Sharing emotions and space - empathy as a basis for cooperative spatial interaction

Boukricha H, Nguyen N, Wachsmuth I. Sharing emotions and space - empathy as a basis for cooperative spatial interaction. In: Kopp S, Marsella S, Thorisson K, Vilhjalmsson HH, eds. Proceedings of the 11th International Conference on Intelligent Virtual Agents (IVA 2011). LNAI. Vol 6895. Berlin, Heidelberg: Springer; 2011: 350-362.Empathy is believed to play a major role as a basis for humans’ cooperative behavior. Recent research shows that humans empathize with each other to different degrees depending on several modulation factors including, among others, their social relationships, their mood, and the situational context. In human spatial interaction, partners share and sustain a space that is equally and exclusively reachable to them, the so-called interaction space. In a cooperative interaction scenario of relocating objects in interaction space, we introduce an approach for triggering and modulating a virtual humans cooperative spatial behavior by its degree of empathy with its interaction partner. That is, spatial distances like object distances as well as distances of arm and body movements while relocating objects in interaction space are modulated by the virtual human’s degree of empathy. In this scenario, the virtual human’s empathic emotion is generated as a hypothesis about the partner’s emotional state as related to the physical effort needed to perform a goal directed spatial behavior

Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)

Deep neural networks (DNN) have shown remarkable success in a variety of machine learning applications. The capacity of these models (i.e., number of parameters), endows them with expressive power and allows them to reach the desired performance. In recent years, there is an increasing interest in deploying DNNs to resource-constrained devices (i.e., mobile devices) with limited energy, memory, and computational budget. To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e.g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e.g., MAC operations are reduced to a few accumulations plus two multiplications). This approach consists of two steps. First, a super-network is created by scaling the dimensions of a pre-trained model (i.e., its width and depth). Subsequently, this super-network is simultaneously pruned (using an entropy constraint) and quantized (that is, ternary values are assigned layer-wise) in a training process, resulting in a sparse and ternary network representation. We validate the proposed approach in CIFAR-10, CIFAR-100, and ImageNet datasets, showing its effectiveness in image classification tasks.Comment: Proceedings of the CVPR'20 Joint Workshop on Efficient Deep Learning in Computer Vision. Code is available at https://github.com/d-becking/efficientCNN

Building and Blocking: The Two Faces of Technology Acquisition

Gaining access to technological assets and patents, in particular, has long been a major motive and objective for firm acquisitions. On the one hand, patents are used as a building instrument for the acquirer's technology portfolio. On the other hand, patents can be attractive because of their strategic value as a bargaining chip, e.g. in licensing negotiations. This is especially the case if patents have the potential to block competitors. Drawing on transaction cost economics and the resource-based view of the firm, we analyze the importance of these two faces of technology acquisition for the valuation of a target firm. Empirical evidence for European firm acquisitions in the period from 1999 to 2003 indicates that the price paid by an acquirer for a target increases with the patent stock, the relatedness, the value and the blocking potential of the target's patents, especially if blocking patents are in technology fields related to the acquiring firm's patent portfolio. Our results have implications for competition authorities, in that M&A transactions may considerably impact technology markets. This would also need to be reflected in the management's technology strategy. --Firm acquisitions,technology,patents,blocking patents

Building and Blocking: The two faces of techology acquisition.

firm acquisitions; technology; patents; blocking patents;

The Innovative Performance of Alliance Block Members: Evidence from the Microelectronics Industry

The primary goal of this paper is to improve our understanding of the complex relationship between the positioning of companies in alliance networks and their innovative performance. In particular, we expect that a firm's innovative performance depends partly on its position in specific network settings (block membership or nonblock membership), with additional effects caused by the technology positioning strategies firms pursue in terms of technological specialization in alliance blocks. Alliance groups derive their competitive advantage from their superior and particular technologies, which they develop and exploit together in the alliance blocks. Incorporating this moderating effect of the degree of technological specialization in alliance blocks (exploitation or exploration) seems to give more insight in the contextual issues in this stream of literature.strategic technology alliances, alliance block membership strategy, microelectronics industry, innovative performance, technology strategies

The effect of duration, serialposition and competitive clutter on consumer memory for television advertising: An extension and a test of generalizability

Advertising;Consumer Behaviour;Television;marketing