Miracle-dz/UniFAR-FTUs huggingface dataset:
TOWARDS A PHILOSOPHY OF REAL MATHEMATICS

David Corfield starts his interesting book with a radical rejection of much that has been written about the philosophy of mathematics. He has no interest in ontology, epistemology, logicism, Platonism, Kantianism, nominalism, fictionalism, and so forth and does not mention most of them. Taking his cue from Nietzsche, Lakatos, and a few others, he likens the traditional approach to the examination of a dead body (pp. 3–5). He criticizes the attitude that has led many philosophers of mathematics to imagine that everything of interest to their subject occurred between 1880 and 1930, contrasting this with a very different attitude among the philosophers of physics. We shall see that events since the publication of his book make it even more relevant now than it might have seemed in 2003. One of Corfield’s strongest arguments for turning away from the philosophy of dead mathematics is that it focuses on logical correctness and is powerless to explain why some topics are regarded as crucial to the subject while others are considered irrelevant, no more than technical games. He argues that if one wants to discuss real mathematics, one has to place the subject in its historical context and focus on the value judgments that mathematicians make and what lies behind them. Chapter 1 is fairly demanding philosophically and is written to explain his rejection of the standard approach and to lay out his stall. Put briefly, he is interested in teasing out philosophical questions from the many ways in which real mathematics has been done. He castigates philosophers for presenting the subject as if it were a purely abstract entity that can and should be discussed in a historical and social vacuum. He has to avoid going Towards a Philosophy of Real Mathematics
On-and off-line identification of linear state space models

A geometrically inspired matrix algorithm is derived for the identification of state space models for multivariable linear time-invariant systems and using possibly noisy input- output measurements data only. In this project, only a limited number of input and output data are required for the determination of the system matrices. The algorithm can be best described and also understood in the matrix formalism and consists in the following two steps. First step, a state vector sequence is realized as the intersection of the row spaces of two block Hankel matrices which is constructed by apply input - output data. Then, the system matrices are obtained at once from the least squares solution of a set of linear equations. When dealing with noisy data, this algorithm draws its excellent performance from repeated use of the numerically stable and accurate singular value decomposition. The algorithm is easily applied to slowly time-varying systems using windowing or exponential weighting.
Does export lead growth? evidence from Japan

: Japan shifted towards export-led growth strategy in the 1980s. This study analyzes the role of export performance in the Japanese economic growth. In order to examine the causal relationship between exports and economic growth, the study applied standard time series techniques. The results indicate that imports are important in positively affecting economic growth, indicating that economies should permit a greater flow of imports into the domestic economy through lowering trade barriers. Secondly, in terms of the role of exports, the evidence of export-led growth for Japan indicates that there is a strong argument for governments to follow an export-promotion strategy thereby providing exporters greater incentives to export, for example, by implementing export subsidies and adopting a favorable exchange rate policy. This analysis indicates that export growth tends to have positive long-run effect on output growth in Japan, which thereby supports the export-led growth hypothesis.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

Distributed systems are hard to design and implement correctly. Recent work has tried to use formal verification techniques to provide rigorous correctness guarantees. These works present a hard choice, though. One must either opt for the power of refinement-based approaches like IronFleet and Verdi, at the cost of large amounts of manual effort; or choose the more automated approach of I4, IC3PO, SWISS and DistAI which give up the ability to prove refinement and the power and scalability that come with it. We propose an alternative approach, Sift, that combines the power of refinement with the ability to automate proofs. Sift is a two-tier methodology that uses a new technique, refinement-guided automation , to leverage automation in a refinement proof and a divide-and-conquer technique to split a system into more refinement layers when necessary. This combination advances the frontier of what systems can be proven correct using a high degree of automation. Contrary to what was possible before, our evaluation shows that our novel approach allows us to prove the correctness of a number of systems with little manual effort, and to extend our proofs to include not just the protocols, but also an executable distributed implementation of these systems.
Qualitative Comparison with SOTA meth

In this supplementary manuscript, we showcase (i) qualitative comparison of final localization maps between different weakly supervised object localization (WSOL) methods for a few query images, (ii) intuitive comparison of GAR and LRP across different transformer backbones, iii) comparison of the patch drop mask visualization across the transformer blocks with respect to the base transformer model DeiT-B, (iv) ablation of hyper-parameters chosen for the p-ADL layer.
AugLy: Data Augmentations for Adversarial Robustness

We introduce AugLy, a data augmentation library with a focus on adversarial robustness. AugLy provides a wide array of augmentations for multiple modalities (audio, image, text, & video). These augmentations were inspired by those that real users perform on social media platforms, some of which were not already supported by existing data augmentation libraries. AugLy can be used for any purpose where data augmentations are useful, but it is particularly well-suited for evaluating robustness and systematically generating adversarial attacks. In this paper we present how AugLy works, benchmark it against existing libraries, and use it to evaluate the robustness of various state-of-the-art models to showcase AugLy’s utility. We found that models trained using a wider variety of augmentations were indeed more robust to AugLy augmentations, which validates the hypothesis that training on augmented data improves robustness against adversarial attacks. The AugLy repository can be found at https://github.com/facebookresearch/AugLy.
Contactless Blood Pressure Measurement via Remote Photoplethysmography with Synthetic Data Generation Using Generative Adversarial Network

Deriving blood pressure in a non-invasive way via photoplethysmography (PPG) signals has become a familiar topic. With the knowledge of the relation between PPG and blood pressure, we expect to further make the measurement contactless for convenience reasons. An alternative signal source is remote photoplethysmography (rPPG) signals. There are mainly two kinds of approaches for exploiting blood pressure through PPG signals, one is by calculating the pulse transit time of the arterial pulse wave at two consecutive sites and the other is based on waveform feature analysis from a single signal. The calibration procedure is necessary for the former way, which leads to some limitations in general use. On the other hand, the properties of the rPPG waveform are far from PPG signals. Hence, the known waveform features in PPG signals are hard to be leveraged in the case of rPPG signals. Recently, convolutional neural networks are also applied for solving this problem. However, the lack of data is an obstacle to the training procedure and evaluation. In this study, a multichannel rPPG-based blood pressure estimator is proposed. To ease the data scarcity issue, the generative adversarial network is adopted to augment synthetic waveform data. Besides, as we know that some physiological states like age and BMI are dominant factors in blood pressure. InfoGAN is chosen in this work to generate the synthetic data with the blood pressure value fluctuating correspondingly to the controlled age and BMI combination. The proposed model outperforms the state-of-the-art methods on MIMIC III and Cuffless datasets. With the synthetic data generation, the mean absolute error (MAE) is reduced to 6.72 and 5.95 mmHg in MAP and DBP respectively. The standard deviations of the MAEs are also reduced. In the rPPG case, the MAE of SBP is 9.13 and 8.76 mmHg for DBP.
Sea Situational Awareness (SeaSAw) Dataset

Vessels move 90% of international cargo by volume, with the marine economy contributing to 5.1% of global GDP. As one of the oldest industries, the marine industry has yet to embrace innovations in modern technology to safeguard the blue economy. Situational awareness from intelligent vessel systems can enable enhanced safety and decision-making for mariners. As the foundation for these intelligent systems, advanced perception technology requires sufficient real-world operational data to leverage recent AI technologies. In this work, we introduce the Sea Situational Awareness (SeaSAw) dataset - a novel dataset that is comprised of 1.9 million images with 14.6 million objects associated with 20.4 million attributes from 12 object classes, making it the largest maritime dataset for object detection, fine-grained classification and tracking. Furthermore, this dataset consists of 9 sources in combination with various RGB cam-eras, mounted on different moving vessels, operating in different geographic locations globally, having variations in scenario, weather and illumination conditions. This data collection took place across 4 years with rigorous efforts on data selection, annotation, management and analysis to enhance the marine perception technology.
Socially Responsible Virtual Assistant for Privacy Protection: Implementing Trustworthy AI

The paper introduces an AI-based virtual assistant VILEM whose primary aim is to strengthen individual right to informational self-determination on the Internet. VILEM helps users to manage their privacy settings, protect themselves against potentially abusive websites, and saves time of users as it presents relevant information on personal data processing in a comprehensible manner. The paper also presents how VILEM fulﬁlls requirements on Trustworthy AI
Designing and implementing future scenarios: Foresight methodology, economics, technologies, society,and ecology

The article discusses several aspects of incorporating foresight into the science, technology, and innovation (STI) policy planning process, as well as a set of recommendations for managing uncertainties and ensuring policy alignment with society's future needs. The goal of this study is to investigate the role of technology foresight in decision-making and to develop recommendations on how to effectively integrate national foresight into the process of STI policy planning. Innovation has emerged as a key driver of global economic development, and it continues to be at the forefront of technological breakthroughs. Developed countries are increasingly focusing their efforts on research and development (R&D) in areas that will determine megatrends of technological and social progress in the coming decades. To identify key priority areas foresight brings together key agents of change and various sources of knowledge. Foresight is not aimed at providing an accurate prediction of the future, but allowing to design alternative scenarios of possible futures and to elaborate policies and strategies to achieve "the desired scenario". The paper states that foresight provides a basis for STI policy planning by identification of key areas for long-term investments and assessing long-term perspectives of science, technology, economy and society development. discuss various aspects of incorporating foresight outputs into decision-making processes and to deliberate recommendations for improving the efficiency of strategic policy planning based on foresight. roadmapping
Strain Detection based on Breath and Motion Features Obtained by a Force Sensor for Smart Toilet Systems

Aging people may be prone to accidents in bathrooms and toilets. The detection of strain motion for a smart toilet application has not been studied sufficiently. In this paper, we propose a method for strain detection from a force sensor placed on a toilet seat for a smart toilet healthcare application. The method first extracts breath and motion features that are assumed to be key components for the strain detection. The method then learns the discriminator model based on the random forest classifier using the aforementioned features. Finally, the method recognizes actions in the toilet room. There were five detection actions: seating, taking up toilet paper, wiping bottom, which are normal actions when sitting on a toilet seat, and strain actions (strong and weak). An experiment with 19 subjects was also conducted. Compared with a microwave sensor-based recognition, which is a conventional method (accuracy = 61.6%), our method was able to recognize the actions with high accuracy of 80.2% (significant test: T = 12.7, P < 0.01) in the experiment. Our strain detection method has the potential to be used as a smart toilet system to prevent blood pressure elevation and collapse caused by strain in the future.
High-Speed Rail and Manufacturing Agglomeration

In China, the roles played by high speed rail (HSR) in improving regional accessibility and promoting urban growth have recently generated significant research interest. However, the question as to how HSR could shape urban development seems rather complex. This paper aims to discuss HSR's role in shaping urban economic development by tracing links from HSR to manufacturing agglomeration with panel data from the Beijing–Guangzhou HSR network in China from 2000 to 2015. On the basis of the new economic geography model, market potential and manufacturing wages are incorporated into the analysis framework, and a treatment effect model is used to account for the endogeneity of HSR in the study. Model results provide three conclusions. First, in general, HSR significantly increases manufacturing agglomeration for HSR cities. Second, an inverted-U relationship between the agglomeration impact of HSR and market potential is confirmed. That is, while the agglomeration effect by HSR is significantly within a threshold level of market potential, HSR in fact has a negative effect, or the dispersion effect, on manufacturing agglomeration beyond the threshold level. Third, the agglomeration impact of HSR differs across regions. For example, the agglomeration impact of HSR is more significant in the second-tier HSR cities than in major HSR cities, indicating the overall trend of regional integration and economic convergence among different regions of HSR through time. Results from this study are useful for policy makers as well as for researchers for analyzing the economic implications of HSR in the future.
Green Space for Mental Health in the COVID-19 Era: A Pathway Analysis in Residential Green Space Users

Residential green space is among the most accessible types of urban green spaces and may help maintain mental health during the COVID-19 pandemic. However, it is insufficiently understood how residents use residential green space for exercise during the epidemic. The pathways between residential green space and mental health also merit further exploration. Therefore, we conducted an online study among Chinese residents in December 2021 to capture data on engagement with urban green space for green exercise, the frequency of green exercise, perceived pollution in green space, perceptions of residential green space, social cohesion, depression, and anxiety. Among the 1208 respondents who engaged in green exercise last month, 967 (80%) reported that green exercise primarily occurred in residential neighborhoods. The rest (20%) reported that green exercise occurred in more distant urban green spaces. The most common reasons that respondents sought green exercise in urban green spaces were better air and environmental qualities. Structural equation modeling (SEM) was then employed to explore the pathways between the perceived greenness of residential neighborhoods and mental health among respondents who used residential green space for exercise. The final model suggested that residential green space was negatively associated with anxiety (β = −0.30, p = 0.001) and depression (β = −0.33, p < 0.001), mainly through indirect pathways. Perceived pollution and social cohesion were the two mediators that contributed to most of the indirect effects. Perceived pollution was also indirectly associated with green exercise through less social cohesion (β = −0.04, p = 0.010). These findings suggest a potential framework to understand the mental health benefits of residential green space and its accompanying pathways during the COVID-19 era.
Ensembling of Various Transformer Based Models for the Fake News Detection Task in the Urdu Language

The spread of misinformation has become a severe issue affecting society. Inaccurate information has enormous potential to cause real-world impacts. Developing algorithms to detect fake news automatically will be very useful in preventing unnecessary panic and damage caused by rumors. This fake news problem is present for all languages, and it becomes crucial to solve it for languages other than English, with scarce datasets. This paper aims to tackle the problem of automatic fake news detection in Urdu, a low-resource language. FIRE-2021 has provided the Urdu dataset used in this paper. We fine-tuned monolingual and multilingual transformers. After searching for hyperparameters, we tried ensembling our models. We submitted our model for the UrduFake task, and it achieved an accuracy of 0.596 and an F1-macro score of 0.449.
A Comparative Study on the Influence of Undersampling and Oversampling Techniques for the Classification of Physical Activities Using an Imbalanced Accelerometer Dataset

Accelerometer data collected from wearable devices have recently been used to monitor physical activities (PAs) in daily life. While the intensity of PAs can be distinguished with a cut-off approach, it is important to discriminate different behaviors with similar accelerometry patterns to estimate energy expenditure. We aim to overcome the data imbalance problem that negatively affects machine learning-based PA classification by extracting well-defined features and applying undersampling and oversampling methods. We extracted various temporal, spectral, and nonlinear features from wrist-, hip-, and ankle-worn accelerometer data. Then, the influences of undersampilng and oversampling were compared using various ML and DL approaches. Among various ML and DL models, ensemble methods including random forest (RF) and adaptive boosting (AdaBoost) exhibited great performance in differentiating sedentary behavior (driving) and three walking types (walking on level ground, ascending stairs, and descending stairs) even in a cross-subject paradigm. The undersampling approach, which has a low computational cost, exhibited classification results unbiased to the majority class. In addition, we found that RF could automatically select relevant features for PA classification depending on the sensor location by examining the importance of each node in multiple decision trees (DTs). This study proposes that ensemble learning using well-defined feature sets combined with the undersampling approach is robust for imbalanced datasets in PA classification. This approach will be useful for PA classification in the free-living situation, where data imbalance problems between classes are common.
TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM

3D object tracking is a critical task in autonomous driving systems. It plays an essential role for the system’s awareness about the surrounding environment. At the same time there is an increasing interest in algorithms for autonomous cars that solely rely on inexpensive sensors, such as cameras. In this paper we investigate the use of triplet embeddings in combination with motion representations for 3D object tracking. We start from an off-the-shelf 3D object detector, and apply a tracking mechanism where objects are matched by an affinity score computed on local object feature embeddings and motion descriptors. The feature embeddings are trained to include information about the visual appearance and monocular 3D object characteristics, while motion descriptors provide a strong representation of object trajectories. We will show that our approach effectively re-identifies objects, and also behaves reliably and accurately in case of occlusions, missed detections and can detect re-appearance across different field of views. Experimental evaluation shows that our approach outperforms state-of-the-art on nuScenes by a large margin. We also obtain competitive results on KITTI.
deepPIC: Deep Perceptual Image Clustering For Identifying Bias In Vision Datasets

Dataset bias in manually collected datasets is a known problem in computer vision. In safety-critical applications such as autonomous driving, these biases can lead to catastrophic errors from models trained on such datasets, jeopardizing the safety of users and their surroundings. Being able to unpuzzle the bias in a given dataset, and across datasets, is an essential tool for building safe and responsible AI. In this paper, we present deepPIC: deep Perceptual Image Clustering, a novel hierarchical clustering pipeline that leverages deep perceptual features to visualize and understand bias in unstructured and unlabeled datasets. It does so by effectively highlighting nuanced subcategories of information embedded within the data (such as multiple but repetitive shadow types) that typically are hard and/or expensive to annotate. Through experiments on a variety of image datasets, both open-source and internal, we demonstrate the effectiveness of deepPIC in (i) singling out errors in metadata from open-source datasets such as BDD100K; (ii) automatic nuanced metadata annotation; (iii) mining for edge cases; (iv) visualizing inherent bias both within and across multiple datasets; and (v) capturing synthetic data limitations; thus highlighting the wide variety of applications this pipeline can be applied to. All clustering results included here have been uploaded with image thumbnails on our project website - https://alchemz.github. io/unpuzzle_dataset_bias/. We recommend zooming in for best impact.
Hydroelastic support in particular for the suspension of a power unit in the body of a motor vehicle

The invention relates to a hydroelastic support to be interposed between two elements to be suspended relative to one another, of the type comprising a body (10) formed by a mass (11) of elastomeric material connecting an upper frame (12) and a lower reinforcement (14), a housing (40), a working chamber (2) and an expansion chamber (3) filled with liquid and communicating with each other by a connecting conduit (4 ) and a valve (20) hydraulically decoupling. The working chamber (2) is delimited at the top by the mass (11) of elastomeric material and at the bottom by a closure piece (30) and the expansion chamber (3) has an annular shape around the working chamber (2) and is delimited at the top by a membrane (50) mounted on the periphery of the body (10) and at the bottom by the hydraulic decoupling valve (20) . L'invention applies to the suspension a drive train on the body of a motor vehicle.