Miracle-dz/UniFAR-FTUs huggingface dataset:
Safe-State Enhancement Method for Autonomous Driving via Direct Hierarchical Reinforcement Learning

Reinforcement learning (RL) has shown excellent performance in the sequential decision-making problem, where safety in the form of state constraints is of great significance in the design and application of RL. Simple constrained end-to-end RL methods might lead to significant failure in a complex system like autonomous vehicles. In contrast, some hierarchical RL (HRL) methods generate driving goals directly, which could be closely combined with motion planning. With safety requirements, some safe-enhanced RL methods add post-processing modules to avoid unsafe goals or achieve expectation-based safety, which accepts the existence of unsafe states and allows some violations of safe constraints. However, ensuring state safety is vital for autonomous vehicles. Therefore, this paper proposes a state-based safety enhancement method for autonomous driving via direct hierarchical reinforcement learning. Finally, we design a constrained reinforcement learner based on the State-based Constrained Markov Decision Process (SCMDP), where a learnable safety module could adjust the constraint strength adaptively. We integrate a dynamic module in the policy training and generate future goals considering safety, temporal-spatial continuity, and dynamic feasibility, which could eliminate dependence on the prior model. Simulations in the typical highway scenes with uncertainties show that the proposed method has better training performance, higher driving safety in interactive scenes, more decision intelligence in traffic congestions, and better economic driving ability on roads with changing slopes.
The Factual Condition of Early Childhood Prosocial Behavior and Its Development by Parents in Public Play Facilities

Penelitian ini dilatarbelakangi oleh rendahnya prososial anak usia dini di arena bermain fasilitas umum, ini terlihat dari perilaku anak yang suka memonopoli permainan, tidah sabar menunggu giliran, suka memukul teman, dan tidak menyukai teman dan lain sebagainya. Hal ini diduga disebabkan karena rendahnya partisipasi orang tua dalam pengembangan sosial anak.Tujuan penelitian ini adalah untuk menggambarkan kondisi faktual prosoaial anak usia dini di arena bermain fasilitas umum dan gambaran penembapartisipasi orang tua dalam pengembangaannya. Penelitian ini menggunakan pendekatan mixed method research. Subjek penelitian ditetapkan orang tua yang membawa anaknya usia dini bermain pada fasilitas bermain umum Setting penelitian ini ditetapkan pada arena bermain fasilitas umum di perumahan Singgalang Padang.Teknik pengumpulan data menggunakan observasi dan observasi partisipatif, wawancara mendalam. Teknik analisis data mengunakan analisis kuantitatif dan kualitatif. Hasil penelitian menunjukkan bahwa (1) Prososial anak diarena Bermain fasilitas umum komplek Singgalang tergolong rendah, (2) Orang tua belum berpartisipasi dalam pengembangan sosial anak di arena bemain umum dengan baik dan benar.
To Regularize or Not to Regularize: The Role of Positivity in Sparse Array Interpolation with a Single Snapshot

We study single-snapshot nested array interpolation with positive sources. The problem of sparse array interpolation is traditionally cast as a low-rank Toeplitz/Hankel matrix completion problem from partial observations. In recent work, we provided the first necessary and sufficient guarantees for nested array interpolation with real measurements in the rank minimization framework. In this work, we strengthen the sufficiency results by proving that in case of positive sources it is possible to interpolate the nested array by performing a simple convex feasibility search instead of solving a rank minimization problem. Simulations demonstrate that this framework is also effective for noisy measurements, and that noisy nested array interpolation outperforms ULA extrapolation.1
Maintenance of an Intraoperative External Fixator Is Associated With Decreased Postoperative Stiffness After Definitive Management of High-Energy Tibial Plateau Fractures

Introduction Postoperative stiffness is a common complication after high-energy tibial plateau fractures. Investigation into reported surgical techniques for the prevention of postoperative stiffness is limited. The purpose of this study was to compare the rates of postoperative stiffness after second-stage definitive surgery for high-energy tibial plateau fractures between groups of patients who had the external fixator prepped into the surgical field and those who did not. Methods Two hundred forty-four patients met the inclusion criteria between the two academic Level I trauma centers, representing the retrospective observational cohort. Patients were separated based on prepping of the external fixator into the surgical field during second-stage definitive open reduction and internal fixation. One hundred sixty-two patients were in the prepped group and 82 were in the non-prepped group. Post-operative stiffness was determined by the need to return to the operating room for subsequent procedures. Results At the final follow-up (mean = 14.6 months), patients in the non-prepped group had an increased rate of stiffness post-operatively (18.3% non-prepped versus 6.8% prepped; p = 0.006). No other investigated variables were associated with increased post-operative stiffness, including the number of days spent in the fixator and operative time. The relative risk for post-operative stiffness associated with complete fixator removal was 2.54 (95% CI 1.26-4.41; p = 0.008 on binary logistic regression; absolute risk reduction 11.5%). Conclusion At the final follow-up, maintenance of an intraoperative external fixator as a reduction aid was associated with a clinically significant decrease in post-operative stiffness after definitive management of high-energy tibial plateau fractures, when compared with complete removal prior to prepping.
Unbiased Unsupervised Stimulus Reconstruction for EEG-Based Auditory Attention Decoding

It is possible to decode auditory attention to speech from electrophysiological brain recordings such as electroencephalography (EEG). Such an auditory attention decoding (AAD) allows, e.g., to determine to which person a listener is attending in a multi-talker scenario. The vast majority of research has focused on developing supervised AAD algorithms in which the decoder is trained based on ground truth labels about the attention to each speaker. However, to work optimally, the trained decoders must be subject-specific and adapt over time to track sudden changes in signal statistics (e.g. electrode failures). Since it is often impractical to regularly retrain these decoders with a dedicated calibration session, an unsupervised algorithm has recently emerged as an alternative.In this paper, we show that the state-of-the-art unsupervised AAD algorithm is biased by its initialisation, which leads to a suboptimal convergence. This bias has the largest effect when only a limited amount of data is available to train it, e.g. to train an unsupervised decoder that can quickly adapt to sudden changes. We show that this bias can be easily removed, leading to a better classification accuracy. However, the gain in accuracy reduces as the number of classified segments increases.
Ordinal outlier detection based on recursive uniform partitioning

Outlier detection plays an important role in intelligent cyber systems, especially for fault-tolerant and adaptive ones. Traditional algorithms always need to evaluate distances or densities, which are very time-consuming. Considering the increasingly urgent demand for real-time application, during the past few years, various novel algorithms have been proposed. They are much faster, but less stable and less accurate. To cope with these problems, based on the core idea of ordinal optimization and the ‘few and different’ characteristics of outliers, by introducing the concept of outlier probability, we propose this ordinal outlier detection algorithm (OOD), which extracts outliers in terms of the order of being isolated in a recursive uniform data space partitioning process. It does not need any distance or density evaluation, and the complexity is reduced to O(n). Experiments show that the CPU time of OOD increases linearly with linearly growing data sets. Furthermore, compared with the recent iForest algorithm, OOD is about 30 times faster, with a 20–30% improvement in accuracy and in particular is much more stable. OOD also has good scalability, so it works well in high-dimensional data sets, which have a huge number of instances and irrelevant attributes.
Fast intersection algorithm between spatial triangles

This paper surveyed the principle and implementation of typically fast and robust triangle-triangle intersection algorithms,which was classified into scalar discrimination algorithm and vector discrimination algorithm according to their principle.Discussed the applicable occasions and performance of every algorithm by analyzing their calculation amount.The results of analysis and test show that the overall performance of the algorithm proposed by Olivier Devillers Philippe Guigue,one of the vector discrimination algorithms,is optimal,and the algorithm proposed by Oren Tropp,one of the scalar discrimination algorithms,is most suitable for the occasions with high triangle-triangle intersection ration.
Research on Key Techniques of Regional Detection Method Based on Significance

In this paper, the separation and detection methods of key components in the detection of airborne transmission lines are studied, and the separation of key components is detected by significant regional detection. In view of the problems in the detection of feature areas, the global feature extraction and local feature extraction are studied respectively in this paper. The problem of regional detection and feature description of local feature extraction is also studied. Keywords—Visual Saliency , Global Feature Extraction, Local feature Extraction, Corner Point Detection
ON FINITE GALOIS STABLE ARITHMETIC GROUPS AND THEIR APPLICATIONS

We prove the existence and finiteness theorems for integral representations stable under Galois operation. An explicit construction of the realization fields for representations of finite groups stable under the natural operation of the Galois group is given. We also compare the representations over fields and the rings of integers, and give a quantitative result on the rarity of integral Galois stable representations. There is a series of related conjectures and applications to arithmetic algebraic geometry, finite flat group schemes, positive definite quadratic lattices and Galois cohomology.
Gambaran Kesehatan Mental Pada Tenaga Kesehatan Kantor Kesehatan Pelabuhan Di Masa Pandemi Coronavirus Disease-19 (COVID-19) Tahun 2021

The increase of COVID-19 cases in Indonesia is in line with the increasing duties of health workers at the point of entry of the country. The task of handling COVID-19 provides psychological pressure and potential to cause depression, stress, and anxiety problems for health workers. This type of research was analytic observational with a cross-sectional design. Data collection was collected by online survey on May to August 2021. The Number of sample in this study was 350 health workers at Health Quarantine Offices in Indonesia. The mental health data collection instrument used DASS-21 questionnaire to check the level of depression, stress, and anxiety. The result showed that there was health workers with moderate to very severe depression in Health Quarantine Offices were 104 respondents (29,7%). The level of anxiety of health workers with moderate to very severe category was 146 respondents (41.7%). Meanwhile, the stress level of health workers with moderate to very severe category was 79 respondents (22,6%). The researcher concludes that the identification of data about the level of depression, anxiety, and stress in health workers at the Quarantine Health Office was needed as an effort to prevent of mental health disorders in health workers. Joint collaborative actions can be pursued by various parties, especially in providing interventions to deal with mental health problems for health workers during the COVID-19 pandemic.
Data assimilation system for the finite-volume community ocean model based on a localized weighted ensemble Kalman filter

Abstract. The unstructured-grid finite-volume community ocean model (FVCOM) offers the potential to build a high-resolution operational prediction system because it can effectively adapt to complex boundaries and flexibly perform local mesh encryption. A matching assimilation system is an indispensable part of an operational forecast system, while the existing assimilation method of the FVCOM [e.g., the ensemble Kalman filter (EnKF)] has difficulty accommodating strong nonlinear model/observation operators and non-Gaussian errors. The localized weighted EnKF (LWEnKF) is a particle filter (PF) that combines the advantages of the EnKF and the PF, which can effectively address nonlinearity and non-Gaussianity and can provide a more accurate initial field for models. An observation preprocessing system and a new data assimilation system based on the LWEnKF for the FVCOM are developed. For the preprocessing system, a K-dimensional tree-based observation thinning method is proposed that can accommodate representative observation efficiently for unstructured grids. Through a 1-month global ocean data assimilation experiment, the effect of our assimilation system is compared with the EnKF and the Mercator analysis forecast product, which comes from a reduced-order Kalman filter system. The statistical verification includes five metrics for various sea surface fields, temperature, and salinity profiles. The results show that the system performs better than the EnKF, although there is still a gap with Mercator in some aspects.
Zinc Finger Proteins in the War on Gastric Cancer: Molecular Mechanism and Clinical Potential

According to the 2020 global cancer data released by the World Cancer Research Fund (WCRF) International, gastric cancer (GC) is the fifth most common cancer worldwide, with yearly increasing incidence and the second-highest fatality rate in malignancies. Despite the contemporary ambiguous molecular mechanisms in GC pathogenesis, numerous in-depth studies have demonstrated that zinc finger proteins (ZFPs) are essential for the development and progression of GC. ZFPs are a class of transcription factors with finger-like domains that bind to Zn2+ extensively and participate in gene replication, cell differentiation and tumor development. In this review, we briefly outline the roles, molecular mechanisms and the latest advances in ZFPs in GC, including eight principal aspects, such as cell proliferation, epithelial–mesenchymal transition (EMT), invasion and metastasis, inflammation and immune infiltration, apoptosis, cell cycle, DNA methylation, cancer stem cells (CSCs) and drug resistance. Intriguingly, the myeloid zinc finger 1 (MZF1) possesses reversely dual roles in GC by promoting tumor proliferation or impeding cancer progression via apoptosis. Therefore, a thorough understanding of the molecular mechanism of ZFPs on GC progression will pave the solid way for screening the potentially effective diagnostic indicators, prognostic biomarkers and therapeutic targets of GC.
Metabolic and osmoionic effects of the recombinant crustacean hyperglycemic hormone (rCHH-A) of the Pacific white shrimp Penaeus vannamei on specimens acutely exposed to extreme salinities

The crustacean hyperglycemic hormone (CHH) is a multifunctional neuropeptide that plays a central role in crustacean metabolism and physiology. Experiments were conducted to examine the metabolic and osmoionic capabilities of the white shrimp Penaeus vannamei injected with its recombinant CHH-A (rCHH-A) peptide and acutely transferred from iso- (26 ppt) to hypo- (10 ppt) and hyper-osmotic (40 ppt) conditions. Hemolymph glucose, protein, osmoregulatory capacity (OC), and ionoregulatory capacity (IC) for sodium, chloride, potassium, and calcium were evaluated at four post-injection times (0.5, 1, 2, and 24 h). The rCHH-A peptide had hyperglycemic activity in all salinity conditions, obtaining maximum values at 1 h post-injection. However, in shrimp transferred to hyper-osmotic condition, rCHH-A caused the most significant reductions in OC (2 h), chloride IC (2 h), and total proteins (0.5-2 h) compared to the phosphate-buffered saline (PBS) control. Contrastingly, in shrimp transferred to hypo-osmotic conditions, rCHH-A decreased OC significantly from 2-24 h, strongly decreased chloride and potassium IC at 1 h post-injection, and increased total protein concentration in the hemolymph (1-2 h) when compared to PBS control. Concerning calcium, the rCHH-A injection decreased calcium IC at 10 ppt (1 h) and 26 ppt (2 h), providing insight into a potential role for CHH-A in calcium regulation. The results suggest that glucose and protein mobilization could enhance energy for osmo-ionic regulation under extreme osmotic conditions. This research study contributed to understanding crustacean endocrinology in P. vannamei and related euryhaline crustaceans. Further research should be performed to understand the osmo- and ionoregulatory mechanisms of the different CHH variants in crustaceans exposed to other stress conditions and the relationship with intermediary energy metabolism regulation.
Screening of Antibiotic and Virulence Genes from Whole Genome Sequenced Cronobacter sakazakii Isolated from Food and Milk-Producing Environments

The objective of this study was to use whole-genome sequencing (WGS) to screen for genes encoding for antibiotic resistance, fitness and virulence in Cronobacter sakazakii strains that had been isolated from food and powdered-milk-producing environments. Virulence (VGs) and antibiotic-resistance genes (ARGs) were detected with the Comprehensive Antibiotic Resistance Database (CARD) platform, ResFinder and PlasmidFinder tools. Susceptibility testing was performed using disk diffusion. Fifteen presumptive strains of Cronobacter spp. were identified by MALDI-TOF MS and ribosomal-MLST. Nine C. sakazakii strains were found in the meningitic pathovar ST4: two were ST83 and one was ST1. The C. sakazakii ST4 strains were further distinguished using core genome MLST based on 3678 loci. Almost all (93%) strains were resistant to cephalotin and 33% were resistant to ampicillin. In addition, 20 ARGs, mainly involved in regulatory and efflux antibiotics, were detected. Ninety-nine VGs were detected that encoded for OmpA, siderophores and genes involved in metabolism and stress. The IncFIB (pCTU3) plasmid was detected, and the prevalent mobile genetic elements (MGEs) were ISEsa1, ISEc52 and ISEhe3. The C. sakazakii isolates analyzed in this study harbored ARGs and VGs, which could have contributed to their persistence in powdered-milk-producing environments, and increase the risk of infection in susceptible population groups.
Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods

Drug discovery and repositioning are important processes for the pharmaceutical industry. These processes demand a high investment in resources and are time-consuming. Several strategies have been used to address this problem, including computer-aided drug design (CADD). Among CADD approaches, it is essential to highlight virtual screening (VS), an in silico approach based on computer simulation that can select organic molecules toward the therapeutic targets of interest. The techniques applied by VS are based on the structure of ligands (LBVS), receptors (SBVS), or fragments (FBVS). Regardless of the type of VS to be applied, they can be divided into categories depending on the used algorithms: similarity-based, quantitative, machine learning, meta-heuristics, and other algorithms. Each category has its objectives, advantages, and disadvantages. This review presents an overview of the algorithms used in VS, describing them and showing their use in drug design and their contribution to the drug development process.
Rhizodeposition through root senescence and root exudation of atmospheric C and N by legumes is controlled by traits indicative of resource acquisition and root development

Legume crop production has many benefits for agricultural systems. Through the rhizodeposition process, they release a significant amount of C and N into the soil, increasing soil organic C and reducing the use of N fertilizer. Rhizodeposition is known as a dynamic process influenced by many factors. The aim of this study was to study the contribution of root exudation and root senescence to the rhizodeposition of atmospheric C and N during vegetative and reproductive growth in annual and perennial legumes and to understand how this is linked to the fixation capacities of C and N and root functional traits. An original approach that combined 13CO2 labelling and the 15N dilution method was developed to measure the rhizodeposition of atmospheric C and N throughout plant growth by two annual grain legumes (pea and faba bean) and two perennial forage legumes (white and crimson clovers). C rhizodeposition was found to increase proportionally with N rhizodeposition during reproductive development and the differences observed between species were related to the C and N fixation abilities. The use of root traits such as specific root length, root tissue density and root dry matter content suggests a strong contribution of root exudation to C rhizodeposition at vegetative growth and a strong contribution of root senescence to both C and N rhizodeposition during reproductive growth. Synthesis. Both C and N rhizodeposition appeared to be controlled by traits indicative of resource acquisition and root development.
On the Effectiveness of Monoaural Target Source Extraction for Distant end-to-end Automatic Speech Recognition

Recent work on enhancement has shown that frequency domain methods may outperform the time domain approaches, while most of the prior art is focused on reporting objective enhancement metrics on simulated noisy data or use less modern hybrid acoustic models for evaluation. In this paper we investigate the effectiveness of target source extraction for improving the robustness of end-to-end automatic speech recognition in noisy and reverberant conditions. A frequency domain source extraction approach is introduced and compared against a state-of-the-art time domain method using several publicly available simulated and real noisy speech test sets. The results show that the frequency domain method outperforms the time domain one only for simulated conditions, and that it is more stable to window size variations. The experiments also indicate that remixing the unprocessed signal with the enhanced speech (referred to as speaker/source reinforcement) yields similar or better results than by using a matched acoustic model retrained using distortions introduced by enhancement.