937 research outputs found
Keyed Non-Parametric Hypothesis Tests
The recent popularity of machine learning calls for a deeper understanding of
AI security. Amongst the numerous AI threats published so far, poisoning
attacks currently attract considerable attention. In a poisoning attack the
opponent partially tampers the dataset used for learning to mislead the
classifier during the testing phase.
This paper proposes a new protection strategy against poisoning attacks. The
technique relies on a new primitive called keyed non-parametric hypothesis
tests allowing to evaluate under adversarial conditions the training input's
conformance with a previously learned distribution . To do so we
use a secret key unknown to the opponent.
Keyed non-parametric hypothesis tests differs from classical tests in that
the secrecy of prevents the opponent from misleading the keyed test
into concluding that a (significantly) tampered dataset belongs to
.Comment: Paper published in NSS 201
Novel electro-optical phase modulator based on GaInAs/InP modulation-doped quantum-well structures
Indirect Low-Intensity Ultrasonic Stimulation for Tissue Engineering
Low-intensity ultrasound (LIUS) treatment has been shown to increase mass transport, which could benefit tissue grafts during the immediate postimplant period, when blood supply to the implanted tissue is suboptimal. In this in vitro study, we investigated effects of LIUS stimulation on dye diffusion, proliferation, metabolism, and tropomyosin expression of muscle cells (C2C12) and on tissue viability and gene expression of human adipose tissue organoids.
We found that LIUS increased dye diffusion within adjacent tissue culture wells and caused anisotropic diffusion patterns. This effect was confirmed by a hydrophone measurement resulting in acoustic pressure 150–341 Pa in wells. Cellular studies showed that LIUS significantly increased proliferation, metabolic activity, and expression of tropomyosin. Adipose tissue treated with LIUS showed significantly increased metabolic activity and the cells had similar morphology to normal unilocular adipocytes. Gene analysis showed that tumor necrosis factor-alpha expression (a marker for tissue damage) was significantly lower for stimulated organoids than for control groups. Our data suggests that LIUS could be a useful modality for improving graft survival in vivo
The evolutionary emergence of stochastic phenotype switching in bacteria
Stochastic phenotype switching – or bet hedging – is a pervasive feature of living systems and common in bacteria that experience fluctuating (unpredictable) environmental conditions. Under such conditions, the capacity to generate variable offspring spreads the risk of being maladapted in the present environment, against offspring likely to have some chance of survival in the future. While a rich subject for theoretical studies, little is known about the selective causes responsible for the evolutionary emergence of stochastic phenotype switching. Here we review recent work – both theoretical and experimental – that sheds light on ecological factors that favour switching types over non-switching types. Of particular relevance is an experiment that provided evidence for an adaptive origin of stochastic phenotype switching by subjecting bacterial populations to a selective regime that mimicked essential features of the host immune response. Central to the emergence of switching types was frequent imposition of ‘exclusion rules’ and ‘population bottlenecks’ – two complementary faces of frequency dependent selection. While features of the immune response, exclusion rules and bottlenecks are likely to operate in many natural environments. Together these factors define a set of selective conditions relevant to the evolution of stochastic switching, including antigenic variation and bacterial persistence
Measuring quality and outcomes of research collaborations: An integrative review
Introduction: Although the science of team science is no longer a new field, the measurement of team science and its standardization remain in relatively early stages of development. To describe the current state of team science assessment, we conducted an integrative review of measures of research collaboration quality and outcomes.
Methods: Collaboration measures were identified using both a literature review based on specific keywords and an environmental scan. Raters abstracted details about the measures using a standard tool. Measures related to collaborations with clinical care, education, and program delivery were excluded from this review.
Results: We identified 44 measures of research collaboration quality, which included 35 measures with reliability and some form of statistical validity reported. Most scales focused on group dynamics. We identified 89 measures of research collaboration outcomes; 16 had reliability and 15 had a validity statistic. Outcome measures often only included simple counts of products; publications rarely defined how counts were delimited, obtained, or assessed for reliability. Most measures were tested in only one venue.
Conclusions: Although models of collaboration have been developed, in general, strong, reliable, and valid measurements of such collaborations have not been conducted or accepted into practice. This limitation makes it difficult to compare the characteristics and impacts of research teams across studies or to identify the most important areas for intervention. To advance the science of team science, we provide recommendations regarding the development and psychometric testing of measures of collaboration quality and outcomes that can be replicated and broadly applied across studies
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