pwc-archive/papers-with-abstracts huggingface dataset:
Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges
Authors: James E. Gallagher, Edward J. Oughton
Date: 2024-09-03 00:00:00
Tasks: Autonomous Vehicles, Multispectral Object Detection, object-detection, Object Detection, Sensor Fusion, Transfer Learning

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of You Only Look Once (YOLO) methods. Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is Red-Green-Blue (RGB) with Long-Wave Infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. 58% of multispectral-YOLO research is being conducted in China, with broadly similar research quality to other countries (with a mean journal impact factor of 4.45 versus 4.36 for papers not originating from Chinese institutions). Future research needs to focus on (i) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications, (ii) exploring methods to generate large synthetic multispectral datasets, (iii) advancing multispectral YOLO transfer learning techniques to address dataset scarcity, and (iv) innovating fusion research with other sensor types beyond RGB and LWIR.
The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology
Authors: Inwoo Seo, Eunkyoung Bae, Joo-Young Jeon, Young-Sang Yoon, Jiho Cha
Date: 2024-09-03 00:00:00
Tasks: Diagnostic

Social problems stemming from the shortage of radiologists are intensifying, and artificial intelligence is being highlighted as a potential solution. Recently emerging large-scale generative AI has expanded from large language models (LLMs) to multi-modal models, showing potential to revolutionize the entire process of medical imaging. However, comprehensive reviews on their development status and future challenges are currently lacking. This scoping review systematically organizes existing literature on the clinical value of large-scale generative AI applications by following PCC guidelines. A systematic search was conducted across four databases: PubMed, EMbase, IEEE-Xplore, and Google Scholar, and 15 studies meeting the inclusion/exclusion criteria set by the researchers were reviewed. Most of these studies focused on improving the efficiency of report generation in specific parts of the interpretation process or on translating reports to aid patient understanding, with the latest studies extending to AI applications performing direct interpretations. All studies were quantitatively evaluated by clinicians, with most utilizing LLMs and only three employing multi-modal models. Both LLMs and multi-modal models showed excellent results in specific areas, but none yet outperformed radiologists in diagnostic performance. Most studies utilized GPT, with few using models specialized for the medical imaging domain. This study provides insights into the current state and limitations of large-scale generative AI-based applications in the medical imaging field, offering foundational data and suggesting that the era of medical imaging foundation models is on the horizon, which may fundamentally transform clinical practice in the near future.
Sherpa: a Mission-Independent Data Analysis Application
Authors: Peter E. Freeman, Stephen Doe, Aneta Siemiginowska
Date: 2001-08-27 00:00:00
Tasks: parameter estimation

The ever-increasing quality and complexity of astronomical data underscores the need for new and powerful data analysis applications. This need has led to the development of Sherpa, a modeling and fitting program in the CIAO software package that enables the analysis of multi-dimensional, multi-wavelength data. In this paper, we present an overview of Sherpa's features, which include: support for a wide variety of input and output data formats, including the new Model Descriptor List (MDL) format; a model language which permits the construction of arbitrarily complex model expressions, including ones representing instrument characteristics; a wide variety of fit statistics and methods of optimization, model comparison, and parameter estimation; multi-dimensional visualization, provided by ChIPS; and new interactive analysis capabilities provided by embedding the S-Lang interpreted scripting language. We conclude by showing example Sherpa analysis sessions.
Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?
Authors: Marina Mayor-Rocher, Nina Melero, Elena Merino-Gómez, María Grandury, Javier Conde, Pedro Reviriego
Date: 2024-09-08 00:00:00
Tasks: Natural Language Understanding, Reading Comprehension

Large Language Models (LLMs) have been profusely evaluated on their ability to answer questions on many topics and their performance on different natural language understanding tasks. Those tests are usually conducted in English, but most LLM users are not native English speakers. Therefore, it is of interest to analyze how LLMs understand other languages at different levels: from paragraphs to morphems. In this paper, we evaluate the performance of state-of-the-art LLMs in TELEIA, a recently released benchmark with similar questions to those of Spanish exams for foreign students, covering topics such as reading comprehension, word formation, meaning and compositional semantics, and grammar. The results show that LLMs perform well at understanding Spanish but are still far from achieving the level of a native speaker in terms of grammatical competence.
StoryBench: A Multifaceted Benchmark for Continuous Story Visualization
Authors: Emanuele Bugliarello, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, Paul Voigtlaender
Date: 2023-08-22 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Story Continuation, Story Generation, Story Visualization, Video Generation

Generating video stories from text prompts is a complex task. In addition to having high visual quality, videos need to realistically adhere to a sequence of text prompts whilst being consistent throughout the frames. Creating a benchmark for video generation requires data annotated over time, which contrasts with the single caption used often in video datasets. To fill this gap, we collect comprehensive human annotations on three existing datasets, and introduce StoryBench: a new, challenging multi-task benchmark to reliably evaluate forthcoming text-to-video models. Our benchmark includes three video generation tasks of increasing difficulty: action execution, where the next action must be generated starting from a conditioning video; story continuation, where a sequence of actions must be executed starting from a conditioning video; and story generation, where a video must be generated from only text prompts. We evaluate small yet strong text-to-video baselines, and show the benefits of training on story-like data algorithmically generated from existing video captions. Finally, we establish guidelines for human evaluation of video stories, and reaffirm the need of better automatic metrics for video generation. StoryBench aims at encouraging future research efforts in this exciting new area.
Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods
Authors: Akhil Mathew Philip, Dr. S Hemalatha
Date: 2022-05-31 00:00:00
Proceeding: journal 2022 5
Tasks: Denoising, ECG Classification, Rhythm

The "Cardio Vascular Diseases (CVDs)" had already attained worrisome proportions in both advanced and emerging nations in recent 
times. Physically inactive behaviors, altered eating, and occupational routines, and reduced daily fitness were all recognized as crucial contextual 
elements, in addition to genetics. Considering CVDs have such a significant morbidity and mortality, accurate and early diagnosis of cardiac 
disease by "ElectroCardioGram (ECG)" allows clinicians to decide suitable therapy for a multitude of cardiovascular disorders. The 
interpretation of ECG signal is an important bio-signal processing area that involves the application of computer science and engineering to 
detect and visualize the functional status of the heart. Therefore, in the present work, a detailed study on ECG signals denoising and 
abnormalities detection using different techniques were performed. Annoying distortions and noisy particles are common in ECG signals. The 
"Biased Finite Impulse Response (BFIR)" preprocessing filtering is employed in this research to eliminate the noises in the raw ECG signals. 
The "Nonlinear-Hamilton" segmentation method is employed to segment the 'R' peak signals. To decrease the extraneous features included in 
the segmented ECG data, the innovative "Enhanced Principal Component Analysis (EPCA)" was applied for feature extraction. A unique 
"Enhanced version of the Support Vector Machine (ESVM)" framework with a "Weighting Kernel" based technique is proposed for classifying 
the ECG data. The 'Q', 'R', and 'S' waves in the given ECG data will be identified by this framework, allowing it to characterize the cardiac 
rhythm. The evaluation metrics of the EPCA-ESVM proposed method is comparatively analyzed with our previous approach EPSO. To estimate 
the results for the dataset from MIT-BIH it was experimented with by the EPSO and the EPCA-ESVM methods focused upon different 
parameters such as Accuracy, F1-score, etc. The final findings of the EPCA-ESVM method were good than the EPSO method in which the 
accuracy is higher even though unbalanced data were present. 
Keywords: Cardio Vascular Diseases, ECG, Enhanced Principal Component Analysis, Enhanced Support Vector Machine
PixelBytes: Catching Unified Embedding for Multimodal Generation
Authors: Fabien Furfaro
Date: 2024-09-03 00:00:00
Tasks: Mamba, multimodal generation, Representation Learning, State Space Models

This report introduces PixelBytes Embedding, a novel approach for unified multimodal representation learning. Our method captures diverse inputs in a single, cohesive representation, enabling emergent properties for multimodal sequence generation, particularly for text and pixelated images. Inspired by state-of-the-art sequence models such as Image Transformers, PixelCNN, and Mamba-Bytes, PixelBytes aims to address the challenges of integrating different data types. We explore various model architectures, including Recurrent Neural Networks (RNNs), State Space Models (SSMs), and Attention-based models, focusing on bidirectional processing and our innovative PxBy embedding technique. Our experiments, conducted on a specialized PixelBytes Pok{\'e}mon dataset, demonstrate that bidirectional sequence models with PxBy embedding and convolutional layers can generate coherent multimodal sequences. This work contributes to the advancement of integrated AI models capable of understanding and generating multimodal data in a unified manner.
MC-SEMamba: A Simple Multi-channel Extension of SEMamba
Authors: Wen-Yuan Ting, Wenze Ren, Rong Chao, Hsin-Yi Lin, Yu Tsao, Fan-Gang Zeng
Date: 2024-09-26 00:00:00
Tasks: Mamba, Speech Enhancement

Transformer-based models have become increasingly popular and have impacted speech-processing research owing to their exceptional performance in sequence modeling. Recently, a promising model architecture, Mamba, has emerged as a potential alternative to transformer-based models because of its efficient modeling of long sequences. In particular, models like SEMamba have demonstrated the effectiveness of the Mamba architecture in single-channel speech enhancement. This paper aims to adapt SEMamba for multi-channel applications with only a small increase in parameters. The resulting system, MC-SEMamba, achieved results on the CHiME3 dataset that were comparable or even superior to several previous baseline models. Additionally, we found that increasing the number of microphones from 1 to 6 improved the speech enhancement performance of MC-SEMamba.
Local Prediction-Powered Inference
Authors: Yanwu Gu, Dong Xia
Date: 2024-09-26 00:00:00
Tasks: Prediction, regression

To infer a function value on a specific point $x$, it is essential to assign higher weights to the points closer to $x$, which is called local polynomial / multivariable regression. In many practical cases, a limited sample size may ruin this method, but such conditions can be improved by the Prediction-Powered Inference (PPI) technique. This paper introduced a specific algorithm for local multivariable regression using PPI, which can significantly reduce the variance of estimations without enlarge the error. The confidence intervals, bias correction, and coverage probabilities are analyzed and proved the correctness and superiority of our algorithm. Numerical simulation and real-data experiments are applied and show these conclusions. Another contribution compared to PPI is the theoretical computation efficiency and explainability by taking into account the dependency of the dependent variable.
LDA-MIG Detectors for Maritime Targets in Nonhomogeneous Sea Clutter
Authors: Xiaoqiang Hua, Linyu Peng, Weijian Liu, Yongqiang Cheng, Hongqiang Wang, Huafei Sun, Zhenghua Wang
Date: 2024-09-26 00:00:00

This paper deals with the problem of detecting maritime targets embedded in nonhomogeneous sea clutter, where limited number of secondary data is available due to the heterogeneity of sea clutter. A class of linear discriminant analysis (LDA)-based matrix information geometry (MIG) detectors is proposed in the supervised scenario. As customary, Hermitian positive-definite (HPD) matrices are used to model the observational sample data, and the clutter covariance matrix of received dataset is estimated as geometric mean of the secondary HPD matrices. Given a set of training HPD matrices with class labels, that are elements of a higher-dimensional HPD matrix manifold, the LDA manifold projection learns a mapping from the higher-dimensional HPD matrix manifold to a lower-dimensional one subject to maximum discrimination. In the current study, the LDA manifold projection, with the cost function maximizing between-class distance while minimizing within-class distance, is formulated as an optimization problem in the Stiefel manifold. Four robust LDA-MIG detectors corresponding to different geometric measures are proposed. Numerical results based on both simulated radar clutter with interferences and real IPIX radar data show the advantage of the proposed LDA-MIG detectors against their counterparts without using LDA as well as the state-of-art maritime target detection methods in nonhomogeneous sea clutter.
Semantic model for the description of energy data in the Module Type Package
Authors: Leif-Thore Reiche, Felix Gehlhoff, Alexander Fay
Date: 2024-09-26 00:00:00

Modular production systems that employ the Module Type Package (MTP) to describe module interfaces can, at present, only communicate energy data through proprietary solutions. Due to this limitation, users face additional effort when calculating energy KPIs for modules or determining the energy efficiency of modules. To address this issue, we present a model that facilitates energy data to be described semantically and uniformly in the MTP on the basis of an industrial standard (OPC 34100). MTPs incorporating this model can transmit semantically consistent energy data from modules to the process control system, making the data available for further applications, such as monitoring or optimization.
Distributed Invariant Unscented Kalman Filter based on Inverse Covariance Intersection with Intermittent Measurements
Authors: Zhian Ruan, Yizhi Zhou
Date: 2024-09-26 00:00:00
Tasks: State Estimation

This paper studies the problem of distributed state estimation (DSE) over sensor networks on matrix Lie groups, which is crucial for applications where system states evolve on Lie groups rather than vector spaces. We propose a diffusion-based distributed invariant Unscented Kalman Filter using the inverse covariance intersection (DIUKF-ICI) method to address target tracking in 3D environments. Unlike existing distributed UKFs confined to vector spaces, our approach extends the distributed UKF framework to Lie groups, enabling local estimates to be fused with intermediate information from neighboring agents on Lie groups. To handle the unknown correlations across local estimates, we extend the ICI fusion strategy to matrix Lie groups for the first time and integrate it into the diffusion algorithm. We demonstrate that the estimation error of the proposed method is bounded. Additionally, the algorithm is fully distributed, robust against intermittent measurements, and adaptable to time-varying communication topologies. The effectiveness of the proposed method is validated through extensive Monte-Carlo simulations.
Risk measures based on target risk profiles
Authors: Jascha Alexander, Christian Laudagé, Jörn Sass
Date: 2024-09-26 00:00:00

We address the problem that classical risk measures may not detect the tail risk adequately. This can occur for instance due to averaging when calculating the Expected Shortfall. The current literature proposes the so-called adjusted Expected Shortfall as a solution. This risk measure is the supremum of Expected Shortfalls for all possible levels, adjusted with a function $g$, the so-called target risk profile. We generalize this idea by using a family of risk measures which allows for more choices than Expected Shortfalls, leading to the concept of adjusted risk measures. An adjusted risk measure quantifies the minimal amount of capital that has to added to a financial position $X$ to ensure that each risk measure out of the chosen family is smaller or equal to the target risk profile $g(p)$ for the corresponding level $p\in[0,1]$. We discuss a variety of assumptions such that desirable properties for risk measures are satisfied in this setup. From a theoretical point of view, our main contribution is the analysis of equivalent assumptions such that an adjusted risk measure is positive homogeneous and subadditive. Furthermore, we show that these conditions hold for several adjusted risk measures beyond the adjusted Expected Shortfall. In addition, we derive their dual representations. Finally, we test the performance in a case study based on the S$\&$P $500$.
Group-Based Phylogenetic Models on 3-Sunlet Networks
Authors: Shelby Cox, Elizabeth Gross, Samuel Martin
Date: 2024-09-26 00:00:00

Phylogenetic networks describe the evolution of a set of taxa for which reticulate events have occurred at some point in their evolutionary history. Of particular interest is when the evolutionary history between a set of just three taxa has a reticulate event. In molecular phylogenetics, substitution models can model the process of evolution at the genetic level, and the case of three taxa with a reticulate event can be modelled using a substitution model on a mixed graph called a 3-sunlet. We investigate a class of substitution models called group-based phylogenetic models on 3-sunlet networks. In particular, we investigate the discrete geometry of the parameter space and how this relates to the dimension of the phylogenetic variety associated to the model. This enables us to give a dimension formula for this variety for general group-based models when the order of the group is odd.
A 2-approximation algorithm for the softwired parsimony problem on binary, tree-child phylogenetic networks
Authors: Martin Frohn, Steven Kelk
Date: 2024-09-26 00:00:00
Tasks: Combinatorial Optimization

Finding the most parsimonious tree inside a phylogenetic network with respect to a given character is an NP-hard combinatorial optimization problem that for many network topologies is essentially inapproximable. In contrast, if the network is a rooted tree, then Fitch's well-known algorithm calculates an optimal parsimony score for that character in polynomial time. Drawing inspiration from this we here introduce a new extension of Fitch's algorithm which runs in polynomial time and ensures an approximation factor of 2 on binary, tree-child phylogenetic networks, a popular topologically-restricted subclass of phylogenetic networks in the literature. Specifically, we show that Fitch's algorithm can be seen as a primal-dual algorithm, how it can be extended to binary, tree-child networks and that the approximation guarantee of this extension is tight. These results for a classic problem in phylogenetics strengthens the link between polyhedral methods and phylogenetics and can aid in the study of other related optimization problems on phylogenetic networks.
Observer-Based Discontinuous Communication in the Secondary Control of AC Microgrids
Authors: Shahabeddin Najafi, Yazdan Batmani, Pouya Shafiee, Charalambos Konstantinou
Date: 2024-09-26 00:00:00

This paper proposes an observer-based event-driven approach to decrease the overuse of communication networks. The suggested approach aims to estimate the required data for sharing between units in line with as much communication reduction as possible. In other words, the proposed approach effectively determines which state variables should be shared (observer concept) among the units during specific time intervals (event-triggered concept). This strategy significantly reduces the overall communication load. It is shown that the estimation error remains bounded and Zeno behavior, characterized by an endless number of transmissions occurring within a limited time frame, does not occur. The proposed methodology can be systematically applied to any communication-based secondary controller in alternating current (AC) microgrids. Simulation results demonstrate a high degree of precision in estimating the states under the proposed approach. Also, the secondary controller performance under the proposed method is evaluated in MATLAB/Simulink environment.
AQMLator -- An Auto Quantum Machine Learning E-Platform
Authors: Tomasz Rybotycki, Piotr Gawron
Date: 2024-09-26 00:00:00
Tasks: AutoML, Quantum Machine Learning

A successful Machine Learning (ML) model implementation requires three main components: training dataset, suitable model architecture and training procedure. Given dataset and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on automatic architecture search -- a meta method that aims at moving human from ML system design process. The success of ML and the development of quantum computing (QC) in recent years led to a birth of new fascinating field called Quantum Machine Learning (QML) that, amongst others, incorporates quantum computers into ML models. In this paper we present AQMLator, an Auto Quantum Machine Learning platform that aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. This way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.