Miracle-dz/UniFAR-FTUs huggingface dataset:
CaloClouds: fast geometry-independent highly-granular calorimeter simulation

Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a major computing constraint. This work achieves a major breakthrough in this task by, for the first time, directly generating a point cloud of a few thousand space points with energy depositions in the detector in 3D space without relying on a fixed-grid structure. This is made possible by two key innovations: i) Using recent improvements in generative modeling we apply a diffusion model to generate photon showers as high-cardinality point clouds. ii) These point clouds of up to 6,000 space points are largely geometry-independent as they are down-sampled from initial even higher-resolution point clouds of up to 40,000 so-called Geant steps. We showcase the performance of this approach using the specific example of simulating photon showers in the planned electromagnetic calorimeter of the International Large Detector (ILD) and achieve overall good modeling of physically relevant distributions.
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization

Deep Neural Networks (DNNs) have recently made significant progress in many fields. However, studies have shown that DNNs are vulnerable to adversarial examples, where imperceptible perturbations can greatly mislead DNNs even if the full underlying model parameters are not accessible. Various defense methods have been proposed, such as feature compression and gradient masking. However, numerous studies have proven that previous methods create detection or defense against certain attacks, which renders the method ineffective in the face of the latest unknown attack methods. The invisibility of adversarial perturbations is one of the evaluation indicators for adversarial example attacks, which also means that the difference in the local correlation of high-frequency information in adversarial examples and normal examples can be used as an effective feature to distinguish the two. Therefore, we propose an adversarial example detection framework based on a high-frequency information enhancement strategy, which can effectively extract and amplify the feature differences between adversarial examples and normal examples. Experimental results show that the feature augmentation module can be combined with existing detection models in a modular way under this framework. Improve the detector's performance and reduce the deployment cost without modifying the existing detection model.
Nicotine reduces discrimination between threat and safety in the hippocampus, nucleus accumbens and amygdala

Nicotine intake by cigarettes is linked to the maintenance and development of anxiety disorders and impairs adaptive discrimination of threat and safety in humans. Yet, it is unclear if nicotine exerts a causal pharmacological effect on the affective and neural mechanisms that underlie aversive learning. We conducted a pre-registered, pseudo-randomly and double-blinded pharmacological fMRI study to investigate the effect of acute nicotine on Fear Acquisition and Extinction in non-smokers (n=88). Participants were pseudo-randomly and double-blinded assigned to one of three experimental groups: 1. nicotine administration before Fear Acquisition, 2. nicotine administration before Extinction training or 3. placebo control group. Our results show that nicotine administration led to decreased discrimination between threat and safety in subjective fear. Nicotine furthermore decreased differential (threat vs. safety) activation in the hippocampus, which was functionally coupled with Nucleus Accumbens and amygdala, compared to placebo controls. Additionally, nicotine led to overactivation of the ventral tegmental area. This study provides mechanistic evidence for impaired discrimination between threat and safety by the acute intake of Nicotine in non-smoking individuals. Nicotine intake directly impairs neural substrate of adaptive aversive learning and might therefore constitute a profound risk factor for the development of pathological anxiety.
On particular integrability in classical mechanics

In this study the notion of particular integrability in Classical Mechanics, introduced in [J. Phys. A: Math. Theor. 46 025203, 2013], is revisited within the formalism of symplectic geometry. A particular integral $\cal I$ is a function not necessarily conserved in the whole phase space $T^*Q$ but when restricted to a certain invariant subspace ${\cal W}\subseteq T^*Q$ it becomes a Liouville first integral. For natural Hamiltonian systems, it is demonstrated that such a function $\cal I$ allows us to construct a lower dimensional Hamiltonian in $\cal W$. This symmetry reduction is intimately related with a phenomenon beyond separation of variables and it is based on an adaptive application of the classical results due to Lie and Liouville on integrability. Three physically relevant systems are used to illustrate the underlying key aspects of the symplectic theory approach to particular integrability: (I) the integrable central-force problem, (II) the chaotic two-body Coulomb system in a constant magnetic field as well as (III) the $N$-body system.
The contribution of epidemiology to the understanding of neurodevelopmental disabilities

Epidemiological approaches have played an important role in creating better understanding of developmental disabilities by delineating their frequency in populations and changes in their frequency over time, by identifying etiological factors, and by documenting pathways to prevention. Both cerebral palsy (CP) and mild intellectual disability are declining in frequency in high‐income countries. The diagnosis of autism spectrum disorder has increased in recent decades, but much of this increase is a result of changing approaches to ascertainment and recording. Epidemiological studies have found that most CP is not of birth‐asphyxial origin, that most febrile seizures do not pose a major risk for epilepsy, and that folic acid deficiency may contribute to developmental disabilities apart from its effect on neural tube defects. Epidemiological research has shown that an important fraction of neural tube defects and virtually all cases of Reye syndrome are preventable, and recent trials have shown ways to prevent CP. Early psychoeducational interventions in children at risk for mild intellectual disability are an effective and valuable societal investment. Very large population‐based studies starting in pregnancy have been launched in Norway, Denmark, and Japan in recent years and these and other population studies promise to continue the epidemiological contribution to a better understanding of developmental disabilities.
Are Nonthermal Velocities in Active Region Coronal Loops Anisotropic?

We have measured line widths in active region coronal loops in order to determine whether the nonthermal broadening is anisotropic with respect to the magnetic field direction. These nonthermal velocities are caused by unresolved fluid motions. Our analysis method combines spectroscopic data and a magnetic field extrapolation. We analyzed spectra from the Extreme Ultraviolet Imaging Spectrometer on Hinode. A differential emission measure analysis showed that many spectral lines that are commonly considered to be formed in the active region have a substantial contribution from the background quiet Sun. From these spectra we identified lines whose emission was dominated by the active region loops rather than background sources. Using these lines, we constructed maps of the nonthermal velocity. With data from the Helioseismic Magnetic Imager on the Solar Dynamics Observatory and the Coronal Modeling System nonlinear force-free magnetic field reconstruction code, we traced several of the magnetic field lines through the active region. Comparing the spectroscopic and magnetic data, we looked for correlations of the nonthermal velocity with the viewing angle between the line of sight and the magnetic field. We found that nonthermal velocities show a weak anticorrelation with the viewing angle. That is, the tendency is for the nonthermal velocity to be slightly larger in the parallel direction. This parallel broadening may be due to acoustic waves or unresolved parallel flows.
Optimized Dimensionality Reduction for Moment-based Distributionally Robust Optimization

Moment-based distributionally robust optimization (DRO) provides an optimization framework to integrate statistical information with traditional optimization approaches. Under this framework, one assumes that the underlying joint distribution of random parameters runs in a distributional ambiguity set constructed by moment information and makes decisions against the worst-case distribution within the set. Although most moment-based DRO problems can be reformulated as semidefinite programming (SDP) problems that can be solved in polynomial time, solving high-dimensional SDPs is still time-consuming. Unlike existing approximation approaches that first reduce the dimensionality of random parameters and then solve the approximated SDPs, we propose an optimized dimensionality reduction (ODR) approach. We first show that the ranks of the matrices in the SDP reformulations are small, by which we are then motivated to integrate the dimensionality reduction of random parameters with the subsequent optimization problems. Such integration enables two outer and one inner approximations of the original problem, all of which are low-dimensional SDPs that can be solved efficiently. More importantly, these approximations can theoretically achieve the optimal value of the original high-dimensional SDPs. As these approximations are nonconvex SDPs, we develop modified Alternating Direction Method of Multipliers (ADMM) algorithms to solve them efficiently. We demonstrate the effectiveness of our proposed ODR approach and algorithm in solving two practical problems. Numerical results show significant advantages of our approach on the computational time and solution quality over the three best possible benchmark approaches. Our approach can obtain an optimal or near-optimal (mostly within 0.1%) solution and reduce the computational time by up to three orders of magnitude.
A Computational Model For Individual Scholars' Writing Style Dynamics

A manuscript's writing style is central in determining its readership, influence, and impact. Past research has shown that, in many cases, scholars present a unique writing style that is manifested in their manuscripts. In this work, we report a comprehensive investigation into how scholars' writing styles evolve throughout their careers focusing on their academic relations with their advisors and peers. Our results show that scholars' writing styles tend to stabilize early on in their careers -- roughly their 13th publication. Around the same time, scholars' departures from their advisors' writing styles seem to converge as well. Last, collaborations involving fewer scholars, scholars from the same gender, or from the same field of study seem to bring about greater change in their co-authors' writing styles with younger scholars being especially influenceable.
Newton--Okounkov bodies and minimal models for cluster varieties

Let $Y$ be a (partial) minimal model of a scheme $V$ with a cluster structure. Under natural assumptions, for every choice of seed we associate a Newton--Okounkov body to every divisor on $Y$ supported on $Y \setminus V$ and show that these Newton--Okounkov bodies are positive sets in the sense of Gross, Hacking, Keel and Kontsevich \cite{GHKK}. This construction essentially reverses the procedure in loc. cit. that generalizes the polytope construction of a toric variety to the framework of cluster varieties. In a closely related setting, we consider cases where $Y$ is a projective variety whose universal torsor $\text{UT}_Y$ is a partial minimal model of a scheme with a cluster structure of type $\mathcal A$. If the theta functions parametrized by the integral points of the associated superpotential cone form a basis of the ring of algebraic functions on $\text{UT}_Y$ and the action of the torus $T_{\text{Pic}(Y)^*}$ on $\text{UT}_Y$ is compatible with the cluster structure, then for every choice of seed we associate a Newton--Okounkov body to every line bundle on $Y$. We prove that any such Newton--Okounkov body is a positive set and that $Y$ is a minimal model of a quotient of a cluster $\mathcal A$-variety by the action of a torus. Our constructions lead to the notion of the intrinsic Newton--Okounkov body associated to a boundary divisor in a partial minimal model of a scheme with a cluster structure. This provides a wide class of examples of Newton-Okoukov bodies exhibiting a wall-crossing phenomenon in the sense of Escobar--Harada \cite{EH20}. This approach includes the partial flag varieties that arise as minimal models of cluster varieties. For the case of Grassmannians, our approach recovers, up to interesting unimodular equivalences, the Newton--Okounkov bodies constructed by Rietsch--Williams in \cite{RW}.
Residual Prompt Tuning: Improving Prompt Tuning with Residual Reparameterization

Prompt tuning is one of the successful approaches for parameter-efficient tuning of pre-trained language models. Despite being arguably the most parameter-efficient (tuned soft prompts constitute<0.1% of total parameters), it typically performs worse than other efficient tuning methods and is quite sensitive to hyper-parameters. In this work, we introduce Residual Prompt Tuning - a simple and efficient method that significantly improves the performance and stability of prompt tuning. We propose to reparameterize soft prompt embeddings using a shallow network with a residual connection. Our experiments show that Residual Prompt Tuning significantly outperforms prompt tuning on SuperGLUE benchmark. Notably, our method reaches +7 points improvement over prompt tuning with T5-Base and allows to reduce the prompt length by 10x without hurting performance. In addition, we show that our approach is robust to the choice of learning rate and prompt initialization, and is effective in few-shot settings.
Riesz networks: scale invariant neural networks in a single forward pass

Scale invariance of an algorithm refers to its ability to treat objects equally independently of their size. For neural networks, scale invariance is typically achieved by data augmentation. However, when presented with a scale far outside the range covered by the training set, neural networks may fail to generalize. Here, we introduce the Riesz network, a novel scale- invariant neural network. Instead of standard 2d or 3d convolutions for combining spatial information, the Riesz network is based on the Riesz transform which is a scale-equivariant operation. As a consequence, this network naturally generalizes to unseen or even arbitrary scales in a single forward pass. As an application example, we consider detecting and segmenting cracks in tomographic images of concrete. In this context, ‘scale’ refers to the crack thickness which may vary strongly even within the same sample. To prove its scale invariance, the Riesz network is trained on one fixed crack width. We then validate its performance in segmenting simulated and real tomographic images featuring a wide range of crack widths. An additional experiment is carried out on the MNIST Large Scale data set.
Rethinking Class Imbalance in Machine Learning

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples in each class is not balanced. The ignorance of the proportion imbalance will result in unfairness between/among classes and poor generalization capability. Previous literature has presented numerous methods for either theoretical/empirical analysis or new methods for imbalance learning. This study presents a new taxonomy of class imbalance in machine learning with a broader scope. Four other types of imbalance, namely, variance, distance, neighborhood, and quality imbalances between/among classes, which may exist in machine learning tasks, are summarized. Two different levels of imbalance including global and local are also presented. Theoretical analysis is used to illustrate the significant impact of the new imbalance types on learning fairness. Moreover, our taxonomy and theoretical conclusions are used to analyze the shortcomings of several classical methods. As an example, we propose a new logit perturbation-based imbalance learning loss when proportion, variance, and distance imbalances exist simultaneously. Several classical losses become the special case of our proposed method. Meta learning is utilized to infer the hyper-parameters related to the three types of imbalance. Experimental results on several benchmark corpora validate the effectiveness of the proposed method.
AvatarReX: Real-time Expressive Full-body Avatars

We present AvatarReX, a new method for learning NeRF-based full-body avatars from video data. The learnt avatar not only provides expressive control of the body, hands and the face together, but also supports real-time animation and rendering. To this end, we propose a compositional avatar representation, where the body, hands and the face are separately modeled in a way that the structural prior from parametric mesh templates is properly utilized without compromising representation flexibility. Furthermore, we disentangle the geometry and appearance for each part. With these technical designs, we propose a dedicated deferred rendering pipeline, which can be executed at a real-time framerate to synthesize high-quality free-view images. The disentanglement of geometry and appearance also allows us to design a two-pass training strategy that combines volume rendering and surface rendering for network training. In this way, patch-level supervision can be applied to force the network to learn sharp appearance details on the basis of geometry estimation. Overall, our method enables automatic construction of expressive full-body avatars with real-time rendering capability, and can generate photo-realistic images with dynamic details for novel body motions and facial expressions.
Uncovering and Experimental Realization of Multimodal 3D Topological Metamaterials for Low‐Frequency and Multiband Elastic Wave Control

Topological mechanical metamaterials unlock confined and robust elastic wave control. Recent breakthroughs have precipitated the development of 3D topological metamaterials, which facilitate extraordinary wave manipulation along 2D planar and layer‐dependent waveguides. The 3D topological metamaterials studied thus far are constrained to function in single‐frequency bandwidths that are typically in a high‐frequency regime, and a comprehensive experimental investigation remains elusive. In this paper, these research gaps are addressed and the state of the art is advanced through the synthesis and experimental realization of a 3D topological metamaterial that exploits multimodal local resonance to enable low‐frequency elastic wave control over multiple distinct frequency bands. The proposed metamaterial is geometrically configured to create multimodal local resonators whose frequency characteristics govern the emergence of four unique low‐frequency topological states. Numerical simulations uncover how these topological states can be employed to achieve polarization‐, frequency‐, and layer‐dependent wave manipulation in 3D structures. An experimental study results in the attainment of complete wave fields that illustrate 2D topological waveguides and multi‐polarized wave control in a physical testbed. The outcomes from this work provide insight that will aid future research on 3D topological mechanical metamaterials and reveal the applicability of the proposed metamaterial for wave control applications.
Learned Wyner–Ziv Compressors Recover Binning

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, real-world applications of this problem have neither been fully developed nor heavily investigated. We propose a data-driven method based on machine learning that leverages the universal function approximation capability of artificial neural networks. We find that our neural network-based compression scheme re-discovers some principles of the optimum theoretical solution of the Wyner-Ziv setup, such as binning in the source space as well as linear decoder behavior within each quantization index, for the quadratic-Gaussian case. These behaviors emerge although no structure exploiting knowledge of the source distributions was imposed. Binning is a widely used tool in information theoretic proofs and methods, and to our knowledge, this is the first time it has been explicitly observed to emerge from data-driven learning.
Train a Real-world Local Path Planner in One Hour via Partially Decoupled Reinforcement Learning and Vectorized Diversity

Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, such application in the real world is immensely limited due to the deficient training efficiency and generalization capability of DRL. To alleviate these two issues, a solution named Color is proposed, which consists of an Actor-Sharer-Learner (ASL) training framework and a mobile robot-oriented simulator Sparrow. Specifically, the ASL intends to improve the training efficiency of DRL algorithms. It employs a Vectorized Data Collection (VDC) mode to expedite data acquisition, decouples the data collection from model optimization by multithreading, and partially connects the two procedures by harnessing a Time Feedback Mechanism (TFM) to evade data underuse or overuse. Meanwhile, the Sparrow simulator utilizes a 2D grid-based world, simplified kinematics, and conversion-free data flow to achieve a lightweight design. The lightness facilitates vectorized diversity, allowing diversified simulation setups across extensive copies of the vectorized environments, resulting in a notable enhancement in the generalization capability of the DRL algorithm being trained. Comprehensive experiments, comprising 57 DRL benchmark environments, 32 simulated and 36 real-world LPP scenarios, have been conducted to corroborate the superiority of our method in terms of efficiency and generalization. The code and the video of this paper are accessible at https://github.com/XinJingHao/Color.
Rotational Slippage Prediction from Segmentation of Tactile Images

Adding tactile sensors to a robotic system is becoming a common practice to achieve more complex manipulation skills than those robotics systems that only use external cameras to manipulate objects. The key of tactile sensors is that they provide extra information about the physical properties of the grasping. In this paper, we implemented a system to predict and quantify the rotational slippage of objects in hand using the vision-based tactile sensor known as Digit. Our system comprises a neural network that obtains the segmented contact region (object-sensor), to later calculate the slippage rotation angle from this region using a thinning algorithm. Besides, we created our own tactile segmentation dataset, which is the first one in the literature as far as we are concerned, to train and evaluate our neural network, obtaining results of 95% and 91% in Dice and IoU metrics. In real-scenario experiments, our system is able to predict rotational slippage with a maximum mean rotational error of 3 degrees with previously unseen objects. Thus, our system can be used to prevent an object from falling due to its slippage.