Unsupervised Triplet Hashing for Fast Image Retrieval

Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this study, we propose a novel hashing method for large-scale image retrieval. Considering the difficulty in obtaining labeled datasets for image retrieval task in large scale, we propose a novel CNN-based unsupervised hashing method, namely Unsupervised Triplet Hashing (UTH). The unsupervised hashing network is designed under the following three principles: 1) more discriminative representations for image retrieval; 2) minimum quantization loss between the original real-valued feature descriptors and the learned hash codes; 3) maximum information entropy for the learned hash codes. Extensive experiments on CIFAR-10, MNIST and In-shop datasets have shown that UTH outperforms several state-of-the-art unsupervised hashing methods in terms of retrieval accuracy

Fabrication and Characterization of Al/NiO Energetic Nanomultilayers

The redox reaction between Al and metallic oxide has its advantage compared with intermetallic reaction and Al/NiO nanomutlilayers are a promising candidate for enhancing the performance of energetic igniter. Al/NiO nanomutlilayers with different modulation periods are prepared on alumina substrate by direct current (DC) magnetron sputtering. The thicknesses of each period are 250 nm, 500 nm, 750 nm, 1000 nm, and 1500 nm, respectively, and the total thickness is 3 μm. The X-ray diffraction (XRD) and scanning electron microscope (SEM) results of the as-deposited Al/NiO nanomutlilayers show that the NiO films are amorphous and the layered structures are clearly distinguished. The X-ray photoelectron spectroscopy (XPS) demonstrates that the thickness of Al2O3 increases on the side of Al monolayer after annealing at 450°C. The thermal diffusion time becomes greater significantly as the amount of thermal boundary conductance across the interfaces increases with relatively smaller modulation period. Differential scanning calorimeter (DSC) curve suggests that the energy release per unit mass is below the theoretical heat of the reaction due to the nonstoichiometric ratio between Al and NiO and the presence of impurities

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Omeprazole Inhibits Cell Proliferation and Induces G0/G1 Cell Cycle Arrest through Up-regulating miR-203a-3p Expression in Barrett’s Esophagus Cells

Existing data suggest that proton pump inhibitors (PPIs), particularly omeprazole, have significant anti-tumor action in monotherapy and or combination chemotherapy. Hedgehog (Hh) signaling pathway represents a leading candidate as a molecular mediator of Barrett’s esophagus (BE). Studies have indicated reduced miRNAs in BE progression, however, little is known about the latent anti-neoplasm effects of miRNAs in BE cells. Here, we investigated whether omeprazole could inhibit BE progression by regulating Hh pathway and explored the promising Hh-targeted miRNAs in BE cells. We conducted qRT-PCR and immunoblotting assay to evaluate the effects of omeprazole on the expression of Hh signaling components and miR-203a-3p in CP-A and CP-B cells. The promising target genes of miR-203a-3p were predicted by bioinformatics methods, and verified by luciferase assays and qRT-PCR. The effects of omeprazole on BE cell proliferation and cell cycle distribution were determined. The overexpression or silencing of miR-203a-3p was performed to test its anti-proliferative effects. Finally, rescue experiments that miR-203a-3p inhibitor alleviated the effects of omeprazole on decreasing the levels of Gli1 mRNA, protein and luciferase were performed. Mechanistic studies showed that omeprazole could inhibit the expression of Gli1 and the nuclear localization of Gli1. Moreover, we determined that omeprazole could selectively up-regulated the expression of miR-203a-3p, and Gli1 was a bona fide target of miR-203a-3p. miR-203a-3p inhibitor alleviated the suppressing effects of omeprazole on Gli1 luciferase activity, mRNA and protein level. The functional assay suggested that omeprazole could dose-dependently inhibit BE cell growth and induce cell cycle arrest in G0/G1 phase. Additionally, overexpression and silencing of miR-203a-3p in BE cells disrupted cell cycle progress, resulting in suppressing and accelerating cell proliferation, respectively. Taken together, these data provide a novel mechanism of potentially anti-neoplastic effects for omeprazole through modulation of miR-203a-3p expression and thus suppressing Hh/Gli1 signaling in BE cells

Human Posture Detection Method Based on Wearable Devices

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system