pwc-archive/papers-with-abstracts huggingface dataset:
An F-ratio-Based Method for Estimating the Number of Active Sources in MEG
Authors: Amita Giri, John C. Mosher, Amir Adler, Dimitrios Pantazis
Date: 2023-06-09 00:00:00
Tasks: Anatomy

Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Our results revealed that the selection of thresholds plays a critical role in determining the method`s overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.
Fusion of Distance Measurements between Agents with Unknown Correlations
Authors: Colin Cros, Pierre-Olivier Amblard, Christophe Prieur, Jean-François da Rocha
Date: 2023-06-09 00:00:00

Cooperative localization is a promising solution to improve the accuracy and overcome the shortcomings of GNSS. Cooperation is often achieved by measuring the distance between users. To optimally integrate a distance measurement between two users into a navigation filter, the correlation between the errors of their estimates must be known. Unfortunately, in large scale networks the agents cannot compute these correlations and must use consistent filters. A consistent filter provides an upper bound on the covariance of the error of the estimator taking into account all the possible correlations. In this paper, a consistent linear filter for integrating a distance measurement is derived using Split Covariance Intersection. Its analysis shows that a distance measurement between two agents can only benefit one of them, i.e., only one of the two can use the distance measurement to improve its estimator. Furthermore, in some cases, none can. A necessary condition for an agent to benefit from the measurement is given for a general class of objective functions. When the objective function is the trace or the determinant, necessary and sufficient conditions are given.
One-shot Learning for Channel Estimation in Massive MIMO Systems
Authors: Kai Kang, Qiyu Hu, Yunlong Cai, Yonina C. Eldar
Date: 2023-06-09 00:00:00
Tasks: Denoising, One-Shot Learning, Self-Supervised Learning

In conventional supervised deep learning based channel estimation algorithms, a large number of training samples are required for offline training. However, in practical communication systems, it is difficult to obtain channel samples for every signal-to-noise ratio (SNR). Furthermore, the generalization ability of these deep neural networks (DNN) is typically poor. In this work, we propose a one-shot self-supervised learning framework for channel estimation in multi-input multi-output (MIMO) systems. The required number of samples for offline training is small and our approach can be directly deployed to adapt to variable channels. Our framework consists of a traditional channel estimation module and a denoising module. The denoising module is designed based on the one-shot learning method Self2Self and employs Bernoulli sampling to generate training labels. Besides,we further utilize a blind spot strategy and dropout technique to avoid overfitting. Simulation results show that the performance of the proposed one-shot self-supervised learning method is very close to the supervised learning approach while obtaining improved generalization ability for different channel environments.
A Novel Satellite Selection Algorithm Using LSTM Neural Networks For Single-epoch Localization
Authors: Ibrahim Sbeity, Christophe Villien, Christophe Combettes, Benoît Denis, E Veronica Belmega, Marwa Chafii
Date: 2023-06-09 00:00:00

This work presents a new approach for detection and exclusion (or de-weighting) of pseudo-range measurements from the Global Navigation Satellite System (GNSS) in order to improve the accuracy of single-epoch positioning, which is an essential prerequisite for maintaining good navigation performance in challenging operating contexts (e.g., under Non-Line of Sight and/or multipath propagation). Beyond the usual preliminary hard decision stage, which can mainly reject obvious outliers, our approach exploits machine learning to optimize the relative contributions from all available satellites feeding the positioning solver. For this, we construct a customized matrix of pseudorange residuals that is used as an input to the proposed longshort term memory neural network (LSTM NN) architecture. The latter is trained to predict several quality indicators that roughly approximate the standard deviations of pseudo-range errors, which are further integrated in the calculation of weights. Our numerical evaluations on both synthetic and real data show that the proposed solution is able to outperform conventional weighting and signal selection strategies from the state-of-theart, while fairly approaching optimal positioning accuracy.
Mitigation of Misalignment Errors Over Inter-Satellite FSO Energy Harvesting
Authors: Baris Donmez, Irfan Azam, Gunes Karabulut Kurt
Date: 2023-06-08 00:00:00

In this paper, the impact of the acquisition, tracking, and pointing (ATP) module utilization on inter-satellite energy harvesting is investigated for 1U (0.1$\times$0.1$\times$0.1 m) and 12U (0.2$\times$0.2$\times$0.3 m) satellites for adaptive beam divergence and the corresponding distances while maintaining the spot diameters. Random elevation and azimuth misalignment error angles at both the transmitter and the receiver are modeled with Gaussian distribution hence the radial pointing error angle is modeled with Rayleigh distribution. The Monte Carlo approach is used to determine mean radial error angles for both transmitter and receiver in the non-ATP and ATP cases. The average harvested powers are analyzed as a function of the transmit powers and inter-satellite distances for both 1U and 12U satellites while considering the minimum power requirements. Our simulation results show that in the non-ATP case, the minimum required average harvested power cannot be achieved beyond 680 and 1360 km distances for 1U and 12U satellites, respectively, with a maximum transmit power of 1 kW. However, 2 W of average harvested power can be achieved at around 750 and 1500 km for 1U and 12U satellites, respectively, with a transmit power of 27 W in the presence of an ATP mechanism.
Impact of resource distributions on the competition of species in stream environment
Authors: Tung D. Nguyen, Yixiang Wu, Tingting Tang, Amy Veprauskas, Ying Zhou, Behzad Djafari Rouhani, Zhisheng Shuai
Date: 2023-06-08 00:00:00

Our earlier work in \cite{nguyen2022population} shows that concentrating the resources on the upstream end tends to maximize the total biomass in a metapopulation model for a stream species. In this paper, we continue our research direction by further considering a Lotka-Voletrra competition patch model for two stream species. We show that the species whose resource allocations maximize the total biomass has competitive advantage.
Noncoding RNAs evolutionarily extend animal lifespan
Authors: Anyou Wang
Date: 2023-06-09 00:00:00

The mechanisms underlying lifespan evolution in organisms have long been mysterious. However, recent studies have demonstrated that organisms evolutionarily gain noncoding RNAs (ncRNAs) that carry endogenous profound functions in higher organisms, including lifespan. This study unveils ncRNAs as crucial drivers driving animal lifespan evolution. Species in the animal kingdom evolutionarily increase their ncRNA length in their genomes, coinciding with trimming mitochondrial genome length. This leads to lower energy consumption and ultimately lifespan extension. Notably, during lifespan extension, species exhibit a gradual acquisition of long-life ncRNA motifs while concurrently losing short-life motifs. These longevity-associated ncRNA motifs, such as GGTGCG, are particularly active in key tissues, including the endometrium, ovary, testis, and cerebral cortex. The activation of ncRNAs in the ovary and endometrium offers insights into why women generally exhibit longer lifespans than men. This groundbreaking discovery reveals the pivotal role of ncRNAs in driving lifespan evolution and provides a fundamental foundation for the study of longevity and aging.
LGEM$^\text{+}$: a first-order logic framework for automated improvement of metabolic network models through abduction
Authors: Alexander H. Gower, Konstantin Korovin, Daniel Brunnsåker, Ievgeniia A. Tiukova, Ross D. King
Date: 2023-06-09 00:00:00
Tasks: scientific discovery

Scientific discovery in biology is difficult due to the complexity of the systems involved and the expense of obtaining high quality experimental data. Automated techniques are a promising way to make scientific discoveries at the scale and pace required to model large biological systems. A key problem for 21st century biology is to build a computational model of the eukaryotic cell. The yeast Saccharomyces cerevisiae is the best understood eukaryote, and genome-scale metabolic models (GEMs) are rich sources of background knowledge that we can use as a basis for automated inference and investigation. We present LGEM+, a system for automated abductive improvement of GEMs consisting of: a compartmentalised first-order logic framework for describing biochemical pathways (using curated GEMs as the expert knowledge source); and a two-stage hypothesis abduction procedure. We demonstrate that deductive inference on logical theories created using LGEM+, using the automated theorem prover iProver, can predict growth/no-growth of S. cerevisiae strains in minimal media. LGEM+ proposed 2094 unique candidate hypotheses for model improvement. We assess the value of the generated hypotheses using two criteria: (a) genome-wide single-gene essentiality prediction, and (b) constraint of flux-balance analysis (FBA) simulations. For (b) we developed an algorithm to integrate FBA with the logic model. We rank and filter the hypotheses using these assessments. We intend to test these hypotheses using the robot scientist Genesis, which is based around chemostat cultivation and high-throughput metabolomics.
Monte Carlo simulation for Barndorff-Nielsen and Shephard model under change of measure
Authors: Takuji Arai, Yuto Imai
Date: 2023-06-09 00:00:00

The Barndorff-Nielsen and Shephard model is a representative jump-type stochastic volatility model. Still, no method exists to compute option prices numerically for the non-martingale case with infinite active jumps. We develop two simulation methods for such a case under change of measure and conduct some numerical experiments.
Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers
Authors: Alvaro Arroyo, Alvaro Cartea, Fernando Moreno-Pino, Stefan Zohren
Date: 2023-06-08 00:00:00
Tasks: Decoder, Informativeness, Survival Analysis

One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g., within the bid-ask spread) for assets with different queue dynamics and trading activity.
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data
Authors: Petar Ivanov, Ivan Koychev, Momchil Hardalov, Preslav Nakov
Date: 2023-05-24 00:00:00
Tasks: Fact Checking, Misinformation

Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers. While previous work on this problem has focused exclusively on the text modality, here we explore the utility of the audio modality as an additional input. We create a new multimodal dataset (text and audio in English) containing 48 hours of speech from past political debates in the USA. We then experimentally demonstrate that, in the case of multiple speakers, adding the audio modality yields sizable improvements over using the text modality alone; moreover, an audio-only model could outperform a text-only one for a single speaker. With the aim to enable future research, we make all our data and code publicly available at https://github.com/petar-iv/audio-checkworthiness-detection.
Simulation and Prediction of Countercurrent Spontaneous Imbibition at Early and Late Times Using Physics-Informed Neural Networks
Authors: Jassem Abbasi, Pål Østebø Andersen
Date: 2023-05-06 00:00:00

The application of Physics-Informed Neural Networks (PINNs) is investigated for the first time in solving the one-dimensional Countercurrent spontaneous imbibition (COUCSI) problem at both early and late time (i.e., before and after the imbibition front meets the no-flow boundary). We introduce utilization of Change-of-Variables as a technique for improving performance of PINNs. We formulated the COUCSI problem in three equivalent forms by changing the independent variables. The first describes saturation as function of normalized position X and time T; the second as function of X and Y=T^0.5; and the third as a sole function of Z=X/T^0.5 (valid only at early time). The PINN model was generated using a feed-forward neural network and trained based on minimizing a weighted loss function, including the physics-informed loss term and terms corresponding to the initial and boundary conditions. All three formulations could closely approximate the correct solutions, with water saturation mean absolute errors around 0.019 and 0.009 for XT and XY formulations and 0.012 for the Z formulation at early time. The Z formulation perfectly captured the self-similarity of the system at early time. This was less captured by XT and XY formulations. The total variation of saturation was preserved in the Z formulation, and it was better preserved with XY- than XT formulation. Redefining the problem based on the physics-inspired variables reduced the non-linearity of the problem and allowed higher solution accuracies, a higher degree of loss-landscape convexity, a lower number of required collocation points, smaller network sizes, and more computationally efficient solutions.
clustering an african hairstyle dataset using pca and k-means
Authors: Teffo Phomolo Nicrocia, Owolawi Pius Adewale, Pholo Moanda Diana
Date: 2023-05-25 00:00:00
Tasks: Clustering, image-classification, Image Classification

The adoption of digital transformation was not expressed in building an African face shape classifier. In this paper, an approach is presented that uses k-means to classify African women images. African women rely on beauty standards recommendations, personal preference, or the newest trends in hairstyles to decide on the appropriate hairstyle for them. In this paper, an approach is presented that uses K-means clustering to classify African women's images. In order to identify potential facial clusters, Haarcascade is used for feature-based training, and K-means clustering is applied for image classification.
Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes
Authors: Chun Liu, Suqiang Ma, Zheng Li, Wei Yang, Zhigang Han
Date: 2023-05-31 00:00:00
Tasks: Classification, Contrastive Learning, Scene Classification, zero-shot-classification, Zero-Shot Learning

Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in recent years. These methods mainly focus on matching the visual features of each image scene with their corresponding semantic descriptors in the latent space. Less attention has been paid to the contrastive relationships between different image scenes and different semantic descriptors. In light of the challenge of large intra-class difference and inter-class similarity among image scenes and the potential noisy samples, these methods are susceptible to the influence of the instances which are far from these of the same classes and close to these of other classes. In this work, we propose a multi-level cross-modal feature alignment method via contrastive learning for zero-shot classification of remote sensing image scenes. While promoting the single-instance level positive alignment between each image scene with their corresponding semantic descriptors, the proposed method takes the cross-instance contrastive relationships into consideration,and learns to keep the visual and semantic features of different classes in the latent space apart from each other. Extensive experiments have been done to evaluate the performance of the proposed method. The results show that our proposed method outperforms state of the art methods for zero-shot remote sensing image scene classification. All the code and data are available at github https://github.com/masuqiang/MCFA-Pytorch
DeepStay: Stay Region Extraction from Location Trajectories using Weak Supervision
Authors: Christian Löwens, Daniela Thyssens, Emma Andersson, Christina Jenkins, Lars Schmidt-Thieme
Date: 2023-06-05 00:00:00
Tasks: Stay Region Extraction, Transportation Mode Detection

Nowadays, mobile devices enable constant tracking of the user's position and location trajectories can be used to infer personal points of interest (POIs) like homes, workplaces, or stores. A common way to extract POIs is to first identify spatio-temporal regions where a user spends a significant amount of time, known as stay regions (SRs). Common approaches to SR extraction are evaluated either solely unsupervised or on a small-scale private dataset, as popular public datasets are unlabeled. Most of these methods rely on hand-crafted features or thresholds and do not learn beyond hyperparameter optimization. Therefore, we propose a weakly and self-supervised transformer-based model called DeepStay, which is trained on location trajectories to predict stay regions. To the best of our knowledge, this is the first approach based on deep learning and the first approach that is evaluated on a public, labeled dataset. Our SR extraction method outperforms state-of-the-art methods. In addition, we conducted a limited experiment on the task of transportation mode detection from GPS trajectories using the same architecture and achieved significantly higher scores than the state-of-the-art. Our code is available at https://github.com/christianll9/deepstay.
Adversarial Attack On Yolov5 For Traffic And Road Sign Detection
Authors: Sanyam Jain
Date: 2023-05-27 00:00:00
Tasks: Adversarial Attack, object-detection, Object Detection

This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm. The paper explores the vulnerability of the YOLOv5 to adversarial attacks in the context of traffic and road sign detection. The paper investigates the impact of different types of attacks, including the Limited memory Broyden Fletcher Goldfarb Shanno (L-BFGS), the Fast Gradient Sign Method (FGSM) attack, the Carlini and Wagner (C&W) attack, the Basic Iterative Method (BIM) attack, the Projected Gradient Descent (PGD) attack, One Pixel Attack, and the Universal Adversarial Perturbations attack on the accuracy of YOLOv5 in detecting traffic and road signs. The results show that YOLOv5 is susceptible to these attacks, with misclassification rates increasing as the magnitude of the perturbations increases. We also explain the results using saliency maps. The findings of this paper have important implications for the safety and reliability of object detection algorithms used in traffic and transportation systems, highlighting the need for more robust and secure models to ensure their effectiveness in real-world applications.
DeepSeaNet: Improving Underwater Object Detection using EfficientDet
Authors: Sanyam Jain
Date: 2023-05-26 00:00:00
Tasks: Object, object-detection, Object Detection

Marine animals and deep underwater objects are difficult to recognize and monitor for safety of aquatic life. There is an increasing challenge when the water is saline with granular particles and impurities. In such natural adversarial environment, traditional approaches like CNN start to fail and are expensive to compute. This project involves implementing and evaluating various object detection models, including EfficientDet, YOLOv5, YOLOv8, and Detectron2, on an existing annotated underwater dataset, called the Brackish-Dataset. The dataset comprises annotated image sequences of fish, crabs, starfish, and other aquatic animals captured in Limfjorden water with limited visibility. The aim of this research project is to study the efficiency of newer models on the same dataset and contrast them with the previous results based on accuracy and inference time. Firstly, I compare the results of YOLOv3 (31.10% mean Average Precision (mAP)), YOLOv4 (83.72% mAP), YOLOv5 (97.6%), YOLOv8 (98.20%), EfficientDet (98.56% mAP) and Detectron2 (95.20% mAP) on the same dataset. Secondly, I provide a modified BiSkFPN mechanism (BiFPN neck with skip connections) to perform complex feature fusion in adversarial noise which makes modified EfficientDet robust to perturbations. Third, analyzed the effect on accuracy of EfficientDet (98.63% mAP) and YOLOv5 by adversarial learning (98.04% mAP). Last, I provide class activation map based explanations (CAM) for the two models to promote Explainability in black box models. Overall, the results indicate that modified EfficientDet achieved higher accuracy with five-fold cross validation than the other models with 88.54% IoU of feature maps.