Miracle-dz/UniFAR-FTUs huggingface dataset:
Late late Albian (Early Cretaceous) shark teeth from Annopol, Poland

Siversson, M. & Machalski, M. , February 2017. Late late Albian (Early Cretaceous) shark teeth from Annopol, Poland. Alcheringa 41, 433–463. Screen washing of the condensed phosporite-bearing sands at the top of the Albian succession at Annopol, Poland, produced 789 selachian teeth of which 264 are determinable to at least genus level. The sediment type and methods of processing prevented recovery of small-toothed taxa, resulting in an assemblage comprising 13, mostly large-toothed taxa. Lamniformes dominates with Dwardius sp. being, by far, the most common taxon. Observations on vertical distribution and preservation of the teeth (with focus on the adhered phosphatic matrix), coupled with biostratigraphic ranges of co-occurring ammonites, indicate that the majority of the shark material is attributable to the Mortoniceras rostratum or, more probably, M. perinflatum Zone (late late Albian; mid-‘Vraconnian’). This is compatible with the composition of the shark assemblage, characterized by the co-occurrence of Paraisurus sp. aff. P. compressus, Cretoxyrhina vraconensis and Squalicorax teeth with strong serrations on the cutting edges. The tightly curved basal edge of the root in lateral teeth of C. vraconensis conforms to that of teeth from the Pawpaw Formation of Texas (M. rostratum Zone) and differs from the more divergent root lobes in younger specimens from the uppermost Albian and/or lowermost Cenomanian of Kolbay, Mangyshlak. Some specimens in the studied assemblage are probably older, within the range from the middle to earliest late late Albian. The strong numerical dominance of either Cretoxyrhina or Dwardius in late late Albian to early Cenomanian selachian faunas indicates competitive exclusion in these similar-sized, apex predatory sharks. Mikael Siversson* [mikael.siversson@museum.wa. gov.au], Department of Earth and Planetary Sciences, Western Australian Museum, 49 Kew Street, Welshpool, Western Australia 6106, Australia; Marcin Machalski [mach@twarda.pan. pl], Institute of Paleobiology, Polish Academy of Sciences, ul. Twarda 51/55, 00-818 Warszawa, Poland. *Also affiliated with: Department of Environment & Agriculture, Curtin University, Kent Street, Bentley, WA 6102, Australia.
Ecotrophic Model for an Ecosystem Approach for Mangrove Fisheries in Thailand

Estuaries, particularly coastal mangrove forests, have been focal points of human settlement and marine resource use throughout history, and they fulfill important socio-economic and environmental functions. Mangroves are currently threatened by various forms of exploitation and coastal development of which conversion to coastal aquaculture is one of the most serious in Thailand. This study is focused on the Mae Klong Estuary, one of the four main mangrove estuaries in the Gulf of Thailand. The estuarine ecosystem of the Mae Klong is described through a mass-balance model (Ecopath) that includes 21 functional groups (state variables), representing 63 exploited fish and commercial invertebrate species as well as the energy (feeding) fluxes among them which pit artisanal fishers, using push net. The parameterization of the model is described in some detail, as are the implications of the ecological and multispecies interactions. The results emphasize the need of management and conservation between the two sectors of the fisheries and forestry, whose present trajectories tend toward further degradation of the Mae Klong ecosystem. The Mae Klong Estuary supports a rich fish fauna in terms of number of species, abundance and biomass. A total of 63 fish species representing 25 families were recorded, with Clupeidae by far the most speciose (9 species). Arius macronotacanthus dominated the biomass of all fish and the biomass of all fish was maximum during the rainy season. Three season-specific Ecopath models were developed and used to compare the biomass, production, consumption, biomass flows and higher order indices of ecosystem functioning of the Mae Klong Estuary in dry, hot and rainy seasons. Several higher order indices related to the ecosystem maturity indicators were computed for the Mae Klong models and were compared with other coastal ecosystems around the world. The results indicate that Mae Klong Estuary has a mixture of characteristics of a mature system (high total system throughput,
Design Space Exploration in the Physical-Design of an AI-Processor at 12 nm Using Relative-Placement Methodology

This paper presents the results of design space exploration (DSE) for the memory-macro placement of a low-power Artificially Intelligent (AI) processor provided by our industry partner in a collaborative project. The commercially available physical design CAD tools for the automatic placement of macros often give unoptimized macro placements that have a large area and total wire length when compared to the manual placement of macros. The goal of this research is to obtain an optimized memory macro placement for the AI processor that provides the lowest possible area, power, wire length, and improved utilization through custom automation to accelerate time. A relative memory macro placement methodology is developed to ease the process of placing the macros. The idea behind the relative placement is that a rectangular object can be placed at 16 different positions around another rectangular object depending on its length and width. Using this methodology for macro placement resulted in a 25% improvement in area requirements, 29% reduction in total power, 40% points improvement in utilization, and a 50% decrease in the wire length when compared with the results for tool placement. Further, the methodology reduced the hand-placement time for memory macros from 2–3 weeks for part of the design to 3–4 days for the complete design.
A Small Claims Court for the NLP: Judging Legal Text Classification Strategies With Small Datasets

Recent advances in language modelling has significantly decreased the need of labelled data in text classification tasks. Transformer-based models, pre-trained on unlabeled data, can outmatch the performance of models trained from scratch for each task. However, the amount of labelled data need to fine-tune such type of model is still considerably high for domains requiring expert-level annotators, like the legal domain. This paper investigates the best strategies for optimizing the use of a small labeled dataset and large amounts of unlabeled data and perform a classification task in the legal area with 50 predefined topics. More specifically, we use the records of demands to a Brazilian Public Prosecutor's Office aiming to assign the descriptions in one of the subjects, which currently demands deep legal knowledge for manual filling. The task of optimizing the performance of classifiers in this scenario is especially challenging, given the low amount of resources available regarding the Portuguese language, especially in the legal domain. Our results demonstrate that classic supervised models such as logistic regression and SVM and the ensembles random forest and gradient boosting achieve better performance along with embeddings extracted with word2vec when compared to BERT language model. The latter demonstrates superior performance in association with the architecture of the model itself as a classifier, having surpassed all previous models in that regard. The best result was obtained with Unsupervised Data Augmentation (UDA), which jointly uses BERT, data augmentation, and strategies of semi-supervised learning, with an accuracy of 80.7% in the aforementioned task.
Advanced UNet++ Architecture for Precise Segmentation of COVID-19 Pulmonary Infections

In the realm of medical image processing using deep learning, accurate segmentation of lesion areas in COVID-19 CT scans remains challenging. We introduce an advanced Unet++ model, tailored for precise segmentation of pulmonary infections in COVID-19-related CT imagery. Our methodology begins with meticulous data preprocessing. Based on the Unet++ framework, we integrate the MulitRes module to amplify lesion edge details in low contrast scenarios. Following feature extraction, the SENet module is incorporated, enriching the receptive field and emphasizing relevant feature channels. Experiments on established datasets indicate our approach not only surpasses existing models in accuracy but also adeptly preserves critical details, especially around lesion boundaries, enhancing the overall segmentation efficacy.
End-to-end Action Quality Assessment with Action Parsing Transformer

Action Quality Assessment (AQA) plays a crucial role in action understanding, and addressing this task poses unique challenges due to the presence of subtle visual differences among actions. Existing action assessment works typically make an overall quality prediction on an entire video. However, the internal structural parsing of actions are important in action quality assessment, which enhances the interpretability of the scoring process. To explore this underlying structural relationship, we propose an action parsing transformer to disintegrate the holistic feature into more fine-grained step-wise representations. Specifically, we utilize a set of learnable queries to represent the step-wise patterns for a specific action and our decoding process converts the video representation to a fixed number of step representations. Moreover, to obtain quality scores, we further devise a score generation module encompassing multiple action scorers, each of which is uniquely associated with specific steps to predict the corresponding step score. Extensive experiments on two public AQA benchmarks suggest that our method well assesses the action quality and achieves outstanding performance.
Overcoming Distribution Shifts in Plug-and-Play Methods with Test- Time Training

Plug-and-Play Priors (PnP) is a well-known class of methods for solving inverse problems in computational imaging. PnP methods combine physical forward models with learned prior models specified as image denoisers. A common issue with the learned models is that of a performance drop when there is a distribution shift between the training and testing data. Test-time training (TTT) was recently proposed as a general strategy for improving the performance of learned models when training and testing data come from different distributions. In this paper, we propose PnP-Ttt as a new method for overcoming distribution shifts in PnP. PnP-TTT uses deep equilibrium learning (DEQ) for optimizing a self-supervised loss at the fixed points of PnP iterations. PnP-Ttt can be directly applied on a single test sample to improve the generalization of PnP. We show through simulations that given a sufficient number of measurements, PnP-Ttt enables the use of image priors trained on natural images for image reconstruction in magnetic resonance imaging (MRI).
Adaptive Containment Control of Heterogeneous Multi-Agent Systems with Unknown Leaders and Communication Delays

This paper investigates the containment control problem of heterogeneous linear multi-agent systems with nonuniform bounded distributed communication delays under an assumption that only the neighboring agents of multiple leaders have access to the information on both the system matrices and the states of multiple leaders. Adaptive distributed observers under nonuniform distributed communication delays are proposed to estimate both the system matrices and the states of multiple leaders without the prior knowledge of communication delays. Then a novel distributed output feedback control strategy is proposed based on the adaptive distributed observer. It is shown that under the proposed control strategy, the output of each follower converges to the convex hull spanned by the outputs of the leaders.
5G Communication Infrastructure for Smart Grids: A Protection Use Case.

A smart grid is an advanced electricity network enabled by the Internet of Things (IoT), Information and Communication Technology (ICT), and advanced grid technologies. 5G and beyond networks are expected to serve as the communication infrastructure of smart grids, providing intelligence, virtualization, and connectivity for millions of devices. This paper investigates the potential of 5G communication infrastructure for smart grids, specifically in the protection of smart grids. We evaluate the communication performance of 5G Standalone (SA) and Non-Standalone (NSA) in a protection scenario, where a User Equipment (UE) acting as an Intelligent Electronic Device (IED) simulates Routable Generic Object Oriented Substation Events (R-GOOSE) and Routable Sampled Values (R-SV) packets and transmits them over the 5G network. We tested the 5G network latency, packet loss, and jitter to evaluate its performance. By visualizing and computing the R-GOOSE and R-SV traffic it is observed that the latency of the majority of packets was 12-15 ms in SA and 20-45 ms in NSA.
A discussion of statistical criteria for assessing awareness with SMR BCI after brain injury

This work discusses the implications of selecting particular statistical metrics and thresholds as criteria to diagnose awareness through Brain-Computer Interface (BCI) technology in patients with Disorders of Consciousness (DOC). We report a first analysis of a novel dataset collected to investigate whether a motor attempt electroencephalography (EEG) paradigm coupled with Functional Electrical Stimulation (FES) can detect command following and, therefore, signs of conscious awareness in DOC. We assessed 22 DOC patients admitted to the acute rehabilitation unit after a brain lesion over one or more sessions. We extracted EEG sensorimotor rhythms and performed a standard open-loop BCI pipeline evaluation, classifying motor attempt against resting-state trials. We validate this approach by correlating classification accuracy with the established clinical scale Coma Recovery Scale Revised. We employ a machine learning (ML)-inspired diagnostic criterion based on confidence intervals over chance-level classification accuracy and show that it yields more conservative and, arguably, reliable inference of Cognitive Motor Dissociation (CMD) by means of command-following, neuroimaging-based tools, compared to diagnoses based on clinical assessments or criteria examining the statistical significance of brain features across different mental states.
Leveraging Task-Specific Context to Improve Unsupervised Adaptation for Myoelectric Control

While there has been renewed interest in the use of myoelectric control for general-purpose applications, the burden of training and maintaining robust models still limits its real-world viability. Online unsupervised adaptation has been proposed to solve this issue by updating the model using predicted pseudo-labels in real time during regular device use. Until now, however, these unsupervised strategies have been limited as they rely on the very classifier outputs they are adapting, making them ill-suited when there is a drastic shift in the input space (e.g., after donning and doffing a device) or there is insufficient training data. In such situations, leveraging context (i.e., task-specific information that can help understand or assess a circumstance) could provide additional guidance for adaptation and improve its robustness. Although difficult to extract in traditional prosthesis control use cases without additional sensors, context may be more readily available in other general-purpose applications, such as in human-computer interaction. In this study, we explore leveraging context, both positive (i.e., reinforcing correct actions) and negative (i.e., correcting poor actions), for conditioning pseudo-label predictions within an adaptive gamified target acquisition setting. The results show that leveraging this additional con-text significantly outperforms the current state-of-the-art high-confidence unsupervised adaptation (p<0.05) using both offline and online performance metrics. This pilot work contributes novel findings and contextual approaches that do not rely on additional sensors, and thus outlines a promising direction of study for myoelectric control as a reliable and effective interaction technique.
Large-Signal Stability of Inverter-Based LV Microgrids: Share of Grid-Forming Units

Considering today’s stakes regarding the reduction of greenhouse gas emissions and the enhancement of the grid resiliency, it is relevant to consider avoiding the use of diesel generators to supply isolated portions of the low-voltage (LV) grid in case of emergency situations. An alternative option would be to operate the isolated portions of the LV grid using the local inverter-based resources only. Initially, all inverters are in grid-following (GFL) mode in order to extract the maximum renewable power and let traditional power plants control the frequency. The islanded operation imposes to switch some inverters to grid-forming (GFM) mode. The global objective is to determine the minimum quantity of modification to be made on existing inverters, without threatening the stability of the islanded grid. This paper focuses on determining the factors which impact the minimum ratio of GFM over GFL capacity required to maintain large-signal stability in the temporary microgrid. For each selected factor, the stability limit when injecting an increasing capacity of GFL power with a fixed GFM capacity is studied in various scenarios of GFM penetration rate, on two representative LV grid topologies. Based on EMT simulations, it is found that factors related to grid topology do not necessarily affect this ratio, while the line $R/X$ ratio does.
Overdispersed Photon-Limited Sparse Signal Recovery Using Nonconvex Regularization

This paper investigates the application of the $\ell_{p}$ quasi-norm, where $0 < p < 1$, in contexts characterized by photon-limited signals such as medical imaging and night vision. In these environments, low-photon count images have typically been modeled using Poisson statistics. In related algorithms, the $\ell_{1}$ norm is commonly employed as a regularization method to promotes sparsity in the reconstruction. However, recent research suggests that using the $\ell_{p}$ quasi-norm may yield lower error results. In this paper, we investigate the use of negative binomial statistics, which are more general models than Poisson models, in conjunction with the $\ell_{p}$ quasi-norm for recovering sparse signals in low-photon count imaging settings.
Reducing Patient Waiting Time at Phlebotomy room of University Hospital using Simulation study

This research is a study of reducing of waiting time at the phlebotomy room of the university hospital by using simulation analysis. There are many patients in the phlebotomy room each morning at the same time, because it is necessary for the patient to abstain breakfast before receiving the service of the phlebotomy room. Another reason is there are fewer number of medical technician than the number of service stations. For this reason, the Simio program was used to simulate a model of the phlebotomy room and measure its performance. The research objective is to proposes an optimal scenario based on reducing of the average waiting time in each service station, the average patient cycle time, measured by the average resource utilization rate of the phlebotomy room. By the data for two types of patients, general patients and VIP patients, was collected. Including data of the patient waiting time, service station service time, and resources used in each process to use in model simulation. From the research results of the simulation model of the phlebotomy room, 3 scenarios were developed and analyzed. Among these scenarios, two emerged as the best models based on different criteria, one considering the hospital's budget and the other without considering it. In conclusion, the best scenario without considering the budget is scenario 1, resulting decreased in average patient cycle time for 63.97 percent, with utilization rate increased by 10.019 percent. The best scenario with affecting the hospital’s budget is scenario 2, resulting decreased in average patient cycle time for 77.317 percent, but utilization rate decreased by 11.071 percent.
Defense Against Spectrum Sensing Data Falsification Attacks Using Cluster Network Channeling with Distance-Based Attacker Detection in Cognitive Radio Ad-Hoc Networks

The growing demand for wireless and smart technologies has led to an unprecedented increase in the utilization of the radio spectrum. However, as the number of users and devices continues to surge, the spectrum is becoming over-crowded, impeding access and efficient utilization. To address this challenge, Cognitive Radio (CR) technology is increasingly becoming the forerunner. Cognitive Radio Ad-Hoc Networks (CRAHNs) consists of cognitive intelligent nodes where the CR nodes operating in an infrastructure-less mode, can sense and hop across different frequencies dynamically. CR nodes adapt broadcast constraints to minimize interference among primary and secondary users. By continuously sensing the radio frequency spectrum, cognitive users identify available licensed channels for communication, selecting optimal channels. CRAHNs are subjected to various security attacks which are unique to such networks only. Spectrum Sensing Data Falsification (SSDF) attack is one such attack where the malicious CR nodes falsify sensing data in an attempt to influence the final fused data. In this work, we simulate a CRAHN consisting of clusters and cluster heads (CH). Apart from implementing routing across the clusters via the CHs, the current work also proposes a distance-based SSDF attacker detection method which operates primarily in the CHs. Comparison with an existing work [1] demonstrates improved detection.
Assessing quality in cross-country comparisons of health care systems and policies : towards a set of generic quality criteria

There is a growing body of cross-country comparisons in health care systems and policy research. However, there is little consensus as to how to assess the quality of these crosscountry comparisons. This is partly due to the fact that this is a field of study and is thus approached differently by researchers from different disciplines and traditions, with different quality concerns. Using the distinction between variable-based and case-based research, we start by identifying the pertinent quality issues related to these comparative strategies. We then present findings from an exploratory survey of health system and policy experts in the European Health Policy Group to elicit their views on the characteristics of high quality cross-country comparisons. Bringing the insights gained by these two exercises together, we have identified the following generic quality criteria for cross-country comparisons: (1) adequate use of theory, (2) explicit selection of comparator countries, (3) rigour of the comparative research design, (4) an appreciation of the complexity of comparison at the country level, and (5) contribution to knowledge. This list may not be exclusive; however, we believe that it will help advance the discussion about quality of cross-country comparisons in health care systems and policy research.