Self-explaining AI as an alternative to interpretable AI

The ability to explain decisions made by AI systems is highly sought after, especially in domains where human lives are at stake such as medicine or autonomous vehicles. While it is often possible to approximate the input-output relations of deep neural networks with a few human-understandable rules, the discovery of the double descent phenomena suggests that such approximations do not accurately capture the mechanism by which deep neural networks work. Double descent indicates that deep neural networks typically operate by smoothly interpolating between data points rather than by extracting a few high level rules. As a result, neural networks trained on complex real world data are inherently hard to interpret and prone to failure if asked to extrapolate. To show how we might be able to trust AI despite these problems we introduce the concept of self-explaining AI. Self-explaining AIs are capable of providing a human-understandable explanation of each decision along with confidence levels for both the decision and explanation. For this approach to work, it is important that the explanation actually be related to the decision, ideally capturing the mechanism used to arrive at the explanation. Finally, we argue it is important that deep learning based systems include a "warning light" based on techniques from applicability domain analysis to warn the user if a model is asked to extrapolate outside its training distribution. For a video presentation of this talk see https://www.youtube.com/watch?v=Py7PVdcu7WY& .Comment: 10pgs, 2 column forma

Optimal allocation and sizing of multi DG units including different load model using evolutionary programming

This paper presents the optimal allocation and sizing of multi distributed generation (DG) units including different load models using evolutionary programming (EP) in solving power system optimization problem. This paper also studies on the effect of multi DG placement in different load model. To optimize the power distribution system, multi DG units were used to reduce losses power distribution system. By using EP, the optimal allocation and sizing of multi-DG was determined in order to obtain maximum benefits from its installation. The propose technique was tested into IEEE 69-bus distribution system. The result shows the placement of DG can reduce power loss 89% to 98%. The placement of multi-DG unit has better performance compare to single DG

Spatial Analysis of the Needs of Green Open Space at Universitas Negeri Padang

The decreasing of environmental quality in padang is caused by the changes of the land, the increasing number of vehicles and the increasing number of population. The solution to overcome these problems is by providing a green open space at universitas negeri padang (unp). The objectives of this study are 1) to analyze the needs of green open space at unp, 2) to plan the construction of open green space at unp. The method employed in this study was survey by using spatial analysis remote sensing data from google earth. The results of the study revealed that unp had open green space as large as 7.643 ha. The area of green open space at unp that fulfilled the width criteria was as much as 10%, and the fulfillment of population and clean air criteria was as much as 20%. However, the minimum width criterion of green open space, which was as much as 30%, was not fulfilled yet. The discrepancy between the area of open green space and the criteria of minimum width (30%) was 0.447 ha. Such lack of green open space can be filled by: optimizing  the unoccupied land as large as 1.4 ha by planting the clump, providing 2308 flower's pots, and making use of building shelter and building lobbies, and campus corridor to be planted with clump, ornamental plants or other types of epiphytes and lianas.  &nbsp

The alternative RelB NF-κB subunit is a novel critical player in diffuse large B-cell lymphoma

: Diffuse large B-cell lymphoma (DLBCL) is the most frequent lymphoid malignancy affecting adults. The NF-κB transcription factor family is activated by 2 main pathways, the canonical and the alternative NF-κB activation pathway, with different functions. The alternative NF-κB pathway leads to activation of the transcriptionally active RelB NF-κB subunit. Alternative NF-κB activation status and its role in DLBCL pathogenesis remain undefined. Here, we reveal a frequent activation of RelB in a large cohort of DLBCL patients and cell lines, independently of their activated B-cell-like or germinal center B-cell-like subtype. RelB activity defines a new subset of patients with DLBCL and a peculiar gene expression profile and mutational pattern. Importantly, RelB activation does not correlate with the MCD genetic subtype, enriched for activated B-cell-like tumors carrying MYD88L265P and CD79B mutations that cooperatively activate canonical NF-κB, thus indicating that current genetic tools to evaluate NF-κB activity in DLBCL do not provide information on the alternative NF-κB activation. Furthermore, the newly defined RelB-positive subgroup of patients with DLBCL exhibits a dismal outcome after immunochemotherapy. Functional studies revealed that RelB confers DLBCL cell resistance to DNA damage-induced apoptosis in response to doxorubicin, a genotoxic agent used in the front-line treatment of DLBCL. We also show that RelB positivity is associated with high expression of cellular inhibitor of apoptosis protein 2 (cIAP2). Altogether, RelB activation can be used to refine the prognostic stratification of DLBCL and may contribute to subvert the therapeutic DNA damage response in a segment of patients with DLBCL