The Hadamard Layer, a simple and computationally efficient way to improve results in semantic segmentation tasks, is presented. This layer has no free parameters that require to be trained. Therefore it does not increase the number of model parameters, and the extra computational cost is marginal. Experimental results show that the new Hadamard layer substantially improves the performance of the investigated models (variants of the Pix2Pix model). The performance's improvement can be explained by the Hadamard layer forcing the network to produce an internal encoding of the classes so that all bins are active. Therefore, the network computation is more distributed. In a sort that the Hadamard layer requires that to change the predicted class, it is necessary to modify $2^{k-1}$ bins, assuming $k$ bins in the encoding. A specific loss function allows a stable and fast training convergence.

Computing started in April 1936 with the invention of the first electronic computer by IBM. It gave new hope towards coding languages which was a previously unknown field. What if someone told you, the hunt for codification in languages started way back? Like many ancient Indian forgotten legends. One example is Panini's "Ashtadhyayi".

You would think something as numerical as income tax law would be similar to mathematical logic, but it is not, Protzenko says, because it is not written with the precision and clarity that would "make it amenable to a very mathematical reading of it." For example, that law does not mention a number may need to be rounded into whole cents. "The law won't tell you what you're supposed to do with rounding numbers and that can lead to ambiguity and a lack of specification of what's supposed to happen," he says. Healthcare law is also very complex. Faisal Khan, senior legal counsel at healthcare law firm Nixon Gwilt Law in Vienna, VA, says, "Software for HIPAA compliance must incorporate algorithms that target and hit on all the top-level statutory requirements and implementing regulations.' To make that happen, Khan says, "There must be a team of compliance-related input as many of the regulations essentially function as guidelines for companies to adhere to." That means a process or ...

A novel bioinspired metaheuristic is proposed in this work, simulating how the Coronavirus spreads and infects healthy people. From an initial individual (the patient zero), the coronavirus infects new patients at known rates, creating new populations of infected people. Every individual can either die or infect and, afterwards, be sent to the recovered population. Relevant terms such as re-infection probability, super-spreading rate or traveling rate are introduced in the model in order to simulate as accurately as possible the coronavirus activity. The Coronavirus Optimization Algorithm has two major advantages compared to other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability of ending after several iterations, without setting this value either. Infected population initially grows at an exponential rate but after some iterations, the high number recovered and dead people starts decreasing the number of infected people in new iterations. As application case, it has been used to train a deep learning model for electricity load forecasting, showing quite remarkable results after few iterations.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

Preference models lie at the core of the formalization for several related notions, such as non-monotonic reasoning,obligations, goals, beliefs, etc. Recently, the interest in integrating dynamic operators in the logics of belief, preference and obligation has gained momentum.This integration sheds light on similarities among several change operations traditionally studied independently of each other. While a prolific approach, important operations, such as the well-known contraction of beliefs or derogation of norms studied in the AGM tradition,have not received proper attention in this framework.In this work, we study codifications of contraction operations, stemming from the work on iterate dbelief change, in the logic of preferences, by means of both semantically defined operations and their counterpart in syntactical priority structures.

Codified narrative is the product of converting human-friendly narrative into computer-friendly code. In past blogs, I discussed my own approach towards this process of codification. Here, I will be covering the idea of spatial, temporal, and contextual distribution of codified narrative. I have never suggested that narrative can or should be used in place of quantitative data. However, I have reflected on how the quantitative regime has tended to dominate discourse; this has perhaps led to data being contextually constrained or deprived. Geography is a type of context that can shape the extent to which people interact with the world. Space provides a medium to distribute resources. It can be involved in forced confinement. An office full of cubicles demonstrates control and dominance over space.

In this chapter, we propose a new practical codification of the elements of the Venn diagram in order to easily manipulate the focal elements. In order to reduce the complexity, the eventual constraints must be integrated in the codification at the beginning. Hence, we only consider a reduced hyper power set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the steps of a general belief function framework. The step of decision is particularly studied, indeed, when we can decide on intersections of the singletons of the discernment space no actual decision functions are easily to use. Hence, two approaches are proposed, an extension of previous one and an approach based on the specificity of the elements on which to decide. The principal goal of this chapter is to provide practical codes of a general belief function framework for the researchers and users needing the belief function theory.