Miracle-dz/UniFAR-FTUs huggingface dataset:
Monocular 3D Object Detection with Depth from Motion

Perceiving 3D objects from monocular inputs is crucial for robotic systems, given its economy compared to multi-sensor settings. It is notably difficult as a single image can not provide any clues for predicting absolute depth values. Motivated by binocular methods for 3D object detection, we take advantage of the strong geometry structure provided by camera ego-motion for accurate object depth estimation and detection. We first make a theoretical analysis on this general two-view case and notice two challenges: 1) Cumulative errors from multiple estimations that make the direct prediction intractable; 2) Inherent dilemmas caused by static cameras and matching ambiguity. Accordingly, we establish the stereo correspondence with a geometry-aware cost volume as the alternative for depth estimation and further compensate it with monocular understanding to address the second problem. Our framework, named Depth from Motion (DfM), then uses the established geometry to lift 2D image features to the 3D space and detects 3D objects thereon. We also present a pose-free DfM to make it usable when the camera pose is unavailable. Our framework outperforms state-of-the-art methods by a large margin on the KITTI benchmark. Detailed quantitative and qualitative analyses also validate our theoretical conclusions. The code will be released at https://github.com/Tai-Wang/Depth-from-Motion.
Trainability Preserving Neural Structured Pruning

Several recent works empirically ﬁnd ﬁnetuning learning rate is critical to the ﬁnal performance in neural network structured pruning. Further researches ﬁnd that the network trainability broken by pruning answers for it, thus calling for an urgent need to recover trainability before ﬁnetuning. Existing attempts propose to exploit weight orthogonal-ization to achieve dynamical isometry for improved trainability. However, they only work for linear MLP networks. How to develop a ﬁlter pruning method that maintains or recovers trainability and is scalable to modern deep networks remains elusive. In this paper, we present trainabil-ity preserving pruning (TPP), a regularization-based structured pruning method that can eﬀectively maintain trainability during sparsiﬁcation. Speciﬁcally, TPP regularizes the gram matrix of convolutional kernels so as to de-correlate the pruned ﬁlters from the kept ﬁlters. Beside the convolutional layers, we also propose to regularize the BN parameters for better preserving trainability. Empirically, TPP can compete with the ground-truth dynamical isometry recovery method on linear MLP networks. On non-linear networks (ResNet56/VGG19, CIFAR datasets), it outperforms the other counterpart solutions by a large margin . Moreover, TPP can also work eﬀectively with modern deep networks (ResNets) on ImageNet, delivering encouraging performance in comparison to many top-performing ﬁlter pruning methods. To our best knowledge, this is the ﬁrst approach that eﬀectively maintains trainability during pruning for the large-scale deep neural networks.
Thermal Properties of New Insulating Juncus Maritimus Fibrous Mortar Composites/Experimental Results and Analytical Laws

This study aims to study the thermal properties and the microstructure of composite materials based on mortar combined with Juncus maritimus fibers. Effective thermophysical properties of the composite materials containing Juncus maritimus fibers are experimentally and theoretically investigated. To better understand the morphology of these new composites, the corresponding microstructures were characterized in 2D by scanning electron microscope and in 3D using micro computed tomography. The local thermal conductivity of the Juncus maritimus fibers was identified using theoretical models and experimental measurement of the effective thermal conductivity of packed bed of crushed fibers. The thermal conductivity of the mortar matrix at given porosity was also determined using experimental measurement data and a theoretical model. The most appropriate analytical laws to predict effective thermal conductivity of mortar composites containing fibers are deduced from experimental thermal conductivity results.
Detection of Na and K in the Atmosphere of the Hot Jupiter HAT-P-1b with P200/DBSP

We present a new optical transmission spectrum of the hot Jupiter HAT-P-1b based on two transits observed with the Double Spectrograph (DBSP) on the Palomar 200-inch (P200) telescope. The DBSP transmission spectrum, covering a wavelength range from 3250 to 10007 Å, is consistent with that observed with the Hubble Space Telescope (HST), but the former has a finer spectral resolution. The DBSP spectrum alone reveals the presence of a pressure-broadened line wing for Na, the line cores for both Na and K, and tentative evidence for H2O. We obtain consistent results from the spectral retrieval analyses performed on the DBSP-only data set and the DBSP, HST, and Spitzer combined data set. Our retrievals suggest a mostly clear atmosphere for HAT-P-1b, with a cloud coverage of 22−3+5 % that is dominated by enhanced haze. We derive subsolar abundances for Na, K, and C, and subsolar to solar for O. Future observations with James Webb Space Telescope and ground-based high-resolution spectrographs should be able to not only confirm the presence of these species but also stringently constrain the formation and migration pathways for HAT-P-1b.
Creating and controlling Dirac fermions, Weyl fermions, and nodal lines in the magnetic antiperovskite 
Eu3PbO

The band topology of magnetic semimetals is of interest both from the fundamental science point of view and with respect to potential spintronics and memory applications. Unfortunately, only a handful of suitable topological semimetals with magnetic order have been discovered so far. One such family that hosts these characteristics is the antiperovskites, A$_3$BO, a family of 3D Dirac semimetals. The A=Eu$^{2+}$ compounds magnetically order with multiple phases as a function of applied magnetic field. Here, by combining band structure calculations with neutron diffraction and magnetic measurements, we establish the antiperovskite Eu$_3$PbO as a new topological magnetic semimetal. This topological material exhibits a multitude of different topological phases with ordered Eu moments which can be easily controlled by an external magnetic field. The topological phase diagram of Eu$_3$PbO includes an antiferromagnetic Dirac phase, as well as ferro- and ferrimagnetic phases with both Weyl points and nodal lines. For each of these phases, we determine the bulk band dispersions, the surface states, and the topological invariants by means of $\textit{ab-initio}$ and tight-binding calculations. Our discovery of these topological phases introduces Eu$_3$PbO as a new platform to study and manipulate the interplay of band topology, magnetism, and transport.
Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids

Plasma is defined as the fourth state of matter, and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison of different studies and determining the most dominant parameters for the antimicrobial effect. The proven applicability of machine learning (ML) in the medical field is encouraging for its application in the field of plasma medicine as well. Thus, ML applications on PALs could present a new perspective to better understand the influences of various parameters on their antimicrobial effects. In this paper, comparative supervised ML models are presented by using previously obtained data to predict the in vitro antimicrobial activity of PALs. A comprehensive literature search was performed, and 12 distinct features related to PAL-microorganism interactions were collected from 33 relevant articles to automatically predict the antimicrobial activity of PALs. After the required normalization, feature encoding, and resampling steps, two supervised ML methods, namely classification and regression, are applied to the data to obtain microbial inactivation (MI) predictions. For classification, MI is labeled in four categories, and for regression, MI is used as a continuous variable. Sixteen different classifiers and 14 regressors are implemented to predict the MI value. Two different robust cross-validation strategies are conducted for classification and regression models to evaluate the proposed method: repeated stratified k-fold cross-validation and k-fold cross-validation, respectively. We also investigate the effect of different features on models. The results demonstrated that the hyperparameter-optimized Random Forest Classifier (oRFC) and Random Forest Regressor (oRFR) provided superior performance compared to other models for classification and regression. Finally, the best test accuracy of 82.68% for oRFC and R 2 of 0.75 for the oRFR are obtained. Furthermore, the determined most important features of predictive models are in line with the outcomes of PALs reported in the literature. An ML framework can accurately predict the antimicrobial activity of PALs without the need for any experimental studies. To the best of our knowledge, this is the first study that investigates the antimicrobial efficacy of PALs with ML. Furthermore, ML techniques could contribute to a better understanding of plasma parameters that have a dominant role in the desired antimicrobial effect. Moreover, such findings may contribute to the definition of a plasma dose in the future.
Dynamical Disruption Timescales and Chaotic Behavior of Hierarchical Triple Systems

We examine the stability of hierarchical triple systems using direct N-body simulations without adopting a secular perturbation assumption. We estimate their disruption timescales in addition to the mere stable/unstable criterion, with particular attention to the mutual inclination between the inner and outer orbits. First, we improve the fit to the dynamical stability criterion by Mardling & Aarseth widely adopted in the previous literature. Especially, we find that that the stability boundary is very sensitive to the mutual inclination; coplanar retrograde triples and orthogonal triples are much more stable and unstable, respectively, than coplanar prograde triples. Next, we estimate the disruption timescales of triples satisfying the stability condition up to 109 times the inner orbital period. The timescales follow the scaling predicted by Mushkin & Katz, especially at high e out where their random walk model is most valid. We obtain an improved empirical fit to the disruption timescales, which indicates that the coplanar retrograde triples are significantly more stable than the previous prediction. We furthermore find that the dependence on the mutual inclination can be explained by the energy transfer model based on a parabolic encounter approximation. We also show that the disruption timescales of triples are highly sensitive to tiny changes of the initial parameters, reflecting the genuine chaotic nature of the dynamics of those systems.
Embodying life-long learning: Transition and capstone experiences

Abstract This paper discusses the principle of Transition as it has been conceptualised by the Curriculum Renewal in Legal Education project. The project sought to develop a principled framework for renewing the final year of tertiary legal education in Australia. Capstone experiences were chosen as the most appropriate mechanism for assisting final year students to manage the transition process. Thoughtfully designed capstones assist students to integrate and synthesise their learning over their entire degree programme, facilitate closure on the undergraduate experience, and assist students to transition from student to emerging professional. We discuss the importance of addressing final year students’ transitional needs and explain how the principle facilitates this process. Although the framework has been developed specifically for legal education in Australia its approach enables transferability across disciplines and institutions. The framework addresses criticisms that universities and law schools are not meeting the needs of final year students by preparing them for the transition to graduate life in a complex and uncertain world.
A human model of bilateral pulmonary vein sampling to assess the effects of one-lung ventilation on neutrophil function

Background Neutrophil activation drives lung complications after cardiopulmonary bypass (CPB). Evidence suggests the healthy, ventilated lung may beneficially re-condition pro-inflammatory neutrophils. However, evidence in humans is lacking, due to a paucity of good models. CPB with simultaneous central venous and bilateral pulmonary vein sampling provides an opportunity to model effects of one-lung ventilation. The study’s primary objectives were to establish a model of intra-operative, bilateral pulmonary vein sampling and to determine whether neutrophil function differed after passing through inflated or deflated lungs. Methods Seventeen patients having “on pump” coronary artery bypass grafting (CABG) with one-lung ventilation (in two cohorts with tidal volume 2ml kg-1 and FiO2 0.21, or tidal volume 4 ml kg-1 and FiO2 0.5 respectively) were recruited. Cohort 1 consisted of 9 patients (7 male, median age 62.0 years) and Cohort 2 consisted of 8 male patients (median age 65.5 years). Recruitment was via prospective screening of scheduled elective and non-elective CABG procedures with cardiopulmonary bypass. Each patient had five blood samples taken—central venous blood pre-operatively; central venous blood pre-CPB; central venous blood post-CPB; pulmonary venous blood draining the ventilated lung post-CPB; and pulmonary venous blood draining the deflated lung post-CPB. Neutrophil phagocytosis and priming status were quantified. Plasma cytokines were measured. Results Phagocytosis and priming were not significantly different in neutrophils returning from the ventilated lung as compared to the non-ventilated lung. Plasma IL-6, IL-8 and IL-10 were significantly elevated by CPB. Conclusions The intra-operative, bilateral pulmonary vein sampling model provides unique opportunities to assess biological effects of interventions to one lung, with the other lung acting as an internal control. Single-lung ventilation during CPB had no significant effects on neutrophil function.
Exon-based Phylogenomics and the Relationships of African Cichlid Fishes: Tackling the Challenges of Reconstructing Phylogenies with Repeated Rapid Radiations

Abstract African cichlids (subfamily: Pseudocrenilabrinae) are among the most diverse vertebrates, and their propensity for repeated rapid radiation has made them a celebrated model system in evolutionary research. Nonetheless, despite numerous studies, phylogenetic uncertainty persists, and riverine lineages remain comparatively underrepresented in higher-level phylogenetic studies. Heterogeneous gene histories resulting from incomplete lineage sorting (ILS) and hybridization are likely sources of uncertainty, especially during episodes of rapid speciation. We investigate the relationships of Pseudocrenilabrinae and its close relatives while accounting for multiple sources of genetic discordance using species tree and hybrid network analyses with hundreds of single-copy exons. We improve sequence recovery for distant relatives, thereby extending the taxonomic reach of our probes, with a hybrid reference guided/de novo assembly approach. Our analyses provide robust hypotheses for most higher-level relationships and reveal widespread gene heterogeneity, including in riverine taxa. ILS and past hybridization are identified as the sources of genetic discordance in different lineages. Sampling of various Blenniiformes (formerly Ovalentaria) adds strong phylogenomic support for convict blennies (Pholidichthyidae) as sister to Cichlidae and points to other potentially useful protein-coding markers across the order. A reliable phylogeny with representatives from diverse environments will support ongoing taxonomic and comparative evolutionary research in the cichlid model system. [African cichlids; Blenniiformes; Gene tree heterogeneity; Hybrid assembly; Phylogenetic network; Pseudocrenilabrinae; Species tree. ]
Salt‐tolerant PGPR strain Priestia endophytica SK1 promotes fenugreek growth under salt stress by inducing nitrogen assimilation and secondary metabolites

Soil salinity is a huge obstacle in crop production worldwide. Saline soil can reduce active chemical contents in medicinal plants of the Leguminosae family through crippled normal nodule function. Intensive efforts are underway to improve yield and medicinal value of leguminous herbs under salt stress condition by using benign microbes. Here, an attempt was made to explore the salt‐tolerant bacteria associated with rhizosphere of fenugreek plant (Trigonella foenum‐graecum L.) and to evaluate their impact on host plant growth and metabolite of pharmaceutical importance.
Deciphering the Mechanisms Shaping the Plastisphere Microbiota in Soil

The increasing pervasive microplastic pollution is creating a new environmental compartment, termed plastisphere. Even though there was conclusive information characterizing the plastisphere, the underlying mechanisms shaping the bacterial communities in the plastisphere in the soil remain unclear. ABSTRACT The gradual accumulation of microplastics has aroused increasing concern for the unique niche, termed “plastisphere. ” As research so far has focused on their characteristics in aquatic ecosystems, our understanding of the colonization and assembly of the attached bacterial communities on microplastics in soil ecosystems remains poor. Here, we aimed to characterize the plastisphere microbiomes of two types of microplastics (polylactic acid [PLA] and polyethylene [PE]) differing in their biodegradability in two different soils. After incubation for 60 days, considerably lower alpha diversity of bacterial community was observed on the microplastic surfaces, and prominent divergences occurred in the microbial community compositions between the plastisphere and the bulk soil. The temperature, rather than polymer type, significantly induced the differences between the plastisphere communities. The rRNA gene operon (rrn) copy numbers were significantly higher in the PLA plastisphere, suggesting potential degradation. The co-occurrence network analysis showed that the PE plastisphere exhibited greater network complexity and stronger stability than those in the PLA plastisphere. The stochasticity ratio indicated the remarkable importance of stochastic process on community assembly in PE and PLA plastispheres, while the null model analysis showed the nonnegligible roles of deterministic processes in shaping the plastisphere communities. Higher contributions of homogenous selection in the PLA plastisphere were observed in comparison with the PE plastisphere, which could probably be attributed to the selective pressure induced by microplastic degradation. Our findings enhance our mechanistic understanding of the diversity patterns and assembly processes of plastisphere in soil environments and have important implications for microbial ecology and microplastic risk assessment. IMPORTANCE The increasing pervasive microplastic pollution is creating a new environmental compartment, termed plastisphere. Even though there was conclusive information characterizing the plastisphere, the underlying mechanisms shaping the bacterial communities in the plastisphere in the soil remain unclear. Therefore, we incubated two types of microplastics (PE and PLA) in two different soils and explored the differences between plastisphere and bulk soil communities. Additionally, the co-occurrence network and the assembly processes of plastisphere were subjected to further analysis. Our results highlight the importance of selective recruitment of microplastics and contribute to the understanding of the diversity patterns and assembly processes of plastisphere in soil environments.
Does past offending behaviors catch up with you? A study examining the relationship between traffic offending history and fatal crash involvement

Abstract Objective The aim of the current study was to compare the traffic histories of drivers fatally injured in a road traffic crash, to alive drivers of the same age and gender in order to determine if key markers of increased fatality-risk could be identified. Methods The case sample comprised 1,139 (82% male) deceased drivers, while the control sample consisted of 1,139 registered Queensland drivers (who were individually matched to the case sample on age and gender). Results Using a logistic regression model, and adjusting for age and gender, it was found that a greater number of offenses predicted greater odds of fatal crash involvement, with each increase in offense frequency category increasing ones’ odds by 1.98 (95% CI: 1.8, 2.18). When each offense type was considered individually, dangerous driving offenses were most influential, predicting a 3.44 (95% CI: 2, 5.93) increased odds of being in the case group, followed by the following offense types: learner/provisional (2.88, 95% CI: 1.75, 4.74), drink and drug driving (2.82, 95% CI: 1.97, 4.04), not wearing a seatbelt/helmet (2.63, 95% CI: 1.53, 4.51), licensing offenses (1.87, 95% CI: 1.41, 2.49), and speeding (1.48, 95% CI: 1.33, 1.66). In contrast, mobile phone and road rules offenses were not identified as significant predictors. Conclusion The findings indicate that engagement in a range of aberrant driving behaviors may result in an increased odds of future fatal crash involvement, which has multiple implications for the sanctioning and management of apprehended offenders.
Functional assessment of total hip arthroplasty using inertial measurement units: Improvement in gait kinematics and association with patient‐reported outcome measures

Inertial measurement units (IMUs) are commonly used for gait assessment, yet their potential for quantifying improvements in gait function and patterns after total hip arthroplasty (THA) has not been fully explored. The primary aim of this study was to compare spatiotemporal parameters and sagittal plane kinematic patterns of patients with hip osteoarthritis (OA) before and after THA, and to asymptomatic controls. The secondary aim was to assess the association between dynamic hip range of motion (ROM) during walking and the Hip Osteoarthritis Outcome Scores (HOOS). Twenty‐four patients with hip OA and 24 matched asymptomatic controls completed gait analyses using the RehaGait® sensor system. Patients were evaluated pre‐ and 1 year postoperatively, controls in a single visit. Differences in kinematic data were analyzed using statistical parametric mapping, and correlations between dynamic hip ROM and HOOS were calculated. Walking speed and stride length significantly increased (+0.08 m/s, p = 0.019; +0.06 m, p = 0.048) after THA but did not reach the level of asymptomatic controls (−0.11 m/s, p = 0.028; −0.14 m, p = 0.001). Preoperative hip and knee kinematics differed significantly from controls. After THA, they improved significantly and did not differ from controls. Dynamic hip flexion‐extension ROM correlated positively with all HOOS subscores (r > 0.417; p ≤ 0.001). The change in HOOS symptoms in patients was explained by the combination of baseline HOOS symptoms and change in dynamic hip ROM (r2 = 0.748) suggesting that the additional information gained with IMU gait analysis helps to complement and objectify patient‐reported outcome measures pre‐ and postoperatively and monitor treatment‐related improvements.