Dr Livingstone, I Presume?

Presumption is often discussed in law, less often in epistemology. Is it an attitude? If so where can we locate it within the taxonomy of epistemic attitudes? Is it a kind of belief, a judgment, an assumption or a supposition? Or is it a species of inference? There are two basic models of presumption: judgmental, as a kind of judgment, and legal, taken from the use of presumptions in law. The legal model suggests that presumption is a practical inference, whereas the judgmental model suggests that presumption is an epistemic attitude. I argue that presumption is neither a practical inference nor a merely epistemic attitude: it involves both, within the category of what we may call the inquiring attitudes.

How Values Ground Value Creation: The practical inference framework

After more than two decades of searching, the holy grail of integrating norms into management and organization research remains elusive. Researchers still lack a clear framework that explains value creation in relation to normative values, and, in turn, a means to incorporate values into research methods and generate value-based practical insights. To fill that need, this article presents an epistemological framework for understanding value creation. The practical inference framework centers on the activity of practical reasoning, a kind of reasoning that is legitimized by intrinsic values. It turns the ordinary epistemic equation on its head by seeking reasons rather than causes, and justifications rather than descriptions. In doing so, it shows how both factor analytic and newer, divergent methods of research can integrate with a robust architecture of value creation in ways that offer relevant knowledge for managers and society.

Balanced Sparsity for Efficient DNN Inference on GPU

In trained deep neural networks, unstructured pruning can reduce redundant weights to lower storage cost. However, it requires the customization of hardwares to speed up practical inference. Another trend accelerates sparse model inference on general-purpose hardwares by adopting coarse-grained sparsity to prune or regularize consecutive weights for efficient computation. But this method often sacrifices model accuracy. In this paper, we propose a novel fine-grained sparsity approach, Balanced Sparsity, to achieve high model accuracy with commercial hardwares efficiently. Our approach adapts to high parallelism property of GPU, showing incredible potential for sparsity in the widely deployment of deep learning services. Experiment results show that Balanced Sparsity achieves up to 3.1x practical speedup for model inference on GPU, while retains the same high model accuracy as finegrained sparsity.

CDMBE: A Case Description Model Based on Evidence

By combining the advantages of argument map and Bayesian network, a case description model based on evidence (CDMBE), which is suitable to continental law system, is proposed to describe the criminal cases. The logic of the model adopts the credibility logical reason and gets evidence-based reasoning quantitatively based on evidences. In order to consist with practical inference rules, five types of relationship and a set of rules are defined to calculate the credibility of assumptions based on the credibility and supportability of the related evidences. Experiments show that the model can get users’ ideas into a figure and the results calculated from CDMBE are in line with those from Bayesian model.