Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining the Circumburst Medium of Gamma-Ray Bursts with X-Ray Afterglows

Long gamma-ray bursts (GRBs) are considered to originate from core collapse of massive stars. It is believed that the afterglow property is determined by the density of the material in the surrounding interstellar medium (ISM). Therefore, the circumburst density can be used to distinguish between an interstellar wind, n(R) ∝ R −k , and a constant-density medium (ISM), n(R)=const . Previous studies with different afterglow samples show that the circumburst medium of GRBs is neither simply supported by an interstellar wind nor completely favored by an ISM. In this work, our new sample consists of 39 GRBs with smoothly onset bump-like features in early X-ray afterglows, in which 20 GRBs have the redshift measurements. By using a smooth broken power-law function to fit the bumps of X-ray light curves, we derive the FWHM as the feature width (ω), as well as the rise and decay timescales of the bumps (T r and T d ). The correlations between the timescales of X-ray bumps are similar to those found previously in the optical afterglows. Based on the fireball forward shock model of the thin shell case, we obtain the distribution of the electron spectral index p and further constrain the medium density distribution index k. The new inferred k is found to be concentrated at 1.0, with a range from 0.2 to 1.8. This finding is consistent with previous studies. The conclusion of our detailed investigation for X-ray afterglows suggests that the ambient medium of the selected GRBs is not homogeneous, i.e., neither ISM nor the typical interstellar wind.
Direct and indirect contributions of molecular genetics to farm animal welfare: a review

Abstract Since domestication, farm animals have played a key role to increase the prosperity of humankind, while animal welfare (AW) is debated even today. This paper aims to comprehensively review the contributions of developing molecular genetics to farm animal welfare (FAW) and to raise awareness among both scientists and farmers about AW. Welfare is a complex trait affected by genetic structure and environmental factors. Therefore, the best welfare status can be achieved not only to enhance environmental factors such as management and feeding practices, but also the genetic structure of animals must be improved. In this regard, advances in molecular genetics have made great contributions to improve the genetic structure of farm animals, which has increased AW. Today, by sequencing and/or molecular markers, genetic diseases may be detected and eliminated in local herds. Additionally, genes related to diseases or adaptations are investigated by molecular techniques, and the frequencies of desired genotypes are increased in farm animals to keep welfare at an optimized level. Furthermore, stress on animals can be reduced with DNA extraction from stool and feather samples which reduces physical contact between animals and veterinarians. Together with molecular genetics, advances in genome editing tools and biotechnology are promising to improve FAW in the future.
A non-monotone smoothing Newton algorithm for solving the system of generalized absolute value equations

The system of generalized absolute value equations (GAVE) has attracted more and more attention in the optimization community. In this paper, by introducing a smoothing function, we develop a smoothing Newton algorithm with non-monotone line search to solve the GAVE. We show that the non-monotone algorithm is globally and locally quadratically convergent under a weaker assumption than those given in most existing algorithms for solving the GAVE. Numerical results are given to demonstrate the viability and efficiency of the approach.
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes

In this paper, we introduce the online and streaming MAP inference and learning problems for Non-symmetric Determinantal Point Processes (NDPPs) where data points arrive in an arbitrary order and the algorithms are constrained to use a single-pass over the data as well as sub-linear memory. The online setting has an additional requirement of maintaining a valid solution at any point in time. For solving these new problems, we propose algorithms with theoretical guarantees, evaluate them on several real-world datasets, and show that they give comparable performance to state-of-the-art offline algorithms that store the entire data in memory and take multiple passes over it.
Stability Analysis of a Feedback-linearization-based Controller with Saturation: A Tilt Vehicle with the Penguin-inspired Gait Plan

Saturations in control signal can challenge the stability proof of a feedback-linearization-based controller, even leading the system unstable [1]. Thus, several approaches are established to avoid reaching the saturation bound [2,3]. Meanwhile, to help design the controller for a quad-tilt-rotor, [1] modeled a tilt vehicle with implementing the feedback-linearization-based controllers. In this article, we provide a gait plan for this tilt vehicle and control it utilizing the feedback linearization. Since saturations exist in the control signals, we study the stability based on Lyapunov theory.
Experimental Studies of Hydrodynamic Properties and Screening of Riser Fairing Concepts for Deep Water Applications

The VIV oscillations of marine risers are known to increase drag, and lead to structural fatigue. One proven method of suppressing this vibration is the use of fairings and strakes. These coverings essentially modify the flow along the cylinder, tripping the production of Karman vortices so that they act less coherently or far enough downstream so they interact less with the body.The Norwegian Deepwater Programme (NDP) has conducted a project with the objective to develop and qualify effective low drag fairing concepts with respect to VIV mitigation and galloping. Furthermore, emphasis is put on easy handling and installation.This paper describes the work and findings in an early phase of the development. This includes small scale model test campaigns. In addition to the bare riser for reference, the behaviour and performance of a total of 10 different fairing concepts are evaluated. Free oscillation tests are performed in a towing tank, where 2D fairings were tested in a pendulum set-up. The set-up enables free vibrations in up to 3 DOF (in-line and cross-flow vibrations and yaw). Fix tests with the purpose of establishing hydrodynamic coefficients for the various fairings have been performed in a large cavitation tunnel.Clear differences in performance have been noticed; particular for drag and galloping responses. Based on the results from the 2D tests, a screening of the fairing designs has been performed and the findings have set the course for further development of the most promising candidates for real life applications.Copyright © 2015 by ASME
Artificial neural networks for online error detection

Hardware reliability is adversely affected by the downscaling of semiconductor devices and the scale-out of systems necessitated by modern applications. Apart from crashes, this unreliability often manifests as silent data corruptions (SDCs), affecting application output. Therefore, we need low-cost and low-human-effort solutions to reduce the incidence rate and the effects of SDCs on the quality of application outputs. We propose Artificial Neural Networks (ANNs) as an effective mechanism for online error detection. We train ANNs using software fault injection. We find that the average overhead of our approach, followed by a costly error correction by re-execution, is 6.45% in terms of CPU cycles. We also report that ANNs discover 94.85% of faults thereby resulting in minimal output quality degradation. To validate our approach we overclock ARM Cortex A53 CPUs, execute benchmarks on them and record the program outputs. ANNs prove to be an efficient error detection mechanism, better than a state of the art approximate error detection mechanism (Topaz), both in terms of performance (12.81% CPU overhead) and quality of application output (94.11% detection coverage).
Performance of Reinforced Concrete Columns Impacted by Water-Borne Shipping Containers

Numerous buildings have been severely damaged during hurricanes and tsunamis due to impact of water-borne massive objects such as barges, boats, heavy vehicles, and shipping containers. Failure of columns which are the primary load-carrying members of a building due to such impact loads, could significantly affect the performance of a building. This paper reports the results of the numerical investigation of reinforced concrete (RC) columns subjected to impact loads from water-borne shipping containers which have been dislodged during tsunamis. Existing methods to estimate peak impact force on structures due to tsunami water-borne massive objects are reviewed and their limitations are discussed. High-fidelity numerical simulation of the shipping container-column impact is carried out using the nonlinear finite element code LS-DYNA. The container model includes all significant structural elements and associated details. Typical RC columns having square and circular cross-sections are considered to investigate the damage to the container and column, and it is shown that the shape of the column cross-section significantly affects the peak impact force. The effects of container specific parameters such as velocity, height above ground level, and container orientation on peak impact force are studied. A series of RC columns is considered to investigate effects of transverse reinforcement ratio, longitudinal reinforcement ratio, column section size, and axial load ratio on peak impact force; and formulae to estimate the peak impact force on RC columns are obtained. Damage to the column is presented in terms of the damage index characterized by degradation of axial load carrying capacity and results will be useful for improving impact resistance of RC columns in buildings located in tsunami inundation zones.
Radar Aided Proactive Blockage Prediction in Real-World Millimeter Wave Systems

Millimeter wave (mmWave) and sub-terahertz communication systems rely mainly on line-of-sight (LOS) links between the transmitters and receivers. The sensitivity of these high-frequency LOS links to blockages, however, challenges the reliability and latency requirements of these communication networks. In this paper, we propose to utilize radar sensors to provide sensing information about the surrounding environment and moving objects, and leverage this information to proactively predict future link blockages before they happen. This is motivated by the low cost of the radar sensors, their ability to efficiently capture important features such as range, angle, velocity of the moving scatterers (candidate blockages), and their capability to capture radar frames at relatively high speed. We formulate the radar-aided proactive blockage prediction problem and develop a solution with deep neural networks. To accurately evaluate the proposed solutions, we build a large-scale real-world dataset, based on the DeepSense framework, gathering co-existing radar and mmWave communication measurements of more than 10 thousand data points and various blockage objects (vehicles, bikes, humans). The evaluation results, based on this dataset, show that the proposed approaches can predicted future blockages 1 second before they happen with more than 90% F1 score (and more than 90% accuracy). These results, among others, highlight a promising solution for blockage prediction and reliability enhancement in future wireless mmWave and terahertz communication systems.
Structural theorems on the distance sets over finite fields

Abstract Let F q \mathbb{F}_{q} be a finite field of order 𝑞. Iosevich and Rudnev [Erdős distance problem in vector spaces over finite fields, Trans. Amer. Math. Soc. 359 (2007), 12, 6127–6142] proved that, for any set A ⊂ F q d A\subset\mathbb{F}_{q}^{d} , if | A | ≫ q d + 1 2 \lvert A\rvert\gg q^{\frac{d+1}{2}} , then the distance set Δ ⁢ ( A ) \Delta(A) contains a positive proportion of all distances. Although this result is sharp in odd dimensions, it is conjectured that the right exponent should be d 2 \frac{d}{2} in even dimensions. During the last 15 years, only some improvements have been made in two dimensions, and the conjecture is still wide open in higher dimensions. To fill the gap, we need to understand more about the structures of the distance sets; the main purpose of this paper is to provide some structural theorems on the distribution of square and non-square distances.
Why MDAC? A Multi-domain Activation Function

In this study, a novel, general and ingenious activation function termed MDAC is proposed to surmount the troubles of gradient vanishing and non-differentiable existence. MDAC approximately inherits the properties of exponential activation function (such as Tanh family) and piecewise linear activation function (such as ReLU family). Specifically, in the positive region, the adaptive linear structure is designed to respond to various domain distributions. In the negative region, the combination of exponent and linearity is considered to conquer the obstacle of gradient vanishing. Furthermore, the non-differentiable existence is eliminated by smooth approximation. Experiments show that MDAC improves performance on both classical models and pre-training optimization models in six domain datasets by simply changing the activation function, which indicates MDAC's effectiveness and progressiveness. MDAC is superior to other prevalent activation functions in robustness and generalization, and can reflect excellent activation performance in multiple domains.
A Random Active Set Method for Strictly Convex Quadratic Problem with Simple Bounds

Active set method aims to find the correct active set of the optimal solution and it is a powerful method for solving strictly convex quadratic problem with bound constraints. To guarantee the finite step convergence, the existing active set methods all need strict conditions or some additional strategies, which greatly affect the efficiency of the algorithm. In this paper, we propose a random active set method which introduces randomness in the update of active set. We prove that it can converge in finite iterations with probability one without any conditions on the problem or any additional strategies. Numerical results show that the algorithm obtains the correct active set within a few iterations, and compared with the existing methods, it has better robustness and efficiency.
HSV-Encephalitis Resembling Acute Cerebral Infarction in a Patient With Atrial Fibrillation

Introduction: Herpes simplex virus-1 (HSV-1) encephalitis, the most common and potentially life-threatening type of encephalitis, may rarely present as a stroke mimic. Prompt diagnosis is of paramount importance for the timely initiation of antiviral treatment and to avert intravenous thrombolysis. Case Report: A 60-year-old man with a history of lone paroxysmal atrial fibrillation without prior antithrombotic treatment was admitted due to mild gait unsteadiness and intermittent dysarthria of acute onset. On admission, the patient was afebrile, whereas neurological examination revealed only a mild pronator drift on the left. Brain magnetic resonance imaging (MRI) showed an extensive right temporo-occipital and thalamic lesion with restricted diffusion and 3 small-sized hemorrhagic foci. Brain MR-angiography did not show large vessel stenosis or occlusion. On the basis of careful observation and the depiction of several imaging discrepancies, such as early vasogenic edema and hemorrhagic transformation, as well as uncus involvement, but also the lack of significant neurological deficits despite the size of the brain lesion we suspected viral encephalitis which was confirmed by the detection of HSV-1 DNA in the cerebrospinal fluid. Conclusion: HSV-encephalitis might occasionally result in the development of unilateral brain MRI lesions with extensive cytotoxic edema, resembling an acute ischemic stroke. Therefore, HSV-encephalitis must be considered in the differential diagnosis of acute ischemic stroke with atypical presentation. The presence of a significant dissociation between the brain MRI lesion volume and the neurological deficits, as well as certain brain MRI imaging discrepancies might serve as “red flags” to extend the diagnostic workup.
Cofilin regulates actin network homeostasis and microvilli length in mouse oocytes

How multiple actin networks coexist in a common cytoplasm, while competing for a shared pool of monomers, is still an ongoing question. This is exemplified by meiotic maturation in the mouse oocyte, which relies on the dynamic remodeling of distinct cortical and cytoplasmic F-actin networks. Here we show that the conserved actin-depolymerizing factor cofilin is activated in a switch-like manner at meiosis resumption from prophase arrest. Interfering with cofilin activation during maturation resulted in widespread microvilli elongation, while cytoplasmic F-actin was depleted, leading to defects in spindle migration and polar body extrusion. In contrast, cofilin inactivation in metaphase II-arrested oocytes resulted in a shutdown of F-actin dynamics, along with a dramatic overgrowth of the polarized actin cap. However, inhibition of the Arp2/3 complex to promote actin cap disassembly elicited ectopic microvilli outgrowth in the polarized cortex. These data establish cofilin as a key player in actin network homeostasis in oocytes, and reveal that microvilli can act as a sink for monomers upon disassembly of a competing network.
Internet Worm Modeling and Analysis in IPv6 Networks

With the development of IPv6, it is taken as a natural cover to defend against Internet worm due to its huge address space. We demonstrate that an intelligent worm can compromise the IPv6 network according to using adaptive means. Based on the analysis of the scanning strategies in IPv6 network and correlative typical propagation technologies, a general worm model named Next Generation Internet worm model (NGIWM) is established for analyzing the propagation of IPv6 worm which is made up of three layers. The three layers have the function to accelerate the spread of NGIWM, and make NGIWM has the capability to form large scale of prevalence. The result indicates the rationality of scanning strategies and the effectiveness of the propagation compared to IPv4 network worm. The simulation shows that the implementation of NGIWM has the potential to become a major threat to IPv6 network. The performance of the worm depends heavily on domain name generator algorithm and scanning strategies in local network. We also analyze the effect of experiment parameters on the model in the simulation part. In the future, detailed implementation of the proposed model and more defense mechanisms are to be studied.
The Hele–Shaw flow as the sharp interface limit of the Cahn–Hilliard equation with disparate mobilities

Abstract In this paper, we study the sharp interface limit for solutions of the Cahn–Hilliard equation with disparate mobilities. This means that the mobility function degenerates in one of the two energetically favorable configurations, suppressing the diffusion in that phase. First, we construct suitable weak solutions to this Cahn–Hilliard equation. Second, we prove precompactness of these solutions under natural assumptions on the initial data. Third, under an additional energy convergence assumption, we show that the sharp interface limit is a distributional solution to the Hele–Shaw flow with optimal energy-dissipation rate.
Smooth Toric Quiver Varieties

We study smoothness of toric quiver varieties. When a quiver Q is deﬁned with the identity dimension vector, the corresponding quiver variety is also a toric variety. So it has both fan representation and quiver representation. We work only on quivers with canonical weight and we classify smooth such toric quiver varieties. We show that a variety corresponding to a quiver with the identity dimension vector and the canonical weight is smooth if and only if it is a product of projective spaces or their blowups.
Action based Network for Conversation Question Reformulation

Conversation question answering requires the ability to interpret a question correctly. Current models, however, are still unsatisfactory due to the difficulty of understanding the co-references and ellipsis in daily conversation. Even though generative approaches achieved remarkable progress, they are still trapped by semantic incompleteness. This paper presents an action-based approach to recover the complete expression of the question. Specifically, we first locate the positions of co-reference or ellipsis in the question while assigning the corresponding action to each candidate span. We then look for matching phrases related to the candidate clues in the conversation context. Finally, according to the predicted action, we decide whether to replace the co-reference or supplement the ellipsis with the matched information. We demonstrate the effectiveness of our method on both English and Chinese utterance rewrite tasks, improving the state-of-the-art EM (exact match) by 3.9\% and ROUGE-L by 1.0\% respectively on the Restoration-200K dataset.
Dynamics of Dirac concentrations in the evolution of quantitative alleles with sexual reproduction

A proper understanding of the links between varying gene expression levels and complex trait adaptation is still lacking, despite recent advances in sequencing techniques leading to new insights on their importance in some evolutionary processes. This calls for extensions of the continuum-of-alleles framework first introduced by Kimura (1965 Proc. Natl Acad. Sci. USA 54 731–36) that bypass the classical Gaussian approximation. Here, we propose a novel mathematical framework to study the evolutionary dynamics of quantitative alleles for sexually reproducing populations under natural selection and competition through an integro-differential equation. It involves a new reproduction operator which is nonlinear and nonlocal. This reproduction operator is different from the infinitesimal operator used in other studies with sexual reproduction because of different underlying genetic structures. In an asymptotic regime where initially the population has a small phenotypic variance, we analyse the long-term dynamics of the phenotypic distributions according to the methodology of small variance (Diekmann et al 2005 Theor. Popul. Biol. 67 257–71). In particular, we prove that the reproduction operator strains the limit distribution to be a product measure. Under some assumptions on the limit equation, we show that the population remains monomorphic, that is the phenotypic distribution remains concentrated as a moving Dirac mass. Moreover, in the case of a monomorphic distribution, we derive a canonical equation describing the dynamics of the dominant alleles.
The PAT Model of Population Dynamics

Abstract. We introduce a population-age-time (PAT) model which describes the temporal evolution of the population distribution in age. The surprising result is that the qualitative nature of the population distribution dynamics is robust with respect to the birth rate and death rate distributions in age, and initial conditions. When the number of children born per woman is 2, the population distribution approaches an asymptotically steady state of a kink shape; thus the total population approaches a constant. When the number of children born per woman is greater than 2, the total population increases without bound; and when the number of children born per woman is less than 2, the total population decreases to zero.