Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

This work introduces a methodology for studying synchronization in adaptive networks with heterogeneous plasticity (adaptation) rules. As a paradigmatic model, we consider a network of adaptively coupled phase oscillators with distance-dependent adaptations. For this system, we extend the master stability function approach to adaptive networks with heterogeneous adaptation. Our method allows for separating the contributions of network structure, local node dynamics, and heterogeneous adaptation in determining synchronization. Utilizing our proposed methodology, we explain mechanisms leading to synchronization or desynchronization by enhanced long-range connections in nonlocally coupled ring networks and networks with Gaussian distance-dependent coupling weights equipped with a biologically motivated plasticity rule.
Birefringence and quasinormal modes of the Einstein-Euler-Heisenberg black hole

In this contribution we study the birefringence and the quasinormal modes (QNM) in the eikonal approximation, of the Einstein-Euler-Heisenberg black hole (EEH-BH) . The EEH-BH is an exact solution of the Einstein equations coupled to the Euler-Heisenberg nonlinear electrodynamics. It is well known that in the presence of strong electromagnetic ﬁelds light rays modify their trajectories and do follow the null geodesics of an eﬀective metric as a result of the nonlinear interaction between light and the intense magnetic or electric background. In the Euler-Heisenberg theory the phenomenon of birefringence arises and then exist two possible light trajectories for polarized light in the vicinity of the EEH-BH. On the other hand, using the correspondence between the parameters of the null unstable geodesics and the quasinormal modes in the eikonal approximation. We determine these QNM for both, the electric and the magnetic EEH-BH and compare them with its linear counterpart, the Reissner-Nordstr¨om black hole, since there are two eﬀective metrics, to each one corresponds one null geodesic that renders two possible QNM from the same compact object.
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation

Recently, token-level adaptive training has achieved promising improvement in machine translation, where the cross-entropy loss function is adjusted by assigning different training weights to different tokens, in order to alleviate the token imbalance problem. However, previous approaches only use static word frequency information in the target language without considering the source language, which is insufficient for bilingual tasks like machine translation. In this paper, we propose a novel bilingual mutual information (BMI) based adaptive objective, which measures the learning difficulty for each target token from the perspective of bilingualism, and assigns an adaptive weight accordingly to improve token-level adaptive training. This method assigns larger training weights to tokens with higher BMI, so that easy tokens are updated with coarse granularity while difficult tokens are updated with fine granularity. Experimental results on WMT14 English-to-German and WMT19 Chinese-to-English demonstrate the superiority of our approach compared with the Transformer baseline and previous token-level adaptive training approaches. Further analyses confirm that our method can improve the lexical diversity.
Neural radiosity

We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing the norm of its residual, similar as in classical radiosity techniques. Traditional basis functions used in radiosity, such as piecewise polynomials or meshless basis functions are typically limited to representing isotropic scattering from diffuse surfaces. Instead, we propose to leverage neural networks to represent the full four-dimensional radiance distribution, directly optimizing network parameters to minimize the norm of the residual. Our approach decouples solving the rendering equation from rendering (perspective) images similar as in traditional radiosity techniques, and allows us to efficiently synthesize arbitrary views of a scene. In addition, we propose a network architecture using geometric learnable features that improves convergence of our solver compared to previous techniques. Our approach leads to an algorithm that is simple to implement, and we demonstrate its effectiveness on a variety of scenes with diffuse and non-diffuse surfaces.
Social-IWSTCNN: A Social Interaction-Weighted Spatio- Temporal Convolutional Neural Network for Pedestrian Trajectory Prediction in Urban Traffic Scenarios

Pedestrian trajectory prediction in urban scenarios is essential for automated driving. This task is challenging because the behavior of pedestrians is influenced by both their own history paths and the interactions with others. Previous research modeled these interactions with pooling mechanisms or aggregating with hand-crafted attention weights. In this paper, we present the Social Interaction-Weighted Spatio- Temporal Convolutional Neural Network (Social-IWSTCNN), which includes both the spatial and the temporal features. We propose a novel design, namely the Social Interaction Extractor, to learn the spatial and social interaction features of pedestrians. Most previous works used ETH and UCY datasets which include five scenes but do not cover urban traffic scenarios extensively for training and evaluation. In this paper, we use the recently released large-scale Waymo Open Dataset in urban traffic scenarios, which includes 374 urban training scenes and 76 urban testing scenes to analyze the performance of our proposed algorithm in comparison to the state-of-the-art (SOTA) models. The results show that our algorithm outperforms SOTA algorithms such as Social-LSTM, Social-GAN, and Social-STGCNN on both Average Displacement Error (ADE) and Final Displacement Error (FDE). Furthermore, our Social- IWSTCNN is 54.8 times faster in data pre-processing speed, and 4.7 times faster in total test speed than the current best SOTA algorithm Social-STGCNN.
Topological Pilot Assignment in Large-Scale Distributed MIMO Networks

We consider the pilot assignment problem in large-scale distributed multi-input multi-output (MIMO) networks, where a large number of remote radio head (RRH) antennas are randomly distributed in a wide area, and jointly serve a relatively smaller number of users (UE) coherently. By artificially imposing structures on the UE-RRH connectivity, we model the network by a partially-connected interference network, so that the pilot assignment problem can be cast as a topological interference management problem with multiple groupcast messages. Building upon such connection, we formulate the topological pilot assignment (TPA) problem in two different ways with respect to whether or not the to-be-estimated channel connectivity pattern is known a priori. When it is known, we formulate the TPA problem as a low-rank matrix completion problem that can be solved by a simple alternating projection algorithm. Otherwise, we formulate it as a sequential maximum weight induced matching problem that can be solved by either a mixed integer linear program or a simple yet efficient greedy algorithm. With respect to two different formulations of the TPA problem, we evaluate the efficiency of the proposed algorithms under the cell-free massive MIMO setting.
Direct Nash Implementation with Evidence

We study full implementation with hard evidence in a bounded environment. By invoking monetary transfers off the equilibrium, we show that a social choice function is Nash implementable in a direct revelation mechanism if and only if it satisfies the measurability condition proposed by Ben-Porath and Lipman (2012). Building on a novel classification of lies according to their refutability with evidence, the mechanism requires only two agents, accounts for mixed-strategy equilibria, accommodates evidentiary costs, and can also be modified to account for limited solvency of the agents. We also establish a necessary and sufficient condition on the evidence structure for renegotiation-proof bilateral contracts, based on the classification of lies.
Adversarial robustness against multiple lp-threat models at the price of one and how to quickly fine-tune robust models to another threat model

Adversarial training (AT) in order to achieve adversarial robustness wrt single l p -threat models has been discussed extensively. However, for safety-critical systems adversarial robustness should be achieved wrt all l p -threat models simultaneously. In this paper we develop a simple and ef-ﬁcient training scheme to achieve adversarial robustness against the union of l p -threat models. Our novel l 1 + l ∞ -AT scheme is based on geometric considerations of the different l p -balls and costs as much as normal adversarial training against a single l p -threat model. Moreover, we show that using our l 1 + l ∞ -AT scheme one can ﬁne-tune with just 3 epochs any l p -robust model (for p ∈ { 1 , 2 , ∞} ) and achieve multiple norm adversarial robustness. In this way we boost the previous state-of-the-art reported for multiple-norm robustness by more than 6% on CIFAR-10 and report up to our knowledge the ﬁrst ImageNet models with multiple norm robustness. Moreover, we study the general transfer of adversarial robustness between different threat models and in this way boost the previous SOTA l 1 -robustness on CIFAR-10 by almost 10% .
Epitaxial Growth and Domain Structure Imaging of Kagome Magnet Fe$_3$Sn$_2$

Magnetic materials with kagome crystal structure exhibit rich physics such as frustrated magnetism, skyrmion formation, topological flat bands, and Dirac/Weyl points. Until recently, most studies on kagome magnets have been performed on bulk crystals or polycrystalline films. Here we report the synthesis of high-quality epitaxial films of topological kagome magnet Fe3Sn2 by atomic layer molecular beam epitaxy. Structural and magnetic characterization of Fe3Sn2 on epitaxial Pt(111) identifies highly ordered films with c-plane orientation and an in-plane magnetic easy axis. Studies of the local magnetic structure by anomalous Nernst effect imaging reveals in-plane oriented micrometer size domains. The realization of high-quality films by atomic layer molecular beam epitaxy opens the door to explore the rich physics of this system and investigate novel spintronic phenomena by interfacing Fe3Sn2 with other materials.
An algorithm-based multiple detection influence measure for high dimensional regression using expectile

The identification of influential observations is an important part of data analysis that can prevent erroneous conclusions drawn from biased estimators. However, in high dimensional data, this identification is challenging. Classical and recently-developed methods often perform poorly when there are multiple influential observations in the same dataset. In particular, current methods can fail when there is masking several influential observations with similar characteristics, or swamping when the influential observations are near the boundary of the space spanned by well-behaved observations. Therefore, we propose an algorithm-based, multi-step, multiple detection procedure to identify influential observations that addresses current limitations. Our three-step algorithm to identify and capture undesirable variability in the data, $\asymMIP,$ is based on two complementary statistics, inspired by asymmetric correlations, and built on expectiles. Simulations demonstrate higher detection power than competing methods. Use of the resulting asymptotic distribution leads to detection of influential observations without the need for computationally demanding procedures such as the bootstrap. The application of our method to the Autism Brain Imaging Data Exchange neuroimaging dataset resulted in a more balanced and accurate prediction of brain maturity based on cortical thickness. See our GitHub for a free R package that implements our algorithm: \texttt{asymMIP} (\url{github.com/AmBarry/hidetify}).
Improved Constraints for the XUV Luminosity Evolution of Trappist-1

We re-examine the XUV luminosity evolution of TRAPPIST-1 utilizing new observational constraints (XUV and bolometric luminosity) from multi-epoch X-ray/UV photometry. Following the formalism presented in Fleming et al., we infer that TRAPPIST-1 maintained a saturated XUV luminosity, relative to the bolometric luminosity, of log10(L XUV/L bol) =−3.03−0.23+0.25 at early times for a period of t sat =3.14−1.46+2.22 Gyr. After the saturation phase, we find L XUV decayed over time by an exponential rate of β XUV =−1.17−0.28+0.27 . Compared to our inferred age of the system, age=7.96−1.87+1.78 Gyr, our result for t sat suggests that there is only a ∼4% chance that TRAPPIST-1 still remains in the saturated phase today, which is significantly lower than the previous estimate of 40%. Despite this reduction in t sat, our results remain consistent in the conclusion that the TRAPPIST-1 planets likely received an extreme amount XUV energy—an estimated integrated XUV energy of ∼1030–1032 erg over the star’s lifetime.
Flat bands, electron interactions, and magnetic order in magic-angle mono-trilayer graphene

Zachary A. H. Goodwin,1,* Lennart Klebl ,2,* Valerio Vitale,1 Xia Liang ,1 Vivek Gogtay,1 Xavier van Gorp,1 Dante M. Kennes ,2,3 Arash A. Mostofi ,1 and Johannes Lischner 1 1Departments of Materials and Physics and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom 2Institute for Theory of Statistical Physics, RWTH Aachen University, and JARA Fundamentals of Future Information Technology, 52062 Aachen, Germany 3Max Planck Institute for the Structure and Dynamics of Matter, Center for Free Electron Laser Science, Luruper Chaussee 149, 22761 Hamburg, Germany
The Effect of Providing Peer Information on Evaluation for Gender-Equalized and ESG-Oriented Firms: An Internet Survey Experiment

Internet survey experiment is conducted to examine how providing peer information of evaluation about progressive firms changed individual’s evaluations. Using large sample including over 13,000 observations collected by two-step experimental surveys, I found; (1) provision of the information leads individuals to expect higher probability of rising of stocks and be more willing to buy it. (2) the effect on willingness to buy is larger than the expected probability of stock price rising, (3) The effect for woman is larger than for man. (4) individuals who prefer environment (woman’s empowerment) become more willing to buy stock of pro-environment (gender-balanced) firms than others if they have the information. (5) The effect of the peer information is larger for individuals with “warm -glow” motivation.
Significant Dust-obscured Star Formation in Luminous Lyman-break Galaxies at z ∼ 7–8

We make use of Atacama Large Millimeter/submillimeter Array continuum observations of 15 luminous Lyman-break galaxies at z ∼ 7–8 to probe their dust-obscured star formation. These observations are sensitive enough to probe obscured star formation rates (SFRs) of 20 M ⊙ yr−1 (3σ). Six of the targeted galaxies show significant (≥3σ) dust-continuum detections, more than doubling the number of known dust-detected galaxies at z > 6.5. Their IR luminosities range from 2.7 × 1011 L ⊙ to 1.1 × 1012 L ⊙, equivalent to obscured SFRs of 25 to 101 M ⊙ yr−1. We use our results to quantify the correlation of the infrared excess (IRX) on the UV-continuum slope β UV and stellar mass. Our results are most consistent with a Small Magellanic Cloud (SMC) attenuation curve for intrinsic UV-slopes βUV,intr of −2.63 and most consistent with an attenuation curve in between SMC and Calzetti for βUV,intr slopes of −2.23, assuming a dust temperature T d of 50 K. Our fiducial IRX–stellar mass results at z ∼ 7–8 are consistent with marginal evolution from z ∼ 0. We then show how both results depend on T d . For our six dust-detected sources, we estimate their dust masses and find that they are consistent with dust production from supernovae if the dust destruction is low (<90%). Finally we determine the contribution of dust-obscured star formation to the SFR density for UV luminous (H<−21.5 mag: ≳1.7 L * UV) z ∼ 7–8 galaxies, finding that the total SFR density at z ∼ 7 and z ∼ 8 from bright galaxies is 0.20−0.10+0.10 dex and 0.23−0.09+0.06 dex higher, respectively; i.e., ∼ 13 of the star formation in ≳1.7 L * UV galaxies at z ∼ 7–8 is obscured by dust.
Stumbling over Planetary Building Blocks: AU Microscopii as an Example of the Challenge of Retrieving Debris-disk Dust Properties

We explore whether assumptions about dust grain shape affect the resulting estimates of the composition and grain size distribution of the AU Microscopii (AU Mic) debris disk from scattered-light data collected by Lomax et al. The near edge-on orientation of the AU Mic debris disk makes it ideal for studying the effect of the scattering phase function on the measured flux ratios as a function of wavelength and projected distance. Previous efforts to model the AU Mic debris disk have invoked a variety of dust grain compositions and explored the effect of porosity, but did not undertake a systematic effort to explore a full range of size distributions and compositions to understand possible degeneracies in fitting the data. The degree to which modeling dust grains with more realistic shapes compounds these degeneracies has also not previously been explored. We find differences in the grain properties retrieved depending on the grain shape model used. We also present here our calculations of porous grains of size parameters x = 0.1 to 48 and complex refractive indices (m = n + iκ) ranging from n = 1.1 to 2.43 and k = 0 to 1.0, covering multiple compositions at visible and near-infrared wavelengths such as ice, silicates, amorphous carbon, and tholins.
Dimensioning V2N Services in 5G Networks through Forecast-based Scaling

With the increasing adoption of intelligent transportation systems and the upcoming era of autonomous vehicles, vehicular services (such as remote driving, cooperative awareness, and hazard warning) will have to operate in an ever-changing and dynamic environment. Anticipating the dynamics of traffic flows on the roads is critical for these services and, therefore, it is of paramount importance to forecast how they will evolve over time. By predicting future events (such as traffic jams) and demands, vehicular services can take proactive actions to minimize Service Level Agreement (SLA) violations and reduce the risk of accidents. In this paper, we compare several techniques, including both traditional time-series and recent Machine Learning (ML)-based approaches, to forecast the traffic flow at different road segments in the city of Torino (Italy). Using the the most accurate forecasting technique, we propose n-max algorithm as a forecast-based scaling algorithm for vertical scaling of edge resources, comparing its benefits against state-of-the-art solutions for three distinct Vehicle-to-Network (V2N) services. Results show that the proposed scaling algorithm outperforms the state-of-the-art, reducing Service Level Objective (SLO) violations for remote driving and hazard warning services.
Aggregating Nested Transformers

Although hierarchical structures are popular in recent vision transformers, they require sophisticated designs and massive datasets to work well. In this work, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical manner. We ﬁnd that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simpliﬁed architecture with minor code changes upon the original vision transformer and obtains improved performance compared to existing methods. Our empirical results show that the proposed method NesT converges faster and requires much less training data to achieve good generalization. For example, a NesT with 68M parameters trained on ImageNet for 100/300 epochs achieves 82 . 3% / 83 . 8% accuracy evaluated on 224 × 224 image size, outperforming previous methods with up to 57% parameter reduction. Training a NesT with 6M parameters from scratch on CIFAR10 achieves 96% accuracy using a single GPU, setting a new state of the art for vision transformers. Beyond image classiﬁcation, we extend the key idea to image generation and show NesT leads to a strong decoder that is 8 × faster than previous transformer based generators. Furthermore, we also propose a novel method for visually interpreting the learned model. Source code is available https://github.com/google-research/nested-transformer .
Blurs Make Results Clearer: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness

Bayesian neural networks (BNNs) have shown success in the areas of uncertainty estimation and robustness. However, a crucial challenge prohibits their use in practice: Bayesian NNs require a large number of predictions to produce reliable results, leading to a signiﬁcant increase in computational cost. To alleviate this issue, we propose spatial smoothing , a method that ensembles neighboring feature map points of CNNs. By simply adding a few blur layers to the models, we empirically show that the spatial smoothing improves accuracy, uncertainty estimation, and robustness of BNNs across a whole range of ensemble sizes. In particular, BNNs incorporating the spatial smoothing achieve high predictive performance merely with a handful of ensembles. Moreover, this method also can be applied to canonical deterministic neural networks to improve the performances. A number of evidences suggest that the improvements can be attributed to the smoothing and ﬂattening of the loss landscape. In addition, we provide a fundamental explanation for prior works—namely, global average pooling, pre-activation, and ReLU6 —by addressing to them as special cases of the spatial smoothing. These not only enhance accuracy, but also improve uncertainty estimation and robustness by making the loss landscape smoother in the same manner as the spatial smoothing. The code is available at https://github.com/xxxnell/spatial-smoothing .