Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning rigid dynamics with face interaction graph networks

Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical dynamics, such as fluids, cloth and articulated bodies, they have been less effective and efficient on rigid-body physics, except with very simple shapes. Existing methods that model collisions through the meshes' nodes are often inaccurate because they struggle when collisions occur on faces far from nodes. Alternative approaches that represent the geometry densely with many particles are prohibitively expensive for complex shapes. Here we introduce the Face Interaction Graph Network (FIGNet) which extends beyond GNN-based methods, and computes interactions between mesh faces, rather than nodes. Compared to learned node- and particle-based methods, FIGNet is around 4x more accurate in simulating complex shape interactions, while also 8x more computationally efficient on sparse, rigid meshes. Moreover, FIGNet can learn frictional dynamics directly from real-world data, and can be more accurate than analytical solvers given modest amounts of training data. FIGNet represents a key step forward in one of the few remaining physical domains which have seen little competition from learned simulators, and offers allied fields such as robotics, graphics and mechanical design a new tool for simulation and model-based planning.
The greenhouse effect and climate change

On any planet with an atmosphere, the surface is warmed not only by the Sun directly but also by downward-propagating infrared radiation emitted by the atmosphere. On the Earth, this phenomenon, known as the greenhouse effect, keeps the mean surface temperature some 33 K warmer than it would otherwise be and is therefore essential to life. The radiative processes which are responsible for the greenhouse effect involve mainly minor atmospheric constituents, the amounts of which can change either naturally or as a by-product of human activities. The growth of the latter is definitely tending to force a general global surface warming, although because of problems in modelling complicated feedback processes, for example those involving water vapour, ozone, clouds, and the oceans, the precise rates of change and the local patterns which should be expected are not yet very well known. The author reviews the physical processes involved in the greenhouse effect and discusses current progress, theoretical and experimental, towards an understanding of the effect on the climate, especially the mean surface temperature, of recent and expected changes in atmospheric composition. It also provides an overview of recent expert forecasts of climatic changes in the next few decades, and discusses their limitations.
Slimmable Pruned Neural Networks

Slimmable Neural Networks (S-Net) is a novel network which enabled to select one of the predefined proportions of channels (sub-network) dynamically depending on the current computational resource availability. The accuracy of each sub-network on S-Net, however, is inferior to that of individually trained networks of the same size due to its difficulty of simultaneous optimization on different sub-networks. In this paper, we propose Slimmable Pruned Neural Networks (SP-Net), which has sub-network structures learned by pruning instead of adopting structures with the same proportion of channels in each layer (width multiplier) like S-Net, and we also propose new pruning procedures: multi-base pruning instead of one-shot or iterative pruning to realize high accuracy and huge training time saving. We also introduced slimmable channel sorting (scs) to achieve calculation as fast as S-Net and zero padding match (zpm) pruning to prune residual structure in more efficient way. SP-Net can be combined with any kind of channel pruning methods and does not require any complicated processing or time-consuming architecture search like NAS models. Compared with each sub-network of the same FLOPs on S-Net, SP-Net improves accuracy by 1.2-1.5% for ResNet-50, 0.9-4.4% for VGGNet, 1.3-2.7% for MobileNetV1, 1.4-3.1% for MobileNetV2 on ImageNet. Furthermore, our methods outperform other SOTA pruning methods and are on par with various NAS models according to our experimental results on ImageNet. The code is available at https://github.com/hideakikuratsu/SP-Net.
COVID-bit: Keep a Distance of (at least) 2m From My Air-Gap Computer!

—Air-gapped systems are isolated from the Internet due to the sensitive information they handle. This paper presents COVID-bit, a new COVert channel attack that leaks sensitive information over the air from highly isolated systems. The information emanates from the air-gapped computer over the air to a distance of 2m and more and can be picked up by a nearby insider or spy with a mobile phone or laptop. Malware on an air-gapped computer can generate radio waves by executing crafted code on the target system. The malicious code exploits the dynamic power consumption of modern computers and manipulates the momentary loads on CPU cores. This technique allows the malware to control the computer’s internal utilization and generate low-frequency electromagnetic radiation in the 0 - 60 kHz band. Sensitive information (e.g., ﬁles, encryption keys, biometric data, and keylogging) can be modulated over the emanated signals and received by a nearby mobile phone at a max speed of 1000 bits/sec. We show that a smartphone or laptop with a small $1 antenna carried by a malicious insider or visitor can be used as a covert receiver. Notably, the attack is highly evasive since it executes from an ordinary user-level process, does not require root privileges, and is effective even within a Virtual Machine (VM). We discuss the attack model and provide technical details. We implement air-gap transmission of texts and ﬁles, and present signal generation and data modulation. We test the covert channel and show evaluation results. Finally, we present a set of countermeasures to this air-gap attack.
RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging

A platoon refers to a group of vehicles traveling together in very close proximity using automated driving technology. Owing to its immense capacity to improve fuel efficiency, driving safety, and driver comfort, platooning technology has garnered substantial attention from the autonomous vehicle research community. Although highly advantageous, recent research has uncovered that an excessively small intra-platoon gap can impede traffic flow during highway on-ramp merging. While existing control-based methods allow for adaptation of the intra-platoon gap to improve traffic flow, making an optimal control decision under the complex dynamics of traffic conditions remains a challenge due to the massive computational complexity. In this paper, we present the design, implementation, and evaluation of a novel reinforcement learning framework that adaptively adjusts the intra-platoon gap of an individual platoon member to maximize traffic flow in response to dynamically changing, complex traffic conditions for highway on-ramp merging. The framework's state space has been meticulously designed in consultation with the transportation literature to take into account critical traffic parameters that bear direct relevance to merging efficiency. An intra-platoon gap decision making method based on the deep deterministic policy gradient algorithm is created to incorporate the continuous action space to ensure precise and continuous adaptation of the intra-platoon gap. An extensive simulation study demonstrates the effectiveness of the reinforcement learning-based approach for significantly improving traffic flow in various highway on-ramp merging scenarios.
Optimal Online Learning using Potential Functions

We study regret-minimizing online algorithms based on potential functions. First, we show that any algorithm with a regret bound that holds for any ǫ is equivalent to a potential minimizing algorithm and vice versa. Second we should a min-max learning algorithm for known horizon. We show a regret bound that is close to optimal when the horizon is not known. Finally we give an algorithm with second order bounds that characterize easy sequences.
Lag-Luminosity Relationship for Interband Lags between Variations in B, V, R, and I Bands in Active Galactic Nuclei

We determine interband lags between variations in the B band and variations in the V, R, and I bands for 14 active galactic nuclei observed at the Crimean Astrophysical Observatory. The computed lags range from tenths of a day to several days, and it is positive (that is, V, R, and I bands lag behind the B band) in most cases, except for a few cases for the V filter. In some cases, the lag is greater than zero, with more than 3 σ confidence. The lag is systematically less for the V filter than for the red filters, and the lag determined from the cross-correlation function (CCF) centroid is systematically greater than the lag determined from the CCF peak. We find that the lag scales with luminosity as Lb, where b ≈ 0.4-0.5. We attribute this lag to the light time travel effect, so it reflects the geometrical size of the region that emits optical continuum. We consider a model in which optical emission is mainly reprocessed emission that arises in the accretion disk heated by an X-ray source above the disk.
Statistical Tools and Methodologies for URLLC - A Tutorial

—Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the ﬁfth generation and beyond cellular networks. Notably, designing and supporting URLLC poses a herculean task due to the fundamental need of identifying and accurately characterizing the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols. In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees. This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems. Speciﬁcally, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, tail approximations, and risk-assessment tools, iv) rare events simulation, v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-ﬁeld games, vi) queuing theory and information freshness, and vii) machine learning. Throughout the paper, we brieﬂy review the state-of-the-art works using the addressed tools and methodologies, and their link to URLLC systems. Moreover, we discuss novel application examples focused on physical and medium access control layers. Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.
Expressiveness of SHACL Features and Extensions for Full Equality and Disjointness Tests

SHACL is a W3C-proposed schema language for expressing structural constraints on RDF graphs. Recent work on formalizing this language has revealed a striking relationship to description logics. SHACL expressions can use three fundamental features that are not so common in description logics. These features are equality tests; disjointness tests; and closure constraints. Moreover, SHACL is peculiar in allowing only a restricted form of expressions (so-called targets) on the left-hand side of inclusion constraints. The goal of this paper is to obtain a clear picture of the impact and expressiveness of these features and restrictions. We show that each of the four features is primitive: using the feature, one can express boolean queries that are not expressible without using the feature. We also show that the restriction that SHACL imposes on allowed targets is inessential, as long as closure constraints are not used. In addition, we show that enriching SHACL with "full" versions of equality tests, or disjointness tests, results in a strictly more powerful language.
Site Assessment and Layout Optimization for Rooftop Solar Energy Generation in Worldview-3 Imagery

With the growth of residential rooftop PV adoption in recent decades, the problem of effective layout design has become increasingly important in recent years. Although a number of automated methods have been introduced, these tend to rely on simplifying assumptions and heuristics to improve computational tractability. We demonstrate a fully automated layout design pipeline that attempts to solve a more general formulation with greater geometric flexibility that accounts for shading losses. Our approach generates rooftop areas from satellite imagery and uses MINLP optimization to select panel positions, azimuth angles and tilt angles on an individual basis rather than imposing any predefined layouts. Our results demonstrate that shading plays a critical role in automated rooftop PV optimization and significantly changes the resulting layouts. Additionally, they suggest that, although several common heuristics are often effective, they may not be universally suitable due to complications resulting from geometric restrictions and shading losses. Finally, we evaluate a few specific heuristics from the literature and propose a potential new rule of thumb that may help improve rooftop solar energy potential when shading effects are considered.
Nonreciprocal total cross section of quantum metasurfaces

Abstract Nonreciprocity originating from classical interactions among nonlinear scatterers has been attracting increasing attention in the quantum community, offering a promising tool to control excitation transfer for quantum information processing and quantum computing. In this work, we explore the possibility of realizing largely nonreciprocal total cross sections for a pair of quantum metasurfaces formed by two parallel periodic arrays of two-level atoms. We show that large nonreciprocal responses can be obtained in such nonlinear systems by controlling the position of the atoms and their transition frequencies, without requiring that the environment in which the atoms are placed is nonreciprocal. We demonstrate the connection of this effect with the asymmetric population of a slowly decaying dark state, which is critical to obtain large nonreciprocal responses.
BiPMAP: A Toolbox for Predictions of Perceived Motion Artifacts on Modern Displays

Presenting dynamic scenes without incurring motion artifacts visible to observers requires sustained effort from the display industry. A tool that predicts motion artifacts and simulates artifact elimination through optimizing the display configuration is highly desired to guide the design and manufacture of modern displays. Despite the popular demands, there is no such tool available in the market. In this study, we deliver an interactive toolkit, Binocular Perceived Motion Artifact Predictor (BiPMAP), as an executable file with GPU acceleration. BiPMAP accounts for an extensive collection of user-defined parameters and directly visualizes a variety of motion artifacts by presenting the perceived continuous and sampled moving stimuli side-by-side. For accurate artifact predictions, BiPMAP utilizes a novel model of the human contrast sensitivity function to effectively imitate the frequency modulation of the human visual system. In addition, BiPMAP is capable of deriving various in-plane motion artifacts for 2D displays and depth distortion in 3D stereoscopic displays.
CT10 next-to-next-to-leading order global analysis of QCD

We present next-to-next-to-leading order (NNLO) parton distribution functions (PDFs) from the CTEQ-TEA group. The CT10NNLO PDF fit is based on essentially the same global data sets used in the CT10 and CT10W NLO PDF analyses. After exploring the goodness of the fits to the HERA combined data and the Tevatron jet data, we present various predictions at NNLO accuracy for both existing and forthcoming precision measurements from the CERN Large Hadron Collider. The range of variations in the gluon distribution introduced by correlated systematic effects in inclusive jet production is also examined.
Capturing the Flow of Art History

Do we really understand how machine classifies art styles? Historically, art is perceived and interpreted by human eyes and there are always controversial discussions over how people identify and understand art. Historians and general public tend to interpret the subject matter of art through the context of history and social factors. Style, however, is different from subject matter. Given the fact that Style does not correspond to the existence of certain objects in the painting and is mainly related to the form and can be correlated with features at different levels. (Ahmed Elgammal et al. 2018), which makes the identification and classification of the characteristics artwork's style and the"transition"- how it flows and evolves - remains as a challenge for both human and machine. In this work, a series of state-of-art neural networks and manifold learning algorithms are explored to unveil this intriguing topic: How does machine capture and interpret the flow of Art History?
Diffusion-SDF: Text-to-Shape via Voxelized Diffusion

With the rising industrial attention to 3D virtual mod-eling technology, generating novel 3D content based on specified conditions (e.g. text) has become a hot issue. In this paper, we propose a new generative 3D modeling framework called Diffusion-SDF for the challenging task of text-to-shape synthesis. Previous approaches lack flexibility in both 3D data representation and shape generation, thereby failing to generate highly diversified 3D shapes conforming to the given text descriptions. To address this, we propose a SDF autoencoder together with the voxelized Diffusion model to learn and generate representations for voxelized signed distance fields (SDFs) of 3D shapes. Specifically, we design a novel Uinll-Net architecture that implants a local-focused inner network inside the standard U-Net architecture, which enables better reconstruction of patch-independent SDF representations. We extend our approach to further text-to-shape tasks including text-conditioned shape completion and manipulation. Experimental results show that Diffusion-SDF generates both higher quality and more diversified 3D shapes that conform well to given text descriptions when compared to previous approaches. Code is available at: https://github.com/ttlmh/Diffusion-SDF.
Double insertions of SMEFT operators in gluon fusion Higgs boson production

Deviations from the Standard Model (SM) can be parameterized in terms of the SM effective field theory (SMEFT), which is typically truncated at dimension-6. Including higher dimension operators -- as well as considering simultaneous insertions of multiple dimension-6 operators -- may be necessary in some processes, in order to correctly capture the properties of the underlying UV theory. As a step towards clarifying this in the Higgs boson production in gluon fusion process, we study double insertions of dimension-6 operators in the 1-loop virtual amplitude. We present needed Feynman rules up to $\mathcal{O}(1/\Lambda^4)$ and we numerically study the impact of various approximations to the $\mathcal{O}(1/\Lambda^4)$ expansion.
intensitynet: Intensity-based Analysis of Spatial Point Patterns Occurring on Complex Networks Structures in R

The statistical analysis of structured spatial point process data where the event locations are determined by an underlying spatially embedded relational system has become a vivid ﬁeld of research. Despite a growing literature on diﬀerent extensions of point process characteristics to linear network domains, most software implementations remain restricted to either directed or undirected network structures and are of limited use for the analysis of rather complex real-world systems consisting of both undirected and directed parts. Formalizing the network through a graph theoretic perspective, this paper discusses a comple-mentary approach for the analysis of network-based event data through generic network intensity functions and gives a general introduction to the intensitynet package implemented in R covering both computational details and applications. By treating the edges as fundamental entities, the implemented approach allows the computation of intensities and other related values related to diﬀerent graph structures containing undirected, directed, or a combination of both edges as special cases. The package includes characteristics for network modeling, data manipulation, intensity estimation, computation of local and global autocorrelation statistics, visualization, and extensions to marked point process scenarios. All functionalities are accompanied by reproducible code examples using the chicago data as toy example to illustrate the application of the package.
Landau level spectrum and magneto-optical conductivity in tilted Weyl semimetal

We present a systematic investigation of the magnetoresponses of the Weyl points (WPs) with a topological charge of n = 2, 3 and 4, and with both linear and quadratic energy tilt. The linear tilt always tends to squeeze the Landau levels (LLs) of both conduction and valence bands of all the WPs, and eventually leads to LL collapse in the type-II phase. However, the quadratic energy tilt has more complex influences on the LLs of the unconventional WPs. For charge-n (n = 2, 4) WP, the influence of the quadratic tilt on the LLs of conduction and valence bands are opposite, i.e. if the LLs of conduction (valence) bands are squeezed, then that of the valence (conduction) bands are broadened, and the squeezed LL spectrum will be collapsed in type-III phase. But, the LL collapse generally can not be found in the type-III charge-3 WP. Moreover, for charge-n (n = 2, 3) WP, the quadratic tilt breaks the degeneracy of the chiral LLs regardless of the direction of the magnetic field, leading to additional optical transitions and magneto-optical conductivity peaks at low frequencies. Interestingly, the four chiral LLs in charge-4 WP are always not degenerate. Hence, there inevitably exist magneto-optical conductivity peaks at low frequencies for charge-4 WP. Since the density of state of the LL spectrum is very large, one can expect that the low-frequency magneto-optical responses in unconventional WPs would be significant and may be used for developing efficient terahertz photodetectors.
Fermi Level Instability as a Way to Tailor Properties of La3Te4

Traditionally, the formation of off-stoichiometric compounds is believed to be the growth effect rather than the intrinsic tendency of the system. However, here, using the example of La3Te4, we demonstrate that in n-type gapped metals having a large internal gap between principal band edges and the Fermi level inside of the principal conduction band, Fermi-level instability can develop, resulting in a reduction of formation energy for acceptor defects. Specifically, La vacancies in La3Te4 form spontaneously to produce the acceptor states and remove a fraction of free carriers from the principal conduction band via electron-hole recombination. Such a unique self-doping mechanism allows to stabilize a range of off-stoichiometric La3-xTe4 compounds, which have different electronic properties. Moreover, we thus show how controlling synthesis conditions can be used as a knob to reach the target functionality, including controllable metal-to-insulator transition.