Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust-GBDT: A Novel Gradient Boosting Model for Noise-Robust Classification

—Robust boosting algorithms have emerged as alternative solutions to traditional boosting techniques for addressing label noise in classification tasks. However, these methods have predominantly focused on binary classification, limiting their applicability to multi-class tasks. Furthermore, they encounter challenges with imbalanced datasets, missing values, and computational efficiency. In this paper, we establish that the loss function employed in advanced Gradient Boosting Decision Trees (GBDT), particularly Newton’s method-based GBDT, need not necessarily exhibit global convexity. Instead, the loss function only requires convexity within a specific region. Consequently, these GBDT models can leverage the benefits of nonconvex robust loss functions, making them resilient to noise. Building upon this theoretical insight, we introduce a new noise-robust boosting model called Robust-GBDT, which seamlessly integrates the advanced GBDT framework with robust losses. Additionally, we enhance the existing robust loss functions and introduce a novel robust loss function, Robust Focal Loss, designed to address class imbalance. As a result, Robust-GBDT generates more accurate predictions, significantly enhancing its generalization capabilities, especially in scenarios marked by label noise and class imbalance. Furthermore, Robust-GBDT is user-friendly and can easily integrate existing open-source code, enabling it to effectively handle complex datasets while improving computational efficiency. Numerous experiments confirm the superiority of Robust-GBDT over other noise-robust methods. The code is available at
Phasor Noise for Dehomogenisation in 2D Multiscale Topology Optimisation

This paper presents an alternative approach to dehomogenisation of elastic Rank-N laminate structures based on the computer graphics discipline of phasor noise. The proposed methodology offers an improvement of existing methods, where high-quality single-scale designs can be obtained efficiently without the utilisation of any least-squares problem or pre-trained models. By utilising a continuous and periodic representation of the translation at each intermediate step, appropriate length-scale and thicknesses can be obtained. Numerical tests verifies the performance of the proposed methodology compared to state-of-the-art alternatives, and the dehomogenised designs achieve structural performance within a few percentages of the optimised homogenised solution. The nature of the phasor-based dehomogenisation is inherently mesh-independent and highly parallelisable, allowing for further efficient implementations and future extensions to 3D problems on unstructured meshes.
sign.mt: Real-Time Multilingual Sign Language Translation Application

This demo paper presents sign.mt, an open-source application pioneering real-time multilingual bi-directional translation between spoken and signed languages. Harnessing state-of-the-art open-source models, this tool aims to address the communication divide between the hearing and the deaf, facilitating seamless translation in both spoken-to-signed and signed-to-spoken translation directions. Promising reliable and unrestricted communication, sign.mt offers offline functionality, crucial in areas with limited internet connectivity. It further enhances user engagement by offering customizable photo-realistic sign language avatars, thereby encouraging a more personalized and authentic user experience. Licensed under CC BY-NC-SA 4.0, sign.mt signifies an important stride towards open, inclusive communication. The app can be used, and modified for personal and academic uses, and even supports a translation API, fostering integration into a wider range of applications. However, it is by no means a finished product. We invite the NLP community to contribute towards the evolution of sign.mt. Whether it be the integration of more refined models, the development of innovative pipelines, or user experience improvements, your contributions can propel this project to new heights. Available at https://sign.mt, it stands as a testament to what we can achieve together, as we strive to make communication accessible to all.
Normalizing Flows for Bayesian Posteriors: Reproducibility and Deployment

We present a computational framework for efficient learning, sampling, and distribution of general Bayesian posterior distributions. The framework leverages a machine learning approach for the construction of normalizing flows for the general probability distributions typically encountered in Bayesian uncertainty quantification studies. This normalizing flow can map a trivial distribution to a more complicated one and can be stored more efficiently than the empirical distribution samples themselves. Once the normalized flow is trained, it further enables parallelized and uncorrelated sampling of the learned distribution. We demonstrate our framework with three test distributions with strong non-linear correlations, multi-modality, and heavy tails, as well as with a realistic posterior distribution obtained from a Bayesian calibration of a nuclear relativistic mean-field model. The performance of the framework, as well as its relatively simple implementation, positions it as one fundamental cornerstone in the development and deployment of continuous calibration pipelines of physical models and as a key component of future reproducible science workflows.
SwimXYZ: A large-scale dataset of synthetic swimming motions and videos

Technologies play an increasingly important role in sports and become a real competitive advantage for the athletes who benefit from it. Among them, the use of motion capture is developing in various sports to optimize sporting gestures. Unfortunately, traditional motion capture systems are expensive and constraining. Recently developed computer vision-based approaches also struggle in certain sports, like swimming, due to the aquatic environment. One of the reasons for the gap in performance is the lack of labeled datasets with swimming videos. In an attempt to address this issue, we introduce SwimXYZ, a synthetic dataset of swimming motions and videos. SwimXYZ contains 3.4 million frames annotated with ground truth 2D and 3D joints, as well as 240 sequences of swimming motions in the SMPL parameters format. In addition to making this dataset publicly available, we present use cases for SwimXYZ in swimming stroke clustering and 2D pose estimation.
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations

Deep classification models can struggle when the number of examples per class varies dramatically [5, 2 45]. This long-tailed setting arises frequently in practice, such as wildlife recognition [5]. Classifiers 3 tend to be biased towards majority classes and perform poorly on class-balanced test distributions, i.e. 4 when there is a shift in the label distribution between training and test. Existing approaches focus 5 on single-domain long-tailed learning, while we study multi-domain long-tailed learning, where 6 each domain has its own long-tailed distribution and the classifiers need to handle distribution shift 7 amidst class imbalance. Here, we focus on two types of distribution shift: subpopulation shift and 8 domain shift. In subpopulation shift, we train a model on data from multiple domains and evaluate 9 the model on a test set with balanced domain-class pairs. A machine learning model trained on the 10 entire population may fail on the test set when this correlation does not hold anymore. In domain 11 shift, we expect the trained model to generalize well to completely new test domains. 12
Narratives of Internationalisation: Legitimacy, Standards and Portfolio Entrepreneurs

Contents: Preface PART I 1. Conversations in SME Internationalisation 2. Approach to Inquiry 3. Method as Experienced PART II 4. A Tale of Growth and Internationalisation 5. Episode 10: The GLT Group in 1996 (June-November 1996) 6. Episode 1: The Start of It (1975 and Before) 7. Episode 2: Born Global (1976-1982) 8. Organisational Legitimacy and Internationalisation 9. Episode 3: Forays in Mini-hydros (1979-1986) 10. Episode 4: Controlling Destiny (June 1982-May 1986) 11. Episode 5: The Marshalling of Forces (June-November 1986) 12. Episode 6: The Dynasphere (December 1985-June 1991) 13. Standards and Institutional Entrepreneurship 14. Episode 7: Forays into Software (May 1986-June 1988) 15. Episode 8: Demise of Alliances and FDI (July 1988-April 1991) 16. Episode 9: Of Elephants (April 1991-July 1994) 17. Portfolio Entrepreneurship 18. Episode 11: Responses to the Takeover Threat (October 1996-February 1997) 19. Episode 12: Vision of the Octopus (March-May 1997) 20. New Directions References Appendices Index
Sparsistency for Inverse Optimal Transport

Optimal Transport is a useful metric to compare probability distributions and to compute a pairing given a ground cost. Its entropic regularization variant (eOT) is crucial to have fast algorithms and reflect fuzzy/noisy matchings. This work focuses on Inverse Optimal Transport (iOT), the problem of inferring the ground cost from samples drawn from a coupling that solves an eOT problem. It is a relevant problem that can be used to infer unobserved/missing links, and to obtain meaningful information about the structure of the ground cost yielding the pairing. On one side, iOT benefits from convexity, but on the other side, being ill-posed, it requires regularization to handle the sampling noise. This work presents an in-depth theoretical study of the l1 regularization to model for instance Euclidean costs with sparse interactions between features. Specifically, we derive a sufficient condition for the robust recovery of the sparsity of the ground cost that can be seen as a far reaching generalization of the Lasso's celebrated Irrepresentability Condition. To provide additional insight into this condition, we work out in detail the Gaussian case. We show that as the entropic penalty varies, the iOT problem interpolates between a graphical Lasso and a classical Lasso, thereby establishing a connection between iOT and graph estimation, an important problem in ML.
Commissioning and first measurements of the initial X-ray and {\gamma}-ray detectors at FACET-II

The upgraded Facility for Advanced Accelerator Experimental Tests (FACET-II) at SLAC National Accelerator Laboratory has been designed to deliver ultra-relativistic electron and positron beams with unprecedented parameters, especially in terms of high peak current and low emittance. For most of the foreseen experimental campaigns hosted at this facility, the high energy radiation produced by these beams at the Interaction Point will be a valuable diagnostic to assess the different physical processes under study. This article describes the X-ray and {\gamma}-ray detectors installed for the initial phase of FACET-II. Furthermore, experimental measurements obtained with these detectors during the first commissioning and user runs are presented and discussed, illustrating the working principles and potential applications of these detectors.
Based on What We Can Control Artificial Neural Networks

How can the stability and efficiency of Artificial Neural Networks (ANNs) be ensured through a systematic analysis method? This paper seeks to address that query. While numerous factors can influence the learning process of ANNs, utilizing knowledge from control systems allows us to analyze its system function and simulate system responses. Although the complexity of most ANNs is extremely high, we still can analyze each factor (e.g., optimiser, hyperparameters) by simulating their system response. This new method also can potentially benefit the development of new optimiser and learning system, especially when discerning which components adversely affect ANNs. Controlling ANNs can benefit from the design of optimiser and learning system, as (1) all optimisers act as controllers, (2) all learning systems operate as control systems with inputs and outputs, and (3) the optimiser should match the learning system. Please find codes: \url{https://github.com/RandomUserName2023/Control-ANNs}.
Multicolor Photometry of Tiny Near-Earth Asteroid 2015 RN35 across a Wide Range of Phase Angles: Possible Mission-accessible A-type Asteroid

Studying small near-Earth asteroids is important in order to understand their dynamical histories and origins as well as to mitigate the damage caused by asteroid impacts on Earth. We report the results of multicolor photometry of the tiny near-Earth asteroid 2015 RN35 using the 3.8 m Seimei telescope in Japan and the TRAPPIST-South telescope in Chile over 17 nights in 2022 December and 2023 January. We observed 2015 RN35 across a wide range of phase angles from 2° to 30° in the g, r, i, and z bands in the Pan-STARRS system. These lightcurves show that 2015 RN35 is in a nonprincipal axis spin state with two characteristic periods of 1149.7 ± 0.3 s and 896.01 ± 0.01 s. We found that the slope of the visible spectrum of 2015 RN35 is as red as asteroid (269) Justitia, one of the very red objects in the main belt, which indicates that 2015 RN35 can be classified as an A- or Z-type asteroid. In conjunction with the shallow slope of the phase curve, we suppose that 2015 RN35 is a high-albedo A-type asteroid. We demonstrated that surface properties of tiny asteroids could be well constrained by intensive observations across a wide range of phase angles. 2015 RN35 is a possible mission-accessible A-type near-Earth asteroid with a small Δv of 11.801 km s−1 in the launch window between 2030 and 2035.
Source locations of secondary microseisms in western Europe: Evidence for both coastal and pelagic sources

[1] We locate the sources of double-frequency (or secondary) microseisms in western Europe by frequency slowness analysis of array data as well as polarization and amplitude analysis at individual stations. Array analysis uses data recorded by a temporary array of broadband stations that we deployed in the Quercy region (southwest of France) and those from the Grafenberg array, from 2 December 2005 to 30 January 2006. We determine attenuation laws for microseisms generated in the Mediterranean Sea and in the Atlantic Ocean, which allow us to use noise amplitudes to estimate distances from the source. We then combine azimuth and amplitude measurements to obtain precise locations of microseisms and estimate their source dimensions. Most of the time, microseismic noise originates in coastal regions where the swell reaches steep rocky coasts with normal incidence, in good agreement with the Longuet-Higgins model for the generation of secondary microseisms. In addition, we find evidence of occasional pelagic sources, which are closely related to moving storms, suggesting that nonlinear interaction between wave components can also generate secondary microseisms.
Edge Computing-Enabled Road Condition Monitoring: System Development and Evaluation

[EMBARGOED UNTIL 8/1/2024] Real-time pavement condition monitoring provides highway agencies with timely and accurate information that could form the basis of pavement maintenance and rehabilitation policies. Existing technologies for pavement condition monitoring rely heavily on manual data processing, are expensive and therefore, difficult to scale for frequent, network level pavement condition monitoring. Additionally, these systems require sending large packets of data to the cloud which requires large storage space, are computationally expensive to process, and results in high latency. The current study proposes a solution that capitalizes on the widespread availability of affordable Micro Electro-Mechanical System (MEMS) sensors, edge computing and internet connection capabilities of microcontrollers, and deployable machine learning models (Tiny ML) to (a) design an Internet of Things (IoT)-enabled device that can be mounted on axles of vehicles to stream live pavement condition data (b) reduce latency through on-device processing and analytics of pavement condition sensor data before sending to the cloud servers (c) facilitate participatory crowd-sensing of pavement condition data at the societal level. In this study, three machine learning models including Random Forest, LightGBM and XGBoost were trained to predict International Roughness Index (IRI) at every 0.1-mile segment. XGBoost has the highest accuracy with an RMSE and MAPE of 16.89in/mi and 20.3 percent, respectively. In terms of the ability to classify the IRI of pavement segments based on ride quality according to MAP-21 criteria, our proposed device achieved an average accuracy of 96.76 percent on I-70EB and 63.15 percent on South Providence (MO-163). Overall, our proposed device demonstrates significant potential in providing real-time International Roughness Index (IRI) data to State Highway Agencies (SHA) and state Department of Transportation (DOTs) with a satisfactory level of accuracy.
Diffusion Models as Masked Audio-Video Learners

Over the past several years, the synchronization between audio and visual signals has been leveraged to learn richer audio-visual representations. Aided by the large availability of unlabeled videos, many unsupervised training frameworks have demonstrated impressive results in various downstream audio and video tasks. Recently, Masked Audio-Video Learners (MAViL) has emerged as a state-of-the-art audio-video pre-training framework. MAViL couples contrastive learning with masked autoencoding to jointly reconstruct audio spectrograms and video frames by fusing information from both modalities. In this paper, we study the potential synergy between diffusion models and MAViL, seeking to derive mutual benefits from these two frameworks. The incorporation of diffusion into MAViL, combined with various training efficiency methodologies that include the utilization of a masking ratio curriculum and adaptive batch sizing, results in a notable 32% reduction in pre-training Floating-Point Operations (FLOPS) and an 18% decrease in pre-training wall clock time. Crucially, this enhanced efficiency does not compromise the model's performance in downstream audio-classification tasks when compared to MAViL's performance.
Physics-constrained symbolic model discovery for polyconvex incompressible hyperelastic materials

We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate basis that can be re-parametrized with a more complex mathematical form, and (2) the resultant elasticity model is guaranteed to fulfill the polyconvexity, which ensures that the acoustic tensor remains elliptic for any deformation. To further improve the interpretability, we use genetic programming to convert each univariate basis into a compact mathematical expression. The resultant multi-variable mathematical models obtained from this proposed framework are not only more interpretable but are also proven to fulfill physical laws. By controlling the compactness of the learned symbolic form, the machine learning-generated mathematical model also requires fewer arithmetic operations than its deep neural network counterparts during deployment. This latter attribute is crucial for scaling large-scale simulations where the constitutive responses of every integration point must be updated within each incremental time step. We compare our proposed model discovery framework against other state-of-the-art alternatives to assess the robustness and efficiency of the training algorithms and examine the trade-off between interpretability, accuracy, and precision of the learned symbolic hyperelastic models obtained from different approaches. Our numerical results suggest that our approach extrapolates well outside the training data regime due to the precise incorporation of physics-based knowledge.
CLAID: Closing the Loop on AI & Data Collection - A Cross-Platform Transparent Computing Middleware Framework for Smart Edge-Cloud and Digital Biomarker Applications

The increasing number of edge devices with enhanced sensing capabilities, such as smartphones, wearables, and IoT devices equipped with sensors, holds the potential for innovative smart-edge applications in healthcare. These devices generate vast amounts of multimodal data, enabling the implementation of digital biomarkers which can be leveraged by machine learning solutions to derive insights, predict health risks, and allow personalized interventions. Training these models requires collecting data from edge devices and aggregating it in the cloud. To validate and verify those models, it is essential to utilize them in real-world scenarios and subject them to testing using data from diverse cohorts. Since some models are too computationally expensive to be run on edge devices directly, a collaborative framework between the edge and cloud becomes necessary. In this paper, we present CLAID, an open-source cross-platform middleware framework based on transparent computing compatible with Android, iOS, WearOS, Linux, macOS, and Windows. CLAID enables logical integration of devices running different operating systems into an edge-cloud system, facilitating communication and offloading between them, with bindings available in different programming languages. We provide Modules for data collection from various sensors as well as for the deployment of machine-learning models. Furthermore, we propose a novel methodology,"ML-Model in the Loop"for verifying deployed machine learning models, which helps to analyze problems that may occur during the migration of models from cloud to edge devices. We verify our framework in three different experiments and achieve 100% sampling coverage for data collection across different sensors as well as an equal performance of a cough detection model deployed on both Android and iOS devices. We evaluate the memory and battery consumption of our framework.
Zero-Point Energy Density at the Origin of the Vacuum Permittivity and Photon Propagation Time Fluctuation

We give a vacuum description with zero-point density for virtual fluctuations. One of the goals is to explain the origin of the vacuum permittivity and permeability and to calculate their values. In particular, we improve on existing calculations by avoiding assumptions on the volume occupied by virtual fluctuations. We propose testing of the models that assume a finite lifetime of virtual fluctuation. If during its propagation, the photon is stochastically trapped and released by virtual pairs, the propagation velocity may fluctuate. The propagation time fluctuation is estimated for several existing models. The obtained values are measurable with available technologies involving ultra-short laser pulses, and some of the models are already in conflict with the existing astronomical observations. The phase velocity is not affected significantly, which is consistent with the interferometric measurements.