Miracle-dz/UniFAR-FTUs huggingface dataset:
Morphological re-description of Aplysia depilans (Gastropoda: Anaspidea): new insights into the anatomy of the anaspideans

Although Aplysia depilans is a widely known European species and the type species of the genus, the distinctions that separate it from other related congeners are not entirely clear. Aimed at improving this scenario, a detailed morphological description is performed herein, showing some unprecedented and useful features. Based on our anatomical examination, this study has found the opaline gland to be dark in colour, as well as a metapodium with an indistinctly delimited pedal sucker, a crop divided into two equal chambers of similar volume by a circular muscle, gizzard plates arranged in groups, and a peculiar penial sheath structure. All of these characters appear to be significantly different from the other species, based on a review of the anatomical literature and several examined samples of ongoing studies. Our central objective is to provide new data on the anatomy of the type species of Aplysiidae; and, based on topological evidence, to propose a new terminology and possible homologies to some important characters, such as parapodia lobation, cephalic tentacles, crop, penial sheath, gizzard plates and visceral ganglion in anaspideans, for future phylogenetic analysis. The subgenus Subaplysia Medina, Collins & Walsh, 2005 is regarded as a junior synonym of subgenus Aplysia Linnaeus, 1767.
Management of intestinal strictures after necrotizing enterocolitis: retrospective study of 14 cases

Objective To study the clinical features and treatment experience of neonates with intestinal strictures after necrotizing enterocolitis.Methods Clinical data of 14 cases was studied retrospectively from January 2005 to December 2010.Results Among them,there were 2 patients with NEC Bell stage 1,9 patients with Bell stage 2,3 patients with Bell stage 3.The main symptoms of intestinal obstruction were intolerance of feeding,gastric retention,vomitting and abdominal distension.A mean time of 29.7 days was found between the recovery from NEC and the diagnosis of late strictures.In 11patients,X-ray showed a fixed and expanded ansa interstinalis.9 patients underwent gastrointestinal contrast study,with 3 showing intestinal strictures.Most intestinal strictures were found in the colon,and 4 patients had multiple strictures.All but one patients receivedoperative management,with resectionof strictures and primary end-to-end anastomosis.One case developed post-operative adhesions and had to undergo second laparotomy.No recurrent stricturewasseen.Conclusions Strictures can develop easily in patients after NEC.A high index of suspicion must be kept in mind for these babies. Key words: Neonate; Enterocolitis,necrotizing; Intestinal stricture
The Influences of Pre-birth Factors in Early Assessment of Child Mortality using Machine Learning Techniques

Analysis of child mortality is crucial as it pertains to the policy and programs of a country. The early assessment of patterns and trends in causes of child mortality help decision-makers assess needs, prioritize interventions, and monitor progress. Post-birth factors of the child, such as real-time clinical data, health data of the child, etc. are frequently used in child mortality studies. However, in the early assessment of child mortality, pre-birth factors would be more practical and beneficial than the post-birth factors. This study aims at incorporating pre-birth factors, such as birth history, maternal history, reproduction history, socioeconomic condition, etc. for classifying child mortality. To assess the relative importance of the features, Information Gain (IG) attribute evaluator is employed. For classifying child mortality, four machine learning algorithms are evaluated. Results show that the proposed approach achieved an AUC score of 0.947 in classifying child mortality which outperformed the clinical standards. In terms of accuracy, precision, recall, and f-1 score, the results are also notable and uniform. In developing countries like Bangladesh, the early assessment of child mortality using pre-birth factors would be effective and feasible as it avoids the uncertainty of the post-birth factors.
Coupling spins to nanomechanical resonators: Toward quantum spin-mechanics

Spin-mechanics studies interactions between spin systems and mechanical vibrations in a nanomechanical resonator and explores their potential applications in quantum information processing. In this tutorial, we summarize various types of spin-mechanical resonators and discuss both the cavity-QED-like and the trapped-ion-like spin-mechanical coupling processes. The implementation of these processes using negatively charged nitrogen vacancy and silicon vacancy centers in diamond is reviewed. Prospects for reaching the full quantum regime of spin-mechanics, in which quantum control can occur at the level of both single spin and single phonon, are discussed with an emphasis on the crucial role of strain coupling to the orbital degrees of freedom of the defect centers.
Rapid Variability of Sgr A* across the Electromagnetic Spectrum

Sagittarius A* (Sgr A*) is the variable radio, near-infrared (NIR), and X-ray source associated with accretion onto the Galactic center black hole. We have analyzed a comprehensive submillimeter (including new observations simultaneous with NIR monitoring), NIR, and 2–8 keV data set. Submillimeter variations tend to lag those in the NIR by ∼30 minutes. An approximate Bayesian computation fit to the X-ray first-order structure function shows significantly less power at short timescales in the X-rays than in the NIR. Less X-ray variability at short timescales, combined with the observed NIR–X-ray correlations, means the variability can be described as the result of two strictly correlated stochastic processes, the X-ray process being the low-pass-filtered version of the NIR process. The NIR–X-ray linkage suggests a simple radiative model: a compact, self-absorbed synchrotron sphere with high-frequency cutoff close to NIR frequencies plus a synchrotron self-Compton scattering component at higher frequencies. This model, with parameters fit to the submillimeter, NIR, and X-ray structure functions, reproduces the observed flux densities at all wavelengths, the statistical properties of all light curves, and the time lags between bands. The fit also gives reasonable values for physical parameters such as magnetic flux density B ≈ 13 G, source size L ≈ 2.2R S , and high-energy electron density n e ≈ 4 × 107 cm−3. An animation illustrates typical light curves, and we make public the parameter chain of our Bayesian analysis, the model implementation, and the visualization code.
Active Inference and Behavior Trees for Reactive Action Planning and Execution in Robotics

In this article, we propose a hybrid combination of active inference and behavior trees (BTs) for reactive action planning and execution in dynamic environments, showing how robotic tasks can be formulated as a free-energy minimization problem. The proposed approach allows handling partially observable initial states and improves the robustness of classical BTs against unexpected contingencies while at the same time reducing the number of nodes in a tree. In this work, we specify the nominal behavior offline, through BTs. However, in contrast to previous approaches, we introduce a new type of leaf node to specify the desired state to be achieved rather than an action to execute. The decision of which action to execute to reach the desired state is performed online through active inference. This results in continual online planning and hierarchical deliberation. By doing so, an agent can follow a predefined offline plan while still keeping the ability to locally adapt and take autonomous decisions at runtime, respecting safety constraints. We provide proof of convergence and robustness analysis, and we validate our method in two different mobile manipulators performing similar tasks, both in a simulated and real retail environment. The results showed improved runtime adaptability with a fraction of the hand-coded nodes compared to classical BTs.
Repeatability of adaptive radiation depends on spatial scale: regional versus global replicates of stickleback in lake versus stream habitats

The repeatability of adaptive radiation is expected to be scale dependent, with determinism decreasing as greater spatial separation among “replicates” leads to their increased genetic and ecological independence. Threespine stickleback (Gasterosteus aculeatus) provide an opportunity to test whether this expectation holds for the early stages of adaptive radiation -their diversification in freshwater ecosystems has been replicated many times. To better understand the repeatability of that adaptive radiation, we examined the influence of geographic scale on levels of parallel evolution by quantifying phenotypic and genetic divergence between lake and stream stickleback pairs sampled at regional (Vancouver Island) and global (North America and Europe) scales. We measured phenotypes known to show lake-stream divergence and used reduced representation genome-wide sequencing to estimate genetic divergence. We assessed the scale-dependence of parallel evolution by comparing effect sizes from multivariate models and also the direction and magnitude of lake-stream divergence vectors. At the phenotypic level, parallelism was greater at the regional than the global scale. At the genetic level, putative selected loci showed greater lake-stream parallelism at the regional than the global scale. Generally, the level of parallel evolution was low at both scales, except for some key univariate traits. Divergence vectors were often orthogonal, highlighting possible ecological and genetic constraints on parallel evolution at both scales. Overall, our results confirm that the repeatability of adaptive radiation decreases at increasing spatial scales. We suggest that greater environmental heterogeneity at larger scales imposes different selection regimes, thus generating lower repeatability of adaptive radiation at larger spatial scales.
“Slow” Radio Bursts from Galactic Magnetars?

Recently, one fast radio burst (FRB), FRB 200428, was detected from the Galactic magnetar soft gamma-ray repeater (SGR) J1935+2154 during one X-ray burst. This suggests that magnetars can make FRBs. On the other hand, the majority of X-ray bursts from SGR J1935+2154 are not associated with FRBs. One possible reason for such rarity of FRB–SGR–burst associations is that the FRB emission is much more narrowly beamed than the SGR–burst emission. If such an interpretation is correct, one would expect to detect radio bursts with viewing angles somewhat outside the narrow emission beam. These “slow” radio bursts (SRBs) would have broader widths and lower flux densities due to the smaller Doppler factor involved. We derive two “closure relations” to judge whether a long, less luminous radio burst could be an SRB. The 2.2 s, 308 Jy ms, ∼111 MHz radio burst detected from SGR J1935+2154 by the BSA/LPI radio telescope may be such an SRB. The ∼2 ms, 60 mJy ms faint burst detected by the Five-hundred-meter Aperture Spherical radio Telescope from the same source could be also an SRB if the corresponding FRB has a narrow spectrum. If the FRB beam is narrow, there should be many more SRBs than FRBs from Galactic magnetars. The lack of detection of abundant SRBs from magnetars would disfavor the hypothesis that all SGR–bursts are associated with narrow-beam FRBs.
Towards Metaheuristics "In the Large"

Following decades of sustained improvement, metaheuristics are one of the great success stories of optimization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientiﬁc and computational infrastructure to support the development, analysis and comparison of new approaches. We argue that, via principled choice of infrastructure support, the ﬁeld can pursue a higher level of scientiﬁc enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the ﬁeld, easing the exploration of the design space of metaheuristics.
Switch-based Markov Chains for Sampling Hamiltonian Cycles in Dense Graphs

We consider the irreducibility of switch-based Markov chains for the approximate uniform sampling of Hamiltonian cycles in a given undirected dense graph on $n$ vertices. As our main result, we show that every pair of Hamiltonian cycles in a graph with minimum degree at least $n/2+7$ can be transformed into each other by switch operations of size at most 10, implying that the switch Markov chain using switches of size at most 10 is irreducible. As a proof of concept, we also show that this Markov chain is rapidly mixing on dense monotone graphs.
On the Request-Trip-Vehicle Assignment Problem

The request-trip-vehicle assignment problem is at the heart of popular decomposition strategies for online vehicle routing. We study an integer linear programming formulation and its linear programming relaxation. Our main result is a simple, linear programming based randomized algorithm that, whenever the instance is feasible, leverages assumptions typically met in practice to return an assignment whose: i) expected cost is at most that of an optimal solution, and ii) expected fraction of unassigned requests is at most $1/e$. If trip-vehicle assignment costs can only be $\alpha$-approximated, we pay an additional factor of $\alpha$ in the expected cost. Unassigned requests are assigned in future rounds with high probability. We can relax the feasibility requirement by including a penalty term for unassigned requests, in which case our performance guarantee is with respect to a modified objective function. Our techniques generalize to a class of set-partitioning problems.
Fully Gap-Dependent Bounds for Multinomial Logit Bandit

We study the multinomial logit (MNL) bandit problem, where at each time step, the seller offers an assortment of size at most $K$ from a pool of $N$ items, and the buyer purchases an item from the assortment according to a MNL choice model. The objective is to learn the model parameters and maximize the expected revenue. We present (i) an algorithm that identifies the optimal assortment $S^*$ within $\widetilde{O}(\sum_{i = 1}^N \Delta_i^{-2})$ time steps with high probability, and (ii) an algorithm that incurs $O(\sum_{i \notin S^*} K\Delta_i^{-1} \log T)$ regret in $T$ time steps. To our knowledge, our algorithms are the first to achieve gap-dependent bounds that fully depends on the suboptimality gaps of all items. Our technical contributions include an algorithmic framework that relates the MNL-bandit problem to a variant of the top-$K$ arm identification problem in multi-armed bandits, a generalized epoch-based offering procedure, and a layer-based adaptive estimation procedure.
The Effects of Road Mortality on Small, Isolated Turtle Populations

Abstract. Roads impact wildlife in a variety of direct and indirect ways. Roads may act as barriers to dispersal, lead to decreasing population size and genetic diversity, change animal behavior, result in direct mortality, and increase habitat disturbance. Road mortality is especially detrimental to long-lived species, such as freshwater turtles, whose population persistence relies on high adult and subadult survivorship to counter high egg and hatchling mortality. The Spotted Turtle (Clemmys guttata) is a small-bodied, freshwater turtle species that is listed as endangered in Canada and proposed for federal listing in the United States. We used a population viability analysis to attempt to quantify the impact that road mortality has on two distinct populations of Spotted Turtles. The baseline model for the North Wetland Complex (NWC) population predicted a probability of quasi-extinction within 150 yr of 20%. The baseline model for the South Wetland Complex (SWC) predicted a probability of quasi-extinction within 150 yr of 24%. Including an estimate of road mortality (modeled as a reduction in adult survival through annual catastrophic events) into the models, the probability of quasiextinction within 150 yr changed to 93% for the NWC and 94% for the SWC. Our results highlight the critical importance that anthropogenic additive adult mortality has on small populations of turtles and the necessity of detailed demographic studies to detect potential declines in populations of long-lived species.
Discovery of a Low-redshift Damped Lyα System in a Foreground Extended Disk Using a Starburst Galaxy Background Illuminator

We present the discovery of a low-redshift damped Lyα (DLA) system in the spectrum of background starburst galaxy SDSS J111323.88+293039.3 (z = 0.17514). The DLA is at an impact parameter of ρ = 36 kpc from the star-forming galaxy, SDSS J111324.08+293051.2 (z = 0.17077). We measure an H i column density of N(H i) = 3.47 × 1020 cm−2 along with multiple low-ionization species such as N i, N ii, Si ii, C ii, and Si iii. We also make an estimate of the covering fraction to be 0.883, giving us a limiting size of the DLA to be A DLA ≥ 3.3 kpc2. Assuming a uniform column density over the entire DLA system, we estimate its mass to be M DLA ≥ 5.3 × 106 M ⊙. The extended illuminator and the low redshift of this DLA give us the unique opportunity to characterize its nature and the connection to its host galaxy. We measure a velocity offset of +131 km s−1 from the systemic velocity of the host for the DLA. This velocity is −84 km s−1 from the projected rotation velocity of the host galaxy as measured using a newly constructed rotation curve. Based on the size of the host galaxy, the H i column density, and the gas kinematics, we believe this DLA is tracing the warm neutral gas in the H i disk of the foreground galaxy. Our detection adds to a small set of low-redshift DLAs that have confirmed host galaxies, and is the first to be found using an extended background source.
Modular Multi Target Tracking Using LSTM Networks

The process of association and tracking of sensor detections is a key element in providing situational awareness. When the targets in the scenario are dense and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a challenging task. The conventional techniques to solve such NP-hard combinatorial optimization problem involves multiple complex models and requires tedious tuning of parameters, failing to provide an acceptable performance within the computational constraints. This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction and filtering using deep learning with memory. The challenging task of association is performed using the Bi-Directional Long short-term memory (LSTM) whereas filtering and prediction are done using LSTM models. The proposed modular blocks can be independently trained and used in multitude of tracking applications including non co-operative (e.g., radar) and co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances the interpretability of the deep learning application. It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.
ACRONYM: A Large-Scale Grasp Dataset Based on Simulation

We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation. The dataset contains 17.7M parallel-jaw grasps, spanning 8872 objects from 262 different categories, each labeled with the grasp result obtained from a physics simulator. We show the value of this large and diverse dataset by using it to train two state-of-the-art learning-based grasp planning algorithms. Grasp performance improves significantly when compared to the original smaller dataset. Data and tools can be accessed at https://sites.google. com/nvidia.com/graspdataset.
Coronal Heating and Solar Wind Formation in Quiet Sun and Coronal Holes: A Unified Scenario

Coronal holes (CHs) are darker than the quiet Sun (QS) when observed in coronal channels. This study aims to understand the similarities and differences between CHs and QS in the transition region using the Si iv 1394 Å line, recorded by the Interface Region Imaging Spectrograph, by considering the distribution of magnetic field measured by the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. We find that Si iv intensities obtained in CHs are lower than those obtained in QS for regions with identical magnetic flux densities. Moreover, the difference in intensities between CHs and QS increases with increasing magnetic flux. For the regions with equal magnetic flux density, QS line profiles are more redshifted than those measured in CHs. Moreover, the blueshifts measured in CHs show an increase with increasing magnetic flux density unlike in the QS. The non-thermal velocities in QS, as well as in CHs, show an increase with increasing magnetic flux. However, no significant difference was observed in QS and CHs, albeit a small deviation at small flux densities. Using these results, we propose a unified model for the heating of the corona in the QS and in CHs and the formation of solar wind.
Neural graph embeddings via matrix factorization for link prediction: smoothing or truncating negatives?

Learning good quality neural graph embeddings has long been achieved by minimzing the pointwise mutual information (PMI) for co-occuring nodes in simulated random walks. This design choice has been mostly popularized by the direct application of the highly-successful word embedding algorithm word2vec to predicting the formation of new links in social, co-citation, and biological networks. However, such a skeumorphic design of graph embedding methods entails a truncation of information coming from pairs of nodes with low PMI. To circumvent this issue, we propose an improved approach to learning low-rank factorization embeddings that incorporate information from such unlikely pairs of nodes and show that it can improve the link prediction performance of baseline methods from 1.2% to 24.2%. Based on our results and observations, we outline further steps that could improve the design of next graph embedding algorithms that are based on matrix factorizaion.
Scalable Graph Neural Networks for Heterogeneous Graphs

Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks by simply operating on graph-smoothed node features, rather than using end-to-end learned feature hierarchies that are challenging to scale to large graphs. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities. We propose Neighbor Averaging over Relation Subgraphs (NARS), which trains a classifier on neighbor-averaged features for randomly-sampled subgraphs of the "metagraph" of relations. We describe optimizations to allow these sets of node features to be computed in a memory-efficient way, both at training and inference time. NARS achieves a new state of the art accuracy on several benchmark datasets, outperforming more expensive GNN-based methods