one setup is more effective than another. In this work, we uncover the underlying characteristics of effective sets. For each training instance, we form a large, diverse pool of candidates and systematically vary the subsets used for calibration fine-tuning. Each selection strategy targets distinct aspects of the sets, such as lexical diversity or the size of the gap between positive and negatives. On three diverse scientific long-form summarization datasets (spanning biomedical, clinical, and chemical domains), we find, among others, that faithfulness calibration is optimal when the negative sets are extractive and more likely to be generated, whereas for relevance calibration, the metric margin between candidates should be maximized and surprise-the disagreement between model and metric defined candidate rankings-minimized. Code to create, select, and optimize calibration sets is available at https://github.com/griff4692/calibrating-summaries.

This paper presents Eclipse, a platform for low-cost urban environmental sensing using solar-powered and cellular-connected devices. Dense sensor networks promise to monitor pollution at fine spatial and temporal resolutions, yet few cities have actually implemented such networks due to high costs and limited accuracy. We address these barriers by developing an end-to-end framework for urban air quality sensing with minimal infrastructure requirements. We designed an unobtrusive device that collects data on fine particulate matter (PM2.5), temperature, relative humidity, and barometric pres-sure. A modular design further includes four low-cost gas sensors - Ozone (03), Nitrogen Dioxide (NO2), Sulfur Dioxide (SO2), and Carbon Monoxide (CO) - selected based on local priorities. We deployed 115 devices across Chicago, reliably collecting data for over 90% of expected sensor-hours from July 2 - September 30, 2021. We further developed a calibration strategy that reduced errors by 41.2 – 98.8%, improving accuracy to levels recommended for hotspot detection (PM2.5and 03) or education (NO2and SO2). Through this work, we offer insights on the real-world deployment of a replicable, large-scale, end-to-end platform for hyperlocal urban environmental sensing.

Year-round ultraviolet exposure silently causes skin damage that goes unnoticed until sunburn. Current personal wearables for monitoring UV exposure have not seen significant uptake, which may be attributed to their one-size-fits-all aesthetic or inapplicability to people with different skin tones. We present EcoPatches, inkjet-printable chemical patches that mediate a person's relationship with their environment by allowing them to create designs and formulations that resonate with them. Supporting human- and machine-interpretability for EcoPatches' visual changes means that users can glance at their EcoPatch during the day to see large exposure changes or take a picture of their EcoPatch with a smartphone app for more accurate and precise readings. We conducted an online survey to elicit visual design recommendations that support these features. We also evaluated both interpretation methods, finding that they achieved strong Pearson correlation coefficients with the \projectnames' known exposure levels (human: 0.79, app: 0.90).

Microfluidic devices promise to automate wetlab procedures by manipulating small chemical or biological samples. This technology comes in many varieties, all of which aim to save time, labor, and supplies by performing lab protocol steps typically done by a technician. However, existing microfluidic platforms remain some combination of inflexible, error-prone, prohibitively expensive, and difficult to program. We address these concerns with a full-stack digital microfluidic automation platform. Our main contribution is a runtime system that provides a high-level API for microfluidic manipulations. It manages fluidic resources dynamically, allowing programmers to freely mix regular computation with microfluidics, which results in more expressive programs than previous work. It also provides real-time error correction through a computer vision system, allowing robust execution on cheaper microfluidic hardware. We implement our stack on top of a low-cost droplet microfluidic device that we have developed. We evaluate our system with the fully-automated execution of polymerase chain reaction (PCR) and a DNA sequencing preparation protocol. These protocols demonstrate high-level programs that combine computational and fluidic operations such as input/output of reagents, heating of samples, and data analysis. We also evaluate the impact of automatic error correction on our system's reliability.

Microfluidic devices promise to automate wetlab procedures by manipulating small chemical or biological samples. We are developing a full-stack microfluidic automation platform that allows and allows users to scale up the complexity of microfluidic programming, encouraging them to mix fluidic manipulations with traditional programming. Puddle is a runtime system that provides a high-level API for microfluidic manipulations. It manages fluidic resources dynamically, allowing programmers to freely mix regular computation with microfluidics, resulting in more expressive programs. It also provides real-time error correction through a computer vision system, allowing robust execution on cheaper digital microfluidic hardware. We have been running Puddle on PurpleDrop, a new digital microfluidic device that is affordable and has novel features such as fully automated input/output of fluids. With this combination, we have demonstrated PCR with automated replenishment, a DNA sequencing preparation protocol, and the complete retrieval of digital data stored in dehydrated spots of DNA on the device's surface. Going forward, we see Puddle and PurpleDrop as part of a platform for further research. PurpleDrop is affordable and extensible, which makes a compelling case for adding new periferials or even scaling out by connecting multiple devices. And Puddle provides a flexible and abstract programming model that could enable microfluidic programs to run on different hardware targets (DMF or liquid handling robots), or even a combination thereof.