Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Differentiable Matrix Square Root

Computing the matrix square root or its inverse in a differentiable manner is important in a variety of computer vision tasks. Previous methods either adopt the Singular Value Decomposition (SVD) to explicitly factorize the matrix or use the Newton-Schulz iteration (NS iteration) to derive the approximate solution. However, both methods are not computationally efficient enough in either the forward pass or in the backward pass. In this paper, we propose two more efficient variants to compute the differentiable matrix square root. For the forward propagation, one method is to use Matrix Taylor Polynomial (MTP), and the other method is to use Matrix Pad\'e Approximants (MPA). The backward gradient is computed by iteratively solving the continuous-time Lyapunov equation using the matrix sign function. Both methods yield considerable speed-up compared with the SVD or the Newton-Schulz iteration. Experimental results on the de-correlated batch normalization and second-order vision transformer demonstrate that our methods can also achieve competitive and even slightly better performances. The code is available at \href{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}{https://github.com/KingJamesSong/FastDifferentiableMatSqrt}.
Kiñit classification in Ethiopian chants, Azmaris and modern music: A new dataset and CNN benchmark

In this paper, we create EMIR, the first-ever Music Information Retrieval dataset for Ethiopian music. EMIR is freely available for research purposes and contains 600 sample recordings of Orthodox Tewahedo chants, traditional Azmari songs and contemporary Ethiopian secular music. Each sample is classified by five expert judges into one of four well-known Ethiopian Kiñits, Tizita, Bati, Ambassel and Anchihoye. Each Kiñit uses its own pentatonic scale and also has its own stylistic characteristics. Thus, Kiñit classification needs to combine scale identification with genre recognition. After describing the dataset, we present the Ethio Kiñits Model (EKM), based on VGG, for classifying the EMIR clips. In Experiment 1, we investigated whether Filterbank, Mel-spectrogram, Chroma, or Mel-frequency Cepstral coefficient (MFCC) features work best for Kiñit classification using EKM. MFCC was found to be superior and was therefore adopted for Experiment 2, where the performance of EKM models using MFCC was compared using three different audio sample lengths. 3s length gave the best results. In Experiment 3, EKM and four existing models were compared on the EMIR dataset: AlexNet, ResNet50, VGG16 and LSTM. EKM was found to have the best accuracy (95.00%) as well as the fastest training time. However, the performance of VGG16 (93.00%) was found not to be significantly worse (P < 0.01). We hope this work will encourage others to explore Ethiopian music and to experiment with other models for Kiñit classification.
High‐dimensional sparse multivariate stochastic volatility models

Although multivariate stochastic volatility models usually produce more accurate forecasts compared with the MGARCH models, their estimation techniques such as Bayesian MCMC typically suffer from the curse of dimensionality. We propose a fast and efficient estimation approach for MSV based on a penalized OLS framework. Specifying the MSV model as a multivariate state‐space model, we carry out a two‐step penalized procedure. We provide the asymptotic properties of the two‐step estimator and the oracle property of the first‐step estimator when the number of parameters diverges. The performances of our method are illustrated through simulations and financial data. Supplementary Material presenting technical proofs is available online.
Scalable Sampling for Nonsymmetric Determinantal Point Processes

A determinantal point process (DPP) on a collection of $M$ items is a model, parameterized by a symmetric kernel matrix, that assigns a probability to every subset of those items. Recent work shows that removing the kernel symmetry constraint, yielding nonsymmetric DPPs (NDPPs), can lead to significant predictive performance gains for machine learning applications. However, existing work leaves open the question of scalable NDPP sampling. There is only one known DPP sampling algorithm, based on Cholesky decomposition, that can directly apply to NDPPs as well. Unfortunately, its runtime is cubic in $M$, and thus does not scale to large item collections. In this work, we first note that this algorithm can be transformed into a linear-time one for kernels with low-rank structure. Furthermore, we develop a scalable sublinear-time rejection sampling algorithm by constructing a novel proposal distribution. Additionally, we show that imposing certain structural constraints on the NDPP kernel enables us to bound the rejection rate in a way that depends only on the kernel rank. In our experiments we compare the speed of all of these samplers for a variety of real-world tasks.
Modelling Agent-Skipping Attacks in Message Forwarding Protocols

Message forwarding protocols are protocols in which a chain of agents handles transmission of a message. Each agent forwards the received message to the next agent in the chain. For example, TLS middleboxes act as intermediary agents in TLS, adding functionality such as filtering or compressing data. In such protocols, an attacker may attempt to bypass one or more intermediary agents. Such an agent-skipping attack can the violate security requirements of the protocol. Using the multiset rewriting model in the symbolic setting, we construct a comprehensive framework of such path protocols. In particular, we introduce a set of security goals related to path integrity: the notion that a message faithfully travels through participants in the order intended by the initiating agent. We perform a security analysis of several such protocols, highlighting key attacks on modern protocols.
The inverse Rytov series for diffuse optical tomography

The Rytov approximation is known in near-infrared spectroscopy including diffuse optical tomography. In diffuse optical tomography, the Rytov approximation often gives better reconstructed images than the Born approximation. Although related inverse problems are nonlinear, the Rytov approximation is almost always accompanied by the linearization of nonlinear inverse problems. In this paper, we will develop nonlinear reconstruction with the inverse Rytov series. By this, linearization is not necessary and higher order terms in the Rytov series can be used for reconstruction. The convergence and stability are discussed. We find that the inverse Rytov series has a recursive structure similar to the inverse Born series.
The Nonlinear Field Equation of the Three-point Correlation Function of Galaxies: to the Second Order of Density Perturbation

Based on the field theory of density fluctuation under Newtonian gravity, we obtain analytically the nonlinear equation of 3-pt correlation function ζ of galaxies in a homogeneous, isotropic, static universe. The density fluctuation has been kept up to second order. By the Fry–Peebles ansatz and the Groth-Peebles ansatz, the equation of ζ becomes closed and differs from the Gaussian approximate equation. Using the boundary condition inferred from the data of SDSS, we obtain the solution ζ(r, u, θ) at fixed u = 2, which exhibits a shallow U-shape along the angle θ and, nevertheless, decreases monotonously along the radial r. We show its difference with the Gaussian solution. As a direct criterion of non-Gaussianity, the reduced Q(r, u, θ) deviates from the Gaussianity plane Q = 1, exhibits a deeper U-shape along θ and varies weakly along r, agreeing with the observed data.
Seamless and Energy-Efficient Maritime Coverage in Coordinated 6G Space–Air–Sea Non-Terrestrial Networks

Non-terrestrial networks (NTNs), which integrate space and aerial networks with terrestrial systems, are a key area in the emerging sixth-generation (6G) wireless networks. As part of 6G, NTNs must provide pervasive connectivity to a wide range of devices, including smartphones, vehicles, sensors, robots, and maritime users. However, due to the high mobility and deployment of NTNs, managing the space-air–sea (SAS) NTN resources, i.e., energy, power, and channel allocation, is a major challenge. The design of an SAS-NTN for energy-efficient resource allocation is investigated in this study. The goal is to maximize system energy efficiency (EE) by collaboratively optimizing user equipment (UE) association, power control, and unmanned aerial vehicle (UAV) deployment. Given the limited payloads of UAVs, this work focuses on minimizing the total energy cost of UAVs (trajectory and transmission) while meeting EE requirements. A mixed-integer nonlinear programming problem is proposed, followed by the development of an algorithm to decompose, and solve each problem distributedly. The binary (UE association) and continuous (power, deployment) variables are separated using the Bender decomposition (BD), and then the Dinkelbach algorithm (DA) is used to convert fractional programming into an equivalent solvable form in the subproblem. A standard optimization solver is utilized to deal with the complexity of the master problem for binary variables. The alternating direction method of multipliers (ADMM) algorithm is used to solve the subproblem for the continuous variables. Our proposed algorithm provides a suboptimal solution, and simulation results demonstrate that the algorithm achieves better EE and spectral efficiency (SE) than baselines.
A Guide to Particle Advection Performance

The performance of particle advection-based flow visualization techniques is complex, since computational work can vary based on many factors, including number of particles, duration, and mesh type. Further, while many approaches have been introduced to optimize performance, the efficacy of a given approach can be similarly complex. In this work, we seek to establish a guide for particle advection performance by conducting a comprehensive survey of the area. We begin by identifying the building blocks for particle advection and establishing a simple cost model incorporating these building blocks. We then survey existing optimizations for particle advection, using two high-level categories: algorithmic optimizations and hardware efficiency. The sub-categories of algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub-categories of hardware efficiency all involve parallelism: shared-memory, distributed-memory, and hybrid. Finally, we conclude the survey by identifying current gaps in particle advection performance, and in particular on achieving a workflow for predicting performance under various optimizations.
Theory of cross phenomena and their coefficients beyond Onsager theorem

Cross phenomena, representing responses of a system to external stimuli, are ubiquitous from quantum to macro scales. The Onsager theorem is often used to describe them, stating that the coefficient matrix of cross phenomena connecting the driving forces and the fluxes of internal processes is symmetric. Here we show that this matrix is intrinsically diagonal when the driving forces are chosen from the gradients of potentials that drive the fluxes of their respective conjugate molar quantities in the combined law of thermodynamics including the contributions from internal processes. Various cross phenomena are discussed in terms of the present theory. IMPACT STATEMENT Through flux equations based on the combined law of thermodynamics, theory of cross phenomena is developed and applied to thermoelectricity, thermodiffusion, diffusion, electromigration, electrocaloric and electromechanical effects, and thermal expansion.
Stochastic Mirror Descent for Convex Optimization with Consensus Constraints

The mirror descent algorithm is known to be effective in situations where it is beneficial to adapt the mirror map to the underlying geometry of the optimization model. However, the effect of mirror maps on the geometry of distributed optimization problems has not been previously addressed. In this paper we study an exact distributed mirror descent algorithm in continuous-time under additive noise. We establish a linear convergence rate of the proposed dynamics for the setting of convex optimization. Our analysis draws motivation from the Augmented Lagrangian and its relation to gradient tracking. To further explore the benefits of mirror maps in a distributed setting we present a preconditioned variant of our algorithm with an additional mirror map over the Lagrangian dual variables. This allows our method to adapt to both the geometry of the primal variables, as well as to the geometry of the consensus constraint. We also propose a Gauss-Seidel type discretization scheme for the proposed method and establish its linear convergence rate. For certain classes of problems we identify mirror maps that mitigate the effect of the graph's spectral properties on the convergence rate of the algorithm. Using numerical experiments we demonstrate the efficiency of the methodology on convex models, both with and without constraints. Our findings show that the proposed method outperforms other methods, especially in scenarios where the model's geometry is not captured by the standard Euclidean norm
A SURVEY OF CONDITION BASED MAINTENANCE TECHNOLOGY FOR ELECTRIC POWER EQUIPMENTS

Applying the condition based maintenance technology in electric power enterprises through expert decision system can reduce the cost and extend the effective operating life of facilities. On the basis of through studying a large amount of literature of condition based maintenance technology of home and abroad, a brief presentation of the development of condition based maintenance technology for electric power facilities is described, the present situation of its application in boilers, steam turbines, generators and transformers and its newest progresses are presented in this paper. The existing problems of this technology are pointed out and some new subjects and ideas for further research in this field are presented.
Improved Random Features for Dot Product Kernels

Dot product kernels, such as polynomial and exponential (softmax) kernels, are among the most widely used kernels in machine learning, as they enable modeling the interactions between input features, which is crucial in applications like computer vision, natural language processing, and recommender systems. We make several novel contributions for improving the efficiency of random feature approximations for dot product kernels, to make these kernels more useful in large scale learning. First, we present a generalization of existing random feature approximations for polynomial kernels, such as Rademacher and Gaussian sketches and TensorSRHT, using complex-valued random features. We show empirically that the use of complex features can significantly reduce the variances of these approximations. Second, we provide a theoretical analysis for understanding the factors affecting the efficiency of various random feature approximations, by deriving closed-form expressions for their variances. These variance formulas elucidate conditions under which certain approximations (e.g., TensorSRHT) achieve lower variances than others (e.g, Rademacher sketch), and conditions under which the use of complex features leads to lower variances than real features. Third, by using these variance formulas, which can be evaluated in practice, we develop a data-driven optimization approach to random feature approximations for general dot product kernels, which is also applicable to the Gaussian kernel. We describe the improvements brought by these contributions with extensive experiments on a variety of tasks and datasets.
Determining ground states of alloys by a symmetry-based classification

Reducing the number of candidate structures is crucial to improve the efficiency of global optimization. Herein, we demonstrate that the generalized Hamiltonian can be described by the atom classification model (ACM) based on symmetry, generating competent candidates for the first-principles calculations to determine ground states of alloy directly. The candidates can be obtained in advance through solving the convex hull step by step, because the correlation functions of ACM can be divided into various subspace according to the defined index $l$. As an important inference, this index can be converted to the number of Wyckoff positions, revealing the dominant effect of geometry symmetry on structural stability. Taking Ni-Pt, Ag-Pd, Os-Ru, Ir-Ru and Mo-Ru as examples, we not only identify the stable structures in previous theoretical and experimental results, but also predict a dozen of configurations with lower formation energies, such as Ag$_{0.5}$Pd$_{0.5}$ ($Fd$-$3m$), Os$_{0.5}$Ru$_{0.5}$ ($Pnma$), Ir$_{1/3}$Ru$_{2/3}$ ($P6_{3}/mmc$), and Mo$_{0.25}$Ru$_{0.75}$ ($Cmcm$).
Cosmic rays in molecular clouds probed by H2 rovibrational lines. Perspectives for the James Webb Space Telescope

Cosmic rays (CRs) at sub-TeV energies play a fundamental role in the chemical and dynamical evolution of molecular clouds, as they control the ionisation, dissociation, and excitation of H$_{2}$. Their characterisation is important both for the interpretation of observations and for the development of theoretical models. The methods used so far for estimating the CR ionisation rate ($\zeta$) in molecular clouds have several limitations due to uncertainties in the adopted chemical networks. We refine and extend the method proposed by Bialy (2020) to estimate $\zeta$ by observing rovibrational transitions of H$_{2}$ at near-infrared wavelengths, which are mainly excited by secondary CR electrons. Combining models of interstellar CR propagation and attenuation with the calculation of the expected secondary electron spectrum and updated H$_{2}$ excitation cross sections by electron collisions, we derive the intensity of the four H$_{2}$ rovibrational transitions observable in dense, cold gas: (1-0)O(2), (1-0)Q(2), (1-0)S(0), and (1-0)O(4). The proposed method allows the estimation of $\zeta$ for a given observed line intensity and H$_{2}$ column density. We are also able to deduce the shape of the low-energy CR proton spectrum impinging upon the molecular cloud. We present a look-up plot and a web-based application that can be used to constrain the low-energy spectral slope of the interstellar CR proton spectrum. We comment on the capability of the James Webb Space Telescope to detect these near-infrared H$_{2}$ lines, making it possible to derive for the first time spatial variation of $\zeta$ in dense gas. Besides the implications for the interpretation of the chemical-dynamic evolution of a molecular cloud, it will be possible to test competing models of CR propagation and attenuation in the interstellar medium, as well as compare CR spectra in different Galactic regions.
LRSVRG-IMC: An SVRG-Based Algorithm for LowRank Inductive Matrix Completion

Low-rank inductive matrix completion (IMC) is currently widely used in IoT data completion, recommendation systems, and so on, as the side information in IMC has demonstrated great potential in reducing sample complexity and computational complexity. Unfortunately, the saddle point remains a major obstacle for the convergence of the nonconvex solutions to IMC. What’s more, carefully choosing the initial solution alone does not usually help remove the saddle points. To address this problem, we propose a stochastic variance reduction gradient-based algorithm called LRSVRGIMC. LRSVRG-IMC can escape from the saddle points under various low-rank and sparse conditions with a properly chosen initial input. We also prove that LRSVRG-IMC achieves both a linear convergence rate and a near-optimal sample complexity. The superiority and applicability of LRSVRG-IMC are verified via experiments on synthetic datasets.
Random Noise vs State-of-the-Art Probabilistic Forecasting Methods : A Case Study on CRPS-Sum Discrimination Ability

The recent developments in the machine-learning domain have enabled the development of complex multivariate probabilistic forecasting models. To evaluate the predictive power of these complex methods, it is pivotal to have a precise evaluation method to gauge the performance and predictability power of these complex methods. To do so, several evaluation metrics have been proposed in the past (such as the energy score, Dawid–Sebastiani score, and variogram score); however, these cannot reliably measure the performance of a probabilistic forecaster. Recently, CRPS-Sum has gained a lot of prominence as a reliable metric for multivariate probabilistic forecasting. This paper presents a systematic evaluation of CRPS-Sum to understand its discrimination ability. We show that the statistical properties of target data affect the discrimination ability of CRPS-Sum. Furthermore, we highlight that CRPS-Sum calculation overlooks the performance of the model on each dimension. These flaws can lead us to an incorrect assessment of model performance. Finally, with experiments on real-world datasets, we demonstrate that the shortcomings of CRPS-Sum provide a misleading indication of the probabilistic forecasting performance method. We illustrate that it is easily possible to have a better CRPS-Sum for a dummy model, which looks like random noise, in comparison to the state-of-the-art method.
Polytope: Practical Memory Access Control for C++ Applications

Designing and implementing secure software is inarguably more important than ever. However, despite years of research into privilege separating programs, it remains difficult to actually do so and such efforts can take years of labor-intensive engineering to reach fruition. At the same time, new intra-process isolation primitives make strong data isolation and privilege separation more attractive from a performance perspective. Yet, substituting intra-process security boundaries for time-tested process boundaries opens the door to subtle but devastating privilege leaks. In this work, we present Polytope, a language extension to C++ that aims to make efficient privilege separation accessible to a wider audience of developers. Polytope defines a policy language encoded as C++11 attributes that separate code and data into distinct program partitions. A modified Clang front-end embeds source-level policy as metadata nodes in the LLVM IR. An LLVM pass interprets embedded policy and instruments an IR with code to enforce the source-level policy using Intel MPK. A run-time support library manages partitions, protection keys, dynamic memory operations, and indirect call target privileges. An evaluation demonstrates that Polytope provides equivalent protection to prior systems with a low annotation burden and comparable performance overhead. Polytope also renders privilege leaks that contradict intended policy impossible to express.