Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Haze Characteristics in Wuxi and Its Impact Factors

The climatic characteristics and causes of hazy weather in Wuxi,Jiangsu Province,are analyzed based on surface observations obtained from Wuxi Meteorological Office from 1980 to 2011 and Wuxi Statistic Yearbook data from 1991 to 2011.The results show that the number of haze days in Wuxi is in an increasing trend,especially in the late 5 years.The haze days appear most in winter,followed by spring,autumn and summer in succession.The haze processes that last for 10 consecutive days all go through high pressure,the rear of high pressure moving to the sea and the ground inverted trough.The air masses in middle and low levels are seen sinking in the back-trajectories simulation during 10 consecutive days of haze and the extremely haze events,and the areas the air masses pass have higher pollutant concentration.The key factor for haze events in 1990s is the dust emission in industrial pollution,and then since 2004 industrial waste gas and vehicle exhaust increase year by year,causing the haze days to increase as well.
Structure‐Dependent Optical Modulation of Propulsion and Collective Behavior of Acoustic/Light‐Driven Hybrid Microbowls

Hybrid light/acoustic‐powered microbowl motors, composed of gold (Au) and titanium dioxide (TiO2) with a structure‐dependent optical modulation of both their movement and collective behavior are reported by reversing the inner and outer positions of Au and TiO2. The microbowl propels in an acoustic field toward its exterior side. UV light activates the photochemical reaction on the TiO2 surface in the presence of hydrogen peroxide and the Au/TiO2 system moves toward its TiO2 side by self‐electrophoresis. Controlling the light intensity allows switching of the dominant propulsion mode and provides braking or reversal of motion direction when TiO2 is on the interior, or accelerated motion when the TiO2 is on its exterior. Theoretical simulations offer an understanding of the acoustic streaming flow and self‐electrophoretic fluid flow induced by the asymmetric distribution of ions around the microbowl. The light‐modulation behavior along with the tunable structure also leads to the control of the swarm behaviors under the acoustic field, including expansion or compaction of ensembles of microbowls with interior and exterior TiO2, respectively. Such structure‐dependent motion control thus paves the way for a variety of complex microscale operations, ranging from cargo transport to drug delivery in biomedical and environmental applications.
Direct seeding of birch, rowan and alder can be a viable technique for the restoration of upland native woodland in the UK

An experiment was established to investigate techniques for improving the reliability of direct seeding for the conversion of commercial conifer forests to native woodland. After 7 years, despite the presence of seed eating predators on site, an equivalent of between 9000 and 12 000 seedlings per hectare, depending on the initial sowing rate used, comprising a mixture of birch (Betula pubescens Ehrh.), rowan (Sorbus aucuparia L.) and alder (Alnus glutinosa (L.) Gaertn. ), had established in the cultivated treatments. Seed pre-treatment and sowing date had little impact on seedling numbers, but cultivation was beneficial. Very few seedlings occurred naturally in the unsown control plots. Our work suggests direct seeding may be a viable technique for converting some less fertile, freely drained, upland restock sites to a woodland consisting of birch, alder and rowan, but only if an appropriate silvicultural regime is put in place. Sowing can be combined, on the same restock site, with enrichment planting of other native species with more expensive seed such as indigenous Scots pine (Pinus sylvestris L.), oak (Quercus petraea Matt. (Liebl.); (Quercus robur L.)) and juniper (Juniper communis L.), which may also increase resilience to extreme climatic events in the first year after seeding.
INDIRECT LIKELIHOOD INFERENCE

Standard indirect Inference (II) estimators take a given finite-dimensional statistic, Zn, and then estimate the parameters by matching the sample statistic with the model-implied population moment. We here propose a novel estimation method that utilizes all available information contained in the distribution of Zn, not just its first moment. This is done by computing the likelihood of Zn, and then estimating the parameters by either maximizing the likelihood or computing the posterior mean for a given prior of the parameters. These are referred to as the maximum indirect likelihood (MIL) and Bayesian Indirect Likelihood (BIL) estimators, respectively. We show that the IL estimators are firstorder equivalent to the corresponding moment-based II estimator that employs the optimal weighting matrix. However, due to higher-order features of Zn, the IL estimators are higher order efficient relative to the standard II estimator. The likelihood of Zn will in general be unknown and so simulated versions of IL estimators are developed. Monte Carlo results for a structural auction model and a DSGE model show that the proposed estimators indeed have attractive finite sample properties.
Studies on Measurements of Plant Physiological Informations and Their Agricultural Applications

These studies were carried out for the development of non-invasive and continuous measurements of the variations of water content in the living plant tissue. The purposes are to measure plant physiological informations, to apply the methods to the estimation of physiological effects of water to the plant growth in agriculture and to analyze the effects of air pollutants to the plants.The following measurements were developed as the results of these studies. (1) Measurement of variations of stem diameter(2) Measurement of variations electrostatic capacity of plant stem(3) Measurement of variations of leaf thicknessThe feasibility studies were done mainly in the growth chamber and the greenhouse, and these measurements were found useful. Especially, the measurement of stem diameter variation was useful even in the field.
The CoLab-score rapidly and efficiently excludes COVID-19 at the emergency department without need for SARS-CoV-2 testing: a multicenter case-control study

BackgroundRapid identification of emergency department (ED) patients with a possible COVID-19 infection is needed. PCR-testing all ED patients is neither feasible nor effective in most centers, therefore a rapid, objective, low-cost screening tool to triage ED patients is necessary.MethodsResults from all routine lab tests from ED patients at the Catharina Hospital were collected from July 2019 to July 2020 and used in a statistical model to obtain the CoLab-score. The score was validated temporally and externally in three independent centers.ResultsThe CoLab-score consists of 10 routine lab results and can be used to safely rule-out a COVID-19 infection in more than one third of ED presentations with a negative predictive value of 0.997 (95% CI: 0.994 - 0.999) . ConclusionsThe CoLab-score is a valuable tool to rule out COVID-19, guide PCR testing and is available to any center with access to routine laboratory tests.
A Primer on Q Methodology

This primer serves two functions: (1) It is a simplified introduction to Q methodology, covering the topics of concourse, Q samples, Q sorting, correlation, factor analysis, theoretical rotation, factor scores, and factor interpretation. (2) It also illustrates different conceptions of Q methodology by taking the concept of "Q methodology" as the subject matter of the study. The factor results show how current understandings about Q are traceable to debates among Stephenson, Burt, and others in the 1930s, '40s, and '50s.
Group-Weighted Conformal Prediction

Conformal prediction (CP) is a method for constructing a prediction interval around the output of a fitted model, whose validity does not rely on the model being correct--the CP interval offers a coverage guarantee that is distribution-free, but relies on the training data being drawn from the same distribution as the test data. A recent variant, weighted conformal prediction (WCP), reweights the method to allow for covariate shift between the training and test distributions. However, WCP requires knowledge of the nature of the covariate shift-specifically,the likelihood ratio between the test and training covariate distributions. In practice, since this likelihood ratio is estimated rather than known exactly, the coverage guarantee may degrade due to the estimation error. In this paper, we consider a special scenario where observations belong to a finite number of groups, and these groups determine the covariate shift between the training and test distributions-for instance, this may arise if the training set is collected via stratified sampling. Our results demonstrate that in this special case, the predictive coverage guarantees of WCP can be drastically improved beyond the bounds given by existing estimation error bounds.
HyperZ·Z·W Operator Connects Slow-Fast Networks for Full Context Interaction

The self-attention mechanism utilizes large implicit weight matrices, programmed through dot product-based activations with very few trainable parameters, to enable long sequence modeling. In this paper, we investigate the possibility of discarding residual learning by employing large implicit kernels to achieve full context interaction at each layer of the network. To accomplish it, we introduce coordinate-based implicit MLPs as a slow network to generate hyper-kernels for another fast convolutional network. To get context-varying weights for fast dynamic encoding, we propose a $\mathrm{Hyper}\mathcal{Z{\cdot}Z{\cdot}W}$ operator that connects hyper-kernels ($\mathcal{W}$) and hidden activations ($\mathcal{Z}$) through simple elementwise multiplication, followed by convolution of $\mathcal{Z}$ using the context-dependent $\mathcal{W}$. Based on this design, we present a novel Terminator architecture that integrates hyper-kernels of different sizes to produce multi-branch hidden representations for enhancing the feature extraction capability of each layer. Additionally, a bottleneck layer is employed to compress the concatenated channels, allowing only valuable information to propagate to the subsequent layers. Notably, our model incorporates several innovative components and exhibits excellent properties, such as introducing local feedback error for updating the slow network, stable zero-mean features, faster training convergence, and fewer model parameters. Extensive experimental results on pixel-level 1D and 2D image classification benchmarks demonstrate the superior performance of our architecture.
ATPPNet: Attention based Temporal Point cloud Prediction Network

Point cloud prediction is an important yet challenging task in the field of autonomous driving. The goal is to predict future point cloud sequences that maintain object structures while accurately representing their temporal motion. These predicted point clouds help in other subsequent tasks like object trajectory estimation for collision avoidance or estimating locations with the least odometry drift. In this work, we present ATPPNet, a novel architecture that predicts future point cloud sequences given a sequence of previous time step point clouds obtained with LiDAR sensor. ATPPNet leverages Conv-LSTM along with channel-wise and spatial attention dually complemented by a 3D-CNN branch for extracting an enhanced spatio-temporal context to recover high quality fidel predictions of future point clouds. We conduct extensive experiments on publicly available datasets and report impressive performance outperforming the existing methods. We also conduct a thorough ablative study of the proposed architecture and provide an application study that highlights the potential of our model for tasks like odometry estimation.
Learning to Stop Cut Generation for Efficient Mixed-Integer Linear Programming

Cutting planes (cuts) play an important role in solving mixed-integer linear programs (MILPs), as they significantly tighten the dual bounds and improve the solving performance. A key problem for cuts is when to stop cuts generation, which is important for the efficiency of solving MILPs. However, many modern MILP solvers employ hard-coded heuristics to tackle this problem, which tends to neglect underlying patterns among MILPs from certain applications. To address this challenge, we formulate the cuts generation stopping problem as a reinforcement learning problem and propose a novel hybrid graph representation model (HYGRO) to learn effective stopping strategies. An appealing feature of HYGRO is that it can effectively capture both the dynamic and static features of MILPs, enabling dynamic decision-making for the stopping strategies. To the best of our knowledge, HYGRO is the first data-driven method to tackle the cuts generation stopping problem. By integrating our approach with modern solvers, experiments demonstrate that HYGRO significantly improves the efficiency of solving MILPs compared to competitive baselines, achieving up to 31% improvement.
Employing Label Models on ChatGPT Answers Improves Legal Text Entailment Performance

The objective of legal text entailment is to ascertain whether the assertions in a legal query logically follow from the information provided in one or multiple legal articles. ChatGPT, a large language model, is robust in many natural language processing tasks, including legal text entailment: when we set the temperature = 0 (the ChatGPT answers are deterministic) and prompt the model, it achieves 70.64% accuracy on COLIEE 2022 dataset, which outperforms the previous SOTA of 67.89%. On the other hand, if the temperature is larger than zero, ChatGPT answers are not deterministic, leading to inconsistent answers and fluctuating results. We propose to leverage label models (a fundamental component of weak supervision techniques) to integrate the provisional answers by ChatGPT into consolidated labels. By that way, we treat ChatGPT provisional answers as noisy predictions which can be consolidated by label models. The experimental results demonstrate that this approach can attain an accuracy of 76.15%, marking a significant improvement of 8.26% over the prior state-of-the-art benchmark. Additionally, we perform an analysis of the instances where ChatGPT produces incorrect answers, then we classify the errors, offering insights that could guide potential enhancements for future research endeavors.
Quantum influences and event relativity

We develop a new interpretation of quantum theory by combining insights from extended Wigner's friend scenarios and quantum causal modelling. In this interpretation, which synthesizes ideas from relational quantum mechanics and consistent histories, events obtain relative to a set of systems, and correspond to projectors that are picked out by causal structure. We articulate these ideas using a precise mathematical formalism. Using this formalism, we show through specific examples and general constructions how quantum phenomena can be modelled and paradoxes avoided; how different scenarios may be classified and the framework of quantum causal models extended; and how one can approach decoherence and emergent classicality without relying on quantum states.
Decentralized control methodology for multi-machine/multi-converter power systems

In this project we evaluate a framework for synchronization of mixed machine-converter power grids. Synchronous machines are assumed to be actuated by mechanical torque injections, while the converters by DC-side current injections. As this approach is based on model-matching, the converter's modulation angle is driven by the DC-side voltage measurement, while its modulation amplitude is assigned analogously to the electrical machine's excitation current. In this way we provide extensions to the swing-equations model, retaining physical interpretation, and design controllers that achieve various objectives: frequency synchronization while stabilizing an angle configuration and a bus voltage magnitude prescribed by an optimal power flow (OPF) set-point. We further discuss decentralization issues related to clock drifts, loopy graphs, model reduction, energy function selection and characterizations of operating points. Finally, a numerical evaluation is based on experiments from three- and two-bus systems.
Let's Roll Back! The Challenging Task of Regulating Temporary Contracts

In this paper, we evaluate the impact of a reform introduced in Italy in 2018 (Decreto Dignit\`a), which increased the rigidity of employment protection legislation (EPL) of temporary contracts, rolling back previous policies, to reduce job instability. We use longitudinal labour force data from 2016 to 2019 and adopt a time-series technique within a Rubin Casual Model (RCM) framework to estimate the causal effect of the reform. We find that the reform was successful in reducing persistence into temporary employment and increasing the flow from temporary to permanent employment, in particular among women and young workers in the North of Italy, with significant effects on the stocks of permanent employment (+), temporary employment (-) and unemployment (-). However, this positive outcome came at the cost of higher persistence into inactivity, lower outflows from unemployment to temporary employment and higher outflows from unemployment to inactivity among males and low-educated workers.
Supporting Meta-model-based Language Evolution and Rapid Prototyping with Automated Grammar Optimization

In model-driven engineering, developing a textual domain-specific language (DSL) involves constructing a meta-model, which defines an underlying abstract syntax, and a grammar, which defines the concrete syntax for the DSL. Language workbenches such as Xtext allow the grammar to be automatically generated from the meta-model, yet the generated grammar usually needs to be manually optimized to improve its usability. When the meta-model changes during rapid prototyping or language evolution, it can become necessary to re-generate the grammar and optimize it again, causing repeated effort and potential for errors. In this paper, we present GrammarOptimizer, an approach for optimizing generated grammars in the context of meta-model-based language evolution. To reduce the effort for language engineers during rapid prototyping and language evolution, it offers a catalog of configurable grammar optimization rules. Once configured, these rules can be automatically applied and re-applied after future evolution steps, greatly reducing redundant manual effort. In addition, some of the supported optimizations can globally change the style of concrete syntax elements, further significantly reducing the effort for manual optimizations. The grammar optimization rules were extracted from a comparison of generated and existing, expert-created grammars, based on seven available DSLs.
Arabic Tweet Act: A Weighted Ensemble Pre-Trained Transformer Model for Classifying Arabic Speech Acts on Twitter

Speech acts are a speakers actions when performing an utterance within a conversation, such as asking, recommending, greeting, or thanking someone, expressing a thought, or making a suggestion. Understanding speech acts helps interpret the intended meaning and actions behind a speakers or writers words. This paper proposes a Twitter dialectal Arabic speech act classification approach based on a transformer deep learning neural network. Twitter and social media, are becoming more and more integrated into daily life. As a result, they have evolved into a vital source of information that represents the views and attitudes of their users. We proposed a BERT based weighted ensemble learning approach to integrate the advantages of various BERT models in dialectal Arabic speech acts classification. We compared the proposed model against several variants of Arabic BERT models and sequence-based models. We developed a dialectal Arabic tweet act dataset by annotating a subset of a large existing Arabic sentiment analysis dataset (ASAD) based on six speech act categories. We also evaluated the models on a previously developed Arabic Tweet Act dataset (ArSAS). To overcome the class imbalance issue commonly observed in speech act problems, a transformer-based data augmentation model was implemented to generate an equal proportion of speech act categories. The results show that the best BERT model is araBERTv2-Twitter models with a macro-averaged F1 score and an accuracy of 0.73 and 0.84, respectively. The performance improved using a BERT-based ensemble method with a 0.74 and 0.85 averaged F1 score and accuracy on our dataset, respectively.
Algorithmic Robust Forecast Aggregation

Forecast aggregation combines the predictions of multiple forecasters to improve accuracy. However, the lack of knowledge about forecasters' information structure hinders optimal aggregation. Given a family of information structures, robust forecast aggregation aims to find the aggregator with minimal worst-case regret compared to the omniscient aggregator. Previous approaches for robust forecast aggregation rely on heuristic observations and parameter tuning. We propose an algorithmic framework for robust forecast aggregation. Our framework provides efficient approximation schemes for general information aggregation with a finite family of possible information structures. In the setting considered by Arieli et al. (2018) where two agents receive independent signals conditioned on a binary state, our framework also provides efficient approximation schemes by imposing Lipschitz conditions on the aggregator or discrete conditions on agents' reports. Numerical experiments demonstrate the effectiveness of our method by providing a nearly optimal aggregator in the setting considered by Arieli et al. (2018).
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection

Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accuracy of intrusion detection models still need further enhancement to render them more adaptable to diverse system categories, particularly within resource-constrained devices, such as those embedded in edge computing systems. This research introduces a three-stage training paradigm, augmented by an enhanced pruning methodology and model compression techniques. The objective is to elevate the system's effectiveness, concurrently maintaining a high level of accuracy for intrusion detection. Empirical assessments conducted on the UNSW-NB15 dataset evince that this solution notably reduces the model's dimensions, while upholding accuracy levels equivalent to similar proposals.
Explaining Predictive Uncertainty by Exposing Second-Order Effects

Explainable AI has brought transparency into complex ML blackboxes, enabling, in particular, to identify which features these models use for their predictions. So far, the question of explaining predictive uncertainty, i.e. why a model 'doubts', has been scarcely studied. Our investigation reveals that predictive uncertainty is dominated by second-order effects, involving single features or product interactions between them. We contribute a new method for explaining predictive uncertainty based on these second-order effects. Computationally, our method reduces to a simple covariance computation over a collection of first-order explanations. Our method is generally applicable, allowing for turning common attribution techniques (LRP, Gradient x Input, etc.) into powerful second-order uncertainty explainers, which we call CovLRP, CovGI, etc. The accuracy of the explanations our method produces is demonstrated through systematic quantitative evaluations, and the overall usefulness of our method is demonstrated via two practical showcases.