Investigation of tool geometry effect and penetration strategies on cutting forces during thread milling

The application of thread milling is increasing in industry because of its inherent advantages over other thread cutting techniques. The objective of this study is to investigate the effect of milling cutter tool geometry on cutting forces during thread milling. The proposed method can compare the performance of milling cutters in spite of the different number of tooth. The best thread milling cutter among the studied tools was determined from the cutting forces point of view. Furthermore, this study also pinpoints the best penetration strategy that provides minimum cutting forces. Lower cutting force variations will lead to fewer vibrations of the tool which in turn will produce accurate part.Postdoc de V Sharma financé par la région Bourgogn

A Degeneracy Framework for Scalable Graph Autoencoders

In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective propagation mechanism, our approach significantly improves scalability and training speed while preserving performance. We evaluate and discuss our method on several variants of existing graph AE and VAE, providing the first application of these models to large graphs with up to millions of nodes and edges. We achieve empirically competitive results w.r.t. several popular scalable node embedding methods, which emphasizes the relevance of pursuing further research towards more scalable graph AE and VAE.Comment: International Joint Conference on Artificial Intelligence (IJCAI 2019

Robust parameter estimation of density functions under fuzzy interval observations

International audienceThis study deals with the derivation of a probabilistic parametric model from interval data using the maximum likelihood principle. In contrast with classical techniques such as the EM algorithm, that define a precise likelihood function by computing the probability of observations viewed as a collection of non-elementary events, our approach presupposes that each imprecise observation underlies a precise one, and that the uncertainty that pervades its observation is epistemic, rather than representing noise. We define an interval-valued likelihood function and apply robust optimisation methods to find a safe plausible estimate of the statistical parameters. The approach is extended to fuzzy data by optimizing the average of lower likelikoods over a collection of data sets obtained from cuts of the fuzzy intervals, as a trade off between optimistic and pessimistic interpretations of fuzzy data. The principles of this method are compared with those of other existing approaches to handle incompleteness of observations, especially the EM technique

A neglected phonetic law: The assimilation of pretonic yod to a following coronal in North-West Semitic

International audienceThis paper shows the existence of a pretonic assimilation of *y to a following coronal consonant (including *y from proto-Semitic *y and *w) in North-West Semitic languages. This rule, which has been obscured by analogy in each of the North-West Semitic languages, explains three independent sets of facts: the formation of irregular maqtal-s in Hebrew, Phoenician and Aramaic; the irregular conjugations of several verbs in Hebrew; and the plural formation of the irregular noun "house" in Hebrew and Aramaic. This proposal also solves the long-standing problem of the etymology of the verb "to give" in North-West Semitic languages (NTN in Hebrew vs. YTN in Phoenician)

Gravity-Inspired Graph Autoencoders for Directed Link Prediction

Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods. In particular, graph AE and VAE were successfully leveraged to tackle the challenging link prediction problem, aiming at figuring out whether some pairs of nodes from a graph are connected by unobserved edges. However, these models focus on undirected graphs and therefore ignore the potential direction of the link, which is limiting for numerous real-life applications. In this paper, we extend the graph AE and VAE frameworks to address link prediction in directed graphs. We present a new gravity-inspired decoder scheme that can effectively reconstruct directed graphs from a node embedding. We empirically evaluate our method on three different directed link prediction tasks, for which standard graph AE and VAE perform poorly. We achieve competitive results on three real-world graphs, outperforming several popular baselines.Comment: ACM International Conference on Information and Knowledge Management (CIKM 2019

Seeking stability of supply chain management decisions under uncertain criteria

The leading theme of MOSIM’12 is "Performance, Interoperability and Safety for sustainable development"International audienceThis paper tackles the question of the anticipation of the supply chain partner's decisional behaviour under uncertain criteria. In other words, we propose a model to support sequential decisions under uncertainty where the decision maker has to make hypothesis about the decision criteria. For example, Hurwicz criterion weights extreme optimism and pessimism positions and a classic criticism of this criterion consisting in the difficulty of the weight assessment and the involving decision instability. To achieve this, we present a method based on fuzzy representation of weight vision. Finally, the model allows sequential decision of a Decision Tree to be compute thanks a pignistic probabilities treatment of the fuzzy representation of the decision maker optimism-pessimism index. This approach is illustrated through an industrial case study

Decision support with ill-known criteria in the collaborative supply chain context

International audienceIn the field of Supply Chain Risk Management, the attitude of managers toward risk affect the tactical decision-making process in collaborative supply chains under an uncertain environment, concerning especially capacity levels, lot-sizing rules, purchasing strategies, production scheduling,…, etc. The issue can be formulated as a sequential decision problem under uncertainty where the customer decisions affect the decisions made by the supplier. In this paper we deal with two kinds of uncertainties. The first one is the uncertainty on the indicators of performance (which are not comparable) used by the decision maker to choose a solution (for example: service quality or inventory cost). Hence, we propose an approach based on subjective probability to evaluate the probability that a decision is optimal for the first actor and the probability that it is optimal for both. From these two evaluations, we propose a ranking function to help the first actor to take into account the second one when selecting a decision. The second kind of uncertainty pertains to the demand. A classical criterion under total uncertainty is Hurwicz criterion where a weight expresses a degree of pessimism. Nevertheless, the degree of pessimism is itself ill-known. Thus, it becomes difficult to take into account the behavior of the actors. Hence, we propose an approach based on possibility theory and the so-called pignistic transform, which computes a subjective probability distribution over the criteria. Then, we apply the method used for uncertain criterion. This approach is illustrated through an example and an industrial case study