PISA: Point-cloud-based Instructed Scene Augmentation

Indoor scene augmentation has become an emerging topic in the field of computer vision with applications in augmented and virtual reality. However, existing scene augmentation methods mostly require a pre-built object database with a given position as the desired location. In this paper, we propose the first end-to-end multi-modal deep neural network that can generate point cloud objects consistent with their surroundings, conditioned on text instructions. Our model generates a seemly object in the appropriate position based on the inputs of a query and point clouds, thereby enabling the creation of new scenarios involving previously unseen layouts of objects. Database of pre-stored CAD models is no longer needed. We use Point-E as our generative model and introduce methods including quantified position prediction and Top-K estimation to mitigate the false negative problems caused by ambiguous language description. Moreover, we evaluate the ability of our model by demonstrating the diversity of generated objects, the effectiveness of instruction, and quantitative metric results, which collectively indicate that our model is capable of generating realistic in-door objects. For a more thorough evaluation, we also incorporate visual grounding as a metric to assess the quality of the scenes generated by our model

Essays in family and labour economics

This thesis explores family and labour economics issues in the context of different countries, the unified motive is to gain policy implication by applying diversified micro-econometric tools into different datasets. The UK has experienced the 1999 Working Family Tax Credit and the 2003 Working and Child Tax Credit reforms. The first chapter provides the first piece of evidence on the effect of single mothers being eligible to income transfer programmes on early childhood outcomes in the Britain. Using the Millennium Cohort Study (MCS), various children’s production functions are used to deal with endogeneity of inputs and unobserved heterogeneity problems. Findings suggest that mothers entitled to in-work benefit has positive effects on both children’s cognitive and non-cognitive outcomes, comparing to the mothers live on welfare. The second chapter presents new evidence on the child quantity- quality (Q-Q) trade-off based on the 1% sample of 1990 Chinese census. The main contribution of this chapter comes from applying a novel Generalised Method of Moments (GMM) approach that accounts for the non-linear distribution of both outcome and endogenous variables. The identification strategy exploits variation in family size that is induced by twin births and first child gender, which allows the test of Q-Q trade-off in a wide range of fertility distribution. I find significantly negative effects of fertility on educational outcome of children, and this trade-off nonlinearly decreases with family size and shows heterogeneous effects by birth order. This chapter provides technique foundation for policies that attempt to reduce contraceptive costs, control population growth and subsidize families with fewer children. The third chapter examines the retirement consumption puzzle using the Chinese Household Income Project data. A failure to smooth the consumption upon retirement would arise considerable concerns for the well-being of elderly people and adjustments of public policies. This chapter employs a regression discontinuity approach and shows that elderly households are able to maintain stable consumption onset of retirement by adjusting expenditure across sub-aggregated categories and household behaviour. This study confirms the prediction of Life Cycle Model and have important implications for using disaggregated consumption data to test the existence of retirement consumption puzzle and for testing consumption theories

What Future Emerging Outsourcing Countries Should Companies Evaluate for Expansion?

[Excerpt] With the rise of globalization and rapid advancements in technology, companies have looked at alternative measures such as outsourcing to keep capital and labor costs low, increase efficiency, improve revenue and profitability, gain competitive business advantages, focus on core business and reduce risks. Also, outsourcing can help companies tap into unexplored territories and talents that the increasingly globalized workforce has to offer. The role of human resources professionals is to decide what and how to source, and manage supplier coordination and development. In this research paper, these questions will be answered: 1) how to make outsourcing decisions, 2) how to manage outsourcing decisions, and 3) what are the potential markets

Nonequidistant two-dimensional antenna arrays are based on Latin squares for registration of cosmic, atmospheric and lithospheric radiation

The possibility of using Latin squares for constructing two-dimensional non-equidistant antenna arrays that can be used for development of radio telescopes and systems for remote sensing of atmospheric and lithospheric radiation has been shown. An algorithm for calculating the coordinates of elements of a non-equidistant antenna array using the values of elements of Latin squares is proposed. It is shown that in this case, it is possible to obtain an almost complete coverage of the grid of spatial frequencies in the region of the arrangement of the elements at small filling coefficients of array. Directional patterns are studied and the side lobes levels are estimated for non-equidistant antennas obtained on the basis of Latin squares. The possibility of synthesizing of large antenna arrays on the basis of composite squares using the embedding of generative Latin squares is shown. The characteristics of the obtained arrays are studied. It is shown that using the shifts and mutual rotations of individual layers is involved in the synthesized array; its characteristics can be substantially improved

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

Controllable group delay in a θ-shaped microfiber resonator with coupled-resonator-induced transparency

The control of Light velocity is theoretically and experimentally demonstrated in a θ-shaped microfiber resonator with coupled-resonator-induced transparency. By adjusting the structure parameters, group delays from -60ps to 200ps are achieved in the all-fiber resonator