Ellerman bombs (EBs) and Ultraviolet (UV) bursts are common brightening phenomena which are usually generated in the low solar atmosphere of emerging flux regions. In this paper, we have investigated the emergence of an initial un-twisted magnetic fl
ux rope based on three-dimensional (3D) magneto-hydrodynamic (MHD) simulations. The EB-like and UV burst-like activities successively appear in the U-shaped part of the undulating magnetic fields triggered by Parker Instability. The EB-like activity starts to appear earlier and lasts for about 80 seconds. Six minutes later, a much hotter UV burst-like event starts to appear and lasts for about 60 seconds. Along the direction vertical to the solar surface, both the EB and UV burst start in the low chromosphere, but the UV burst extends to a higher altitude in the up chromosphere. The regions with apparent temperature increase in the EB and UV burst are both located inside the small twisted flux ropes generated in magnetic reconnection processes, which are consistent with the previous 2D simulations that most hot regions are usually located inside the magnetic islands. However, the twisted flux rope corresponding to the EB is only strongly heated after it floats up to an altitude much higher than the reconnection site during that period. Our analyses show that the EB is heated by the shocks driven by the strong horizontal flows at two sides of the U-shaped magnetic fields. The twisted flux rope corresponding to the UV burst is heated by the driven magnetic reconnection process.
Tissue-agnostic trials enroll patients based on their genetic biomarkers, not tumor type, in an attempt to determine if a new drug can successfully treat disease conditions based on biomarkers. The Bayesian hierarchical model (BHM) provides an attrac
tive approach to design phase II tissue-agnostic trials by allowing information borrowing across multiple disease types. In this article, we elucidate two intrinsic and inevitable issues that may limit the use of BHM to tissue-agnostic trials: sensitivity to the prior specification of the shrinkage parameter and the competing interest among disease types in increasing power and controlling type I error. To address these issues, we propose the optimal BHM (OBHM) approach. With OBHM, we first specify a flexible utility function to quantify the tradeoff between type I error and power across disease type based on the study objectives, and then we select the prior of the shrinkage parameter to optimize the utility function of clinical and regulatory interest. OBMH effectively balances type I and II errors, addresses the sensitivity of the prior selection, and reduces the unwarranted subjectivity in the prior selection. Simulation study shows that the resulting OBHM and its extensions, clustered OBHM (COBHM) and adaptive OBHM (AOBHM), have desirable operating characteristics, outperforming some existing methods with better balanced power and type I error control. Our method provides a systematic, rigorous way to apply BHM and solve the common problem of blindingly using a non-informative inverse-gamma prior (with a large variance) or priors arbitrarily chosen that may lead to pathological statistical properties.
By studying a complex Monge-Amp`ere equation, we present an alternate proof to a recent result of Chu-Lee-Tam concerning the projectivity of a compact Kahler manifold $N^n$ with $Ric_k< 0$ for some integer $k$ with $1<k<n$, and the ampleness of the canonical line bundle $K_N$.
UV bursts and Ellerman bombs are transient brightenings observed in the low solar atmospheres of emerging flux regions. Observations have discovered the cospatial and cotemporal EBs and UV bursts, and their formation mechanisms are still not clear. T
he multi-thermal components with a large temperature span in these events challenge our understanding of magnetic reconnection and heating mechanisms in the low solar atmosphere. We have studied magnetic reconnection between the emerging and background magnetic fields. The initial plasma parameters are based on the C7 atmosphere model. After the current sheet with dense photosphere plasma is emerged to $0.5$ Mm above the solar surface, plasmoid instability appears. The plasmoids collide and coalesce with each other, which makes the plasmas with different densities and temperatures mixed up in the turbulent reconnection region. Therefore, the hot plasmas corresponding to the UV emissions and colder plasmas corresponding to the emissions from other wavelenghts can move together and occur at about the same height. In the meantime, the hot turbulent structures basically concentrate above $0.4$ Mm, whereas the cool plasmas extend to much lower heights to the bottom of the current sheet. These phenomena are consistent with the observations of Chen et al. 2019, ApJL. The synthesized Si IV line profiles are similar to the observed one in UV bursts, the enhanced wing of the line profiles can extend to about $100$ km s$^{-1}$. The differences are significant among the numerical results with different resolutions, which indicate that the realistic magnetic diffusivity is crucial to reveal the fine structures and realistic plasmas heating in these reconnection events. Our results also show that the reconnection heating contributed by ambipolar diffusion in the low chromosphere around the temperature minimum region is not efficient.
Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. For example, a multiple-choice QA model, tested without any input of question, is surprisingly capable to predict the most of correct options.
In this paper, we inspect such spurious capability of QA models using causal inference. We find the crux is the shortcut correlation, e.g., unrobust word alignment between passage and options learned by the models. We propose a novel approach called Counterfactual Variable Control (CVC) that explicitly mitigates any shortcut correlation and preserves the comprehensive reasoning for robust QA. Specifically, we leverage multi-branch architecture that allows us to disentangle robust and shortcut correlations in the training process of QA. We then conduct two novel CVC inference methods (on trained models) to capture the effect of comprehensive reasoning as the final prediction. For evaluation, we conduct extensive experiments using two BERT backbones on both multi-choice and span-extraction QA benchmarks. The results show that our CVC achieves high robustness against a variety of adversarial attacks in QA while maintaining good interpretation ability.
Use of historical data and real-world evidence holds great potential to improve the efficiency of clinical trials. One major challenge is how to effectively borrow information from historical data while maintaining a reasonable type I error. We propo
se the elastic prior approach to address this challenge and achieve dynamic information borrowing. Unlike existing approaches, this method proactively controls the behavior of dynamic information borrowing and type I errors by incorporating a well-known concept of clinically meaningful difference through an elastic function, defined as a monotonic function of a congruence measure between historical data and trial data. The elastic function is constructed to satisfy a set of information-borrowing constraints prespecified by researchers or regulatory agencies, such that the prior will borrow information when historical and trial data are congruent, but refrain from information borrowing when historical and trial data are incongruent. In doing so, the elastic prior improves power and reduces the risk of data dredging and bias. The elastic prior is information borrowing consistent, i.e. asymptotically controls type I and II errors at the nominal values when historical data and trial data are not congruent, a unique characteristics of the elastic prior approach. Our simulation study that evaluates the finite sample characteristic confirms that, compared to existing methods, the elastic prior has better type I error control and yields competitive or higher power.
VQA models may tend to rely on language bias as a shortcut and thus fail to sufficiently learn the multi-modal knowledge from both vision and language. Recent debiasing methods proposed to exclude the language prior during inference. However, they fa
il to disentangle the good language context and bad language bias from the whole. In this paper, we investigate how to mitigate language bias in VQA. Motivated by causal effects, we proposed a novel counterfactual inference framework, which enables us to capture the language bias as the direct causal effect of questions on answers and reduce the language bias by subtracting the direct language effect from the total causal effect. Experiments demonstrate that our proposed counterfactual inference framework 1) is general to various VQA backbones and fusion strategies, 2) achieves competitive performance on the language-bias sensitive VQA-CP dataset while performs robustly on the balanced VQA v2 dataset without any augmented data. The code is available at https://github.com/yuleiniu/cfvqa.
Magnetic reconnection has been intensively studied in fully ionized plasmas. However, plasmas are often partially ionized in astrophysical environments. The interactions between the neutral particles and ionized plasmas might strongly affect the reco
nnection mechanisms. We review magnetic reconnection in partially ionized plasmas in different environments from theoretical, numerical, observational and experimental points of view. We focus on mechanisms which make magnetic reconnection fast enough to compare with observations, especially on the reconnection events in the low solar atmosphere. The heating mechanisms and the related observational evidence of the reconnection process in the partially ionized low solar atmosphere are also discussed. We describe magnetic reconnection in weakly ionized astrophysical environments, including the interstellar medium and protostellar disks. We present recent achievements about fast reconnection in laboratory experiments for partially ionized plasmas.
The recent observations from CMB have imposed a very stringent upper-limit on the tensor/scalar ratio $r$ of inflation models, $r < 0.064$, which indicates that the primordial gravitational waves (PGW), even though possible to be detected, should hav
e a power spectrum of a tiny amplitude. However, current experiments on PGW is ambitious to detect such a signal by improving the accuracy to an even higher level. Whatever their results are, it will give us much information about the early Universe, not only from the astrophysical side but also from the theoretical side, such as model building for the early Universe. In this paper, we are interested in analyzing what kind of inflation models can be favored by future observations, starting with a kind of general action offered by the effective field theory (EFT) approach. We show a general form of $r$ that can be reduced to various models, and more importantly, we show how the accuracy of future observations can put constraints on model parameters by plotting the contours in their parameter spaces.
We show that a current-carrying coherent electron conductor can be treated as effective bosonic energy reservoir involving different types of electron-hole pair excitation. For weak electron-boson coupling, hybrid energy transport between nonequilibr
ium electrons and bosons can be described by a Landauer-like formula. This allows for unified account of a variety of heat transport problems in hybrid electron-boson systems. As applications, we study the non-reciprocal heat transport between electrons and bosons, thermoelectric current from a cold-spot and electronic cooling of the bosons. Our unified framework provides an intuitive way of understanding hybrid energy transport between electrons and bosons. It opens the way of nonequilibrium reservoir engineering for efficient energy control between different quasi-particles in the nanoscale.
