Legal Representation in the Chinese Criminal Court

Abstract: Legal representation plays an important role in criminal sentencing decisions. China has recently stipulated a mandatory legal representation clause for all offenders facing capital charges in its Criminal Procedural Law (1996). This study uses data generated from criminal court case documents involving three serious violent crimes: murder, intentional assault, and robbery. All these crimes carry a maximum of sentence of death. The study examines whether and under what conditions legal representation has an effect on criminal sentencing decisions in China. While the overall multi-regression model did not find that having a legal representation significantly reduces the criminal sentence, a further analysis of the types of criminal defense reveals that sentencing decisions are significantly correlated with the type of defense, and in particular, the court’s appraisal of the defense. Theoretical and practical implications are discussed

Induction of MET Receptor Tyrosine Kinase Down-Regulation through Antibody-Mediated Receptor Clustering

The proto-oncoprotein MET is a receptor tyrosine kinase that plays a key role in cancer cell growth and invasion. We have used fluorescence-tagged antibodies to activate MET in live serum-starved glioblastoma cells and monitor the fate of antibody-bound MET receptor in single cell-based assays. We found that the antibodies induced rapid and transient formation of highly polarized MET clusters on the plasma membrane and promoted the activation of MET, resembling the initial effects of binding to its ligand, HGF. However, the antibody-induced clustering and activation of MET led to the rapid removal of the receptor from cell surface and altered its intracellular processing, resulted in rapid degradation of the receptor. Consequently, while cells pre-treated with HGF remain competent to respond to further HGF stimulation, cells pre-treated with antibodies are refractory to further HGF stimulation due to antibody-mediated MET depletion. Removal of MET by sustained treatment of antibodies blocked cancer cell migration and invasion. Our studies reveal a novel mechanism to alter the recycling process of MET in glioblastoma cancer cells by promoting the receptor degradation through a proteasome-sensitive and lysosome-dependent pathway through the ligand-independent activation of MET using anti-MET antibodies

A Bayesian non-parametric model for small population mortality

International audienceThis paper proposes a Bayesian non-parametric mortality model for a small population, when a benchmark mortality table is also available and serves as part of the prior information. In particular, we extend the Poisson-gamma model of Hardy and Panjer to incorporate correlated and age-specific mortality coefficients. These coefficients, which measure the difference in mortality levels between the small and the benchmark population, follow an age-indexed autoregressive gamma process, and can be stochastically extrapolated to ages where the small population has no historical exposure. Our model substantially improves the computation efficiency of existing two-population Bayesian mortality models by allowing for closed form posterior mean and variance of the future number of deaths, and an efficient sampling algorithm for the entire posterior distribution. We illustrate the proposed model with a life insurance portfolio from a French insurance company

Simultaneously encoding movement and sEMG-based stiffness for robotic skill learning

Transferring human stiffness regulation strategies to robots enables them to effectively and efficiently acquire adaptive impedance control policies to deal with uncertainties during the accomplishment of physical contact tasks in an unstructured environment. In this work, we develop such a physical human-robot interaction (pHRI) system which allows robots to learn variable impedance skills from human demonstrations. Specifically, the biological signals, i.e., surface electromyography (sEMG) are utilized for the extraction of human arm stiffness features during the task demonstration. The estimated human arm stiffness is then mapped into a robot impedance controller. The dynamics of both movement and stiffness are simultaneously modeled by using a model combining the hidden semi-Markov model (HSMM) and the Gaussian mixture regression (GMR). More importantly, the correlation between the movement information and the stiffness information is encoded in a systematic manner. This approach enables capturing uncertainties over time and space and allows the robot to satisfy both position and stiffness requirements in a task with modulation of the impedance controller. The experimental study validated the proposed approach