Определение интервалов квазистационарности экономических систем

В работе рассмотрен вопрос определения оптимального интервала адаптации алгоритма динамического управления капиталом для нестационарного случая методами расчета показателя Херста и построения автокорреляционной функции для анализа временных рядов. Проведен анализ влияния выбора интервала адаптации на эффективность алгоритма. Из анализа полученных результатов следует, что метод расчета показателя Херста позволяет более эффективно, чем метод построения автокорреляционной функции, определить интервал стационарности модели функционирования экономической системы.Робота присвячена питанню визначення оптимального інтервалу адаптації алгоритму динамічного керування капіталом для нестаціонарного випадку за допомогою методів розрахунку показника Херста і побудови автокореляційної функції задля аналізу часових рядів. Проведено аналіз впливу вибору інтервалу адаптації на ефективність алгоритму. Порівняння результатів проведеного аналізу дозволяє стверджувати, що метод розрахунку показника Херста дозволяє більш ефективно, ніж метод побудови автокореляційної функції, визначити інтервал стаціонарності моделі функціонування економічної системи

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Introducing v0.5 of the AI Safety Benchmark from MLCommons

This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safety risks of AI systems that use chat-tuned language models. We introduce a principled approach to specifying and constructing the benchmark, which for v0.5 covers only a single use case (an adult chatting to a general-purpose assistant in English), and a limited set of personas (i.e., typical users, malicious users, and vulnerable users). We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0.5 benchmark. We plan to release version 1.0 of the AI Safety Benchmark by the end of 2024. The v1.0 benchmark will provide meaningful insights into the safety of AI systems. However, the v0.5 benchmark should not be used to assess the safety of AI systems. We have sought to fully document the limitations, flaws, and challenges of v0.5. This release of v0.5 of the AI Safety Benchmark includes (1) a principled approach to specifying and constructing the benchmark, which comprises use cases, types of systems under test (SUTs), language and context, personas, tests, and test items; (2) a taxonomy of 13 hazard categories with definitions and subcategories; (3) tests for seven of the hazard categories, each comprising a unique set of test items, i.e., prompts. There are 43,090 test items in total, which we created with templates; (4) a grading system for AI systems against the benchmark; (5) an openly available platform, and downloadable tool, called ModelBench that can be used to evaluate the safety of AI systems on the benchmark; (6) an example evaluation report which benchmarks the performance of over a dozen openly available chat-tuned language models; (7) a test specification for the benchmark

Embedding 2-Dimensional Grids Into Optimal Hypercubes With Edge-Congestion 1 Or 2

This paper explores one-to-one embeddings of 2-dimensional grids into hypercubes. It is shown that each 2-dimensional grid can be embedded with edge-congestion 2 into its optimal hypercube (the smallest hypercube with at least as many nodes as the grid). Additionally, a technique is developed to embed many 2-dimensional grids into their optimal hypercubes with edge-congestion 1

Efficient Embeddings of Grids into Grids (Extended Abstract)

In this paper we explore one-to-one embeddings of 2-dimensional grids into their ideal 2-dimensional grids. The presented results are optimal or considerably close to the optimum. For embedding grids into grids of smaller aspect ratio, we prove a new lower bound on the dilation matching a known upper bound. The edgecongestion provided by our matrix-based construction differs from the here presented tight lower bound by at most one. For embedding grids into grids of larger aspect ratio, we establish five as an upper bound on the dilation and four as an upper bound on the edge-congestion, which are improvements of previous results

Implementation of a Parallel and Distributed Mapping Kernel for PARIX

This paper describes the mapping kernel of the virtual topology library for the commercial run-time system PARIX 3. The mapping kernel is composed of a collection of injective embedding functions for special interconnection structures of process graphs (virtual topologies) onto a 2-dimensional grid architecture of parallel machines of the MIMD type. Each of these functions realizes a concrete virtual topology by placing each process on a different processor and establishing the communication channels as virtual links with communication primitives of PARIX. The implemented functions were selected under the criteria of fast distributed computation, universal applicability, and small dilation, awell-known cost measure for graph embedding. The virtual topology library supports the implementation of parallel applications and leads to a portable programming and an efficient usage of MIMD-systems

Embedding 3-dimensional Grids into Optimal Hypercubes

The hypercube is a particularly versatile network for parallel computing. It is wellknown that 2-dimensional grid machines can be simulated on a hypercube with a small constant communication overhead. We introduce new easily computable functions which embed many 3-dimensional grids into their optimal hypercubes with dilation 2. Moreover, we show that one can reduce the open problem to recognize whether it is possible to embed every 3-dimensional grid into its optimal hypercube with dilation at most 2 by constructing embeddings for a particular class of grids. We embed some of these grids, and thus for the first time one can guarantee that every 3-dimensional grid with at most 2 9 \Gamma 18 nodes is embeddable into its optimal hypercube with dilation 2. Key words: embedding, hypercubes, 3-dimensional grids, dilation, GrayCode 1 Introduction Within the last decade parallel computing has received considerable attention in computer science. The main reasons for the growing interest are..

On Efficient Embeddings of Grids into Grids in PARIX

A hardware independent method of programming a massively parallel machine (MPP) can best be supported by a well-designed run-time environment. An important problem in this design is the ability of efficiently simulating networks different from the hardware topology. We will describe the mapping kernel of the virtual processors library for the commercial run-time system PARIX. This kernel contains description classes for several topologies (so-called virtual topologies) and implementations of respective embeddings which map given instances of virtual topologies onto others or onto the hardware. Using these functions, PARIX is able to establish concrete virtual topologies with corresponding communication channels. The implemented functions were selected with respect to the well-known criteria for graph embeddings: equal load and small dilation. Additionally, we focus on fast distributed computation and universal applicability. As an example, we will show new methods for efficient..

Efficient Mapping Library for PARIX

An important problem in the design of run-time environments for massively parallel systems (MPP) is the ability of efficiently simulating networks different from the hardware topology which leads to a hardware independent kind of programming. We will describe the mapping kernel of the virtual processor library for the commercial run-time system PARIX as well as aspects of this environment. The mapping kernel contains description classes for several topologies (so-called virtual topologies) and implementations of respective embeddings which map given instances of virtual topologies onto others or onto the hardware interconnection network of a parallel machine. Using these functions, PARIX can establish concrete virtual topologies with corresponding communication channels. The implemented functions were selected w.r.t. well-known criteria for graph embeddings: fast distributed computation, universal applicability, equal load, and small dilation. 1 Introduction To achieve a high system p..