Higher School of Economics (HSE; Russian: «Высшая школа экономики», ВШЭ), officially the National Research University Higher School of Economics (Russian: Национальный исследовательский университет «Высшая школа экономики») is a public research university founded in 1992 and headquartered in Moscow, Russia. Along with its main campus located in the capital, the university maintains three other regional campuses in Nizhny Novgorod, Perm and Saint Petersburg.. Widely regarded among the best and most prestigious universities in Russia and the CIS, it acquired the status of "national research university" in 2009. HSE was the first educational institution in Russia to successfully introduce Bachelor's and Master's degrees, having also taken part in the development and implementation of the Unified State Exam to modernize education and health care systems of Russia. Starting from 2013, HSE University has also participated in the 5-100 Russian Academic Excellence Project (the project was initiated by the Ministry of Education and Science to promote at least five Russian universities to the top hundred universities according to Times Higher Education World University Rankings, QS World University Rankings, and Academic Ranking of World Universities). Furthermore, university representatives are part of the Civic Chamber of the Russian Federation and the Expert Council under the Government of Russia.
An approximate method for calculating drift velocity and other kinetic coefficients of heavy ions in a light gas is generalized to gaseous mixtures. Obtained equations are used to calculate the mobilities of O_4^ - and O_2^ - ions, as well as rate constants for inelastic ion–molecule processes with these ions in helium with small additions of O2 under an electric field. Calculated results are compared with the results of Monte Carlo simulation. Simple approximate methods allow good agreement to be reached with more complicated Monte Carlo simulation when describing the strong effect of small O2 concentrations on the ion kinetic properties in He and to shorten computational time by orders of magnitude.
3D object detection is fundamental for spatial understanding. Real-world environments demand models capable of recognizing diverse, previously unseen objects, which remains a major limitation of closed-set methods. Existing open-vocabulary 3D detectors relax annotation requirements but still depend on training scenes, either as point clouds or images. We take this a step further by introducing Zoo3D, the first training-free 3D object detection framework. Our method constructs 3D bounding boxes via graph clustering of 2D instance masks, then assigns semantic labels using a novel open-vocabulary module with best-view selection and view-consensus mask generation. Zoo3D operates in two modes: the zero-shot Zoo3D_0, which requires no training at all, and the self-supervised Zoo3D_1, which refines 3D box prediction by training a class-agnostic detector on Zoo3D_0-generated pseudo labels. Furthermore, we extend Zoo3D beyond point clouds to work directly with posed and even unposed images. Across ScanNet200 and ARKitScenes benchmarks, both Zoo3D_0 and Zoo3D_1 achieve state-of-the-art results in open-vocabulary 3D object detection. Remarkably, our zero-shot Zoo3D_0 outperforms all existing self-supervised methods, hence demonstrating the power and adaptability of training-free, off-the-shelf approaches for real-world 3D understanding. Code is available at https://github.com/col14m/zoo3d .
The Hough transform (HT) is widely used in computer vision, tomography, and neural networks. Numerous algorithms for HT computation have been proposed, making their systematic comparison essential. However, existing comparative methodologies are either non-universal and limited to certain HT formulations or task-oriented, relying on application-specific criteria that do not fully capture algorithmic properties. This paper introduces a novel unified methodology for the systematic comparison of HT algorithms. It evaluates key characteristics, including computational complexity, accuracy, and auxiliary space complexity, while explicitly accounting for the property of self-adjointness. The methodology integrates both implementation-level and theoretical considerations related to the interpretation of HT as a discrete approximation of the Radon transform. A set of mathematically justified evaluation functions, not previously described in the literature, is proposed to support our methodology. Importantly, the methodology is universal, applicable across diverse HT paradigms, encompasses pattern-based and Fourier-based fast HT (FHT) algorithms, and offers a comprehensive alternative to existing task-specific methodologies. Its application to several state-of-the-art FHT algorithms (FHT2DT, FHT2SP, ASD2, KHM, and Fast Slant Stack) yields new experimentally confirmed theoretical insights, identifies ASD2 as the most balanced algorithm, and provides practical guidelines for algorithm selection. In particular, the methodology reveals that for image sizes up to 3000, the maximum normalized computational complexity increases as follows: FHT2DT (1.1), ASD2 (15.3), and KHM (30.6), while the remaining algorithms exhibit at least 1.1 times higher values. The maximum orthotropic approximation error equals 0.5 for ASD2, KHM, and Fast Slant Stack; lies between 0.5 and 1.5 for FHT2SP; and reaches 2.1 for FHT2DT. In terms of worst-case normalized auxiliary space complexity, the lowest values are achieved by FHT2DT (2.0), Fast Slant Stack (4.0, lower bound), and ASD2 (6.8), with all other algorithms requiring at least 8.2 times more memory.
Vector search, which returns the vectors most similar to a given query vector from a large vector dataset, underlies many important applications such as search, recommendation, and LLMs. To be economic, vector search needs to be efficient to reduce the resources required by a given query workload. However, existing vector search libraries (e.g., Faiss and DiskANN) are optimized for x86 CPU architectures (i.e., Intel and AMD CPUs) while Huawei Kunpeng CPUs are based on the ARM architecture and competitive in compute power. In this paper, we present KBest as a vector search library tailored for the latest Kunpeng 920 CPUs. To be efficient, KBest incorporates extensive hardware-aware and algorithmic optimizations, which include single-instruction-multiple-data (SIMD) accelerated distance computation, data prefetch, index refinement, early termination, and vector quantization. Experiment results show that KBest outperforms SOTA vector search libraries running on x86 CPUs, and our optimizations can improve the query throughput by over 2x. Currently, KBest serves applications from both our internal business and external enterprise clients with tens of millions of queries on a daily basis.
Пусть $\Delta$ - $n$-симплекс в $\mathbb{R}^n$ с целочисленными вершинами, содержащий ровно одну целочисленную точку $a$, отличную от своих вершин. В работе доказывается, что если $a$ находится во внутренности $\Delta$ или в относительной внутренности некоторой гиперграни симплекса, то объем $\Delta$ ограничен величиной, зависящей только от размерности $n$, в противном случае объем $\Delta$ может быть сколь угодно большим. Этот результат применяется для вывода асимптотической формулы для среднего числа вершин полиэдров Клейна. Усреднение проводится по полиэдрам Клейна $s$-мерных целочисленных решеток фиксированного определителя $N$, где $N$ - растущий параметр. Ранее такая формула была известна только при $s=2,3$. Если $\Gamma$ - решетка в $\mathbb{R}^s$ ранга $s$, то полиэдр Клейна определяется как выпуклая оболочка ненулевых узлов $\Gamma$, содержащихся в некотором ортанте. Библиография: 27 наименований.
