Miracle-dz/UniFAR-FTUs huggingface dataset:
Initiation efficiency and cytotoxicity of novel water-soluble two-photon photoinitiators for direct 3D microfabrication of hydrogels

The lack of efficient water-soluble two-photon absorption (TPA) photoinitiators has been a critical obstruction for three dimensional hydrogel microfabrications with high water load by two-photon induced polymerization (TPIP). In this paper, a series of cyclic benzylidene ketone-based two-photon initiators, containing carboxylic acid sodium salts to improve water solubility, were synthesized via classical aldol condensation reactions. The cytotoxicity of cyclopentanone-based photoinitiators is as low as that of the well-known biocompatible photoinitiator Irgacure 2959 as assessed in the dark with MG63 cell line. In z-scan measurement, the TPA cross sections of the investigated initiators are only moderate in water, while the TPA values for hydrophobic analogues measured in chloroform were much higher. All novel initiators exhibited broad processing windows in TPIP tests using hydrophilic photopolymers with up to 50 wt% of water. Impressively, microfabrication of hydrogels with excellent precision was possible at a writing speed as high as 100 mm s−1.
Accretion disc cooling and narrow absorption lines in the tidal disruption event AT 2019dsg

We present the results of a large multiwavelength follow-up campaign of the tidal disruption event (TDE) AT 2019dsg, focusing on low to high resolution optical spectroscopy, X-ray, and radio observations. The galaxy hosts a super massive black hole of mass $\rm (5.4\pm 3.2)\times 10^6\, M_\odot$ and careful analysis finds no evidence for the presence of an active galactic nucleus, instead the TDE host galaxy shows narrow optical emission lines that likely arise from star formation activity. The transient is luminous in the X-rays, radio, UV, and optical. The X-ray emission becomes undetected after ∼100 d, and the radio luminosity density starts to decay at frequencies above 5.4 GHz by ∼160 d. Optical emission line signatures of the TDE are present up to ∼200 d after the light-curve peak. The medium to high resolution spectra show traces of absorption lines that we propose originate in the self-gravitating debris streams. At late times, after ∼200 d, narrow Fe lines appear in the spectra. The TDE was previously classified as N-strong, but after careful subtraction of the host galaxy’s stellar contribution, we find no evidence for these N lines in the TDE spectrum, even though O Bowen lines are detected. The observed properties of the X-ray emission are fully consistent with the detection of the inner regions of a cooling accretion disc. The optical and radio properties are consistent with this central engine seen at a low inclination (i.e. seen from the poles).
AU-Guided Unsupervised Domain Adaptive Facial Expression Recognition

The domain diversities including inconsistent annotation and varied image collection conditions inevitably exist among different facial expression recognition (FER) datasets, which pose an evident challenge for adapting the FER model trained on one dataset to another one. Recent works mainly focus on domain-invariant deep feature learning with adversarial learning mechanism, ignoring the sibling facial action unit (AU) detection task which has obtained great progress. Considering AUs objectively determine facial expressions, this paper proposes an AU-guided unsupervised Domain Adaptive FER (AdaFER) framework. In AdaFER, we first leverage an advanced model for AU detection on both source and target domain. Then, we compare the AU results to perform AU-guided annotating, i.e., target faces that own the same AUs with source faces would inherit the labels from source domain. Meanwhile, to achieve domain-invariant compact features, we utilize an AU-guided triplet training which randomly collects anchor-positive-negative triplets on both domains with AUs. We conduct extensive experiments on several popular benchmarks and show that AdaFER achieves state-of-the-art results on all the benchmarks.
Assessing Pattern Recognition Performance of Neuronal Cultures through Accurate Simulation

Previous work has shown that it is possible to train neuronal cultures on Multi-Electrode Arrays (MEAs), to recognize very simple patterns. However, this work was mainly focused to demonstrate that it is possible to induce plasticity in cultures, rather than performing a rigorous assessment of their pattern recognition performance. In this paper, we address this gap by developing a methodology that allows us to assess the performance of neuronal cultures on a learning task. Specifically, we propose a digital model of the real cultured neuronal networks; we identify biologically plausible simulation parameters that allow us to reliably reproduce the behavior of real cultures; we use the simulated culture to perform handwritten digit recognition and rigorously evaluate its performance; we also show that it is possible to find improved simulation parameters for the specific task, which can guide the creation of real cultures.
Efficient Verification of Optimized Code: Correct High-speed Curve25519

Code that is highly optimized poses a problem for program-level verification. Programmers can employ various clever tricks that are non-trivial to reason about. For cryptography on low-power devices, it is nonetheless crucial that implementations be functionally correct, secure, and efficient. These are usually crafted in hand-optimized machine code that eschew conventional control flow as much as possible. We have formally verified such code: a library which implements elliptic curve cryptography on 8-bit AVR microcontrollers. The chosen implementation is the most efficient currently known for this microarchitecture. It consists of over 3000 lines of assembly instructions. Building on earlier work, we use the Why3 platform to model the code and generate verification conditions, which are proven using automated provers. The approach is re-usable and adaptable, and allows for validation. Furthermore, an error in the original implementation was found and corrected, at the same time reducing its memory footprint. This shows that practical verification of cutting-edge code is not only possible, but can in fact add to its efficiency -- and is clearly necessary.
Exact clustering in tensor block model: Statistical optimality and computational limit

High‐order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non‐convex and discontinuous nature of this problem pose significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, high‐order Lloyd algorithm (HLloyd), and high‐order spectral clustering (HSC), for high‐order clustering. The convergence guarantees and statistical optimality are established for the proposed procedure under a mild sub‐Gaussian noise assumption. Under the Gaussian tensor block model, we completely characterise the statistical‐computational trade‐off for achieving high‐order exact clustering based on three different signal‐to‐noise ratio regimes. The analysis relies on new techniques of high‐order spectral perturbation analysis and a ‘singular‐value‐gap‐free’ error bound in tensor estimation, which are substantially different from the matrix spectral analyses in the literature. Finally, we show the merits of the proposed procedures via extensive experiments on both synthetic and real datasets.
Temporal Bilinear Encoding Network of Audio-Visual Features at Low Sampling Rates

Current deep learning based video classification architectures are typically trained end-to-end on large volumes of data and require extensive computational resources. This paper aims to exploit audio-visual information in video classification with a 1 frame per second sampling rate. We propose Temporal Bilinear Encoding Networks (TBEN) for encoding both audio and visual long range temporal information using bilinear pooling and demonstrate bilinear pooling is better than average pooling on the temporal dimension for videos with low sampling rate. We also embed the label hierarchy in TBEN to further improve the robustness of the classifier. Experiments on the FGA240 fine-grained classification dataset using TBEN achieve a new state-of-the-art (hit@1=47.95%). We also exploit the possibility of incorporating TBEN with multiple decoupled modalities like visual semantic and motion features: experiments on UCF101 sampled at 1 FPS achieve close to state-of-the-art accuracy (hit@1=91.03%) while requiring significantly less computational resources than competing approaches for both training and prediction.
Gradient-based optimization of 3D MHD equilibria

Using recently developed adjoint methods for computing the shape derivatives of functions that depend on magnetohydrodynamic (MHD) equilibria (Antonsen et al., J. Plasma Phys., vol. 85, issue 2, 2019; Paul et al., J. Plasma Phys., vol. 86, issue 1, 2020), we present the first example of analytic gradient-based optimization of fixed-boundary stellarator equilibria. We take advantage of gradient information to optimize figures of merit of relevance for stellarator design, including the rotational transform, magnetic well and quasi-symmetry near the axis. With the application of the adjoint method, we reduce the number of equilibrium evaluations by the dimension of the optimization space (${\sim }50\text {--}500$) in comparison with a finite-difference gradient-based method. We discuss regularization objectives of relevance for fixed-boundary optimization, including a novel method that prevents self-intersection of the plasma boundary. We present several optimized equilibria, including a vacuum field with very low magnetic shear throughout the volume.
Link Identiﬁability with Two Monitors: Auxiliary Algorithms and Proof of Selected Theorems

Complexity Analysis: In Algorithm A, partitioning G into biconnected components (line 1) and then triconnected components (line 3) takes O(V (G) + L(G)) time [3], [4]. The classification of triconnected components (lines 4-10) can be performed during the partitioning with a constant-factor increase in the complexity. Therefore, Algorithm A (also line 1 of Algorithm DIL-2M) has O(|V (G)| + |L(G)|) time complexity. In Algorithm DIL-2M, lines 2-4 take O(1) time. The complexity of lines 6-12 is O(|L(Ti)|) for each triconnected component Ti under consideration (Algorithm B takes O(1) time). Therefore, the overall complexity of Algorithm DIL-2M is O(|V (G)| + |L(G)|).
High Dimensional Level Set Estimation with Bayesian Neural Network

Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques suffer from the scalability issue, that is, these methods do not work well with high dimensional inputs. This paper proposes novel methods to solve the high dimensional LSE problems using Bayesian Neural Networks. In particular, we consider two types of LSE problems: (1) explicit LSE problem where the threshold level is a fixed user-specified value, and, (2) implicit LSE problem where the threshold level is defined as a percentage of the (unknown) maximum of the objective function. For each problem, we derive the corresponding theoretic information based acquisition function to sample the data points so as to maximally increase the level set accuracy. Furthermore, we also analyse theoretical time complexity of our proposed acquisition functions, and suggest a practical methodology to efficiently tune the network hyper-parameters to achieve high model accuracy. Numerical experiments on both synthetic and real-world datasets show that our proposed methods can achieve better results compared to existing state-of-the-art approaches.
Spin and charge excitations in the correlated multiband metal 
Ca3Ru2O7

We use Ru ${L}_{3}$-edge resonant inelastic x-ray scattering to study the full range of excitations in ${\mathrm{Ca}}_{3}{\mathrm{Ru}}_{2}{\mathrm{O}}_{7}$ from meV-scale magnetic dynamics through to the eV-scale interband transitions. This bilayer $4d$-electron correlated metal expresses a rich phase diagram, displaying long-range magnetic order below 56 K followed by a concomitant structural, magnetic, and electronic transition at 48 K. In the low-temperature phase, we observe a magnetic excitation with a bandwidth of $\ensuremath{\sim}30$ meV and a gap of $\ensuremath{\sim}8$ meV at the zone center, in excellent agreement with inelastic neutron scattering data. The dispersion can be modeled using a Heisenberg Hamiltonian for a bilayer $S=1$ system with single-ion anisotropy terms. At a higher energy loss, $dd$-type excitations show heavy damping in the presence of itinerant electrons, giving rise to a fluorescencelike signal appearing between the ${t}_{2g}$ and ${e}_{g}$ bands. At the same time, we observe a resonance originating from localized ${t}_{2g}$ excitations, in analogy to the structurally related Mott insulator ${\mathrm{Ca}}_{2}\mathrm{Ru}{\mathrm{O}}_{4}$. But whereas ${\mathrm{Ca}}_{2}\mathrm{Ru}{\mathrm{O}}_{4}$ shows sharp separate spin-orbit excitations and Hund's-rule driven spin-state transitions, here we identify only a single broad asymmetric feature. These results indicate that local intraionic interactions underlie the correlated physics in ${\mathrm{Ca}}_{3}{\mathrm{Ru}}_{2}{\mathrm{O}}_{7}$, even as the excitations become strongly mixed in the presence of itinerant electrons.
A Surrogate Lagrangian Relaxation-based Model Compression for Deep Neural Networks

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline, i.e., training, pruning and retraining (fine-tuning) significantly increases the overall training trails. For instance, the retraining process could take up to 80 epochs for ResNet-18 on ImageNet, that is 70% of the original model training trails. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem while ensuring fast convergence. We decompose the weight-pruning problem into subproblems, which are coordinated by updating Lagrangian multipliers. Convergence is then accelerated by using quadratic penalty terms. We evaluate the proposed method on image classification tasks, i.e., ResNet-18, ResNet-50 and VGG-16 using ImageNet and CIFAR-10, as well as object detection tasks, i.e., YOLOv3 and YOLOv3-tiny using COCO 2014, PointPillars using KITTI 2017, and Ultra-Fast-Lane-Detection using TuSimple lane detection dataset. Numerical testing results demonstrate that with the adoption of the Surrogate Lagrangian Relaxation method, our SLR-based weight-pruning optimization approach achieves a high model accuracy even at the hard-pruning stage without retraining for many epochs, such as on PointPillars object detection model on KITTI dataset where we achieve 9.44x compression rate by only retraining for 3 epochs with less than 1% accuracy loss. As the compression rate increases, SLR starts to perform better than ADMM and the accuracy gap between them increases. SLR achieves 15.2% better accuracy than ADMM on PointPillars after pruning under 9.49x compression. Given a limited budget of retraining epochs, our approach quickly recovers the model accuracy.
Flow-based Generative Models for Learning Manifold to Manifold Mappings

Many measurements or observations in computer vision and machine learning manifest as non-Euclidean data. While recent proposals (like spherical CNN) have extended a number of deep neural network architectures to manifold-valued data, and this has often provided strong improvements in performance, the literature on generative models for manifold data is quite sparse. Partly due to this gap, there are also no modality transfer/translation models for manifold-valued data whereas numerous such methods based on generative models are available for natural images. This paper addresses this gap, motivated by a need in brain imaging - in doing so, we expand the operating range of certain generative models (as well as generative models for modality transfer) from natural images to images with manifold-valued measurements. Our main result is the design of a two-stream version of GLOW (flow-based invertible generative models) that can synthesize information of a field of one type of manifold-valued measurements given another. On the theoretical side, we introduce three kinds of invertible layers for manifold-valued data, which are not only analogous to their functionality in flow-based generative models (e.g., GLOW) but also preserve the key benefits (determinants of the Jacobian are easy to calculate). For experiments, on a large dataset from the Human Connectome Project (HCP), we show promising results where we can reliably and accurately reconstruct brain images of a field of orientation distribution functions (ODF) from diffusion tensor images (DTI), where the latter has a 5 × faster acquisition time but at the expense of worse angular resolution.
Classiﬁcation of Object Point Clouds: A Practical Perspective

As a 3D counterpart of object classiﬁcation in images, object point cloud classi-ﬁcation is fundamental to 3D scene understanding, and has drawn great research attention since the release of benchmarking datasets, such as the ModelNet and the ShapeNet. These benchmarks assume point clouds covering complete surfaces of object instances, for which plenty of high-performing methods have been developed. However, their settings deviate from those often met in practice, where, due to (self-)occlusion, a point cloud covering partial surface of an object is captured from an arbitrary view. We show in this paper that performance of existing point cloud classiﬁcation methods drops drastically under the considered practical single-view, partial setting; the phenomenon is consistent with the observation that semantic category of a partial object surface is less ambiguous only when its distribution on the whole surface is clearly speciﬁed. To this end, we argue for a single-view, partial setting where supervised learning of object pose estimation should be accompanied with classiﬁcation. Technically, we propose a baseline method of Pose-Accompanied Point cloud classiﬁcation Network (PAP-Net) ; built upon SE (3) -equivariant convolutions, the PAPNet learns intermediate pose transformations for equivariant features deﬁned on vector ﬁelds, which makes the subsequent classiﬁcation easier (ideally) in the category-level, canonical pose. We adapt existing ModelNet40 and ScanNet datasets on point set classiﬁcation to the introduced single-view, partial setting to verify our hypothesis. Thorough experiments conﬁrm the necessity of object pose estimation; our PAPNet also outperforms existing methods greatly on the new benchmarks.
Learning from History for Byzantine Robust Optimization

Byzantine robustness has received significant attention recently given its importance for distributed and federated learning. In spite of this, we identify severe flaws in existing algorithms even when the data across the participants is identically distributed. First, we show realistic examples where current state of the art robust aggregation rules fail to converge even in the absence of any Byzantine attackers. Secondly, we prove that even if the aggregation rules may succeed in limiting the influence of the attackers in a single round, the attackers can couple their attacks across time eventually leading to divergence. To address these issues, we present two surprisingly simple strategies: a new robust iterative clipping procedure, and incorporating worker momentum to overcome time-coupled attacks. This is the first provably robust method for the standard stochastic optimization setting. Our code is open sourced at https://github.com/epfml/byzantine-robust-optimizer.
Parallel Implementation of Distributed Global Optimization (DGO)

Parallel implementations of distributed global optimization (DGO) [13] on MP-1 and NCUBE parallel computers revealed an approximate O(n) increase in the performance of this algorithm. Therefore, the implementation of the DGO on parallel processors can remedy the only draw back of this algorithm which is the O(n) of execution time as the number of the dimensions increase. The speed up factor of the parallel implementations of DGO is measured with respect to the sequential execution time of the identical problem on SPARC IV computer. The best speed up was achieved by the SIMD implementation of the algorithm on the MP-1 with the total speedup of 126 for an optimization problem with n = 9. This optimization problem was distributed across 128 PEs of Mas-Par.
Fair for All: Best-effort Fairness Guarantees for Classification

Standard approaches to group-based notions of fairness, such as \emph{parity} and \emph{equalized odds}, try to equalize absolute measures of performance across known groups (based on race, gender, etc.). Consequently, a group that is inherently harder to classify may hold back the performance on other groups; and no guarantees can be provided for unforeseen groups. Instead, we propose a fairness notion whose guarantee, on each group $g$ in a class $\mathcal{G}$, is relative to the performance of the best classifier on $g$. We apply this notion to broad classes of groups, in particular, where (a) $\mathcal{G}$ consists of all possible groups (subsets) in the data, and (b) $\mathcal{G}$ is more streamlined. For the first setting, which is akin to groups being completely unknown, we devise the {\sc PF} (Proportional Fairness) classifier, which guarantees, on any possible group $g$, an accuracy that is proportional to that of the optimal classifier for $g$, scaled by the relative size of $g$ in the data set. Due to including all possible groups, some of which could be too complex to be relevant, the worst-case theoretical guarantees here have to be proportionally weaker for smaller subsets. For the second setting, we devise the {\sc BeFair} (Best-effort Fair) framework which seeks an accuracy, on every $g \in \mathcal{G}$, which approximates that of the optimal classifier on $g$, independent of the size of $g$. Aiming for such a guarantee results in a non-convex problem, and we design novel techniques to get around this difficulty when $\mathcal{G}$ is the set of linear hypotheses. We test our algorithms on real-world data sets, and present interesting comparative insights on their performance.
Spacecraft Collision Risk Assessment with Probabilistic Programming

Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. Furthermore, the predicted growth of the space sector and the planned launch of megaconstellations will add even more complexity, therefore causing the collision risk and the burden on space operators to increase. Managing this complex framework with internationally agreed methods is pivotal and urgent. In this context, we build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages, calibrated using real data. By conditioning on observations, we use the model to obtain posterior distributions via Bayesian inference. We show that the probabilistic programming approach to conjunction assessment can help in making predictions and in finding the parameters that explain the observed data in conjunction data messages, thus shedding more light on key variables and orbital characteristics that more likely lead to conjunction events. Moreover, our technique enables the generation of physically accurate synthetic datasets of collisions, answering a fundamental need of the space and machine learning communities working in this area.