Miracle-dz/UniFAR-FTUs huggingface dataset:
Evolutionarily Significant Units of Gray-Sided Vole (Myodes rufocanus) in Hokkaido, Japan

The gray-sided vole, Myodes rufocanus (= Clethrionomys rufocanus) is a widespread species in Hokkaido, Japan. We applied an integrative approach to determine adaptive and neutral genetic variation in the gray-sided vole populations to identify full or partial Evolutionarily Significant Units. We surveyed 38 mainland populations and six island populations. The mitochondrial DNA control region was analyzed for neutral genetic variation and cranial measurements were taken as a proxy for adaptive variation. Data on neutral genetic variation indicate that most island populations are differentiated from the mainland populations. The islands of Teuri and Yagishiri, Rishiri and, Kunashiri can be regarded as partial ESUs based on their unique haplotypes. Partial ESUs based on morphological features can be identified in the eastern part of mainland with populations from Akkeshi and Shibecha. A full ESU designation can be given to Daikoku Island based on its unique haplotype and unique morphological feature.
Exploring the Pharmacological Properties of 4 Sul Compounds as Potential Novel Treatments for COPD

Although chronic obstructive pulmonary disease (COPD patients are treated with (combinations of ) bronchodilators and anti-inflammatory glucocorticosteroids, many patients respond poorly to these drugs. Therefore there is a great need for novel therapy in this disease. Drugs that possesses both anti-inflammatory and bronchodilatory properties would yield an optimal treatment of COPD. In the present study, we explored the antiinflammatory and airway smooth muscle (ASM)-relaxing properties of 4 compounds that have shown anti-oxidative effects (Sul-90, Sul-121, Sul-127 and Sul-136), using in vitro models. We found that Sul-90 and Sul-121 (but not Sul-127 and Sul-136) dose-dependently prevented cigarette smoke extract (CSE)-induced release of interleukin-8 (IL-8) from human ASM cells, without significantly affecting basal IL-8 release. Moreover, Sul-90 and Sul-121 (but not Sul-127 and Sul-136) both induced relaxation of pre-contracted bovine tracheal smooth muscle (BTSM) strips, which was unaffected by the nonselective β-adrenoceptor antagonist propranolol. In addition, Sul-121 relaxed BTSM strips at a concentration that did not affect cell viability, whereas Sul-90 only induced BTSM relaxation at a concentration that could decreases cell viability. In conclusion, Sul-121 represents a promising candidate for further screening in cell and animal models of COPD as it possesses the attractive combination of anti-inflammatory and (non-β 2 -adrenoceptor-mediated) ASM-relaxing properties without a detrimental effect on cell viability. Sul-121 may therefore present a novel compound for the treatment of COPD.
MARINE DURABILITY OF 15-YEAR-OLD UNCRACKED AND PRECRACKED CONCRETE SPECIMENS MADE WITH DIFFERENT CEMENTS

The marine durability of 15-year-old uncracked and precracked concrete specimens made with ordinary Portland (OPC), slag (types A (SCA), B (SCB) and C (SCC)), and fly ash (type B (FACB)) cements is investigated. Compressive strength, chloride ingress, corrosion of steel reinforcing bars, and crack healing are evaluated. The degree of chloride ingress and degree of steel bar corrosion are found to be ordered as OPC>FACB>SCA>SCB>SCC. Crack healing is observed for small crack widths (≦0.5 mm) irrespective of the cement type. Large crack widths, such as 1.5, 2 and 5 mm, do not heal and significant loss of diameter is observed. Voids at the steel-concrete interface lead to the formation of corrosion pits.
Heterogeneous Conductance‐Based Locally Shape‐Morphable Soft Electrothermal Actuator

The development of soft electrothermal actuators (ETAs) that can be designed in arbitrary shapes and can easily handle soft objects has recently attracted much attention. However, the existing ETAs cannot be locally designed with a single substrate geometry, which places certain limitations on their applications. In addition, some limited materials (e.g., highly aligned carbon nanotubes) are used as heating layers for improving actuation controllability, which results in a high driving voltage, thus limiting the diversity and utility of ETAs. Herein, a novel method for a heterogeneous conductance‐based locally shape‐morphable electrothermal actuator (HC‐ETA) is suggested, revealing that conductance programming allows control of the heat distribution in ETAs. Thus, by controlling the actuator heat distribution, the designed actuation motion can be implemented. Furthermore, Ag nanowire/carbon nanotube composites are used as conductive nanomaterials for a heating layer to realize a low‐driving‐voltage actuator. Additionally, combining a porous fabric substrate with a bipolymer actuator structure allows mechanical interlocking and the chemical bonding between actuator multilayers, resulting in significantly enhanced mechanical durability. Finally, the utility of the proposed actuator and the corresponding design method is successfully demonstrated by fabricating a biomimetic self‐walking robot and an object lifting soft robot for vacuum chamber applications.
Predictive modeling of large scale historic masonry monuments: uncertainty quantification and model validation

This paper focuses on establishing confidence in model predictions through the validation of numerical models with a specific focus on large scale masonry structures. Through an overview of various case study applications, this presentation will discuss the relevant phases of model validation: numerical model development, field experimentation, model calibration and validation. The main goals of this paper are (i) to discuss where modeling errors and uncertainties originate and (ii) to present an approach to obtain model predictions that are statistically consistent with their respective measurements. Our approach considers all major sources of error and uncertainty, which originate from the numerical solutions of differential equations (numerical uncertainty), imprecise model input parameter values (parameter uncertainty), incomplete definitions of underlying physics due to assumptions and idealizations (bias error) and variability in measurements (experimental uncertainty). This paper provides an overview of the best-practices for developing finite element models for large-scale masonry monuments, designing and executing calibration experiments as well as calibrating numerical models under uncertainty.
A Constant Voltage Design for Inductive Power Transfer System with Multiple Loads

Compensation circuit design has been extensively studied in literatures to achieve constant voltage for single load in inductive power transfer (IPT) systems. In this work, we propose secondary-side compensation circuit for IPT system which can obtain load-independent constant voltages for multiple loads. This topology can be beneficial to applications that require difference voltages for multiple loads. In this paper, as a primitive investigation, we demonstrate how to design compensation circuit to achieve constant voltages for two loads. Simulation and experiment results are provided to confirm effectiveness of the proposed scheme.
Parathyroid hormone changes following denosumab treatment in postmenopausal osteoporosis

Denosumab is a new potent antiresorptive treatment of osteoporosis that can potentially induce a compensatory increase in parathyroid hormone (PTH) levels. We aimed to evaluate the alteration of PTH 1 and 6 months after denosumab's administration with different regimens of calcium and vitamin D (Ca/D) supplementation.
A Novel Bicistronic Expression Strategy for Neuronal Calcium Imaging in C. elegans*

Calcium imaging has been widely used to monitor the activity of various neurons in C. elegans. However, it is a challenge to determine the calcium transient in a freely moving worm for two reasons. One reason is the challenge in ensuring the co-expression of the genetically encoded calcium indicator and reference fluorescent protein in the same target neurons. Another reason is the common problem with spectrum cross-talk between the fluorescent pair used most often: G-CaMPs (calcium indicator) and DsReds (the reference fluorescent protein). Spectrum cross-talk occasionally introduces artifacts in the calcium transient measurements. Herein, we developed a novel bicistronic expression strategy to ensure co-expression efficiency and simplify the labeling for the target neurons. Moreover, we presented a new fluorescent protein pair for calcium imaging, mKate2 and G-CaMPs, which have less spectrum cross-talk. We successfully tested our new labeling strategy in the C. elegans ASH sensory neuron. We anticipate that this improved technique will facilitate neural circuit studies in C. elegans.
New Demapper for MIMO-LDM in ATSC 3.0

In this paper, we propose a new demapping approach for the multiple-input multiple-output aided layered division multiplexing (MIMO-LDM) system. The new demapping approach demaps the core layer (CL) signal using a part of enhanced layer (EL) signal and processes the other part of EL signal as noise. The new demapping approach has a near optimal performance and acceptable demapping complexity that is independent with the modulation order of EL signal. The simulation results also shows that the new demapper outperforms the original Gaussian approximation (GA) demapper that came with LDM on CL performance at low injection level (IL) and high code rate, and usually achieves the same performance with the optimal maximum a posteriori (MAP) demapper. Furthermore, unlike the GA, the new demapping approach is feasible for almost all the cases of IL and code rate listed in the standard ATSC 3.0.
Sampling Prediction-Matching Examples in Neural Networks: A Probabilistic Programming Approach

Though neural network models demonstrate impressive performance, we do not understand exactly how these black-box models make individual predictions. This drawback has led to substantial research devoted to understand these models in areas such as robustness, interpretability, and generalization ability. In this paper, we consider the problem of exploring the prediction level sets of a classifier using probabilistic programming. We define a prediction level set to be the set of examples for which the predictor has the same specified prediction confidence with respect to some arbitrary data distribution. Notably, our sampling-based method does not require the classifier to be differentiable, making it compatible with arbitrary classifiers. As a specific instantiation, if we take the classifier to be a neural network and the data distribution to be that of the training data, we can obtain examples that will result in specified predictions by the neural network. We demonstrate this technique with experiments on a synthetic dataset and MNIST. Such level sets in classification may facilitate human understanding of classification behaviors.
Electron Broadening Operator Including Penetrating Collisions for Hydrogen

The expression of the electron broadening operator including the effect of penetrating collisions, i.e., for which the incoming electron enters the extent of bound-electron wave-functions, is rather complicated, even for hydrogen. It involves integrals of special functions, the evaluation of which deserves scrutiny. We present a simple approximate form of the electron collision operator for hydrogen including penetration effects, both in direct and interference terms. The new expression is accurate and easy to compute. In the Penetration Standard Theory, the collision operator is convergent whatever the value of the maximum impact parameter. However, when penetration theory is not valid anymore, it should be questioned. We discuss the problem of strong collisions when penetration effects are taken into account.
Transformer ratio enhancement at wakefield excitation in blowout regime in plasma by electron bunch with semi-gaussian charge distribution.

Using 2d3v code LCODE, the numerical simulation of nonlinear wakefield excitation in plasma by shaped relativistic electron bunch with charge distribution, which increases according to Gaussian charge distribution up to the maximum value, and then decreases sharply to zero, has been performed. Transformer ratio, as the ratio of the maximum accelerating field to the maximum decelerating field inside the bunch, and accelerating the wakefield have been investigated taking into account nonlinearity of the wakefield. The dependence of the transformer ratio and the maximum accelerating field on the length of the bunch was investigated with a constant charge of the bunch. It was taken into account that the length of the nonlinear wakefield increases with increasing length of the bunch. It is shown that the transformer ratio reaches its maximum value for a certain length of the bunch. The maximum value of the transformer ratio reaches six as due to the profiling of the bunch, and due to the non-linearity of the wakefield.
Temperature effects on the neutron matter equation of state obtained from chiral effective field theory

Temperature effects on the neutron matter equation of state are investigated in the framework of chiral effective field theory. Latest, state-of-the-art chiral two-nucleon forces are applied from third to fifth order in the chiral expansion together with chiral three-nucleon forces, allowing for a determination of the truncation error of the theoretical predictions. The thermodynamic quantities considered include the chemical potential, the internal energy, the entropy, and the free energy. In general, good order-by-order convergence of all predictions is observed. As to be expected, temperature effects are largest at low density. The temperature dependence of the chiral three-nucleon force turns out to be weak.
Learning Landmark Guided Embeddings for Animal Re-identification

Re-identification of individual animals in images can be ambiguous due to subtle variations in body markings between different individuals and no constraints on the poses of animals in the wild. Person re-identification is a similar task and it has been approached with a deep convolutional neural network (CNN) that learns discriminative embeddings for images of people. However, learning discriminative features for an individual animal is more challenging than for a person’s appearance due to the relatively small size of ecological datasets compared to labelled datasets of person’s identities. We propose to improve embedding learning by exploiting body landmarks information explicitly. Body landmarks are provided to the input of a CNN as confidence heatmaps that can be obtained from a separate body landmark predictor. The model is encouraged to use heatmaps by learning an auxiliary task of reconstructing input heatmaps. Body landmarks guide a feature extraction network to learn the representation of a distinctive pattern and its position on the body. We evaluate the proposed method on a large synthetic dataset and a small real dataset. Our method outperforms the same model without body landmarks input by 26% and 18% on the synthetic and the real datasets respectively. The method is robust to noise in input coordinates and can tolerate an error in coordinates up to 10% of the image size.
Reconstructing the Observed Ionizing Photon Production Efficiency at z ∼ 2 Using Stellar Population Models

The ionizing photon production efficiency, ξion, is a critical parameter that provides a number of physical constraints to the nature of the early universe, including the contribution of galaxies to the timely completion of the reionization of the universe. Here, we use KECK/MOSFIRE and ZFOURGE multiband photometric data to explore the ξion of a population of galaxies at z ∼ 2 with . Our 130 Hα detections show a median of 24.8 ± 0.5 when dust corrected using a Calzetti et al. dust prescription. Our values are typical of mass/magnitude selected ξion values observed in the z ∼ 2 universe. Using BPASSv2.2. 1 and Starburst99 stellar population models with simple parametric star formation histories (SFH), we find that even with models that account for effects of stellar evolution with binaries/stellar rotation, model galaxies at have low Hα equivalent widths (EWs) and redder colors compared to our z ∼ 2 observed sample. We find that introducing starbursts to the SFHs resolve the tension with the models; however, due to the rapid time evolution of ξion, Hα EWs, and rest-frame optical colors, our Monte Carlo simulations of starbursts show that random distributions of starbursts in evolutionary time of galaxies are unlikely to explain the observed distribution. Thus, either our observed sample is specially selected based on their past SFH, or stellar models require additional mechanisms to reproduce the observed high UV luminosity of galaxies for a given production rate of hydrogen ionizing photons.
Deep Network Approximation for Smooth Functions

This paper establishes optimal approximation error characterization of deep ReLU networks for smooth functions in terms of both width and depth simultaneously. To that end, we first prove that multivariate polynomials can be approximated by deep ReLU networks of width $\mathcal{O}(N)$ and depth $\mathcal{O}(L)$ with an approximation error $\mathcal{O}(N^{-L})$. Through local Taylor expansions and their deep ReLU network approximations, we show that deep ReLU networks of width $\mathcal{O}(N\ln N)$ and depth $\mathcal{O}(L\ln L)$ can approximate $f\in C^s([0,1]^d)$ with a nearly optimal approximation rate $\mathcal{O}(\|f\|_{C^s([0,1]^d)}N^{-2s/d}L^{-2s/d})$. Our estimate is non-asymptotic in the sense that it is valid for arbitrary width and depth specified by $N\in\mathbb{N}^+$ and $L\in\mathbb{N}^+$, respectively.
An Internal Covariate Shift Bounding Algorithm for Deep Neural Networks by Unitizing Layers’ Outputs

Batch Normalization (BN) techniques have been proposed to reduce the so-called Internal Covariate Shift (ICS) by attempting to keep the distributions of layer outputs unchanged. Experiments have shown their effectiveness on training deep neural networks. However, since only the first two moments are controlled in these BN techniques, it seems that a weak constraint is imposed on layer distributions and furthermore whether such constraint can reduce ICS is unknown. Thus this paper proposes a measure for ICS by using the Earth Mover (EM) distance and then derives the upper and lower bounds for the measure to provide a theoretical analysis of BN. The upper bound has shown that BN techniques can control ICS only for the outputs with low dimensions and small noise whereas their control is not effective in other cases. This paper also proves that such control is just a bounding of ICS rather than a reduction of ICS. Meanwhile, the analysis shows that the high-order moments and noise, which BN cannot control, have great impact on the lower bound. Based on such analysis, this paper furthermore proposes an algorithm that unitizes the outputs with an adjustable parameter to further bound ICS in order to cope with the problems of BN. The upper bound for the proposed unitization is noise-free and only dominated by the parameter. Thus, the parameter can be trained to tune the bound and further to control ICS. Besides, the unitization is embedded into the framework of BN to reduce the information loss. The experiments show that this proposed algorithm outperforms existing BN techniques on CIFAR-10, CIFAR-100 and ImageNet datasets.
On Optimal Locally Repairable Codes and Generalized Sector-Disk Codes

Optimal locally repairable codes with information locality are considered. Optimal codes are constructed, whose length is also order-optimal with respect to a new bound on the code length derived in this paper. The length of the constructed codes is super-linear in the alphabet size, which improves upon the well known pyramid codes, whose length is only linear in the alphabet size. The recoverable erasure patterns are also analyzed for the new codes. Based on the recoverable erasure patterns, we construct generalized sector-disk (GSD) codes, which can recover from disk erasures mixed with sector erasures in a more general setting than known sector-disk (SD) codes. Additionally, the number of sectors in the constructed GSD codes is superlinear in the alphabet size, compared with known SD codes, whose number of sectors is only linear in the alphabet size.
Killing Stubborn Mutants with Symbolic Execution

We introduce SEMu, a Dynamic Symbolic Execution technique that generates test inputs capable of killing stubborn mutants (killable mutants that remain undetected after a reasonable amount of testing). SEMu aims at mutant propagation (triggering erroneous states to the program output) by incrementally searching for divergent program behaviors between the original and the mutant versions. We model the mutant killing problem as a symbolic execution search within a specific area in the programs’ symbolic tree. In this framework, the search area is defined and controlled by parameters that allow scalable and cost-effective mutant killing. We integrate SEMu in KLEE and experimented with Coreutils (a benchmark frequently used in symbolic execution studies). Our results show that our modeling plays an important role in mutant killing. Perhaps more importantly, our results also show that, within a two-hour time limit, SEMu kills 37% of the stubborn mutants, where KLEE kills none and where the mutant infection strategy (strategy suggested by previous research) kills 17%.
Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control

Reasoning about human motion through an environment is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist a multitude of methods for trajectory forecasting, many of them have only been evaluated with one semantic class of agents and only use prior trajectory information, ignoring a plethora of information available online to autonomous systems from common sensors. Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of agents with distinct semantic classes while incorporating heterogeneous data (e.g. semantic maps and camera images). Our model is designed to be tightly integrated with robotic planning and control frameworks; it is capable of producing predictions that are conditioned on ego-agent motion plans. We demonstrate the performance of our model on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.