Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the spatial and temporal variability of primary production from a combination of in situ and remote sensing data a southern Benguela case study

The aim of this thesis is to produce fine resolution estimates of primary production in three-dimensional space at the temporal scale that these events develop. It is hypothesized that complex relationships among time sequences of physical and biological processes that influence primary production can be automatically discovered from archives of data. This study uses an archive of in situ ship-board data containing subsurface temperature and phytoplankton distribution profiles. Each profile is associated in time and space with satellite remotely-sensed wind, sea surface temperature and surface chlorophyll a data. The bottom depth, season and location of each profile are also recorded. The archive of depth profiles is simplified by mapping each profile onto one of twelve representative profile clusters obtained using the k-means clustering algorithm so that each cluster contains a set of similar profiles and their corresponding data. Relationships between remotely sensed surface features and chlorophyll a profiles are first obtained from a static Bayesian network using same day data. This is then taken further by analysing time-series of satellite data to predict likely temperature and chlorophyll a profiles for each pixel of a 4 km resolution satellite image. Un ive rsi ty of Ca pe To wn viii The time-series of data associated with each profile and their influence on the profile shape is determined using statistical models based on Bayesian theory and methods based on rule discovery. These sequences are then used to predict the profile shape. The predicted chlorophyll a profile is used in a primary production model to estimate hourly production in the water column of the St Helena Bay sub-region. The model is run over sixteen months to illustrate the short-term variability of primary production on a fine scale. The total annual production estimated by the model shows very good agreement with other estimates made in the Benguela system. An average annual production rate of 3.2 gC.m. day is obtained for the sub-region. The model differs from previous methods for estimating annual production in that the primary production estimates are three-dimensional and derived from physical processes, which allows for analysis of complex tropho-dynamic relationships and impacts of hypothetical climate scenarios.
Unity3D-based Virtual Click Interaction Implementation

In order to solve the problem of broken colliders caused by the fact that the object models for interaction are not constructed in one piece, but are built in pieces in the Unity3D macro virtual scene, this paper proposes a collider combining method, which enables the combining of colliders of objects in the scene, so that when the effective part of the object to be interacted with is clicked, the click of the object will be triggered event to display the data information. Finally, the collider combining method is applied in the virtual industrial park, and the colliders of the models to be interacted with in the virtual industrial park are successfully combined. The results show that this method can effectively interact with the target object model.
A study on the application of contrastive learning in the brain-computer interface of motor imagery

In brain-computer interface (BCI) systems, acquiring EEG signals is relatively easy, but labeling EEG signals is challenging. Supervised learning is widely used in existing BCI research, but its generalization ability is poor due to the influence of labeled data, which is not conducive to the promotion and application of BCI. In the fields of computer vision and natural language processing, self-supervised learning is becoming a popular research direction that can use unlabeled data for learning. In this study, we propose a self-supervised contrastive learning method for identifying motor imagery states by performing different data transformation operations on EEG data to generate positive and negative sample pairs. This allows the pre-trained network to learn general feature representations from the signals, enabling it to converge faster and achieve better classification performance when performing downstream tasks. The network's performance is significantly related to the amount of unlabeled data used in the self-supervised learning process. Through the evaluation of the collected data, we achieved an accuracy of 82.56% in classifying motor imagery states with 40% amount of labeled data, demonstrating the practical value of our method.
PCB defect detection algorithm based on improved YOLOv5

Aiming at the problem of small contrast difference between the defective region and the background region in PCB images, This Article presents an enhanced YOLOv5 algorithm with a multi-scale detection approach. To enhance the YOLOv5 algorithm, this Article upgrades its backbone network by replacing Characteristics Abstraction network with the ASPP module. This change aims to improve the network's perceptual field and Characteristics extraction capability. Secondly, in order to improve the attention of the model to other regions, this Article introduces the attention mechanism Coordinate Attention module, which embeds the location information into the channel attention and achieves multi-scale processing and Characteristics fusion. Finally, this Article uses different sizes of anchor frames for multiscale detection of defective targets. The experimental results show that the size of the improved multiscale network model is only 83% of the size of the original YOLOv5 model, and the mAP on the dataset reaches 97.2%. The algorithm proposed in this Article can effectively detect various defects in PCB images and has high detection precision and low false detection rate, which has good practical value and prospect of popularization and application.
Belt defect detection based on f-AnoGAN

A belt defect detection model based on unsupervised generative adversarial network is proposed for the problem of low number of defective samples in belt defect detection. The model is trained on defect-free normal samples and the detection results are obtained by calculating the outliers between the input samples and the reconstructed samples at the time of detection. Considering the information loss problem of the encoder compressing the input samples, this paper adds a selfattention mechanism to the model to enhance the extraction of useful features. In addition, the LeakyPeLU activation function in the model is replaced with PReLU to improve the fitting ability of the model, and the method is experimentally proven to achieve better image reconstruction results. The experimental results show that the accuracy of the assay using this method is 98.13%, AUC value is 99.89% and AP value is 99.75%, which was better than the other two comparative models. The method has good reconfiguration and defect detection capabilities.
GCN-based table-to-text generation research

Table-to-text generation is an important area of text generation, and the process of structured table-to-text generation faces problems such as the "illusion" of over-understanding table data, and the problem of selecting and ordering the content of the generated text from table data. In this paper, we present some important model mechanisms in the history of its development and a new outlook on the future development of table-to-text generation and possible technical routes. A two-stage encoder-decoder approach is analyzed as to why it is superior to its predecessors, and a new outlook is proposed for implementing table-to-text generation tasks based on graph convolutional neural networks.
Prediction of CF4/N2 adsorption and separation performance in organic frameworks based on machine learning algorithms

The separation of different components in a binary mixture of CF4 and N2 is a challenging task. The adsorption of 603 experimental (CoRE) organic frameworks on CF4 and N2 binary mixture gases at room temperature and 10 different pressures was simulated using GCMC in this study. The performance of adsorption separation is described by the product of the adsorption amount of CF4 and the logarithm of the CF4 adsorption selectivity. Among the nine features extracted are pressure, structural features of adsorbent, and two easily accessible user-defined descriptors (AVG_SIG and AVG_SQRT_EPS). The importance of AVG_SIG and AVG_SQRT_EPS was assessed using random forest and found to play an important role in predicting adsorption separation performance. By using Harris Hawks Optimization (HHO) to explore the potential of different models, it was found that XGBoost and GangNeuron performed excellently in predicting the adsorption and separation performance of CF4 and N2, with prediction accuracy of 99.2% and 98.8%, respectively.
Robust Location Prediction over Sparse Spatiotemporal Trajectory Data: Flashback to the Right Moment!

As a fundamental problem in human mobility modeling, location prediction forecasts a user’s next location based on historical user mobility trajectories. Recurrent neural networks (RNNs) have been widely used to capture sequential patterns of user visited locations for solving location prediction problems. Due to the sparse nature of real-world user mobility trajectories, existing techniques strive to improve RNNs by incorporating spatiotemporal contexts into the recurrent hidden state passing process of RNNs using context-parameterized transition matrices or gates. However, such a scheme mismatches universal spatiotemporal mobility laws and thus cannot fully benefit from rich spatiotemporal contexts encoded in user mobility trajectories. Against this background, we propose Flashback++, a general RNN architecture designed for modeling sparse user mobility trajectories. It not only leverages rich spatiotemporal contexts to search past hidden states with high predictive power but also learns to optimally combine them via a hidden state re-weighting mechanism, which significantly improves the robustness of the models against different settings and datasets. Our extensive evaluation compares Flashback++ against a sizable collection of state-of-the-art techniques on two real-world location-based social networks datasets and one on-campus mobility dataset. Results show that Flashback++ not only consistently and significantly outperforms all baseline techniques by 20.56% to 44.36% but also achieves better robustness of location prediction performance against different model settings (different RNN architectures and numbers of hidden states to flash back), different levels of trajectory sparsity, and different train-testing splitting ratios than baselines, yielding an improvement of 31.05% to 94.60%.
The End of the Policy Analyst? Testing the Capability of Artificial Intelligence to Generate Plausible, Persuasive, and Useful Policy Analysis

Policy advising in government centers on the analysis of public problems and the developing of recommendations for dealing with them. In carrying out this work, policy analysts consult a variety of sources and work to synthesize that body of evidence into useful decision support documents commonly called briefing notes. Advances in natural language processing (NLP) have led to the continuing development of tools that can undertake a similar task. Given a brief prompt, a large language model (LLM) can synthesize information in content databases. This article documents the findings from an experiment that tested whether contemporary NLP technology is capable of producing public policy relevant briefing notes that expert evaluators judge to be useful. The research involved two stages. First, briefing notes were created using three models: NLP generated; human generated; and NLP generated / human edited. Next, two panels of retired senior public servants (with only one panel informed of the use of NLP in the experiment) were asked to judge the briefing notes using a heuristic evaluation rubric. The findings indicate that contemporary NLP tools were not able to, on their own, generate useful policy briefings. However, the feedback from the expert evaluators indicates that automatically-generated briefing notes might serve as a useful supplement to the work of human policy analysts. And the speed with which the capabilities of NLP tools are developing, supplemented with access to a larger corpus of previously prepared policy briefings and other policy-relevant material, suggests that the quality of automatically-generated briefings may improve significantly in the coming years. The article concludes with reflections on what such improvements might mean for the future practice of policy analysis.
Does urbanization lead to parallel demographic shifts across the world in a cosmopolitan plant?

Urbanization is occurring globally, leading to dramatic environmental changes that are altering the ecology and evolution of species. In particular, the expansion of human infrastructure and the loss and fragmentation of natural habitats in cities is predicted to increase genetic drift and reduce gene flow by reducing the size and connectivity of populations. Alternatively, the “urban facilitation model” suggests that some species will have greater gene flow into and within cities leading to higher diversity and lower differentiation in urban populations. These alternative hypotheses have not been contrasted across multiple cities. Here, we used the genomic data from the Global Urban Evolution project (GLUE), to study the effects of urbanization on non-adaptive evolutionary processes of white clover (Trifolium repens) at a global scale. We found that white clover populations presented high genetic diversity and no evidence of a reduction in Ne linked to urbanization. On the contrary, we found that urban populations were less likely to experience a recent decrease in effective population size than rural ones. In addition, we found little genetic structure among populations both globally and between urban and rural populations, which showed extensive gene flow between habitats. Interestingly, white clover displayed overall higher gene flow within urban areas than within rural habitats. Our study provides one of the largest comprehensive tests of demographic effects of urbanization and our results contrast the common perception that heavily altered and fragmented urban environments will reduce the effective population size and genetic diversity of populations and contribute to their isolation.
A fluorescent sex-sorting technique for insects with the demonstration in Drosophila melanogaster

Recent advances in insect genetic engineering offer alternative genetic biocontrol solutions to control populations of pests and disease vectors. While success has been achieved, sex-sorting remains problematic for scaling many genetic biocontrol interventions. Here we describe the development of a sex-sorting technique for female and male selection with a proof-of-concept in D. melanogaster termed SEPARATOR (Sexing Element Produced by Alternative RNA-splicing of A Transgenic Observable Reporter). This approach utilizes dominant fluorescent proteins and differentially spliced introns to ensure sex-specific expression. The system has the potential for adaptability to various insect species and application for high-throughput insect sex-sorting.
Increasing NPYergic transmission in the hippocampus rescues aging-related deficits of long-term potentiation in the mouse dentate gyrus

Loss of neuropeptide Y (NPY)-expressing interneurons in the hippocampus and decaying cholinergic neuromodulation are thought to contribute to impaired cognitive function during aging. However, the interaction of these two neuromodulatory systems in maintaining hippocampal synaptic plasticity during healthy aging has not been explored so far. Here we report profound sex differences in the Neuropeptide-Y (NPY) levels in the dorsal dentate gyrus (DG) with higher NPY concentrations in the male mice compared to their female counterparts and a reduction of NPY levels during aging specifically in males. This change in aged males is accompanied by a deficit in theta burst-induced long-term potentiation (LTP) in the medial perforant path-to-dorsal DG (MPP-DG) synapse, which can be rescued by enhancing cholinergic activation with the acetylcholine esterase blocker, physostigmine. Importantly, NPYergic transmission is required for this rescue of LTP. Moreover, exogenous NPY application alone is sufficient to recover LTP induction in aged male mice, even in the absence of the cholinergic stimulator. Together, our results suggest that in male mice NPYergic neurotransmission is a critical factor for maintaining dorsal DG LTP during aging.
A deep neural network estimation of brain age is sensitive to cognitive impairment and decline

The greatest known risk factor for Alzheimer’s disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62×10−32; T1: r=0.61, p=1.45×10−26, FW+T1: r=0.77, p=6.48×10−50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: β=−1.094, p=6.32×10−7; T1: β=−1.331, p=6.52×10−7; FW+T1: β=−1.476, p=2.53×10−10; executive function, FW: β=−1.276, p=1.46×10−9; T1: β=−1.337, p=2.52×10−7; FW+T1: β=−1.850, p=3.85×10−17) and longitudinal cognition (memory, FW: β=−0.091, p=4.62×10−11; T1: β=−0.097, p=1.40×10−8; FW+T1: β=−0.101, p=1.35×10−11; executive function, FW: β=−0.125, p=1.20×10−10; T1: β=−0.163, p=4.25×10−12; FW+T1: β=−0.158, p=1.65×10−14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.
Mechanism of proton-powered c-ring rotation in a mitochondrial ATP synthase

Proton-powered c-ring rotation in mitochondrial ATP synthase is crucial to convert the transmembrane protonmotive force into torque to drive the synthesis of ATP. Capitalizing on recent cryo-EM structures, we aim at a structural and energetic understanding of how functional directional rotation is achieved. We performed multi-microsecond atomistic simulations to determine the free energy profiles along the c-ring rotation angle before and after the arrival of a new proton. Our results reveal that rotation proceeds by dynamic sliding of the ring over the a-subunit surface, during which interactions with conserved polar residues stabilize distinct intermediates. Ordered water chains line up for a Grotthuss-type proton transfer in one of these intermediates. After proton transfer, a high barrier prevents backward rotation and an overall drop in free energy favors forward rotation, ensuring the directionality of c-ring rotation required for the thermodynamically disfavored ATP synthesis. The essential arginine of the a-subunit stabilizes the rotated configuration through a salt-bridge with the c-ring. Overall, we describe a complete mechanism for the rotation step of the ATP synthase rotor, thereby illuminating a process critical to all life at atomic resolution.
L-Carnitine on contrast-induced nephropathy in diabetic rats

Background: The use of contrast media in coronary interventions favors diagnostic success. Contrast-induced nephropathy (CIN) is considered a frequent adverse event in the presence of the risk factor diabetes mellitus (DM). L-Carnitine is a lipid metabolism compound with a possible antioxidant effect. Objectives: to evaluate the effect of L-Carnitine on contrast-induced nephropathy in rats with diabetes mellitus. Method: twenty-eight male Wistar rats (250-300g) were randomized into four groups: Citrate (control); Diabetes (65mg/kg of intravenous streptozotocin); Diabetes+Iodinated contrast (meglumine sodium ioxathalamate 6ml/kg intraperitoneal-i.p, single dose); Diabetes+Iodinated contrast+L-Carnitine (50 mg/kg i.p. administered for 5 days). Physiological parameters were evaluated (weight, feed and water intake, glycemia and ratio of kidney weight to animal weight); renal function (inulin clearance, serum creatinine, urinary NGAL and urinary albumin); renal hemodynamics and oxidative profile (urine peroxides, thiols in renal tissue, thiobarbituric acid reactive substances in urine and urinary nitric oxide). Results: Contrast-induced nephropathy was confirmed by reduced renal blood flow, increased renal vascular resistance, reduced inulin clearance, increased serum creatinine, and urinary NGAL with increased oxidative peroxide and consumption of antioxidant enzymes. L-Carnitine favored the improvement of renal function and hemodynamics with recovery of redox imbalance. Conclusion: the use of L-Carnitine demonstrated a renoprotective and antioxidant effect.
Robust Weak Supervision with Variational Auto-Encoders

Recent advances in programmatic weak supervision (WS) techniques allow to mitigate the enormous cost and effort of human data annotation for supervised machine learning by automating it using simple rule-based labelling functions (LFs). However, LFs need to be carefully designed, often requiring expert domain knowledge and extensive validation for existing WS methods to be effective. To tackle this, we propose the Weak Su-pervision Variational Auto-Encoder (WS-VAE), a novel framework that combines unsupervised representation learning and weak labelling to reduce the dependence of WS on expert and manual engineering of LFs. Our technique learns from inputs and weak labels jointly to capture the input signals distribution with a latent space. The unsupervised representation component of the WS-VAE regularises the inference of weak labels, while a specifically designed decoder allows the model to learn the relevance of LFs for each input. These unique features lead to considerably improved robustness to the quality of LFs, compared to existing methods. An extensive empirical evaluation on a standard WS benchmark shows that our WS-VAE is competitive to state-of-the-art methods and substantially more robust to LF engineering.