On Unconstrained Quasi-Submodular Function Optimization

With the extensive application of submodularity, its generalizations are constantly being proposed. However, most of them are tailored for special problems. In this paper, we focus on quasi-submodularity, a universal generalization, which satisfies weaker properties than submodularity but still enjoys favorable performance in optimization. Similar to the diminishing return property of submodularity, we first define a corresponding property called the {\em single sub-crossing}, then we propose two algorithms for unconstrained quasi-submodular function minimization and maximization, respectively. The proposed algorithms return the reduced lattices in $\mathcal{O}(n)$ iterations, and guarantee the objective function values are strictly monotonically increased or decreased after each iteration. Moreover, any local and global optima are definitely contained in the reduced lattices. Experimental results verify the effectiveness and efficiency of the proposed algorithms on lattice reduction.Comment: 11 page

On the Global Convergence Rates of Softmax Policy Gradient Methods

We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ rate, with constants depending on the problem and initialization. This result significantly expands the recent asymptotic convergence results. The analysis relies on two findings: that the softmax policy gradient satisfies a \L{}ojasiewicz inequality, and the minimum probability of an optimal action during optimization can be bounded in terms of its initial value. Second, we analyze entropy regularized policy gradient and show that it enjoys a significantly faster linear convergence rate $O(e^{-t})$ toward softmax optimal policy. This result resolves an open question in the recent literature. Finally, combining the above two results and additional new $\Omega(1/t)$ lower bound results, we explain how entropy regularization improves policy optimization, even with the true gradient, from the perspective of convergence rate. The separation of rates is further explained using the notion of non-uniform \L{}ojasiewicz degree. These results provide a theoretical understanding of the impact of entropy and corroborate existing empirical studies.Comment: 64 pages, 5 figures. Published in ICML 202

Asian CBDCs on the rise: An in-depth analysis of developments and implications

In this paper, we present an in-depth analysis of Central Bank Digital Currencies (CBDCs), focusing on their definition, purpose, design considerations and recent developments. We also delve into the potential advantages of CBDCs for Asia, such as enhancing convenience, precisely quantifying economic metrics, managing anonymity, catalyzing innovation, and promoting financial inclusion. Moreover, we examine how CBDCs can fortify monetary and fiscal policies, ensure safe distribution, reduce costs and combat corruption. We also address the risks associated with CBDC adoption in Asia and explore potential outcomes such as substitution effects, valuation fluctuations, and foreign currency dependence, while highlighting the importance of managing financial imbalances, holdings concentration and public apprehension toward digital currencies

Dual regulatory switch through interactions of Tcf7l2/Tcf4 with stage-specific partners propels oligodendroglial maturation

Constitutive activation of Wnt/β-catenin inhibits oligodendrocyte myelination. Tcf7l2/Tcf4, a β-catenin transcriptional partner, is required for oligodendrocyte differentiation. How Tcf7l2 modifies β-catenin signalling and controls myelination remains elusive. Here we define a stage-specific Tcf7l2-regulated transcriptional circuitry in initiating and sustaining oligodendrocyte differentiation. Multistage genome occupancy analyses reveal that Tcf7l2 serially cooperates with distinct co-regulators to control oligodendrocyte lineage progression. At the differentiation onset, Tcf7l2 interacts with a transcriptional co-repressor Kaiso/Zbtb33 to block β-catenin signalling. During oligodendrocyte maturation, Tcf7l2 recruits and cooperates with Sox10 to promote myelination. In that context, Tcf7l2 directly activates cholesterol biosynthesis genes and cholesterol supplementation partially rescues oligodendrocyte differentiation defects in Tcf712 mutants. Together, we identify stage-specific co-regulators Kaiso and Sox10 that sequentially interact with Tcf7l2 to coordinate the switch at the transitions of differentiation initiation and maturation during oligodendrocyte development, and point to a previously unrecognized role of Tcf7l2 in control of cholesterol biosynthesis for CNS myelinogenesis

Ultra-broadband and tunable infrared absorber based on VO2 hybrid multi-layer nanostructure

We propose an ultra-broadband near- to mid-infrared (NMIR) tunable absorber based on VO2 hybrid multi-layer nanostructure by hybrid integration of the upper and the lower parts. The upper part is composed of VO2 nanocylinder arrays prepared on the front illuminated surface of quartz substrate, and VO2 square films and VO2/SiO2/VO2 square nanopillar arrays prepared on the back surface. The lower part is an array of SiO2/Ti/VO2 nanopillars on Ti substrate. The effects of different structural parameters and temperature on the absorption spectra were analyzed by the finite-difference time-domain method. An average absorption rate of up to 94.7% and an ultra-wide bandwidth of 6.5 μm were achieved in NMIR 1.5–8 μm. Neither vertical incident light with different polarization angles nor large inclination incident light has a significant effect on the absorption performance of the absorber. The ultra-broadband high absorption performance of this absorber will be widely used in NMIR photodetectors and other new optoelectronic devices

First-Principles-Based Kinetic Monte Carlo Simulation of Nitric Oxide Reduction over Platinum Nanoparticles under Lean-Burn Conditions

This article discusses first-principles-based kinetic Monte Carlo simulation of Nitric Oxide reduction

Locality Preserving Hashing

Hashing has recently attracted considerable attention for large scale similarity search. However, learning compact codes with good performance is still a challenge. In many cases, the real-world data lies on a low-dimensional manifold embedded in high-dimensional ambient space. To capture meaningful neighbors, a compact hashing representation should be able to uncover the intrinsic geometric structure of the manifold, e.g., the neighborhood relationships between subregions. Most existing hashing methods only consider this issue during mapping data points into certain projected dimensions. When getting the binary codes, they either directly quantize the projected values with a threshold, or use an orthogonal matrix to refine the initial projection matrix, which both consider projection and quantization separately, and will not well preserve the locality structure in the whole learning process. In this paper, we propose a novel hashing algorithm called Locality Preserving Hashing to effectively solve the above problems. Specifically, we learn a set of locality preserving projections with a joint optimization framework, which minimizes the average projection distance and quantization loss simultaneously. Experimental comparisons with other state-of-the-art methods on two large scale datasets demonstrate the effectiveness and efficiency of our method