Abstract-Manipulation of complex deformable semi-solids such as food objects is an important skill for personal robots to have. In this work, our goal is to model and learn the physical properties of such objects. We design actions involving use of tools such as forks and knives that obtain haptic data containing information about the physical properties of the object. We then design appropriate features and use supervised learning to map these features to certain physical properties (hardness, plasticity, elasticity, tensile strength, brittleness, adhesiveness). Additionally, we present a method to compactly represent the robot's beliefs about the object's properties using a generative model, which we use to plan appropriate manipulation actions. We extensively evaluate our approach on a dataset including haptic data from 12 categories of food (including categories not seen before by the robot) obtained in 941 experiments. Our robot prepared a salad during 60 sequential robotic experiments where it made a mistake in only 4 instances.
Abstract-For an aerial robot, perceiving and avoiding obstacles are necessary skills to function autonomously in a cluttered unknown environment. In this work, we use a single image captured from the onboard camera as input, produce obstacle classifications, and use them to select an evasive maneuver. We present a Markov Random Field based approach that models the obstacles as a function of visual features and non-local dependencies in neighboring regions of the image. We perform efficient inference using new low-power parallel neuromorphic hardware, where belief propagation updates are done using leaky integrate and fire neurons in parallel, while consuming less than 1 W of power. In outdoor robotic experiments, our algorithm was able to consistently produce clean, accurate obstacle maps which allowed our robot to avoid a wide variety of obstacles, including trees, poles and fences.
Two main approaches to using social network information in recommendation have emerged: augmenting collaborative filtering with social data and algorithms that use only ego-centric data. We compare the two approaches using movie and music data from Facebook, and hashtag data from Twitter. We find that recommendation algorithms based only on friends perform no worse than those based on the full network, even though they require much less data and computational resources. Further, our evidence suggests that locality of preference, or the non-random distribution of item preferences in a social network, is a driving force behind the value of incorporating social network information into recommender algorithms. When locality is high, as in Twitter data, simple k-nn recommenders do better based only on friends than they do if they draw from the entire network. These results help us understand when, and why, social network information is likely to support recommendation systems, and show that systems that see ego-centric slices of a complete network (such as websites that use Facebook logins) or have computational limitations (such as mobile devices) may profitably use ego-centric recommendation algorithms.
Introduction: Important immunotherapy drugs targeting PD-L1 are approved for first and second line treatment for various stages of NSCLC. Reproducible and precise evaluation of PD-L1 expression is essential to accurately evaluate patients’ eligibility for treatment and for enrollment in clinical trials. Current guidelines rely on pathologists to interpret tumor samples, which is challenging in part because different PD-L1 assays have distinct scoring criteria. As a result, determining eligibility by manual assessment can be inconsistent and inaccurate, leading to untreated patients. To support pathologist quantification of PD-L1 in clinical trials, PathAI has developed scanner-and antibody-agnostic machine learning (ML) models, AI-based histologist measurement of PD-L1 in NSCLC (AIM-PD-L1-NSCLC), for the quantification of PD-L1 expression in NSCLC using four PD-L1 immunohistochemistry (IHC) clones. Methods: AIM-PD-L1-NSCLC was trained using convolutional neural networks to identify and quantify PD-L1-positive cells in digitized whole slide images (WSI) of tissue samples. Models were developed using over 5,000 diverse clinical biopsies and resections, including primary and metastatic adenocarcinoma and squamous cell carcinoma samples collected from 10 clinical trials and from two clinical laboratories, each stained for PD-L1 with one of four IHC clones: SP263 (N=1,320), SP142 (N=1,829) (both Ventana Medical Systems Inc., Tucson, AZ), 28-8 (N=1,331), or 22C3 (N=843) (both Agilent Technologies, Santa Clara, USA). Slides were digitized using Aperio, Philips, and Ventana scanners, and WSI were split into training (N=3,818) and test (N=1,505) datasets. The training dataset was annotated by board certified pathologists (313,770 annotations) to label tissue regions and cells. Human Interpretable features representing the number of tumor cells were automatically extracted from the model and a slide level Tumor Proportion Score (TPS) calculated as the proportion of PD-L1+ cancer cells divided by total cancer cells in tumor regions. Model predicted slide level TPS were compared with the median TPS of five pathologists’ scores using intraclass correlation coefficient (ICC) statistics. Results: There was high concordance between ML model-predicted and median pathologists’ slide level TPS for all PD-L1 clones (ICC 0.93 (95% CI 0.90-0.94), and for each individual clone: 22C3 ICC 0.93 (95% CI 0.89-0.96); SP142 ICC 0.88 (95% CI 0.79-0.93); SP263 ICC 0.96 (95% CI 0.93-0.97; 28-8 ICC 0.90 (95% CI 0.85-0.93). Conclusions: AIM PD-L1 NSCLC is highly concordant with the gold standard pathologist consensus score across four PD-L1 clones in a large diverse dataset. This model could support patient enrollment and stratification in prospective clinical trials, as well as quality control of staining and pathology drift. Citation Format: Michael Griffin, Mevlana Gemici, Ashar Javed, Nishant Agrawal, Murray Resnick, Limin Yu, Sara Hoffman, Victoria Mountain, Jamie Harisiades, Megan Rothney, Benjamin Glass, Ilan Wapinski, Andrew Beck, Eric Walk. AIM PD-L1-NSCLC: Artificial intelligence-powered PD-L1 quantification for accurate prediction of tumor proportion score in diverse, multi-stain clinical tissue samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 471.
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