Right for the Right Reason: Training Agnostic Networks

We consider the problem of a neural network being requested to classify images (or other inputs) without making implicit use of a "protected concept", that is a concept that should not play any role in the decision of the network. Typically these concepts include information such as gender or race, or other contextual information such as image backgrounds that might be implicitly reflected in unknown correlations with other variables, making it insufficient to simply remove them from the input features. In other words, making accurate predictions is not good enough if those predictions rely on information that should not be used: predictive performance is not the only important metric for learning systems. We apply a method developed in the context of domain adaptation to address this problem of "being right for the right reason", where we request a classifier to make a decision in a way that is entirely 'agnostic' to a given protected concept (e.g. gender, race, background etc.), even if this could be implicitly reflected in other attributes via unknown correlations. After defining the concept of an 'agnostic model', we demonstrate how the Domain-Adversarial Neural Network can remove unwanted information from a model using a gradient reversal layer.Comment: Author's original versio

Bigger Than You Think: The Economic Impact of Microbusinesses in the U.S.

AEO embarked on a two-year study to build the data and the evidence base that documents the economic impact of microbusiness in the U.S. The evidence gathered paints a compelling portrait of a remarkably vigorous microbusiness community that plays an essential role in American economic productivity

Structural, electronic, and magnetic characteristics of Np_2Co_(17)

A previously unknown neptunium-transition-metal binary compound Np_2Co_(17) has been synthesized and characterized by means of powder x-ray diffraction, ^(237)Np Mössbauer spectroscopy, superconducting-quantum-interference-device magnetometry, and x-ray magnetic circular dichroism (XMCD). The compound crystallizes in a Th_2Ni_(17)-type hexagonal structure with room-temperature lattice parameters α=8.3107(1) Å and c=8.1058(1) Å. Magnetization curves indicate the occurrence of ferromagnetic order below T_C>350 K. Mössbauer spectra suggest a Np^(3+) oxidation state and give an ordered moment of μ_(Np)=1.57(4) μ_B and μ_(Np)=1.63(4) μ_B for the Np atoms located, respectively, at the 2b and 2d crystallographic positions of the P6_3/mmc space group. Combining these values with a sum-rule analysis of the XMCD spectra measured at the neptunium M_(4,5) absorption edges, one obtains the spin and orbital contributions to the site-averaged Np moment [μ_S=−1.88(9) μ_B, μ_L=3.48(9) μ_B]. The ratio between the expectation value of the magnetic-dipole moment and the spin magnetic moment (m_(md)/μS=+1.36) is positive as predicted for localized 5f electrons and lies between the values calculated in intermediate-coupling (IC) and jj approximations. The expectation value of the angular part of the spin-orbit-interaction operator is in excellent agreement with the IC estimate. The ordered moment averaged over the four inequivalent Co sites, as obtained from the saturation value of the magnetization, is μ_(Co)≃1.6 μ_B. The experimental results are discussed against the predictions of first-principles electronic-structure calculations based on the spin-polarized local-spin-density approximation plus the Hubbard interaction

In-ovo feeding with creatine monohydrate: implications for chicken energy reserves and breast muscle development during the pre-post hatching period

The most dynamic period throughout the lifespan of broiler chickens is the pre-post-hatching period, entailing profound effects on their energy status, survival rate, body weight, and muscle growth. Given the significance of this pivotal period, we evaluated the effect of in-ovo feeding (IOF) with creatine monohydrate on late-term embryos’ and hatchlings’ energy reserves and post-hatch breast muscle development. The results demonstrate that IOF with creatine elevates the levels of high-energy-value molecules (creatine and glycogen) in the liver, breast muscle and yolk sac tissues 48 h post IOF, on embryonic day 19 (p < 0.03). Despite this evidence, using a novel automated image analysis tool on day 14 post-hatch, we found a significantly higher number of myofibers with lower diameter and area in the IOF creatine group compared to the control and IOF NaCl groups (p < 0.004). Gene expression analysis, at hatch, revealed that IOF creatine group had significantly higher expression levels of myogenin (MYOG) and insulin-like growth factor 1 (IGF1), related to differentiation of myogenic cells (p < 0.01), and lower expression of myogenic differentiation protein 1 (MyoD), related to their proliferation (p < 0.04). These results imply a possible effect of IOF with creatine on breast muscle development through differential expression of genes involved in myogenic proliferation and differentiation. The findings provide valuable insights into the potential of pre-hatch enrichment with creatine in modulating post-hatch muscle growth and development

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

GOPHER, an HPC framework for large scale graph exploration and inference

Biological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to investigate the complex relationship that exists between the phenome and the genome. The interpretation of the encoded information requires methods that efficiently interoperate between multiple ontologies providing molecular details of disease-related features. To this aim, we present GenOtype PHenotype ExplOrer (GOPHER), a framework to infer associations between HPO and GO terms harnessing machine learning and large-scale parallelism and scalability in High-Performance Computing. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. GOPHER can improve the interpretation of molecular processes involved in pathological conditions, displaying a vast range of applications in biomedicine.This work has been developed with the support of the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015- 65316-P); and the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement.Peer ReviewedPostprint (author's final draft