Pr2_2Ir2_2O7_7: when Luttinger semimetal meets Melko-Hertog-Gingras spin ice state

We study the band structure topology and engineering from the interplay between local moments and itinerant electrons in the context of pyrochlore iridates. For the metallic iridate Pr$_2$Ir$_2$O$_7$, the Ir $5d$ conduction electrons interact with the Pr $4f$ local moments via the $f$-$d$ exchange. While the Ir electrons form a Luttinger semimetal, the Pr moments can be tuned into an ordered spin ice with a finite ordering wavevector, dubbed "Melko-Hertog-Gingras" state, by varying Ir and O contents. We point out that the ordered spin ice of the Pr local moments generates an internal magnetic field that reconstructs the band structure of the Luttinger semimetal. Besides the broad existence of Weyl nodes, we predict that the magnetic translation of the "Melko-Hertog-Gingras" state for the Pr moments protects the Dirac band touching at certain time reversal invariant momenta for the Ir conduction electrons. We propose the magnetic fields to control the Pr magnetic structure and thereby indirectly influence the topological and other properties of the Ir electrons. Our prediction may be immediately tested in the ordered Pr$_2$Ir$_2$O$_7$ samples. We expect our work to stimulate a detailed examination of the band structure, magneto-transport, and other properties of Pr$_2$Ir$_2$O$_7$.Comment: 10 pages, 7 figures, added more ref

MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning

Placement is an essential task in modern chip design, aiming at placing millions of circuit modules on a 2D chip canvas. Unlike the human-centric solution, which requires months of intense effort by hardware engineers to produce a layout to minimize delay and energy consumption, deep reinforcement learning has become an emerging autonomous tool. However, the learning-centric method is still in its early stage, impeded by a massive design space of size ten to the order of a few thousand. This work presents MaskPlace to automatically generate a valid chip layout design within a few hours, whose performance can be superior or comparable to recent advanced approaches. It has several appealing benefits that prior arts do not have. Firstly, MaskPlace recasts placement as a problem of learning pixel-level visual representation to comprehensively describe millions of modules on a chip, enabling placement in a high-resolution canvas and a large action space. It outperforms recent methods that represent a chip as a hypergraph. Secondly, it enables training the policy network by an intuitive reward function with dense reward, rather than a complicated reward function with sparse reward from previous methods. Thirdly, extensive experiments on many public benchmarks show that MaskPlace outperforms existing RL approaches in all key performance metrics, including wirelength, congestion, and density. For example, it achieves 60%-90% wirelength reduction and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip layout design. The deliverables are released at https://laiyao1.github.io/maskplace

Longitudinal Causal Inference of Cognitive Function and Depressive Symptoms in Elderly People

Objective: the association between depressive symptoms (Center for Epidemiologic Studies Depression Scale [CES-D]) and subsequent cognitive function (Mini-Mental State Examination [MMSE]) is equivocal in literature. To examine the causal relationship between them, we use longitudinal data on MMSE and CESD and causal inference to illustrate the relationship between two health outcomes. Method:  Data were obtained from the Hispanic Established Populations for Epidemiologic Studies of the Elderly. Participants included 3050 noninstitutionalized Mexican Americans aged 65 and older followed from 1993-2001. Cognitive function and depressive symptoms were assessed using the MMSE and CESD at baseline and at 2, 5, and 7 years of follow-up. Independent variables were sociodemographics, CESD, medical conditions. Marginal structural causal models were employed to evaluate the extent to which cognitive function depend not only on depressive symptoms measured at a single point in time but also on an individual’s entire depressive symptoms history.  Discussion: our results indicate that if intervention to reduce 1 points of depressive symptoms were made at two years prior to assessing cognitive function, they would result in average improvement in cognitive function of 0.12, 95% CI [0.06, 0.18],P<.0001. Conclusion: The results suggest that health intervention of depressive symptoms would be useful in prevention of cognitive impair. &nbsp

Joint Modeling of Multivariate Longitudinal Depressive Symptoms and Survival with Application to an Aging Study

Objective The primary aim in this study was to use a joint analysis approach to examine the association between longitudinal depressive symptoms and survival in Mexican Americans. Methods:  The separate Cox regression and joint modeling were applied to data from the Hispanic Established Population for Epidemiological Study of the Elderly (HEPESE). Depressive symptoms were measured by the Center of Epidemiological Studies Depression Scale (CES-D). The trajectories of CES-D, modeled by random effect, were used as independent variables to fit the mortality curve adjusted by other variables including demographics and physical functioning. Results: The separate Cox regression couldn’t identify association between depressive symptoms and survival. The joint analysis indicated that the  slope of  CES-D score was not associated with mortality in older Mexican-Americans, however the intercept had negative effects on mortality. Conclusion: There was significant association between baseline depression symptoms and mortality, while no association with slope in older Mexican American