Diurnal cycle of deep tropical convection

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences, 1992.Title as it appears in the M.I.T. Graduate List, Feb. 1992: Diurnal cycle of deep cloud cover in tropics.Includes bibliographical references (leaf 53).by Sewon Park.M.S

Formalizing Hyperspaces for Extracting Efficient Exact Real Computation

We propose a framework for certified computation on hyperspaces by formalizing various higher-order data types and operations in a constructive dependent type theory. Our approach builds on our previous work on axiomatization of exact real computation where we formalize nondeterministic first-order partial computations over real and complex numbers. Based on the axiomatization, we first define open, closed, compact and overt subsets in an abstract topological way that allows short and elegant proofs with computational content coinciding with standard definitions in computable analysis. From these proofs we extract programs for testing inclusion, overlapping of sets, et cetera. To improve extracted programs, our framework specializes the Euclidean space ?^m making use of metric properties. To define interesting operations over hyperspaces of Euclidean space, we introduce a nondeterministic version of a continuity principle valid under the standard type-2 realizability interpretation. Instead of choosing one of the usual formulations, we define it in a way similar to an interval extension operator, which often is already available in exact real computation software. We prove that the operations on subsets preserve the encoding, and thereby define a small calculus to built new subsets from given ones, including limits of converging sequences with regards to the Hausdorff metric. From the proofs, we extract programs that generate drawings of subsets of ?^m with any given precision efficiently. As an application we provide a function that constructs fractals, such as the Sierpinski triangle, from iterated function systems using the limit operation, resulting in certified programs that errorlessly draw such fractals up to any desired resolution

Regular Time-series Generation using SGM

Score-based generative models (SGMs) are generative models that are in the spotlight these days. Time-series frequently occurs in our daily life, e.g., stock data, climate data, and so on. Especially, time-series forecasting and classification are popular research topics in the field of machine learning. SGMs are also known for outperforming other generative models. As a result, we apply SGMs to synthesize time-series data by learning conditional score functions. We propose a conditional score network for the time-series generation domain. Furthermore, we also derive the loss function between the score matching and the denoising score matching in the time-series generation domain. Finally, we achieve state-of-the-art results on real-world datasets in terms of sampling diversity and quality.Comment: 9 pages, appendix 3 pages, under revie

Sound wave scattering by cyclindrical shells with internal structures

Thesis (Ocean. E.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1995.Includes bibliographical references (leaves 52-58).by Sewon Park.Ocean.E

MadSGM: Multivariate Anomaly Detection with Score-based Generative Models

The time-series anomaly detection is one of the most fundamental tasks for time-series. Unlike the time-series forecasting and classification, the time-series anomaly detection typically requires unsupervised (or self-supervised) training since collecting and labeling anomalous observations are difficult. In addition, most existing methods resort to limited forms of anomaly measurements and therefore, it is not clear whether they are optimal in all circumstances. To this end, we present a multivariate time-series anomaly detector based on score-based generative models, called MadSGM, which considers the broadest ever set of anomaly measurement factors: i) reconstruction-based, ii) density-based, and iii) gradient-based anomaly measurements. We also design a conditional score network and its denoising score matching loss for the time-series anomaly detection. Experiments on five real-world benchmark datasets illustrate that MadSGM achieves the most robust and accurate predictions

Role of TNF-α in vascular dysfunction

Healthy vascular function is primarily regulated by several factors including EDRF (endothelium-dependent relaxing factor), EDCF (endothelium-dependent contracting factor) and EDHF (endothelium-dependent hyperpolarizing factor). Vascular dysfunction or injury induced by aging, smoking, inflammation, trauma, hyperlipidaemia and hyperglycaemia are among a myriad of risk factors that may contribute to the pathogenesis of many cardiovascular diseases, such as hypertension, diabetes and atherosclerosis. However, the exact mechanisms underlying the impaired vascular activity remain unresolved and there is no current scientific consensus. Accumulating evidence suggests that the inflammatory cytokine TNF (tumour necrosis factor)-α plays a pivotal role in the disruption of macrovascular and microvascular circulation both in vivo and in vitro. AGEs (advanced glycation end-products)/RAGE (receptor for AGEs), LOX-1 [lectin-like oxidized low-density lipoprotein receptor-1) and NF-κB (nuclear factor κB) signalling play key roles in TNF-α expression through an increase in circulating and/or local vascular TNF-α production. The increase in TNF-α expression induces the production of ROS (reactive oxygen species), resulting in endothelial dysfunction in many pathophysiological conditions. Lipid metabolism, dietary supplements and physical activity affect TNF-α expression. The interaction between TNF-α and stem cells is also important in terms of vascular repair or regeneration. Careful scrutiny of these factors may help elucidate the mechanisms that induce vascular dysfunction. The focus of the present review is to summarize recent evidence showing the role of TNF-α in vascular dysfunction in cardiovascular disease. We believe these findings may prompt new directions for targeting inflammation in future therapies

High-Power Hybrid Solid-State Lithium-Metal Batteries Enabled by Preferred Directional Lithium Growth Mechanism

Solid electrolytes are revolutionizing the field of lithium-metal batteries; however, their practical implementa-tion has been impeded by the interfacial instability between lithium metal electrodes and solid electrolytes. While various interlayers have been suggested to address this issue in recent years, long-term stability with repeated lithium deposition/ stripping has been challenging to attain. Herein, we successfully operate a high-power lithium-metal battery by inducing the preferred directional lithium growth with a rationally designed interlayer, which employs (i) crystalline-direction-controlled carbon material providing isotropic lithium transports, with (ii) prelithium deposits that guide the lithium nucleation direction toward the current collector. This combination ensures that the morphology of the interlayer is mechanically robust while regulating the preferred lithium growth underneath the interlayer without disrupting the initial interlayer/electrolyte interface, enhancing the durability of the interface. We illustrate how these material/geometric optimizations are conducted from the thermodynamic considerations, and its applicability is demonstrated for the garnet-type Li7-xLa3-aZr2-bO12 (LLZO) solid electrolytes paired with the capacity cathode. It is shown that a lithium-metal cell with the optimized amorphous carbon interlayer with prelithium deposits exhibits outstanding room-temperature cycling performance (99. 6% capacity retention after 250 cycles), delivering 4.0 mAh cm-2 at 2.5 mA cm-2 without significant degradation of the capacity. The successful long-term operation of the hybrid solid-state cell at room temperature (approximately a cumulative deliverable capacity of over 1000 mAh cm-2) is unprecedented and records the highest performance reported for lithium-metal batteries with LLZO electrolytes until date