Combining Background Subtraction Algorithms with Convolutional Neural Network

Accurate and fast extraction of foreground object is a key prerequisite for a wide range of computer vision applications such as object tracking and recognition. Thus, enormous background subtraction methods for foreground object detection have been proposed in recent decades. However, it is still regarded as a tough problem due to a variety of challenges such as illumination variations, camera jitter, dynamic backgrounds, shadows, and so on. Currently, there is no single method that can handle all the challenges in a robust way. In this letter, we try to solve this problem from a new perspective by combining different state-of-the-art background subtraction algorithms to create a more robust and more advanced foreground detection algorithm. More specifically, an encoder-decoder fully convolutional neural network architecture is trained to automatically learn how to leverage the characteristics of different algorithms to fuse the results produced by different background subtraction algorithms and output a more precise result. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that the proposed method outperforms all the considered single background subtraction algorithm. And we show that our solution is more efficient than other combination strategies

Metal-Organic Framework Nanosheets for Electrocatalysis

Metal-organic frameworks (MOFs) have aroused great interest in many fields due to their appearing properties such as tailorable structure and function, high specific surface area and porosity. Electrocatalysis is attractive and significant for the academia and industry because it underpins various clean and renewable technologies like water splitting, fuel cell, metal-air batteries, etc. In recent years, MOFs-derived materials prepared through post hightemperature calcination have been widely investigated for electrocatalysis. However, the pyrolysis process always destroys the structure of the MOFs, resulting in the agglomeration of metal nodes and loss of organic ligands, which are not favourable for electrocatalysis. Meanwhile, only very limited number of works directly used pristine MOFs as electrocatalysts. To this end, this thesis aims to design and synthesize 2D MOF nanosheets and 2D MOF-based hybrid nanosheets for electrocatalysis. The first aspect of this thesis is about Ni-MOF nanosheets with high oxidation state for urea oxidation reaction (UOR). High oxidation state of metal cations is critical in achieving outstanding performance of many transition metal-based materials towards electrochemical oxidation reactions such as UOR, which acts as a vital half reaction for several practical applications. However, it is still a great challenge to explore such a kind of materials for high-performance oxidation reactions. Herein, 2D MOF comprising nickel species and organic ligand of 1,4-benzenedicarboxylic acid (BDC) is fabricated and explored as an electrocatalyst for UOR, which exhibits high activity (120 mA cm-2 at 1.6 V vs. RHE) and strong catalyst durability after continuous operation for 10 hours. The excellent UOR performance is due to high active site density of the 2D MOF, and high oxidation state of the nickel species, which are proved by both X-ray photoelectron spectroscopy and Synchrotronbased X-ray absorption near edge spectra. Our findings provide a suitable material for practical application of UOR, and this 2D MOF strategy could be used to fabricate other electrocatalyst with high oxidation state for a wide range of oxidation reactions. The second aspect of this thesis is about Ni-BDC/Ni(OH)2 hybrid nanosheets for oxygen evolution reaction (OER). Just like graphene, 2D MOF has an unwanted tendency to aggregate, which reduces the specific surface area. Ni(OH)2 is a typical catalyst for OER, but the reaction activity is far from satisfactory probably due to its low oxidation state. The Ni- BDC/Ni(OH)2 hybrid nanosheets prepared through a facile sonication-assisted solution method can perfectly solve these two problems. After hybridization with Ni(OH)2, the large 1 surface area of Ni-BDC is well retained. Moreover, due to the strong electron interactions between BDC from Ni-BDC and Ni cations from Ni(OH)2, the electronic structure of Ni cations from Ni(OH)2 component can be well modified, leading to the generation of Ni cations with higher oxidation state, which surely contribute to enhanced OER activity. As a result, the Ni-BDC/Ni(OH)2 hybrid nanosheets exhibited remarkable OER performance in 1.0 M KOH, outperforming pure Ni-BDC, Ni(OH)2 and even commercial Ir/C. The third aspect of this thesis is about Co-BDC/MoS2 hybrid nanosheets for alkaline hydrogen evolution reaction (HER). Generally, the reaction activity of a catalyst for alkaline HER is about 2-3 orders of magnitude lower than that for acidic HER. This is because the hydrogen intermediate (H*) comes from the dissociation of water in alkaline solution, and this step introduces an additional energy barrier for alkaline HER. At present, the oxidation reaction (e.g. OER) performance of MOFs are comparable or even superior to benchmark noble metals, but the HER activities of MOFs are far from satisfactory. To this end, Co- BDC/MoS2 hybrid nanosheets are constructed for alkaline HER. The pristine 2H-MoS2 are transformed to 1T-MoS2 partially after the hybridization. This is beneficial for HER as 1TMoS2 is a much better HER catalyst. Moreover, the well-constructed Co-BDC/MoS2 interface is vital for alkaline HER, as both components play specific roles in different elementary steps of alkaline HER. In specific, Co-BDC facilitates the dissociation of water to provide enough protons to the nearby MoS2, while phase-modified MoS2 is favourable for the following H2 generation. As expected, the as-fabricated Co-BDC/MoS2 nanosheets exhibit remarkable HER performance in 1.0 M KOH, outperforming those of Co-BDC nanosheets, MoS2 nanosheets and almost all the previously reported MOFs-based electrocatalysts.Thesis (Ph.D.) -- University of Adelaide, School of Chemical Engineering & Advanced Materials, 201

Background Subtraction with Real-time Semantic Segmentation

Accurate and fast foreground object extraction is very important for object tracking and recognition in video surveillance. Although many background subtraction (BGS) methods have been proposed in the recent past, it is still regarded as a tough problem due to the variety of challenging situations that occur in real-world scenarios. In this paper, we explore this problem from a new perspective and propose a novel background subtraction framework with real-time semantic segmentation (RTSS). Our proposed framework consists of two components, a traditional BGS segmenter $\mathcal{B}$ and a real-time semantic segmenter $\mathcal{S}$. The BGS segmenter $\mathcal{B}$ aims to construct background models and segments foreground objects. The real-time semantic segmenter $\mathcal{S}$ is used to refine the foreground segmentation outputs as feedbacks for improving the model updating accuracy. $\mathcal{B}$ and $\mathcal{S}$ work in parallel on two threads. For each input frame $I_t$, the BGS segmenter $\mathcal{B}$ computes a preliminary foreground/background (FG/BG) mask $B_t$. At the same time, the real-time semantic segmenter $\mathcal{S}$ extracts the object-level semantics ${S}_t$. Then, some specific rules are applied on ${B}_t$ and ${S}_t$ to generate the final detection ${D}_t$. Finally, the refined FG/BG mask ${D}_t$ is fed back to update the background model. Comprehensive experiments evaluated on the CDnet 2014 dataset demonstrate that our proposed method achieves state-of-the-art performance among all unsupervised background subtraction methods while operating at real-time, and even performs better than some deep learning based supervised algorithms. In addition, our proposed framework is very flexible and has the potential for generalization

The effects of grain structure on electromigration failure of the lead-free solder bump

This paper carries out an electromigration (EM) acceleration test on ball grid array (BGA) samples with Sn96.5/Ag3.0/Cu0.5 solder bumps under constant temperature, and characterizes the structure of β-Sn grains in the lead-free solder bumps. The EM failure modes of the solder bumps of different grain structures were analysed, aiming to disclose the effect of grain structure on the EM failure. Considering the driving forces of the EM (i.e. electron wind force, stress gradient, temperature gradient and atomic density gradient), the atomic density integral (ADI) method was introduced to simulate the void formation and failure lifetime of the EM. The simulation results show that solder bump reliability and failure mode are greatly affected by grain orientation, in that the EM failure occurs rapidly when the c-axis of grain structure of the solder bump is strongly misaligned, or almost perpendicular, to the current direction. The double grain solder bump with grain boundary parallel to current direction boasts a small EM failure and thus a long lifetime

Overexpression of members of the microRNA-183 family is a risk factor for lung cancer: A case control study

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the leading cause of cancer-related deaths worldwide. Early detection is considered critical for lung cancer treatment. MicroRNAs (miRNAs) have shown promise as diagnostic and prognostic indicators. This study was to identify specific miRNAs with diagnostic and prognostic value for patients with lung cancer, and to explore the correlation between expression profiles of miRNAs and patient survival.</p> <p>Methods</p> <p>Gene expression of members of the miR-183 family (miR-96, miR-182, and miR-183) were examined in 70 paired samples from lung cancer patients (primary cancer and non-cancerous tissues and sera), as well as 44 serum samples from normal volunteers and lung cancer cell lines by quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR). The correlation between the expression of miRNAs in tissues, sera, and patient overall survival were also examined by log-rank and Cox regression analysis.</p> <p>Results</p> <p>Expression levels of members of the miR-183 family in lung cancer tumor and sera were higher than that of their normal counterparts. The miR-96 expression in tumors was positively associated with its expression in sera. Log-rank and Cox regression analyses demonstrated that high expression of tumor and serum miRNAs of the miR-183 family were associated with overall poor survival in patients with lung cancer.</p> <p>Conclusions</p> <p>Our results suggest that the expressions of miR-96, miR-182, and miR-183 in tumor and sera may be considered potential novel biomarkers for the diagnosis and prognosis of lung cancer.</p

Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M_W 7.9 Wenchuan Earthquake

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase-Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 M_W 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The deployment of CPIC takes less than 12 h to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases

Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models

Video-based large language models (Video-LLMs) have been recently introduced, targeting both fundamental improvements in perception and comprehension, and a diverse range of user inquiries. In pursuit of the ultimate goal of achieving artificial general intelligence, a truly intelligent Video-LLM model should not only see and understand the surroundings, but also possess human-level commonsense, and make well-informed decisions for the users. To guide the development of such a model, the establishment of a robust and comprehensive evaluation system becomes crucial. To this end, this paper proposes \textit{Video-Bench}, a new comprehensive benchmark along with a toolkit specifically designed for evaluating Video-LLMs. The benchmark comprises 10 meticulously crafted tasks, evaluating the capabilities of Video-LLMs across three distinct levels: Video-exclusive Understanding, Prior Knowledge-based Question-Answering, and Comprehension and Decision-making. In addition, we introduce an automatic toolkit tailored to process model outputs for various tasks, facilitating the calculation of metrics and generating convenient final scores. We evaluate 8 representative Video-LLMs using \textit{Video-Bench}. The findings reveal that current Video-LLMs still fall considerably short of achieving human-like comprehension and analysis of real-world videos, offering valuable insights for future research directions. The benchmark and toolkit are available at: \url{https://github.com/PKU-YuanGroup/Video-Bench}.Comment: Benchmark is available at https://github.com/PKU-YuanGroup/Video-Benc