Miracle-dz/UniFAR-FTUs huggingface dataset:
An overabundance of oxygen in planetary nebulae of the solar neighborhood

We study the oxygen abundance in five H II regions and seven planetary nebulae (PNe) located within 2 kpc from the Sun that have available spectra of high quality. Our analysis uses a similar procedure and the same atomic data to derive abundances in all the objects. The results calculated with collisionally excited lines for the H II regions indicate that the local interstellar medium is very homogeneous, with 12+log(O/H)=8.45-8.54. As for the PNe, six out of seven show significantly higher abundances: 12+log(O/H)=8.65-8.80. This overabundance of oxygen in PNe also holds when we consider the abundances implied by recombination lines.
Targeted delivery of triptolide by dendritic cell‐derived exosomes for colitis and rheumatoid arthritis therapy in murine models

Triptolide (TP) elicits a beneficial effect in the treatment of autoimmune diseases, such as ulcerative colitis (UC) and rheumatoid arthritis (RA). However, its multiorgan toxicity needs to be resolved. Dendritic cells (DCs) are the primary target of TP, which induces immunosuppression, and DC‐derived exosomes (DEX) can selectively enter DCs in vivo. Here, we encapsulated TP with DEX (DEXTP) to generate TP‐targeted delivery to reduce toxicity.
Liguleless narrow and narrow odd dwarf act in overlapping pathways to regulate maize development and metabolism

SUMMARY Narrow odd dwarf (nod) and Liguleless narrow (Lgn) are pleiotropic maize mutants that both encode plasma membrane proteins, cause similar developmental patterning defects, and constitutively induce stress signaling pathways. To investigate how these mutants coordinate maize development and physiology, we screened for protein interactors of NOD by affinity purification. LGN was identified by this screen as a strong candidate interactor, and we confirmed the NOD‐LGN molecular interaction through orthogonal experiments. We further demonstrated that LGN, a receptor‐like kinase, can phosphorylate NOD in vitro, hinting that they could act in intersecting signal transduction pathways. To test this hypothesis, we generated Lgn‐R;nod mutants in two backgrounds (B73 and A619), and found that these mutations enhance each other, causing more severe developmental defects than either single mutation on its own, with phenotypes including very narrow leaves, increased tillering, and failure of the main shoot. Transcriptomic and metabolomic analyses of the single and double mutants in the two genetic backgrounds revealed widespread induction of pathogen defense genes and a shift in resource allocation away from primary metabolism in favor of specialized metabolism. These effects were similar in each single mutant and heightened in the double mutant, leading us to conclude that NOD and LGN act cumulatively in overlapping signaling pathways to coordinate growth‐defense tradeoffs in maize.
Rpd3 regulates single-copy origins independently of the rDNA array by opposing Fkh1-mediated origin stimulation

Significance The faithful replication of eukaryotic genomes requires balancing the replication capacities of different genomic regions, such as repetitive versus single-copy genetic elements, which may compete for limiting replication resources, possibly leading to replication stress and genome instability. We examined the function of histone deacetylases Rpd3 and Sir2 in balancing replication between unique genome sequences and the multicopy ribosomal DNA genes. Our findings support prior conclusions that Sir2 directly suppresses early firing of rDNA origins, thereby enabling balanced replication of the genome. We further show that Rpd3’s function in delaying firing of later-firing, single-copy origins is independent of Sir2 and rDNA load. Instead, Rpd3 appears to oppose the Fkh1/2 origin activation pathway by regulating binding of the origin-stimulator Fkh1.
Binding of adenovirus species C hexon to prothrombin and the influence of hexon on vector properties in vitro and in vivo

The majority of adenovirus (Ad) vectors are based on human Ad type 5, which is a member of Ad species C. Species C also includes the closely-related types 1, 2, 6, 57 and 89. It is known that coagulation factors bind to Ad5 hexon and play a key role in the liver tropism of Ad5 vectors, but it is unclear how coagulation factors affect vectors derived from other species C Ads. We evaluated species C Ad vectors both in vitro and following intravenous injection in mice. To assess the impact of hexon differences, we constructed chimeric Ad5 vectors that contain the hexon hypervariable regions from other species C types, including vectors with hexon mutations that decreased coagulation factor binding. After intravenous injection into mice, vectors with Ad5 or Ad6 hexon had strong liver tropism, while vectors with chimeric hexon from other Ad types had weaker liver tropism due to inhibition by natural antibodies and complement. In addition, we discovered a novel ability of hexon to bind prothrombin, which is the most abundant coagulation factor in blood, and we found striking differences in the affinity of Ads for human, mouse and bovine coagulation factors. When compared to Ad5, vectors with non-Ad5 species C hexons had considerably higher affinity for both human and mouse prothrombin. Most of the vectors tested were strongly dependent on coagulation factors for liver transduction, but vectors with chimeric Ad6 hexon showed much less dependence on coagulation factors than other vectors. We found that in vitro neutralization experiments with mouse serum predicted in vivo behavior of Ad5 vectors, but in vitro experiments did not predict the in vivo behavior of vectors based on other Ad types. In sum, hexons from different human Ad species C viruses confer diverse properties on vectors, including differing abilities to target the liver.
Macular thickness and its associated factors in a Chinese rural adult population: the Handan Eye Study

Purpose To describe the normal macular thickness and assess its associations. Methods The Handan Eye Follow-up Study was conducted between 2012 and 2013. Macular thickness was scanned by spectral-domain optical coherence tomography (OCT). The built-in software generated a retinal thickness (RT) map, which was divided into three regions (central, internal and external regions) and nine quadrants (one in central and four in internal and external regions each). Results For 5394 subjects in the Handan Eye Follow-up Study, 4793 received OCT examination, 2946 of whom (accounting for 61.46% of the total subjects, mean age 58.91±10.95, 55.6% were women) were included for analysis. The mean RT in central macula, inner and outer rings were (237.38 µm±23.05 µm), (309.77 µm±18.36 µm) and (278.29 µm±14.38 µm), respectively (overall difference, p＜0.001). In inner ring, the RT in temporal was thinnest, followed by nasal, superior and inferior. In outer ring, the RT in superior was thinnest, with the next subfields being temporal, inferior and nasal, respectively. The RT in central macula, inner and outer rings were significantly thicker in men than in women. Multivariate linear regression analysis showed that in central macula, RT increased in subjects younger than 60 years and thinned above the age of 60. In inner and outer rings, RT thinned along with age (p＜0.001). Conclusions This study finds that RT in central macula is the thinnest, followed by the outer ring, the RT in the inner ring is the thickest. Age and gender are related to RT. These associated factors need to be considered when explaining RT.
Morningness/eveningness in gestational diabetes mellitus: clinical characteristics and maternal-neonatal outcomes

ABSTRACT Objective: This study aims to evaluate the impact of morning-evening preference in pregnancy outcomes in gestational diabetes mellitus (GDM). Materials and methods: This is a prospective cohort study of 2nd-3rd trimester GDM outpatient care in Fortaleza, Brazil (2018-2020). Eveningness was defined by the Horne-Östberg Morningness-Eveningness-Questionnaire (MEQ ≤ 41). Furthermore, we obtained a 7-day actigraphic register. Subjective sleep quality, daytime somnolence, insomnia, fatigue and depressive symptoms were also evaluated. Associations with pregnancy outcomes were investigated. Results: Among 305 patients with GDM, evening preference was found in 21 (6.9%). Patients with evening preference had worse sleep quality (p < 0.01), greater severity of insomnia (p < 0.005), fatigue (p < 0.005) and depressive symptoms (<0.009). Evening chronotype was associated with preeclampsia [p = 0.01; OR = 0.27; CI 0.09-0.79] and a greater need for admission to a neonatal intensive care unit (NICU) [p = 0.02; OR = 0.23; CI . 0.06-0.80]. A lower MEQ score confirmed an association with preeclampsia [p = 0.002; OR = 0.94; CI 0.90-0.97] and this was maintained after controlling for age, arterial hypertension, sleep quality, fatigue and depressive symptoms [p < 005; OR = 0.91; CI 0.87-0.95]. Conclusion: In GDM, patients with evening preference had worse sleep quality, more insomnia, fatigue, and depressive symptoms. Furthermore, eveningness was independently associated with preeclampsia. These results indicate the important role of eveningness in adverse pregnancy outcomes.
Nanoscale structuring of tungsten tip yields most coherent electron point-source

This report demonstrates the most spatially-coherent electron source ever reported. A coherence angle of 14.3 ± 0.5° was measured, indicating a virtual source size of 1.7 ± 0.6 Å using an extraction voltage of 89.5 V. The nanotips under study were crafted using a spatially-confined, field-assisted nitrogen etch which removes material from the periphery of the tip apex resulting in a sharp, tungsten–nitride stabilized, high-aspect ratio source. The coherence properties are deduced from holographic measurements in a low-energy electron point source microscope with a carbon nanotube bundle as sample. Using the virtual source size and emission current the brightness normalized to 100 kV is found to be 7.9 × 108 A sr−1 cm2.
HMM-V: Heterogeneous Memory Management for Virtualization

The memory demand of virtual machines (VMs) is increasing, while DRAM has limited capacity and high power consumption. Non-volatile memory (NVM) is an alternative to DRAM, but it has high latency and low bandwidth. We observe that the VM with heterogeneous memory may incur up to a $1.5\times$ slowdown compared to a DRAM VM, if not managed well. However, none of the state-of-the-art heterogeneous memory management designs are customized for virtualization on a real system. In this paper, we propose HMM-V, a Heterogeneous Memory Management system for Virtualization. HMM-V automatically determines page hotness and migrates pages between DRAM and NVM to achieve performance close to the DRAM system. First, HMM-V tracks memory accesses through page table manipulation, but reduces the cost by leveraging Intel page-modification logging (PML) and a multi-level queue. Second, HMM-V quantifies the ``temperature'' of page and determines the hot set with bucket-sorting. HMM-V then efficiently migrates pages with minimal access pause and handles dirty pages with the assistance of PML. Finally, HMM-V provides pooling management to balance precious DRAM across multiple VMs to maximize utilization and overall performance. HMM-V is implemented on a real system with Intel Optane DC persistent memory. The four-VM co-running results show that HMM-V outperforms NUMA balancing and hardware management (Intel Optane memory mode) by $51\%$ and $31\%$, respectively.
Training Efficient Controllers via Analytic Policy Gradient

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but require high computing power. Conversely, learning-based offline optimization approaches, such as Reinforcement Learning (RL), allow fast and efficient execution on the robot but hardly match the accuracy of MPC in trajectory tracking tasks. In systems with limited compute, such as aerial vehicles, an accurate controller that is efficient at execution time is imperative. We propose an Analytic Policy Gradient (APG) method to tackle this problem. APG exploits the availability of differentiable simulators by training a controller offline with gradient descent on the tracking error. We address training instabilities that frequently occur with APG through curriculum learning and experiment on a widely used controls benchmark, the CartPole, and two common aerial robots, a quadrotor and a fixed-wing drone. Our proposed method outperforms both model-based and model-free RL methods in terms of tracking error. Concurrently, it achieves similar performance to MPC while requiring more than an order of magnitude less computation time. Our work provides insights into the potential of APG as a promising control method for robotics. To facilitate the exploration of APG, we open-source our code and make it available atgithub.com/lis-epfl/apg_trajectory_tracking.
Real-space obstruction in quantum spin Hall insulators

The recently introduced classification of two-dimensional insulators in terms of topological crystalline invariants has been applied so far to"obstructed"atomic insulators characterized by a mismatch between the centers of the electronic Wannier functions and the ionic positions. We extend this notion to quantum spin Hall insulators in which the ground state cannot be described in terms of time-reversal symmetric localized Wannier functions. A system equivalent to graphene in all its relevant electronic and topological properties except for a real-space obstruction is identified and studied via symmetry analysis as well as with density functional theory. The low-energy model comprises a local spin-orbit coupling and a non-local symmetry breaking potential, which turn out to be the essential ingredients for an obstructed quantum spin Hall insulator. An experimental fingerprint of the obstruction is then measured in a large-gap triangular quantum spin Hall material.
Totally-ordered Sequential Rules for Utility Maximization

High-utility sequential pattern mining (HUSPM) is a significant and valuable activity in knowledge discovery and data analytics with many real-world applications. In some cases, HUSPM can not provide an excellent measure to predict what will happen. High-utility sequential rule mining (HUSRM) discovers high utility and high confidence sequential rules, so it can solve the issue in HUSPM. However, all existing HUSRM algorithms aim to find high-utility partially-ordered sequential rules (HUSRs), which are not consistent with reality and may generate fake HUSRs. Therefore, in this paper, we formulate the problem of high-utility totally-ordered sequential rule mining and propose a novel algorithm, called TotalSR, which aims to identify all high-utility totally-ordered sequential rules (HTSRs). TotalSR introduces a left-first expansion strategy that can utilize the anti-monotonic property to use a confidence pruning strategy. TotalSR also designs a new utility upper bound: RSPEU, which is tighter than the existing upper bounds. TotalSR can drastically reduce the search space with the help of utility upper bounds pruning strategies, avoiding much more meaningless computation. To effectively compute the information, TotalSR proposes an auxiliary antecedent record table that can efficiently calculate the antecedent’s support and a utility prefix sum list that can compute the upper bound in O(1) time for a sequence. Finally, there are numerous experimental results on both real and synthetic datasets demonstrating that TotalSR is more efficient than the existing algorithms.
Pareto Driven Surrogate (ParDen-Sur) Assisted Optimisation of Multi-period Portfolio Backtest Simulations

Portfolio management is a multi-period multi-objective optimisation problem subject to a wide range of constraints. However, in practice, portfolio management is treated as a single-period problem partly due to the computationally burdensome hyper-parameter search procedure needed to construct a multi-period Pareto frontier. This study presents the \gls{ParDen-Sur} modelling framework to efficiently perform the required hyper-parameter search. \gls{ParDen-Sur} extends previous surrogate frameworks by including a reservoir sampling-based look-ahead mechanism for offspring generation in \glspl{EA} alongside the traditional acceptance sampling scheme. We evaluate this framework against, and in conjunction with, several seminal \gls{MO} \glspl{EA} on two datasets for both the single- and multi-period use cases. Our results show that \gls{ParDen-Sur} can speed up the exploration for optimal hyper-parameters by almost $2\times$ with a statistically significant improvement of the Pareto frontiers, across multiple \glspl{EA}, for both datasets and use cases.
Hypothesis Testing for Detecting Outlier Evaluators

In epidemiological studies, very often, evaluators obtain measurements of disease outcomes for study participants. In this paper, we propose a two-stage procedure for detecting outlier evaluators. In the ﬁrst stage, a regression model is ﬁtted to obtain the evaluators’ eﬀects. The outlier evaluators are considered as those with diﬀerent eﬀects compared with the normal evaluators. In the second stage, stepwise hypothesis testings are performed to detect outlier evaluators. The true positive rate and true negative rate of the proposed procedure are assessed in a simulation study. We apply the proposed method to detect potential outlier audiologists among the audiologists who measured hearing threshold levels of the participants in the Audiology Assessment Arm of the Conservation of Hearing Study, which is an epidemiological study for examining risk factors of hearing loss.
Video-based estimation of pain indicators in dogs

Dog owners are typically capable of recognizing behavioral cues that reveal subjective states of their dogs, such as pain. But automatic recognition of the pain state is very challenging. This paper proposes a novel video-based, two-stream deep neural network approach for this problem. We extract and preprocess body keypoints, and compute features from both keypoints and the RGB representation over the video. We propose an approach to deal with self-occlusions and missing keypoints. We also present a unique video-based dog behavior dataset, collected by veterinary professionals, and annotated for presence of pain, and report good classification results with the proposed approach. This study is one of the first works on machine learning based estimation of dog pain state. Code is available at https://github.con/s04240051/pain_detection
Paused Agent Replay Refresh

Reinforcement learning algorithms have become more complex since the invention of target networks. Unfortunately, target networks have not kept up with this increased complexity, instead requiring approximate solutions to be computationally feasible. These approximations increase noise in the Q-value targets and in the replay sampling distribution. Paused Agent Replay Refresh (PARR) is a drop-in replacement for target networks that supports more complex learning algorithms without this need for approximation. Using a basic Q-network architecture, and refreshing the novelty values, target values, and replay sampling distribution, PARR gets 2500 points in Montezuma's Revenge after only 30.9 million Atari frames. Finally, interpreting PARR in the context of carbon-based learning offers a new reason for sleep.
A Novel Dataset for Evaluating and Alleviating Domain Shift for Human Detection in Agricultural Fields

In this paper we evaluate the impact of domain shift on human detection models trained on well known object detection datasets when deployed on data outside the distribution of the training set, as well as propose methods to alleviate such phenomena based on the available annotations from the target domain. Specifically, we introduce the OpenDR Humans in Field dataset, collected in the context of agricultural robotics applications, using the Robotti platform, allowing for quantitatively measuring the impact of domain shift in such applications. Furthermore, we examine the importance of manual annotation by evaluating three distinct scenarios concerning the training data: a) only negative samples, i.e., no depicted humans, b) only positive samples, i.e., only images which contain humans, and c) both negative and positive samples. Our results indicate that good performance can be achieved even when using only negative samples, if additional consideration is given to the training process. We also find that positive samples increase performance especially in terms of better localization. The dataset is publicly available for download at https://github.com/opendr-eu/datasets.
Understanding Hindsight Goal Relabeling Requires Rethinking Divergence Minimization

Hindsight goal relabeling has become a foundational technique for multi-goal reinforcement learning (RL). The idea is quite simple: any arbitrary trajectory can be seen as an expert demonstration for reaching the trajectory’s end state. Intuitively, this procedure trains a goal-conditioned policy to imitate a sub-optimal expert. However, this connection between imitation and hindsight relabeling is not well understood. Modern imitation learning algorithms are described in the language of divergence minimization
Simultaneous Acquisition of High Quality RGB Image and Polarization Information using a Sparse Polarization Sensor

This paper proposes a novel polarization sensor structure and network architecture to obtain a high-quality RGB image and polarization information. Conventional polarization sensors can simultaneously acquire RGB images and polarization information, but the polarizers on the sensor degrade the quality of the RGB images. There is a trade-off between the quality of the RGB image and polarization information as fewer polarization pixels reduce the degradation of the RGB image but decrease the resolution of polarization information. Therefore, we propose an approach that resolves the trade-off by sparsely arranging polarization pixels on the sensor and compensating for low-resolution polarization information with higher resolution using the RGB image as a guide. Our proposed network architecture consists of an RGB image refinement network and a polarization information compensation network. We confirmed the superiority of our proposed network in compensating the differential component of polarization intensity by comparing its performance with state-of-the-art methods for similar tasks: depth completion. Furthermore, we confirmed that our approach could simultaneously acquire higher quality RGB images and polarization information than conventional polarization sensors, resolving the tradeoff between the quality of RGB images and polarization information. The baseline code and newly generated real and synthetic large-scale polarization image datasets are available for further research and development.
Passive Non-line-of-sight Imaging for Moving Targets with an Event Camera

Non-line-of-sight (NLOS) imaging is an emerging technique for detecting objects behind obstacles or around corners. Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods, which show limitations in recognition of moving targets. To the best of our knowledge, we propose a novel event-based passive NLOS imaging method. We acquire asynchronous event-based data which contains detailed dynamic information of the NLOS target, and efficiently ease the degradation of speckle caused by movement. Besides, we create the first event-based NLOS imaging dataset, NLOS-ES, and the event-based feature is extracted by time-surface representation. We compare the reconstructions through event-based data with frame-based data. The event-based method performs well on PSNR and LPIPS, which is 20% and 10% better than frame-based method, while the data volume takes only 2% of traditional method.