Narrow Results

The importance of the public finance in tackling climate change has been widely recognized by the global communities. As the operating entity of the financial mechanism of the UNFCCC and the Paris Agreement, the $10.3 billion Green Climate Fund (GCF) holds a potential to be the champion in the international climate finance architecture. Within two and a half years, the GCF approved 76 projects worthy of $3.7 billion and has established partnership with 59 accredited entities. Integrating different concerns into its governance and operational modalities, the GCF maintains an inclusive participation and has profound implication for the international climate change cooperation. While with these achievements, the GCF still faces financial and policy challenges going forward. If the current pace of the project approval continues, the GCF will soon exhaust its resource. The existing policy gaps will also jeopardize GCF meeting its climate goals. To ensure a sustainable and bright future, the GCF needs to take advantage of its opportunities and address the challenges in a wise and strategic way. Given the real scarcity of the public resources available, a top-down combined with bottom-up replenishment modality may be worth exploring.

When science began considering the brain from a chemistry rather than electrical standpoint, this marked the beginning of new perspectives on how this complex organ operated. Seminal work relating to dopamine helped to introduce the breakthrough concept of neurotransmitters leading to the understanding of the numerous circuitries within the brain. The observance of decreased dopamine levels when anti-psychotic drugs were administered helped to launch new medical treatments especially for patients suffering from Parkinson’s disease. Today stabilizer compounds are being tested to provide beneficial therapies for both elevated or decreased levels of dopamine as well as for other medical conditions.

Large Language Models (LLMs), exemplified by GPT-4, have transcended traditional boundaries in language processing, demonstrating remarkable capabilities in understanding and generating nuanced text. Crucially, these models are pioneering a paradigm shift in Artificial Intelligence (AI) applications — from solving narrowly defined problems to navigating complex, real-world scenarios. Such a shift is based on a simple and fundamental principle: LLMs can process any data that can be serialized and tokenized, enabling them to engage in multifaceted reasoning and utilize diverse tools. This capability positions LLMs to operate effectively in broader, more intricate contexts, marking a leap in AI’s practical applicability and potential.

When science began considering the brain from a chemistry rather than electrical standpoint, this marked the beginning of new perspectives on how this complex organ operated. Seminal work relating to dopamine helped to introduce the breakthrough concept of neurotransmitters leading to the understanding of the numerous circuitries within the brain. The observance of decreased dopamine levels when anti-psychotic drugs were administered helped to launch new medical treatments especially for patients suffering from Parkinson’s disease. Today stabilizer compounds are being tested to provide beneficial therapies for both elevated or decreased levels of dopamine as well as for other medical conditions.

Covariance of space and time in General Relativity (GR) entails a number of technical and conceptual difficulties. Remarkably, these can be resolved by a paradigm shift from full 4-dimensional general coordinate invariance to invariance only with respect to spatial diffeomorphisms. The framework for a theory of gravity with this paradigm shift, from quantum to classical regimes, is presented; GR is contained as a special case. Appositely formulated as a master constraint, the Hamiltonian constraint now determines only dynamics; and is relieved of its dual role of generating symmetry transformations. The Dirac algebra, in which 4-dimensional diffeomorphism symmetry is only realized on-shell, is replaced by the master constraint algebra which possesses only spatial diffeomorphism gauge symmetry, both on- and off-shell. Decomposition of the spatial metric into unimodular and determinant, q, factors results in mutually commuting pairs of canonical variables. The classical content of GR can be captured with a Hamiltonian constraint linear in the trace of the momentum. This implies a theory of quantum gravity can be described by a Schrodinger equation first order in intrinsic time ln q accompanied with positive semi-definite probability density. The semi-classical Hamilton-Jacobi equation is also first order in intrinsic time, with the implication of being complete; and gauge-invariant physical observables can be constructed from integration constants of its complete integral solution. Classical space-time, with direct correlation of its proper times and intrinsic time intervals, emerges from constructive interference; and the physical content of GR can be regained from a theory with a true Hamiltonian generating intrinsic time translations, but with only spatial diffeomorphism symmetry. The framework also prompts natural extensions towards a well-behaved quantum theory of gravity.