1,763 research outputs found
Large Scale Constrained Linear Regression Revisited: Faster Algorithms via Preconditioning
In this paper, we revisit the large-scale constrained linear regression
problem and propose faster methods based on some recent developments in
sketching and optimization. Our algorithms combine (accelerated) mini-batch SGD
with a new method called two-step preconditioning to achieve an approximate
solution with a time complexity lower than that of the state-of-the-art
techniques for the low precision case. Our idea can also be extended to the
high precision case, which gives an alternative implementation to the Iterative
Hessian Sketch (IHS) method with significantly improved time complexity.
Experiments on benchmark and synthetic datasets suggest that our methods indeed
outperform existing ones considerably in both the low and high precision cases.Comment: Appear in AAAI-1
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method
Graph-Based Network Analysis of Resting-State Functional MRI
In the past decade, resting-state functional MRI (R-fMRI) measures of brain activity have attracted considerable attention. Based on changes in the blood oxygen level-dependent signal, R-fMRI offers a novel way to assess the brain's spontaneous or intrinsic (i.e., task-free) activity with both high spatial and temporal resolutions. The properties of both the intra- and inter-regional connectivity of resting-state brain activity have been well documented, promoting our understanding of the brain as a complex network. Specifically, the topological organization of brain networks has been recently studied with graph theory. In this review, we will summarize the recent advances in graph-based brain network analyses of R-fMRI signals, both in typical and atypical populations. Application of these approaches to R-fMRI data has demonstrated non-trivial topological properties of functional networks in the human brain. Among these is the knowledge that the brain's intrinsic activity is organized as a small-world, highly efficient network, with significant modularity and highly connected hub regions. These network properties have also been found to change throughout normal development, aging, and in various pathological conditions. The literature reviewed here suggests that graph-based network analyses are capable of uncovering system-level changes associated with different processes in the resting brain, which could provide novel insights into the understanding of the underlying physiological mechanisms of brain function. We also highlight several potential research topics in the future
The Metabolism of Baicalin in Rat and the Biological Activities of the Metabolites
Baicalin is one of the major bioactive constituents of Scutellariae Radix, but the biotransformation of it is poorly understood. In this paper, the metabolism of baicalin in rat was studied. Nine metabolites including one new compound were isolated and identified structurally. The plausible scheme for the biotransformation pathways of baicalin in the rats was deduced. And the main metabolites were evaluated for their antioxidation and anti-inflammation biological activities for the first time
On PAC Learning Halfspaces in Non-interactive Local Privacy Model with Public Unlabeled Data
In this paper, we study the problem of PAC learning halfspaces in the
non-interactive local differential privacy model (NLDP). To breach the barrier
of exponential sample complexity, previous results studied a relaxed setting
where the server has access to some additional public but unlabeled data. We
continue in this direction. Specifically, we consider the problem under the
standard setting instead of the large margin setting studied before. Under
different mild assumptions on the underlying data distribution, we propose two
approaches that are based on the Massart noise model and self-supervised
learning and show that it is possible to achieve sample complexities that are
only linear in the dimension and polynomial in other terms for both private and
public data, which significantly improve the previous results. Our methods
could also be used for other private PAC learning problems.Comment: To appear in The 14th Asian Conference on Machine Learning (ACML
2022
Waste Cathode Rays Tube: An Assessment of Global Demand for Processing
AbstractThe management of used Cathode rays tube (CRT) devices is a major problem worldwide due to rapid uptake of the technology and early obsolescence of CRT devices, which is considered an environment hazard if disposed improperly. Previously, their production has grown in step with computer and television demand but later on with rapid technological change; TVs and computer screens has been replaced by new products such as Liquid Crystal Displays (LCDs) and Plasma Display Panel (PDPs). This change creates a large volume of waste stream of obsolete CRTs waste in developed countries and developing countries will become major CRTs waste producers in the forthcoming decades. This article provides a concise overview of world's current CRTs waste scenario, namely magnitude of the demand and processing, current disposal and recycling operations
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