Miracle-dz/UniFAR-FTUs huggingface dataset:
On ground and in orbit microvibrations measurement comparison

Since the 90’s, the accuracy and the stability required for the Earth observation satellites instruments line of sight have increased. This is why the microvibrations generated by the mechanisms became a potential problem. During the SPOT4 satellite project development, some work was performed to improve the knowledge of these phenomena. The first activity consisted of the quantification of torques and forces generated by the wheels and a frequency response functions (FRF) measurement on ground, suspended satellite. A microvibratory characterisation test bench was developed. The second way has been to develop an experiment called MICROMEDY as a technological passenger of the SPOT4 satellite to monitor the orbital microvibrations environment and structural behaviour. This paper presents the on Ground tests, the orbital measurements and a comparison between the obtained results is made.
More evidence for extinction of activity in galaxies

This Research Note amends an article in which we showed that radio-loud quasars can become radio-quiet. Exploring the analogy between galactic nuclei and X-ray binaries (XRB), we pointed out there that this transition in quasars could be identified with a switch from low/hard to high/soft state in microquasars. Here, we present the evidence that traces of past occurrences of this kind of phenomena can be found in normal but once active galaxies. Based on the properties of a few such "post-active" galaxies that are representative for a much wider group, it has been argued that they have reached the evolutionary stages when their nuclei, which were radio-loud in the past, now, mimicking the behaviour of XRBs, remain in the intermediate state on their way towards quiescence or even have already entered the quiescent state. It follows that the full evolutionary track of XRBs can be mapped onto the evolution of galaxies. The above findings are in line with those reported recently for IC 2497, a galaxy that 70,000 years ago or less hosted a quasar but now appears as a normal one. This scenario stems from the presence of Hanny's Voorwerp, a nebulous object in its vicinity excited by that QSO in the epoch when IC 2497 was active. The post-active galaxies we deal with here are accompanied by extremely weak and diffuse relic radio lobes that were inflated during their former active period. These relics can be regarded as radio analogues of Hanny's Voorwerp.
Cost‐Effectiveness of Weight‐Loss Interventions Prior to Total Knee Replacement for Patients With Class III Obesity

Class III obesity (body mass index >40 kg/m2) is associated with higher complications following total knee replacement (TKR), and weight loss is recommended. We aimed to establish the cost‐effectiveness of Roux‐en‐Y gastric bypass (RYGB), laparoscopic sleeve gastrectomy (LSG), and lifestyle nonsurgical weight loss (LNSWL) interventions in knee osteoarthritis patients with class III obesity considering TKR.
Virtual reality to foster empathy in disability workers: A feasibility study during COVID‐19

Abstract Background Empathy is a critical skill required by disability workers. Virtual reality may increase access to staff training to foster empathy. Method A mixed methods feasibility study investigated a custom‐built virtual reality program, IMercyVE, which aimed to immerse a disability worker in a first‐person perspective of having intellectual disability. Data were collected through online surveys (n = 9) completed before and after use of IMercyVE, and by videoconference focus groups (n = 6). Results Qualitative findings included that IMercyVE delivered a novel experience for participants and a distinctive mode of learning that assisted participants to deepen their levels of empathy. Descriptive analyses of the surveys indicated that participants' empathy increased after engaging with IMercyVE. Conclusions IMercyVE has the potential to be offered as a remotely delivered and flexible staff training modality suitable for building empathy. Further research with a larger sample is warranted.
Interplay between hydrogen and chalcogen bonds in cysteine

Protein structures are stabilized by several types of chemical interactions between amino acids, which can compete with each other. This is the case of chalcogen and hydrogen bonds formed by the thiol group of cysteine, which can form three hydrogen bonds with one hydrogen acceptor and two hydrogen donors and a chalcogen bond with a nucleophile along the extension of the CS bond. A survey of the Protein Data Bank shows that hydrogen bonds are about 40–50 more common than chalcogen bonds, suggesting that they are stronger and, consequently, prevail, though not always. It is also observed that frequently a thiol group that forms a chalcogen bond is also involved, as a hydrogen donor, in a hydrogen bond.
Moderate intakes of soluble corn fibre or inulin do not cause gastrointestinal discomfort and are well tolerated in healthy children

Abstract We investigated the gastrointestinal (GI) tolerance of soluble corn fibre (SCF) compared with inulin in children 3–9 years old. SCF (3–8 g/d for 10d) was tolerated as well as inulin: no differences were identified in stool frequency and consistency, proportion of subjects with at least one loose stool or reporting symptoms during bowel movement. Compared to inulin, 6 g/d of SCF lowered gas severity in children aged 3–5 years old. No differences were noted for alpha and beta diversity, relative abundance of Bacteroidota, Firmicutes, Ruminococcaceae, or the Firmicutes to Bacteroidota ratio. Relative abundance of some specific strains (i.e. Anaerostipes, Bifidobacterium, Fusicatenibacter, Parabacteroides) varied depending on the fibre type and dose level. Fortification at a level of 6–8 g/d of SCF and/or inulin could help addressing the fibre gap without any GI discomfort.
CSI-Fingerprinting Indoor Localization via Attention-Augmented Residual Convolutional Neural Network

Deep learning has been widely adopted for channel state information (CSI)-fingerprinting indoor localization systems. These systems usually consist of two main parts, ${i}$ . ${e}$ . , a positioning network that learns the mapping from high-dimensional CSI to physical locations and a tracking system that utilizes historical CSI to reduce the positioning error. This paper presents a new localization system with high accuracy and generality. On the one hand, the receptive field of the existing convolutional neural network (CNN)-fingerprinting positioning networks is limited, restricting their performance as useful information in CSI is not explored thoroughly. As a solution, we propose a novel attention-augmented residual CNN to utilize the local information and global context in CSI exhaustively. On the other hand, considering the generality of a tracking system, we decouple the tracking system from the CSI environments so that one tracking system for all environments becomes possible. Specifically, we remodel the tracking problem as a denoising task and solve it with deep trajectory prior. Furthermore, we investigate how the precision difference of inertial measurement units will adversely affect the tracking performance and adopt plug-and-play to solve the precision difference problem. Experiments show the superiority of our methods over existing approaches in performance and generality improvement.
Structured Systems of Nonlinear Equations

In a"structured system"of equations, each equation depends on a specified subset of the variables. In this article, we explore properties common to"almost every"system with a fixed structure and how the properties can be read from the corresponding connection graph. A solution $p$ of a system $F(p)=c$ is called robust if it persists despite small changes in $F$. We establish methods for determining robustness that depends on the structure, as expressed in the properties of the corresponding directed graph of the structured system. The keys to understanding linear and nonlinear structured systems are subsets of variables that we call forward and backward bottlenecks. In particular, when robustness fails in a structured system, it is due to the existence of a unique"backward bottleneck", that we call a"minimax bottleneck". We present a numerical method for locating the minimax bottleneck. We show how to remove it by adding edges to the graph.
PCR: Pessimistic Consistency Regularization for Semi-Supervised Segmentation

—Currently, state-of-the-art semi-supervised learning (SSL) segmentation methods employ pseudo labels to train their models, which is an optimistic training manner that supposes the predicted pseudo labels are correct. However, their models will be optimized incorrectly when the above assumption does not hold. In this paper, we propose a Pessimistic Consistency Regularization (PCR) which considers a pessimistic case that pseudo labels are not always correct. PCR makes it possible for our model to learn the ground truth (GT) in pessimism by adaptively providing a candidate label set containing K proposals for each unlabeled pixel. Speciﬁcally, we propose a pessimistic consistency loss which trains our model to learn the possible GT from multiple candidate labels. In addition, we develop a candidate label proposal method to adaptively decide which pseudo labels are provided for each pixel. Our method is easy to implement and could be applied to existing baselines without changing their frameworks. Theoretical analysis and experiments on various benchmarks demonstrate the superiority of our approach to state-of-the-art alternatives.
On a Low-Rank Matrix Single-Index Model

In this paper, we conduct a theoretical examination of a low-rank matrix single-index model. This model has recently been introduced in the field of biostatistics, but its theoretical properties for jointly estimating the link function and the coefficient matrix have not yet been fully explored. In this paper, we make use of the PAC-Bayesian bounds technique to provide a thorough theoretical understanding of the joint estimation of the link function and the coefficient matrix. This allows us to gain a deeper insight into the properties of this model and its potential applications in different fields.
Demystifying CNNs for Images by Matched Filters

The success of convolution neural networks (CNN) has been revolutionising the way we approach and use intelligent machines in the Big Data era. Despite success, CNNs have been consistently put under scrutiny owing to their black-box nature, an ad hoc manner of their construction, together with the lack of theoretical support and physical meanings of their operation. This has been prohibitive to both the quantitative and qualitative understanding of CNNs, and their application in more sensitive areas such as AI for health. We set out to address these issues, and in this way demystify the operation of CNNs, by employing the perspective of matched ﬁltering. We ﬁrst illuminate that the convolution operation, the very core of CNNs, represents a matched ﬁlter which aims to identify the presence of features in input data. This then serves as a vehicle to interpret the convolution-activation-pooling chain in CNNs under the theoretical umbrella of matched ﬁltering, a common operation in signal processing. We further provide extensive examples and experiments to illustrate this connection, whereby the learning in CNNs is shown to also perform matched ﬁltering, which further sheds light onto physical meaning of learnt parameters and layers. It is our hope that this material will provide new insights into the understanding, constructing and analysing of CNNs, as well as paving the way for developing new methods and architectures of CNNs.
A New Spatio-Temporal Loss Function for 3D Motion Reconstruction and Extended Temporal Metrics for Motion Evaluation

We propose a new loss function that we call Laplacian loss, based on spatio-temporal Laplacian representation of the motion as a graph. This loss function is intended to be used in training models for motion reconstruction through 3D human pose estimation from videos. It compares the differential coordinates of the joints obtained from the graph representation of the ground truth against the one of the estimation. We design a fully convolutional temporal network for motion reconstruction to achieve better temporal consistency of estimation. We use this generic model to study the impact of our proposed loss function on the benchmarks provided by Human3.6M. We also make use of various motion descriptors such as velocity, acceleration to make a thorough evaluation of the temporal consistency while comparing the results to some of the state-of-the-art solutions.
A Utility-Preserving Obfuscation Approach for YouTube Recommendations

Online content platforms optimize engagement by providing personalized recommendations to their users. These recommendation systems track and profile users to predict relevant content a user is likely interested in. While the personalized recommendations provide utility to users, the tracking and profiling that enables them poses a privacy issue because the platform might infer potentially sensitive user interests. There is increasing interest in building privacy-enhancing obfuscation approaches that do not rely on cooperation from online content platforms. However, existing obfuscation approaches primarily focus on enhancing privacy but at the same time they degrade the utility because obfuscation introduces unrelated recommendations. We design and implement DeHarpo, an obfuscation approach for YouTube's recommendation system that not only obfuscates a user's video watch history to protect privacy but then also denoises the video recommendations by YouTube to preserve their utility. In contrast to prior obfuscation approaches, DeHarpo adds a denoiser that makes use of a ``secret'' input (i.e., a user's actual watch history) as well as information that is also available to the adversarial recommendation system (i.e., obfuscated watch history and corresponding ``nois`` recommendations). Our large-scale evaluation of DeHarpo shows that it outperforms the state-of-the-art by a factor of 2x in terms of preserving utility for the same level of privacy, while maintaining stealthiness and robustness to de-obfuscation.
Encoding subshifts through sliding block codes

We prove a generalization of Krieger’s embedding theorem, in the spirit of zero-error information theory. Specifically, given a mixing shift of finite type X, a mixing sofic shift Y, and a surjective sliding block code $\pi : X \to Y$ , we give necessary and sufficient conditions for a subshift Z of topological entropy strictly lower than that of Y to admit an embedding $\psi : Z \to X$ such that $\pi \circ \psi $ is injective.
Disordered systems insights on computational hardness

In this review article we discuss connections between the physics of disordered systems, phase transitions in inference problems, and computational hardness. We introduce two models representing the behavior of glassy systems, the spiked tensor model and the generalized linear model. We discuss the random (non-planted) versions of these problems as prototypical optimization problems, as well as the planted versions (with a hidden solution) as prototypical problems in statistical inference and learning. Based on ideas from physics, many of these problems have transitions where they are believed to jump from easy (solvable in polynomial time) to hard (requiring exponential time). We discuss several emerging ideas in theoretical computer science and statistics that provide rigorous evidence for hardness by proving that large classes of algorithms fail in the conjectured hard regime. This includes the overlap gap property, a particular mathematization of clustering or dynamical symmetry-breaking, which can be used to show that many algorithms that are local or robust to changes in their input fail. We also discuss the sum-of-squares hierarchy, which places bounds on proofs or algorithms that use low-degree polynomials such as standard spectral methods and semidefinite relaxations, including the Sherrington–Kirkpatrick model. Throughout the manuscript we present connections to the physics of disordered systems and associated replica symmetry breaking properties.
Stability of Accuracy for the Training of DNNs Via the Uniform Doubling Condition

We study the stability of accuracy during the training of deep neural networks (DNNs). In this context, the training of a DNN is performed via the minimization of a cross-entropy loss function, and the performance metric is accuracy (the proportion of objects that are classified correctly). While training results in a decrease of loss, the accuracy does not necessarily increase during the process and may sometimes even decrease. The goal of achieving stability of accuracy is to ensure that if accuracy is high at some initial time, it remains high throughout training. A recent result by Berlyand, Jabin, and Safsten introduces a doubling condition on the training data, which ensures the stability of accuracy during training for DNNs using the absolute value activation function. For training data in $\mathbb{R}^n$, this doubling condition is formulated using slabs in $\mathbb{R}^n$ and depends on the choice of the slabs. The goal of this paper is twofold. First, to make the doubling condition uniform, that is, independent of the choice of slabs. This leads to sufficient conditions for stability in terms of training data only. In other words, for a training set $T$ that satisfies the uniform doubling condition, there exists a family of DNNs such that a DNN from this family with high accuracy on the training set at some training time $t_0$ will have high accuracy for all time $t>t_0$. Moreover, establishing uniformity is necessary for the numerical implementation of the doubling condition. The second goal is to extend the original stability results from the absolute value activation function to a broader class of piecewise linear activation functions with finitely many critical points, such as the popular Leaky ReLU.
On the Relationship Between Variational Inference and Auto-Associative Memory

In this article, we propose a variational inference formulation of auto-associative memories, allowing us to combine perceptual inference and memory retrieval into the same mathematical framework. In this formulation, the prior probability distribution onto latent representations is made memory dependent, thus pulling the inference process towards previously stored representations. We then study how different neural network approaches to variational inference can be applied in this framework. We compare methods relying on amortized inference such as Variational Auto Encoders and methods relying on iterative inference such as Predictive Coding and suggest combining both approaches to design new auto-associative memory models. We evaluate the obtained algorithms on the CIFAR10 and CLEVR image datasets and compare them with other associative memory models such as Hopfield Networks, End-to-End Memory Networks and Neural Turing Machines.
A Saccaded Visual Transformer for General Object Spotting

This paper presents the novel combination of a visual transformer style patch classifier with saccaded local attention. A novel optimisation paradigm for training object models is also presented, rather than the optimisation function minimising class membership probability error the network is trained to estimate the normalised distance to the centroid of labelled objects. This approach builds a degree of transnational invariance directly into the model and allows fast saccaded search with gradient ascent to find object centroids. The resulting saccaded visual transformer is demonstrated on human faces.
Logical Zonotope: A Set Representation for Binary Vectors

In this paper, we propose a new set representation for binary vectors called logical zonotopes. A logical zonotope is constructed by XOR-ing a binary vector with a combination of binary vectors called generators. A logical zonotope can efficiently represent up to 2 𝛾 binary vectors using only 𝛾 generators. Instead of the explicit enumeration of the zonotopes’ members, logical operations over sets of binary vectors are applied directly to a zonotopes’ generators. Thus, logical zonotopes can be used to greatly reduce the computational complexity of a variety of operations over sets of binary vectors, including logical operations (e.g. XOR, NAND, AND, OR) and semi-tensor products. Additionally, we show that, similar to the role classical zonotopes play for formally verifying dynamical systems defined over real vector spaces, logical zonotopes can be used to efficiently analyze the forward reachability of dynamical systems defined over binary vector spaces (e.g. logical circuits or Boolean networks). To showcase the utility of logical zonotopes, we illustrate three use cases: (1) discovering the key of a linear-feedback shift register with a linear time complexity, (2) verifying the safety of a logical vehicle intersection crossing protocol, and (3) performing reachability analysis for a high-dimensional Boolean function.
RoS-KD: A Robust Stochastic Knowledge Distillation Approach for Noisy Medical Imaging

AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses. However, it usually suffers from a lack of high-quality datasets due to high annotation cost, interobserver variability, human annotator error, and errors in computer-generated labels. Deep learning models trained on noisy labelled datasets are sensitive to the noise type and lead to less generalization on the unseen samples. To address this challenge, we propose a Robust Stochastic Knowledge Distillation (RoS-KD) framework which mimics the notion of learning a topic from multiple sources to ensure deterrence in learning noisy information. More specifically, RoS-KD learns a smooth, well-informed, and robust student manifold by distilling knowledge from multiple teachers trained on overlapping subsets of training data. Our extensive experiments on popular medical imaging classification tasks (cardiopulmonary disease and lesion classification) using real-world datasets, show the performance benefit of RoS-KD, its ability to distill knowledge from many popular large networks (ResNet-50, DenseNet-121, MobileNetV2) in a comparatively small network, and its robustness to adversarial attacks (PGD, FSGM). More specifically, RoS-KD achieves >2% and > 4% improvement on F1-score for lesion classification and cardiopulmonary disease classification tasks, respectively, when the underlying student is ResNet-18 against recent competitive knowledge distillation baseline. Additionally, on cardiopulmonary disease classification task, RoS-KD outperforms most of the SOTA baselines by ~1% gain in AUC score.
Beyond the Worst Case: Semi-Random Complexity Analysis of Winner Determination

The computational complexity of winner determination is a classical and important problem in computational social choice. Previous work based on worst-case analysis has established NP-hardness of winner determination for some classic voting rules, such as Kemeny, Dodgson, and Young. In this paper, we revisit the classical problem of winner determination through the lens of semi-random analysis, which is a worst average-case analysis where the preferences are generated from a distribution chosen by the adversary. Under a natural class of semi-random models that are inspired by recommender systems, we prove that winner determination remains hard for Dodgson, Young, and some multi-winner rules such as the Chamberlin-Courant rule and the Monroe rule. Under another natural class of semi-random models that are extensions of the Impartial Culture, we show that winner determination is hard for Kemeny, but is easy for Dodgson. This illustrates an interesting separation between Kemeny and Dodgson.