Miracle-dz/UniFAR-FTUs huggingface dataset:
PiRNA-DQ541777 Contributes to Neuropathic Pain via Targeting Cdk5rap1

Piwi-Interacting RNA (piRNA) is the largest class of small noncoding RNA and is involved in various physiological and pathological processes. However, whether it has a role in pain modulation remains unknown. In the present study, we found that spinal piRNA-DQ541777 (piR-DQ541777) was significantly increased in the male mouse model of sciatic nerve chronic constriction injury (CCI)-induced neuropathic pain. Knockdown of spinal piR-DQ541777 alleviated CCI-induced thermal hyperalgesia and mechanical allodynia and spinal neuronal sensitization. However, the overexpression of spinal piR-DQ541777 in naive mice produced pain behaviors and increased spinal neuron sensitization. Furthermore, we found that piR-DQ541777 regulates pain behaviors by targeting CDK5 regulatory subunit-associated protein 1 (Cdk5rap1). CCI increased the methylation level of CpG islands in the cdk5rap1 promoter and consequently reduced the expression of Cdk5rap1, which was reversed by the knockdown of piR-DQ541777 and mimicked by the overexpression of piR-DQ541777 in naive mice. Finally, piR-DQ541777 increased the methylation level of CpG islands by recruiting DNA methyltransferase 3A (DNMT3a) to cdk5rap1 promoter. In conclusion, this study represents a novel role of piR-DQ541777 in the regulation of neuropathic pain through the methylation of cdk5rap1. SIGNIFICANCE STATEMENT Chronic pain affects ∼20% of the population of the world and is a major global public health problem. Although we have studied the neurobiological mechanism of neuropathic pain for decades, there is still no ideal drug available to treat it. This work indicates that a novel role of Piwi-interacting RNA (piRNA) DQ541777 in the regulation of neuropathic pain through the methylation of cdk5rap1. Our findings provide the first evidence of the regulatory effect of piRNAs on neuropathic pain, which may improve our understanding of pain mechanisms and lead to the discovery of novel drug targets for the prevention and treatment of neuropathic pain.
Verbal insight revisited: fMRI evidence for early processing in bilateral insulae for solutions with AHA! experience shortly after trial onset

In insight problem solving solutions with AHA! experience have been assumed to be the consequence of restructuring of a problem which usually takes place shortly before the solution. However, evidence from priming studies suggests that solutions with AHA! are not spontaneously generated during the solution process but already relate to prior subliminal processing. We test this hypothesis by conducting an fMRI study using a modified compound remote associates paradigm which incorporates semantic priming. We observe stronger brain activity in bilateral anterior insulae already shortly after trial onset in problems that were later solved with than without AHA! This early activity was independent of semantic priming but may be related to other lexical properties of attended words helping to reduce the amount of solutions to look for. In contrast, there was more brain activity in bilateral anterior insulae during solutions that were solved without than with AHA! This timing (after trial start/during solution) x solution experience (with/without AHA! ) interaction was significant. The results suggest that (a) solutions accompanied with AHA! relate to early solution‐relevant processing and (b) both solution experiences differ in timing when solution‐relevant processing takes place. In this context, we discuss the potential role of the anterior insula as part of the salience network involved in problem solving by allocating attentional resources.
Active learning for level set estimation under cost-dependent input uncertainty

As part of a quality control process in manufacturing it is often necessary to test whether all parts of a product satisfy a required property, with as few inspections as possible. When multiple inspection apparatuses with different costs and precision exist, it is desirable that testing can be carried out cost-effectively by properly controlling the trade-off between the costs and the precision. In this paper, we formulate this as a level set estimation (LSE) problem under cost-dependent input uncertainty - LSE being a type of active learning for estimating the level set, i.e., the subset of the input space in which an unknown function value is greater or smaller than a pre-determined threshold. Then, we propose a new algorithm for LSE under cost-dependent input uncertainty with theoretical convergence guarantee. We demonstrate the effectiveness of the proposed algorithm by applying it to synthetic and real datasets.
Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning

Purpose To investigate the feasibility of automatic identification and classification of hip fractures using deep learning, which may improve outcomes by reducing diagnostic errors and decreasing time to operation. Materials and Methods Hip and pelvic radiographs from 1118 studies were reviewed, and 3026 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous open reduction and internal fixation, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (or DenseNet) was trained on a subset of the bounding box images, and its performance was evaluated on a held-out test set and by comparison on a 100-image subset with two groups of human observers: fellowship-trained radiologists and orthopedists; senior residents in emergency medicine, radiology, and orthopedics. Results The binary accuracy for detecting a fracture of this model was 93.7% (95% confidence interval [CI]: 90.8%, 96.5%), with a sensitivity of 93.2% (95% CI: 88.9%, 97.1%) and a specificity of 94.2% (95% CI: 89.7%, 98.4%). Multiclass classification accuracy was 90.8% (95% CI: 87.5%, 94.2%). When compared with the accuracy of human observers, the accuracy of the model achieved an expert-level classification, at the very least, under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance in the multiclass classification. Conclusion A deep learning model identified and classified hip fractures with expert-level performance, at the very least, and when used as an aid, improved human performance, with aided resident performance approximating that of unaided fellowship-trained attending physicians.Supplemental material is available for this article. © RSNA, 2020.
Codebook-Based Max–Min Energy-Efficient Resource Allocation for Uplink mmWave MIMO-NOMA Systems

In this paper, we investigate the energy-efficient resource allocation problem in an uplink non-orthogonal multiple access (NOMA) millimeter wave system, where the fully-connected-based sparse radio frequency chain antenna structure is applied at the base station (BS). To relieve the pilot overhead for channel estimation, we propose a codebook-based analog beam design scheme, which only requires to obtain the equivalent channel gain. On this basis, users belonging to the same analog beam are served via NOMA. Meanwhile, an advanced NOMA decoding scheme is proposed by exploiting the global information available at the BS. Under predefined minimum rate and maximum transmit power constraints for each user, we formulate a max-min user energy efficiency (EE) optimization problem by jointly optimizing the detection matrix at the BS and transmit power at the users. We first transform the original fractional objective function into a subtractive one. Then, we propose a two-loop iterative algorithm to solve the reformulated problem. Specifically, the inner loop updates the detection matrix and transmit power iteratively, while the outer loop adopts the bi-section method. Meanwhile, to decrease the complexity of the inner loop, we propose a zero-forcing (ZF)-based iterative algorithm, where the detection matrix is designed via the ZF technique. Finally, simulation results show that the proposed schemes obtain a better performance in terms of spectral efficiency and EE than the conventional schemes.
On the Existence of Optimal Potentials on Unbounded Domains

We consider elliptic equations of Schrodinger type with a right-hand side fixed and with the linear part of order zero given by a potential V . The main goal is to study the optimization problem for an integral cost depending on the solution uV , when V varies in a suitable class of admissible potentials. These problems can be seen as the natural extension of shape optimization problems to the framework of potentials. The main result is an existence theorem for optimal potentials, and the main difficulty is to work in the whole Euclidean space Rd, which implies a lack of compactness in several crucial points. In the last section we present some numerical simulations.
The CFHT Large Area U-band Deep Survey (CLAUDS)

The Canada-France-Hawaii Telescope (CFHT) Large Area U-band Deep Survey (CLAUDS) uses data taken with the MegaCam mosaic imager on CFHT to produce images of 18.60 deg2 with median seeing of FWHM=0.92″ and to a median depth of U = 27.1 AB (5σ in 2″ apertures), with selected areas that total 1.36 deg2 reaching a median depth of U = 27.7 AB. These are the deepest U-band images assembled to date over this large an area. These data are located in four fields also imaged to comparably faint levels in grizy and several narrowband filters as part of the Hyper Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP). These CFHT and Subaru datasets will remain unmatched in their combination of area and depth until the advent of the Large Synoptic Survey Telescope (LSST). This paper provides an overview of the scientific motivation for CLAUDS and gives details of the observing strategy, observations, data reduction, and data merging with the HSC-SSP. Three early applications of these deep data are used to illustrate the potential of the dataset: deep U-band galaxy number counts, z∼3 Lyman break galaxy (LBG) selection, and photometric redshifts improved by adding CLAUDS U to the Subaru HSC grizy photometry.
Transferable Adversarial Robustness using Adversarially Trained Autoencoders

Adversarial machine learning is a well-studied field of research where an adversary causes predictable errors in a machine learning algorithm through careful manipulation of the input. Numerous techniques have been proposed to harden machine learning algorithms and mitigate the effect of adversarial attacks. Of these techniques, adversarial training, which augments the training data with adversarial samples, has proven to be an effective defensive technique with respect to a certain class of attacks. However, adversarial training is computationally expensive and its improvements are limited to a single classifier. In this paper, we propose Adversarially-Trained Autoencoder Augmentation, the first transferable adversarial defense that is robust to certain adaptive adversaries. We disentangle adversarial robustness from the classification pipeline by adversarially training an autoencoder with respect to the classification loss of a naturally trained classifier. The main advantage of our work is that the autoencoders can be reused to protect other vulnerable classifiers without additional training. We show that our approach improves the adversarial robustness of a naturally trained classifier by at least 45\% in MNIST and Fashion-MNIST datasets despite no additional training. On CIFAR-10, we can train a single autoencoder to protect multiple naturally trained classifiers and achieve adversarial performance on par or better than adversarial training. Finally, using a natural image corruption dataset, we show that our approach improves robustness to naturally corrupted images, which has been identified as strongly indicative of true adversarial robustness.
Carrier Density Oscillation in photoexcited Semiconductors

The perturbation of a semiconductor from the thermodynamic equilibrium often leads to the display of nonlinear dynamics and formation of spatiotemporal patterns due to the spontaneous generation of competing processes. Here, we describe the ultrafast imaging of nonlinear carrier transport in silicon, excited by an intense femtosecond laser pulse. We use scanning ultrafast electron microscopy (SUEM) to show that, at a sufficiently high excitation fluence, the transport of photoexcited carriers slows down by turning into an oscillatory process. We attribute this nonlinear response to the electric field, generated by the spatial separation of these carriers under intrinsic and photo-induced fields; we then provide an advection-diffusion model that mimics the experimental observation. Our finding provides a direct imaging evidence for the electrostatic oscillation of hot carriers in highly excited semiconductors and offers new insights into their spatiotemporal evolution as the equilibrium is recovered.
Spin transport in thick insulating antiferromagnetic films

Spin transport of magnonic excitations in uniaxial insulating antiferromagnets (AFs) is investigated. In linear response to spin biasing and a temperature gradient, the spin transport properties of normal-metal--insulating antiferromagnet--normal-metal heterostructures are calculated. We focus on the thick-film regime, where the AF is thicker than the magnon equilibration length. This regime allows the use of a drift-diffusion approach, which is opposed to the thin-film limit considered by Bender {\it et al.} 2017, where a stochastic approach is justified. We obtain the temperature- and thickness-dependence of the structural spin Seebeck coefficient $\mathcal{S}$ and magnon conductance $\mathcal{G}$. In their evaluation we incorporate effects from field- and temperature-dependent spin conserving inter-magnon scattering processes. Furthermore, the interfacial spin transport is studied by evaluating the contact magnon conductances in a microscopic model that accounts for the sub-lattice symmetry breaking at the interface. We find that while inter-magnon scattering does slightly suppress the spin Seebeck effect, transport is generally unaffected, with the relevant spin decay length being determined by non-magnon-conserving processes such as Gilbert damping. In addition, we find that while the structural spin conductance may be enhanced near the spin flip transition, it does not diverge due to spin impedance at the normal metal|magnet interfaces.
Insights from the APOKASC determination of the evolutionary state of red-giant stars by consolidation of different methods

The internal working of low-mass stars is of great significance to both the study of stellar structure and the history of the Milky Way. Asteroseismology has the power to directly sense the internal structure of stars and allows for the determination of the evolutionary state – i.e. has helium burning commenced or is the energy generated only by the fusion in the hydrogen-burning shell? We use observational data from red-giant stars in a combination (known as APOKASC) of asteroseismology (from the Kepler mission) and spectroscopy (from SDSS/APOGEE). The new feature of the analysis is that the APOKASC evolutionary state determination is based on the comparison of diverse approaches to the investigation of the frequency-power spectrum. The high level of agreement between the methods is a strong validation of the approaches. Stars for which there is not a consensus view are readily identified. The comparison also facilitates the identification of unusual stars including those that show evidence for very strong coupling between p and g cavities. The comparison between the classification based on the spectroscopic data and asteroseismic data have led to a new value for the statistical uncertainty in APOGEE temperatures. These consensus evolutionary states will be used as an input for methods that derive masses and ages for these stars based on comparison of observables with stellar evolutionary models (‘grid-based modelling’) and as a training set for machine-learning and other data-driven methods of evolutionary state determination.
Constraints on the astrophysical environment of binaries with gravitational-wave observations

Aims. The dynamics of coalescing compact binaries can be affected by the environment in which the systems evolve, leaving detectable signatures in the emitted gravitational signal. In this paper, we investigate the ability of gravitational-wave detectors to constrain the nature of the environment in which compact binaries merge. Methods. We parametrized a variety of environmental effects by modifying the phase of the gravitational signal emitted by black hole and neutron star binaries. We infer the bounds on such effects by current and future generations of interferometers, studying their dependence on the binary’s parameters. Results. We show that the strong dephasing induced by accretion and dynamical friction can constrain the density of the surrounding medium to orders of magnitude below those of accretion disks. Planned detectors, such as LISA or DECIGO, will be able to probe densities typical of those of dark matter.
Solving Ergodic Markov Decision Processes and Perfect Information Zero-sum Stochastic Games by Variance Reduced Deflated Value Iteration

Recently, Sidford, Wang, Wu and Ye (2018) developed an algorithm combining variance reduction techniques with value iteration to solve discounted Markov decision processes. This algorithm has a sublinear complexity when the discount factor is fixed. Here, we extend this approach to mean-payoff problems, including both Markov decision processes and perfect information zero-sum stochastic games. We obtain sublinear complexity bounds, assuming there is a distinguished state which is accessible from all initial states and for all policies. Our method is based on a reduction from the mean payoff problem to the discounted problem by a Doob h-transform, combined with a deflation technique. The complexity analysis of this algorithm uses at the same time the techniques developed by Sidford et al. in the discounted case and non-linear spectral theory techniques (Collatz-Wielandt characterization of the eigenvalue).
Guided Learning Convolution System for DCASE 2019 Task 4

In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We employ a convolutional neural network (CNN) with an embedding-level attention pooling module to solve it. By considering the interference caused by the co-occurrence of multiple events in the unbalanced dataset, we utilize the disentangled feature to raise the performance of the model. To take advantage of the unlabeled data, we adopt Guided Learning for semi-supervised learning. A group of median filters with adaptive window sizes is utilized in the post-processing of output probabilities of the model. We also analyze the effect of the synthetic data on the performance of the model and finally achieve an event-based F-measure of 45.43% on the validation set and an event-based F-measure of 42.7% on the test set. The system we submitted to the challenge achieves the best performance compared to those of other participates.
New Lower Bounds on the Size-Ramsey Number of a Path

We prove that for all graphs with at most $(3.75-o(1))n$ edges there exists a 2-coloring of the edges such that every monochromatic path has order less than $n$. This was previously known to be true for graphs with at most $2.5n-7.5$ edges. We also improve on the best-known lower bounds in the $r$-color case.
On the use of Hadamard expansions in hyperasymptotic evaluation. I. Real variables

We demonstrate that the Hadamard expansion, first introduced for the modified Bessel function I0(x) in 1908, can be used as a computational tool for hyperasymptotic evaluation. Such expansions, which are absolutely convergent series involving the incomplete gamma function, have early terms possessing a rapid asymptotic–like decay (when the variable is large) with late terms going over into a slow algebraic decay. Rearrangement of the slowly convergent tail shows that the Hadamard expansion can be converted into an absolutely convergent series representation whose late terms exhibit a rapid decay comparable with that of the early terms. The theory is illustrated by means of the confluent hypergeometric functions of real variable. Numerical examples involving the modified Bessel functions Iv(x) and Kv(x), when x > 0, are given to show the level of accuracy that can be achieved by this pseudoasymptotic procedure.
Projectice subgroups for grouping

The history of grouping in computer vision stretches a long way back. One strand could be called geometry–based and focuses on shapes with special regularities such as symmetry. From a mathematical point of view, the subparts of such shapes are related by special transformations. The presented work proposes to systematically classify such ‘special’ transformations by studying projective subgroups that come with fixed structures. These are geometric entities such as points or lines that remain fixed under the projectivities in the subgroups. As subgroups have their own invariants, these can then be used to guide the search for the corresponding groupings.
A market model for stochastic implied volatility

In this paper a stochastic volatility model is presented that directly prescribes the stochastic development of the implied Black–Scholes volatilities of a set of given standard options. Thus the model is able to capture the stochastic movements of a full term structure of implied volatilities. The conditions are derived that have to be satisfied to ensure absence of arbitrage in the model and its numerical implementation is discussed.
Pictorial relief

‘Pictorial relief’ is a surface in 3D ‘pictorial space’. It is perceived in single flat pictures and clearly has nothing to do with binocular stereopsis but with the interpretation of image structure in terms of relations in the external world. Ways to perform geometrical measurements in pictorial space are presented and a number of empirical results are reviewed. Applications to the theory of optical instruments aiding human vision are discussed.
Fluctuating paradigm

A prominent paradigm in evolutionary biology over the last ten years has been the role of fluctuating asymmetry in sexual selection. Fluctuating asymmetry in bilaterally paired traits, and in particular sexual traits, has been proposed to be a reliable indicator of individual quality and the focus of selection through sexual competition and attractiveness. We surveyed the literature on fluctuating asymmetry and sexual selection and found a marked chronological decline in the proportion of studies supporting the paradigm through the 1990s. Our data are supported by an independent meta–analysis of the literature. The data conform with the notion of a scientific revolution in which the early phase of a paradigm change is characterized by a publication bias, a less critical approach to research, or both. The patterns we observe in the fluctuating asymmetry literature suggest caution in drawing general conclusions from meta–analyses conducted before revolutions have settled.