Promoting sex and gender inclusivity in the classroom: A re-evaluation of discipline norms.

Creating curricula that are inclusive of sex and gender diverse students starts with redefining the classroom environment and how course materials are discussed. Such inclusive environments help increase diversity and representation in STEM. However, traditional approaches to teaching and the use of gendered language can reinforce unsupported notions regarding sex and gender. These notions persist throughout STEM and only serve to alienate our sex and gender diverse students and impact their safety. Thus, we have been exploring how best to approach the topics of sex and gender. Our context is in Biology, as our courses can explore the complexity of biological sex, why it is different from gender, and thus why transgender and non-binary individuals are part of natural variation instead of outliers. However, gendered language and misconceptions can appear in any discipline, in the terminology, metaphors, and examples given in class. In addition, all disciplines must consider how best to foster inclusivity among their students. In this presentation, we invite participants to share experiences and concerns regarding inclusivity in their classes, discuss case studies, and brainstorm with each other on how best to incorporate sex and gender awareness in their disciplines. Participants will come away with an appreciation for why sex and gender inclusivity is important, a framework for applying sex and gender inclusivity to their own curricula, a desire to learn more, and resources to help them on their journey

Correlated Rounding of Multiple Uniform Matroids and Multi-Label Classification

We introduce correlated randomized dependent rounding where, given multiple points y^1,...,y^n in some polytope Psubseteq [0,1]^k, the goal is to simultaneously round each y^i to some integral z^i in P while preserving both marginal values and expected distances between the points. In addition to being a natural question in its own right, the correlated randomized dependent rounding problem is motivated by multi-label classification applications that arise in machine learning, e.g., classification of web pages, semantic tagging of images, and functional genomics. The results of this work can be summarized as follows: (1) we present an algorithm for solving the correlated randomized dependent rounding problem in uniform matroids while losing only a factor of O(log{k}) in the distances (k is the size of the ground set); (2) we introduce a novel multi-label classification problem, the metric multi-labeling problem, which captures the above applications. We present a (true) O(log{k})-approximation for the general case of metric multi-labeling and a tight 2-approximation for the special case where there is no limit on the number of labels that can be assigned to an object

An Automated Room Temperature Flip-Chip Mounting Process for Hybrid Printed Electronics

Printing technology and mounting technology enable the novel field of hybrid printed electronics. To establish a hybrid printed system, one challenge is that the applied mounting process meets the requirements of functional inks and substrates. One of the most common requirements is low process temperature. Many functional inks and substrates cannot withstand the high temperatures required by traditional mounting processes. In this work, a standardized interconnection and an automated bump-less flip-chip mounting process using a room temperature curing conductive adhesive are realised. With the proposed process, the conductive adhesive selected for the standardized interconnection can be dispensed uniformly, despite its increase of viscosity already during pot time. Electrical and mechanical performance of the interconnection are characterized by four terminal resistance measurement and shear test. The herein proposed automated process allows for fabrication of hybrid printed devices in larger batch sizes than manual assembly processes used beforehand and thus, more comprehensive evaluation of device parameters. This is successfully demonstrated in a first application, a novel hybrid printed security device. The room temperature mounting process eliminates any potentially damaging thermal influence on the performance of the printed circuits that might result from other assembly techniques like soldering

Combining subspace approach and short time Fourier analysis for locating structural damage storeys

This study proposes a novel and efficient approach for locating the floors of a building whose stiffnesses change after being subject to a strong earthquake. The floors that may be damaged are determined by comparing the unitary stiffness matrix in different stages in the life cycle of a building. To evaluate the coefficient matrices of a state-space model, the proposed procedure applies a subspace approach in conjunction with the short time Fourier analysis. The dynamic characteristics of a structure are determined from the coefficient matrices. Next, the unitary stiffness matrix is constructed by identifying natural frequencies and mode shapes. The effectiveness of the proposed procedure is verified using the measured earthquake acceleration responses of a three-storey structure that sustains damage on one or two floors and an eight-storey steel frame under a 200 Gal and a 1200 Gal earthquake, such as the Chi-chi earthquake that shook Taiwan on September 1999. The proposed scheme is compared to mode shape based approaches in identifying damaged floors, and is demonstrated to be superior to both MAC (Modal Assurance Criterion) and COMAC (Coordinate Modal Assurance Criterion)

Systematic Investigation of Novel, Controlled Low‐Temperature Sintering Processes for Inkjet Printed Silver Nanoparticle Ink

Functional inks enable manufacturing of flexible electronic devices by means of printing technology. Silver nanoparticle (Ag NP) ink is widely used for printing conductive components. A sintering process is required to obtain sufficient conductivity. Thermal sintering is the most commonly used method, but the heat must be carefully applied to avoid damaging low-temperature substrates such as polymer films. In this work, two alternative sintering methods, damp heat sintering and water sintering are systematically investigated for inkjet-printed Ag tracks on polymer substrates. Both methods allow sintering polyvinyl pyrrolidone (PVP) capped Ag NPs at 85°C. In this way, the resistance is significantly reduced to only 1.7 times that of the samples on polyimide sintered in an oven at 250°C. The microstructure of sintered Ag NPs is analyzed. Taking the states of the capping layer under different conditions into account, the explanation of the sintering mechanism of Ag NPs at low temperatures is presented. Overall, both damp heat sintering and water sintering are viable options for achieving high conductivity of printed Ag tracks. They can broaden the range of substrates available for flexible electronic device fabrication while mitigating substrate damage risks. The choice between them depends on the specific application and the substrate used

Hybrid low-voltage physical unclonable function based on inkjet-printed metal-oxide transistors

Modern society is striving for digital connectivity that demands information security. As an emerging technology, printed electronics is a key enabler for novel device types with free form factors, customizability, and the potential for large-area fabrication while being seamlessly integrated into our everyday environment. At present, information security is mainly based on software algorithms that use pseudo random numbers. In this regard, hardware-intrinsic security primitives, such as physical unclonable functions, are very promising to provide inherent security features comparable to biometrical data. Device-specific, random intrinsic variations are exploited to generate unique secure identifiers. Here, we introduce a hybrid physical unclonable function, combining silicon and printed electronics technologies, based on metal oxide thin film devices. Our system exploits the inherent randomness of printed materials due to surface roughness, film morphology and the resulting electrical characteristics. The security primitive provides high intrinsic variation, is non-volatile, scalable and exhibits nearly ideal uniqueness

Deep Learning with Coherent VCSEL Neural Networks

Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain with high clock rates, parallelism and low-loss data transmission. However, to explore the potential of ONNs, it is necessary to investigate the full-system performance incorporating the major DNN elements, including matrix algebra and nonlinear activation. Existing challenges to ONNs are high energy consumption due to low electro-optic (EO) conversion efficiency, low compute density due to large device footprint and channel crosstalk, and long latency due to the lack of inline nonlinearity. Here we experimentally demonstrate an ONN system that simultaneously overcomes all these challenges. We exploit neuron encoding with volume-manufactured micron-scale vertical-cavity surface-emitting laser (VCSEL) transmitter arrays that exhibit high EO conversion (<5 attojoule/symbol with $V_\pi$=4 mV), high operation bandwidth (up to 25 GS/s), and compact footprint (<0.01 mm$^2$ per device). Photoelectric multiplication allows low-energy matrix operations at the shot-noise quantum limit. Homodyne detection-based nonlinearity enables nonlinear activation with instantaneous response. The full-system energy efficiency and compute density reach 7 femtojoules per operation (fJ/OP) and 25 TeraOP/(mm$^2\cdot$ s), both representing a >100-fold improvement over state-of-the-art digital computers, with substantially several more orders of magnitude for future improvement. Beyond neural network inference, its feature of rapid weight updating is crucial for training deep learning models. Our technique opens an avenue to large-scale optoelectronic processors to accelerate machine learning tasks from data centers to decentralized edge devices.Comment: 10 pages, 5 figure

Is telomere length socially patterned? Evidence from the West of Scotland Twenty-07 study

Lower socioeconomic status (SES) is strongly associated with an increased risk of morbidity and premature mortality, but it is not known if the same is true for telomere length, a marker often used to assess biological ageing. The West of Scotland Twenty-07 Study was used to investigate this and consists of three cohorts aged approximately 35 (N = 775), 55 (N = 866) and 75 years (N = 544) at the time of telomere length measurement. Four sets of measurements of SES were investigated: those collected contemporaneously with telomere length assessment, educational markers, SES in childhood and SES over the preceding twenty years. We found mixed evidence for an association between SES and telomere length. In 35-year-olds, many of the education and childhood SES measures were associated with telomere length, i.e. those in poorer circumstances had shorter telomeres, as was intergenerational social mobility, but not accumulated disadvantage. A crude estimate showed that, at the same chronological age, social renters, for example, were nine years (biologically) older than home owners. No consistent associations were apparent in those aged 55 or 75. There is evidence of an association between SES and telomere length, but only in younger adults and most strongly using education and childhood SES measures. These results may reflect that childhood is a sensitive period for telomere attrition. The cohort differences are possibly the result of survival bias suppressing the SES-telomere association; cohort effects with regard different experiences of SES; or telomere possibly being a less effective marker of biological ageing at older ages

Accessible Data Curation and Analytics for International-Scale Citizen Science Datasets

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. Over 4.7 million participants and 189 million unique assessments have been logged since its introduction in March 2020. The success of the Covid Symptom Study creates technical challenges around effective data curation for two reasons. Firstly, the scale of the dataset means that it can no longer be easily processed using standard software on commodity hardware. Secondly, the size of the research group means that replicability and consistency of key analytics used across multiple publications becomes an issue. We present ExeTera, an open source data curation software designed to address scalability challenges and to enable reproducible research across an international research group for datasets such as the Covid Symptom Study dataset