Stainless steels reinforced with intermetallics useful against corrosion and wear

Powder Technology Research Group has developed an innovative family of composite materials is presented. Metallic matrix are austenitic and ferritic stainless steels, and as reinforcements, intermetallics, have been used in quantities from 1% to 15% (vol.). These materials combine excellent properties against corrosion and wear, so they become very useful for structural applications, in areas like aerospace and automotive. The research group is trying to find companies in order to establish license agreements and/or collaborative projects for the technology development and validation. The companies profile sectors would be the manufacturers of materials, components or structures for aerospace and automotive areas.Contrato Programa de Comercialización e Internacionalización. Sistema Regional de Investigación Científica e Innovación Tecnológica. (Comunidad de Madrid; Universidad Carlos III de Madrid

Anticipating Visual Representations from Unlabeled Video

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. We present a framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. Visual representations are a promising prediction target because they encode images at a higher semantic level than pixels yet are automatic to compute. We then apply recognition algorithms on our predicted representation to anticipate objects and actions. We experimentally validate this idea on two datasets, anticipating actions one second in the future and objects five seconds in the future.Comment: CVPR 201

Lex-Partitioning: A New Option for BDD Search

For the exploration of large state spaces, symbolic search using binary decision diagrams (BDDs) can save huge amounts of memory and computation time. State sets are represented and modified by accessing and manipulating their characteristic functions. BDD partitioning is used to compute the image as the disjunction of smaller subimages. In this paper, we propose a novel BDD partitioning option. The partitioning is lexicographical in the binary representation of the states contained in the set that is represented by a BDD and uniform with respect to the number of states represented. The motivation of controlling the state set sizes in the partitioning is to eventually bridge the gap between explicit and symbolic search. Let n be the size of the binary state vector. We propose an O(n) ranking and unranking scheme that supports negated edges and operates on top of precomputed satcount values. For the uniform split of a BDD, we then use unranking to provide paths along which we partition the BDDs. In a shared BDD representation the efforts are O(n). The algorithms are fully integrated in the CUDD library and evaluated in strongly solving general game playing benchmarks.Comment: In Proceedings GRAPHITE 2012, arXiv:1210.611

Feedstocks development for Metal Injection Moulding

Today, more tan 90% of used feedstock for MIM in Europe, came from BASF (exclusive patent), with low possibility for change compositions or costs (in Japan or USA, the percentage is quite smaller). In our research group (Powder Technology Group) we can develop new feedstocks formulation that can be used directly by the MIM parts manufacturers and fulfilling their composition requirements. Interest in licensing the applied patent or commercial agreement with technical assistance with companies that would like to incorporate this technology

Design and manufacturing of master alloys for sintering activation in high performance structural parts

Nowadays, the development of high performance structural parts, is limited by the fact that the alloying systems are being modifying by requirements associated to envorimental guideline as well as to the increase in the price of raw materials. The use of masteralloys allows to activate the mass transport processes during sintering with a minimum modification of final composition (low cost) acting on densification, and hence, on final properties. The research group of “Powder Technology” from Carlos III University, has a wide experience and qualification on the design of new alloying systems and in manufacturing the powders by atomization and mechanical alloying techniques. The Group is looking for companies interested in technical cooperation or manufacturing agreement

Predicting Motivations of Actions by Leveraging Text

Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a person has performed an action in images. This problem has many applications in human activity understanding, such as anticipating or explaining an action. To study this problem, we introduce a new dataset of people performing actions annotated with likely motivations. However, the information in an image alone may not be sufficient to automatically solve this task. Since humans can rely on their lifetime of experiences to infer motivation, we propose to give computer vision systems access to some of these experiences by using recently developed natural language models to mine knowledge stored in massive amounts of text. While we are still far away from fully understanding motivation, our results suggest that transferring knowledge from language into vision can help machines understand why people in images might be performing an action.Comment: CVPR 201

Precautionary savings and the importance of business owners

In this paper, we show the pivotal role business owners play in estimating the importance of the precautionary saving motive. The fact that business owners hold higher-than-average wealth while facing higher income risk than other households leads to a correlation between wealth and labor income risk regardless of whether or not a precautionary motive is important. Using data from the Panel Study of Income Dynamics in the 1980s and the 1990s, we show that within separate samples of both business owners and non-business owners the size of precautionary savings with respect to labor income risk is modest and accounts for less than ten percent of total household wealth. However, pooling together these two groups leads to an artificially high estimate of the importance of precautionary savings. Data from the Survey of Consumer Finances further confirms that precautionary savings account for less than ten percent of total wealth for both business owners and non-business owners. Thus, while a precautionary saving motive exists and affects all households, it does not give rise to high amounts of wealth in the economy, particularly among those households who face the most volatile labor earnings. Klassifizierung: D9

Interpreting Deep Visual Representations via Network Dissection

The success of recent deep convolutional neural networks (CNNs) depends on learning hidden representations that can summarize the important factors of variation behind the data. However, CNNs often criticized as being black boxes that lack interpretability, since they have millions of unexplained model parameters. In this work, we describe Network Dissection, a method that interprets networks by providing labels for the units of their deep visual representations. The proposed method quantifies the interpretability of CNN representations by evaluating the alignment between individual hidden units and a set of visual semantic concepts. By identifying the best alignments, units are given human interpretable labels across a range of objects, parts, scenes, textures, materials, and colors. The method reveals that deep representations are more transparent and interpretable than expected: we find that representations are significantly more interpretable than they would be under a random equivalently powerful basis. We apply the method to interpret and compare the latent representations of various network architectures trained to solve different supervised and self-supervised training tasks. We then examine factors affecting the network interpretability such as the number of the training iterations, regularizations, different initializations, and the network depth and width. Finally we show that the interpreted units can be used to provide explicit explanations of a prediction given by a CNN for an image. Our results highlight that interpretability is an important property of deep neural networks that provides new insights into their hierarchical structure.Comment: *B. Zhou and D. Bau contributed equally to this work. 15 pages, 27 figure