Digestive hemorrhage secondary to fistula between hepatic artery and jejunal stump following duodenocephalopancreasectomy. A clinical case

The authors describe a case of one patient who underwent duodenocephalopancreasectomy for CBD cancer 6 years ago and developed gastrointestinal bleeding caused by a fistula between the hepatic artery and the jejunal stump. They discuss the differential diagnosis problem in the gastrointestinal bleeding syndrome caused by this rare pathology.     

Cryptosporidium parvum initiates inflammatory bowel disease in germfree T cell receptor-alpha-deficient mice

Flora-bearing mice with targeted disruption of T cell receptor (TCR)-alpha or -beta genes spontaneously develop intestinal inflammation with features similar to ulcerative colitis in humans. TCR-alpha-deficient mice maintained germfree or colonized with a limited number of intestinal bacteria failed to develop inflammatory bowel disease (IBD)-like lesions. Evidently, inflammation in these mice does not develop spontaneously or result from a generalized antigenic stimulation, but rather requires induction by a heretofore unidentified specific stimulus. We describe the development of IBD-like lesions in germfree TCR-alpha-deficient mice monoassociated with the protozoan Cryptosporidium parvum. Lesions were seen in distal ileum, cecum, and colon and were most severe in the cecum. A prominent leukocytic infiltrate within the lamina propria was a common characteristic of the lesions observed in the C. parvum-infected germfree TCR-alpha-deficient mice. The leukocytic infiltrate was composed of aggregates of B220+ cells, the majority of which expressed surface IgD (ie, conventional B lymphocytes). It has been proposed that antigenic stimulation by a microorganism(s) is needed to initiate intestinal inflammation in TCR-alpha-deficient mice. Our results indicate that a single microbial species, C. parvum, is capable of triggering the development of IBD-like lesions in germfree TCR-alpha-deficient mice.     

On the evolution of homogeneous two-robot teams: clonal versus aclonal approaches

This study compares two different evolutionary approaches (clonal and aclonal) to the design of homogeneous two-robot teams (i.e. teams of morphologically identical agents with identical controllers) in a task that requires the agents to specialise to different roles. The two approaches differ mainly in the way teams are formed during evolution. In the clonal approach, a team is formed from a single genotype within one population of genotypes. In the aclonal approach, a team is formed from multiple genotypes within one population of genotypes. In both cases, the goal is the synthesis of individual generalist controllers capable of integrating role execution and role allocation mechanisms for a team of homogeneous robots. Our results diverge from those illustrated in a similar comparative study, which supports the superiority of the aclonal versus the clonal approach. We question this result and its theoretical underpinning, and we bring new empirical evidence showing that the clonal outperforms the aclonal approach in generating homogeneous teams required to dynamically specialise for the benefit of the team. The results of our study suggest that task-specific elements influence the evolutionary dynamics more than the genetic relatedness of the team members. We conclude that the appropriateness of the clonal approach for role allocation scenarios is mainly determined by the specificity of the collective task, including the evaluation function, rather than by the way in which the solutions are evaluated during evolution.     

Geiringer theorems: from population genetics to computational intelligence, memory evolutive systems and Hebbian learning

The classical Geiringer theorem addresses the limiting frequency of occurrence of various alleles after repeated application of crossover. It has been adopted to the setting of evolutionary algorithms and, a lot more recently, reinforcement learning and Monte-Carlo tree search methodology to cope with a rather challenging question of action evaluation at the chance nodes. The theorem motivates novel dynamic parallel algorithms that are explicitly described in the current paper for the first time. The algorithms involve independent agents traversing a dynamically constructed directed graph that possibly has loops and multiple edges. A rather elegant and profound category-theoretic model of cognition in biological neural networks developed by a well-known French mathematician, professor Andree Ehresmann jointly with a neurosurgeon, Jan Paul Vanbremeersch over the last thirty years provides a hint at the connection between such algorithms and Hebbian learning.     

Combining stochastic programming and optimal control to decompose multistage stochastic optimization problems

The paper suggests a possible cooperation between stochastic programming and optimal control for the solution of multistage stochastic optimization problems. We propose a decomposition approach for a class of multistage stochastic programming problems in arborescent form (i.e. formulated with implicit non-anticipativity constraints on a scenario tree). The objective function of the problem can be either linear or nonlinear, while we require that the constraints are linear and involve only variables from two adjacent periods (current and lag 1). The approach is built on the following steps. First, reformulate the stochastic programming problem into an optimal control one. Second, apply a discrete version of Pontryagin maximum principle to obtain optimality conditions. Third, discuss and rearrange these conditions to obtain a decomposition that acts both at a time stage level and at a nodal level. To obtain the solution of the original problem we aggregate the solutions of subproblems through an enhanced mean valued fixed point iterative scheme.     

On the second-order sliding mode control of nonlinear systems with uncertain control direction

This note deals with the implementation of a second-order sliding mode control algorithm for a class of nonlinear systems in which the sign of the high-frequency gain, though constant, is unknown. A specific second-order sliding mode control algorithm, the “Suboptimal” algorithm, is properly modified in order to face the uncertainty in the control direction. It is shown that after a finite time the uncertain sign is identified and the standard finite time convergence takes place from that time on. Simulation results are provided.     

Feedforward networks in financial predictions: the future that modifies the present

The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test.     

"Residence times difference" fluxgate magnetometers

We present analytical and experimental results on fluxgate magnetometers that make use of a readout technique based on residence times. This approach allows for enhancing sensitivity to weak target signals in particular when the reduction of the sensor dimensions are considered. Our approach, exploiting the inherent nonlinear character of the bistable core dynamics, is based on the time domain characterization of the transitions between the two saturation states of the hysteresis loop that is inherent in the ferromagnetic core dynamics. This readout technique can be implemented with bias signals having lower amplitude and frequency than those used in conventional fluxgate processing schemes, thus reducing the device power requirements. The efficacy of this strategy is shown through an analytical approach and via experimental results which suggests guidelines for optimal device design and realization. The experiments have been carried out on a miniaturized laboratory fluxgate prototype; this device shows numerous desirable characteristics, including very good sensitivity and resolution, as well as ease of operation and a very low cost.