Miracle-dz/UniFAR-FTUs huggingface dataset:
MeshCNN Fundamentals: Geometric Learning through a Reconstructable Representation

Mesh-based learning is one of the popular approaches nowadays to learn shapes. The most established backbone in this field is MeshCNN. In this paper, we propose infusing MeshCNN with geometric reasoning to achieve higher quality learning. Through careful analysis of the way geometry is represented through-out the network, we submit that this representation should be rigid motion invariant, and should allow reconstructing the original geometry. Accordingly, we introduce the first and second fundamental forms as an edge-centric, rotation and translation invariant, reconstructable representation. In addition, we update the originally proposed pooling scheme to be more geometrically driven. We validate our analysis through experimentation, and present consistent improvement upon the MeshCNN baseline, as well as other more elaborate state-of-the-art architectures. Furthermore, we demonstrate this fundamental forms-based representation opens the door to accessible generative machine learning over meshes.
MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intravehicle networks (IVNs) to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-to-everything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intravehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyber-attacks. In this article, the vulnerabilities of intravehicle and external networks are discussed, and a multitiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the IVN data and 99.88% accuracy on the CICIDS2017 data set illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two data sets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.
When Liebig's Barrel Meets Facial Landmark Detection: A Practical Model

In recent years, significant progress has been made in the research of facial landmark detection. However, few prior works have thoroughly discussed about models for practical applications. Instead, they often focus on improving a couple of issues at a time while ignoring the others. To bridge this gap, we aim to explore a practical model that is accurate, robust, efficient, generalizable, and end-to-end trainable at the same time. To this end, we first propose a baseline model equipped with one transformer decoder as detection head. In order to achieve a better accuracy, we further propose two lightweight modules, namely dynamic query initialization (DQInit) and query-aware memory (QAMem). Specifically, DQInit dynamically initializes the queries of decoder from the inputs, enabling the model to achieve as good accuracy as the ones with multiple decoder layers. QAMem is designed to enhance the discriminative ability of queries on low-resolution feature maps by assigning separate memory values to each query rather than a shared one. With the help of QAMem, our model removes the dependence on high-resolution feature maps and is still able to obtain superior accuracy. Extensive experiments and analysis on three popular benchmarks show the effectiveness and practical advantages of the proposed model. Notably, our model achieves new state of the art on WFLW as well as competitive results on 300W and COFW, while still running at 50+ FPS.
The COS-legacy survey of C iv absorbers: properties and origins of the intervening systems

We present here results from a survey of intervening C iv absorbers at z < 0.16 conducted using 223 sightlines from the Hubble Spectroscopic Legacy Archive. Most systems (83%) out of the total sample of 69 have simple kinematics with 1 or 2 C iv components. In the 22 C iv systems with well constrained H i column densities, the temperatures from the b-values imply predominantly photoionized plasma (T ≤ 105K) and non-thermal dynamics. These systems also have solar or highermetallicities. We obtain a C iv line density of dN/dX = 5.1±1.0 for log[N (C iv) (cm−2)] ≥ 12.9, andΩC iv = (8.01±1.62)×10−8 for 12.9 ≤ log[N (C iv) (cm−2)] ≤ 15.0. The C iv bearing diffuse gas in the z < 0.16Universe has ametallicity of (2.07± 0.43) × 10−3 Z , an order of magnitude more than the metal abundances in the IGM at high redshifts (z & 5), and consistent with the slow build-up of metals in the diffuse circum/intergalactic space with cosmic time. For z < 0.015 (complete above L > 0.01L), the Sloan Digital Sky Survey provides a tentative evidence of declining covering fraction for strong C iv (N > 1013.5 cm−2) with ρ (impact parameter) and ρ/Rvir. However, the increase at high separations suggests that strong systems are not necessarily coincident with such galaxies. We also find that strong C iv absorption at z < 0.051 is not coincident with galaxy over-dense regions complete for L > 0.13L.
Moment preserving Fourier-Galerkin spectral methods and application to the Boltzmann equation

Spectral methods, thanks to the high accuracy and the possibility of using fast algorithms, represent an effective way to approximate collisional kinetic equations in kinetic theory. On the other hand, the loss of some local invariants can lead to the wrong long time behavior of the numerical solution. We introduce in this paper a novel Fourier-Galerkin spectral method that improves the classical spectral method by making it conservative on the moments of the approximated distribution, without sacrificing its spectral accuracy or the possibility of using fast algorithms. The method is derived directly using a constrained best approximation in the space of trigonometric polynomials and can be applied to a wide class of problems where preservation of moments is essential. We then apply the new spectral method to the evaluation of the Boltzmann collision term, and prove spectral consistency and stability of the resulting Fourier-Galerkin approximation scheme. Various numerical experiments illustrate the theoretical findings.
An extension of the orthogonal derivative with adjustable precision

The orthogonal derivative is defined as a limit of an integral whose kernel contains an orthogonal polynomial with its measure. When in practice no limit is taken, it means that the accuracy of the derivative depends on the second derivative of the given function. Liptaj shows that it is possible to define a kernel in such a way that the accuracy depends on a higher derivative at your own choice. The accuracy is therefore much greater than with the orthogonal derivative. However Diekema and Koornwinder find a similar extension starting directly from of the orthogonal derivative. The new kernel is not orthogonal for order greater then one. The transfer function for this new derivative is given.
PAC: Practical Accountability for CCF

Permissioned ledger systems execute transactions on a set of replicas governed by members of a consortium. They use Byzantine fault tolerance protocols to distribute trust among the replicas, and thus can ensure linearizability if fewer than 1/3 of the replicas misbehave. With more misbehaving replicas, current systems provide no guarantees, and all replicas and members share the blame. We describe PAC, a permissioned ledger system that assigns blame to misbehaving replicas while supporting governance transactions to change the consortium membership and the set of replicas. PAC signs and stores protocol messages in the ledger and provides clients with signed, universally-veriﬁable receipts as evidence that a transaction executed at a certain ledger position. If clients obtain a sequence of receipts that violate linearizability, anyone can audit the ledger and the sequence of receipts to assign blame to at least 1/3 of the replicas, even if all replicas and members misbehave. Audit-ing assigns blame by ﬁnding contradictory statements signed by the same replica. Since the set of replicas changes, PAC determines the valid signing keys at any point in the ledger using a shorter sub-ledger of governance transactions. PAC provides a strong disincentive to misbehavior at low cost: it can execute more than 48,000 transactions per second, and clients receive receipts in two network round trips.
The effect of kick velocities on the spatial distribution of millisecond pulsars and implications for the Galactic center excess

Recently it has become apparent that the Galactic center excess (GCE) is spatially correlated with the stellar distribution in the Galactic bulge. This has given extra motivation for the unresolved population of millisecond pulsars (MSPs) explanation for the GCE. However, in the “recycling” channel the neutron star forms from a core collapse supernovae that undergoes a random “kick” due to the asymmetry of the explosion. This would imply a smoothing out of the spatial distribution of the MSPs. We use N-body simulations to model how the MSP spatial distribution changes. We estimate the probability distribution of natal kick velocities using the resolved gamma-ray MSP proper motions, where MSPs have random velocities relative to the circular motion with a scale parameter of 77 ± 6 km/s. We find that, due to the natal kicks, there is an approximately 10% increase in each of the bulge MSP spatial distribution dimensions and also the bulge MSP distribution becomes less boxy but is still far from being spherical.
Diverse and Controllable Speech Synthesis with GMM-Based Phone-Level Prosody Modelling

—Generating natural speech with diverse and smooth prosody pattern is a challenging task. Although random sampling with phone-level prosody distribution has been investigated to generate different prosody patterns, the diversity of the generated speech is still very limited and far from what can be achieved by human. This is largely due to the use of uni-modal distribution, such as single Gaussian, in the prior works of phone-level prosody modelling. In this work, we propose a novel approach that models phone-level prosodies with a GMM-based mixture density network and then extend it for multi-speaker TTS using speaker adaptation transforms of Gaussian means and variances. Furthermore, we show that we can clone the prosodies from a reference speech by sampling prosodies from the Gaussian components that produce the reference prosodies. Our experiments on LJSpeech and LibriTTS dataset show that the proposed GMM-based method not only achieves signiﬁcantly better diversity than using a single Gaussian in both single-speaker and multi-speaker TTS, but also provides better naturalness. The prosody cloning experiments demonstrate that the prosody similarity of the proposed GMM-based method is comparable to recent proposed ﬁne-grained VAE while the target speaker similarity is better.
Evidence for multiple origins of fast declining Type II supernovae from spectropolarimetry of SN 2013ej and SN 2017ahn

T. Nagao,1,2† F. Patat, S. Taubenberger, D. Baade, T. Faran, A. Cikota,5,6 D. J. Sand, M. Bulla, H. Kuncarayakti,1,9 J. R. Maund, L. Tartaglia, S. Valenti, and D. E. Reichart 1 Department of Physics and Astronomy, University of Turku, FI-20014 Turku, Finland 2 European Southern Observatory, Karl-Schwarzschild-Str. 2, 85748 Garching b. München, Germany Max-Planck-Institut für Astrophysik, Karl-Schwarzschild-Str 1, 85748 Garching b. München, Germany Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem 91904, Israel 5 European Southern Observatory, Alonso de Cordova 3107, Vitacura, Casilla 19001, Santiago de Chile, Chile Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA Department of Astronomy and Steward Observatory, University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85719, USA The Oskar Klein Centre, Department of Astronomy, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden 9 Finnish Centre for Astronomy with ESO (FINCA), FI-20014 University of Turku, Finland 10 Department of Physics and Astronomy, University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH, UK INAF Osservatorio Astronomico di Padova, Vicolo dell’Osservatorio 5, I-35122 Padova, Italy Department of Physics, University of California, Davis, CA 95616, USA Department of Physics and Astronomy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
What sustained multi-disciplinary research can achieve: The space weather modeling framework

Magnetohydrodynamics (MHD)-based global space weather models have mostly been developed and maintained at academic institutions. While the “free spirit” approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan, that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).
Directed Acyclic Graph Network for Conversational Emotion Recognition

The modeling of conversational context plays a vital role in emotion recognition from conversation (ERC). In this paper, we put forward a novel idea of encoding the utterances with a directed acyclic graph (DAG) to better model the intrinsic structure within a conversation, and design a directed acyclic neural network, namely DAG-ERC, to implement this idea. In an attempt to combine the strengths of conventional graph-based neural models and recurrence-based neural models, DAG-ERC provides a more intuitive way to model the information flow between long-distance conversation background and nearby context. Extensive experiments are conducted on four ERC benchmarks with state-of-the-art models employed as baselines for comparison. The empirical results demonstrate the superiority of this new model and confirm the motivation of the directed acyclic graph architecture for ERC.
Risk Quantization by Magnitude and Propensity

We propose a novel approach in the assessment of a random risk variable <i>X</i> by introducing magnitude-propensity risk measures (<i>m<sub>X</sub>, p<sub>X</sub></i>). This bivariate measure intends to account for the dual aspect of risk, where the magnitudes <i>x</i> of <i>X</i> tell how high are the losses incurred, whereas the probabilities <i>P(X = x)</i> reveal how often one has to expect to suffer such losses. The basic idea is to simultaneously quantify both the severity <i>mX</i> and the propensity <i>pX</i> of the real-valued risk <i>X</i>. This is to be contrasted with traditional univariate risk measures, like VaR or Expected shortfall, which typically conflate both effects. <br><br>In its simplest form, <i>(m<sub>X</sub>, p <sub>X</sub>)</i> is obtained by mass transportation in Wasserstein metric of the law <i>P<sup>X</sup></i> of <i>X</i> to a two-points {0,<i>m<sub>X</sub></i>} discrete distribution with mass <i>p<sub>X</sub></i> at <i>m<sub>X</sub></i>. The approach can also be formulated as a constrained optimal quantization problem. <br><br>This allows for an informative comparison of risks on both the magnitude and propensity scales. Several examples illustrate the proposed approach.
Simulated Data Generation through Algorithmic Force Coefficient Estimation for AI-Based Robotic Projectile Launch Modeling

Modeling of non-rigid object launching and manipulation is complex considering the wide range of dynamics affecting trajectory, many of which may be unknown. Using physics models can be inaccurate because they cannot account for unknown factors and the effects of the deformation of the object as it is launched; moreover, deriving force coefficients for these models is not possible without extensive experimental testing. Recently, advancements in data-powered artificial intelligence methods have allowed learnable models and systems to emerge. It is desirable to train a model for launch prediction on a robot, as deep neural networks can account for immeasurable dynamics. However, the inability to collect large amounts of experimental data decreases performance of deep neural networks. Through estimating force coefficients, the accepted physics models can be leveraged to produce adequate supplemental data to artificially increase the size of the training set, yielding improved neural networks. In this paper, we introduce FCE-NN, a new framework for algorithmic estimation of force coefficients for non-rigid object launching, which can be generalized to other domains, in order to generate large datasets. We implement a novel training algorithm and objective for our deep neural network to accurately model launch trajectory of non-rigid objects and predict whether they will hit a series of targets. Our experimental results demonstrate the effectiveness of using simulated data from force coefficient estimation and shows the importance of simulated data for training an effective neural network.11Code is available at https:/igithub.com/ayaanzhaque/FCE-NN
XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data

Abstract The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This ‘black box’ problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we present XOmiVAE, a variational autoencoder (VAE)-based interpretable deep learning model for cancer classification using high-dimensional omics data. XOmiVAE is capable of revealing the contribution of each gene and latent dimension for each classification prediction and the correlation between each gene and each latent dimension. It is also demonstrated that XOmiVAE can explain not only the supervised classification but also the unsupervised clustering results from the deep learning network. To the best of our knowledge, XOmiVAE is one of the first activation level-based interpretable deep learning models explaining novel clusters generated by VAE. The explainable results generated by XOmiVAE were validated by both the performance of downstream tasks and the biomedical knowledge. In our experiments, XOmiVAE explanations of deep learning-based cancer classification and clustering aligned with current domain knowledge including biological annotation and academic literature, which shows great potential for novel biomedical knowledge discovery from deep learning models.
Federated Meta-Learning Enhanced Acoustic Radio Cooperative Framework for Ocean of Things

Ocean of Things, consisting of multiple buoys distributed on the sea, is an acoustic radio cooperative wireless network that aims to acquire underwater information. In this paper, a deep neural network (DNN)-based receiver with data augmentation, termed chirp (C)-DNN, is developed for a buoy that uses chirp modulation-based underwater acoustic communications. To further solve the problem that the training data at a single buoy may not be sufficient, a federated meta-learning (FML) scheme is proposed to train the DNN-based receiver by exploiting the model parameters from multiple buoys. We analyze the convergence performance of FML and derive a closed-form expression for the convergence rate, accounting for the impacts of scheduling ratios, local epochs, and data volumes on a single node. Simulation results show that the proposed C-DNN receiver that is trained with sufficient data achieves better bit error rate performance and lower complexity than classical matched filter (MF)-based detectors. The proposed FML also outperforms the MF-based method after several communication rounds and achieves better generalization performance than federated learning based systems.
The Schwartz correspondence for the complex motion group on ${\mathbb C}^2$

If (G,K) is a Gelfand pair, with G a Lie group of polynomial growth and K a compact subgroup of G, the Gelfand spectrum Σ of the bi-K-invariant algebra L1(K\G/K) admits natural embeddings into R spaces as a closed subset. For any such embedding, define S(Σ) as the space of restrictions to Σ of Schwartz functions on R. We call Schwartz correspondence for (G,K) the property that the spherical transform is an isomorphism of S(K\G/K) onto S(Σ). In all the cases studied so far, Schwartz correspondence has been proved to hold true. These include all pairs with G = K ⋉H and K abelian and a large number of pairs with G = K ⋉H and H nilpotent. In this paper we study what is probably the simplest of the pairs with G = K ⋉ H , K non-abelian and H non-nilpotent, with H = M2(C), the complex motion group, and K = U2 acting on it by inner automorphisms.
Compiling Halide Programs to Push-Memory Accelerators

Image processing and machine learning applications benefit tremendously from hardware acceleration, but existing compilers target either FPGAs, which sacrifice power and performance for flexible hardware, or ASICs, which rapidly become obsolete as applications change. Programmable domain-specific accelerators have emerged as a promising middle-ground between these two extremes, but such architectures have traditionally been difficult compiler targets. The main obstacle is that these accelerators often use a different memory abstraction than CPUs and GPUs: push memories that send a data stream from one computation kernel to other kernels, possibly reordered. To address the compilation challenges caused by push memories, we propose that the representation of memory in the middle and backend of the compiler be altered to combine storage with address generation and control logic in a single structure -- a unified buffer. We show that this compiler abstraction can be implemented efficiently on a programmable accelerator, and design a memory mapping algorithm that combines polyhedral analysis and software vectorization techniques to target our accelerator. Our evaluation shows that the compiler supports programmability while maintaining high performance. It can compile a wide range of image processing and machine learning applications to our accelerator with 4.7x better runtime and 4.3x better energy-efficiency as compared to an FPGA.