Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Supervised Depth Completion for Active Stereo

Active stereo systems are used in many robotic applications that require 3D information. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth estimates. In this work, we present the first self- supervised depth completion method for active stereo systems that predicts accurate dense depth maps. Our system leverages a feature- based visual inertial SLAM system to produce motion estimates and accurate (but sparse) 3D landmarks. The 3D landmarks are used both as model input and as supervision during training. The motion estimates are used in our novel reconstruction loss that relies on a combination of passive and active stereo frames, resulting in significant improvements in textureless areas that are common in indoor environments. Due to the non-existence of publicly available active stereo datasets, we release a real dataset together with additional information for a publicly available synthetic dataset (Tartanair) needed for active depth completion and prediction. Through rigorous evaluations we show that our method outperforms state of the art on both datasets. Additionally we show how our method obtains more complete, and therefore safer, 3D maps when used in a robotic platform.
Robust Segmentation of Cell Nuclei in 3-D Microscopy Images

Accurate segmentation of 3-D cell nuclei in microscopy images is essential for studying the nuclear organization, gene expression, and cell morphodynamics. Current image segmentation methods are challenged by the complexity and variability of microscopy images and often over-segment or under-segment the cell nuclei. Thus, there is a need to improve segmentation accuracy and reliability, as well as the level of automation. This paper presents an automated algorithm for segmentation of 3-D cell nuclei using the concepts of keypoint features, random walk, graph theory, and energy minimization as the foundation. We first use a seed detection algorithm to find a seed voxel for each individual cell nucleus. Next, using the concept of random walk on a graph we find the probability of all the voxels in the 3-D image to reach each of the seed voxels. We then generate a 3-D response image by combining these probabilities for each voxel and apply the marker controlled watershed transform on this response image to obtain an initial segmentation of the cell nuclei. Finally, we apply local region-based active contours to obtain final segmentation of the cell nuclei. The advantage of using such an approach is its ability to accurately segment highly textured cells having inhomogeneous intensities, and varying shapes and sizes. The proposed algorithm was compared with three other nucleus segmentation algorithms for segmentation accuracy using overlap measure, Tanimoto index, Rand index, F-score, and Hausdorff measure. Quantitative and qualitative results show that our algorithm provides improved segmentation accuracy compared to existing algorithms.
A Fast and Effective Large-Scale Two-Sample Test Based on Kernels

Kernel two-sample tests have been widely used and the development of efficient methods for high-dimensional large-scale data is gaining more and more attention as we are entering the big data era. However, existing methods, such as the maximum mean discrepancy (MMD) and recently proposed kernel-based tests for large-scale data, are computationally intensive to implement and/or ineffective for some common alternatives for high-dimensional data. In this paper, we propose a new test that exhibits high power for a wide range of alternatives. Moreover, the new test is more robust to high dimensions than existing methods and does not require optimization procedures for the choice of kernel bandwidth and other parameters by data splitting. Numerical studies show that the new approach performs well in both synthetic and real world data.
A Survey on Evidential Deep Learning For Single-Pass Uncertainty Estimation

Popular approaches for quantifying predictive uncertainty in deep neural networks often involve a set of weights or models, for instance via ensembling or Monte Carlo Dropout. These techniques usually produce overhead by having to train multiple model instances or do not produce very diverse predictions. This survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they admit “what they don’t know” and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting. Finally, we survey the application of the same paradigm to regression problems. We also provide a reflection on the strengths and weaknesses of the mentioned approaches compared to existing ones and provide the most central theoretical results in order to inform future research.
Improving MC-Dropout Uncertainty Estimates with Calibration Error-based Optimization

—Uncertainty quantiﬁcation of machine learning and deep learning methods plays an important role in enhancing trust to the obtained result. In recent years, a numerous number of uncertainty quantiﬁcation methods have been introduced. Monte Carlo dropout (MC-Dropout) is one of the most well-known techniques to quantify uncertainty in deep learning methods. In this study, we propose two new loss functions by combining cross entropy with Expected Calibration Error (ECE) and Predictive Entropy (PE). The obtained results clearly show that the new proposed loss functions lead to having a calibrated MC-Dropout method. Our results conﬁrmed the great impact of the new hybrid loss functions for minimising the overlap between the distributions of uncertainty estimates for correct and incorrect predictions without sacriﬁcing the model’s overall performance.
Advanced Eco-Friendly Formulations of Guar Biopolymer-Based Textile Conditioners

Fabric conditioners are household products used to impart softness and fragrance to textiles. They are colloidal dispersions of cationic double chain surfactants that self-assemble in vesicles. These surfactants are primarily derived from palm oil chemical modification. Reducing the content of these surfactants allows to obtain products with lower environmental impact. Such a reduction, without adverse effects on the characteristics of the softener and its performance, can be achieved by adding hydrophilic biopolymers. Here, we review the role of guar biopolymers modified with cationic or hydroxyl-propyl groups, on the physicochemical properties of the formulation. Electronic and optical microscopy, dynamic light scattering, X-ray scattering and rheology of vesicles dispersion in the absence and presence of guar biopolymers are analyzed. Finally, the deposition of the new formulation on cotton fabrics is examined through scanning electron microscopy and a new protocol based on fluorescent microscopy. With this methodology, it is possible to quantify the deposition of surfactants on cotton fibers. The results show that the approach followed here can facilitate the design of sustainable home-care products.
Future constraints on primordial black holes from XGIS-THESEUS

Current observations allow Primordial Black Holes (PBHs) in asteroid mass range $10^{17}-10^{22}$ g to constitute the entire dark matter (DM) energy density (barring a small mass range constrained by 21 cm observations). In this work, we explore the possibility of probing PBH with masses $10^{17}-10^{19}\,{\rm g}$ via upcoming X and Gamma Imaging Spectrometer (XGIS) telescope array on-board the Transient High-Energy Sky and Early Universe Surveyor (THESEUS) mission. While our projected limits are comparable with those proposed in the literature for $10^{16}\,{\rm g}\,<\,M_{\mathrm{PBH}}\,<\,10^{17}\,{\rm g}$, we show that the XGIS-THESEUS mission can potentially provide the strongest bound for $10^{17} \mathrm{~g}<M_{\mathrm{PBH}} \lesssim 3\times 10^{18} \mathrm{~g}$ for non-rotating PBHs. The bounds become more stringent by nearly an order of magnitude for maximally rotating PBHs in the mass range $5\times10^{15}\,{\rm g}\,<\,M_{\rm PBH}\,\lesssim\,10^{19}\,{\rm g}$.
A Fast Randomized Algorithm for Massive Text Normalization

Many popular machine learning techniques in natural language processing and data mining rely heavily on high-quality text sources. However real-world text datasets contain a significant amount of spelling errors and improperly punctuated variants where the performance of these models would quickly deteriorate. Moreover, real-world, web-scale datasets contain hundreds of millions or even billions of lines of text, where the existing text cleaning tools are prohibitively expensive to execute over and may require an overhead to learn the corrections. In this paper, we present FLAN, a scalable randomized algorithm to clean and canonicalize massive text data. Our algorithm relies on the Jaccard similarity between words to suggest correction results. We efficiently handle the pairwise word-to-word comparisons via Locality Sensitive Hashing (LSH). We also propose a novel stabilization process to address the issue of hash collisions between dissimilar words, which is a consequence of the randomized nature of LSH and is exacerbated by the massive scale of real-world datasets. Compared with existing approaches, our method is more efficient, both asymptotically and in empirical evaluations, and does not rely on additional features, such as lexical/phonetic similarity or word embedding features. In addition, FLAN does not require any annotated data or supervised learning. We further theoretically show the robustness of our algorithm with upper bounds on the false positive and false negative rates of corrections. Our experimental results on real-world datasets demonstrate the efficiency and effi-cacy of FLAN. Leveraging recent advances in efficiently computing minhash signatures, FLAN requires much less computational time compared to baselines text normalization techniques on large-scale Twitter and Reddit datasets. In a human evaluation of the quality of the normalization, FLAN achieves 5% and 14% improvement against the baselines over the Reddit and Twitter dataset correspondingly. Our method also improves performance on the perturbed GLUE benchmark datasets, where we introduce errors into the text, and Twitter sentiment classification applications. methods. We also demonstrate the impact of FLAN on downstream NLP tasks. On the Twitter sentiment analysis and various perturbed GLUE benchmark tasks, FLAN demonstrates consistent improvement over the baselines. We also conduct an ablation study over the impact of threshold parameter on the algorithm’s performance. We further provide a case study of applying FLAN in an industrial setting on the task of normalizing search queries and product titles on a dataset sampled from the search logs of a large this dataset with of
Robustness and reliability when training with noisy labels

Labelling of data for supervised learning can be costly and time-consuming and the risk of incorporating label noise in large data sets is imminent. When training a ﬂexible discriminative model using a strictly proper loss, such noise will inevitably shift the solution towards the conditional distribution over noisy labels. Nevertheless, while deep neural networks have proven capable of ﬁtting random labels, regularisation and the use of robust loss functions empirically mitigate the eﬀects of label noise. However, such observations concern robustness in accuracy, which is insuﬃcient if reliable uncertainty quantiﬁcation is critical. We demonstrate this by analysing the properties of the conditional distribution over noisy labels for an input-dependent noise model. In addition, we evaluate the set of robust loss functions characterised by noise-insensitive, asymptotic risk minimisers. We ﬁnd that strictly proper and robust loss functions both oﬀer asymptotic robustness in accuracy, but neither guarantee that the ﬁnal model is calibrated. Moreover, even with robust loss functions, overﬁtting is an issue in practice. With these results, we aim to explain observed robustness of common training practices, such as early stopping, to label noise. In addition, we aim to encourage the development of new noise-robust algorithms that not only preserve accuracy but that also ensure reliability.
Two Higgs Doublet Model with Scalar Mediation via Yukawa Interactions

Scalar sector offers a valuable avenue to explore its implications in the particle physics phenomenology for new physics and several model dependent phenomena. Two Higgs doublet models are among the promising models for such explorations. In the presence of a standard model Higgs, a two Higgs doublet model is studied in which the two Higgs fields are connected with each other via a scalar singlet field. The study is conducted using Dyson Schwinger equations under the Yukawa vertices set at their tree level form up to certain constants. Among the field propagators, the two Higgs propagators are found to be least effected in the parameter space which also reflects in the insignificant beyond the bare mass contributions to the renormalized Higgs masses. Evidence of universality in terms of identical coupling for both Higgs is found in the model. The model is found to be nontrivial in the explored parameter space. There are cutoff effects in the model. Stability in the calculated correlation functions and parameters generally ensues beyond 100 TeV cutoff.
Disentangling deep neural networks with rectified linear units using duality

Despite their success deep neural networks (DNNs) are still largely considered as black boxes. The main issue is that the linear and non-linear operations are entangled in every layer, making it hard to interpret the hidden layer outputs. In this paper, we look at DNNs with rectified linear units (ReLUs), and focus on the gating property (‘on/off’ states) of the ReLUs. We extend the recently developed dual view in which the computation is broken path-wise to show that learning in the gates is more crucial, and learning the weights given the gates is characterised analytically via the so called neural path kernel (NPK) which depends on inputs and gates. In this paper, we present novel results to show that convolution with global pooling and skip connection provide respectively rotational invariance and ensemble structure to the NPK. To address ‘black box’-ness, we propose a novel interpretable counterpart of DNNs with ReLUs namely deep linearly gated networks (DLGN): the pre-activations to the gates are generated by a deep linear network, and the gates are then applied as external masks to learn the weights in a different network. The DLGN is not an alternative architecture per se, but a disentanglement and an interpretable re-arrangement of the computations in a DNN with ReLUs. The DLGN disentangles the computations into two ‘mathematically’ interpretable linearities (i) the ‘primal’ linearity between the input and the pre-activations in the gating network and (ii) the ‘dual’ linearity in the path space in the weights network characterised by the NPK. We compare the performance of DNN, DGN and DLGN on CIFAR-10 and CIFAR-100 to show that, the DLGN recovers more than 83.5% of the performance of state-of-the-art DNNs, i.e., while entanglement in the DNNs enable their improved performance, the ‘disentangled and interpretable’ computations in the DLGN recovers most part of the performance. This brings us to an interesting question: ‘Is DLGN a universal spectral approximator? ’
Fast Methods for Posterior Inference of Two-Group Normal-Normal Models

We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical mixed effects models with partial pooling over one group of predictors, as well as random effects models with partial pooling over two groups of predictors. We demonstrate the performance of the methods on two applications, one involving U.S. opinion polls and one involving the modeling of COVID-19 outbreaks in Israel using survey data. The algorithms involve analytical marginalization of regression coefficients followed by numerical integration of the remaining low-dimensional density. The dominant cost of the algorithms is an eigendecomposition computed once for each value of the outside parameter of integration. Our approach drastically reduces run times compared to state-of-the-art Markov chain Monte Carlo (MCMC) algorithms. The latter, in addition to being computationally expensive, can also be difficult to tune when applied to hierarchical models.
SERAB: A Multi-Lingual Benchmark for Speech Emotion Recognition

Recent developments in speech emotion recognition (SER) often leverage deep neural networks (DNNs). Comparing and benchmarking different DNN models can often be tedious due to the use of different datasets and evaluation protocols. To facilitate the process, here, we present the Speech Emotion Recognition Adaptation Benchmark (SERAB), a framework for evaluating the performance and generalization capacity of different approaches for utterance-level SER. The benchmark is composed of nine datasets for SER in six languages. Since the datasets have different sizes and numbers of emotional classes, the proposed setup is particularly suitable for estimating the generalization capacity of pre-trained DNN-based feature extractors. We used the proposed framework to evaluate a selection of standard hand-crafted feature sets and state-of-the-art DNN representations. The results highlight that using only a subset of the data included in SERAB can result in biased evaluation, while compliance with the proposed protocol can circumvent this issue.
SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap

Autonomous vision-based spaceborne navigation is an enabling technology for future on-orbit servicing and space logistics missions. While computer vision in general has benefited from Machine Learning (ML), training and validating space-borne ML models are extremely challenging due to the impracticality of acquiring a large-scale labeled dataset of images of the intended target in the space environment. Existing datasets, such as Spacecraft PosE Estimation Dataset (SPEED), have so far mostly relied on synthetic images for both training and validation, which are easy to mass-produce but fail to resemble the visual features and illumination variability inherent to the target spaceborne images. In order to bridge the gap between the current practices and the intended applications in future space missions, this paper introduces SPEED+: the next generation spacecraft pose estimation dataset with specific emphasis on domain gap. In addition to 60,000 synthetic images for training, SPEED+ includes 9,531 hardware-in-the-loop images of a spacecraft mockup model captured from the Testbed for Rendezvous and Optical Navigation (TRON) facility. TRON is a first-of-a-kind robotic testbed capable of capturing an arbitrary number of target images with accurate and maximally diverse pose labels and high-fidelity spaceborne illumination conditions. SPEED+ is used in the second international Satellite Pose Estimation Challenge co-hosted by SLAB and the Advanced Concepts Team of the European Space Agency to evaluate and compare the robustness of spaceborne ML models trained on synthetic images.
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates

Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correlations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically"unlearn"confounding features. In this work, we propose influence tuning--a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.
Characterizing and Tailoring Spatial Correlations in Multimode Parametric Down-Conversion

Photons entangled in their position-momentum degrees of freedom (DoFs) serve as an elegant manifestation of the Einstein-Podolsky-Rosen paradox, while also enhancing quantum technologies for communication, imaging, and computation. The multi-mode nature of photons generated in parametric downconversion has inspired a new generation of experiments on high-dimensional entanglement, ranging from complete quantum state teleportation to exotic multi-partite entanglement. However, precise characterisation of the underlying position-momentum state is notoriously difficult due to limitations in detector technology, resulting in a slow and inaccurate reconstruction riddled with noise. Furthermore, theoretical models for the generated two-photon state often forgo the importance of the measurement system, resulting in a discrepancy between theory and experiment. Here we formalise a description of the two-photon wavefunction in the spatial domain, referred to as the collected joint-transverse-momentum-amplitude (JTMA), which incorporates both the generation and measurement system involved. We go on to propose and demonstrate a practical and efficient method to accurately reconstruct the collected JTMA using a simple phase-step scan known as the $2D\pi$-measurement. Finally, we discuss how precise knowledge of the collected JTMA enables us to generate tailored high-dimensional entangled states that maximise discrete-variable entanglement measures such as entanglement-of-formation or entanglement dimensionality, and optimise critical experimental parameters such as photon heralding efficiency. By accurately and efficiently characterising photonic position-momentum entanglement, our results unlock its full potential for discrete-variable quantum information science and lay the groundwork for future quantum technologies based on multi-mode entanglement.
Constraining Localized Vote Tampering in the 2020 US Presidential Election

Voter fraud in the United States is rare and the vote-counting system is robust against tampering, but there remains widespread distrust in the security of election infrastructure among the public. We consider statistical means of detecting anomalous election results that would be indicative of large-scale fraud, focusing on scenarios in which votes are modified in in a localized setting. The technique we develop, based on standard regression analysis, makes use of the fact that vote share is correlated with demographics. We apply our method to the results of the 2020 US presidential election as a proof-of-concept, resulting in uncertainties at the few-percent level. We are able to readily detect an artificial signal of such fraud in some cases, ruling out some scenarios of localized fraud and placing constraints on other scenarios.