Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

A bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition.

Hormone resistance, invasiveness, and metastatic potential in breast cancer

Critical phenotypic changes that occur during the progression of breast cancer include the loss of hormone-dependence, acquired resistance to systemic therapies, and increased metastatic potential. We have isolated a series of MCF-7 human breast cancer variants which exhibit hormone-independent growth, antiestrogen resistance, and increased metastatic potential. Analysis of the phenotypes of these variants strongly suggests that changes in the expression of specific genes may be critical to the generation of phenotypic diversity in the process of malignant progression in breast cancer. Epigenetic changes may contribute significantly to the generation of these phenotypic changes observed during breast cancer progression. Many of the characteristics of the progressed phenotypes appear to have arisen in response to appropriate selective pressures (growth in ovariectomized nude mice; growth in the presence of antiestrogens). These observations are consistent with the concept of clonal selection and expansion in the process of malignant progression.

Robust multiple-sensing-modality data fusion using Gaussian Process Implicit Surfaces

The ability to build high-fidelity 3D representations of the environment from sensor data is critical for autonomous robots. Multi-sensor data fusion allows for more complete and accurate representations. Furthermore, using distinct sensing modalities (i.e. sensors using a different physical process and/or operating at different electromagnetic frequencies) usually leads to more reliable perception, especially in challenging environments, as modalities may complement each other. However, they may react differently to certain materials or environmental conditions, leading to catastrophic fusion. In this paper, we propose a new method to reliably fuse data from multiple sensing modalities, including in situations where they detect different targets. We first compute distinct continuous surface representations for each sensing modality, with uncertainty, using Gaussian Process Implicit Surfaces (GPIS). Second, we perform a local consistency test between these representations, to separate consistent data (i.e. data corresponding to the detection of the same target by the sensors) from inconsistent data. The consistent data can then be fused together, using another GPIS process, and the rest of the data can be combined as appropriate. The approach is first validated using synthetic data. We then demonstrate its benefit using a mobile robot, equipped with a laser scanner and a radar, which operates in an outdoor environment in the presence of large clouds of airborne dust and smoke.

Modelling attitudes to climate change : an order effect and a test between alternatives

Quantum-like models can be fruitfully used to model attitude change in a social context. Next steps require data, and higher dimensional models. Here, we discuss an exploratory study that demonstrates an order effect when three question sets about Climate Beliefs, Political Affiliation and Attitudes Towards Science are presented in different orders within a larger study of n=533 subjects. A quantum-like model seems possible, and we propose a new experiment which could be used to test between three possible models for this scenario.

Non-parametric consistency test for multiple-sensing-modality data fusion

Fusing data from multiple sensing modalities, e.g. laser and radar, is a promising approach to achieve resilient perception in challenging environmental conditions. However, this may lead to \emph{catastrophic fusion} in the presence of inconsistent data, i.e. when the sensors do not detect the same target due to distinct attenuation properties. It is often difficult to discriminate consistent from inconsistent data across sensing modalities using local spatial information alone. In this paper we present a novel consistency test based on the log marginal likelihood of a Gaussian process model that evaluates data from range sensors in a relative manner. A new data point is deemed to be consistent if the model statistically improves as a result of its fusion. This approach avoids the need for absolute spatial distance threshold parameters as required by previous work. We report results from object reconstruction with both synthetic and experimental data that demonstrate an improvement in reconstruction quality, particularly in cases where data points are inconsistent yet spatially proximal.

Understanding PK/PD

Pharmacology is the science underpinning dosing, mechanisms of action and effectiveness of drugs. Central to pharmacology, are the studies of pharmacokinetics (PK) and pharmacodynamics (PD). On one hand, PK defines the time-course of drug concentrations in the body and incorporates the broad concepts of drug absorption, distribution, metabolism and elimination. On the other hand, PD describes the relationship between drug concentrations and pharmacological effects. In practice, PK is often referred as “what the body does to the drug” whilst PD as “what the drug does to the body”. Thus, PK/PD describes the relationship between drug dose and pharmacological effects with changes in drug concentrations leading to different pharmacological effects.

Reproducibility of fatmax and fat oxidation rates during exercise in recreationally trained males

Aerobic exercise training performed at the intensity eliciting maximal fat oxidation (Fatmax) has been shown to improve the metabolic profile of obese patients. However, limited information is available on the reproducibility of Fatmax and related physiological measures. The aim of this study was to assess the intra-individual variability of: a) Fatmax measurements determined using three different data analysis approaches and b) fat and carbohydrate oxidation rates at rest and at each stage of an individualized graded test. Fifteen healthy males [body mass index 23.1±0.6 kg/m2, maximal oxygen consumption () 52.0±2.0 ml/kg/min] completed a maximal test and two identical submaximal incremental tests on ergocycle (30-min rest followed by 5-min stages with increments of 7.5% of the maximal power output). Fat and carbohydrate oxidation rates were determined using indirect calorimetry. Fatmax was determined with three approaches: the sine model (SIN), measured values (MV) and 3rd polynomial curve (P3). Intra-individual coefficients of variation (CVs) and limits of agreement were calculated. CV for Fatmax determined with SIN was 16.4% and tended to be lower than with P3 and MV (18.6% and 20.8%, respectively). Limits of agreement for Fatmax were −2±27% of with SIN, −4±32 with P3 and −4±28 with MV. CVs of oxygen uptake, carbon dioxide production and respiratory exchange rate were <10% at rest and <5% during exercise. Conversely, CVs of fat oxidation rates (20% at rest and 24–49% during exercise) and carbohydrate oxidation rates (33.5% at rest, 8.5–12.9% during exercise) were higher. The intra-individual variability of Fatmax and fat oxidation rates was high (CV>15%), regardless of the data analysis approach employed. Further research on the determinants of the variability of Fatmax and fat oxidation rates is required.

Not “just words”: Exposure to homophobic epithets leads to dehumanizing and physical distancing from gay men

We examined whether homophobic epithets (e.g., faggot) function as labels of deviance for homosexuals that contribute to their dehumanization and physical distance. Across two studies, participants were supraliminally (Study 1) and subliminally (Study 2) exposed to a homophobic epithet, a category label, or a generic insult. Participants were then asked to associate human related and animal-related words to homosexuals and heterosexuals. Results showed that after exposure to a homophobic epithet, compared with a category label or a generic insult, participants associated less human-related words with homosexuals, indicating dehumanization. In Study 2, we also assessed the effect of a homophobic epithet on physical distance from a target group member and found that homophobic epithets led to greater physical distancing of a gay man. These findings indicate that homophobic epithets foster dehumanization and avoidance of gay people, in ways that other insults or labels do not.