Miracle-dz/UniFAR-FTUs huggingface dataset:
First detection of VHE gamma-ray emission from TXS~1515--273, study of its X-ray variability and spectral energy distribution

We report here on the first multi-wavelength (MWL) campaign on the blazar TXS 1515-273, undertaken in 2019 and extending from radio to very-high-energy gamma rays (VHE). Up until now, this blazar had not been the subject of any detailed MWL observations. It has a rather hard photon index at GeV energies and was considered a candidate extreme high-synchrotronpeaked source. MAGIC observations resulted in the first-time detection of the source in VHE with a statistical significance of 7.6$\sigma$. The average integral VHE flux of the source is 6 $\pm$ 1% of the Crab nebula flux above 400 GeV. X-ray coverage was provided by Swift-XRT, XMMNewton, and NuSTAR. The long continuous X-ray observations were separated by $\sim$ 9 h, both showing clear hour scale flares. In the XMM-Newton data, both the rise and decay timescales are longer in the soft X-ray than in the hard X-ray band, indicating the presence of a particle cooling regime. The X-ray variability timescales were used to constrain the size of the emission region and the strength of the magnetic field. The data allowed us to determine the synchrotron peak frequency and classify the source as a flaring high, but not extreme, synchrotron peaked object. Considering the constraints and variability patterns from the X-ray data, we model the broad-band spectral energy distribution. We applied a simple one-zone model, which could not reproduce the radio emission and the shape of the optical emission, and a two-component leptonic model with two interacting components, enabling us to reproduce the emission from radio to VHE band.
SynthSeg: Domain Randomisation for Segmentation of Brain MRI Scans of any Contrast and Resolution

—Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) have difﬁculties generalising to unseen target domains. When applied to segmentation of brain MRI scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MR modality, decreases in performance can be observed across datasets. We introduce SynthSeg, the ﬁrst segmentation CNN agnostic to brain MRI scans of any contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model inspired by Bayesian segmentation. Crucially, we adopt a domain randomisation strategy where we fully randomise the generation parameters to maximise the variability of the training data. Consequently, SynthSeg can segment preprocessed and unpreprocessed real scans of any target domain, without retraining or ﬁne-tuning. Because SynthSeg only requires segmentations to be trained (no images), it can learn from label maps obtained automatically from existing datasets of different populations (e.g., with atrophy and lesions), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,500 scans of 6 modalities and 10 resolutions, where it exhibits unparalleled generalisation compared to supervised CNNs, test time adaptation, and Bayesian segmentation. The code and trained model are available at github.com/BBillot/SynthSeg.
Slope inequalities for KSB‐stable and K‐stable families

We prove some higher dimensional generalizations of the slope inequality originally due to G. Xiao, and to M. Cornalba and J. Harris. We give applications to families of KSB‐stable and K‐stable pairs, as well as to the study of the ample cone of the moduli space of KSB‐stable varieties. Our proofs rely on the study of the Harder–Narasimhan filtration, and some generalizations of Castelnuovo's and Noether's inequalities.
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features

Deep neural networks (DNNs) are under threat from adversarial example attacks. The adversary can easily change the outputs of DNNs by adding small well-designed perturbations to inputs. Adversarial example detection is a fundamental work for robust DNNs-based service. Adversarial examples show the difference between humans and DNNs in image recognition. From a human-centric perspective, image features could be divided into dominant features that are comprehensible to humans, and recessive features that are incomprehensible to humans, yet are exploited by DNNs. In this paper, we reveal that imperceptible adversarial examples are the product of recessive features misleading neural networks, and an adversarial attack is essentially a kind of method to enrich these recessive features in the image. The imperceptibility of the adversarial examples indicates that the perturbations enrich recessive features, yet hardly affect dominant features. Therefore, adversarial examples are sensitive to filtering off recessive features, while benign examples are immune to such operation. Inspired by this idea, we propose a label-only adversarial detection approach that is referred to as feature-filter. Feature-filter utilizes discrete cosine transform to approximately separate recessive features from dominant features, and gets a mutant image that is filtered off recessive features. By only comparing DNN's prediction labels on the input and its mutant, feature-filter can real-time detect imperceptible adversarial examples at high accuracy and few false positives.
Spacetime symmetries and the qubit Bloch ball: A physical derivation of finite-dimensional quantum theory and the number of spatial dimensions

Quantum theory and relativity are the pillar theories on which our understanding of physics is based. Poincaré invariance is a fundamental physical principle stating that the experimental results must be the same in all inertial reference frames in Minkowski spacetime. It is a basic condition imposed on quantum theory in order to construct quantum field theories, hence, it plays a fundamental role in the standard model of particle physics too. As is well known, Minkowski spacetime follows from clear physical principles, like the relativity principle and the invariance of the speed of light. Here, we reproduce such a derivation, but leave the number of spatial dimensions n as a free variable. Then, assuming that spacetime is Minkowski in 1 + n dimensions and within the framework of general probabilistic theories, we reconstruct the qubit Bloch ball and finite dimensional quantum theory, and obtain that the number of spatial dimensions must be n = 3, from Poincaré invariance and other physical postulates. Our results suggest a fundamental physical connection between spacetime and quantum theory.
Gravitino Thermal Production

In this talk we present a new calculation of the gravitino production rate, using its full one-loop corrected thermal self-energy, beyond the hard thermal loop approximation. Gravitino production $2 \to 2$ processes, that are not related to its self-energy have been taken properly into account. Our result, compared to the latest estimation, differs by almost 10%. In addition, we present a handy parametrization of our finding, that can be used to calculate the gravitino thermal abundance, as a function of the reheating temperature.
Asymptotic Escape of Spurious Critical Points on the Low-rank Matrix Manifold

We show that on the manifold of fixed-rank and symmetric positive semi-definite matrices, the Riemannian gradient descent algorithm almost surely escapes some spurious critical points on the boundary of the manifold. Our result is the first to partially overcome the incompleteness of the low-rank matrix manifold without changing the vanilla Riemannian gradient descent algorithm. The spurious critical points are some rank-deficient matrices that capture only part of the eigen components of the ground truth. Unlike classical strict saddle points, they exhibit very singular behavior. We show that using the dynamical low-rank approximation and a rescaled gradient flow, some of the spurious critical points can be converted to classical strict saddle points in the parameterized domain, which leads to the desired result. Numerical experiments are provided to support our theoretical findings.
High-Power and High-Capacity Mobile Optical SWIPT

The increasing demands of power supply and data rate for mobile devices promote the research of simultaneous information and power transfer (SWIPT). Optical SWIPT, as known as simultaneous light information and power transfer (SLIPT), can provide high-capacity communication and highpower charging. However, light emitting diodes (LEDs)-based SLIPT technologies have low efficiency due to energy dissipation over the air. Laser-based SLIPT technologies face the challenge in mobility, as it needs accurate positioning, fast beam steering, and real-time tracking. In this paper, we propose a mobile SLIPT scheme based on spatially separated laser resonator (SSLR) and intra-cavity second harmonic generation (SHG). The power and data are transferred via separated frequencies, while they share the same self-aligned resonant beam path, without the needs of receiver positioning and beam steering. We establish the analysis model of the resonant beam power and its second harmonic power. We also evaluate the system performance on deliverable power and channel capacity. Numerical results show that the proposed system can achieve watt-level battery charging power and above 20-bit/s/Hz communication capacity over 8-m distance, which satisfies the requirements of most indoor mobile devices.
DeepSMILE: Self-supervised heterogeneity-aware multiple instance learning for DNA damage response defect classification directly from H&E whole-slide images

We propose a Deep learning-based weak label learning method for analysing whole slide images (WSIs) of Hema-toxylin and Eosin (H&E) stained tumorcells not requiring pixel-level or tile-level annotations using Self-supervised pre-training and heterogeneity-aware deep Multiple Instance LEarning (DeepSMILE). We apply DeepSMILE to the task of Homologous recombination deﬁciency (HRD) and microsatellite instability (MSI) prediction. We utilize contrastive self-supervised learning to pre-train a feature extractor on histopathology tiles of cancer tissue. Additionally, we use variability-aware deep multiple instance learning to learn the tile feature aggregation function while modeling tumor heterogeneity. Compared to state-of-the-art genomic label classiﬁcation methods, DeepSMILE improves classiﬁcation performance for HRD from 70 . 43 ± 4 . 10% to 83 . 79 ± 1 . 25% AUC and MSI from 78 . 56 ± 6 . 24% to 90 . 32 ± 3 . 58% AUC in a multi-center breast and colorectal cancer dataset, respectively. These improvements suggest we can improve genomic label classiﬁcation performance without collecting larger datasets. In the future, this may reduce the need for expensive genome sequencing techniques, provide personalized therapy recommendations based on widely available WSIs of cancer tissue, and improve patient care with quicker treatment decisions - also in medical centers without access
Automated Test Cases Prioritization for Self-driving Cars in Virtual Environments

Testing with simulation environments help to identify critical failing scenarios emerging autonomous systems such as self-driving cars (SDCs) and are safer than in-field operational tests. However, these tests are very expensive and are too many to be run frequently within limited time constraints. In this paper, we investigate test case prioritization techniques to increase the ability to detect SDC regression faults with virtual tests earlier. Our approach, called SDC-Prioritizer , prioritizes virtual tests for SDCs according to static features of the roads used within the driving scenarios. These features are collected without running the tests and do not require past execution results. SDC-Prioritizer utilizes meta-heuristics to prioritize the test cases using diversity metrics (black-box heuristics) computed on these static features. Our empirical study conducted in the SDC domain shows that SDC-Prioritizer doubles the number of safety-critical failures that virtual tests can detect at the same level of execution time compared to baselines: random and greedy-based test case orderings. Furthermore, this meta-heuristic search performs statistically better than both baselines in terms of detecting safety-critical failures. SDC-Prioritizer effectively prioritize test cases for SDCs with a large improvement in fault detection while its overhead (up to 0.34% of the test execution cost) is negligible.
Data Hiding With Deep Learning: A Survey Unifying Digital Watermarking and Steganography

The advancement of secure communication and identity verification fields has significantly increased through the use of deep learning techniques for data hiding. By embedding information into a noise-tolerant signal, such as audio, video, or images, digital watermarking and steganography techniques can be used to protect sensitive intellectual property (IP) and enable confidential communication, ensuring that the information embedded is only accessible to authorized parties. This survey provides an overview of recent developments in deep learning techniques deployed for data hiding, categorized systematically according to model architectures and noise injection methods. In addition, potential future research directions that unite digital watermarking and steganography on software engineering to enhance security and mitigate risks are suggested and deliberated. This contribution furthers the creation of a more trustworthy digital world and advances responsible artificial intelligence (AI).
Solar inertial modes: Observations, identification, and diagnostic promise

The oscillations of a slowly rotating star have long been classified into spheroidal and toroidal modes. The spheroidal modes include the well-known 5-min acoustic modes used in helioseismology. Here we report observations of the Sun’s toroidal modes, for which the restoring force is the Coriolis force and whose periods are on the order of the solar rotation period. By comparing the observations with the normal modes of a differentially rotating spherical shell, we are able to identify many of the observed modes. These are the high-latitude inertial modes, the critical-latitude inertial modes, and the equatorial Rossby modes. In the model, the high-latitude and critical-latitude modes have maximum kinetic energy density at the base of the convection zone, and the high-latitude modes are baroclinically unstable due to the latitudinal entropy gradient. As a first application of inertial-mode helioseismology, we constrain the superadiabaticity and the turbulent viscosity in the deep convection zone.
Local entropy theory and descriptive complexity

We investigate local entropy theory, particularly the property of having completely positive entropy (CPE), from a descriptive set-theoretic point of view. We aim to determine descriptive complexity of different families of dynamical systems with CPE. For a large class of compact $X$, we show that the family of dynamical systems on $X$ with CPE is complete coanalytic and hence not Borel. When we restrict our attention to dynamical systems having special properties such as the mixing property or the shadowing property, we obtain some contrasting behavior. In particular, the notion of CPE and the notion of uniform positive entropy, a Borel property, coincide for mixing maps on topological graphs. On the other hand, the class of mixing map on the Cantor space is coanalytic and not Borel. For dynamical systems with the shadowing property, the notions CPE and uniform positive entropy coincide regardless of the phase space.
Towards Privacy-preserving Explanations in Medical Image Analysis

The use of Deep Learning in the medical field is hindered by the lack of interpretability. Case-based interpretability strategies can provide intuitive explanations for deep learning models' decisions, thus, enhancing trust. However, the resulting explanations threaten patient privacy, motivating the development of privacy-preserving methods compatible with the specifics of medical data. In this work, we analyze existing privacy-preserving methods and their respective capacity to anonymize medical data while preserving disease-related semantic features. We find that the PPRL-VGAN deep learning method was the best at preserving the disease-related semantic features while guaranteeing a high level of privacy among the compared state-of-the-art methods. Nevertheless, we emphasize the need to improve privacy-preserving methods for medical imaging, as we identified relevant drawbacks in all existing privacy-preserving approaches.
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression

Due to the high communication cost in distributed and federated learning, methods relying on compressed communication are becoming increasingly popular. Besides, the best theoretically and practically performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of communications (faster convergence), e.g., Nesterov's accelerated gradient descent (Nesterov, 1983, 2004) and Adam (Kingma and Ba, 2014). In order to combine the benefits of communication compression and convergence acceleration, we propose a \emph{compressed and accelerated} gradient method based on ANITA (Li, 2021) for distributed optimization, which we call CANITA. Our CANITA achieves the \emph{first accelerated rate} $O\bigg(\sqrt{\Big(1+\sqrt{\frac{\omega^3}{n}}\Big)\frac{L}{\epsilon}} + \omega\big(\frac{1}{\epsilon}\big)^{\frac{1}{3}}\bigg)$, which improves upon the state-of-the-art non-accelerated rate $O\left((1+\frac{\omega}{n})\frac{L}{\epsilon} + \frac{\omega^2+\omega}{\omega+n}\frac{1}{\epsilon}\right)$ of DIANA (Khaled et al., 2020) for distributed general convex problems, where $\epsilon$ is the target error, $L$ is the smooth parameter of the objective, $n$ is the number of machines/devices, and $\omega$ is the compression parameter (larger $\omega$ means more compression can be applied, and no compression implies $\omega=0$). Our results show that as long as the number of devices $n$ is large (often true in distributed/federated learning), or the compression $\omega$ is not very high, CANITA achieves the faster convergence rate $O\Big(\sqrt{\frac{L}{\epsilon}}\Big)$, i.e., the number of communication rounds is $O\Big(\sqrt{\frac{L}{\epsilon}}\Big)$ (vs. $O\big(\frac{L}{\epsilon}\big)$ achieved by previous works). As a result, CANITA enjoys the advantages of both compression (compressed communication in each round) and acceleration (much fewer communication rounds).
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning

Deep Metric Learning (DML) aims to find representations suitable for zero-shot transfer to a priori unknown test distributions. However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider a broad spectrum of distribution shifts with potentially varying degree and difficulty. In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization under out-of-distribution shifts in DML. ooDML is designed to probe the generalization performance on much more challenging, diverse train-to-test distribution shifts. Based on our new benchmark, we conduct a thorough empirical analysis of state-of-the-art DML methods. We find that while generalization tends to consistently degrade with difficulty, some methods are better at retaining performance as the distribution shift increases. Finally, we propose few-shot DML as an efficient way to consistently improve generalization in response to unknown test shifts presented in ooDML. Code available here: https://github.com/CompVis/Characterizing_Generalization_in_DML.
Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

We present DrQ-v2, a model-free reinforcement learning (RL) algorithm for visual continuous control. DrQ-v2 builds on DrQ, an off-policy actor-critic approach that uses data augmentation to learn directly from pixels. We introduce several improvements that yield state-of-the-art results on the DeepMind Control Suite. Notably, DrQ-v2 is able to solve complex humanoid locomotion tasks directly from pixel observations, previously unattained by model-free RL. DrQ-v2 is conceptually simple, easy to implement, and provides significantly better computational footprint compared to prior work, with the majority of tasks taking just 8 hours to train on a single GPU. Finally, we publicly release DrQ-v2's implementation to provide RL practitioners with a strong and computationally efficient baseline.
Optical shock-enhanced self-photon acceleration

Photon accelerators can spectrally broaden laser pulses with high efficiency in moving electron density gradients driven in a rapidly ionizing plasma. When driven by a conventional laser pulse, the group velocity walk-off experienced by the accelerated photons and deterioration of the gradient from diffraction and plasma-refraction limit the extent of spectral broadening. Here we show that a laser pulse with a shaped space-time and transverse intensity profile overcomes these limitations by creating a guiding density profile at a tunable velocity. Self-photon acceleration in this profile leads to dramatic spectral broadening and intensity steepening, forming an optical shock that further enhances the rate of spectral broadening. In this new regime, multi-octave spectra extending from 400nm− 60 nm wavelengths, which support near-transform limited < 400 as pulses, are generated over < 100μm of interaction length.
Using Shape Constraints for Improving Symbolic Regression Models

We describe and analyze algorithms for shape-constrained symbolic regression, which allows the inclusion of prior knowledge about the shape of the regression function. This is relevant in many areas of engineering – in particular whenever a data-driven model obtained from measurements must have certain properties (e.g. positivity, monotonicity or convexity/concavity). We implement shape constraints using a soft-penalty approach which uses multi-objective algorithms to minimize constraint violations and training error. We use the non-dominated sorting genetic algorithm (NSGA-II) as well as the multi-objective evolutionary algorithm based on decomposition (MOEA/D). We use a set of models from physics textbooks to test the algorithms and compare against earlier results with single-objective algorithms. The results show that all algorithms are able to ﬁnd models which conform to all shape constraints. Using shape constraints helps to improve extrapolation behavior of the models.
Dynamical structure factor of the SU(3) Heisenberg chain: Variational Monte Carlo approach

We compute the dynamical spin structure factor S(k, ω) of the SU(3) Heisenberg chain variationally using a truncated Hilbert space spanned by the Gutzwiller projected particle-hole excitations of the Fermi sea, introduced in [B. Dalla Piazza et al., Nature Physics 11, 62 (2015)], with a modified importance sampling. We check the reliability of the method by comparing the S(k, ω) to exact diagonalization results for 18 sites and to the two-soliton continuum of the Bethe Ansatz for 72 sites. We get an excellent agreement in both cases. Detailed analysis of the finite-size effects shows that the method captures the critical Wess-Zumino-Witten SU(3)1 behavior and reproduces the correct exponent, with the exception of the size dependence of the weight of the bottom of the conformal tower. We also calculate the single-mode approximation for the SU(N) Heisenberg model and determine the velocity of excitations. Finally, we apply the method to the SU(3) Haldane-Shastry model and find that the variational method gives the exact wave function for the lowest excitation at k = ±2π/3.