Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description

We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). We present two approaches to generate the events: unconditional generator and generator conditioned on missing transverse energy. We assess generation performances in a realistic LHC data-analysis environment, with a pileup mitigation algorithm applied.
Learning from Interventions using Hierarchical Policies for Safe Learning

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.
Let's Get Dirty: GAN Based Data Augmentation for Soiling and Adverse Weather Classification in Autonomous Driving

Cameras are getting more and more important in autonomous driving. Wide-angle fisheye cameras are relatively cheap sensors and very suitable for automated parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a complete and detailed view around the vehicle. These cameras are usually directly exposed to harsh environmental settings and therefore can get soiled very easily by mud, dust, water, frost, etc. The soiling on the camera lens has a direct impact on the further processing of the images they provide. While adverse weather conditions, such as rain, are getting attention recently, there is limited work on lens soiling. We believe that one of the reasons is that it is difficult to build a diverse dataset for this task, which is moreover expensive to annotate. We propose a novel GAN based algorithm for generating artificial soiling data along with the corresponding annotation masks. The manually annotated soiling dataset and the generated augmentation dataset will be made public. We demonstrate the generalization of our fisheye trained soiling GAN model on the Cityscapes dataset. Additionally, we provide an empirical evaluation of the degradation of the semantic segmentation algorithm with the soiled data.
Revisiting Non Local Sparse Models for Image Restoration

We propose a differentiable algorithm for image restoration inspired by the success of sparse models and self-similarity priors for natural images. Our approach builds upon the concept of joint sparsity between groups of similar image patches, and we show how this simple idea can be implemented in a differentiable architecture, allowing end-to-end training. The algorithm has the advantage of being interpretable, performing sparse decompositions of image patches, while being more parameter efficient than recent deep learning methods. We evaluate our algorithm on grayscale and color denoising, where we achieve competitive results, and on demoisaicking, where we outperform the most recent state-of-the-art deep learning model with 47 times less parameters and a much shallower architecture.
Polarized Low-Density Parity-Check Codes on the BSC

The connections between variable nodes and check nodes have a great influence on the performance of low-density parity-check (LDPC) codes. Inspired by the unique structure of polar code's generator matrix, we proposed a new method of constructing LDPC codes that achieves a polarization effect. The new code, named as polarized LDPC codes, is shown to achieve lower or no error floor in the binary symmetric channel (BSC).
Quantum butterfly effect in polarized Floquet systems

We explore quantum dynamics in Floquet many-body systems with local conservation laws in one spatial dimension, focusing on sectors of the Hilbert space which are highly polarized. We numerically compare the predicted charge diffusion constants and quantum butterfly velocity of operator growth between models of chaotic Floquet dynamics (with discrete spacetime translation invariance) and random unitary circuits which vary both in space and time. We find that for small but finite polarization per length (in the thermodynamic limit), the random unitary circuit correctly predicts the scaling of the butterfly velocity but incorrectly predicts the scaling of the diffusion constant. We argue that this is a consequence of quantum coherence on short time scales. Our work clarifies the settings in which random unitary circuits provide correct physical predictions for non-random chaotic systems, and sheds light into the origin of the slow down of the butterfly effect in highly polarized systems or at low temperature.
Collective Learning

In this paper, we introduce the concept of collective learning (CL) which exploits the notion of collective intelligence in the field of distributed semi-supervised learning. The proposed framework draws inspiration from the learning behavior of human beings, who alternate phases involving collaboration, confrontation and exchange of views with other consisting of studying and learning on their own. On this regard, CL comprises two main phases: a self-training phase in which learning is performed on local private (labeled) data only and a collective training phase in which proxy-labels are assigned to shared (unlabeled) data by means of a consensus-based algorithm. In the considered framework, heterogeneous systems can be connected over the same network, each with different computational capabilities and resources and everyone in the network may take advantage of the cooperation and will eventually reach higher performance with respect to those it can reach on its own. An extensive experimental campaign on an image classification problem emphasizes the properties of CL by analyzing the performance achieved by the cooperating agents.
The Search for Sparse, Robust Neural Networks

Recent work on deep neural network pruning has shown there exist sparse subnetworks that achieve equal or improved accuracy, training time, and loss using fewer network parameters when compared to their dense counterparts. Orthogonal to pruning literature, deep neural networks are known to be susceptible to adversarial examples, which may pose risks in security- or safety-critical applications. Intuition suggests that there is an inherent trade-off between sparsity and robustness such that these characteristics could not co-exist. We perform an extensive empirical evaluation and analysis testing the Lottery Ticket Hypothesis with adversarial training and show this approach enables us to find sparse, robust neural networks. Code for reproducing experiments is available here: this https URL.
Age demographics of the Milky Way disc and bulge

We exploit the extensive Gaia Data Release 2 set of long-period variables to select a sample of O-rich Miras throughout the Milky Way disc and bulge for study. Exploiting the relation between Mira pulsation period and stellar age/chemistry, we slice the stellar density of the Galactic disc and bulge as a function of period. We find that the morphology of both components evolves as a function of stellar age/chemistry with the stellar disc being stubby at old ages, becoming progressively thinner and more radially extended at younger stellar ages, consistent with the picture of inside-out and upside-down formation of the Milky Way’s disc. We see evidence of a perturbed disc, with large-scale stellar overdensities visible both in and away from the stellar plane. We find that the bulge is well modelled by a triaxial boxy distribution with an axial ratio of ∼1:0.4:0.3. The oldest of the Miras (∼9–10 Gyr) show little bar-like morphology, while the younger stars appear inclined at a viewing angle of ∼21° to the Sun–Galactic Centre line. This suggests that bar formation and buckling took place 8–9 Gyr ago, with the older Miras being hot enough to avoid being trapped by the growing bar. We find the youngest Miras to exhibit a strong peanut morphology, bearing the characteristic X-shape of an inclined bar structure.
Combining Q-Learning and Search with Amortized Value Estimates

We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS). In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values. The new Q-estimates are then used in combination with real experience to update the prior. This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search. SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari. SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets. By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.
Evaluation Metrics for Item Recommendation under Sampling

The task of item recommendation requires ranking a large catalogue of items given a context. Item recommendation algorithms are evaluated using ranking metrics that depend on the positions of relevant items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates sampled metrics in more detail and shows that sampled metrics are inconsistent with their exact version. Sampled metrics do not persist relative statements, e.g., 'algorithm A is better than B', not even in expectation. Moreover the smaller the sampling size, the less difference between metrics, and for very small sampling size, all metrics collapse to the AUC metric.
Maintenance rituximab versus observation after R-CHOP or R-EPOCH in patients with untreated poor-prognosis diffuse large B-cell lymphoma and grade III follicular lymphoma.

8084 Background: The prognosis of diffuse large-B-cell lymphoma (DLBCL) with poor risk (International Prognostic Index, IPI=3-5 or bulky disease) is poor. A considerable proportion of patients with poor-risk relapsed after rituximab in combination with CHOP or CHOP-like chemotherapy. It has been widely demonstrated that maintenance rituximab (MR) can improve survival rate in follicular lymphoma (FL), but it's unclear in DLBCL. The objective of this study is to investigate the efficacy and safety of maintenance rituximab therapy in DLBCL patients with poor risk. Methods: 50 untreated DLBCL patients (including 6 FL III) with poor risk (IPI=3-5 or bulky disease) who achieve complete remission (CR) or complete remission unconfirmed (CRu) after 6∼8 cycles of R-CHOP or R-EPOCH (±invasion field radiotherapy) were enrolled. They were assigned to MR (n=21) for two years or till to relapse or death or observation only (OBS) (n=29). Results: Two-year disease-free survival (DFS) and overall survival (OS) rates for MR.
Normalizing Flows for Probabilistic Modeling and Inference

Normalizing flows provide a general mechanism for defining expressive probability distributions, only requiring the specification of a (usually simple) base distribution and a series of bijective transformations. There has been much recent work on normalizing flows, ranging from improving their expressive power to expanding their application. We believe the field has now matured and is in need of a unified perspective. In this review, we attempt to provide such a perspective by describing flows through the lens of probabilistic modeling and inference. We place special emphasis on the fundamental principles of flow design, and discuss foundational topics such as expressive power and computational trade-offs. We also broaden the conceptual framing of flows by relating them to more general probability transformations. Lastly, we summarize the use of flows for tasks such as generative modeling, approximate inference, and supervised learning.
Functional Convergence of U-processes with Size-Dependent Kernels

We consider sequences of $U$-processes based on symmetric kernels of a fixed order, that possibly depend on the sample size. Our main contribution is the derivation of a set of analytic sufficient conditions, under which the aforementioned $U$-processes weakly converge to a linear combination of time-changed independent Brownian motions. In view of the underlying symmetric structure, the involved time-changes and weights remarkably depend only on the order of the U-statistic, and have consequently a universal nature. Checking these sufficient conditions requires calculations that have roughly the same complexity as those involved in the computation of fourth moments and cumulants. As such, when applied to the degenerate case, our findings are infinite-dimensional extensions of the central limit theorems (CLTs) proved in de Jong (1990) and Dobler and Peccati (2017). As important tools in our analysis, we exploit the multidimensional central limit theorems established in Dobler and Peccati (2019) together with upper bounds on absolute moments of degenerate $U$-statistics by Ibragimov and Sharakhmetov (2002), and also prove some novel multiplication formulae for degenerate symmetric $U$-statistics --- allowing for different sample sizes --- that are of independent interest. We provide applications to random geometric graphs and to a class of $U$-statistics of order two, whose Gaussian fluctuations have been recently studied by Robins et al. (2016), in connection with quadratic estimators in non-parametric models. In particular, our application to random graphs yields a class of new functional central limit theorems for subgraph counting statistics, extending previous findings in the literature. Finally, some connections with invariance principles in changepoint analysis are established.
Large-Scale Semi-Supervised Learning via Graph Structure Learning over High-Dense Points

We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of large-scale unlabeled data, existing SSL methods usually encounter either suboptimal performance because of an improper graph or the high computational complexity of the large-scale optimization problem. In this paper, we propose to address both challenging problems by constructing a proper graph for graph-based SSL methods. Different from existing approaches, we simultaneously learn a small set of vertexes to characterize the high-dense regions of the input data and a graph to depict the relationships among these vertexes. A novel approach is then proposed to construct the graph of the input data from the learned graph of a small number of vertexes with some preferred properties. Without explicitly calculating the constructed graph of inputs, two transductive graph-based SSL approaches are presented with the computational complexity in linear with the number of input data. Extensive experiments on synthetic data and real datasets of varied sizes demonstrate that the proposed method is not only scalable for large-scale data, but also achieve good classification performance, especially for extremely small number of labels.
3D Objectness Estimation via Bottom-up Regret Grouping

3D objectness estimation, namely discovering semantic objects from 3D scene, is a challenging and significant task in 3D understanding. In this paper, we propose a 3D objectness method working in a bottom-up manner. Beginning with over-segmented 3D segments, we iteratively group them into object proposals by learning an ingenious grouping predictor to determine whether two 3D segments can be grouped or not. To enhance robustness, a novel regret mechanism is presented to withdraw incorrect grouping operations. Hence the irreparable consequences brought by mistaken grouping in prior bottom-up works can be greatly reduced. Our experiments show that our method outperforms state-of-the-art 3D objectness methods with a small number of proposals in two difficult datasets, GMU-kitchen and CTD. Further ablation study also demonstrates the effectiveness of our grouping predictor and regret mechanism.
Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning actionconditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.
Sifat-sifat fizik dan kandungan kimia lempung kaolin Segamat, Johor

Kaolin is a source of secondary mineral as a product of a weathering process of primary minerals. Its main component is fine grain kaolinite (<2μm) and it also contains other elements such as aluminium and iron phyllosilicate as the pigment. Aluminium rich kaolin is light in colour with high plasticity and is normally used in the ceramic, plastic, paint, paper, pesticide, pharmacology and cosmetic industries. The physical and chemical characteristics of kaolins are important for its potential application. In this study, about 25 kaolin samples were hand-augered from depths of 1-2 m at Buloh Kasap Segamat, Johor, Malaysia. Chemical analysis carried out included determination of oxides and types of minerals by X-ray diffraction and X-ray fluorescence. Shrinkage rate, rupture modulus and water absorption rate tests were carried out in the physical properties analysis. Plastic and liquid limits of the kaolin were also measured for plastic index. The Segamat kaolin was light in colour due to its high silicate composition. The highest mineral content in the kaolin was kaolinite and quartz occured as impurities. The low shrinkage rate showed that the kaolin was dense with little voids, hence very suitable for use in the ceramic industry. This kaolin has low water absorption, plasticity and durable according to the rupture modulus test.
Implantação dos Times de Reposta Rápida: experiência de um hospital de alta complexidade em cardiopneumologia* Implementation of Rapid Response Teams: the experience in a highly complex cardiopneumology hospital

JUSTIFICATIVA E OBJETIVOS: A implantação dos times de resposta rápida (TRR) para o atendimento de emergências clínicas e parada cardiorrespiratória (PCR) tem mostrado impacto positivo na sobrevida e morbidade dos pacientes atendidos. Este estudo teve como objetivo descrever a sua implantação no Instituto do Coração do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. RELATO DE EXPERIÊNCIA: Trata-se de implantação dos TRR. Foram criados três TRR de acordo com o tipo de emergência clínica e a idade do paciente: Código Vermelho e Azul – atendimento de PCR em cardiopediatria e adultos, respectivamente – e Código Amarelo – atendimento de emergências clínicas em adultos. Para viabilizar a implantação, houve necessidade Implantação dos Times de Reposta Rápida: experiência de um hospital de alta complexidade em cardiopneumologia*
RESEARCH NOTE CONTROLLABLE SYNTHESIS OF FLOWER-LIKE ZnO NANOSTRUCTURE WITH HYDROTHERMAL METHOD

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Effective Bond Lengths in a Protein by NMR in a Dilute Liquid Crystalline Phase

Weak alignment of solute macromolecules with the magnetic field can be achieved in a dilute, aqueous liquid crystalline phase of planar phospholipid micelles, consisting of mixtures of dimyristoylphos- phatidylcholine (DMPC) and dihexanoylphosphatidylcholine (DHPC). Alignment of proteins in such a medium is sufficiently weak to retain the simplicity of the isotropic solution NMR spectrum but strong enough to permit accurate measurement of residual one-bond dipolar couplings. Highly accurate one-bond N-H N ,C R - H R ,C R -C', and C'-N and two-bond C'-H N dipolar couplings were measured in 13 C/ 15 N-enriched ubiquitin. Together with knowledge of the protein's three-dimensional structure, the dipolar couplings permit calculation of the relative, vibrationally corrected average bond lengths for these interactions. Assuming a C '-N bond length of 1.329 A (Engh, R. A.; Huber, R.Acta Crystallogr. 1992, A47, 392-400), the relative C R -Cdistance of 1.526 A is found to be in excellent agreement with results from Engh and Huber (1.525 A). Using a C-N bond length of 1.329 A as a reference, N -H N (1.041 ( 0.006 A) and C R -H R (1.117 ( 0.007 A) are considerably longer than equilibrium or average internuclear distances derived from ab initio calculations, electron diffraction, neutron diffraction, or microwave spectroscopy. The increase in effective N-H N and C R -H R bond lengths is attributed to a decrease in the corresponding dipolar couplings resulting from fast librations, which must be of considerably larger amplitude than the C R -Cand C'-N angular fluctuations. Accurate knowledge of the relative effective N-H N ,C R -H R ,C R -C' ,C '-N, and two-bond C'-H N effective internuclear distances is essential for determining the magnitude of the molecular alignment tensor, for using the dipolar couplings in macromolecular structure determination, and for extracting angular information from recently described cross correlation experiments.