Miracle-dz/UniFAR-FTUs huggingface dataset:
Last layer state space model for representation learning and uncertainty quantification

As sequential neural architectures become deeper and more complex, uncertainty estimation is more and more challenging. Efforts in quantifying uncertainty often rely on specific training procedures, and bear additional computational costs due to the dimensionality of such models. In this paper, we propose to decompose a classification or regression task in two steps: a representation learning stage to learn low-dimensional states, and a state space model for uncertainty estimation. This approach allows to separate representation learning and design of generative models. We demonstrate how predictive distributions can be estimated on top of an existing and trained neural network, by adding a state space-based last layer whose parameters are estimated with Sequential Monte Carlo methods. We apply our proposed methodology to the hourly estimation of Electricity Transformer Oil temperature, a publicly benchmarked dataset. Our model accounts for the noisy data structure, due to unknown or unavailable variables, and is able to provide confidence intervals on predictions.
Pretraining Conformer with ASR or ASV for Anti-Spoofing Countermeasure

Finding synthetic artifacts of spoofing data will help the anti-spoofing countermeasures (CMs) system discriminate between spoofed and real speech. The Conformer combines the best of convolutional neural network and the Transformer, allowing it to aggregate global and local information. This may benefit the CM system to capture the synthetic artifacts hidden both locally and globally. In this paper, we present the transfer learning based MFA-Conformer structure for CM systems. By pre-training the Conformer encoder with different tasks, the robustness of the CM system is enhanced. The proposed method is evaluated on both Chinese and English spoofing detection databases. In the FAD clean set, proposed method achieves an EER of 0.04%, which dramatically outperforms the baseline. Our system is also comparable to the pre-training methods base on Wav2Vec 2.0. Moreover, we also provide a detailed analysis of the robustness of different models.
Impact of UAVs Equipped with ADS-B on the Civil Aviation Monitoring System

In recent years, there is an increasing demand for unmanned aerial vehicles (UAVs) to complete multiple applications. However, as unmanned equipments, UAVs lead to some security risks to general civil aviations. In order to strengthen the flight management of UAVs and guarantee the safety, UAVs can be equipped with automatic dependent surveillance-broadcast (ADS-B) devices. In addition, as an automatic system, ADS-B can periodically broadcast flight information to the nearby aircrafts or the ground stations, and the technology is already used in civil aviation systems. However, due to the limited frequency of ADS-B technique, UAVs equipped with ADS-B devices result in the loss of packets to both UAVs and civil aviation. Further, the operation of civil aviation are seriously interfered. Hence, this paper firstly examines the packets loss of civil planes at different distance, then analyzes the impact of UAVs equipped with ADS-B on the packets updating of civil planes. The result indicates that the 1090MHz band blocking is affected by the density of UAVs. Besides, the frequency capacity is affected by the requirement of updating interval of civil planes. The position updating probability within 3s is 92.3% if there are 200 planes within 50km and 20 UAVs within 5km. The position updating probability within 3s is 86.9% if there are 200 planes within 50km and 40 UAVs within 5km.
Homotopy cofinality for Non-Abelian homology of group diagrams

We prove that a homotopy cofinal functor between small categories induces a weak equivalence between homotopy colimits of pointed simplicial sets. This is used to prove that the non-Abelian homology of a group diagram is isomorphic to the homology of its inverse image under a homotopy cofinal functor. This also made it possible to establish that the non-Abelian homology of group diagrams are invariant under the left Kan extension along virtual discrete cofibrations. With the help of these results, we have constructed a non-Abelian Gabriel-Zisman homology theory for simplicial sets with coefficients in group diagrams. Moreover, we have generalized this theory to presheaves over an arbitrary small category, which play the role of simplicial sets. Sufficient conditions are found for the isomorphism of non-Abelian homology of presheaves over a small category with coefficients in group diagrams. It is proved that the non-Abelian homology of the factorization category for the small category is isomorphic to the Baues-Wirsching homology. A condition is found under which a functor between small categories induces an isomorphism of non-Abelian Baues-Wirsching homology.
Non-linear regime of cosmic structure formation

In the near future numerous galaxy surveys are going to provide a cartography of the distribution of matter in the universe, with which it will be possible to constrain the cosmological parameters with an unprecedented precision. This means that theoretical predictions of the observables at issue need to attain a high level of precision, so that we can extract the maximum cosmological information when comparing them to observations. Upcoming surveys have been conceived to understand the nature of dark energy, a component of which we have postulated the existence to explain the acceleration of the expansion of the universe observed at low redshift. In the standard cosmological model this component is represented as a cosmological constant Λ that dominate the energy budget of the universe today. Even if the ΛCDM model remains in good agreement with observations, a number of theoretical problems regarding the nature of the cosmological constant justify the study of a great number of alternative models, that now have to be put to the test with observations. To do so, it is necessary to take into account the effects of the non-linear dynamics of the gravitational collapse process. This thesis is placed in this context, using N-body simulations as a tool to link observations to the theory of cosmic structure formation in non-linear regime. On one side we are going to explore the impact of non-linear effects on the estimation of cosmological parameters in a statistical framework. This requires the computation of the covariance matrix of the matter power spectrum. The estimation of this matrix using simulations allows the quantification of the importance of non-linear effects that, if neglected, can bias the results or lead to an under-estimation of the statistical errors on the cosmological parameters. On the other side, we are going to present the numerical methods to solve the evolution of a dark energy fluid with perturbations that have a very low speed of propagation with respect to the speed of light. Collapsing dark energy perturbations leave a distinctive imprint on the distribution of matter in the universe. In this case, dark energy cannot be treated as a homogeneous fluid and its evolution has to be followed in the non-linear regime together with the dark matter component. These numerical methods, once coupled to an N-body code, will allow the production of the first simulations of the evolution of an inhomogeneous dark energy model.
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses

Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently, Haim et al. (2022) proposed a scheme to reconstruct training samples from multilayer perceptron binary classifiers, effectively demonstrating that a large portion of training samples are encoded in the parameters of such networks. In this work, we extend their findings in several directions, including reconstruction from multiclass and convolutional neural networks. We derive a more general reconstruction scheme which is applicable to a wider range of loss functions such as regression losses. Moreover, we study the various factors that contribute to networks' susceptibility to such reconstruction schemes. Intriguingly, we observe that using weight decay during training increases reconstructability both in terms of quantity and quality. Additionally, we examine the influence of the number of neurons relative to the number of training samples on the reconstructability.
Convolutions Through the Lens of Tensor Networks

Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the generalization of theoretical and algorithmic ideas. We provide a new perspective onto convolutions through tensor networks (TNs) which allow reasoning about the underlying tensor multiplications by drawing diagrams, and manipulating them to perform function transformations, sub-tensor access, and fusion. We demonstrate this expressive power by deriving the diagrams of various autodiff operations and popular approximations of second-order information with full hyper-parameter support, batching, channel groups, and generalization to arbitrary convolution dimensions. Further, we provide convolution-specific transformations based on the connectivity pattern which allow to re-wire and simplify diagrams before evaluation. Finally, we probe computational performance, relying on established machinery for efficient TN contraction. Our TN implementation speeds up a recently-proposed KFAC variant up to 4.5x and enables new hardware-efficient tensor dropout for approximate backpropagation.
From NeurODEs to AutoencODEs: a mean-field control framework for width-varying Neural Networks

The connection between Residual Neural Networks (ResNets) and continuous-time control systems (known as NeurODEs) has led to a mathematical analysis of neural networks, which has provided interesting results of both theoretical and practical significance. However, by construction, NeurODEs have been limited to describing constant-width layers, making them unsuitable for modelling deep learning architectures with layers of variable width. In this paper, we propose a continuous-time Autoencoder, which we call AutoencODE, based on a modification of the controlled field that drives the dynamics. This adaptation enables the extension of the mean-field control framework originally devised for conventional NeurODEs. In this setting, we tackle the case of low Tikhonov regularisation, resulting in potentially non-convex cost landscapes. While the global results obtained for high Tikhonov regularisation may not hold globally, we show that many of them can be recovered in regions where the loss function is locally convex. Inspired by our theoretical findings, we develop a training method tailored to this specific type of Autoencoders with residual connections, and we validate our approach through numerical experiments conducted on various examples.
Iterative hard thresholding based algorithms for low-rank tensor recovery

Low-rank tensor recovery (LRTR), i.e., the recovery of tensors having low-rank properties from underdetermined linear measurements, is a very important problem for numerous application areas, like medical/hyperspectral imaging, intelligent transport systems and computer network engineering, among others. This problem can be viewed as an extension of the low-rank matrix recovery (LRMR) problem to higher order arrays. Numerous approaches have been devised to address it. Unlike the matrix setting, however, no convex approach for LRTR is known to be both tractable and efficient (in terms of sampling requirements). Among the non-convex approaches, iterative hard thresholding (IHT) algorithms are particularly appealing due to their simplicity and effectiveness. In the first part of our talk, we will present a brief overview of LRTR methods, and more specifically of existing IHT algorithms. Then, we will introduce a new IHT algorithm which applies sequential per-mode SVD truncation as its thresholding operator, which has lower cost in comparison with the standard truncated HOSVD. Guarantees based on restricted isometry constants will be given for the proposed IHT solution. Although these guarantees lead to suboptimal sampling bounds, our numerical results suggest IHT algorithms have optimal recovery performance (order-wise) for Gaussian measurements. Heuristics for step size selection and for choosing an appropriate model complexity will be discussed. Several simulation results will be presented to illustrate the performance of our approach and compare it with existing ones.
Evaluation of the Therapeutic Efficacy of Functional Devices of Planas: Longitudinal Study of a Population of Young Children Aged 7 to 9 Years, Presenting Incisor Crowding Associated with Skeletal Class I or Class II

Objects: Evaluate at the beginning of mixed dentition, the effect of planas plates tracks on disharmony between tooth and jaw size and transverse dimensions of maxillae, on mandibular retrognathia and mandibular growth direction, and on masticatory function. Material and Methods: The study was carried out on a population of 82 school children, aged 7 to 9 years, from the city of Blida, presenting incisor crowding on a skeletal class I or class II. They were divided, by drawing lots, into two comparable groups: a control group and a group having received treatment with track plates associated with selective grinding. Measurements on mouldings, on photos and on face and profile teleradiographies, were performed on each patient before and after one year of follow-up. Results: A significant difference was noted between the two groups concerning the dento-alveolar and masticatory parameters. For the skeletal parameters, the difference is not statistically significant between the two samples. Conclusion: The results confirm the effectiveness of the Planas appliance in the treatment of dento-maxillary disharmony, by expansion and plead in favor of an early treatment, in mixed dentition of this malocclusion. They also show the restoration of chewing function. With regard to the shift of the skeletal bases, if the clinical observations showed its reduction, the measurements did not demonstrate it, due to an excessively small sample size.
New Resistance Mutations Linked to Decreased Susceptibility to Solithromycin in Streptococcus pneumoniae Revealed by Chemogenomic Screens

Two strains of Streptococcus pneumoniae, one expressing the methyltransferase Erm(B) and the other negative for erm(B), were selected for solithromycin resistance in vitro either with direct drug selection or with chemical mutagenesis followed by drug selection. We obtained a series of mutants that we characterized by next-generation sequencing. ABSTRACT Two strains of Streptococcus pneumoniae, one expressing the methyltransferase Erm(B) and the other negative for erm(B), were selected for solithromycin resistance in vitro either with direct drug selection or with chemical mutagenesis followed by drug selection. We obtained a series of mutants that we characterized by next-generation sequencing. We found mutations in various ribosomal proteins (L3, L4, L22, L32, and S4) and in the 23S rRNA. We also found mutations in subunits of the phosphate transporter, in the DEAD box helicase CshB, and in the erm(B)L leader peptide. All mutations were shown to decrease solithromycin susceptibility when transformed into sensitive isolates. Some of the genes derived from our in vitro screens were found to be mutated also in clinical isolates with decreased susceptibility to solithromycin. While many mutations were in coding sequences, some were found in regulatory regions. These included novel phenotypic mutations in the intergenic regions of the macrolide resistance locus mef(E)/mel and in the vicinity of the ribosome binding site of erm(B). Our screens highlighted that macrolide-resistant S. pneumoniae can easily acquire resistance to solithromycin, and they revealed many new phenotypic mutations.
Segmentation en arcs discrets en temps linéaire

Nous proposons un algorithme lineaire reposant sur une approche de geometrie discrete pour segmenter une courbe en arcs et cercles discrets. Cette methode utilise une representation originale des arcs et cercles discrets. En utilisant cette representation, nous transformons le probleme de reconnaissance d'arcs discrets en un probleme de reconnaissance de droites discretes et nous en deduisons un algorithme de segmentation.
On the Wintering Ecology of Montagu's Harriers in West Africa: Itinerancy in Relation to Varying Annual Environmental Conditions

Palearctic migrants wintering in Africa commonly use several sites throughout the winter, a strategy known as ‘itinerancy’. In this way, migrants track spatiotemporal variation in resources. Despite the importance of this strategy for migratory landbirds, we still lack detailed understanding of how variation in environmental conditions affects site use and the timing of movements between sites. We tracked 125 adult Montagu’s Harriers Circus pygargus from Western European breeding populations between 2005 and 2018 using satellite transmitters and GPS trackers. In total, data on 129 complete wintering seasons were obtained, including 33 individuals that were followed in two or more winters. Montagu’s Harriers were itinerant, using on average 3.3 wintering sites, to which they showed high site fidelity between years. The first sites harriers used after arriving in their wintering range were situated in the northern Sahel and were dominated by natural and sparse vegetation. Subsequent sites, situated further south in the Sahel, were mainly dominated by agricultural and natural habitats. Sites used by harriers had higher habitat diversity compared to random sites. Home range size and activity (time flying per day, daily distance) peaked at the last sites harriers used (i.e. the site from which they commenced spring migration). For individuals tracked in multiple seasons, we showed that home range size did not depend on vegetation greenness. However, the birds covered longer daily distances at the same site in drier years compared to greener (wetter) years. Importantly, the timing of the movements between wintering sites was affected by local environmental conditions, with individuals staying for shorter durations and departing earlier from first sites in drier years and arriving earlier at last sites in greener years. We conclude that within the context of a strategy of itinerancy, Montagu’s Harriers are faithful to the sites they use between years (spatial component), but flexible in the timing of use of these sites (temporal component), which they adjust to annual variation in environmental conditions.
Sheffield System for the Second Clarity Enhancement Challenge

This report describes the Sheffield system for the 2nd Clarity Enhancement Challenge (CEC2) concerning maximising speech intelligibility for hearing impaired listeners. The CEC2 database provides a large number of simulated domestic scenes, each of which contains a target speech degraded by two or three interfering sources, e.g. domestic noises, music, and other speech signals. An enhancement system is wanted to produce an enhanced binaural speech signal given three pairs of binaural noisy speech signals with an ideal latency within 5 ms. The Sheffield system consists of a denoising module and an amplification module. The denoising module aims to suppress interfering sources and restore target speech, and the amplification module is optimised to compensate for hearing losses. We take advantage of a causal multi-channel densely connected convolutional U-Net with target speaker extraction for denoising, with a Short-time Fourier Transform (STFT) window introducing 4 ms latency. For amplification, we use a simple neural network mapping the audiogram of an impaired ear to a finite impulse response (FIR) filter, which introduces less than 4 ms latency. The overall ideal latency of the proposed cascaded sys-tem thus meets the requirement.
Trading Partially Stuck Cells with Errors

—This work considers coding for partially stuck memory cells. Such memory cells can only store partial information as some of their levels cannot be used due to, e.g., wearout. First, we present 2 µ -ary partially stuck cell code construction (over the finite field F 2 µ , where integer µ > 1 ) for masking partially stuck cells while correcting substitution errors. "Masking" finds a word whose entries coincide with writable levels at the (partially) stuck cells. Then we investigate a technique where the encoder, after a first masking step, introduces errors at some partially stuck positions of a codeword in order to satisfy the stuck-at constraints. It turns that treating some of the partially stuck cells as erroneous cells can decrease the required redundancy for some parameters, e.g., by Lemma 2.
Global Patent Analysis of Foreign Object Detection in Wireless Charging

In recent years, wireless charging has become a hot topic, and many component suppliers, car companies, and technology companies have begun testing wireless charging for alternative fuel vehicles. This study uses relevant patent analysis tools to analyze the output of global patents on foreign object detection for wireless charging through patent bibliometric methods. It explores the focus, hot spots, and frontiers of patent research in this scientiﬁc ﬁeld, which helps researchers master the status and development trends of scientiﬁc research in this ﬁeld.
Resective Epilepsy Surgery, QUality of life and Economic evaluation (RESQUE): the change in quality of life after resective epilepsy surgery–protocol for a multicentre, prospective cohort study

Introduction Resective epilepsy surgery is often seen as a last resort when treating drug-resistant epilepsy. Positive results on quality of life (QoL) and economic benefits after surgery argue for a less restrictive attitude towards epilepsy surgery for drug-resistant epilepsy. QoL and economic benefits are country-dependent. The objective of the Resective Epilepsy Surgery, QUality of life and Economic evaluation (RESQUE) trial is to evaluate the change in QoL before and after epilepsy surgery in Dutch people with drug-resistant epilepsy. The results will form part of an economic evaluation of epilepsy surgery in people with epilepsy (PWE) in The Netherlands. Methods and analysis A longitudinal prospective multicentre cohort study involving 100 PWE undergoing epilepsy surgery between 2019 and 2025 is being performed in three Dutch academic hospitals. Excluded are PWE who have a lower level of intelligence (TIQ<70) or who do not master the Dutch language. Before surgery and 3, 6, 12 and 24 months after surgery, PWE receive validated online questionnaires (QOLIE-31, EQ-5D, iMCQ and iPCQ) on QoL, cost of care, expectations and satisfaction. Primary outcome is the change in QoL. Secondary outcomes are change in generic QoL, seizure reduction (International League Against Epilepsy Outcome Classification), medical consumption, productivity, the correlation between QoL and seizure reduction and expectation of and satisfaction with the surgery. Ethics and dissemination The study design has been approved by the Medical Ethics Review Committee (METC) of Maastricht UMC+ (2019–1134) and the Amsterdam UMC (vu). At the time of writing, UMC Utrecht is in the process of considering approval. The study will be conducted according to the Dutch Medical Research Involving Human Subjects Act and the Declaration of Helsinki. The results will be publicly disclosed and submitted for publication in international peer-reviewed scientific journals. There is no veto on publication by the involved parties. Trial registration NL8278; Pre-results.