Поиск оптимальной стратегии для выхода на новые рынки леса и пиломатериалов

Предложено комплексное решение трех задач линейного программирования: производственной (классическая поста-новка), задачи размещения центров, а также максимально- го потока. Подобные задачи в предложенной комплексной постановке часто возникают на предприятиях в процессе производства и реализации продукции. Рассмотрены основ- ные алгоритмы поиска оптимального решения, сформули- рована комплексная задача, построена модель и реализован алгоритм решения, проведено сравнение существующего метода и авторского. Предложенная модель может быть использована на любом предприятии, где необходимо найти оптимальный комбинаторный вариант для производства с целью минимизации производственных издержек и затрат на транспортировку готовой продукции, а также получе- ния максимальной прибыли

Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions

Movement synchrony refers to the dynamic temporal connection between the motions of interacting people. The applications of movement synchrony are wide and broad. For example, as a measure of coordination between teammates, synchrony scores are often reported in sports. The autism community also identifies movement synchrony as a key indicator of children's social and developmental achievements. In general, raw video recordings are often used for movement synchrony estimation, with the drawback that they may reveal people's identities. Furthermore, such privacy concern also hinders data sharing, one major roadblock to a fair comparison between different approaches in autism research. To address the issue, this paper proposes an ensemble method for movement synchrony estimation, one of the first deep-learning-based methods for automatic movement synchrony assessment under privacy-preserving conditions. Our method relies entirely on publicly shareable, identity-agnostic secondary data, such as skeleton data and optical flow. We validate our method on two datasets: (1) PT13 dataset collected from autism therapy interventions and (2) TASD-2 dataset collected from synchronized diving competitions. In this context, our method outperforms its counterpart approaches, both deep neural networks and alternatives.Comment: IEEE ICPR 2022. 8 pages, 3 figure

A Branch-and-Bound Framework for Unsupervised Common Event Discovery

Event discovery aims to discover a temporal segment of interest, such as human behavior, actions or activities. Most approaches to event discovery within or between time series use supervised learning. This becomes problematic when some relevant event labels are unknown, are difficult to detect, or not all possible combinations of events have been anticipated. To overcome these problems, this paper explores Common Event Discovery (CED), a new problem that aims to discover common events of variable-length segments in an unsupervised manner. A potential solution to CED is searching over all possible pairs of segments, which would incur a prohibitive quartic cost. In this paper, we propose an efficient branch-and-bound (B&B) framework that avoids exhaustive search while guaranteeing a globally optimal solution. To this end, we derive novel bounding functions for various commonality measures and provide extensions to multiple commonality discovery and accelerated search. The B&B framework takes as input any multidimensional signal that can be quantified into histograms. A generalization of the framework can be readily applied to discover events at the same or different times (synchrony and event commonality, respectively). We consider extensions to video search and supervised event detection. The effectiveness of the B&B framework is evaluated in motion capture of deliberate behavior and in video of spontaneous facial behavior in diverse interpersonal contexts: interviews, small groups of young adults, and parent-infant face-to-face interaction