Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors

Trajectory prediction modules are key enablers for safe and efficient planning of autonomous vehicles (AVs), particularly in highly interactive traffic scenarios. Recently, learning-based trajectory predictors have experienced considerable success in providing state-of-the-art performance due to their ability to learn multimodal behaviors of other agents from data. In this paper, we present an algorithm called multi-predictor fusion (MPF) that augments the performance of learning-based predictors by imbuing them with motion planners that are tasked with satisfying logic-based rules. MPF probabilistically combines learning- and rule-based predictors by mixing trajectories from both standalone predictors in accordance with a belief distribution that reflects the online performance of each predictor. In our results, we show that MPF outperforms the two standalone predictors on various metrics and delivers the most consistent performance

Sex cord stromal tumor of ovary masquerading as polycystic ovarian syndrome

Virilization is a portentous sign that suggests the possibility of an ovarian or adrenal neoplasm. Diagnosis may be delayed in some patients due to nonspecific symptoms and overlapping symptoms with that of polycystic ovarian syndrome (PCOS). However, it must be remembered that PCOS usually causes mild to moderate elevation of serum testosterone with hirsutism whereas serum testosterone levels are many times elevated in cases of androgen secreting tumors and virilization is a norm. So high testosterone level with new onset virilization rule out PCOS. Authors are reporting two cases of Sertoli Leydig cell tumor despite their similar histopathology and equivalent levels of serum testosterone had a varied clinical spectrum of virilization

ADAPTIVE MEETING ROOM TEMPERATURE CONTROL

Smart buildings are typically able to facilitate temperature controls in meeting rooms today by monitoring temperature within the rooms using sensors, thermostat devices, and the like. Presented herein are techniques through which room temperature can be adapted in a more dynamic manner, by accounting for the number of people in a room and heat from electronic devices either present in or carried into the room. This adaptive temperature control uses real-time factors that other solutions fail to include, thereby providing both improved comfort and reduced power consumption

Evaluation of Phytochemicals, Antioxidant and Antibacterial Activity of Hyophila involuta (Hook.) Jaeg. and Entodon plicatus C.Muell. (Bryophyta) from Rajasthan, India

Abstract. Plants are the ultimate source of drugs against many communicable diseases since time immemorial. Multitudinous drugs have been obtained from countless plant species. However, invariably angiosperms were the preferred choice of most of the workers and other plant groups remain somewhat unexplored in this direction. Hence, the aim of the present study was to examine the phytochemicals, antioxidant and antibacterial activity of two such neglected mosses (bryophyta), Hyophila involuta (Hook.) Jaeg. and Entodon plicatus C. Muell. (Bryopsida). In preluded phytochemical analysis cardiac glycosides, flavonoids, saponins, anthroquinone, terpenoids, tannins, phenolic, proteins, fats and fixed oils were observed by using standard tests. It was found that E. plicatus contains more amount of phytochemicals than H. involuta. The antioxidant activity of both the plants was also determined according to standard protocols and appealing results were found. Comparative analysis was done for the activity of Catalase, Peroxidase, Ascorbate peroxidase, Glutathione reductase and Superoxide dismutase and interesting observations were made. Antimicrobial activity of methanolic extracts of both the plants was evaluated against Bacillus subtilis, B. cereus and Escherichia coli by using the Agar well diffusion method. Extract of H. involuta showed a greater inhibitory activity than Entodon plicatus. Bacillus spp. (gram+ve) were found more affected than E. coli (gram-ve)

A System-Level View on Out-of-Distribution Data in Robotics

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy