A counterexample to Belgun-Moroianu conjecture

We construct an example of a closed manifold with a nonflat reducible locally metric connection such that it preserves a conformal structure and such that it is not the Levi-Civita connection of a Riemannian metric.Comment: are as always welcom

Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition

With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. Researchers and industry prefer to use end-to-end ASR systems for on-device speech recognition tasks. This is because end-to-end systems can be made resource-efficient while maintaining a higher quality compared to hybrid systems. However, building end-to-end models requires a significant amount of speech data. Another challenging task associated with speech assistants is personalization, which mainly lies in handling out-of-vocabulary (OOV) words. In this work, we consider building an effective end-to-end ASR system in low-resource setups with a high OOV rate, embodied in Babel Turkish and Babel Georgian tasks. To address the aforementioned problems, we propose a method of dynamic acoustic unit augmentation based on the BPE-dropout technique. It non-deterministically tokenizes utterances to extend the token's contexts and to regularize their distribution for the model's recognition of unseen words. It also reduces the need for optimal subword vocabulary size search. The technique provides a steady improvement in regular and personalized (OOV-oriented) speech recognition tasks (at least 6% relative WER and 25% relative F-score) at no additional computational cost. Owing to the use of BPE-dropout, our monolingual Turkish Conformer established a competitive result with 22.2% character error rate (CER) and 38.9% word error rate (WER), which is close to the best published multilingual system.Comment: 16 pages, 7 figure

Institutional aspect of the Russian economy regional development

The urgency of the issue under research takes place due to the fact that the institutional system forms prerequisites for economic development of regions and affects the rate of economic growth and welfare of the population not only at the local, but also at the federal level. The article reveals the peculiarities of the institutional aspect of regional development, gives recommendations on the improvement of institutions to smooth the unevenness of regional development. As a scientific and methodological basis, the methods of analysis and synthesis are used in the work, which allow to reveal the features of regional institutions at the present stage. The use of statistical data reflecting the main characteristics of Russian regions made it possible to reveal that regions develop unevenly both in time and in space. The reasons for this are insufficient performance efficiency of institutions, which duplicates the powers of federal and regional authorities, the state’s participation in realizing ownership rights in the private property system, insufficient protection of property rights, bureaucratic procedures, and financial problems in the Russian economy. The above-mentioned shortcomings in the system of regional institutes reduce the competitiveness of regions, and reduce the volume of investments, which leads to further strengthening of their uneven development, and exacerbating the issue of income differences of the population. The authors believe that in order to improve the system of regional institutions and overcome the asymmetry, it is necessary to systematize resources, develop differentiated interaction at the regional and federal levels, and evaluate the effectiveness of the decisions made with the help of leading indicators in business cycle phases

A theoretical and experimental investigation on the SHS synthesis of (HfTiCN)-TiB2 high-entropy composite

In this work, a fundamental possibility of obtaining a high-entropy ceramic (HfTiCN)-TiB2 composite material by the coupled self-propagating high-temperature synthesis is shown. To search for a stable fixed composition of the HfTiCN compound, the USPEX code was used with the CASTEP interface at 0K. According to the XRD analysis, the obtained SHS product is represented by HfTiCN phase (60 wt%) and TiB2 phase (40 wt%). Based on the results of XRD, elemental analysis, and the heat pattern of combustion of the Hf-Ti-C-N-B powder mixture, a probable mechanism for the formation of the (HfTiCN)-TiB2 composite material during the coupled self-propagating high-temperature synthesis was proposed

Energy-Saving Vibration Impulse Coal Degradation at Finely Dispersed Coal-Water Slurry Preparation

Theoretical and experimental research results of processes of finely dispersed coal-water slurry preparation for further generation of energetic gas in direct flow and vortex gas generator plants have been presented. It has been stated that frequency parameters of parabolic vibration impulse mill influence degradation degree. Pressure influence on coal parameters in grinding cavity has been proven. Experimental researches have proven efficiency of vibration impulse mill with unbalanced mass vibrator generator development. Conditions of development on intergranular walls of coal cracks have been defined

ATTACK DETECTION IN ENTERPRISE NETWORKS BY MACHINE LEARNING METHODS

Detection of network attacks is currently one of the most important problems of secure use of enterprise networks. Network signature-based intrusion detection systems cannot detect new types of attacks. Thus, the urgent task is to quickly classify network traffic to detect network attacks. The article describes algorithms for detecting attacks in enterprise networks based on data analysis that can be collected in them. The UNSW-NB15 data set was used to compare machine learning methods for classifying attack or-normal traffic, as well as to identify nine more popular classes of typical attacks, such as Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms. Balanced accuracy is used as the main metric for assessing the accuracy of the classification. The main advantage of this metric is an adequate assessment of the accuracy of classification algorithms given the strong imbalance in the number of marked records for each class of data set. As a result of the experiment, it was found that the best algorithm for identifying the presence of an attack is RandomForest, to clarify its type - AdaBoost

Efficiency of Finding Muon Track Trigger Primitives in CMS Cathode Strip Chambers

In the CMS Experiment, muon detection in the forward direction is accomplished by cathode strip chambers~(CSC). These detectors identify muons, provide a fast muon trigger, and give a precise measurement of the muon trajectory. There are 468 six-plane CSCs in the system. The efficiency of finding muon trigger primitives (muon track segments) was studied using~36 CMS CSCs and cosmic ray muons during the Magnet Test and Cosmic Challenge~(MTCC) exercise conducted by the~CMS experiment in~2006. In contrast to earlier studies that used muon beams to illuminate a very small chamber area ($< \! 0.01$~m$^2$), results presented in this paper were obtained by many installed CSCs operating {\em in situ} over an area of $\approx \! 23$~m$^2$ as a part of the~CMS experiment. The efficiency of finding 2-dimensional trigger primitives within 6-layer chambers was found to be~$99.93 \pm 0.03\%$. These segments, found by the CSC electronics within $800$~ns after the passing of a muon through the chambers, are the input information for the Level-1 muon trigger and, also, are a necessary condition for chambers to be read out by the Data Acquisition System