330,563 research outputs found
The Effect of Incorporating End-User Customization into Additive Manufacturing Designs
In the realm of additive manufacturing there is an increasing trend among makers to create designs that allow for end-users to alter them prior to printing an artifact. Online design repositories have tools that facilitate the creation of such artifacts. There are currently no rules for how to create a good customizable design or a way to measure the degree of customization within a design. This work defines three types of customizations found in additive manufacturing and presents three metrics to measure the degree of customization within designs based on the three types of customization. The goal of this work is to ultimately provide a consistent basis for which a customizable design can be evaluated in order to assist makers in the creation of new customizable designs that can better serve end-user. The types of customization were defined by doing a search of Thingiverse’s online data base of customizable designs and evaluating commonalities between designs. The three types of customization defined by this work are surface, structure, and personal customization. The associated metrics are used to quantify the adjustability of a set of online designs which are then plot against the daily use rate and each other on separate graphs. The use rate data used in this study is naturally biased towards hobbyists due to where the designs used to create the data resides. A preliminary analysis is done on the metrics to evaluate their correlation with design use rate as well as the dependency of the metrics in relation to each other. The trends between the metrics are examined for an idea of how best to provide customizable designs. This work provides a basis for measuring the degree of customization within additive manufacturing design and provides an initial framework for evaluating the usability of designs based on the measured degree of customization relative to the three types of defined customizations
Optimal modeling for complex system design
The article begins with a brief introduction to the theory describing optimal data compression systems and their performance. A brief outline is then given of a representative algorithm that employs these lessons for optimal data compression system design. The implications of rate-distortion theory for practical data compression system design is then described, followed by a description of the tensions between theoretical optimality and system practicality and a discussion of common tools used in current algorithms to resolve these tensions. Next, the generalization of rate-distortion principles to the design of optimal collections of models is presented. The discussion focuses initially on data compression systems, but later widens to describe how rate-distortion theory principles generalize to model design for a wide variety of modeling applications. The article ends with a discussion of the performance benefits to be achieved using the multiple-model design algorithms
Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks
Under the epidemic of the novel coronavirus disease 2019 (COVID-19), chest
X-ray computed tomography imaging is being used for effectively screening
COVID-19 patients. The development of computer-aided systems based on deep
neural networks (DNNs) has been advanced, to rapidly and accurately detect
COVID-19 cases, because the need for expert radiologists, who are limited in
number, forms a bottleneck for the screening. However, so far, the
vulnerability of DNN-based systems has been poorly evaluated, although DNNs are
vulnerable to a single perturbation, called universal adversarial perturbation
(UAP), which can induce DNN failure in most classification tasks. Thus, we
focus on representative DNN models for detecting COVID-19 cases from chest
X-ray images and evaluate their vulnerability to UAPs generated using simple
iterative algorithms. We consider nontargeted UAPs, which cause a task failure
resulting in an input being assigned an incorrect label, and targeted UAPs,
which cause the DNN to classify an input into a specific class. The results
demonstrate that the models are vulnerable to nontargeted and targeted UAPs,
even in case of small UAPs. In particular, 2% norm of the UPAs to the average
norm of an image in the image dataset achieves >85% and >90% success rates for
the nontargeted and targeted attacks, respectively. Due to the nontargeted
UAPs, the DNN models judge most chest X-ray images as COVID-19 cases. The
targeted UAPs make the DNN models classify most chest X-ray images into a given
target class. The results indicate that careful consideration is required in
practical applications of DNNs to COVID-19 diagnosis; in particular, they
emphasize the need for strategies to address security concerns. As an example,
we show that iterative fine-tuning of the DNN models using UAPs improves the
robustness of the DNN models against UAPs.Comment: 17 pages, 5 figures, 3 table
On the Origins and Control of Community Types in the Human Microbiome
Microbiome-based stratification of healthy individuals into compositional
categories, referred to as "community types", holds promise for drastically
improving personalized medicine. Despite this potential, the existence of
community types and the degree of their distinctness have been highly debated.
Here we adopted a dynamic systems approach and found that heterogeneity in the
interspecific interactions or the presence of strongly interacting species is
sufficient to explain community types, independent of the topology of the
underlying ecological network. By controlling the presence or absence of these
strongly interacting species we can steer the microbial ecosystem to any
desired community type. This open-loop control strategy still holds even when
the community types are not distinct but appear as dense regions within a
continuous gradient. This finding can be used to develop viable therapeutic
strategies for shifting the microbial composition to a healthy configurationComment: Main Text, Figures, Methods, Supplementary Figures, and Supplementary
Tex
Recommended from our members
Web navigation for individuals with dyslexia: An exploratory study
In this paper, we present an exploratory study of the web navigation experiences of dyslexic users. Findings indicate that dyslexics exhibit distinctive web navigation behaviour and preferences. We believe that the outcomes of this study add to our understanding of the particular needs of this web user population and have implications for the design of effective navigation structures
- …