IRAC Deep Survey of COSMOS

Over the last four years, we have developed the COSMOS survey field with complete multi-wavelength coverage from radio to X-ray, including a total of 600 hours of Spitzer Legacy time (166 hours IRAC, 460 hours MIPS). Here we propose to deepen the IRAC 3.6 µm and 4.5 µm coverage with 3000 hours over 2.3 deg^2 area included in deep Subaru imaging. This extended mission deep survey will increase the sensitivity by a factor of 3–5. The most important impact will be that the COSMOS survey will then provide extremely sensitive photometric redshifts and stellar mass estimates for approximately a million galaxies out to z~6. We expect these data to detect approximately 1000 objects at z = 6 to 10. The data will also provide excellent temporal coverage for variability studies on timescales from days to the length of the extended mission

Using Multimodal LLM for Material Type and Emissivity Determination to Enhance Accuracy of Infrared Temperature Measurement

Smart devices equipped with infrared (IR) sensors offer convenient temperature measurement capabilities. However, the accuracy of temperature measurement using IR sensors is dependent on estimating the emissivity of the target surface. Currently, manual categorization of the object is necessary so that its material properties can be determined. Requiring such manual input is inconvenient and detracts from the user experience. This disclosure describes techniques that utilize a multimodal large language model (LLM) to automatically determine material type of an object whose temperature is to be determined. Per the techniques, an image of the object captured by a camera is provided to the multimodal LLM along with a suitable prompt instructing the LLM to determine the material type for the object. The LLM outputs the material type which is used in determining the temperature of the object based on infrared sensors. Alternatively, the LLM can be prompted to provide an emissivity estimate of the object directly and the emissivity estimate can be used to determine the object temperature. The techniques leverage the capability of a multimodal LLM of analyzing images to provide detailed information regarding an input image without requiring extensive training for various use cases

Clientele Impact for Beef Producers from a Grass-Roots Style of Extension Programming

The Alachua County Master Cattlemen program is developed for small beef producers to help increase profitability. Because small beef producers are at a disadvantage in marketing truck loads of cattle and retaining ownership, educational programs relating to beef cattle management are used to give producers tools to manage their cattle in order to become more profitable. As a result of these programs, a small beef cooperative has been formed to take advantage of marketing alternatives. This cooperative has shown a significant increase in price per pound received, and this has resulted in a cumulative economic impact of $42,500

Display Device for Wearable and Other Products

In a watch or other display device, the functionality of the device can be enhanced by using a display that pairs a transparent organic light-emitting diode (TOLED) layer with an underlying display, with the TOLED layer being placed on top of the underlying display instead of below it. There are two main embodiments: (a) one in which the display is a liquid crystal display (LCD) or other transparent display paired with a TOLED layer, and (b) one in which an electrophoretic display such as E-Ink or E-paper is paired with a TOLED layer. Additional layers can, of course, be added in other embodiments

Eliminating Mole Size in Melanoma Classification

While skin cancer classification has been a popular and valuable deep learning application for years, there has been little consideration of the context in which testing images are taken. Traditional melanoma classifiers rely on the assumption that their testing environments are analogous to the structured images on which they are trained. This paper combats this notion, arguing that mole size, a vital attribute in professional dermatology, is a red herring in automated melanoma detection. Although malignant melanomas are consistently larger than benign melanomas, this distinction proves unreliable and harmful when images cannot be contextually scaled. This implementation builds a custom model that eliminates size as a training feature to prevent overfitting to incorrect parameters. Additionally, random rotation and contrast augmentations are performed to simulate the real-world use of melanoma detection applications. Several custom models with varying forms of data augmentation are implemented to demonstrate the most significant features of the generalization abilities of mole classifiers. These implementations show that user unpredictability is crucial when utilizing such applications. The caution required when manually modifying data is acknowledged, as data loss and biased conclusions are necessary considerations in this process. Additionally, mole size inconsistency and its significance are discussed in both the dermatology and deep learning communities

Transcending the Attention Paradigm: Representation Learning from Geospatial Social Media Data

While transformers have pioneered attention-driven architectures as a cornerstone of language modeling, their dependence on explicitly contextual information underscores limitations in their abilities to tacitly learn overarching textual themes. This study challenges the heuristic paradigm of performance benchmarking by investigating social media data as a source of distributed patterns. In stark contrast to networks that rely on capturing complex long-term dependencies, models of online data inherently lack structure and are forced to detect latent structures in the aggregate. To properly represent these abstract relationships, this research dissects empirical social media corpora into their elemental components, analyzing over two billion tweets across population-dense locations. We create Bag-of-Word embedding specific to each city and compare their respective representations. This finds that even amidst noisy data, geographic location has a considerable influence on online communication, and that hidden insights can be uncovered without the crutch of advanced algorithms. This evidence presents valuable geospatial implications in social science and challenges the notion that intricate models are prerequisites for pattern recognition in natural language. This aligns with the evolving landscape that questions the embrace of absolute interpretability over abstract understanding and bridges the divide between sophisticated frameworks and intangible relationships

Towards a model policy for implementing ecoregional conservation in the Northern Appalachian/Acadian forest ecoregion

Using the conceptual framework of ecoregional conservation, this thesis provides an assessment of the current conservation frameworks operating within the Northern Appalachian/Acadian Forest ecoregion. The ecoregion, which covers three provinces, four states and two countries, stretches from the Adirondack Plateau in New York, through northern New England, the Eastern Townships and the Gaspé Peninsula of Québec, and includes all of New Brunswick and Nova Scotia. Much of the ecoregion was severely deforested and exploited in the first 400 years of European settlement and has now reached another crossroads in its evolution as the landscape is subjected to a further surge of human-induced stressors and habitat destruction. The impacts range from urban and rural sprawl, unsustainable resource extraction, and habitat fragmentation related to road construction and development. A policy analysis, combined with interviews with conservation practitioners, is used to identify strengths and weaknesses within the existing policy regime and to develop an alternative model policy for ecoregional conservation. This Model Policy addresses the impacts of land use conversion, unsustainable resource extraction, and facilitates cooperation across borders by defining the baseline biodiversity conditions of the ecoregion, regulating land use and development, assessing cumulative impacts, and designating a connected network of protected areas

An Exploratory Study into the Use of Psychology Participant Panels in Psychology Departments in the United Kingdom

Psychology Participant Pools (PPP) are known to be used within psychology departments in the United Kingdom as a way to promote understanding of psychological research and as a means to aid students and researchers to collect data. However, there is currently no information regarding the different practices undertaken in each department. This article represents a first exploration in this endeavour by asking representatives from these departments to complete a survey. General findings revealed that the number of studies conducted were either under 20 or over 40, Level 4 students had to obtain slightly more credits than Level 5 students, a range of activities were observed for those participants who did not obtain all their credits, and the PPP was more often than not tied to a research methods module. Despite receiving responses from around only a third of departments, the results revealed a wide range of behaviours across the departments. We feel that these are useful for departments who wish to establish, or update, their own PPP, but also recognise that a larger study is required to more accurately capture the use of PPPs in the United Kingdom

Post-license Education for Novice Drivers: Evaluation of a Training Programme implemented in Spain

Introduction: This study evaluated the implementation of a 2nd phase training programme for novice drivers in Spain, which puts the primary focus of the training on the higher hierarchical levels of driver behaviour. Method: Two hundred and sixty-three participants took part throughout the study, which was implemented as an experimental design with the test and control groups assessed before and after the one day safety training. Measurement of the impact of the training program focused on the participants¿ self-evaluation and self reporting of some driving behaviour indicators related to accident risk. Results: Data analysis showed a change in the expected direction in the scale related to the skills for careful driving, but not for the other four scales considered. A feedback survey about the training course offered some important input for evaluating the organization, contents, tuition, and results of the three parts of the training programme (discussion group, on-road and track training) as reported by the participants in the test group. Conclusions and suggestions: The results of the experiment show that using a one day driver safety course, it is possible to change some of the drivers¿ evaluations connected to safe driving style into safe direction. The follow-up period was exceptionally long (9 months) and the design (randomly divided experimental and control groups with before and after measurements) was reliable. More effort should be devoted to improving the on-road part of the training, which was often perceived as a typical driving lesson rather than a feedback drive

Fast Adaptive Voltage and Boost Frequencies for Central Processing Units

This publication describes methods, techniques, and apparatuses that enable a user equipment (UE) to quickly increase or lower the supply voltage and/or the clock frequency to handle changes in load operating conditions of the components of a system on chip (SoC). The UE uses a dynamic voltage and frequency scaling (DVFS) to handle changes in load operating conditions. During the DVFS, an application processor (AP) writes the supply voltage and the clock frequency settings to shared memory between the SoC, the AP, and a microcontroller unit (MCU). The MCU, then, can change the supply voltage using a voltage controller and/or change the clock frequency using a clock controller, which includes multiple phase-locked loops (PLLs). The utilization of a clock controller with multiple PLLs enables the MCU to trigger a switch between preset clock frequencies much faster than when using a clock controller with a single PLL. Further, the MCU can anticipate the load operating conditions of the components of the SoC and can quickly adjust the supply voltage and the clock frequency settings to run the anticipated load, enabling the UE to save power and increase performance