Scalable Image Retrieval by Sparse Product Quantization

Fast Approximate Nearest Neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is Product Quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors and thus inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called Sparse Product Quantization (SPQ) to encoding the high-dimensional feature vectors into sparse representation. We optimize the sparse representations of the feature vectors by minimizing their quantization errors, making the resulting representation is essentially close to the original data in practice. Experiments show that the proposed SPQ technique is not only able to compress data, but also an effective encoding technique. We obtain state-of-the-art results for ANN search on four public image datasets and the promising results of content-based image retrieval further validate the efficacy of our proposed method.Comment: 12 page

Low molecular weight linear polyethylenimine-b-poly(ethylene glycol)-b-polyethylenimine triblock copolymers: synthesis, characterization, and in vitro gene transfer properties

Novel ABA triblock copolymers consisting of low molecular weight linear polyethylenimine (PEI) as the A block and poly(ethylene glycol) (PEG) as the B block were prepared and evaluated as polymeric transfectant. The cationic polymerization of 2-methyl-2-oxazoline (MeOZO) using PEG−bis(tosylate) as a macroinitiator followed by acid hydrolysis afforded linear PEI−PEG−PEI triblock copolymers with controlled compositions. Two copolymers, PEI−PEG−PEI 2100−3400−2100 and 4000−3400−4000, were synthesized. Both copolymers were shown to interact with and condense plasmid DNA effectively to give polymer/DNA complexes (polyplexes) of small sizes (<100 nm) and moderate ζ-potentials (+10 mV) at polymer/plasmid weight ratios ≥1.5/1. These polyplexes were able to efficiently transfect COS-7 cells and primary bovine endothelial cells (BAECs) in vitro. For example, PEI−PEG−PEI 4000−3400−4000 based polyplexes showed a transfection efficiency comparable to polyplexes of branched PEI 25000. The transfection activity of polyplexes of PEI−PEG−PEI 4000−3400−4000 in BAECs using luciferase as a reporter gene was 3-fold higher than that for linear PEI 25000/DNA formulations. Importantly, the presence of serum in the transfection medium had no inhibitive effect on the transfection activity of the PEI−PEG−PEI polyplexes. These PEI−PEG−PEI triblock copolymers displayed also an improved safety profile in comparison with high molecular weight PEIs, since the cytotoxicity of the polyplex formulations was very low under conditions where high transgene expression was found. Therefore, linear PEI−PEG−PEI triblock copolymers are an attractive novel class of nonviral gene delivery systems

Controlled synthesis of biodegradable lactide polymers and copolymers using novel in situ generated or single-site stereoselective polymerisation initiators

Polylactides and their copolymers are key biodegradable polymers used widely in biomedical, pharmaceutical and ecological applications. The development of synthetic pathways and catalyst/initiator systems to produce pre-designed polylactides, as well as the fundamental understanding of the polymerization reactions, has continuously been an important topic. Here, we will address the recent advances in the ring-opening polymerization of lactides, with an emphasis on the highly versatile in situ generated initiator systems and single-site stereoselective initiators. The in situ generated initiators including in situ formed yttrium, calcium and zinc alkoxides all have been shown to bring about a rapid and living polymerization of lactides under mild conditions, which facilitated the preparation of a variety of advanced lactide-based biomaterials. For example, well-defined di- and tri-block copolymers consisting of hydrophilic poly(ethylene glycol) blocks and hydrophobic polyester blocks, which form novel biodegradable polymersomes or biodegradable thermosensitive hydrogels, have been prepared. In the past few years, significant progress has also been made in the area of stereoselective polymerization of lactides. This new generation of initiators has enabled the production of polylactide materials with novel microstructures and/or properties, such as heterotactic (–RRSSRRSSRRSS–) polylactide, crystalline syndiotactic (–RSRSRSRSRSRS–) polylactide and isotactic stereoblock (–RnSnRnSn–) polylactide, exhibiting a high melting temperature. The recently developed polymerizations using in situ generated initiators and stereoselective polymerizations have no doubt opened a brand-new avenue for the design and exploration of polylactides and their copolymers

(1R,3S)-Methyl 3-[(S)-2-(hydroxy­diphenyl­meth­yl)pyrrolidin-1-ylmeth­yl]-2,2-dimethyl­cyclo­propane­carboxyl­ate

The asymmetric unit of the title compound, C25H31NO3, prepared from (−)-1R-cis-caronaldehyde, contains three independent mol­ecules with similar conformations. The hydr­oxy groups are involved in intra­molecular O—H⋯N hydrogen bonds. The crystal packing exhibits weak inter­molecular O—H⋯O and C—H⋯O hydrogen bonds

SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website:https://github.com/7ohnKeats/SCVCNet.Comment: 12 page