Miracle-dz/UniFAR-FTUs huggingface dataset:
SEQUENCE STRATIGRAPHIC INTERPRETATION OF CARBONATE WIRELINE LOG MOTIFS: AN EXAMPLE FROM THE NORTH WEST SHELF OF AUSTRALIA

An integrated biostratigraphic, wireline and seismic sequence stratigraphic study has been conducted to constrain the timing and evolution of Late Cretaceous to Tertiary depocentres along the North West Shelf of Australia. During this study a model for the sequence stratigraphic interpretation of wireline logs in this carbonate-dominated regime has been developed. A series of readily identifiable, lowstand clastic deposits interspersed within the predominantly carbonate passive margin section of the North West Shelf provide well-defined correctable events with which to divide the section. Biostratigraphic data have indicated the presence of missing section at the base of these clastic deposits and their shelfal equivalents. These events have been correlated to define sequence boundaries that are represented on wireline log data by a sharp increase in the gamma signature. Lowstand systems tracts exhibit an irregular sonic and upwardly increasing gamma signature. Transgressive systems tracts show characteristically upward-decreasing gamma and sonic profiles. Maximum flooding surfaces have been identified as the point of cleanest carbonate sedimentation represented by gamma minima on wireline logs. Log motifs exhibiting little character have been interpreted as highstand systems tracts. On seismic these sequence stratigraphic events are represented by stratal geometries that would be expected for these systems tracts. The model has enabled the definition of a higher resolution chronostratigraphic framework for the Mid Cretaceous to Recent section of the North West Shelf than has previously been possible. Forty basin-wide events have been identified from the biostratigraphic and wireline log analysis, thirty of which can be tied throughout the Barrow, Dampier and Roebuck basins.
Nebula: Distributed edge cloud for data-intensive computing

Today, centralized data-centers or clouds have become the de-facto platform for data-intensive computing in the commercial, and increasingly, scientific domains. This is because clouds such as Amazon AWS and Microsoft Azure offer large amounts of monetized co-located computation and storage well suited to typical processing tasks such as batch analytics. However, many Big Data applications rely on data that is geographically distributed, and is not collocated with the centralized computational resources provided by clouds. Examples of such applications include analysis of user data such as blogs, video feeds taken from geographically separated cameras, monitoring and log analysis of server and content distribution network (CDN) logs, and scientific data collected from distributed instruments and sensors. Such applications lead to a number of challenges for efficient data analytics in today's cloud platforms. First, in many applications, data is both large and widely distributed and data upload may constitute a non-trivial portion of the execution time. Second, centralized cloud resources present a single point of failure and network partitions between the data sources and the cloud can also lead to service disruptions. Third, the cost to transport, store, and process data may be outside of the budget of the small-scale application designer or end-user. The paper present Nebula: a dispersed edge cloud infrastructure that provides both computation and data storage to address the above challenges. The use of edge resources is attractive for several reasons. First, there is an increasing amount of computing and storage resources available on the edge, as evidenced by their use in several volunteer computing, filesharing as well as content delivery (CDN) environments. This capacity is likely to increase further with the provision of powerful multi-core, multi-node desktop and home machines coupled with increasing amount of high bandwidth Internet connectivity. Second, edge resources provide locality to data and users naturally, and hence, can be exploited easily for insitu processing. Finally, if cost is an issue, then volunteer edge resources can be utilized at a relatively low cost.
SC_LPR: Semantically Consistent LiDAR Place Recognition Based on Chained Cascade Network in Long-Term Dynamic Environments

In large-scale long-term dynamic environments, high-frequency dynamic objects inevitably lead to significant changes in the appearance of the scene at the same location at different times, which is catastrophic for place recognition (PR). Therefore, how to eliminate the influence of dynamic objects to achieve robust PR has universal practical value for mobile robots and autonomous vehicles. To this end, we suggest a novel semantically consistent LiDAR PR method based on chained cascade network, called SC_LPR, which mainly consists of a LiDAR semantic image inpainting network (LSI-Net) and a semantic pyramid Transformer-based PR network (SPT-Net). Specifically, LSI-Net is a coarse-to-fine generative adversarial network (GAN) with a gated convolutional autoencoder as the backbone. To effectively address the challenges posed by variable-scale dynamic object masks, we integrate the updated Transformer block with mask attention and gated trident block into LSI-Net. Sequentially, in order to generate a discriminative global descriptor representing the point cloud, we design an encoder with pyramid Transformer block to efficiently encode long-range dependencies and global contexts between different categories in the inpainted semantic image, followed by an augmented NetVALD, a generalized VLAD (Vector of Locally Aggregated Descriptors) layer that adaptively aggregates salient local features. Last but not least, we first attempt to create a LiDAR semantic inpainting dataset, called LSI-Dataset, to effectively validate the proposed method. Experimental comparisons show that our method not only improves semantic inpainting performance by about 6%, but also improves PR performance in dynamic environments by about 8% compared to the representative optimal baseline. LSI-Dataset will be publicly available at https://github.KD. LPR.com/.
Automatic capturing and timeline generation for reality TV

Reality TV shows have gained popularity, motivating many production houses to bring new variants for us to watch. Compared to traditional TV shows, reality TV shows have spontaneous unscripted footage. Computer vision techniques could partially replace the manual labour needed to record and process this spontaneity. However, automated real-world video recording and editing is a challenging topic. In this paper, we propose a system that utilises state-of-the-art video and audio processing algorithms to, on the one hand, automatically steer cameras, replacing camera operators and on the other hand, detect all audiovisual action cues in the recorded video, to ease the job of the film editor. This publication has hence two main contributions. The first, automating the steering of multiple Pan-Tilt-Zoom PTZ cameras to take aesthetically pleasing medium shots of all the people present. These shots need to comply with the cinematographic rules and are based on the poses acquired by a pose detector. Secondly, when a huge amount of audio-visual data has been collected, it becomes labour intensive for a human editor retrieve the relevant fragments. As a second contribution, we combine state-of-the-art audio and video processing techniques for sound activity detection, action recognition, face recognition, and pose detection to decrease the required manual labour during and after recording. These techniques used during postprocessing produce meta-data allowing for footage filtering, decreasing the search space. We extended our system further by producing timelines uniting generated meta-data, allowing the editor to have a quick overview. We evaluated our system on three in-the-wild reality TV recording sessions of 24 hours (× 8 cameras) each taken in real households.
AN EFFICIENT AND SCALABLE LOCATION-AWARE RECOMMENDER SYSTEM

LARS,a location-aware recommender system that uses location-based ratings to produce recommendations is proposed. Traditional recommender systems do not consider spatial properties of users nor items; LARS, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS exploits item locations using travel penalty, a technique that favors recommendation candidates closer in travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS can apply these techniques separately, or together, depending on the type of location-based rating available. LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
From rateless to distanceless: enabling sparse sensor network deployment in large areas

This paper presents a distanceless networking approach for wireless sensor networks sparsely deployed in large areas. By leveraging rateless codes, we provide distanceless transmission to expand the communication range of sensor motes and fully exploit network diversity. We address a variety of practical challenges to accommodate rateless coding on resource-constrained sensor motes and devise a communication protocol to efficiently coordinate the distanceless link transmissions. We propose a new metric (expected distanceless transmission time) for routing selection and further adapt the distanceless transmissions to low duty-cycled sensor networks. We implement the proposed scheme in TinyOS on the TinyNode platform and deploy the sensor network in a real-world project, in which 12 wind measurement sensors are installed around a large urban reservoir of 2.5km * 3.0km to monitor the field wind distribution. Extensive experiments show that our proposed scheme significantly outperforms the state-of-the-art approaches for data collection in sparse sensor networks.
NEWT , a new taxonomy portal

NEWT is a new taxonomy portal to the SWISS-PROT protein sequence knowledgebase. It contains taxonomy data, which is updated daily, for the complete set of species represented in SWISS-PROT, as well as those stored at the NCBI. Users can navigate through the taxonomy tree and access corresponding SWISS-PROT protein entries. In addition, a manually curated selection of external links allows access to specific information on selected species. NEWT is available at http://www.ebi. ac.uk/newt/.
Generalized Anxiety Disorder

Therapists and clinical scientists involved in the study, assessment, and treatment of generalized anxiety disorder (GAD) undoubtedly encounter individuals preoccupied with intense and pervasive worry and anxiety. Whereas worry is a universal experience, common in both nonpathological and anxious populations, individuals with GAD stand apart, as their worry is more pervasive and less controllable, thereby engendering greater distress and life interference. Typically describing themselves as lifelong worriers, these individuals perceive their worrisome thinking and associated anxiety as facets of their personality, enduring traits rather than phenomena prone to fluctuations that can be monitored, targeted, and effectively changed. In fact, worry may be viewed as such a central part of life, a primary coping strategy used to avoid perceived threat and changes in emotional reactivity, that treatment may not even be considered (Newman, Crits-Christoph, & Szkodny, in press). GAD has been referred to as the “basic” anxiety disorder (Brown, Barlow, & Liebowitz, 1994), an appellation that suggests understanding the development and maintenance of GAD is important for understanding all anxiety disorders. Given GAD’s course and documented resistance to change, research has centered not only on elucidating the nature and etiology of this disorder, but also on developing treatments that improve upon standard versions of cognitive behavioral therapy (CBT). This has been most critical since worry is a means to avoid anticipated threats, as opposed to tangible, anxiety-provoking stimuli, and thus is not as easily addressed with exposure interventions commonly executed in the treatment of other anxiety disorders (Newman & Borkovec, 2002). The principal objective of this chapter is to present an overview of CBT for GAD. First, the symptomatology of GAD is discussed, followed by a presentation of the cognitive behavioral treatment rationale and CBT techniques. Additionally, empirical evidence supporting the efficacy of CBT for GAD
Life-History Characteristics and Fishery Dynamics of Red Drum (Sciaenops ocellatus), in the North-Central Gulf of Mexico

ii ACKNOWLEDGMENTS . iii DEDICATION . iv LIST OF TABLES . vii LIST OF ILLUSTRATIONS . viii CHAPTER I – ESCAPEMENT RATE OF MISSISSIPPI’S RED DRUM STOCK . 1 1.
TCSA: A dynamic job scheduling algorithm for computational grids

Despite the popularity of parallel computing and the existence of distributed computing for several years, Grid computing, has adopted the ongoing demand for new and better ways to compute. In computational Grids, managing users' jobs and assigning them to appropriate resources become the first and foremost activities. In order to efficiently execute users' jobs and utilize the distributed resources in an appropriate way, this paper presents an effective job scheduling algorithm, called two choices scheduling algorithm (TCSA), to dynamically allocate jobs to resources so as to minimize the job execution time and maximize resource utilization. In this algorithm, instead of selecting just one of the available nodes to carry out a user submitted job, the allocation decided after examining a small number of the available nodes sampled randomly. To evaluate the performance of the proposed method, this study compares TCSA with those of several existing scheduling algorithms by conducting several simulation experiments under different scenarios. The obtained results show that the proposed TCSA algorithm can effectively reduce the makespan as well as balancing the load among resources compared with the other scheduling algorithms evaluated in this paper.
A SSESSING DIFFERENTIALLY PRIVATE DEEP LEARNING

Releasing data in the form of trained deep neural networks with differential privacy promises meaningful anonymization. However, there is an inherent privacy-accuracy trade-off in differential privacy which is challenging to assess for non-privacy experts. Moreover, local and central differential privacy mechanisms are available to either anonymize the training data or the learnt neural network, and the privacy parameter cannot be used to compare these two mechanism categories. We propose to measure privacy by a membership inference attack, and compare the privacy-accuracy trade-off for different local and central differential privacy mechanisms. Furthermore, we evaluate whether differential privacy is a useful mechanism against membership inference in practice since differential privacy will especially be used by data scientists if membership inference precision and recall are lowered more than neural network accuracy. We experiment with several datasets and show that neither local differential privacy nor central differential privacy yields a consistently better privacyaccuracy trade-off among all datasets. We also show that the relative privacy-accuracy trade-off is not strictly declining over and only favorable within a small interval. For this purpose, we propose φ, a ratio expressing the relative privacy-accuracy trade-off.
A Comparison of Outdoor Bioaerosol and Particulate Measurements in Las Vegas

iii Acknowledgements . v List of Tables . x List of Figures . xii List of Equations . xvi Chapter 1: Introduction . 1 Chapter 2: Literature Review . 4 Pollen . 4 Fungal Spores . 5 Airborne Bacteria . 6 Particulate Matter (2.5 and 10). 7 Purpose of the Study . 9 Research Questions/Hypotheses . 9 Chapter 3: Comparison of Airborne Pollen Concentrations in Five Monitoring Locations in Las Vegas, NV.11 Abstract . 11.1
Random Web Surfer PageRank Algorithm

this paper analyzes how the Google web search engine implements the PageRank algorithm to define prominent status to web pages in a network. It describes the PageRank algorithm as a Markov process, web page as state of Markov chain, Link structure of web as Transitions probability matrix of Markov chains, the solution to an eigenvector equation and Vector iteration power method. It mainly focus on how to relate the eigenvalues and eigenvector of Google matrix to PageRank values to guarantee that there is a single stationary distribution vector to which the PageRank algorithm converges and efficiently compute the PageRank for large sets of web Pages. Finally, it will demonstrate example of the PageRank algorithm.
Making a Case for an Autism-Specific Multimorbidity Index: A Comparative Cohort Study

Autistic people experience challenges in healthcare, including disparities in health outcomes and multimorbidity patterns distinct from the general population. This study investigated the efficacy of existing multimorbidity indices in predicting COVID-19 mortality among autistic adults and proposes a bespoke index, the ASD-MI, tailored to their specific health profile. Using data from the CVD-COVID-UK/COVID-IMPACT Consortium, encompassing England's entire population, we identified 1,027 autistic adults hospitalized for COVID-19, among whom 62 died due to the virus. Employing logistic regression with 5-fold cross-validation, we selected diabetes, coronary heart disease, and thyroid disorders as predictors for the ASD-MI, outperforming the Quan Index, a general population-based measure, with an AUC of 0.872 versus 0.828, respectively. Notably, the ASD-MI exhibited better model fit (pseudo-R2 0.25) compared to the Quan Index (pseudo-R2 0.20). These findings underscore the need for tailored indices in predicting mortality risks among autistic individuals. However, caution is warranted in interpreting results, given the limited understanding of morbidity burden in this population. Further research is needed to refine autism-specific indices and elucidate the complex interplay between long-term conditions and mortality risk, informing targeted interventions to address health disparities in autistic adults. This study highlights the importance of developing healthcare tools tailored to the unique needs of neurodivergent populations to improve health outcomes and reduce disparities.
Characterizing the skeletal muscle immune microenvironment for sarcopenia using transcriptome analysis and histological validation

Sarcopenia is a condition characterized by the age-related loss of skeletal muscle mass and function. The pathogenesis of the disease is influenced by chronic low-grade inflammation. However, the specific changes in the immune landscape changes of sarcopenic muscle are not yet fully understood. To gain insights into the immune cell composition and interactions, we combined single-nuclei RNA sequencing data, bulk RNA sequencing datasets, and comprehensive bioinformatic analyses on skeletal muscle samples from young, aged, and sarcopenic individuals. Histological staining was then performed on skeletal muscles to validate the distribution of immune cells in clinical samples. Overall, we analyzed the transcriptomes of 101,862 single nuclei, revealing a total of 10 major cell types and 6 subclusters of immune cell types within the human skeletal muscle tissues. Among the immune cells, macrophages constituted the largest immune fraction. A specific marker gene LYVE1 for skeletal muscle macrophages was further identified. Cellular subclasses included four distinct groups of resident macrophages, which play a different role in physiological or non-physiological conditions. Using bulk RNA sequencing data, we identified strong enrichment for a macrophage- rich inflammation in sarcopenia. Our findings demonstrate age-related changes in the composition and cross-talk of immune cells, which contribute to chronic inflammation. Furthermore, macrophages emerge as a potential therapeutic target, thus advancing our understanding of the pathogenesis of sarcopenia.
Dartmouth Digital Commons Dartmouth Digital Commons

The original Dartmouth wireless network study [KE02, KE03, KE05] used SNMP to query the college’s Cisco 802.11b access points. The perl scripts that performed the SNMP queries suffered from some problems, in that they queried inappropriate SNMP values, or misunderstood the meaning of other values. This data was also used in a subsequent analysis [Lee03]. This document outlines these problems and indicates which of the data collected by the original scripts may be invalid.
Implementation and Experimental Results of a Three-Transmitter Three-Receiver OFDM / BLAST Testbed

A three-transmitter three-receiver orthogonal frequency-division multiplexing Bell Laboratories layered space-time testbed is set up, which achieves a peak data rate of 281.25 Mb/s and a spectral efficiency of 14.4 b/Hz/s. The transmitter of the testbed consists of three signal generators transmitting three independent OFDM signals at 25 Msamples/s synchronously. Three synchronized receiving links are used, each of which includes an RF receiver, an analog-to-digital converter, a digital downconverter, and a PowerPC processor for baseband processing. The performance and complexity of three typical BLAST detection techniques (linear detection, ordered decision feedback detection, and partial decision detection) are evaluated and compared using the data from the experiments conducted in both line-of-sight and non-line-of-sight indoor environments. Implementation and Experimental Results of a Three-Transmitter Three-Receiver OFDM/BLAST Testbed
NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features

We present a description of the system submitted to the Semantic Textual Similarity (STS) shared task at SemEval 2016. The task is to assess the degree to which two sentences carry the same meaning. We have designed two different methods to automatically compute a similarity score between sentences. The ﬁrst method combines a variety of semantic similarity measures as features in a machine learning model. In our second approach, we employ training data from the Interpretable Similarity subtask to create a combined word-similarity measure and assess the importance of both aligned and unaligned words. Finally, we combine the two methods into a single hybrid model. Our best-performing run attains a score of 0 . 7732 on the 2015 STS evaluation data and 0 . 7488 on the 2016 STS evaluation data.
Bootstrapping Deep Neural Networks from Image Processing and Computer Vision Pipelines

Complex image processing and computer vision systems often consist of a"pipeline"of"black boxes"that each solve part of the problem. We intend to replace parts or all of a target pipeline with deep neural networks to achieve benefits such as increased accuracy or reduced computational requirement. To acquire a large amounts of labeled data necessary to train the deep neural network, we propose a workflow that leverages the target pipeline to create a significantly larger labeled training set automatically, without prior domain knowledge of the target pipeline. We show experimentally that despite the noise introduced by automated labeling and only using a very small initially labeled data set, the trained deep neural networks can achieve similar or even better performance than the components they replace, while in some cases also reducing computational requirements.