dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation and hardware Our MCEV tool is developed with Unity 2019. 4 LTS 1  , and the execution engine is implemented using ROS2 2. We used Google vocal recognition API to recognize the vocal commands. The MCEV tool ran on an Oculus Quest 2 headset connected to a laptop (RTX 2080, Intel Core I9-9900K, 32Go RAM) with the link mode. The desktop tool was developed with the Python library PySimpleGUI 3  and was executed on a laptop (RTX 2070, Intel Core I7-10750H, 16Go RAM) for the OTF experiment. A Kinect (on a camera tripod) and a USB microphone were connected to this PC. The lightbulbs were Philips connected lightbulbs.
Collected data The completion times were recorded to compare the time performance in the OTF experiment and VR. Moreover, to compare the quality of the configurations created with both tools, we recorded the devices positioning each time the participants tested their configuration. We implemented a percentage scoring system to impose a minimal configuration quality on the devices positioning and coverage of the interaction space illustrated by the red square in Figure  5c. A configuration was considered acceptable if the score for each device was high enough (above 75%). In practice, it was easily obtained as long as both the devices were not too far from the chairs and the camera had the chairs in its field of view. Thus, there was no specific optimal positioning that could be inferred from the score.
Conclusion and future Work In this article, we have proposed a methodology and software tool based on VR to facilitate the configuration and evaluation of MIBS. Thanks to 3D interactions, it aims to facilitate the selection and positioning of devices and their association with newly configured multimodal interaction techniques and services. The created MIBS can then be evaluated in immersion without needing the real environment. Finally, the evaluated configurations can be shared with our proposed collaborative features to improve the MIBS or facilitate the installation in the real environment. We conducted a user experiment to compare the efficiency and usability of our tool to the ones of a desktop tool we developed to adapt our methodology process to OTF experiments. The results show that the configuration and installation process is more efficient with our VR tool, and that it has at least the same usability as the desktop tool.
, 2020)  for more exhaustive surveys of bias in NLP. Recently, adversarial methods  (Beutel et al. , 2017; Zhang et al. , 2018; Elazar and Goldberg, 2018)  have been investigated to mitigate biases. Han et al. (2021c,b)  respectively suggest using several discriminators where each learns different hidden representations and applying an adversarial approach in-and cross-domain to train the adversary on a different dataset, with methods called Adv and GATE. Differently, recent contributions focus on directly constraining the objective to improve fairness  (Shen et al. , 2022b; Han et al. , 2021a). For instance, by adding some fairness metric, such as the Equal opportunity that we will define later in this paper, to the objective function. Our approach is at the crossroad of these two philosophies: on the one hand, we propose to train a biased model whose sole purpose is to predict the sensitive attribute and use this latter to enforce fairness in our main prediction model.
(2017) : max ω,||Cw|| L ≤1 E zy,zs∼p(zy,zs) [C ω (z y , z s )] -E zy∼p(zy),zs∼p(zs) [C ω (z y , z s )]. (10) In this context, C ω is a MLP, referred to as the critic in Figure  2. To enforce the Lipschitz constraint, we clamp the weights to given values ([-0. 01, 0. 01]) at each optimization step 2. For a batch of documents, the critic takes as input the concatenation of z y and z s on the one hand, and the concatenation of z y and z s randomly drawn from the dataset (equivalent to z y ∼ p(z y ), z s ∼ p(z s )), on the other hand. We then follow the training procedure introduced by  Arjovsky et al. (2017)  which alternate maximizing Equation 10 in the critic parameters for n c iterations and minimizing Equation 9 for n d iterations in the f y classifier parameters. The overview of the training process is detailed in the appendix B.
Dataset We employ two widely-used datasets to evaluate fairness in the context of text classification, building upon prior research  (Ravfogel et al. , 2020; Han et al. , 2021c; Shen et al. , 2022a). Both datasets are readily available in FairLib. Bias in Bios  (De-Arteaga et al. , 2019). This dataset, referred to as 'Bios dataset' in the rest of the paper, consists of brief biographies associated with occupations (a total of 28) and genders (male or female). As per the partitioning prepared by  (Ravfogel et al. , 2020) , the training, validation, and test sets comprise 257, 000, 40, 000 and 99, 000 samples, respectively. Moji  (Blodgett et al. , 2016). This dataset contains tweets written in either "Standard American English" (SAE) or "African American English" (AAE), annotated with positive or negative polarity.
Baselines Except for the classical cross-entropy loss without fairness constraint (CE) that we run ourselves, we report the results from  (Shen et al. , 2022a; Han et al. , 2022)  on these two datasets. The considered baselines are INLP  (Ravfogel et al. , 2020) , the ADV method  (Han et al. , 2021c) , FairBatch  (Roh et al. , 2021) , GATE  (Han et al. , 2021a)  and Con, displaying the dp and eo versions  (Shen et al. , 2022a).
We use the representation generated by a base BERT model as an input to the MLPs. For Bios, the demonic MLP is trained on 1% of the training set and obtains 99% accuracy for predicting the sensitive attributes on the test set. Similarly, the demonic MLP obtains 88. 5% accuracy on Moji. Task 2: Demonic transfer We conduct these experiments for Bios and train a demonic MLP either on the EEC dataset  (Kiritchenko and Mohammad, 2018)  or the Marked Personas dataset  (Cheng et al. , 2023). We then evaluate the performance of the demonic MLP to predict gender on the Bios test dataset. When training on the EEC dataset we obtain 98. 1% of accuracy, and on the Marked Personas dataset, we obtain 98. 4% of accuracy. We repeat Task 1, with those variants of the demonic MLP. We focus on Bios in this experiment.
For Moji, it would require to have access to other datasets with the same protected attribute (ethnicity). Task 3: Use of representations from different layers In the previous experiments, following approaches presented in  (Han et al. , 2022) , the Wasserstein distance is approximated using the last hidden representations of the 3-layer MLP. We compare this approach, on both datasets, with the use of the first hidden representations of the MLP and with the output logits. For the latter, the Wasserstein is estimated between distributions of different dimensions: for example, in the case of Bios, 2 for the demonic MLP corresponding to the sensitive attributes and 28 for the classification MLP corresponding to the labels. Task 4: Independence with predicted hard sensitive attributes To evaluate the impact of using the representation z s , we reproduce Task 1, but replace z s with the sensitive attributes predicted by the demonic MLP: ŝ and concatenate z y and ŝ dependently and independently when computing the Wasserstein distance.
Eventually, even when our method does not outperform the baselines (e. g. , Bios dataset), it still exhibits noteworthy properties, particularly its ability to achieve competitive performances without access to the sensitive attributes in the training set. We evaluate this capability in the next subsection. We also explore the ability of our proposition to improve the leakage. We initially aim at improving the fairness while maintaining the accuracy of the model. Yet, our method allows to improve leakage by increasing the value of β in Equation  9 , in other words, we give more importance to the Wasserstein regularization in the loss. We note in Table  2  that with a higher β, the leakage decreases. However, on both datasets, the accuracy, that we want to preserve, decreases and the trade-off worsens as we get better for the leakage.
Conclusion We presented WFC a novel method that enforces fairness constraints using a pre-trained neural network on the sensitive attributes and Wasserstein regularization. Our model is theoretically well-motivated and has interesting properties over existing models. The most important one is the fact that it does not require annotation of the sensitive attribute at both training and inference time. We obtain competitive results compared to baselines on the Bios dataset and outperform them on the fairness score with comparable accuracy on Moji dataset. Furthermore, we present a solution for our algorithm to be trained when sensitive attributes are not available for a given dataset, paving the way for its use under realistic applications. In further studies, we will focus on applying this method using different text encoders or decoders, datasets, and downstream tasks, as this method can generalize to tasks out of the text classification scope, notably, regression and even unsupervised objectives.
Limitations The proposed approach is rather flexible as it can handle various types of sensitive attributes. However, due to the lack of available datasets, we were not able to assess our performance for continuous sensitive attributes, e. g. age. In addition, we are aware that gender may embrace a n-ary definition, in all our experiments, we were limited to consider-ing only men vs women classification, due to data availability. For Task 2 defined in the experiments section, we were able to show empirically that our method works well when the demonic model is pre-trained on a different dataset when no sensitive attributes or very few of them are available on the main training dataset. However, we do not provide sufficient generalization guarantees to consider an out-of-thebox large-scale deployment.
Degraded resolution beyond the design limits will impact the ability of SPICE to achieve its science goals. This paper reports on some issues that degrade SPICE's resolution and discusses a novel data processing methodology that will remove the degradation. We use a set of coordinated SPICE and high spectral and spatial resolution Interface Region Imaging Spectrograph (IRIS; De  Pontieu et al. 2014 ) data for the examples and demonstrations in this paper, with IRIS data used as "ground truth" to assess the quality of the PSF correction and constrain the effective SPICE PSF. IRIS observes spectral lines that sample the same regions of the corona as SPICE, with very similar temperature response, and its high resolution makes a good starting point for degrading resolution to that of SPICE. The observation data were taken on March 7, 2022, when SPICE's orbit placed it on the Earth-Sun line, with the same perspective as IRIS.
Each of these coincide with bright features (e. g. , ridges) in intensity, as can be seen in the lower panels of Fig. 3. Because the features are absent from the original (plus down-binning) IRIS data, their appearance in the IRIS with the SPICE PSF clearly implicates the PSF in creating these artifacts. A similar effect and artifacts have been previously noted for the Hinode EUV Imaging Spectrometer (EIS;  Culhane et al. 2007). This is primarily discussed in an online EIS software note (Ugarte-Urra 2016) but is also alluded to in other papers, such as  Young et al. (2012)    The Doppler shift artifacts have fortunately been reduced thanks to the efforts of the SPICE team. This was done by adjusting the SPICE focus position and checking the results during instrument calibration campaigns. Consequently, the team has determined a focus position where the aberrations are significantly reduced.
Solvers: GMRes and Bicstab Since these forward matrices are very large and sparse (though they would not be solvable if they were not sparse), they require sparse solvers. These generally rely on some sort of iterative process in order to avoid dealing with the fully populated matrix. The code for applying these corrections was developed in Python, so we have been using the solver algorithms included in the sparse package of SciPy  (Virtanen et al. 2020b ). Specifically, the stabilized biconjugate gradient (bicgstab; van der Vorst 1992) and the "loose" generalized minimal residual (lgmres;  Baker et al. 2005)  algorithms. Both of these algorithms have a long heritage. They are variants of algorithms mentioned in the 1992 edition of  Press et al. (2002) , namely, the biconjugate gradient and generalized minimal residual methods.
This is shown in Fig. 10. In this figure, our method is called the Sparse NLMap (for "nonlinear map") reconstruction. The figure also shows a standard Richardson-Lucy (R-L) deconvolution that does not capture spatial variation of the PSF for comparison. We used the average of the PSFs in the field for the R-L deconvolution. The specific R-L algorithm used is the Python skimage package's restoration. richardson_lucy code. Since the exact shape of the PSF can be difficult to discern from a complex Solar Dynamics Observatory Atmospheric Imaging Assembly (AIA;  Lemen et al. 2012)   Notes. "Region 2" is the second region discussed in Sect. 4. 2 (Region 1 is shown in Fig. 1  and Region 2 in Fig. 15 ). The PSF size parameters had to be converted to natural detector plane units (microns) before the PSF was evaluated; otherwise, they are dimensionally inconsistent.
Further coordinated observation campaigns with IRIS along those lines are already in progress. We hope to produce, as a result, a map of the SPICE PSF at each point on the detector plane. Future A52, page 18 of 19 work will also include a survey of the results of PSF correction for each spectral window, including effectiveness of the correction for the spectral windows and for various signal-to-noise ratios. The corrections shown in this work were all done on a laptop computer with a 2. 6 GHz 6 core Intel Core i7. Although the results shown have been reduced in size, we also carried out correction of full resolution data on the same laptop. These took approximately 2 h, considerably less than the average acquisition rate of the SPICE data set as a whole (the cropped and binned down regions we showed took approximately 6 min).
Therefore, the CPU load is easily within the capacity of a central server to reprocess the data as well as of individual users who would reprocess the data on their own. Currently, the code base has been provided to other members of the SPICE team for beta testing. Once this is finished, the code will be made publicly available. In the meantime, the code will be provided upon request, pending approval of the SPICE team. The code was written in Python, making use of Numpy  (Harris et al. 2020 ), Astropy (Astropy Collaboration 2013 , 2018) , SciPy  (Virtanen et al. 2020b) , and (optionally) SunPy  (SunPy et al. 2020 ).
The experiments were performed on a computer with an In-tel®Xeon®CPU E5-2697 v3 @ 2. 60GHz, 264 GB of RAM, and the CentOS Linux 7 OS, using one CPU core per network. All code was implemented and run in Python 3. 5; the code is available at: https://msioutis. gitlab. io/software/ The results for the ParaLog variants and the FindOnePS problem, along with the metrics used, are qualitatively similar for both RCC8 and IA, and are shown in Table  1  and Figure  3  for the case of IA (the detailed results for the similar case of RCC8 are omitted to conserve space). The performance of ParaLog r is superior to the rest of the variants with respect to every important metric used, especially the ones regarding the use of the solver as a SAT oracle and the overall runtime. However, it is interesting to note that all variants are somewhat comparable with regard to the # of extra base relations that they report, with ParaLog r being only slightly better in general.
Setup Receivers. To obtain the data, we consider two probes made of the same components. The GNSS receiver used is the u-blox ZED-F9P, supplied by an embedded antenna Tallysman TW1889. Using these, we obtain raw data from the following signals: GPS (L1C/A, L2C), GLONASS (L1OF, L2OF), Galileo (E1B/C, E5b) and BeiDou (B1I, B2I). Experiment. The two probes are placed in a location with a clear sky above 15 •. They are positioned side by side, at 10. 8cm. We set the rate at 1s for 15 minutes, so as to obtain 900 epochs of data. the last mode, which is a combination of the two previous ones. The "Mixed" mode comprises two steps. First, we fix a limit for the residuals of the wide lane and compute Q ϕ the covariance matrix for the wide-lane combinations. We then run our search process, keeping every ambiguity vector such that ǫ ⋆ is smaller than our limit.
Recently, the Vision Transformer (ViT) model has been used for various computer vision tasks, due to its advantages to extracting long-range features. To better integrate the long-range features useful for classification, the standard ViT adds a class token, in addition to patch tokens. Despite state-of-the-art results on some traditional vision tasks, the ViT model typically requires large datasets for supervised training, and thus, it still face challenges in areas where it is difficult to build large datasets, such as medical image analysis. In the ViT model, only the output corresponding to the class token is fed to a Multi-Layer Perceptron (MLP) head for classification, and the outputs corresponding to the patch tokens are exposed. In this paper, we propose an improved ViT architecture (called ViT-Patch), which adds a shared MLP head to the output of each patch token to balance the feature learning on the class and patch tokens.
In addition to the primary task, which uses the output of the class token to discriminate whether the image is malignant, a secondary task is introduced, which uses the output of each patch token to determine whether the patch overlaps with the tumor area. More interestingly, due to the correlation between the primary and secondary tasks, the supervisory information added to the patch tokens help with improving the performance of the primary task on the class token. The introduction of secondary supervision information also improves the attention interaction among the class and patch tokens. And by this way, ViT reduces the demand on dataset size. The proposed ViT-Patch is validated on a publicly available dataset, and the experimental results show its effectiveness for both malignant identification and tumor localization.
Yi et al. [21]  proposed a local-global transformer (LGT) to preserve the intra-unit information and inter-unit correlation. Yuan et al. [22]  proposed tokens-to-token to imitate the convolution by reshaping the tokens in the transformer, which is useful for the interaction of tokens. In  [23] , Liu et al. proposed Swin-Transformer, to assist cross-window connection by the Shifted windows. And, as indicated by  [9] , compared to the CNN-based models, the ViT-based models usually require larger datasets to support their training, due to a more number of learnable parameters. However, for many medical related tasks such as the ultrasound image analysis also focused on in this paper, it is often difficult to build a large dataset like the natural image dataset ImageNet, due to the difficulties in acquiring and labeling data, which has resulted in the ViT models performing less efficiently than expected on many medical image analysis tasks.
The standard ViT architecture for image classification adds a class token in addition to the patch tokens to better aggregate the features extracted from each image patch. At the end of the transformer encoder, only the output corresponding to the class token is fed to a Multi-Layer Perceptron (MLP) head for classification, while all of the outputs corresponding to the patch tokens are directly discarded. To improve the training of ViT models on small training datasets, this work proposes an improved ViT architecture, called ViT-Patch, by introducing a secondary task on the patch tokens, which is related to the classification task (i. e. , the primary task) on the class token. An MLP head is added for each patch token in ViT-Patch to determine whether the patch overlaps with the tumor area (i.
e. , the secondary task). Since the primary and secondary tasks are closely related, the proposed ViT-Patch can obtain more supervised information from a single image sample to train model parameters, thus reducing the requirement for training datasets, compared with the standard ViT that relies only on the output of the class token. The proposed ViT-Patch is validated based on a publicly available breast ultrasound dataset. The experimental results show that ViT-Patch can effectively improve the efficiency of feature learning on the class token and patch tokens. Compared with the standard ViT, the ViT-Patch not only improves the performance of the primary task (malignant identifica-tion) on the class token, but also enables patch-based tumor localization using the outputs of patch tokens, which will be useful for subsequent detection or segmentation tasks.
Standard ViT for Malignant Identification The ViT replaces the traditional convolutional computation with a self-attentive mechanism in vision tasks. Figure  1  shows the standard ViT, which is composed of three modules: patch and position embedding, transformer encoder, and MLP head. Patch and position embedding: Since the input to transformer is a sequence of vectors, the ViT first needs to divide the input image into different patches and encode them to form a sequence of embedded vectors, i. e. , the initial patch tokens. Suppose that the input image is I ∈ R H×W×C , where C is the number of channels, and H and W represent the height and weight of the image, respectively. The size of each patch is S × S. There are N = HW/S 2 patches in total, and by patch and position embedding, N embedded vectors (patch tokens) are obtained.
Improved ViT-Patch Architecture The standard ViT usually requires large training datasets due to the large number of parameters. This makes it difficult for ViT models to take advantage of long-range feature extraction on some medical image analysis tasks where data acquisition and labeling costs are high, resulting in performance that is often inferior to that of CNN-based models. As can be seen from Figure  1 , only the class token is covered by an MLP head, while all patch tokens are not roofed in the standard ViT. This single MLP-head architecture tends to lead to the unbalanced training of class token and patch tokens. During the training phase, the supervised information is only back-propagated from the class token to the transformer encoder, which may lead to weaker and weaker associations between the features learned by each patch token and its initial embedded features, as the number of encoding blocks increases, since the MHSA mechanism allows for the lateral flow and fusion of embedded features between tokens.
As shown in Figure  2 , each row of the last layer token of ViT is much similar with the others. At the deeper levels, the patch tokens and class tokens have similar functionalities, and their output features are prone to redundancy, which can cause the model to be prone to overfitting on small datasets. We expect the model to processing image information like a real doctor, which can constantly interact with global and local information. And, we believe that the patch tokens contain valid information for category classification, so we hope to find a way to introduce the patch tokens into the training process. In this work, We try to let each token keep its own characteristic information, and so a secondary task is imposed on the patch tokens, which is related to the primary task (malignant identification) imposed on the class token.