Miracle-dz/UniFAR-FTUs huggingface dataset:
Cost-efficient Crowdsourcing for Span-based Sequence Labeling: Worker Selection and Data Augmentation

Crowdsourcing is a scalable data collecting 001 method used in many NLP tasks. Due to the dis-002 parity of expertise among crowd workers, prior 003 studies utilize worker selection to improve the 004 quality of the crowdsourced dataset. However, 005 most of them are designed for and tested on 006 simple classification tasks. In this paper, we 007 focus on span-based sequence labeling tasks in 008 NLP, which are more challenging as nearby la-009 bels have complex inter-dependencies. We pro-010 pose a new worker selection algorithm based 011 on combinatorial multi-armed bandit (CMAB). 012 Our algorithm maximizes the quality of the an-013 notations while reducing the overall cost by 014 using both majority-voted and expert annota-015 tions for evaluations. A key challenge is that 016 practical datasets are highly imbalanced and 017 of small scale, which makes offline simulation 018 of worker selection difficult. To address this 019 issue, we present a novel data augmentation 020 method called shifting , expanding , and shrink-021 ing (SES), which is customized for sequence 022 labeling. We augment two datasets, CoNLL 023 2003 NER and Chinese OEI, on which we ex-024 tensively test our worker selection algorithm. 025 The results show that our algorithm achieves up 026 to 100.04% F 1 score compared with an expert-027 evaluation-only (i.e., all annotations evaluated 028 by experts) baseline, saving up to 65.97% of 029 costs to ask experts. We also include a dataset-030 independent test in which the annotation evalu-031 ation is simulated through a Bernoulli distribu-032 tion. Similarly, our algorithm achieves 97.56% 033 F 1 and saves 59.88% expert costs. 034
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning

With the increasing number of \acp{uav} as users of the cellular network, the research community faces particular challenges in providing reliable \ac{uav} connectivity. A challenge that has limited research is understanding how the local building and \ac{bs} density affects \ac{uav}'s connection to a cellular network, that in the physical layer is related to its spectrum efficiency. With more \acp{bs}, the \ac{uav} connectivity could be negatively affected as it has \ac{los} to most of them, decreasing its spectral efficiency. On the other hand, buildings could be blocking interference from undesirable \ac{bs}, improving the link of the \ac{uav} to the serving \ac{bs}. This paper proposes a \ac{rl}-based algorithm to optimise the height of a UAV, as it moves dynamically within a range of heights, with the focus of increasing the UAV spectral efficiency. We evaluate the solution for different \ac{bs} and building densities. Our results show that in most scenarios \ac{rl} outperforms the baselines achieving up to 125\% over naive constant baseline, and up to 20\% over greedy approach with up front knowledge of the best height of UAV in the next time step.
Restormer-Plus for Real World Image Deraining: the Runner-up Solution to the GT-RAIN Challenge (CVPR 2023 UG2+ Track 3)

This technical report presents our Restormer-Plus approach, which was submitted to the GT-RAIN Challenge (CVPR 2023 UG$^2$+ Track 3). Details regarding the challenge are available at http://cvpr2023.ug2challenge. org/track3.html. Restormer-Plus outperformed all other submitted solutions in terms of peak signal-to-noise ratio (PSNR), and ranked 4th in terms of structural similarity (SSIM). It was officially evaluated by the competition organizers as a runner-up solution. It consists of four main modules: the single-image de-raining module (Restormer-X), the median filtering module, the weighted averaging module, and the post-processing module. Restormer-X is applied to each rainy image and built on top of Restormer. The median filtering module is used as a median operator for rainy images associated with each scene. The weighted averaging module combines the median filtering results with those of Restormer-X to alleviate overfitting caused by using only Restormer-X. Finally, the post-processing module is utilized to improve the brightness restoration. These modules make Restormer-Plus one of the state-of-the-art solutions for the GT-RAIN Challenge. Our code can be found at https://github.com/ZJLAB-AMMI/Restormer-Plus.
A Real-time Contribution Measurement Method for Participants in Federated Learning

In recent years, individuals, business organizations or the country have paid more and more attention to their data privacy. At the same time, with the rise of federated learning, federated learning is involved in more and more fields. However, there is no good evaluation standard for each agent participating in federated learning. This paper proposes an online evaluation method for federated learning and compares it with the results obtained by Shapley Value in game theory. The method proposed in this paper is more sensitive to data quality and quantity.
Asymptotics for subcritical fully nonlinear equations with isolated singularities

In this thesis, we consider this equation σk(A) = u(p− n+2 n−2 )k, where n ≥ 3 and p ∈ ( n n−2 , n+2 n−2 ). Here σk denotes the kth elementary symmetric function of the eigenvalues of Au, and Au = − 2 n − 2 − n+2 n−2 D2u + 2n (n − 2)2 u − 2n n−2∇u ⊗ ∇u − 2 (n − 2)2 u − 2n n−2 |∇u|2I, where∇u denotes the gradient of u and D2u denotes the Hessian of u. This equation arises naturally from the σk Yamabe equation. When k = 1, it amounts to −∆u = up, which is simply a classical subcritical semilinear elliptic equation. We study the asymptotic behavior of solutions in a punctured ball. For 1 ≤ k < 2 , we prove that an admissible solution to this equation with a non-removable isolated singular point is asymptotic to a radial solution. Then we are able to obtain higher order expansion of solutions using analysis of the linearized operators. These results generalize earlier pioneering work of Caffarelli, Gidas and Spruck. As a side effect, we also obtain Schoen’s Harnack type inequality in Euclidean balls, asymptotic behavior of an entire solution. Based on the asymptotic behavior, we are able to give another proof of the Liouville type theorem obtained by Li and Li.
Improving color lensless microscopy reconstructions by self-calibration

Lensless color microscopy is a recent 3D quantitative imaging method allowing to retrieve physical parameters characterizing microscopic objects spread in a volume. The main advantages of this technique are related to its simplicity, compactness, low sensitivity of the setup to vibrations and the possibility to accurately characterize objects. The cost-effectiveness of the method can be further increased using low-end laser diodes as coherent sources and CMOS color sensor equipped with a Bayer filter array. However, the central wavelength delivered by this type of laser is generally known only with a limited precision and can evolve because of its dependence on temperature and power supply voltage. In addition, Bayer-type filters of conventional color sensors are not very selective, resulting in spectral mixing (crosstalk phenomenon) of signals from each color channel. Ignoring these phenomena leads to significant errors in holographic reconstructions. We have proposed a maximum likelihood estimation method to calibrate the setup (central wavelength of the laser sources and spectral mixing introduced by the Bayer filters) using spherical objects naturally present in the field of view or added (calibration objects). This calibration method provides accurate estimates of the wavelengths and of the crosstalk, with an uncertainty comparable to that of a high-resolution spectrometer. To perform the image reconstruction from color holograms following the self-calibration of the setup, we describe a regularized inversion method that includes a linear hologram formation model, sparsity constraints and an edge-preserving regularization. We show on holograms of calibrated objects that the self-calibration of the setup leads to an improvement of the reconstructions.
DropNet: Reducing Neural Network Complexity via Iterative Pruning

In this supplementary material, we: (i) present more results for Model C (Figs. 2, 3, 4, 5, 6, 7), ResNet18 (Figs. 8, 9) and VGG19 (Figs. 10, 11) and (ii) explore the effect of random initialization on ResNet18 (Figs. 12, 13) and VGG19 (Figs. 14, 15). The results show that DropNet is robust for larger models, and the final pruned model is able to achieve a similar performance even after reinitialization.
DropNet: Reducing Neural Network Complexity via Iterative Pruning (

The supplementary experiments performed use the same methodology as the main paper. In addition, to show DropNet’s scalability, we also perform experiments on the Tiny ImageNet dataset. We demonstrate how effective pruning using DropNet can be done on larger models like Model C (Conv4), ResNet18 and VGG19. For ResNet18 and VGG19, the model architecture follows closely from the original papers (Simonyan & Zisserman, 2014; He et al., 2016) and are detailed in Fig. 1.
Value of Bargaining Contract in a Supply Chain System With Sustainability Investment: An Incentive Analysis

Sustainability is a prevalent topic as the consumers and the governments are much more concerned about the environmental pollution. To reduce the carbon emission, in real world practice, governments have executed different forms of policies to motivate the firms to operate in a sustainable manner. This paper aims to examine the value of bargaining contract when the supply chain member invests in the sustainability projects, and explore the effectiveness of the direct (e.g., cash grant) and indirect (e.g., an increased environmental tax rate) subsidies in reducing the total carbon emission. We first derive the optimal pricing and sustainability effort under the wholesale pricing contract when either the manufacturer or the retailer makes the sustainability investment. We then consider the supply chain coordination under the two-part tariff, revenue sharing, and bargaining contracts. We uncover that only the bargaining contract can coordinate the supply chain and a Pareto improvement is achieved when the bargaining power of the manufacturer is in a specific range. Finally, we reveal that both the direct and indirect subsidies can increase the sustainability effort but they are not always an effective tool to reduce the total carbon emissions.
Multi-Agent Reinforcement Learning:a critical survey

We survey the recent work in AI on multi-agent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent literature, we identify four well-defined problems in multi-agent reinforcement learning, single out the problem that in our view is most suitable for AI, and make some remarks about how we believe progress is to be made on this problem.
Multiagent Reinforcement Learning With Unshared Value Functions

One important approach of multiagent reinforcement learning (MARL) is equilibrium-based MARL, which is a combination of reinforcement learning and game theory. Most existing algorithms involve computationally expensive calculation of mixed strategy equilibria and require agents to replicate the other agents' value functions for equilibrium computing in each state. This is unrealistic since agents may not be willing to share such information due to privacy or safety concerns. This paper aims to develop novel and efficient MARL algorithms without the need for agents to share value functions. First, we adopt pure strategy equilibrium solution concepts instead of mixed strategy equilibria given that a mixed strategy equilibrium is often computationally expensive. In this paper, three types of pure strategy profiles are utilized as equilibrium solution concepts: pure strategy Nash equilibrium, equilibrium-dominating strategy profile, and nonstrict equilibrium-dominating strategy profile. The latter two solution concepts are strategy profiles from which agents can gain higher payoffs than one or more pure strategy Nash equilibria. Theoretical analysis shows that these strategy profiles are symmetric meta equilibria. Second, we propose a multistep negotiation process for finding pure strategy equilibria since value functions are not shared among agents. By putting these together, we propose a novel MARL algorithm called negotiation-based Q-learning (NegoQ). Experiments are first conducted in grid-world games, which are widely used to evaluate MARL algorithms. In these games, NegoQ learns equilibrium policies and runs significantly faster than existing MARL algorithms (correlated Q-learning and Nash Q-learning). Surprisingly, we find that NegoQ also performs well in team Markov games such as pursuit games, as compared with team-task-oriented MARL algorithms (such as friend Q-learning and distributed Q-learning).
Decision making with dynamically arriving information

Decision making is the ability to decide on the best alternative among a set of candidates based on their value. In many real-world domains the value depends on events that occur dynamically, so that the decision is based on dynamically changing uncertain information. When there is a cost to waiting for more information, the question is when to make the decision. Do you stop and make the best decision you can, given the information you have so far, or do you wait until more information arrives so you can make a better decision? We propose a model that characterizes the influence of dynamic information on the utility of the decision. Based on this model, we present an optimal algorithm that guarantees the best time to stop. Unfortunately, its complexity is exponential in the number of candidates. We present an alternative framework in which the different candidates are solved separately. We formally analyze the alternative framework, and show how it leads to a range of specific heuristic algorithms. We evaluate the optimal and the simplest heuristic algorithms through experiments, and show that the heuristic algorithm is much faster than the optimal algorithm, and the utility of the winner it finds is close to the optimum.
A framework for supporting quality of service requirements in a data stream management system

Currently, a large class of data-intensive applications, in which data are presented in the form of continuous data streams rather than static relations, has been widely recognized in the database community. Not only is the size of the data for these applications unbounded and the data arrives in a highly bursty mode, but these applications have to conform to Quality of Service (QoS) requirements for processing continuous queries (CQs) over data streams. These characteristics make it infeasible to simply load the arriving data streams into a traditional database management system and use currently available techniques for their processing. Therefore, a data stream management system (DSMS) is needed to process continuous streaming data effectively and efficiently. In this thesis, we discuss and provide solutions to many aspects of a DSMS with the emphasis on supporting QoS requirements and event and rule processing. Specifically, we address the following problems: System capacity planning and QoS metrics estimation. We propose a queueing theory based model for analyzing a multiple continuous query processing system. Using our queueing model, we provide a solution to the system capacity planning problem and its reverse problem: given the resources and CQs, how to estimate QoS metrics? The estimated QoS metrics not only can be used to verify whether the defined QoS requirements of CQs in a DSMS have been satisfied, but also form the base in a DSMS to manage and control various QoS delivery mechanisms such as scheduling strategies, load shedding, admission control, and others. Run-Time resource allocation (scheduling strategies). We propose a family of scheduling strategies for run-time resource allocation in DSMSs, which includes the operator path capacity strategy (PC) to minimize the overall tuple latency, the operator segment strategy and its variances: the memory-optimal segment strategy (MOS), which minimizes the total memory requirement, and the simplified segment strategy, and the threshold strategy, a hybrid of the PC and the MOS strategy. QoS delivery mechanism (load shedding). We develop a set of comprehensive techniques to handle the bursty nature of input data streams by activating/deactivating a set of shedders to gracefully discard tuples during overload periods in a general DSMS. We first formalize the problem and discuss the physical implementation of shedders. We then develop a set of algorithms to estimate system capacity, to compute the optimal location of shedders in CQs, and to allocate the total shedding load among non-active shedders. Event and rule processing. We develop an integrated model, termed Estream, to process complicated event expressions and rules under the context of data stream processing through a group of enhancements to a DSMS. Our Estream model greatly improves the expressiveness and computation ability of DSMSs in terms of processing complex real-life events and makes DSMSs actively respond to defined events over data streams and carry out defined sequences of actions automatically. (Abstract shortened by UMI.)
The Case for Shared Nothing

There are three dominent themes in building high transaction rate multiprocessor systems, namely shared memory (e.g. Synapse, IBM/AP configurations), shared disk (e.g. VAX/cluster, any multi-ported disk system), and shared nothing (e.g. Tandem, Tolerant). This paper argues that shared nothing is the preferred approach.
Optimizing phase object reconstruction using an in-line digital holographic microscope and a reconstruction based on a Lorenz-Mie model

Among the various configurations that may be used in digital holography, the original in-line “Gabor” configuration is the simplest setup, with a single beam. It requires sparsity of the sample but it is free from beam separation device and associated drawbacks. This option is particularly suited when cost, compact design or stability are important. This configuration is also easier to adapt on a traditional microscope. Finally, from the metrological point of view, this configuration, combined with parametric inverse reconstructions using Lorenz-Mie Theory, has proven to make possible highly accurate estimation of spherical particles parameters (3D location, radius and refractive index) with sub-micron accuracy. Experimental parameters such as the defocus distance, the choice of the objective, or the coherence of the source have a strong influence on the accuracy of the estimation. They are often studied experimentally on specific setups. We previously demonstrated the benefit of using statistical signal processing tools as the Cram´er-Rao Lower Bounds to predict best theoretical accuracy reachable for opaque object. This accuracy depends on the image/hologram formation model, the noise model and the signal to noise ratio in the holograms. In a co-design framework, we propose here to investigate the influence of experimental parameters on the estimation of the radius and refractive index of micrometer-sized transparent spherical objects. In this context, we use Lorenz-Mie Theory to simulate spherical object holograms, to compute Cram´er-Rao Lower bounds, and to numerically reconstruct the objects parameters using an inverse problem approach. Then, these theoretical studies are used to challenge our digital holographic microscopy setup and conclude about accuracy, limitations and possible enhancements.
Flow characteristics around a deformable stenosis under pulsatile flow condition

A specific portion of a vulnerable stenosis is deformed periodically under a pulsatile blood flow condition. Detailed analysis of such deformable stenosis is important because stenotic deformation can increase the likelihood of rupture, which may lead to sudden cardiac death or stroke. Various diagnostic indices have been developed for a nondeformable stenosis by using flow characteristics and resultant pressure drop across the stenosis. However, the effects of the stenotic deformation on the flow characteristics remain poorly understood. In this study, the flows around a deformable stenosis model and two different rigid stenosis models were investigated under a pulsatile flow condition. Particle image velocimetry was employed to measure flow structures around the three stenosis models. The deformable stenosis model was deformed to achieve high geometrical slope and height when the flow rate was increased. The deformation of the stenotic shape enhanced jet deflection toward the opposite vessel wall of the stenosis. The jet deflection in the deformable model increased the rate of jet velocity and turbulent kinetic energy (TKE) production as compared with those in the rigid models. The effect of stenotic deformation on the pulsating waveform related with the pressure drop was analyzed using the TKE production rate. The deformable stenosis model exhibited a phase delay of the peak point in the waveform. These results revealed the potential use of pressure drop waveform as a diagnostic index for deformable stenosis.
Asymmetric diffraction based on a passive parity-time grating

Optical structures with balanced loss and gain provide an efficient platform to study the features of light propagation under non-Hermitian parity-time symmetry. Here, we report a feasible design of one-dimensional parity-time symmetric diffraction grating, where the real and imaginary parts of refractive index are separately modulated. Due to the spontaneous breaking of parity-time symmetry at the exceptional point, asymmetric diffractions are observed between a pair of oblique incident light. This asymmetric phenomenon, determined by the modulation direction of the introduced parity-time symmetry, is also polarization-dependent. The coupled-mode theory is implemented to theoretically analyze the polarization dependent asymmetric diffraction, showing consistence with numerical simulations. Our findings may provide a feasible way for manipulating light and instructively inspire the development of diffraction optics.
Asymptotic Performance of Thompson Sampling for Batched Multi-Armed Bandits

We study the asymptotic performance of the Thompson sampling algorithm in the batched multi-armed bandit setting where the time horizon <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> is divided into batches, and the agent is not able to observe the rewards of her actions until the end of each batch. We show that in this batched setting, Thompson sampling achieves the same asymptotic performance as in the case where instantaneous feedback is available after each action, provided that the batch sizes increase subexponentially. This result implies that Thompson sampling can maintain its performance even if it receives delayed feedback in <inline-formula> <tex-math notation="LaTeX">$\omega (\log T)$ </tex-math></inline-formula> batches. We further propose an adaptive batching scheme that reduces the number of batches to <inline-formula> <tex-math notation="LaTeX">$O(\log \log T)$ </tex-math></inline-formula> while maintaining the same performance. Although the batched multi-armed bandit setting has been considered in several recent works, previous results rely on tailored algorithms for the batched setting, which optimize the batch structure and prioritize exploration in the beginning of the experiment to eliminate suboptimal actions. We show that Thompson sampling, on the other hand, is able to achieve a similar asymptotic performance in the batched setting without any modifications.