The Causal Boundary of spacetimes revisited

We present a new development of the causal boundary of spacetimes, originally introduced by Geroch, Kronheimer and Penrose. Given a strongly causal spacetime (or, more generally, a chronological set), we reconsider the GKP ideas to construct a family of completions with a chronology and topology extending the original ones. Many of these completions present undesirable features, like those appeared in previous approaches by other authors. However, we show that all these deficiencies are due to the attachment of an ``excessively big'' boundary. In fact, a notion of ``completion with minimal boundary'' is then introduced in our family such that, when we restrict to these minimal completions, which always exist, all previous objections disappear. The optimal character of our construction is illustrated by a number of satisfactory properties and examples.Comment: 37 pages, 10 figures; Definition 6.1 slightly modified; multiple minor changes; one figure added and another replace

Exploring quantum criticality based on ultracold atoms in optical lattices

Critical behavior developed near a quantum phase transition, interesting in its own right, offers exciting opportunities to explore the universality of strongly-correlated systems near the ground state. Cold atoms in optical lattices, in particular, represent a paradigmatic system, for which the quantum phase transition between the superfluid and Mott insulator states can be externally induced by tuning the microscopic parameters. In this paper, we describe our approach to study quantum criticality of cesium atoms in a two-dimensional lattice based on in situ density measurements. Our research agenda involves testing critical scaling of thermodynamic observables and extracting transport properties in the quantum critical regime. We present and discuss experimental progress on both fronts. In particular, the thermodynamic measurement suggests that the equation of state near the critical point follows the predicted scaling law at low temperatures.Comment: 15 pages, 6 figure

Strongly intermittent far scrape-off layer plasma fluctuations in Alcator C-Mod plasmas close to the empirical discharge density limit

Intermittent plasma fluctuations in the boundary region of the Alcator C-Mod device were comprehensively investigated using data times-series from gas puff imaging and mirror Langmuir probe diagnostics. Fluctuations were sampled during stationary plasma conditions in ohmically heated, lower single null diverted configurations with scans in both line-averaged density and plasma current, with Greenwald density fractions up to $0.85$. Utilizing a stochastic model, we describe the plasma fluctuations as a super-position of uncorrelated pulses, with large-amplitude events corresponding to blob-like filaments moving through the scrape-off layer. A deconvolution method is used to estimate the pulse arrivals and amplitudes. The analysis reveals a significant increase of pulse amplitudes and waiting times between pulses as the line-averaged density approaches the empirical discharge density limit. Broadened and flattened average radial profiles are thus accompanied by strongly intermittent and large-amplitude fluctuations. Although these filaments are arriving less frequently at high line-averaged densities, we show that there are significant increases in radial far-SOL particle and heat flux which will further enhance plasma--wall interactions. The stochastic model has been used as the framework for study of the scalings in the intermittency, flux and mean waiting times and mean amplitudes and is being used to inform predictive capability for the effects of filamentary transport as a function of Greenwald fraction

Towards Impartial Multi-task Learning

Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifically, for the task-shared parameters, we optimize the scaling factors via a closed-form solution, such that the aggregated gradient (sum of raw gradients weighted by the scaling factors) has equal projections onto individual tasks. For the task-specific parameters, we dynamically weigh the task losses so that all of them are kept at a comparable scale. Further, we find the above gradient balance and loss balance are complementary and thus propose a hybrid balance method to further improve the performance. Our impartial multi-task learning (IMTL) can be end-to-end trained without any heuristic hyper-parameter tuning, and is general to be applied on all kinds of losses without any distribution assumption. Moreover, our IMTL can converge to similar results even when the task losses are designed to have different scales, and thus it is scale-invariant. We extensively evaluate our IMTL on the standard MTL benchmarks including Cityscapes, NYUv2 and CelebA. It outperforms existing loss weighting methods under the same experimental settings

Dynamics and Berry phase of two-species Bose-Einstein condensates

In terms of exact solutions of the time-dependent Schrodinger equation for an effective giant spin modeled from a coupled two-mode Bose-Einstein condensate (BEC) with adiabatic and cyclic time-varying Raman coupling between two hyperfine states of the BEC, we obtain analytic time-evolution formulas of the population imbalance and relative phase between two components with various initial states, especially the SU(2)coherent state. We find the Berry phase depending on the number parity of atoms, and particle number dependence of the collapse revival of population-imbalance oscillation. It is shown that self-trapping and phase locking can be achieved from initial SU(2) coherent states with proper parameters.Comment: 18 pages,5 figure

Genetic Diversity, Inbreeding Level, and Genetic Load in Endangered Snub-Nosed Monkeys (Rhinopithecus)

The snub-nosed monkey genus (Rhinopithecus) comprises five closely related species (R. avunculus, R. bieti, R. brelichi, R. roxellana, and R. strykeri). All are among the world's rarest and most endangered primates. However, the genomic impact associated with their population decline remains unknown. We analyzed population genomic data of all five snub-nosed monkey species to assess their genetic diversity, inbreeding level, and genetic load. For R. roxellana, R. bieti, and R. strykeri, population size is positively correlated with genetic diversity and negatively correlated with levels of inbreeding. Other species, however, which possess small population sizes, such as R. brelichi and R. avunculus, show high levels of genetic diversity and low levels of genomic inbreeding. Similarly, in the three populations of R. roxellana, the Shennongjia population, which possesses the lowest population size, displays a higher level of genetic diversity and lower level of genomic inbreeding. These findings suggest that although R. brelichi and R. avunculus and the Shennongjia population might be at risk, it possess significant genetic diversity and could thus help strengthen their long-term survival potential. Intriguingly, R. roxellana with large population size possess high genetic diversity and low level of genetic load, but they show the highest recent inbreeding level compared with the other snub-nosed monkeys. This suggests that, despite its large population size, R. roxellana has likely been experiencing recent inbreeding, which has not yet affected its mutational load and fitness. Analyses of homozygous-derived deleterious mutations identified in all snub-nosed monkey species indicate that these mutations are affecting immune, especially in smaller population sizes, indicating that the long-term consequences of inbreeding may be resulting in an overall reduction of immune capability in the snub-nosed monkeys, which could provide a dramatic effect on their long-term survival prospects. Altogether, our study provides valuable information concerning the genomic impact of population decline of the snub-nosed monkeys. We revealed multiple counterintuitive and unexpected patterns of genetic diversity in small and large population, which will be essential for conservation management of these endangered species

GenDet: Meta Learning to Generate Detectors From Few Shots

Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes that the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without directly modeling the detectors for novel classes. In this article, we propose GenDet, a new meta-learning-based framework that can effectively generate object detectors for novel classes from few shots and, thus, conducts few-shot detection tasks explicitly. The detector generator is trained by numerous few-shot detection tasks sampled from base classes each with sufficient samples, and thus, it is expected to generalize well on novel classes. An adaptive pooling module is further introduced to suppress distracting samples and aggregate the detectors generated from multiple shots. Moreover, we propose to train a reference detector for each base class in the conventional way, with which to guide the training of the detector generator. The reference detectors and the detector generator can be trained simultaneously. Finally, the generated detectors of different classes are encouraged to be orthogonal to each other for better generalization. The proposed approach is extensively evaluated on the ImageNet, VOC, and COCO data sets under various few-shot detection settings, and it achieves new state-of-the-art results

Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis.

Background In healthy subjects, there is an association between amplitude of low-frequency fluctuations (ALFF) and regional cerebral blood flow (rCBF). To date, no published meta-analysis has investigated changes in the regional ALFF in medication-free depressed patients. Methods In this study, we aimed to explore whether resting-state rCBF and ALFF changes co-occur in the depressed brain without the potential confound of medication. Using signed differential mapping (SDM), we conducted two meta-analyses, one of rCBF studies and one of ALFF studies, involving medication-free patients with major depressive disorder (MDD). In addition, we conducted a multimodal meta-analysis to identify brain regions that showed abnormalities in both rCBF and ALFF. Results A total of 16 studies were included in this series. We identified abnormalities in resting-state rCBF and ALFF in the left insula in medication-free MDD patients compared with healthy controls (HC). In addition, we observed altered resting-state rCBF in the limbic-subcortical-cortical circuit and altered ALFF in the default mode network (DMN) and some motor-related brain regions. Limitations The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous. Conclusions The conjoint alterations in ALFF and rCBF in the left insula may represent core neuropathological changes in medication-free patients with MDD and merit further studying