Landau meets Newton: time translation symmetry breaking in classical mechanics

Every classical Newtonian mechanical system can be equipped with a nonstandard Hamiltonian structure, in which the Hamiltonian is the square of the canonical Hamiltonian up to a constant shift, and the Poisson bracket is nonlinear. In such a formalism, time translation symmetry can be spontaneously broken, provided the potential function becomes negative. A nice analogy between time translation symmetry breaking and the Landau theory of second order phase transitions is established, together with several example cases illustrating time translation breaking ground states. In particular, the $\Lambda$CDM model of FRW cosmology is reformulated as the time translation symmetry breaking ground states.Comment: 10 pages, 1 figure. V2: minor correction

Magnetic Field Effect on Charmonium Production in High Energy Nuclear Collisions

It is important to understand the strong external magnetic field generated at the very beginning of high energy nuclear collisions. We study the effect of the magnetic field on the charmonium yield and anisotropic distribution in Pb+Pb collisions at the LHC energy. The time dependent Schr\"odinger equation is employed to describe the motion of $c\bar{c}$ pairs. We compare our model prediction of non- collective anisotropic parameter $v_2$ of $J/\psi$s with CMS data at high transverse momentum. This is the first attempt to measure the magnetic field in high energy nuclear collisions.Comment: 5 pages, 4 figure

Dysfunction of the motivational brain:evidence from anxiety and schizophrenia

Impairments in motivational systems are at the core of distinct maladaptive goal-directed behaviors in psychiatric disorders. In this thesis, we examine the neurocognitive deficits underlying abnormally low approach motivation in schizophrenia with negative symptoms, and abnormally high avoidant motivation in anxiety with overdefensive behaviors. We first systematically integrate previous neuroimaging findings on different stages of reward processing in schizophrenia by conducting a coordinate-based meta-analysis. Next, we examine the dopaminergic brain system in schizophrenia in relation to social amotivation by using resting state functional magnetic resonance imaging. Converging results suggest that hypo-active dopaminergic brain areas and their weakened connections with brain areas related to top-down control may contribute to amotivation and maladaptive approach behaviors in schizophrenia. By manipulating emotional context of risk decision making, and experimentally dissociating subjective aversion to risk from aversion to loss, we examined the neural basis of risk preference in anxiety. Collectively, we show that heightened risk-avoidant behaviors in anxiety are associated with hyperactivation of brain areas involved in emotional processing but lower coupling of brain systems implicated in top-down control. Together with previous findings, we proposed a model of maladaptive motivation in psychiatric disorders, which highlights adequate top-down/bottom-up modulations on valuation of approach-avoidance motivation in adaptive behaviors and the underlying neural pathways of psychiatric disorders, especially for anxiety and schizophrenia. This thesis provides neuroimaging evidence and scientific understanding of the neurocognitive mechanisms underlying maladaptive goal-directed behaviors in anxiety and schizophrenia, which have widespread implications for the improvement of diagnostics and treatment for various psychiatric disorders

Learning over Knowledge-Base Embeddings for Recommendation

State-of-the-art recommendation algorithms -- especially the collaborative filtering (CF) based approaches with shallow or deep models -- usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines