Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions

Local Dimensionality Reduction for Non-Parametric Regression

Locally-weighted regression is a computationally-efficient technique for non-linear regression. However, for high-dimensional data, this technique becomes numerically brittle and computationally too expensive if many local models need to be maintained simultaneously. Thus, local linear dimensionality reduction combined with locally-weighted regression seems to be a promising solution. In this context, we review linear dimensionalityreduction methods, compare their performance on non-parametric locally-linear regression, and discuss their ability to extend to incremental learning. The considered methods belong to the following three groups: (1) reducing dimensionality only on the input data, (2) modeling the joint input-output data distribution, and (3) optimizing the correlation between projection directions and output data. Group 1 contains principal component regression (PCR); group 2 contains principal component analysis (PCA) in joint input and output space, factor analysis, and probabilistic PCA; and group 3 contains reduced rank regression (RRR) and partial least squares (PLS) regression. Among the tested methods, only group 3 managed to achieve robust performance even for a non-optimal number of components (factors or projection directions). In contrast, group 1 and 2 failed for fewer components since these methods rely on the correct estimate of the true intrinsic dimensionality. In group 3, PLS is the only method for which a computationally-efficient incremental implementation exists

Deep active object recognition by joint label and action prediction

An active object recognition system has the advantage of acting in the environment to capture images that are more suited for training and lead to better performance at test time. In this paper, we utilize deep convolutional neural networks for active object recognition by simultaneously predicting the object label and the next action to be performed on the object with the aim of improving recognition performance. We treat active object recognition as a reinforcement learning problem and derive the cost function to train the network for joint prediction of the object label and the action. A generative model of object similarities based on the Dirichlet distribution is proposed and embedded in the network for encoding the state of the system. The training is carried out by simultaneously minimizing the label and action prediction errors using gradient descent. We empirically show that the proposed network is able to predict both the object label and the actions on GERMS, a dataset for active object recognition. We compare the test label prediction accuracy of the proposed model with Dirichlet and Naive Bayes state encoding. The results of experiments suggest that the proposed model equipped with Dirichlet state encoding is superior in performance, and selects images that lead to better training and higher accuracy of label prediction at test time

In Vivo Antitumor and Antimetastatic Efficacy of a Polyacetal-Based Paclitaxel Conjugate for Prostate Cancer Therapy

Prostate cancer (PCa), one of the leading causes of cancer-related deaths, currently lacks effective treatment for advanced-stage disease. Paclitaxel (PTX) is a highly active chemotherapeutic drug and the first-line treatment for PCa; however, conventional PTX formulation causes severe hypersensitivity reactions and limits PTX use at high concentrations. In the pursuit of high molecular weight, biodegradable, and pH-responsive polymeric carriers, we conjugated PTX to a polyacetal-based nanocarrier to yield a tert-Ser-PTX polyacetal conjugate. tert-Ser-PTX conjugate provides sustained release of PTX over two weeks in a pHresponsive manner while also obtaining a degree of epimerization of PTX to 7-epi-PTX. Serum proteins stabilize tert-Ser-PTX, with enhanced stability in human serum vs. PBS (pH 7.4). In vitro efficacy assessments in PCa cells demonstrated IC50 values above those for the free form of PTX due to the differential cell trafficking modes; however, in vivo tolerability assays demonstrated that tert-Ser-PTX significantly reduced the systemic toxicities associated with free PTX treatment. tert-Ser-PTX also effectively inhibited primary tumor growth and hematologic, lymphatic, and coelomic dissemination, as confirmed by in vivo and ex vivo bioluminescence imaging and histopathological evaluations in mice carrying orthotopic LNCaP tumors. Overall, our results suggest the application of tert-Ser-PTX as a robust anti-tumor/antimetastatic treatment for PCa.JRC.F.2 - Consumer Products Safet

Stereotypes Possess Heterogeneous Directionality: A Theoretical and Empirical Exploration of Stereotype Structure and Content

We advance a theory-driven approach to stereotype structure, informed by connectionist theories of cognition. Whereas traditional models define or tacitly assume that stereotypes possess inherently Group → Attribute activation directionality (e.g., Black activates criminal), our model predicts heterogeneous stereotype directionality. Alongside the classically studied Group → Attribute stereotypes, some stereotypes should be bidirectional (i.e., Group ⇄ Attribute) and others should have Attribute → Group unidirectionality (e.g., fashionable activates gay). We tested this prediction in several large-scale studies with human participants (NCombined = 4,817), assessing stereotypic inferences among various groups and attributes. Supporting predictions, we found heterogeneous directionality both among the stereotype links related to a given social group and also between the links of different social groups. These efforts yield rich datasets that map the networks of stereotype links related to several social groups. We make these datasets publicly available, enabling other researchers to explore a number of questions related to stereotypes and stereotyping. Stereotype directionality is an understudied feature of stereotypes and stereotyping with widespread implications for the development, measurement, maintenance, expression, and change of stereotypes, stereotyping, prejudice, and discrimination