Cooperative Transmission for a Vector Gaussian Parallel Relay Network

In this paper, we consider a parallel relay network where two relays cooperatively help a source transmit to a destination. We assume the source and the destination nodes are equipped with multiple antennas. Three basic schemes and their achievable rates are studied: Decode-and-Forward (DF), Amplify-and-Forward (AF), and Compress-and-Forward (CF). For the DF scheme, the source transmits two private signals, one for each relay, where dirty paper coding (DPC) is used between the two private streams, and a common signal for both relays. The relays make efficient use of the common information to introduce a proper amount of correlation in the transmission to the destination. We show that the DF scheme achieves the capacity under certain conditions. We also show that the CF scheme is asymptotically optimal in the high relay power limit, regardless of channel ranks. It turns out that the AF scheme also achieves the asymptotic optimality but only when the relays-to-destination channel is full rank. The relative advantages of the three schemes are discussed with numerical results.Comment: 35 pages, 10 figures, submitted to IEEE Transactions on Information Theor

Deterministic Relay Networks with State Information

Motivated by fading channels and erasure channels, the problem of reliable communication over deterministic relay networks is studied, in which relay nodes receive a function of the incoming signals and a random network state. An achievable rate is characterized for the case in which destination nodes have full knowledge of the state information. If the relay nodes receive a linear function of the incoming signals and the state in a finite field, then the achievable rate is shown to be optimal, meeting the cut-set upper bound on the capacity. This result generalizes on a unified framework the work of Avestimehr, Diggavi, and Tse on the deterministic networks with state dependency, the work of Dana, Gowaikar, Palanki, Hassibi, and Effros on linear erasure networks with interference, and the work of Smith and Vishwanath on linear erasure networks with broadcast.Comment: 5 pages, to appear in proc. IEEE ISIT, June 200

Transnational Marriages in the Steel Industry: Experience and Lessons for Global Business

Drawing upon case studies of firms in the steel industry, the authors show that companies competing internationally can pool their strengths to offset their individual weaknesses, enabling them to build economically successful entities more easily than if each company tried to go it alone in competition with rivals. In doing so they show how the world steel industry emerged into a group of international joint ventures and how in each of these transnational marriages the whole became greater than the sum of its parts. Among the authors\u27 main points are: cultural conflicts are minimized by economic success but magnified by failure; expertise and commitment can overcome national differences, and even failing international joint ventures can be rehabilitated. Important reading for professionals in all areas of international business and for their colleagues in the academic community. Included in each case study is a history of the firms and the emerging joint venture. Authors described the condition of facilities, the rehabilitation and construction of new facilities, the financial relationships between firms and the sources of funding, and their corporate structures. Cultural differences between firms and their impact on the success of the relationship are examined closely, with particular emphasis on personnel selection, training supervision, labor relations, retention and promotion policies and policies on tenure and layoff. Authors look at labor productivity and the use of participative management and other team approaches, relating them to such measurable variables as product quality, corporate profitability, and indeed the ultimate survival of each newly created firm. From there the authors show how the experiences of the steel industry and the lessons learned from its transnational alliances can be applied to other industries and to their own joint ventures.https://scholarship.richmond.edu/bookshelf/1046/thumbnail.jp

Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment

This paper proposes Mutual Information Regularized Assignment (MIRA), a pseudo-labeling algorithm for unsupervised representation learning inspired by information maximization. We formulate online pseudo-labeling as an optimization problem to find pseudo-labels that maximize the mutual information between the label and data while being close to a given model probability. We derive a fixed-point iteration method and prove its convergence to the optimal solution. In contrast to baselines, MIRA combined with pseudo-label prediction enables a simple yet effective clustering-based representation learning without incorporating extra training techniques or artificial constraints such as sampling strategy, equipartition constraints, etc. With relatively small training epochs, representation learned by MIRA achieves state-of-the-art performance on various downstream tasks, including the linear/k-NN evaluation and transfer learning. Especially, with only 400 epochs, our method applied to ImageNet dataset with ResNet-50 architecture achieves 75.6% linear evaluation accuracy.Comment: NeurIPS 202