12,049 research outputs found
Incremental Learning Using a Grow-and-Prune Paradigm with Efficient Neural Networks
Deep neural networks (DNNs) have become a widely deployed model for numerous
machine learning applications. However, their fixed architecture, substantial
training cost, and significant model redundancy make it difficult to
efficiently update them to accommodate previously unseen data. To solve these
problems, we propose an incremental learning framework based on a
grow-and-prune neural network synthesis paradigm. When new data arrive, the
neural network first grows new connections based on the gradients to increase
the network capacity to accommodate new data. Then, the framework iteratively
prunes away connections based on the magnitude of weights to enhance network
compactness, and hence recover efficiency. Finally, the model rests at a
lightweight DNN that is both ready for inference and suitable for future
grow-and-prune updates. The proposed framework improves accuracy, shrinks
network size, and significantly reduces the additional training cost for
incoming data compared to conventional approaches, such as training from
scratch and network fine-tuning. For the LeNet-300-100 and LeNet-5 neural
network architectures derived for the MNIST dataset, the framework reduces
training cost by up to 64% (63%) and 67% (63%) compared to training from
scratch (network fine-tuning), respectively. For the ResNet-18 architecture
derived for the ImageNet dataset and DeepSpeech2 for the AN4 dataset, the
corresponding training cost reductions against training from scratch (network
fine-tunning) are 64% (60%) and 67% (62%), respectively. Our derived models
contain fewer network parameters but achieve higher accuracy relative to
conventional baselines
Photochemical transformation of perfluoroalkyl acid precursors in water using engineered nanomaterials
The production of perfluoroalkyl acids (PFAAs) has been phased out over recent decades; however, no significant decline in their environmental concentrations has been observed. This is partly due to the photochemical decomposition of PFAAs precursors (PrePFAAs) which remain in extensive use. The decomposition of PrePFAAs may be accelerated by the light-activated engineered nanomaterials (ENMs) in water. In light of this hypothesis, we investigated the photochemical transformation of three PrePFAAs, which are 8:2 fluorotelomer sulfonic acid (8:2 FTSA), 8:2 fluorotelomer alcohol (8:2 FTOH), and 2-(N-ethylperfluorooctane-1-sulfonamido ethyl] phosphate (SAmPAP), in the presence of six ENMs under simulated sunlight irradiation. The transformation rates of 8:2 FTSA and 8:2 FTOH were increased by 2–6 times when in the presence of six ENMs. However, most of ENMs appeared to inhibit the decomposition of SAmPAP. The transformation rates of PrePFAAs were found to depend on the yield of reactive oxygen species generated by ENMs, but the rates were also related to compound photo-stability, adsorption to surfaces, and photo-shielding effects. The PrePFAAs are transformed to perfluorooctanoic acid (PFOA) or/and perfluorooctane sulfonate (PFOS) with higher toxicity and longer half-life, PFOA or PFOS and a few PFAAs having shorter carbon chain lengths. Higher concentrations of the PFAAs photodegradation products were observed in the presence of most of the ENMs
Veech's Theorem of acting freely on and Structure Theorem of a.a. flows
Veech's Theorem claims that if is a locally compact\,(LC) Hausdorff
topological group, then it may act freely on . We prove
Veech's Theorem for being only locally quasi-totally bounded, not
necessarily LC. And we show that the universal a.a. flow is the maximal almost
1-1 extension of the universal minimal a.p. flow and is unique up to almost 1-1
extensions. In particular, every endomorphism of Veech's hull flow induced by
an a.a. function is almost 1-1; for or , acts
freely on its canonical universal a.a. space. Finally, we characterize Bochner
a.a. functions on a LC group in terms of Bohr a.a. function on (due to
Veech 1965 for the special case that is abelian, LC, -compact, and
first countable).Comment: 54 page
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