El humanismo no protege: Las memorias escolares de Alfred Andersch

Se interpretan dos fuentes de memorias escolares, los recuerdos del escritor Alfred Andersch, que los condensa en su novela «El padre de un asesino», y las memorias personales del autor. Ambientadas ambas memorias en el mismo instituto de educación secundaria en Múnich, constituyen un material idóneo para realizar una comparación histórica, sobre todo en referencia al contexto entre estructuras de instituciones educativas, la personalidad de sus profesores y acontecimientos históricos. Aunque algunos elementos del autoritarismo del año 1928 y de la época dictatorial posterior seguían vivos en 1978, se puede constatar que el paso del tiempo había tenido sus efectos sobre las normas que determinaban el comportamiento de los profesores, en el sentido de que las acciones agresivas de profesores contra sus alumnos en 1978 fueron más limitadas que en 1928. Además, queda la sensación de que las condiciones de una sociedad enferma se reflejan en su sistema escolar y de que los fenómenos de autoritarismo, antisemitismo y frustración social, presentes en 1928, constituyeron un caldo de cultivo para originar la catástrofe histórica del holocausto.Two sources of school memoirs are interpreted, the records of the writer Alfred Andersch, who condenses them in his novel “The Father of a Murderer”, and the personal memoirs of the author.Both memoirs refer to the same secondary school in Munich and constitute an ideal material for a historical comparison, above of all in reference to the context between the structure of educational institutions, the personality of their teachers and historical events. Even though some elements of the authoritarianism in 1928 and the following dictatorship were still alive in 1978, the course of time had had its impact on the rules, which determined the teachers’ behaviour, in the sense that aggressive actions of teachers against their students were more limited in 1978 than in 1928. Furthermore, the impression remains that the conditions of a sick society are reflected in its school system and that the phenomena of authoritarianism, anti-Semitism and social frustration, which were present in 1928, constituted a fertile soil in order to cause the historical catastrophe of the holocaust

Bone Densitometry of the Femoral Midshaft the Protein-Deprived Rat

Densitometric assessment of radiographs of the femoral midshafts in protein-deprived and age-matched control rats, has shown a significant loss of total bone density in the protein-deprived group. This reduction is no greater than can be accounted for by the loss of cortical bone surface area, suggesting that while bone mass is reduced as a result of protein deprivation, the mineral composition of the residual bone is likely to be normal. These findings are supported by data on the ash content of extirpated bone in the same group of animals.S. Afr. Med. J., 47, 1 (1973

The type I insulin-like growth factor regulates the liver stromal response to metastatic colon carcinoma cells.

Hepatic stellate cells (HSC) play a major role in initiating the liver fibrogenic (wounding) response of the liver and can also orchestrate a pro-metastatic microenvironment in the liver in response to invading cancer cells. Here we explored the role of the hepatic stellate cells in colon carcinoma liver metastasis with emphasis on the contribution of the insulin-like growth factor (IGF) axis to their activation and function. To this end, we used mice with a Tamoxifen inducible liver IGF-I deficiency. We found that in mice with a sustained IGF-I deficiency, recruitment and activation of HSC into tumor-infiltrated areas of the liver were markedly diminished, resulting in decreased collagen deposition and reduced tumor expansion. In addition, IGF-I could rescue HSC from apoptosis induced by pro-inflammatory factors such as TNF-α known to be upregulated in the early stages of liver metastasis. Moreover, in surgical specimens, activated IGF-IR was observed on HSC-like stromal cells surrounding colorectal carcinoma liver metastases. Finally, IGF-targeting in vivo using an IGF-Trap caused a significant reduction in HSC activation in response to metastatic colon cancer cells. Therefore, our data identify IGF as a survival factor for HSC and thereby, a promoter of the pro-metastatic microenvironment in the liver. IGF-targeting could therefore provide a strategy for curtailing the pro-metastatic host response of the liver during the early stages of liver metastasis

Polarized consensus-based dynamics for optimization and sampling

In this paper we propose polarized consensus-based dynamics in order to make consensus-based optimization (CBO) and sampling (CBS) applicable for objective functions with several global minima or distributions with many modes, respectively. For this, we “polarize” the dynamics with a localizing kernel and the resulting model can be viewed as a bounded confidence model for opinion formation in the presence of common objective. Instead of being attracted to a common weighted mean as in the original consensus-based methods, which prevents the detection of more than one minimum or mode, in our method every particle is attracted to a weighted mean which gives more weight to nearby particles. We prove that in the mean-field regime the polarized CBS dynamics are unbiased for Gaussian targets. We also prove that in the zero temperature limit and for sufficiently well-behaved strongly convex objectives the solution of the Fokker–Planck equation converges in the Wasserstein-2 distance to a Dirac measure at the minimizer. Finally, we propose a computationally more efficient generalization which works with a predefined number of clusters and improves upon our polarized baseline method for high-dimensional optimization

Polarized consensus-based dynamics for optimization and sampling

In this paper we propose polarized consensus-based dynamics in order to make consensus-based optimization (CBO) and sampling (CBS) applicable for objective functions with several global minima or distributions with many modes, respectively. For this, we ``polarize'' the dynamics with a localizing kernel and the resulting model can be viewed as a bounded confidence model for opinion formation in the presence of common objective. Instead of being attracted to a common weighted mean as in the original consensus-based methods, which prevents the detection of more than one minimum or mode, in our method every particle is attracted to a weighted mean which gives more weight to nearby particles. We prove that in the mean-field regime the polarized CBS dynamics are unbiased for Gaussian targets. We also prove that in the zero temperature limit and for sufficiently well-behaved strongly convex objectives the solution of the Fokker--Planck equation converges in the Wasserstein-2 distance to a Dirac measure at the minimizer. Finally, we propose a computationally more efficient generalization which works with a predefined number of clusters and improves upon our polarized baseline method for high-dimensional optimization.Comment: Added mean-field convergence theore

Adversarial flows: A gradient flow characterization of adversarial attacks

A popular method to perform adversarial attacks on neuronal networks is the so-called fast gradient sign method and its iterative variant. In this paper, we interpret this method as an explicit Euler discretization of a differential inclusion, where we also show convergence of the discretization to the associated gradient flow. To do so, we consider the concept of p-curves of maximal slope in the case $p=\infty$. We prove existence of $\infty$-curves of maximum slope and derive an alternative characterization via differential inclusions. Furthermore, we also consider Wasserstein gradient flows for potential energies, where we show that curves in the Wasserstein space can be characterized by a representing measure on the space of curves in the underlying Banach space, which fulfill the differential inclusion. The application of our theory to the finite-dimensional setting is twofold: On the one hand, we show that a whole class of normalized gradient descent methods (in particular signed gradient descent) converge, up to subsequences, to the flow, when sending the step size to zero. On the other hand, in the distributional setting, we show that the inner optimization task of adversarial training objective can be characterized via $\infty$-curves of maximum slope on an appropriate optimal transport space