dataesr/scientific-paragraphs-categorization huggingface dataset:
Hence, the radar signatures presented different background clutter from static furniture and walls, as well as different distances between the radar and the subjects. This is an important point, as in the majority of the open literature on radar-based human activities classification, data are generally recorded in just one environment, typically in controlled laboratory conditions. Furthermore, the recordings involved 72 subjects of which 26 were female, which is substantial compared to the typical size of radar-based proprietary datasets in human activity classification literature. The age of these subjects ranged from 21 to 98 years, therefore offering the possibility of analyzing differences in gait and activity signatures compared with datasets that only have younger subjects  [4] ,  [7] ,  [15].
While 1754 samples is still rather small compared to datasets for other sensing technologies, to the best of our knowledge the UoG dataset is a good addition to the research landscape of the radar community in this application area. The number of samples per activity are detailed in Table  II. The first step of the challenge consisted in classifying seen data from elderly volunteers from 60-98 years of age including only activities A1-A5 as the ederly did not perform the fall activity (A6) for safety and ethical reasons. There were 20 of each for the 100 sample "Seen data" for validation used in step 1 of the challenge. Step 2 in the challenge consisted in 100 samples taken from 108 recordings with 18 repetitions of each activities A1 to A6 (details in Table  III ). Fig. 1  shows an example of the spectrograms (time-velocity patterns) for a sequence of 6 activities.
III. CLASSIFICATION CHALLENGE TASK DESCRIPTION The majority of the current research in radar-based human activity recognition is mainly tested on data from a small cohort composed of graduate students or postdocs between the age of 20-30 in the laboratory or in controlled conditions. The data collected for this challenge was organised for a more generalpurpose to validate human activities classification in more realistic environments, including clutter from common pieces of furniture in the surroundings. For the evaluation, which ran from 02/2020 to 12/2020, a total of 12 finalist teams made 188 valid submissions out of the many submitted by participants of the challenge who did not submit a paper to the conference. Note that the participants processed the data locally and submitted only the output of their predictions to the radar challenge website for scoring and analysis purposes.
IV. ANALYSIS OF THE RESULTS This section presents key results from the finalist submissions to this challenge in terms of performance, pre-processing, and classification methods reported in Section IV-A. The submitted papers for this challenge are labeled and referenced as  [27] ,  [28] ,  [29] ,  [30] ,  [31] ,  [32] ,  [33] ,  [34] ,  [35] ,  [36] ,  [37] ,  [38]. By finalist submissions, we mean that 12 teams submitted results for the seen data and unseen data, as well as a paper submission for the online hosting conference. In Section IV-B, we will give some insight into some of the pre-processing strategies to prepare the data for the machine learning algorithms. Among 12 papers that were submitted to this challenge as part of the IET radar conference, convolutional neural network (CNN) methods dominated the best performing models and will be discussed in Section IV-C.
A. Challenge Outcomes The evaluation results reported from the validated 12 submissions can be found in Table  IV. Among these reported results, submission  [34]  only reported the best run for individual action accuracy using 10-fold crossvalidation. Submission  [35]  only reported the best run for average classification accuracy. The leader table of unseen data and seen data evaluation results are shown in Fig. 4. The finalists achieved high accuracy > 90% for the seen data, which is very promising for the challenge of human activities classification, given the results reported in prior studies ranging from 65% to 99% seen in the open literature from various datasets  [4] ,  [7] ,  [15]. It is worth emphasizing here that the 1st place holders for the unseen data  [27] ,  [35] ,  [36] ,  [37]  have used various forms of  ensemble strategies.
An alternative approach used range-time maps and micro-Doppler spectrograms  [37]. Liu et al. proposed a feature extraction method based on motion pattern from singular value decomposition (SVD) on limbs and torso  [36]. The micro-Doppler features of limbs and torsos were extracted into principal Doppler patterns and principal human activity timing patterns. Finally, a transfer learning method was used as part of preprocessing to provide weights initialization. It is interesting to notice that transfer learning did not boost the classification performance in most cases. However, it provided fast training, as reported in other works in the literature  [25]. Fig. 5  shows a box-plot chart from the performance of the unseen data evaluation submissions. It is interesting to see that the overall performance for classification in data collected from volunteer 1 is worse than for the data collected from the other volunteers.
This may be related to the fact that volunteer 1 is female and the other volunteers in the unseen data are all males. Furthermore, also the larger training dataset is collected from a majority of male participants. This suggests the importance of considering gender balance when planning data collections on which data-driven approaches will be based. While, to the best of the authors' knowledge, detailed studies on gender-related differences in the radar signatures of human activities do not exist in the open literature, several studies hypothesized the fact that such differences occur and can be perceived with modern radar sensors, as it is possible for example with cameras  [40]. Specifically,  [41]  is among the first papers to claim that differences in movements of male and female can be perceived by radar, whereas  [42]  presents some evidence based on quantitative gait parameters extracted from radar signatures of male and female volunteers.
Region of Interest selection: Before moving to the range-Doppler domain, some submissions  [27] ,  [28] ,  [29] ,  [30] ,  [33] ,  [35] ,  [37] ,  [38]  selected a region of interest from the range-time domain to reduce the noise in the following domain by only incorporating range bins with sufficient energy present before applying the next pre-processing step, namely, the STFT. [28] ,  [29] ,  [30] ,  [33] ,  [37] ,  [38]  set the range bin interval to be used manually. This means they looked into the database and empirically determined the region of interest. Note  [33]  used multiple intervals to create diversity for training. The others used adaptive methods that are more pragmatic for real scenarios. [35]  determines a threshold based on the average energy in that range bin and only keeping values active above that threshold.
Zhang et al. [38]  is also worthy of notice, as this team implemented a dynamic time warping (DTW) technique. The DTW algorithm was originally used to match sequence data that are of different lengths. In essence, this corresponds to comparing samples that might be scaled in time to one another and estimating a distance to other samples present in the database to determine which class it belongs to. The disadvantage is that it requires more operations as the size of the dataset increases. The number of parameters indicates the complexity of neural network architectures. It is interesting to see that the network's performance was not linearly correlated with the number of parameters. Table VI lists the number of parameters reported in the main paper submissions. All these networks achieved an average classification accuracy greater than 89%.
A. Insight Into the Training Dataset Most of the teams used micro-Doppler spectrograms for classification, a data domain for which the six activities in the dataset could generally be distinguished clearly. For example, "sitting down" produced negative Doppler, "standing up" positive Doppler, and "drinking water" 'almost random' Doppler as the arm movement to grab the glass was not tied to a specific direction. Leveraging these differences from the predefined directions at which the activities were recorded, even simple classifiers (e. g. , a 5-layer CNN) could achieve an average of over 90% in classification accuracy. However, the two activities, "pick up an object" and "drink water," were the hardest to classify for most teams and proposed methods. Besides the varying direction of the arm movement for the "drinking" activity, one should also notice that these two are not single actions but more interaction activities consisting of several actions in sequences.
To ensure consistency in the inferences of the network and their measures, the proposed machine learning algorithms must be stress-tested under different conditions. Various strategies are proposed in  [57]  to get the best out of neural networks and determine the source of underperformance. Recently, different strategies such as training with a dynamic gradient, using the Levenberg-Marquardt training function, cross-validation, and hold out are evaluated on the same datasets to understand the effect of initialization and the architecture on the key performance indicators. Our challenge proposed a two-stage crossvalidation method, which is a step forward to benchmark models in terms of generalization. Further benchmarks for evaluation training kernels will be needed in future work using the classification algorithms across different datasets e.
VI. CONCLUSION We organized the 'Human Activity Radar Challenge', a classification challenge for radar-based human activities where raw radar data (i. e. , without any pre-processing) were provided to participants as input to further processing in view of the application of machine learning for classification. This Challenge was open in 2020 and affiliated with the IET International Radar Conference of the same year, later postponed to 2021 and then canceled because of the COVID-19 pandemic. In this paper, we presented a summary of the main methods proposed in the Challenge, including the task, data, performance metric, results, and performance analyses. The primary objectives were to systematically measure the recent progress in human activities classification in radar, particularly in the machine learning classification domain, and stimulate new ideas and collaborations.
This paper's results and analyses indicate great progress in human activities classification in radar data, with the performance for walking as accurate as 100% for the leading methods. This opens the way for further gait analysis to extract parameters for timed-up and go test and balance tests that rely on gait features and speed  [55]. Nevertheless, the performance gap in certain activities remains relatively large, at least for motion-based activities. This motivates further research towards developing a more robust technology that can maintain performance across a wide range of activities, also leveraging on the increasingly available public datasets  [59]  such as  [53] ,  [60] ,  [61] ,  [62] ,  [63] ,  [64] ,  [65] ,  [66] ,  [67]. Future benchmark challenges should consider more comprehensive metrics to define a figure of merit to better gauge the performances of various software especially in terms of deployment and footprint considerations.
The metrics that should be considered in the future are convergence, training time, inference time, number of hyperparameters, pre-processing and classification (Giga Operations / second), Accuracy, Sensitivity, Precision and F1 score. This would encompass both the machine learning performance and the pre-processing required to format the data prior to classification for resource-constrained embedded platforms. Furthermore, the teams will need to adhere to prescribed data splits for machine learning and cross-validation to ensure that the comparisons are statistically equivalent for the 'seen' dataset addressing one of the shortcomings of this challenge. Authors will be asked to submit their algorithms as well to run the training time and inference time on the same platform to have fairer performances comparison using the same implementation for an improved benchmarking.
This work was supported in part by the  British Council  under Grants  515095884  and  514739586 , in part by  Campus France   44764WK -PHC Alliance France-UK , in part by the  UK EPSRC  under Grant  INSHEP EP/R041679/1 , and in part by the  Dutch Research Council NWO  (grant  RAD-ART ). This challenge was supported by the  IET Electromagnetics Network. (Corresponding author:  Julien Le Kernec. ).
Text generation with large language models A second category of methods generates counterfactual examples in NLP with large pre-trained generative language models. A first approach  (Madaan et al. , 2022)  applies a Plug and Play language model  (Dathathri et al. , 2020)  methodology to generate text under the control of the classifier to explain. It consists in learning latent space perturbations from encoder-decoder models such as BART  (Lewis et al. , 2020)  in order to flip the outcome. Polyjuice  (Wu et al. , 2021)  proposes to finetune a GPT-2  (Radford et al. , 2019)  model on a set of predefined tasks. It results in a generative language model capable of performing negation, quantification, insertion of tokens or sentiment flipping based on prompt engineering. Polyjuice needs to be trained in a supervised way on ground truth counterfactual examples in order to be able to generate the expected text.
Global-CFI IG-CFI and TIGTEC-CFI both are local explainers, as they explain the prediction for a given instance of interest, x 0. This section turns to the generation of explanations at a global scale. We present g-CFI that provides information about a given token couple (t 1 , t 2 ), computing the importance of substituting t 1 with t 2 for the considered classifier. We propose to build global Counterfactual Feature Importance (g-CFI) by summing up local CFI on the whole dataset. Following the previously introduced notations, we denote the instance of interest x 0 and its counterfactual example x cf obtained with a counterfactual generator M, and the counterfactual feature importance operator h that measures the importance of a token change noted h(x 0 , x cf ) = h(x 0 , M(x 0 )) in the label flipping.
Statistical Analysis Two-way ANOVA together with LSD values at a 1% probability level  [73]  was used for analyzing collected data using Statistix 9 (Analytical Software, Inc. , Tallahassee, FL, USA). Principal component analysis (PCA) is a statistical technique that allows easier analysis of a large dataset with visualization by reducing the complexity and noise of the data, and highlighting the most important features and relationships between observed parameters. In this study, the relationships between the growth parameters (shoot and root length and fresh and dry biomass), chlorophyll content, nutrient uptake (N, P, K, Ca, Mg, and Na), antioxidant activity, proline, and 2AP level accumulation of KDML105 rice seedlings as affected by ST-PGPR inoculation were evaluated using PCA. The measured parameters were introduced as variables in the PCA using R 1.
Acknowledgments: The authors are indebted to the farmers of the  Community Enterprise of Thamo Organic Agricultural Group, Surin Province , and the  Na So Rice Farmers Group (Nature Conservation Club) ,  Kut Chum, Yasothon Province , for their help during field sampling, as well as to the  Alliance of Biodiversity International and Center International of Tropical Agriculture (CIAT), Asia Hub, Common Microbial Biotechnology Platform (CMBP), Hanoi, Vietnam , and the  Institute for Plant Sciences of Montpellier (IPSiM) ,  Univ Montpellier, CNRS ,  INRAE ,  Institut Agro, Montpellier, France. W. P. and K. C. acknowledge the partial support from  Chiang Mai University  through the  Research Center of Microbial Diversity and Sustainable Utilization. D. TL. and K. C. acknowledge the partial support from the  Junior Research Fellowship Program  of the  France Embassy in Thailand  and the  Agropolis Fondation, Montpellier, France.
Funding: This study was funded by the  Junior Research Fellowship Program 2022  of the  France Embassy in Thailand  and the  Agropolis Fondation, Montpellier, France , to K. C. and W. P. -A. D. T. L. and K. C. acknowledge the partial support from  Chiang Mai University  through the  Research Center of Microbial Diversity and Sustainable Utilization and Lanna Rice Research and Innovation Center  ( RG 25/2566 ). C. P. -u. -t. and K. C. acknowledge the partial support from  Chiang Mai University.
We propose an optimal architecture for deep neural networks of given size. The optimal architecture obtains from maximizing the lower bound of the maximum number of linear regions approximated by a deep neural network with a ReLu activation function. The accuracy of the approximation function relies on the neural network structure characterized by the number, dependence and hierarchy between the nodes within and across layers. We show how the accuracy of the approximation improves as we optimally choose the width and depth of the network. A Monte-Carlo simulation exercise illustrates the outperformance of the optimized architecture against cross-validation methods and gridsearch for linear and nonlinear prediction models. The application of this methodology to the Boston Housing dataset confirms empirically the outperformance of our method against state-of the-art machine learning models.
We overcome some of these criticisms with our proposed NAS optimization for rectifier networks. Other aspects such as the difference between the practical and theoretical expressivity of the neural networks are of an empirical nature and need to be studied on a case-by-case basis. We assess empirically the performance of our optimal rectifier network architecture by measuring its predictive ability in different simulation experiments and in a toy example widely used in the empirical machine learning literature. The simulation exercise evaluates the outof-sample performance of deep neural networks equipped with our optimal architecture against different benchmarks, depending on the data generating process considered. The main competitors that we consider for nonlinear data generating processes are state-of-the-art methods such as 𝑘-fold cross-validation and randomized grid search.
In this setting, the predictive ability of our approach is shown to be superior to those methods. The simulation section also considers a linear data generating process that helps us understand the limits of our optimization procedure. We do this by comparing the predictions of our optimal DNN against the predictions of ordinary least squares (OLS) methods, that are known to be best linear unbiased estimators (BLUE) in linear settings. The out-of-sample mean square prediction error (MSPE) and mean absolute prediction error (MAPE) obtained from our optimized ReLU DNN are comparable to those obtained from OLS methods in this unfavorable setting. Empirically, we apply our optimal architecture to a ReLu DNN with the aim of predicting median house prices from the Boston Housing Dataset. The dataset originally adopted by  Harrison and Rubinfeld (1978)  for modeling willingness to pay for clean air in the Boston area is a toy example extensively used in the machine learning literature to validate new learning techniques; such as Al  Bataineh and Kaur (2018) ,  Papadopoulos and Haralambous (2011), Granitto et al.
(2001) ,  Nado et al. (2018) ,  Bakker and Heskes (2003) ,  Myshkov and Julier (2016), and Zhou et al. (2001). The optimal structure selected by our novel methodology is a DNN with three layers and nodes equal to  [52, 39, 39]. Our results provide an improvement in prediction accuracy (MSE) in between 28. 55% and 47. 32% in comparison to those studies and highlight the performance gains of our optimal DNN for prediction relative to the aforementioned studies. The accuracy of the approximation function provided by our optimal DNN model not only depends on the disposition of the network in terms of width and depth. It also depends on the number of nodes in the neural network. Our method is flexible on the number of nodes and allows to experiment with different choices depending on the complexity of the problem and dimension of the dataset.
The rest of the paper is organized as follows: Section 2 reports the definitions and notations used in the paper, briefly reviewing the relationship between function class expressivity and universal approximation theorems for rectifier networks. Section 3 presents the novel methodology used for neural architecture search (NAS), reporting numerical results for the optimization. Section 4 presents a Monte Carlo simulation exercise that provides empirical evidence in finite samples of the performance of our method in improving the out-of-sample goodness of fit of deep neural networks for different data generating processes and input dimensions. In Section 5 the novel methodology is applied to the Boston Housing dataset. Section 6 concludes.
The question of interest is to approximate the unknown function 𝑓 (𝐱). Under the assumption that the function 𝑓 (𝐱) is linear on the observable input variables, OLS estimation provides unbiased, consistent and efficient estimators of the coefficients associated to 𝐱. Nonlinear specifications of 𝑓 (𝐱), the presence of unobserved heterogeneity, high-dimensional problems and complex datasets, require of alternative methods to approximate the function 𝑓 (𝐱). Recent advances in machine learning have shown that neural network methods provide accurate predictions that do not require of specific knowledge of the data generating process and, hence, are not affected by model misspecification. Neural networks approximate the function of interest by combining and composing different types of activation functions over one or more layers of nodes.
Accuracy test The main objective of this subsection is to compare the out-ofsample performance of the proposed novel methodology against a structure selected via 𝑘-fold cross-validation. By performing this methodology, the structure that returns the lowest out-of-sample mean square error (MSE) will be selected. The procedure is as follows. We simulate 1500 observations from the different DGPs; 1200 observations are used for training the model and 300 observations are used as validation set for the final comparison. A 3 -fold cross-validation over a randomized grid search 10 is performed over the 1200 observations to select the structure that returns the lowest out-of-sample MSE. Thus, being both neural networks fitted using the same number of training observations and on the same dataset, the comparison carried out for the nonlinear DGP processes analyses two comparable neural networks.
Simulation results Table  2  reports the results from the Monte Carlo simulation. When the linear data generating process is considered the out-of-sample accuracy of the neural network with structure obtained via the proposed maximization (to which we will refer as optimal ReLu DNN) is compared against a linear regression and thus, a model that correctly identifies the DGP. In this case when considering the null hypothesis (20), the out-of-sample MSE of the linear model will estimate the loss function 𝐿 𝐵 ; failing to reject the null hypothesis indicates that there is no statistically significant difference between the out-of-sample MSE of the optimal ReLu DNN and the corresponding MSE of the OLS estimator. Both models are indistinguishable in terms of predictive accuracy in mean. From Table  2 , one could notice that the null hypothesis is rejected at 0.