Tighter bounds of the First Fit algorithm for the bin-packing problem

AbstractIn this paper, we present improved bounds for the First Fit algorithm for the bin-packing problem. We prove CFF(L)≤1710C∗(L)+710 for all lists L, and the absolute performance ratio of FF is at most 127

La\u3csub\u3e0.85\u3c/sub\u3eSr\u3csub\u3e0.15\u3c/sub\u3eMnO\u3csub\u3e3−\u3c/sub\u3e Infiltrated Y\u3csub\u3e0.5\u3c/sub\u3eBi\u3csub\u3e1.5\u3c/sub\u3eO\u3csub\u3e3\u3c/sub\u3e Cathodes for Intermediate-Temperature Solid Oxide Fuel Cells

Porous yttria-stabilized bismuth oxides (YSB) were investigated as the backbones for La0.85Sr0.15MnO3−(LSM) infiltrated cathodes in intermediate-temperature solid oxide fuel cells. The cathodes were evaluated using anode-supported single cells with scandia-stabilized zirconia as the electrolytes. With humidified H2 as the fuel, the cell showed peak power density of 0.33, 0.52, and 0.74 W cm−2 at 650, 700, and 750°C, respectively. At 650°C, the cell polarization resistance was only 1.38 Ω cm2, \u3c50% of the lowest value previously reported, indicating that YSB is a promising backbone for the LSM infiltrated cathode

Attitude Determination and Control System Design for STU-2A CubeSat and In-Orbit Results

STU-2A, a 3U CubeSat developed by Shanghai Engineering Center for Microsatellites, along with the other two 2U CubeSats and one MicroSat, has been sent into a 481km sun-synchronous orbit by CZ-11 launch vehicle with its maiden journey. As the first batch of CubeSats in China that is made in accordance with CubeSat standard, the 2.9kg satellite is featured with the on-board CMOS color camera for taking pictures of polar glacier, Gamalink for Cubesats Networking, MEMS based cold-gas micropropulsion for attitude/orbit maneuver and formation flight and precise ADCS module for technology demonstration. The ADCS module, equipped with two 3-axis magnetometers, 1 fine Sun sensor, five coarse Sun sensors, one three-axis MEMS gyro, one Nano-scaled star tracker, three magnetic coils and three reaction wheels, would provide three-axis stabilization and maneuver capability. Combining the attitude sensors, TRAID and unscented Kalman Filter (UKF) algorithms are adopted to determine the attitude knowledge. Some attitude control modes, such as damping control, Sun-pointing, magnetic-based nadir pointing, momentum-biased stabilization and reaction wheels-based control, are designed to achieve the prefect attitude.In-Orbit data received by ground station verified the performance of ADCS of STU-2A

Code Generation as a Dual Task of Code Summarization

Code summarization (CS) and code generation (CG) are two crucial tasks in the field of automatic software development. Various neural network-based approaches are proposed to solve these two tasks separately. However, there exists a specific intuitive correlation between CS and CG, which have not been exploited in previous work. In this paper, we apply the relations between two tasks to improve the performance of both tasks. In other words, exploiting the duality between the two tasks, we propose a dual training framework to train the two tasks simultaneously. In this framework, we consider the dualities on probability and attention weights, and design corresponding regularization terms to constrain the duality. We evaluate our approach on two datasets collected from GitHub, and experimental results show that our dual framework can improve the performance of CS and CG tasks over baselines.Comment: To appear at the 33rd Conference on Neural Information Processing Systems (NeurIPS) 201

Rethinking Pseudo-LiDAR Representation

The recently proposed pseudo-LiDAR based 3D detectors greatly improve the benchmark of monocular/stereo 3D detection task. However, the underlying mechanism remains obscure to the research community. In this paper, we perform an in-depth investigation and observe that the efficacy of pseudo-LiDAR representation comes from the coordinate transformation, instead of data representation itself. Based on this observation, we design an image based CNN detector named Patch-Net, which is more generalized and can be instantiated as pseudo-LiDAR based 3D detectors. Moreover, the pseudo-LiDAR data in our PatchNet is organized as the image representation, which means existing 2D CNN designs can be easily utilized for extracting deep features from input data and boosting 3D detection performance. We conduct extensive experiments on the challenging KITTI dataset, where the proposed PatchNet outperforms all existing pseudo-LiDAR based counterparts. Code has been made available at: https://github.com/xinzhuma/patchnet.Comment: ECCV2020. Supplemental Material attache

Single-image based deep learning for precise atomic defects identification

Defect engineering has been profoundly employed to confer desirable functionality to materials that pristine lattices inherently lack. Although single atomic-resolution scanning transmission electron microscopy (STEM) images are widely accessible for defect engineering, harnessing atomic-scale images containing various defects through traditional image analysis methods is hindered by random noise and human bias. Yet the rise of deep learning (DL) offering an alternative approach, its widespread application is primarily restricted by the need for large amounts of training data with labeled ground truth. In this study, we propose a two-stage method to address the problems of high annotation cost and image noise in the detection of atomic defects in monolayer 2D materials. In the first stage, to tackle the issue of data scarcity, we employ a two-state transformation network based on U-GAT-IT for adding realistic noise to simulated images with pre-located ground truth labels, thereby infinitely expanding the training dataset. In the second stage, atomic defects in monolayer 2D materials are effectively detected with high accuracy using U-Net models trained with the data generated in the first stage, avoiding random noise and human bias issues. In both stages, we utilize segmented unit-cell-level images to simplify the model's task and enhance its accuracy. Our results demonstrate that not only sulfur vacancies, we are also able to visualize oxygen dopants in monolayer MoS2, which are usually overwhelmed by random background noise. As the training was based on a few segmented unit-cell-level realistic images, this method can be readily extended to other 2D materials. Therefore, our results outline novel ways to train the model with minimized datasets, offering great opportunities to fully exploit the power of machine learning (ML) applicable to a broad materials science community

Genome-wide identification of the AcMADS-box family and functional validation of AcMADS32 involved in carotenoid biosynthesis in Actinidia

MADS-box is a large transcription factor family in plants and plays a crucial role in various plant developmental processes; however, it has not been systematically analyzed in kiwifruit. In the present study, 74 AcMADS genes were identified in the Red5 kiwifruit genome, including 17 type-I and 57 type-II members according to the conserved domains. The AcMADS genes were randomly distributed across 25 chromosomes and were predicted to be mostly located in the nucleus. A total of 33 fragmental duplications were detected in the AcMADS genes, which might be the main force driving the family expansion. Many hormone-associated cis-acting elements were detected in the promoter region. Expression profile analysis showed that AcMADS members had tissue specificity and different responses to dark, low temperature, drought, and salt stress. Two genes in the AG group, AcMADS32 and AcMADS48, had high expression levels during fruit development, and the role of AcMADS32 was further verified by stable overexpression in kiwifruit seedlings. The content of α-carotene and the ratio of zeaxanthin/β-carotene was increased in transgenic kiwifruit seedlings, and the expression level of AcBCH1/2 was significantly increased, suggesting that AcMADS32 plays an important role in regulating carotenoid accumulation. These results have enriched our understanding of the MADS-box gene family and laid a foundation for further research of the functions of its members during kiwifruit development

Identification and validation of IgG N-glycosylation biomarkers of esophageal carcinoma

Introduction: Altered Immunoglobulin G (IgG) N-glycosylation is associated with aging, inflammation, and diseases status, while its effect on esophageal squamous cell carcinoma (ESCC) remains unknown. As far as we know, this is the first study to explore and validate the association of IgG N-glycosylation and the carcinogenesis progression of ESCC, providing innovative biomarkers for the predictive identification and targeted prevention of ESCC. Methods: In total, 496 individuals of ESCC (n=114), precancerosis (n=187) and controls (n=195) from the discovery population (n=348) and validation population (n=148) were recruited in the study. IgG N-glycosylation profile was analyzed and an ESCC-related glycan score was composed by a stepwise ordinal logistic model in the discovery population. The receiver operating characteristic (ROC) curve with the bootstrapping procedure was used to assess the performance of the glycan score. Results: In the discovery population, the adjusted OR of GP20 (digalactosylated monosialylated biantennary with core and antennary fucose), IGP33 (the ratio of all fucosylated monosyalilated and disialylated structures), IGP44 (the proportion of high mannose glycan structures in total neutral IgG glycans), IGP58 (the percentage of all fucosylated structures in total neutral IgG glycans), IGP75 (the incidence of bisecting GlcNAc in all fucosylated digalactosylated structures in total neutral IgG glycans), and the glycan score are 4.03 (95% CI: 3.03-5.36, P \u3c 0.001), 0.69 (95% CI: 0.55-0.87, P \u3c 0.001), 0.56 (95% CI: 0.45-0.69, P \u3c 0.001), 0.52 (95% CI: 0.41-0.65, P \u3c 0.001), 7.17 (95% CI: 4.77-10.79, P \u3c 0.001), and 2.86 (95% CI: 2.33-3.53, P \u3c 0.001), respectively. Individuals in the highest tertile of the glycan score own an increased risk (OR: 11.41), compared with those in the lowest. The average multi-class AUC are 0.822 (95% CI: 0.786-0.849). Findings are verified in the validation population, with an average AUC of 0.807 (95% CI: 0.758-0.864). Discussion: Our study demonstrated that IgG N-glycans and the proposed glycan score appear to be promising predictive markers for ESCC, contributing to the early prevention of esophageal cancer. From the perspective of biological mechanism, IgG fucosylation and mannosylation might involve in the carcinogenesis progression of ESCC, and provide potential therapeutic targets for personalized interventions of cancer progression

A study on China's R&D innovation: From the perspective of patent economic value and green patents

The connection between world economic development and technological innovation is getting closer and closer, and enterprises, as the main body of technological innovation, play an essential role in it. This thesis takes China, an emerging economy, as the background to conduct an in-depth exploration and discussion of the factors influencing the R&D innovation of its listed companies. More specifically, we focus on patents produced by R&D innovation, taking the economic value of patents as the primary research goal, and also on the green patents obtained by enterprises. This thesis mainly conducts an in-depth exploration of three different research questions. First, given that the calculation of the economic value of patents is based on the reaction of the stock market, and stock liquidity is an essential characteristic of stocks, its impact on the economic value of patents is still unknown. We use the data of China's A-share listed companies between 2000 and 2020 as a sample to study the impact of stock liquidity on the economic value of corporate patents. This thesis finds that stock liquidity can significantly increase the economic value and innovation returns of patents obtained by companies. This conclusion is still robust after passing the test of instrumental variables and quasi-natural experiments. Furthermore, we found that companies in high-tech industries are the leading promoters of the positive relationship between the two, and this relationship is not significant in other industries. We also found that increased stock liquidity can increase stock transparency, reduce information asymmetry, improve market operation and pricing efficiency, and enable patents to be accurately priced, thereby increasing the economic value of patents and innovation returns. In addition, we also used six other alternative measures of stock liquidity for testing, and the results were similar. Secondly, China's patent types are divided into invention patents, utility models and design patents (non-invention patents). The latter's proportion in the total number of patents is always significantly higher than the former. However, there is always a need for empirical conclusions on the value between them. This thesis conducts a quantitative comparison and analysis. We found that the individual economic value of invention patents is significantly higher than non-invention patents. However, the higher the proportion of the latter, the higher the company's total patent economic value and innovation returns. In addition, compared with state-owned enterprises, private enterprises have more invention patents, a more significant number and proportion of R&D staff, and a lower proportion of non-invention patents. The high-tech industry has more invention and non-invention patents and R&D staff, but the proportion of non-invention patents is significantly lower than that of other industries. Moreover, government innovation subsidies and increased R&D employees can significantly suppress the proportion of non-invention patents held by enterprises. If the market operates more efficiently, the higher the proportion of utility models and designs, the higher the average economic value of patents owned by the company, and the total economic value of patents and the return on innovation will be significantly lower. That is similar to when market competition is more intense or when a company's competitive position is higher. Finally, given the increasingly severe global environmental problems such as global warming and the gradual gaining popularity of the concept of green development, we explored the factors that affect corporate green innovation. This thesis takes institutional investors, essential players in the market, as the research object and finds that general institutional investors cannot promote green innovation in enterprises. In contrast, a particular group of institutional investors, green institutional investors, can significantly promote green innovation in enterprises. Furthermore, we found that green institutional investors can play a more significant role in promoting green innovation in companies with high green cost expenditures and private companies. The positive relationship between the two is insignificant among low green cost expenditures and enterprises and state-owned enterprises. In addition, this thesis finds that companies with the participation of green institutional investors tend to disclose environmental information actively, and these companies also have relatively higher ESG ratings. Establishing a corporate reputation attracts more investors inclined to make green investments, promoting corporate green innovation