We report a novel correlated background in the antineutrino detection using the inverse beta decay reaction. Spontaneous fissions and $(alpha,n)$ reactions in peripheral materials of the antineutrino detector, such as borosilicate glass of photomulti
pliers, produce fast neutrons and prompt gamma rays. If the shielding from the material to the detector target were not thick enough, neutrons and gammas could enter the target volume and mimic antineutrino signals. This paper revisits the yields and energy spectra of neutrons produced in B$(alpha,n)$N and F$(alpha,n)$Na reactions. A Geant4 based simulation has been carried out using a simplified detector geometry for the present generation reactor neutrino experiments. The background rates in these experiments are estimated. If this background was not taken into account, the value of the neutrino mixing angle $sin^22theta_{13}$ would be underestimated. We recommend that Daya Bay, RENO, Double Chooz, and JUNO, carefully examine the masses and radiopurity levels of detector materials that are close to the target and rich in boron and fluorine.
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging. The fastMRI dataset, unique in that it contains large volum
es of raw MRI data, has enabled significant advances in accelerating MRI using deep learning-based reconstruction methods. While the impact of the fastMRI dataset on the field of medical imaging is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond.
Ferrimagnets, which contain the advantages of both ferromagnets (detectable moments) and antiferromagnets (ultrafast spin dynamics), have recently attracted great attention. Here we report the optimization of epitaxial growth of a tetragonal perpendi
cularly magnetized ferrimagnet Mn2Ga on MgO. Electrical transport, magnetic properties and the anomalous Hall effect (AHE) were systematically studied. Furthermore, we successfully integrated high-quality epitaxial ferrimagnetic Mn2Ga thin films onto ferroelectric PMN-PT single crystals with a MgO buffer layer. It was found that the AHE of such a ferrimagnet can be effectively modulated by a small electric field over a large temperature range in a nonvolatile manner. This work thus demonstrates the great potential of ferrimagnets for developing high-density and low-power spintronic devices.
The reionization process is expected to be prolonged by the small-scale absorbers (SSAs) of ionizing photons, which have been seen as Lyman-limit systems in quasar absorption line observations. We use a set of semi-numerical simulations to investigat
e the effects of absorption systems on the reionization process, especially their impacts on the neutral islands during the late epoch of reionization (EoR). Three model are studied, i.e. the extreme case of no-SSA model with a high level of ionizing background, the moderate-SSA model with a relatively high level of ionizing background, and the dense-SSA model with a low level of ionizing background. We find that while the characteristic scale of neutral regions decreases during the early and middle stages of reionization, it stays nearly unchanged at about 10 comoving Mpc during the late stage for the no-SSA and moderate-SSA models. However, in the case of weak ionizing background in the dense-SSA model, the characteristic island scale shows obvious evolution, as large islands break into many small ones that are slowly ionized. The evolutionary behavior of neutral islands during the late EoR thus provides a novel way to constrain the abundance of SSAs. We discuss the 21-cm observation with the upcoming Square Kilometre Array (SKA). The different models can be distinguished by the 21-cm power spectrum measurement, and it is also possible to extract the characteristic island scale from the imaging observation with a proper choice of the 21-cm brightness threshold.
Present-day federated learning (FL) systems deployed over edge networks have to consistently deal with a large number of workers with high degrees of heterogeneity in data and/or computing capabilities. This diverse set of workers necessitates the de
velopment of FL algorithms that allow: (1) flexible worker participation that grants the workers capability to engage in training at will, (2) varying number of local updates (based on computational resources) at each worker along with asynchronous communication with the server, and (3) heterogeneous data across workers. To address these challenges, in this work, we propose a new paradigm in FL called ``Anarchic Federated Learning (AFL). In stark contrast to conventional FL models, each worker in AFL has complete freedom to choose i) when to participate in FL, and ii) the number of local steps to perform in each round based on its current situation (e.g., battery level, communication channels, privacy concerns). However, AFL also introduces significant challenges in algorithmic design because the server needs to handle the chaotic worker behaviors. Toward this end, we propose two Anarchic FedAvg-like algorithms with two-sided learning rates for both cross-device and cross-silo settings, which are named AFedAvg-TSLR-CD and AFedAvg-TSLR-CS, respectively. For general worker information arrival processes, we show that both algorithms retain the highly desirable linear speedup effect in the new AFL paradigm. Moreover, we show that our AFedAvg-TSLR algorithmic framework can be viewed as a {em meta-algorithm} for AFL in the sense that they can utilize advanced FL algorithms as worker- and/or server-side optimizers to achieve enhanced performance under AFL. We validate the proposed algorithms with extensive experiments on real-world datasets.
The mutual information (MI) of Gaussian multi-input multi-output (MIMO) channels has been evaluated by utilizing random matrix theory (RMT) and shown to asymptotically follow Gaussian distribution, where the ergodic mutual information (EMI) converges
to a deterministic quantity. However, with non-Gaussian channels, there is a bias between the EMI and its deterministic equivalent (DE), whose evaluation is not available in the literature. This bias of the EMI is related to the bias for the trace of the resolvent in large RMT. In this paper, we first derive the bias for the trace of the resolvent, which is further extended to compute the bias for the linear spectral statistics (LSS). Then, we apply the above results on non-Gaussian MIMO channels to determine the bias for the EMI. It is also proved that the bias for the EMI is -0.5 times of that for the variance of the MI. Finally, the derived bias is utilized to modify the central limit theory (CLT) and approximate the outage probability. Numerical results show that the modified CLT significantly outperforms the previous results in approximating the distribution of the MI and can accurately determine the outage probability.
Aspect based sentiment analysis (ABSA), exploring sentim- ent polarity of aspect-given sentence, has drawn widespread applications in social media and public opinion. Previously researches typically derive aspect-independent representation by sentenc
e feature generation only depending on text data. In this paper, we propose a Position-Guided Contributive Distribution (PGCD) unit. It achieves a position-dependent contributive pattern and generates aspect-related statement feature for ABSA task. Quoted from Shapley Value, PGCD can gain position-guided contextual contribution and enhance the aspect-based representation. Furthermore, the unit can be used for improving effects on multimodal ABSA task, whose datasets restructured by ourselves. Extensive experiments on both text and text-audio level using dataset (SemEval) show that by applying the proposed unit, the mainstream models advance performance in accuracy and F1 score.
Nowadays advanced image editing tools and technical skills produce tampered images more realistically, which can easily evade image forensic systems and make authenticity verification of images more difficult. To tackle this challenging problem, we i
ntroduce TransForensics, a novel image forgery localization method inspired by Transformers. The two major components in our framework are dense self-attention encoders and dense correction modules. The former is to model global context and all pairwise interactions between local patches at different scales, while the latter is used for improving the transparency of the hidden layers and correcting the outputs from different branches. Compared to previous traditional and deep learning methods, TransForensics not only can capture discriminative representations and obtain high-quality mask predictions but is also not limited by tampering types and patch sequence orders. By conducting experiments on main benchmarks, we show that TransForensics outperforms the stateof-the-art methods by a large margin.
This paper proposes a novel sparse principal component analysis algorithm with self-learning ability for successive modes, where synaptic intelligence is employed to measure the importance of variables and a regularization term is added to preserve t
he learned knowledge of previous modes. Different from traditional multimode monitoring methods, the monitoring model is updated based on the current model and new data when a new mode arrives, thus delivering prominent performance for sequential modes. Besides, the computation and storage resources are saved in the long run, because it is not necessary to retrain the model from scratch frequently and store data from previous modes. More importantly, the model furnishes excellent interpretability owing to the sparsity of parameters. Finally, a numerical case and a practical pulverizing system are adopted to illustrate the effectiveness of the proposed algorithm.
With nontrivial entropy production, first passage process is one of the most common nonequilibrium process in stochastic thermodynamics. Using one dimensional birth and death precess as a model framework, approximated expressions of mean first passag
e time (FPT), mean total number of jumps (TNJ), and their coefficients of variation (CV), are obtained for the case far from equilibrium. Consequently, uncertainty relations for FPT and TNJ are presented. Generally, mean FPT decreases exponentially with entropy production, while mean TNJ decreases exponentially first and then tends to a starting site dependent limit. For forward biased process, the CV of TNJ decreases exponentially with entropy production, while that of FPT decreases exponentially first and then tends to a starting site dependent limit. For backward biased process, both CVs of FPT and TNJ tend to one for large absolute values of entropy production. Related properties about the case of equilibrium are also addressed briefly for comparison.
