Miracle-dz/UniFAR-FTUs huggingface dataset:
Rethinking the Value of Gazetteer in Chinese Named Entity Recognition

Gazetteer is widely used in Chinese named entity recognition (NER) to enhance span boundary detection and type classification. However, to further understand the generalizability and effectiveness of gazetteers, the NLP community still lacks a systematic analysis of the gazetteer-enhanced NER model. In this paper, we first re-examine the effectiveness several common practices of the gazetteer-enhanced NER models and carry out a series of detailed analysis to evaluate the relationship between the model performance and the gazetteer characteristics, which can guide us to build a more suitable gazetteer. The findings of this paper are as follows: (1) the gazetteer improves most of the situations that the traditional NER model datasets are difficult to learn. (2) the performance of model greatly benefits from the high-quality pre-trained lexeme embeddings. (3) a good gazetteer should cover more entities that can be matched in both the training set and testing set.
-Synthesis-Based Generalized Robust Framework for Grid-Following and Grid-Forming Inverters

Grid-following and grid-forming inverters are integral components of microgrids and for integration of renewable energy sources with the grid. For grid following (GFL) inverters, which need to emulate controllable current sources, a significant challenge is to address the large uncertainty of the grid impedance. For grid forming (GFM) inverters, which need to emulate a controllable voltage source, large uncertainty due to varying loads has to be addressed. This article presents a generalized control framework by leveraging the voltage-current duality in the plant dynamic model of GFL and GFM inverters. The modeling of uncertainties is also generalized under the control framework by quantifying the uncertainties in grid impedance parameters and the uncertainties in equivalent loading parameters for GFL and GFM inverters, respectively. Based on the generalized control framework, a <inline-formula><tex-math notation="LaTeX">$\boldsymbol{\mu}$</tex-math></inline-formula>-synthesis-based robust control design methodology is proposed for both GFL and GFM inverters. The control objectives, while designing the proposed optimal controllers, are reference tracking, disturbance rejection, and harmonic compensation capability with <inline-formula><tex-math notation="LaTeX">${i})$</tex-math></inline-formula> sufficient <inline-formula><tex-math notation="LaTeX">${LCL}$</tex-math></inline-formula> resonance damping under large variations of grid impedance uncertainty for GFL inverters and <inline-formula><tex-math notation="LaTeX">${ii})$</tex-math></inline-formula> with enhanced dynamic response under large variations of equivalent loading uncertainty for GFM inverters. A combined system-in-the-loop, controller hardware-in-the-loop, and power hardware-in-the-loop based experimental validation on <inline-formula><tex-math notation="LaTeX">$\mathbf {10}$</tex-math></inline-formula>-kVA microgrid system with two physical inverter systems is conducted in order to evaluate the efficacy and viability of the proposed controllers.
SocChain: Blockchain with Swift Proportional Governance for Bribery Mitigation

Blockchain governance is paramount to lead securely a large group of users towards the same goal without disputes about the legit-imacy of a blockchain instance over another. As of today, there is no efficient way of protecting this governance against an oligarchy. This paper aims to offer a new dimension to the security of blockchains by defining the swift proportional governance problem. This problem is to rapidly elect governance users that proportionally represent voters without the risk of dictatorship. We then design and implement an open permissioned blockchain called SocChain (Social Choice Blockchain) that mitigates bribery by building upon results in social choice theory. We deploy SocChain and evaluate our new multi-winner election DApp running on top of it. Our results indicate that, using our DApp, 150 voters can elect a proportionally representative committee of 150 members within 5 minutes. Hence we show that SocChain can elect as many representatives as members in various global organizations.
5/6G: Networks of the Future or Defuturing Networks?

—Energy efﬁciency is at the core of sustainability solutions for 5/6G networks. We argue this is a too narrow perspective on sustainability, as it ignores the effects of the increased trafﬁc demand these networks stimulate and the need for additional equipment that this demand requires. The hope is that techniques to reduce the network’s energy consumption in operation will be able to compensate for increases in trafﬁc demand. However, we argue that there are more challenges than just reducing the energy that the network requires to function and that it is not clear whether higher energy efﬁciency will be able to cope with increasing demand. The need for more equipment related to deployment of 5/6G networks (both at the user end, network level and the cloud and grid) may result in im- portant environmental impacts related to: i) increased pressures on material extraction, which imply socio-environmental conﬂicts, ecosystem destruction and displacement, ii) more manufacturing and shipment, with effects on greenhouse gas emissions and pollution; iii) increased disposal complexities and challenges to recycle components of such equipments. By extending our view on sustainability to include the aforementioned often ignored implications, we are able to identify design requirements and technical pillars of 5/6G networks that need to be rethought. We also devise new paths forward to address these challenges. We argue that it is crucial to think of alternative applications and requirements that aim to serve user demands explicitly, instead of incessantly creating new needs. We also claim that acknowledging material limitations in the production of new hardware is critical, promoting retroﬁtting and modular design in future network development. The conclusions of this article show that it is time to start rethinking the course of mobile network development in order to align it to current environmental objectives to tackle the climate emergency the world is experiencing.
Integrated mass loss of evolved stars in M4 using asteroseismology

Mass loss remains a major uncertainty in stellar modelling. In low-mass stars, mass loss is most significant on the red giant branch (RGB), and will impact the star’s evolutionary path and final stellar remnant. Directly measuring the mass difference of stars in various phases of evolution represents one of the best ways to quantify integrated mass loss. Globular clusters (GCs) are ideal objects for this. M4 is currently the only GC for which asteroseismic data exists for stars in multiple phases of evolution. Using K2 photometry, we report asteroseismic masses for 75 red giants in M4, the largest seismic sample in a GC to date. We find an integrated RGB mass loss of $\Delta \overline{M} = 0.17 \pm 0.01 ~\mathrm{M}_{\odot }$, equivalent to a Reimers’ mass-loss coefficient of ηR = 0.39. Our results for initial mass, horizontal branch mass, ηR, and integrated RGB mass loss show remarkable agreement with previous studies, but with higher precision using asteroseismology. We also report the first detections of solar-like oscillations in early asymptotic giant branch (EAGB) stars in GCs. We find an average mass of $\overline{M}_{\text{EAGB}}=0.54 \pm 0.01 ~\mathrm{M}_{\odot }$, significantly lower than predicted by models. This suggests larger-than-expected mass loss on the horizontal branch. Alternatively, it could indicate unknown systematics in seismic scaling relations for the EAGB. We discover a tentative mass bi-modality in the RGB sample, possibly due to the multiple populations. In our red horizontal branch sample, we find a mass distribution consistent with a single value. We emphasise the importance of seismic studies of GCs since they could potentially resolve major uncertainties in stellar theory.
Knowing Earlier What Right Means to You: A Comprehensive VQA Dataset for Grounding Relative Directions via Multi-task Learning

Spatial reasoning poses a particular challenge for intelligent agents and is at the same time a prerequisite for their successful interaction and communication in the physical world. One such reasoning task is to describe the position of a target object with respect to the intrinsic orientation of some reference object via relative directions. In this paper, we introduce GRiD-A-3D, a novel diagnostic visual question-answering (VQA) dataset based on abstract objects. Our dataset allows for a fine-grained analysis of end-to-end VQA models' capabilities to ground relative directions. At the same time, model training requires considerably fewer computational resources compared with existing datasets, yet yields a comparable or even higher performance. Along with the new dataset, we provide a thorough evaluation based on two widely known end-to-end VQA architectures trained on GRiD-A-3D. We demonstrate that within a few epochs, the subtasks required to reason over relative directions, such as recognizing and locating objects in a scene and estimating their intrinsic orientations, are learned in the order in which relative directions are intuitively processed.
Cosmology in $f(Q)$ gravity: A unified dynamical system analysis at background and perturbation levels

Motivated by the fact that cosmological models based on $f(Q)$ gravity are very efficient in fitting observational datasets at both background and perturbation levels, we perform a combined dynamical system analysis of both background and perturbation equations in order to examine the validity of this result through an independent method. We examine two studied $f(Q)$ models of the literature, namely the power-law and the exponential ones. For both cases, we obtain a matter-dominated saddle point characterized by the correct growth rate of matter perturbations, followed by the transition to a stable dark-energy dominated accelerated universe in which matter perturbations remain constant. Furthermore, analyzing the behavior of $f \sigma_8$, we find that the models fit observational data successfully, obtaining a behavior similar to that of $\Lambda$CDM scenario, although the exponential model does not possess the latter as a particular limit. Hence, through the independent approach of dynamical systems, we do verify the results of observational confrontation, namely that $f(Q)$ gravity can be considered as a very promising alternative to the $\Lambda$CDM concordance model.
Nodal domain theorem of signed hypergraphs

In 2001, Davies, Gladwell, Leydold, and Stadler proved discrete nodal domain theorems for eigenfunctions of generalized Laplacians. In 2019, Jost and Mulas generalized the normalized combinatorial Laplace operator of graphs to signed hypergraphs. In this paper, we establish nodal domain theorems for the normalized combinatorial Laplace operator in signed hypergraphs. We also obtain a lower bound estimates for the number of strong nodal domains.
A truncated inner disc in the Seyfert 1 galaxy WKK 4438

Understanding if and when the accretion disc extends down to the innermost stable circular orbit (ISCO) is important since it is the fundamental assumption behind measuring black hole spin. Here, we examine the 2013 and 2018 NuSTAR and Swift data (0.5 − 50 keV) of the narrow-line Seyfert 1 galaxy, WKK 4438. The X-ray emission can be fitted well with models depicting a corona and blurred reflection originating from a disc around a low spin (a* ≈ 0) black hole. However, such models result in unconventional values for some of the parameters (e.g. inverted emissivity profile and high coronal height). Alternatively, equally good fits can be achieved if the disc is truncated at ∼10 rg and the black hole is spinning at the Thorne limit (a* = 0.998). In these cases, the model parameters are consistent with the interpretation that the corona is centrally located close to the black hole and illuminating the disc at a larger distance.
A Two-Step Quasi-Newton Identification Algorithm for Stochastic Systems with Saturated Observations

This paper investigates the identiﬁcation and prediction problems for stochastic dynamical systems with saturated observations, which arise from various ﬁelds in engineering and social systems, but still lack comprehensive theoretical studies up to now. The main contributions of this paper are: (i) To introduce a two-step Quasi-Newton (TSQN) identiﬁcation algorithm which is applicable to a typical class of nonlinear stochastic systems with outputs observed under possibly varying saturations. (ii) To establish the convergence of both the parameter estimators and adaptive predictors and to prove the asymptotic normality, under a weakest possible non-persistent excitation (PE) condition, which can be applied to stochastic feedback systems with general non-stationary and correlated system signals or data. (iii) To establish probabilistic estimation error bounds for any given ﬁnite number of data, by using either martingale inequalities or Monte Carlo experiments. A numerical example is also provided to illustrated the performance of our identiﬁcation algorithm. the nonlinear Quasi-Newton estimation algorithm, we have proposed a new two-step TSQN algorithm to estimate the unknown parameters. It is shown that the strong consistency and the
Free-Space Point-to-Multiplepoint Optical Frequency Transfer With Lens Assisted Integrated Beam Steering

We report on the realization of high-performance silica integrated 2-D lens assisted beam steering (LABS) arrays along with the first-of-their-kind point-to-multiplepoint optical frequency transfer. The LABS equips with <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> antennas and has the capability to produce arbitrary number of output beams with different output angles with simple control complexity. We demonstrate that the LABS has 16 scanning angles, which can support the access capability for the maximum of simultaneous 16 user nodes. The coaxial configuration for transmitting and receiving the light as a monolithic transceiver allows us to reduce the out-of-loop phase noise significantly. Finally, the LABS-based nonblocking point-to-multiplepoint indoor free-space optical frequency transfer links with 24- and 50-m free-space links are shown. After being compensated for the free-space link up to 50 m, the fractional frequency instability of <inline-formula> <tex-math notation="LaTeX">$4.5\,\,\times 10^{-17}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$7.7\,\,\times 10^{-20}$ </tex-math></inline-formula> at the averaging time of 1 and 20000 s, respectively, can be achieved. The present work proves the potential application of 2-D LABS in free-space optical time–frequency transfer and provides a guidance for developing a chip-scale optical time–frequency transfer system.
Careful seeding for the k-medoids algorithm with incremental k++ cluster construction

The k -medoids algorithm is a popular variant of the k -means algorithm and widely used in pattern recognition and machine learning. A main drawback of the k -medoids algorithm is that it can be trapped in local optima. An improved k -medoids algorithm (INCKM) was recently proposed to overcome this drawback, based on constructing a candidate medoids subset with a parameter choosing procedure, but it may fail when dealing with imbalanced datasets. In this paper, we propose a novel incremental k -medoids algorithm (INCKPP) which dynamically increases the number of clusters from 2 to k through a nonparametric and stochastic k -means++ search procedure. Our algorithm can overcome the parameter selection problem in the improved k -medoids algorithm, improve the clustering performance, and deal with imbalanced datasets very well. But our algorithm has a weakness in computation eﬃciency. To address this issue, we propose a fast INCKPP algorithm (called INCKPP sample ) which preserves the computational eﬃciency of the simple and fast k -medoids algorithm with an improved clustering performance. The proposed algorithm is compared with three state-of-the-art algorithms: the improved k -medoids algorithm (INCKM), the simple and fast k -medoids algorithm (FKM) and the k -means++ algorithm (KPP). Extensive experiments on both synthetic and real world datasets including imbalanced datasets illustrate the eﬀectiveness of the proposed algorithm.
Is the U-NET Directional-Relationship Aware?

CNNs are often assumed to be capable of using contextual information about distinct objects (such as their directional relations) inside their receptive field. However, the nature and limits of this capacity has never been explored in full. We explore a specific type of relationship – directional – using a standard U-Net trained to optimize a cross-entropy loss function for segmentation. We train this network on a pretext segmentation task requiring directional relation reasoning for success and state that, with enough data and a sufficiently large receptive field, it succeeds to learn the proposed task. We further explore what the network has learned by analysing scenarios where the directional relationships are perturbed, and show that the network has learned to reason using these relationships.
Multi-Contrast MRI Segmentation Trained on Synthetic Images

In our comprehensive experiments and evaluations, we show that it is possible to generate multiple contrast (even all synthetically) and use synthetically generated images to train an image segmentation engine. We showed promising segmentation results tested on real multi-contrast MRI scans when delineating muscle, fat, bone and bone marrow, all trained on synthetic images. Based on synthetic image training, our segmentation results were as high as 93.91%, 94.11%, 91.63%, 95.33%, for muscle, fat, bone, and bone marrow delineation, respectively. Results were not significantly different from the ones obtained when real images were used for segmentation training: 94.68%, 94.67%, 95.91%, and 96.82%, respectively. Clinical relevance— Synthetically generated images could potentially be used in large-scale training of deep networks for segmentation purpose. Small data set problem of many clinical imaging problems can potentially be addressed with the proposed algorithm.
Mean-field limits for non-linear Hawkes processes with inhibition on a Erdős-Rényi-graph

We study a multivariate, non-linear Hawkes process $Z^N$ on a $q$-Erd\H{o}s-R\'{e}nyi-graph with $N$ nodes. Each vertex is either excitatory (probability $p$) or inhibitory (probability $1-p$). If $p\neq\tfrac12$, we take the mean-field limit of $Z^N$, leading to a multivariate point process $\bar Z$. We rescale the interaction intensity by $N$ and find that the limit intensity process solves a deterministic convolution equation and all components of $\bar Z$ are independent. The fluctuations around the mean field limit converge to the solution of a stochastic convolution equation. In the critical case, $p=\tfrac12$, we rescale by $N^{1/2}$ and discuss difficulties, both heuristically and numerically.
Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation

Asset allocation using reinforcement learning has advantages such as flexibility in goal setting and utilization of various information. However, existing asset allocation methods do not consider the following viewpoints in solving the asset allocation problem. First, State design without considering portfolio management and financial market characteristics. Second, Model Overfitting. Third, Model training design without considering the statistical structure of financial time series data. To solve the problem of the existing asset allocation method using reinforcement learning, we propose a new reinforcement learning asset allocation method. First, the state of the portfolio managed by the model is considered as the state of the reinforcement learning agent. Second, Monte Carlo simulation data are used to increase training data complexity to prevent model overfitting. These data can have different patterns, which can increase the complexity of the data. Third, Monte Carlo simulation data are created considering various statistical structures of financial markets. We define the statistical structure of the financial market as the correlation matrix of the assets constituting the financial market. We show experimentally that our method outperforms the benchmark at several test intervals.
A concurrent program logic with a future and history

Verifying fine-grained optimistic concurrent programs remains an open problem. Modern program logics provide abstraction mechanisms and compositional reasoning principles to deal with the inherent complexity. However, their use is mostly confined to pencil-and-paper or mechanized proofs. We devise a new separation logic geared towards the lacking automation. While local reasoning is known to be crucial for automation, we are the first to show how to retain this locality for (i) reasoning about inductive properties without the need for ghost code, and (ii) reasoning about computation histories in hindsight. We implemented our new logic in a tool and used it to automatically verify challenging concurrent search structures that require inductive properties and hindsight reasoning, such as the Harris set.
Difference in Euclidean Norm Can Cause Semantic Divergence in Batch Normalization

In this paper, we show that the difference in Euclidean norm of samples can make a contribution to the semantic divergence and even confusion, after the spatial translation and scaling transformation in batch normalization. To address this issue, we propose an intuitive but effective method to equalize the Euclidean norms of sample vectors. Concretely, we l2-normalize each sample vector before batch normalization, and therefore the sample vectors are of the same magnitude. Since the proposed method combines the l2 normalization and batch normalization, we name our method as L2BN. The L2BN can strengthen the compactness of intraclass features and enlarge the discrepancy of inter-class features. In addition, it can help the gradient converge to a stable scale. TheL2BN is easy to implement and can exert its effect without any additional parameters and hyper-parameters. Therefore, it can be used as a basic normalization method for neural networks. We evaluate the effectiveness ofL2BN through extensive experiments with various models on image classification and acoustic scene classification tasks. The experimental results demonstrate that the L2BN is able to boost the generalization ability of various neural network models and achieve considerable performance improvements.
BFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization

In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed. This approach can be applied to design non-adaptive learning rate and adaptive learning rate. Firstly, I will introduce the non-adaptive learning rate optimization method: Binary Forward Exploration (BFE), and then the corresponding adaptive per-parameter learning rate method: Adaptive BFE (AdaBFE) is possible to be developed. This approach could be an alternative method to optimize the learning rate based on the stochastic gradient descent (SGD) algorithm besides the current non-adaptive learning rate methods e.g. SGD, momentum, Nesterov and the adaptive learning rate methods e.g. AdaGrad, AdaDelta, Adam. The purpose to develop this approach is not to beat the benchmark of other methods but just to provide a different perspective to optimize the gradient descent method, although some comparative study with previous methods will be made in the following sections. This approach is expected to be heuristic or inspire researchers to improve gradient-based optimization combined with previous methods.