and Cox-Ingersoll-Ross (CIR) models originally designed for interest rate pricing but transformed by us into a forecasting tool. For the latter, which we denoted CIR*, both the Euler-Maruyama method and the Milstein method were used. Forecasts obtained with the maximum likelihood method have been validated with 95\% confidence intervals and with statistical measures of goodness of fit, such as the root mean square error (RMSE). We demonstrate that the accuracy of the obtained results is consistent with the observations and sufficiently accurate to the point that the proposed CIR* framework could be considered a valid alternative to the classical ARIMAX for modelling pandemics.
Lagrangian Formalism in Biology: II. Non-Standard and Null Lagrangians and their Role in Population Dynamics
Authors: Diana T. Pham, Zdzislaw E. Musielak
Date: 2023-01-20 00:00:00

Non-standard Lagrangians do not display any discernible energy-like terms, yet they give the same equations of motion as standard Lagrangians, which have easily identifiable energy-like terms. A new method to derive non-standard Lagrangians for second-order nonlinear differential equations with damping is developed and the limitations of this method are explored. It is shown that the limitations do not exist only for those nonlinear dynamical systems that can be converted into linear ones. The obtained results are applied to selected population dynamics models for which non-standard Lagrangians and their corresponding null Lagrangians and gauge functions are derived, and their roles in the population dynamics are discussed.
Statistical reproducibility of meta-analysis research claims for medical mask use in community settings to prevent COVID infection
Authors: S. Stanley Young, Warren B. Kindzierski
Date: 2023-01-22 00:00:00
Tasks: Medical Diagnosis

The coronavirus pandemic (COVID) has been an exceptional test of current scientific evidence that inform and shape policy. Many US states, cities, and counties implemented public orders for mask use on the notion that this intervention would delay and flatten the epidemic peak and largely benefit public health outcomes. P-value plotting was used to evaluate statistical reproducibility of meta-analysis research claims of a benefit for medical (surgical) mask use in community settings to prevent COVID infection. Eight studies (seven meta-analyses, one systematic review) published between 1 January 2020 and 7 December 2022 were evaluated. Base studies were randomized control trials with outcomes of medical diagnosis or laboratory-confirmed diagnosis of viral (Influenza or COVID) illness. Self-reported viral illness outcomes were excluded because of awareness bias. No evidence was observed for a medical mask use benefit to prevent viral infections in six p-value plots (five meta-analyses and one systematic review). Research claims of no benefit in three meta-analyses and the systematic review were reproduced in p-value plots. Research claims of a benefit in two meta-analyses were not reproduced in p-value plots. Insufficient data were available to construct p-value plots for two meta-analyses because of overreliance on self-reported outcomes. These findings suggest a benefit for medical mask use in community settings to prevent viral, including COVID infection, is unproven.
Forecasting local hospital bed demand for COVID-19 using on-request simulations
Authors: Angelo D'Ambrosio, Raisa Kociurzynski, Alexis Papathanassopoulos, Fabian Bürkin, Hajo Grundmann, Tjibbe Donker
Date: 2023-01-22 00:00:00

For hospitals, realistic forecasting of bed demand during impending epidemics of infectious diseases is essential to avoid being overwhelmed by a potential sudden increase in the number of admitted patients. Short-term forecasting can aid hospitals in adjusting their planning and freeing up beds in time. We created an easy-to-use online on-request tool based on local data to forecast COVID-19 bed demand for individual hospitals. The tool is flexible and adaptable to different settings. It is based on a stochastic compartmental model for estimating the epidemic dynamics and coupled with an exponential smoothing model for forecasting. The models are written in R and Julia and implemented as an R-shiny dashboard. The model is parameterized using COVID-19 incidence, vaccination, and bed occupancy data at customizable geographical resolutions, loaded from official online sources or uploaded manually. Users can select their hospital's catchment area and adjust the number of COVID-19 occupied beds at the start of the simulation. The tool provides short-term forecasts of disease incidence and past and forecasted estimation of the epidemic reproductive number at the chosen geographical level. These quantities are then used to estimate the bed occupancy in both general wards and intensive care unit beds. The platform has proven efficient, providing results within seconds while coping with many concurrent users. By providing ad-hoc, local data informed forecasts, this platform allows decision-makers to evaluate realistic scenarios for allocating scarce resources, such as ICU beds, at various geographic levels.
The Combinational Mutation Strategy of Differential Evolution Algorithm for Pricing Vanilla Options and Its Implementation on Data during Covid-19 Pandemic
Authors: Werry Febrianti, Kuntjoro Adji Sidarto, Novriana Sumarti
Date: 2023-01-23 00:00:00

Investors always want to know about the profit and the risk that they will be get before buying some assets. Our main focus is getting the profit and the probability of getting that profit using the differential evolution algorithm for vanilla option pricing on data before and during COVID-19 pandemic. Therefore, we model the pricing of an option using a bi-objective optimization problem using data before and during COVID-19 pandemic for one year expiration date. We change this problem into an optimization problem using adaptive weighted sum method. We use metaheuristics algorithm like Differential Evolution (DE) algorithm to solve this bi-objective optimization problems. In this paper, we also use modification of Differential Evolution for getting Pareto optimal solutions on vanilla option pricing for all contract. The algorithm is called Combinational Mutation Strategy of Differential Evolution (CmDE) algorithm. The results of our algorithm are satisfactory close to the real option price in the market data. Besides that, we also compare our result with the Black-Scholes results for validation. The results show that our results can approximate the real market options more accurate than Black-Scholes results. Hence, our bi-objective optimization using Combinational Mutation Strategy of Differential Evolution algorithm can be used to approximate the market real vanilla option pricing before and during COVID-19 pandemic.
Model Based Reinforcement Learning with Non-Gaussian Environment Dynamics and its Application to Portfolio Optimization
Authors: Huifang Huang, Ting Gao, Pengbo Li, Jin Guo, Peng Zhang, Nan Du
Date: 2023-01-23 00:00:00
Tasks: Algorithmic Trading, Decision Making, Gaussian Processes, Model-based Reinforcement Learning, Portfolio Optimization, reinforcement-learning, Reinforcement Learning (RL)

With the fast development of quantitative portfolio optimization in financial engineering, lots of AI-based algorithmic trading strategies have demonstrated promising results, among which reinforcement learning begins to manifest competitive advantages. However, the environment from real financial markets is complex and hard to be fully simulated, considering the observation of abrupt transitions, unpredictable hidden causal factors, heavy tail properties and so on. Thus, in this paper, first, we adopt a heavy-tailed preserving normalizing flows to simulate high-dimensional joint probability of the complex trading environment and develop a model-based reinforcement learning framework to better understand the intrinsic mechanisms of quantitative online trading. Second, we experiment with various stocks from three different financial markets (Dow, NASDAQ and S&P) and show that among these three financial markets, Dow gets the best performance based on various evaluation metrics under our back-testing system. Especially, our proposed method is able to mitigate the impact of unpredictable financial market crises during the COVID-19 pandemic period, resulting in a lower maximum drawdown. Third, we also explore the explanation of our RL algorithm. (1), we utilize the pattern causality method to study the interactive relation among different stocks in the environment. (2), We analyze the dynamic loss and actor loss to ensure the convergence of our strategies. (3), by visualizing high dimensional state transition data comparisons from real and virtual buffers with t-SNE, we uncover some effective patterns of better portfolio optimization strategies. (4), we also utilize eigenvalue analysis to study the convergence properties of the environmen's model.
Earthquake Magnitude and b value prediction model using Extreme Learning Machine
Authors: Gunbir Singh Baveja, Jaspreet Singh
Date: 2023-01-23 00:00:00
Tasks: Disaster Response, Earthquake prediction, Management, Value prediction

Earthquake prediction has been a challenging research area for many decades, where the future occurrence of this highly uncertain calamity is predicted. In this paper, several parametric and non-parametric features were calculated, where the non-parametric features were calculated using the parametric features. $8$ seismic features were calculated using Gutenberg-Richter law, the total recurrence, and the seismic energy release. Additionally, criterions such as Maximum Relevance and Maximum Redundancy were applied to choose the pertinent features. These features along with others were used as input for an Extreme Learning Machine (ELM) Regression Model. Magnitude and time data of $5$ decades from the Assam-Guwahati region were used to create this model for magnitude prediction. The Testing Accuracy and Testing Speed were computed taking the Root Mean Squared Error (RMSE) as the parameter for evaluating the mode. As confirmed by the results, ELM shows better scalability with much faster training and testing speed (up to a thousand times faster) than traditional Support Vector Machines. The testing RMSE came out to be around $0.097$. To further test the model's robustness -- magnitude-time data from California was used to calculate the seismic indicators which were then fed into an ELM and then tested on the Assam-Guwahati region. The model proves to be robust and can be implemented in early warning systems as it continues to be a major part of Disaster Response and management.
Backprop Induced Feature Weighting for Adversarial Domain Adaptation with Iterative Label Distribution Alignment
Authors: Thomas Westfechtel, Hao-Wei Yeh, Qier Meng, Yusuke Mukuta, Tatsuya Harada
Date: 2023-01-01 00:00:00
Proceeding: WACV 2023 1
Tasks: Classification, Domain Adaptation, Unsupervised Domain Adaptation

The requirement for large labeled datasets is one of the limiting factors for training accurate deep neural networks. Unsupervised domain adaptation tackles this problem of limited training data by transferring knowledge from one domain, which has many labeled data, to a different domain for which little to no labeled data is available. One common approach is to learn domain-invariant features for example with an adversarial approach. Previous methods often train the domain classifier and label classifier network separately, where both classification networks have little interaction with each other. In this paper, we introduce a classifier-based backprop-induced weighting of the feature space. This approach has two main advantages. Firstly, it lets the domain classifier focus on features that are important for the classification, and, secondly, it couples the classification and adversarial branch more closely. Furthermore, we introduce an iterative label distribution alignment method, that employs results of previous runs to approximate a class-balanced dataloader. We conduct experiments and ablation studies on three benchmarks Office-31, OfficeHome, and DomainNet to show the effectiveness of our proposed algorithm.
DiffMotion: Speech-Driven Gesture Synthesis Using Denoising Diffusion Model
Authors: Fan Zhang, Naye Ji, Fuxing Gao, Yongping Li
Date: 2023-01-24 00:00:00
Tasks: Denoising

Speech-driven gesture synthesis is a field of growing interest in virtual human creation. However, a critical challenge is the inherent intricate one-to-many mapping between speech and gestures. Previous studies have explored and achieved significant progress with generative models. Notwithstanding, most synthetic gestures are still vastly less natural. This paper presents DiffMotion, a novel speech-driven gesture synthesis architecture based on diffusion models. The model comprises an autoregressive temporal encoder and a denoising diffusion probability Module. The encoder extracts the temporal context of the speech input and historical gestures. The diffusion module learns a parameterized Markov chain to gradually convert a simple distribution into a complex distribution and generates the gestures according to the accompanied speech. Compared with baselines, objective and subjective evaluations confirm that our approach can produce natural and diverse gesticulation and demonstrate the benefits of diffusion-based models on speech-driven gesture synthesis.
Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
Authors: Zanjia Tong, Yuhang Chen, Zewei Xu, Rong Yu
Date: 2023-01-24 00:00:00
Tasks: object-detection, Object Detection, regression

The loss function for bounding box regression (BBR) is essential to object detection. Its good definition will bring significant performance improvement to the model. Most existing works assume that the examples in the training data are high-quality and focus on strengthening the fitting ability of BBR loss. If we blindly strengthen BBR on low-quality examples, it will jeopardize localization performance. Focal-EIoU v1 was proposed to solve this problem, but due to its static focusing mechanism (FM), the potential of non-monotonic FM was not fully exploited. Based on this idea, we propose an IoU-based loss with a dynamic non-monotonic FM named Wise-IoU (WIoU). The dynamic non-monotonic FM uses the outlier degree instead of IoU to evaluate the quality of anchor boxes and provides a wise gradient gain allocation strategy. This strategy reduces the competitiveness of high-quality anchor boxes while also reducing the harmful gradient generated by low-quality examples. This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance. When WIoU is applied to the state-of-the-art real-time detector YOLOv7, the AP-75 on the MS-COCO dataset is improved from 53.03% to 54.50%. Code is available at https://github.com/Instinct323/wiou.
RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving
Authors: Angelika Ando, Spyros Gidaris, Andrei Bursuc, Gilles Puy, Alexandre Boulch, Renaud Marlet
Date: 2023-01-24 00:00:00
Proceeding: CVPR 2023 1
Tasks: 3D Semantic Segmentation, Autonomous Driving, Inductive Bias, Semantic Segmentation

Casting semantic segmentation of outdoor LiDAR point clouds as a 2D problem, e.g., via range projection, is an effective and popular approach. These projection-based methods usually benefit from fast computations and, when combined with techniques which use other point cloud representations, achieve state-of-the-art results. Today, projection-based methods leverage 2D CNNs but recent advances in computer vision show that vision transformers (ViTs) have achieved state-of-the-art results in many image-based benchmarks. In this work, we question if projection-based methods for 3D semantic segmentation can benefit from these latest improvements on ViTs. We answer positively but only after combining them with three key ingredients: (a) ViTs are notoriously hard to train and require a lot of training data to learn powerful representations. By preserving the same backbone architecture as for RGB images, we can exploit the knowledge from long training on large image collections that are much cheaper to acquire and annotate than point clouds. We reach our best results with pre-trained ViTs on large image datasets. (b) We compensate ViTs' lack of inductive bias by substituting a tailored convolutional stem for the classical linear embedding layer. (c) We refine pixel-wise predictions with a convolutional decoder and a skip connection from the convolutional stem to combine low-level but fine-grained features of the the convolutional stem with the high-level but coarse predictions of the ViT encoder. With these ingredients, we show that our method, called RangeViT, outperforms existing projection-based methods on nuScenes and SemanticKITTI. The code is available at https://github.com/valeoai/rangevit.
Bipartite Graph Diffusion Model for Human Interaction Generation
Authors: Baptiste Chopin, Hao Tang, Mohamed Daoudi
Date: 2023-01-24 00:00:00
Tasks: Diversity, model

The generation of natural human motion interactions is a hot topic in computer vision and computer animation. It is a challenging task due to the diversity of possible human motion interactions. Diffusion models, which have already shown remarkable generative capabilities in other domains, are a good candidate for this task. In this paper, we introduce a novel bipartite graph diffusion method (BiGraphDiff) to generate human motion interactions between two persons. Specifically, bipartite node sets are constructed to model the inherent geometric constraints between skeleton nodes during interactions. The interaction graph diffusion model is transformer-based, combining some state-of-the-art motion methods. We show that the proposed achieves new state-of-the-art results on leading benchmarks for the human interaction generation task.