Miracle-dz/UniFAR-FTUs huggingface dataset:
Contributions to the Fauna (Corals, Brachiopods) and Stable Isotopes of the Late Triassic Steinplatte Reef/Basin-Complex, Northern Calcareous Alps, Austria

Based on the study of 60-isotopes and using the equation of SHACKLETON & KENNETT (1975) a water temperature range of 18-24°C can be inferred for the Oberrhätkalk-Capping Facies and also for the limestones of the Kössen Formation, while for the Liassic deeper water limestones 14-19°C can be assumed. Our stable isotope data till now do not provide any indications for vadose cementation within the Oberrhätkalk-Capping Facies. So far in all 23 scleractinian coral species are known from the Steinplatte region. Seven species are mentioned for the first time in this paper, namely six from the Oberrhätkalk-Capping Facies of Plattenkogel Coral Garden ("Erik's Coral Garden"): Retiophyllia defilippi (STOPPANI), R. gosaviensis RONIEWICZ, R. multiramis RONIEWICZ, R. norica (FRECH), R. oppeli (REUSS), and Margarosmilia charliana (FRECH), while Parathecosmilia sellae (STOPPANI) and Oedalmia norica (FRECH) are part of the Kössen-Patch Reef A assemblage. Retiophyllia multiramis RONIEWICZ was observed in the Oberrhätkalk-Capping Facies and also in the coral-assemblage of Patch Reef A. The brachiopod assemblages of the Oberrhätkalk-Capping Facies and of the various Kössen facies are very similar. However, so far Sinucosta emmrichi (SUESS) was not observed in the Oberrhätkalk-Capping Facies assemblage, while up to date Bactrynium bicarinatum EMMR. , Laballa suessi (ZUGMAYER), Triadithyris gregariaeformis (ZUGMAYER), Rhaetina aff. elliptica DAGYS. and "Rhynchonella" ex gr. subrimosa (SCHAFHAUTL) were not encountered in the Kössen assemblage
Neurosymbolic Knowledge Distillation

The rapid advancement of neural networks has permeated industries worldwide. However, this remarkable success often faces adversity while deploying on devices with limited resources due to the high computational power demands and storage requirements. To address these challenges, a model compressing technique such as knowledge distillation (KD) has emerged as an effective process for training compact models. However, due to the lack of interpretability, knowledge distillation is often hard to explain. In this paper, our focus is to implement a knowledge distillation framework that can provide interpretability alongside improved performance. With the help of our experiments, we demonstrate that this can be achieved by integrating first-order logical formulas into a neurosymbolic learning approach within the knowledge distillation framework. Diverging from the prior research, in this paper, we introduce a Neurosymbolic Knowledge Distillation Framework (KD-LTN), which is a composition of Logic Tensor Network (LTN) and Knowledge Distillation (KD). Notably, our KD-LTN network not only enhances interpretability but also achieves accuracy improvements, compared with the conventional knowledge distillation framework.
Helmet Pose Measurement Method Based on Feature Learning

In modern aviation systems, the helmet sight (HMS) assists the pilot to cooperate with the warplane to quickly complete the target tracking and aiming, and the helmet pose measurement is the core technology of the helmet sight. Since the helmet pose measurement technology involved in aviation safety, this technology is kept confidential at home and abroad, and there is a lack of open source helmet data set and high-precision helmet posture measurement algorithm. For this purpose, this paper produced the first helmet dataset, Helment 8K, and proposed the CSPDarkNet-Ht helmet pose measurement algorithm based on feature learning. The target is to form the helmet dataset image on the helmet model according to the unique feature points of the helmet model design. Helment 8K data set is made by building a helmet model with 3dmax and then rendering 8282 helmet images at different angles with targets. The CSPDarkNet-Ht algorithm divides the yaw angle and pitch angle of the helmet pose angle into two sub-tasks for classification and regression. Firstly, The algorithm uses CSPDarkNet to extract the low, medium and high semantic information of the target features for the backbone network. Then, different semantic feature layers are classified from different angles and regression of the highest semantic feature layer is performed to obtain the predicted pose angles. Finally, the loss function was optimized to include the cross-entropy loss of the five coarse and fine categories as the total loss. Moreover, the average absolute error of the angular prediction on the Helment 8K dataset is 0.5, and the prediction accuracy is 91.09% within 1 degree error, which is consistent with the accurate measurement of the helmet pose.
CONSMI: Contrastive Learning in the Simplified Molecular Input Line Entry System Helps Generate Better Molecules

In recent years, the application of deep learning in molecular de novo design has gained significant attention. One successful approach involves using SMILES representations of molecules and treating the generation task as a text generation problem, yielding promising results. However, the generation of more effective and novel molecules remains a key research area. Due to the fact that a molecule can have multiple SMILES representations, it is not sufficient to consider only one of them for molecular generation. To make up for this deficiency, and also motivated by the advancements in contrastive learning in natural language processing, we propose a contrastive learning framework called CONSMI to learn more comprehensive SMILES representations. This framework leverages different SMILES representations of the same molecule as positive examples and other SMILES representations as negative examples for contrastive learning. The experimental results of generation tasks demonstrate that CONSMI significantly enhances the novelty of generated molecules while maintaining a high validity. Moreover, the generated molecules have similar chemical properties compared to the original dataset. Additionally, we find that CONSMI can achieve favorable results in classifier tasks, such as the compound–protein interaction task.
Localized polar cap precipitation in association with nonstorm time airglow patches

Although airglow patches are traditionally regarded as high‐density ionospheric plasma unrelated to local precipitation, past observations were limited to disturbed conditions. Recent nonstorm time observations show patches to be associated with ionospheric flow channels and localized field‐aligned currents. We examine whether nonstorm time patches are related also to polar cap precipitation using Fast Auroral Snapshot‐imager conjunctions. We have identified localized precipitation that is enhanced within patches in comparison to the weak polar rain outside patches. The precipitation consists of structured or diffuse soft electron fluxes. While the latter resembles polar rain only with higher fluxes, the former consists of discrete fluxes enhanced by 1–2 orders of magnitude from several to several hundred eV. Therefore, patches should be regarded as part of a localized magnetosphere‐ionosphere coupling system along open magnetic field lines and their transpolar evolution as a reflection of mesoscale magnetotail lobe processes. The precipitation a minor contributor to patch ionization.
Uncertainty Estimation for a Dual-Embedding based Entity Extraction Service

In this paper, we present our approach for quantifying uncertainty in our dual-embedding based entity extraction system. First, we review the state-of-the-art uncertainty estimation methods. We then present our entity extraction system and discuss practical challenges of applying each of the uncertainty estimation solutions to our system. Finally, we present our uncertainty estimate approach and its performance in production. Our goal is to present the challenges and concerns that one should consider while designing an uncertainty estimation solution in the real world and, therefore, help the research community to develop more practical uncertainty estimation solutions.
Direct observations of microbial community succession on sinking marine particles

Abstract Microbial community dynamics on sinking particles control the amount of carbon that reaches the deep ocean and the length of time that carbon is stored, with potentially profound impacts on Earth’s climate. A mechanistic understanding of the controls on sinking particle distributions has been hindered by limited depth- and time-resolved sampling and methods that cannot distinguish individual particles. Here, we analyze microbial communities on nearly 400 individual sinking particles in conjunction with more conventional composite particle samples to determine how particle colonization and community assembly might control carbon sequestration in the deep ocean. We observed community succession with corresponding changes in microbial metabolic potential on the larger sinking particles transporting a significant fraction of carbon to the deep sea. Microbial community richness decreased as particles aged and sank; however, richness increased with particle size and the attenuation of carbon export. This suggests that the theory of island biogeography applies to sinking marine particles. Changes in POC flux attenuation with time and microbial community composition with depth were reproduced in a mechanistic ecosystem model that reflected a range of POC labilities and microbial growth rates. Our results highlight microbial community dynamics and processes on individual sinking particles, the isolation of which is necessary to improve mechanistic models of ocean carbon uptake.
Mitigating Representation Bias in Data Transformations: A Constraint-based Optimization Approach

The development of discrimination-aware solutions is one of the main current research directions in the design of automated decision systems that make deep use of people-related data. Two main groups of techniques have been designed so far: the first focuses on fair machine learning, i.e., the development of algorithms that can detect and correct the bias in the context of a learning process; the second targets approaches for detecting and mitigating bias due to the under-representation of specific groups of people in the used dataset (representation bias), independently from the further analytical tasks to be executed. In both cases, bias can originate from how and where the data was originally collected or it can be introduced, sometimes amplified, during the data preparation steps preceding any analytical task. In this paper, we focus on representation bias and we present an approach for mitigating representation bias in datasets generated through data transformation, a specific data preparation phase. Differently from similar proposals, the proposed technique can mitigate bias defined in terms of multiple types of constraints, including coverage and fairness. To show the applicability of the proposed approach, we consider dropout prediction in the educational context as a case study. The preliminary reported experimental results show that the proposed technique can mitigate representation bias in an effective and efficient way.
Adversarial Data-Augmented Resilient Intrusion Detection System for Unmanned Aerial Vehicles

With the growing adoption of unmanned aerial vehicles (UAVs) across various domains, the security of their operations is paramount. UAVs, heavily dependent on GPS navigation, are at risk of jamming and spoofing cyberattacks, which can severely jeopardize their performance, safety, and mission integrity. Intrusion detection systems (IDSs) are typically employed as defense mechanisms, often leveraging traditional machine learning techniques. However, these IDSs are susceptible to adversarial attacks that exploit machine learning models by introducing input perturbations. In this work, we propose a novel IDS for UAVs to enhance resilience against such attacks using generative adversarial networks (GAN). We also comprehensively study several evasion-based adversarial attacks and utilize them to compare the performance of the proposed IDS with existing ones. The resilience is achieved by generating synthetic data based on the identified weak points in the IDS and incorporating these adversarial samples in the training process to regularize the learning. The evaluation results demonstrate that the proposed IDS is significantly robust against adversarial machine learning-based attacks compared to the state-of-the-art IDSs while maintaining a low false positive rate.
Clinical Outcomes of Intravesical Prostatic Protrusion in Patients With Benign Prostatic Hyperplasia

Background Benign prostatic hyperplasia (BPH) is a disorder that is characterized by the hyperplasia of the cellular elements of the prostate, leading to an enlarged prostate. One of the parameters affecting urinary outflow is intravesical prostatic protrusion (IPP). It is a phenomenon wherein the enlargement of the prostate protrudes into the bladder along the plane of least resistance. This condition can lead to various clinical effects, including symptoms such as the feeling of incomplete void and weak, interrupted urine stream. Hence, investigating the potential associations between different grades of IPP and clinical urological outcomes holds crucial implications for optimizing patient care, refining risk stratification, and enhancing treatment approaches. Methodology We examined patients who were following up at the urology outpatient clinics due to BPH between June 1, 2021, and December 31, 2022. All patients included in this study were required to undergo a transabdominal prostate ultrasound. Patient records were reviewed for various factors, including demographic stratification, the presence of urine routine or culture with evidence of urinary tract infection (UTI) within the past two years, and whether patients were scheduled for surgical intervention. The radiological parameters were recorded by viewing the midsagittal and transverse ultrasound images retrospectively by two specialist radiology physicians. The parameters measured included IPP Grade, prostate volume (PV), presence of bladder stones, anatomical abnormalities (such as bladder diverticulum), and post-void volume. Results The total sample size was 184 patients. Out of these, 53 (28.8%) had IPP Grade I, 72 (39.1%) were classified as Grade II, 42 (22.8%) had Grade III, and 17 (9.2%) were categorized as Grade IV. The data collected also showed that 12 (6.5%) patients had bladder stones on ultrasound examination. Additionally, 17 (9.2%) patients had bladder diverticulum. Furthermore, when controlled for age and PV, multivariate analysis using logistic regression models to calculate the odds ratio (OR) showed that increasing IPP Grade is associated with an increased risk of developing UTIs, acute urinary retention, and the need for surgical intervention. The highest risk group of patients is IPP Grade IV, with odds ratios (ORs) of 6.8, 7.2, and 6.4 for developing UTIs, experiencing acute urinary retention, and requiring surgical intervention, respectively. Conclusions The results provide compelling evidence of the adverse relationships between higher grades of IPP and worsening urological outcomes and patient morbidity. Hence, we recommend further studies be conducted on the clinical effects of IPP and that these measurements should be considered as part of routine ultrasound prostate imaging to aid in the management of BPH cases.
Design of Intelligent Recommendation System for Graduate Employment Based on Artificial Intelligence

The traditional graduate employment recommendation system mainly relies on the employment information provided by graduates. The content of the information is single, and the recommended employment information is relatively outdated, which cannot meet the individual needs of graduates when choosing a career. The article designs an intelligent recommendation system for graduate employment based on artificial intelligence. Based on the collection and processing of graduates' job-seeking information, the system provides graduates with multidimensional and personalized employment information recommendation services through machine learning technology and recommendation algorithms. It improves the success rate of job hunting for graduates, and realizes precise and intelligent employment recommendation for college students. The experimental results show that using the artificial intelligence-based graduate employment intelligent recommendation system, the employment success rate of graduates can reach up to 88.7%.
SYK-623, a δ Opioid Receptor Inverse Agonist, Mitigates Chronic Stress-Induced Behavioral Abnormalities and Disrupted Neurogenesis

The δ opioid receptor (DOR) inverse agonist has been demonstrated to improve learning and memory impairment in mice subjected to restraint stress. Here, we investigated the effects of SYK-623, a new DOR inverse agonist, on behavioral, immunohistochemical, and biochemical abnormalities in a mouse model of imipramine treatment-resistant depression. Male ddY mice received daily treatment of adrenocorticotropic hormone (ACTH) combined with chronic mild stress exposure (ACMS). SYK-623, imipramine, or the vehicle was administered once daily before ACMS. After three weeks, ACMS mice showed impaired learning and memory in the Y-maze test and increased immobility time in the forced swim test. SYK-623, but not imipramine, significantly suppressed behavioral abnormalities caused by ACMS. Based on the fluorescent immunohistochemical analysis of the hippocampus, ACMS induced a reduction in astrocytes and newborn neurons, similar to the reported findings observed in the postmortem brains of depressed patients. In addition, the number of parvalbumin-positive GABA neurons, which play a crucial role in neurogenesis, was reduced in the hippocampus, and western blot analysis showed decreased glutamic acid decarboxylase protein levels. These changes, except for the decrease in astrocytes, were suppressed by SYK-623. Thus, SYK-623 mitigates behavioral abnormalities and disturbed neurogenesis caused by chronic stress.
Poisoning of Pt/γ-Al2O3 Aqueous Phase Reforming Catalysts by Ketone and Diketone-Derived Surface Species

Strong adsorption of ketone and diketone byproducts and their fragmentation products during the aqueous phase reforming of biomass derived oxygenates is believed to be responsible for the deactivation of supported Pt catalysts. This study involves a combined experimental and theoretical approach to demonstrate the interactions of several model di/ketone poisons with Pt/γ-Al2O3 catalysts. Particular di/ketones were selected to reveal the effects of hydroxyl groups (acetone, hydroxyacetone), conjugation with C=C bonds (mesityl oxide), intramolecular distance between carbonyls in diketones (2,3-butanedione, 2,4-pentanedione), and length of terminal alkyl chains (3,4-hexanedione). The formation of adsorbed carbon monoxide (1900–2100 cm–1) as a decarbonylation product was probed using infrared spectroscopy and to calculate the extent of poisoning during subsequent methanol dehydrogenation based on the reduction of the ν(C≡O) band integral relative to experiments in which only methanol was dosed. Small Pt particles appeared less active in decarbonylation and were perhaps poisoned by strongly adsorbed di/ketones on undercoordinated metal sites and bulky conjugated species formed on the γ-Al2O3 support from aldol self-condensation. Larger Pt particles were more resistant to di/ketone poisoning due to higher decarbonylation activity yet still fell short of the expected yield of adsorbed CO from subsequent methanol activity. Vibrational spectra acquired using inelastic neutron scattering showed evidence for strongly binding methyl and acyl groups resulting from di/ketone decarbonylation on a Pt sponge at 250 °C. Adsorption energies and molecular configurations were obtained for di/ketones on a Pt(111) slab using density functional theory, revealing potential descriptors for predicting decarbonylation activity on highly coordinated metal sites. Calculated reaction energies suggest it is energetically favorable to reform surface methyl groups into adsorbed CO and H. However, the rate of this surface reaction is limited by a high activation barrier indicating that either improved APR catalyst designs or regeneration procedures may be necessary.
Deep Sparse Depth Completion Using Multi-Scale Residuals and Channel Shuffle

Depth completion aims to recover dense depth maps from sparse depth maps. Recent approaches have used additional modalities as guidance to improve depth completion performance. Image-guided depth completion uses scene information from color images, but it still produces inaccurate object boundaries. In this paper, we propose deep sparse depth completion using multi-scale residuals and channel shuffle, named ReCSNet. ReCSNet is a dual-branch network based on a U-shaped architecture. ReCSNet consists of one VIS-Semantic-Guided Branch (VSGB) and one Sparse Depth Guided Branch (SDGB) to get global color, edge information, and local accurate depth information. VSGB utilizes two encoders to extract features from the VIS-Semantic image pairs and the sparse depth maps, and employs a feature channel shuffle mechanism to blend the two sets of encoded features. The semi-dense depth map generated by VSGB is concatenated with the original sparse depth map and input into SDGB to predict the second semi-dense depth map. The confidence maps generated by the two branches are adaptively fused to generate the final depth map. Moreover, we incorporate multi-scale residuals obtained from the VIS image and concatenate them with the decoded features to further enhance the constraint on object boundaries. At the rear of the dual-branch network, we add a Repetitive Deformable Convolution Module (RDCM) to further refine the depth values in object edges. Experimental results show that ReCSNet achieves outstanding performance on the KITTI depth completion validation dataset with an improvement of 16mm in the root mean square error (RMSE) metric.
A new species of Pholidobolus (Squamata: Gymnophthalmidae) from the Andes of southern Ecuador

—We describe a new species of Pholidobolus lizard from the Amazonian slopes of the Andes of southern Ecuador. Among other characters, the new species differs from other species of Pholidobolus in having a distinct diagonal white stripe extending from the fourth genial scale to the fore limb. We present a phylogeny based on mitochondrial DNA sequence data as additional evidence supporting delimitation of the new species, which is sister to all other species of Pholidobolus. Our phylogeny further supports the south-to-north speciation hypothesis proposed for other lizard clades from the northern Andes.