pwc-archive/papers-with-abstracts huggingface dataset:
The OPS-SAT benchmark for detecting anomalies in satellite telemetry
Authors: Bogdan Ruszczak, Krzysztof Kotowski, David Evans, Jakub Nalepa
Date: 2024-06-29 00:00:00
Tasks: Anomaly Detection

Detecting anomalous events in satellite telemetry is a critical task in space operations. This task, however, is extremely time-consuming, error-prone and human dependent, thus automated data-driven anomaly detection algorithms have been emerging at a steady pace. However, there are no publicly available datasets of real satellite telemetry accompanied with the ground-truth annotations that could be used to train and verify anomaly detection supervised models. In this article, we address this research gap and introduce the AI-ready benchmark dataset (OPSSAT-AD) containing the telemetry data acquired on board OPS-SAT -- a CubeSat mission which has been operated by the European Space Agency which has come to an end during the night of 22--23 May 2024 (CEST). The dataset is accompanied with the baseline results obtained using 30 supervised and unsupervised classic and deep machine learning algorithms for anomaly detection. They were trained and validated using the training-test dataset split introduced in this work, and we present a suggested set of quality metrics which should be always calculated to confront the new algorithms for anomaly detection while exploiting OPSSAT-AD. We believe that this work may become an important step toward building a fair, reproducible and objective validation procedure that can be used to quantify the capabilities of the emerging anomaly detection techniques in an unbiased and fully transparent way.
Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms
Authors: Mónica Mora, Lucile Riaboff, Ingrid David, Juan Pablo Sánchez, Miriam Piles
Date: 2024-06-28 00:00:00
Tasks: Management

This study explores how wearable accelerometers, small devices that measure acceleration, can help monitor the activity of growing rabbits. We equipped 16 rabbits with these devices and filmed them for two weeks. By watching the videos and using a special software we figure out what the rabbits were doing -- things like lying down, eating, moving around, and more. These activitties were grouped into two states: active or inactive. Then, this information along acceleration data was used to teach a computer program to recognize when the rabbits were active or not. This technology offers a reliable way to understand rabbit behavior, which could lead to better management practices in animal production.
Demonstration of a Real-Time Testbed for D-Band Integrated Sensing and Communication
Authors: Sven Wittig, Rodrigo Hernangomez, Karen Vardanyan, Ramez Askar, Amr Haj-Omar, Michael Peter, Slawomir Stanczak
Date: 2024-06-28 00:00:00
Tasks: Integrated sensing and communication, ISAC

The D-band, spanning 110 GHz to 170 GHz, has emerged as a relevant frequency range for future mobile communications and radar sensing applications, particularly in the context of 6G technologies. This demonstration presents a high-bandwidth, real-time integrated sensing and communication (ISAC) platform operating in the upper D-band at 160 GHz. The platform comprises a software-defined intermediate frequency transceiver and a D-band radio frequency module. Its flexible software design allows for rapid integration of signal processing algorithms. Highlighting its potential for advanced research in ISAC systems, the platforms's efficacy is showcased through a live demonstration with an algorithm implementing human target tracking and activity classification.
Dynamical Embedding of Single Channel Electroencephalogram for Artifact Subspace Reconstruction
Authors: Doli Hazarika, Vishnu KN, Ramdas Ransing, Cota Navin Gupta
Date: 2024-06-28 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

This study introduces a novel framework to apply Artifact Subspace Reconstruction (ASR) algorithm on single-channel Electroencephalogram (EEG) data. ASR, renowned for its automated capability to effectively eliminate various artifacts like eye-blinks and eye movements from EEG signals. Importantly it has been implemented on android smartphones, but relied on multiple channels for principal component subspace calculations. To overcome this limitation, we incorporate the established dynamical embedding approach into the algorithm, naming it Embedded-ASR (E-ASR). In our proposed method, an embedded matrix is first constructed from a single-channel EEG data using series of delay vectors. ASR is then applied to this embedded matrix, and the resulting cleaned single-channel EEG is reconstructed by removing the time lag and concatenating the rows of the embedded matrix. Data was collected from four subjects in resting states with eyes open from pre-frontal (Fp1 and Fp2) electrodes using CameraEEG app. To assess the effectiveness of the E-ASR algorithm on an EEG dataset with artifacts, we employed performance metrics such as relative root mean square error (RRMSE), correlation coefficient (CC), average power ratio as well as estimated the number of eye-blinks with and without the E-ASR approach. E-ASR was able to reduce artifacts from the semi-simulated EEG data, with an RRMSE of 45.45% and a CC of 0.91. For real EEG data, the counted eye-blinks were manually cross-checked with ground truth obtained from CameraEEG video data across all subjects for individual Fp1 and Fp2 electrodes. In conclusion, our study suggests E-ASR framework can remove artifacts from single channel EEG data. This promising algorithm might have potential for smartphone-based natural environment EEG applications, where minimal number of electrodes is a critical factor.
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
Authors: Aidan Furlong, Farah Alsafadi, Scott Palmtag, Andrew Godfrey, Xu Wu
Date: 2024-06-27 00:00:00
Tasks: Uncertainty Quantification

The development of Crud-Induced Power Shift (CIPS) is an operational challenge in Pressurized Water Reactors that is due to the development of crud on the fuel rod cladding. The available predictive tools developed previously, usually based on fundamental physics, are computationally expensive and have shown differing degrees of accuracy. This work proposes a completely top-down approach to predict CIPS instances on an assembly level with reactor-specific calibration built-in. Built using artificial neural networks, this work uses a three-dimensional convolutional approach to leverage the image-like layout of the input data. As a classifier, the convolutional neural network model predicts whether a given assembly will experience CIPS as well as the time of occurrence during a given cycle. This surrogate model is both trained and tested using a combination of calculated core model parameters and measured plant data from Unit 1 of the Catawba Nuclear Station. After the evaluation of its performance using various metrics, Monte Carlo dropout is employed for extensive uncertainty quantification of the model predictions. The results indicate that this methodology could be a viable approach in predicting CIPS with an assembly-level resolution across both clean and afflicted cycles, while using limited computational resources.
UAV-Assisted Weather Radar Calibration: A Theoretical Model for Wind Influence on Metal Sphere Reflectivity
Authors: Jiabiao Zhao, Da Li, Jiayuan Cui, Houjun Sun, Jianjun Ma
Date: 2024-06-20 00:00:00
Tasks: Position

The calibration of weather radar for detecting meteorological phenomena has advanced rapidly, aiming to enhance accuracy. Utilizing an unmanned aerial vehicle (UAV) equipped with a suspended metal sphere introduces an efficient calibration method by allowing dynamic adjustment of the UAV's position, effectively acting as a mobile calibration platform. However, external factors such as wind can introduce bias in reflectivity measurements by causing the sphere to deviate from its intended position. This study develops a theoretical model to assess the impact of the metal sphere's one-dimensional oscillation on reflectivity. The findings offer valuable insights for UAV based radar calibration efforts.
Volatility modeling in a Markovian environment: Two Ornstein-Uhlenbeck-related approaches
Authors: Anita Behme
Date: 2024-07-08 00:00:00
Tasks: Time Series

We introduce generalizations of the COGARCH model of Kl\"uppelberg et al. from 2004 and the volatility and price model of Barndorff-Nielsen and Shephard from 2001 to a Markov-switching environment. These generalizations allow for exogeneous jumps of the volatility at times of a regime switch. Both models are studied within the framework of Markov-modulated generalized Ornstein-Uhlenbeck processes which allows to derive conditions for stationarity, formulas for moments, as well as the autocovariance structure of volatility and price process. It turns out that both models inherit various properties of the original models and therefore are able to capture basic stylized facts of financial time-series such as uncorrelated log-returns, correlated squared log-returns and non-existence of higher moments in the COGARCH case.
Subleading correction to the Asian options volatility in the Black-Scholes model
Authors: Dan Pirjol
Date: 2024-07-06 00:00:00

The short maturity limit $T\to 0$ for the implied volatility of an Asian option in the Black-Scholes model is determined by the large deviations property for the time-average of the geometric Brownian motion. In this note we derive the subleading $O(T)$ correction to this implied volatility, using an asymptotic expansion for the Hartman-Watson distribution. The result is used to compute subleading corrections to Asian options prices in a small maturity expansion, sharpening the leading order result obtained using large deviations theory. We demonstrate good numerical agreement with precise benchmarks for Asian options pricing in the Black-Scholes model.
The second-order Esscher martingale densities for continuous-time market models
Authors: Tahir Choulli, Ella Elazkany, Michèle Vanmaele
Date: 2024-07-04 00:00:00

In this paper, we introduce the second-order Esscher pricing notion for continuous-time models. Depending whether the stock price $S$ or its logarithm is the main driving noise/shock in the Esscher definition, we obtained two classes of second-order Esscher densities called linear class and exponential class respectively. Using the semimartingale characteristics to parametrize $S$, we characterize the second-order Esscher densities (exponential and linear) using pointwise equations. The role of the second order concept is highlighted in many manners and the relationship between the two classes is singled out for the one-dimensional case. Furthermore, when $S$ is a compound Poisson model, we show how both classes are related to the Delbaen-Haenzendonck's risk-neutral measure. Afterwards, we restrict our model $S$ to follow the jump-diffusion model, for simplicity only, and address the bounds of the stochastic Esscher pricing intervals. In particular, no matter what is the Esscher class, we prove that both bounds (upper and lower) are solutions to the same linear backward stochastic differential equation (BSDE hereafter for short) but with two different constraints. This shows that BSDEs with constraints appear also in a setting beyond the classical cases of constraints on gain-processes or constraints on portfolios. We prove that our resulting constrained BSDEs have solutions in our framework for a large class of claims' payoffs including any bounded claim, in contrast to the literature, and we single out the monotonic sequence of BSDEs that ``naturally" approximate it as well.
Statistics for Phylogenetic Trees in the Presence of Stickiness
Authors: Lars Lammers, Tom M. W. Nye, Stephan F. Huckemann
Date: 2024-07-04 00:00:00

Samples of phylogenetic trees arise in a variety of evolutionary and biomedical applications, and the Fr\'echet mean in Billera-Holmes-Vogtmann tree space is a summary tree shown to have advantages over other mean or consensus trees. However, use of the Fr\'echet mean raises computational and statistical issues which we explore in this paper. The Fr\'echet sample mean is known often to contain fewer internal edges than the trees in the sample, and in this circumstance calculating the mean by iterative schemes can be problematic due to slow convergence. We present new methods for identifying edges which must lie in the Fr\'echet sample mean and apply these to a data set of gene trees relating organisms from the apicomplexa which cause a variety of parasitic infections. When a sample of trees contains a significant level of heterogeneity in the branching patterns, or topologies, displayed by the trees then the Fr\'echet mean is often a star tree, lacking any internal edges. Not only in this situation, the population Fr\'echet mean is affected by a non-Euclidean phenomenon called stickness which impacts upon asymptotics, and we examine two data sets for which the mean tree is a star tree. The first consists of trees representing the physical shape of artery structures in a sample of medical images of human brains in which the branching patterns are very diverse. The second consists of gene trees from a population of baboons in which there is evidence of substantial hybridization. We develop hypothesis tests which work in the presence of stickiness. The first is a test for the presence of a given edge in the Fr\'echet population mean; the second is a two-sample test for differences in two distributions which share the same sticky population mean.
Single-Sequence-Based Protein Secondary Structure Prediction using One-Hot and Chemical Encodings of Amino Acids
Authors: Hoa Trinh, Satish Kumar Thittamaranahalli
Date: 2024-07-06 00:00:00
Tasks: Dimensionality Reduction, Protein Secondary Structure Prediction

In protein secondary structure prediction, each amino acid in sequence is typically treated as a distinct category and represented by a one-hot vector. In this study, we developed two novel chemical representations for amino acids utilizing molecular fingerprints and the dimensionality reduction algorithm FastMap. We demonstrate that the two new chemical encodings can provide additional information about the interactions of amino acids in sequences that an LSTM-based model cannot capture with one-hot encoding alone. Compared to the latest LSTM-based model used in the single-sequence-based method SPOT-1D-Single, our ensemble model utilizing one-hot and chemical encodings achieves better accuracy across most test sets while requiring approximately nine times fewer trainable parameters for each encoding model. Our single-sequence-based method is valuable for its simplicity, lower resource requirements, and independence from external sequence data. It is beneficial when quick or preliminary predictions are needed or when data on homologous sequences is scarce.
Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters
Authors: Benoit Baillif, Jason Cole, Patrick McCabe, Andreas Bender
Date: 2024-07-05 00:00:00
Tasks: Benchmarking, valid

Three-dimensional (3D) deep molecular generative models offer the advantage of goal-directed generation based on 3D-dependent properties, such as binding affinity for structure-based design within binding pockets. Traditional benchmarks created to evaluate SMILES or molecular graphs generators, such as GuacaMol or MOSES, are limited to evaluate 3D generators as they do not assess the quality of the generated molecular conformation. In this work, we hence developed GenBench3D, which implements a new benchmark for models producing molecules within a binding pocket. Our main contribution is the Validity3D metric, evaluating the conformation quality using the likelihood of bond lengths and valence angles based on reference values observed in the Cambridge Structural Database. The LiGAN, 3D-SBDD, Pocket2Mol, TargetDiff, DiffSBDD and ResGen models were benchmarked. We show that only between 0% and 11% of generated molecules have valid conformations. Performing local relaxation of generated molecules in the pocket considerably improved the Validity3D for all models by a minimum increase of 40%. For LiGAN, 3D-SBDD, or TargetDiff, the set of valid relaxed molecules shows on average higher Vina score (i.e. worse) than the set of raw generated molecules, indicating that the binding affinity of raw generated molecules might be overestimated. Using the other scoring functions, that give higher importance to ligand strain, only yield improved scores when using valid relaxed molecules. Using valid relaxed molecules, TargetDiff and Pocket2Mol show better median Vina, Glide and Gold PLP scores than other models. We have publicly released GenBench3D on GitHub for broader use: https://github.com/bbaillif/genbench3d
Analysis of genetic diversity among some Iraqi durum wheat cultivars revealed by different molecular markers
Authors: Mihraban Sharif Maeruf, Djshwar Dhahir Lateef, Kamil Mahmood Mustafa, Hero Fatih Hamakareem, Shang Hasseb Abdalqadir, Dastan Ahmad Ahmad, Shokhan Mahmood Sleman, Kamaran Salh Rasul
Date: 2024-07-08 00:00:00
Tasks: Diversity

Durum wheat has been cultivated since the beginning of crop domestication, occupying now the tenth ranking among the global most significant cultivated crops. Despite the fact that, the extent of the crop genetic diversity has not yet fully incorporated into modern varieties through breeding programs. In this study, a total of 35 markers (11 RAPD, 12 ISSR, and 12 CDDP) were utilized to assess the genetic variability and population structure of sixteen different cultivars of Iraqi durum wheat. Out of 294 bands obtained, 171 were identified as polymorphic: 47.00 polymorphic alleles from 98 RAPD bands, 53 polymorphic alleles from a total of 89 ISSR bands, and 71 alleles from 107 CDDP bands. The average number of observed alleles (Na), effective number of alleles (Ne), Shannon's information index (I), expected heterozygosity or gene diversity (He), unbiased expected heterozygosity (uHe), and polymorphic information content (PIC) (1.45, 1.38, 0.32, 0.22, 0.24, and 0.28, respectively) were obtained for RAPDs , (1.63, 1.45, 0.40, 0.27, 0.29, and 0.32, respectively) ISSRs and (1.35, 1.35, 0.31, 0.21, 0.23, and 0.30, respectively) for the CDDP markers. A dendrogram of two main clades (unweighted pair group method with arithmetic mean; UPGMA) and three populations of structure analysis, were obtained based on the three markers data. The analysis of molecular variance indicated 97.00%, 97.00%, and 90.00% variability within populations, applying RAPD, ISSR, and CDDP markers, respectively. The highest diversity indices were revealed in population 2 under the RAPD and CDDP markers, whereas population 1 had the highest values of these indices according to the ISSR markers. The results provide greater knowledge on the genetic makeup of Iraqi durum wheat cultivars, that facilitate future breeding programs of this crop.
Metagenomic analysis reveals shared and distinguishing features in horse and donkey gut microbiome and maternal resemblance of the microbiota in hybrid equids
Authors: Yihang Zhou
Date: 2024-07-06 00:00:00

Mammalian gut microbiomes are essential for host functions like digestion, immunity, and nutrient utilization. This study examines the gut microbiome of horses, donkeys, and their hybrids, mules and hinnies, to explore the role of microbiomes in hybrid vigor. We performed whole-genome sequencing on rectal microbiota from 18 equids, generating detailed microbiome assemblies. Our analysis revealed significant differences between horse and donkey microbiomes, with hybrids showing a pronounced maternal resemblance. Notably, Firmicutes were more abundant in the horse-maternal group, while Fibrobacteres were richer in the donkey-maternal group, indicating distinct digestive processes. Functional annotations indicated metabolic differences, such as protein synthesis in horses and energy metabolism in donkeys. Machine learning predictions of probiotic species highlighted potential health benefits for each maternal group. This study provides a high-resolution view of the equid gut microbiome, revealing significant taxonomic and metabolic differences influenced by maternal lineage, and offers insights into microbial contributions to hybrid vigor.
Metagenomic analysis revealed significant changes in cattle rectum microbiome and antimicrobial resistome under fescue toxicosis
Authors: Yihang Zhou
Date: 2024-07-06 00:00:00

Fescue toxicity causes reduced growth and reproductive issues in cattle grazing endophyte-infected tall fescue. To characterize the gut microbiota and its response to fescue toxicosis, we collected fecal samples before and after a 30-days toxic fescue seeds supplementation from eight Angus Simmental pregnant cows and heifers. We sequenced the 16 metagenomes using the whole-genome shotgun approach and generated 157 Gbp of metagenomic sequences. Through de novo assembly and annotation, we obtained a 13.1 Gbp reference contig assembly and identified 22 million microbial genes for cattle rectum microbiota. We discovered a significant reduction of microbial diversity after toxic seed treatment (P<0.01), suggesting dysbiosis of the microbiome. Six bacterial families and 31 species are significantly increased in the fecal microbiota (P-adj<0.05), including members of the top abundant rumen core taxa. This global elevation of rumen microbes in the rectum microbiota suggests a potential impairment of rumen microbiota under fescue toxicosis. Among these, Ruminococcaceae bacterium P7, an important species accounting for ~2% of rumen microbiota, was the most impacted with a 16-fold increase from 0.17% to 2.8% in feces (P<0.01). We hypothesized that rumen Ruminococcaceae bacterium P7 re-adapted to the large intestine environment under toxic fescue stress, causing this dramatic increase in abundance. Functional enrichment analysis revealed that the overrepresented pathways shifted from energy metabolism to antimicrobial resistance and DNA replication. In conclusion, we discovered dramatic microbiota alterations in composition, abundance, and functional capacities under fescue toxicosis, and our results suggest Ruminococcaceae bacterium P7 as a potential biomarker for fescue toxicosis management.
MotifbreakR v2: extended capability and database integration
Authors: Simon G. Coetzee, Dennis J. Hazelett
Date: 2024-07-03 00:00:00

MotifbreakR is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in motifbreakR v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR implements querying the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR, in addition to the existing interface, we have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.