There is no Such Thing as a Sham Trust

The Court of Appeal decision in Official Assignee v Wilson is the leading New Zealand case on "sham trusts". Obiter, O'Regan and Robertson JJ held that for a sham trust to exist, the settlor and trustee must have a common intention to not create a trust. Post-Wilson, debate continues over the precise elements that render a trust a sham. The Law Commission suggested that the sham doctrine, as a means of analysing the validity of an express trust, may not be the best approach. A better starting point would be a return to the certainty of intention requirement. In arguing that the Law Commission's recommendation is correct, this article will discuss three legal issues: whether an express trust is a unilateral or bilateral transaction; whether the excluded evidence has always been part of the objective intention requirement; and whether the legislative and policy factors have made foreign trust law distinct from New Zealand trust law. Finally, this article will expand on the test proposed by the Law Commission

Joining the Aotearoa New Zealand Constitutional Debate: Constitutional Environmental Rights in our Future ‘Constitution’

In 2013, the Constitutional Advisory Panel invited New Zealanders to think about our vision of what New Zealand should look like in the future and to consider how our constitutional arrangements would support that vision. In response, New Zealanders have suggested the inclusion of an environmental protection regime in our future constitutional landscape. The author supports this prevailing opinion. This paper will use the experiences gained from international and regional human rights and environmental law treaties and other countries’ constitutions to explore the best model to achieve that goal. This comparative law analysis will identify the key theoretical and legal issues that must be addressed by Parliament to ensure the successful implementation and enforcement of an environmental protection regime through the courts. While international developments are important, any environmental constitutional framework must reflect New Zealand’s unique and distinctive history, environment, people, and cultural values. With this in mind, this paper will tentatively canvass a new environmental constitutional framework and lay foundations for further legal research and public debate

Interaction-driven topological phase diagram of twisted bilayer MoTe2_2

Twisted bilayer MoTe$_2$ is a promising platform to investigate the interplay between topology and many-body interaction. We present a theoretical study of its interaction-driven quantum phase diagrams based on a three-orbital model, which can be viewed as a generalization of the Kane-Mele-Hubbard model with an additional orbital and realistic Coulomb repulsion. We predict a cascade of phase transitions tuned by the twist angle $\theta$. At the hole filling factor $\nu=1$ (one hole per moir\'e unit cell), the ground state can be in the multiferroic phase with coexisting spontaneous layer polarization and magnetism, the quantum anomalous Hall phase, and finally the topologically trivial magnetic phases, as $\theta$ increases from $1.5^{\circ}$ to $5^{\circ}$. At $\nu=2$, the ground state can have a second-order phase transition between an antiferromagnetic phase and the quantum spin Hall phase as $\theta$ passes through a critical value. The dependence of the phase boundaries on model parameters such as the gate-to-sample distance, the dielectric constant, and the moir\'e potential amplitude is examined. The predicted phase diagrams can guide the search for topological phases in twisted transition metal dichalcogenide homobilayers.Comment: 12 pages, 7 figures. Comments and Collaborations are Welcome

Mapping solar array location, size, and capacity using deep learning and overhead imagery

The effective integration of distributed solar photovoltaic (PV) arrays into existing power grids will require access to high quality data; the location, power capacity, and energy generation of individual solar PV installations. Unfortunately, existing methods for obtaining this data are limited in their spatial resolution and completeness. We propose a general framework for accurately and cheaply mapping individual PV arrays, and their capacities, over large geographic areas. At the core of this approach is a deep learning algorithm called SolarMapper - which we make publicly available - that can automatically map PV arrays in high resolution overhead imagery. We estimate the performance of SolarMapper on a large dataset of overhead imagery across three US cities in California. We also describe a procedure for deploying SolarMapper to new geographic regions, so that it can be utilized by others. We demonstrate the effectiveness of the proposed deployment procedure by using it to map solar arrays across the entire US state of Connecticut (CT). Using these results, we demonstrate that we achieve highly accurate estimates of total installed PV capacity within each of CT's 168 municipal regions

Collaboration of Pre-trained Models Makes Better Few-shot Learner

Few-shot classification requires deep neural networks to learn generalized representations only from limited training images, which is challenging but significant in low-data regimes. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. Based on this point, we question if the large-scale pre-training can alleviate the few-shot data deficiency and also assist the representation learning by the pre-learned knowledge. In this paper, we propose CoMo, a Collaboration of pre-trained Models that incorporates diverse prior knowledge from various pre-training paradigms for better few-shot learning. Our CoMo includes: CLIP's language-contrastive knowledge, DINO's vision-contrastive knowledge, and DALL-E's language-generative knowledge. Specifically, CoMo works in two aspects: few-shot data expansion and diverse knowledge ensemble. For one, we generate synthetic images via zero-shot DALL-E to enrich the few-shot training data without any manpower. For the other, we introduce a learnable Multi-Knowledge Adapter (MK-Adapter) to adaptively blend the predictions from CLIP and DINO. By such collaboration, CoMo can fully unleash the potential of different pre-training methods and unify them to perform state-of-the-art for few-shot classification. We conduct extensive experiments on 11 datasets to demonstrate the superiority and generalization ability of our approach.Comment: 10 pages, 6 figure

SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension

Based on powerful Large Language Models (LLMs), recent generative Multimodal Large Language Models (MLLMs) have gained prominence as a pivotal research area, exhibiting remarkable capability for both comprehension and generation. In this work, we address the evaluation of generative comprehension in MLLMs as a preliminary step towards a comprehensive assessment of generative models, by introducing a benchmark named SEED-Bench. SEED-Bench consists of 19K multiple choice questions with accurate human annotations (x 6 larger than existing benchmarks), which spans 12 evaluation dimensions including the comprehension of both the image and video modality. We develop an advanced pipeline for generating multiple-choice questions that target specific evaluation dimensions, integrating both automatic filtering and manual verification processes. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 18 models across all 12 dimensions, covering both the spatial and temporal understanding. By revealing the limitations of existing MLLMs through evaluation results, we aim for SEED-Bench to provide insights for motivating future research. We will launch and consistently maintain a leaderboard to provide a platform for the community to assess and investigate model capability.Comment: Technical Report; Project released at: https://github.com/AILab-CVC/SEED-Benc

Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit

[EN] Growth and development of hair follicles (HF) is a complex and dynamic process in most mammals. As HF growth and development regulate rabbit wool yield, exploring the role of genes involved in HF growth and development may be relevant. In this study, the coding sequence of the Angora rabbit (Oryctolagus cuniculus) WIF1 gene was cloned. The length of the coding region sequence was found to be 1140 bp, which encodes 379 amino acids. Bioinformatics analysis indicated that the WIF1 protein was unstable, hydrophilic and located in the extracellular region, contained a putative signal peptide and exhibited a high homology in different mammals. Moreover, WIF1 was significantly downregulated in the high wool production in the Angora rabbit group. Overexpression and knockdown studies revealed that WIF1 regulates HF growth and development-related genes and proteins, such as LEF1 and CCND1. WIF1 activated β-catenin/TCF transcriptional activity, promoted cell apoptosis and inhibited cellular proliferation. These results indicate that WIF1 might be important for HF development. This study, therefore, provides a theoretical foundation for investigating WIF1 in HF growth and development.This research was funded by This research was funded by National Natural Science Foundation of China (Grant No. 32102529), China Agriculture Research System of MOF and MARA (CARS-43-A-1).Zhao, B.; Li, J.; Zhang, X.; Bao, Z.; Chen, Y.; Wu, X. (2022). Characterisation and functional analysis of the WIF1 gene and its role in hair follicle growth and development of the Angora rabbit. World Rabbit Science. 30(3):209-218. https://doi.org/10.4995/wrs.2022.1735320921830

SEED-Bench-2: Benchmarking Multimodal Large Language Models

Multimodal large language models (MLLMs), building upon the foundation of powerful large language models (LLMs), have recently demonstrated exceptional capabilities in generating not only texts but also images given interleaved multimodal inputs (acting like a combination of GPT-4V and DALL-E 3). However, existing MLLM benchmarks remain limited to assessing only models' comprehension ability of single image-text inputs, failing to keep up with the strides made in MLLMs. A comprehensive benchmark is imperative for investigating the progress and uncovering the limitations of current MLLMs. In this work, we categorize the capabilities of MLLMs into hierarchical levels from $L_0$ to $L_4$ based on the modalities they can accept and generate, and propose SEED-Bench-2, a comprehensive benchmark that evaluates the \textbf{hierarchical} capabilities of MLLMs. Specifically, SEED-Bench-2 comprises 24K multiple-choice questions with accurate human annotations, which spans 27 dimensions, including the evaluation of both text and image generation. Multiple-choice questions with groundtruth options derived from human annotation enables an objective and efficient assessment of model performance, eliminating the need for human or GPT intervention during evaluation. We further evaluate the performance of 23 prominent open-source MLLMs and summarize valuable observations. By revealing the limitations of existing MLLMs through extensive evaluations, we aim for SEED-Bench-2 to provide insights that will motivate future research towards the goal of General Artificial Intelligence. Dataset and evaluation code are available at \href{https://github.com/AILab-CVC/SEED-Bench}Comment: Project released at: https://github.com/AILab-CVC/SEED-Bench. arXiv admin note: text overlap with arXiv:2307.1612