GPU Scheduler for De Novo Genome Assembly with Multiple MPI Processes

$\textit{De Novo}$ Genome assembly is one of the most important tasks in computational biology. ELBA is the state-of-the-art distributed-memory parallel algorithm for overlap detection and layout simplification steps of $\textit{De Novo}$ genome assembly but exists a performance bottleneck in pairwise alignment. In this work, we introduce 3 GPU schedulers for ELBA to accommodate multiple MPI processes and multiple GPUs. The GPU schedulers enable multiple MPI processes to perform computation on GPUs in a round-robin fashion. Both strong and weak scaling experiments show that 3 schedulers are able to significantly improve the performance of baseline while there is a trade-off between parallelism and GPU scheduler overhead. For the best performance implementation, the one-to-one scheduler achieves $\sim$7-8$\times$ speed-up using 25 MPI processes compared with the baseline vanilla ELBA GPU scheduler

Stroke Extraction of Chinese Character Based on Deep Structure Deformable Image Registration

Stroke extraction of Chinese characters plays an important role in the field of character recognition and generation. The most existing character stroke extraction methods focus on image morphological features. These methods usually lead to errors of cross strokes extraction and stroke matching due to rarely using stroke semantics and prior information. In this paper, we propose a deep learning-based character stroke extraction method that takes semantic features and prior information of strokes into consideration. This method consists of three parts: image registration-based stroke registration that establishes the rough registration of the reference strokes and the target as prior information; image semantic segmentation-based stroke segmentation that preliminarily separates target strokes into seven categories; and high-precision extraction of single strokes. In the stroke registration, we propose a structure deformable image registration network to achieve structure-deformable transformation while maintaining the stable morphology of single strokes for character images with complex structures. In order to verify the effectiveness of the method, we construct two datasets respectively for calligraphy characters and regular handwriting characters. The experimental results show that our method strongly outperforms the baselines. Code is available at https://github.com/MengLi-l1/StrokeExtraction.Comment: 10 pages, 8 figures, published to AAAI-23 (oral

MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild

Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github.io/MAFW.Comment: This paper has been accepted by ACM MM'2

Impact of load ramping on power transformer dissolved gas analysis

Dissolved gas in oil analysis (DGA) is one of the most reliable condition monitoring techniques, which is currently used by the industry to detect incipient faults within the power transformers. While the technique is well matured since the development of various offline and online measurement techniques along with various interpretation methods, no much attention was given so far to the oil sampling time and its correlation with the transformer loading. A power transformer loading is subject to continuous daily and seasonal variations, which is expected to increase with the increased penetration level of renewable energy sources of intermittent characteristics, such as photovoltaic (PV) and wind energy into the current electricity grids. Generating unit transformers also undergoes similar loading variations to follow the demand, particularly in the new electricity market. As such, the insulation system within the power transformers is expected to exhibit operating temperature variations due to the continuous ramping up and down of the generation and load. If the oil is sampled for the DGA measurement during such ramping cycles, results will not be accurate, and a fault may be reported due to a gas evolution resulting from such temporarily loading variation. This paper is aimed at correlating the generation and load ramping with the DGA measurements through extensive experimental analyses. The results reveal a strong correlation between the sampling time and the generation/load ramping. The experimental results show the effect of load variations on the gas generation and demonstrate the vulnerabilities of misinterpretation of transformer faults resulting from temporary gas evolution. To achieve accurate DGA, transformer loading profile during oil sampling for the DGA measurement should be available. Based on the initial investigation in this paper, the more accurate DGA results can be achieved after a ramping down cycle of the load. This sampling time could be defined as an optimum oil sampling time for transformer DGA

Polarization bandgaps and fluid-like elasticity in fully solid elastic metamaterials

Elastic waves exhibit rich polarization characteristics absent in acoustic and electromagnetic waves. By designing a solid elastic metamaterial based on three-dimensional anisotropic locally resonant units, here we experimentally demonstrate polarization bandgaps together with exotic properties such as 'fluid-like' elasticity. We construct elastic rods with unusual vibrational properties, which we denote as 'meta-rods'. By measuring the vibrational responses under flexural, longitudinal and torsional excitations, we find that each vibration mode can be selectively suppressed. In particular, we observe in a finite frequency regime that all flexural vibrations are forbidden, whereas longitudinal vibration is allowed-a unique property of fluids. In another case, the torsional vibration can be suppressed significantly. The experimental results are well interpreted by band structure analysis, as well as effective media with indefinite mass density and negative moment of inertia. Our work opens an approach to efficiently separate and control elastic waves of different polarizations in fully solid structures.G.M., C.F., and P.S. acknowledge the support of the Hong Kong Research Grants Council (Grant No. AoE/P-02/12). Y.L. and G.W. thank the State Key Program for Basic Research of China (No. 2014CB360505, No. 2012CB921501), National Natural Science Foundation of China (No. 11374224, No. 61671314), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). J.C. acknowledges the support from the European Research Council (ERC) through the Starting Grant 714577 PHONOMETA

Polymer Electret Improves the Performance of the Oxygen-Doped Organic Field-Effect Transistors

Chemical doping is widely used in the electronic devices. In p-type semiconductor thin films, oxygen doping fills the hole traps and increases hole concentrations, improving the performance of the organic field-effect transistors (OFETs). Due to the low ionization potential for p-type semiconductors, the superfluous holes induced by the oxygen doping degrades the OFETs off-state leakage performance. On the other hand, for p-type semiconductors with high ionization potential (up to 5.5-6.0 eV), the limited oxidation of oxygen is hard to achieve satisfactory doping concentrations to fill the trap states. This refers to the well-known intrinsic incompatibility between the oxygen doping and high-performance OFETs. Herein, a novel strategy is introduced to overcome the incompatibility and achieve high-performance OFETs by using the structural polymer electret. That is, moderate hole concentrations induced by low-pressure (30 Pa) oxygen plasma fill the hole traps within semiconductor. And the built-in field resulted from polymer electret accumulates the holes inside semiconductor near the semiconductor/electret interface, thus improving the OFETs performance. Using a model organic semiconductor with high ionization potential-2,7-didodecyl[1]benzothieno [3,2-b][1]benzothiophene (C12-BTBT) as an example, the high-performance OFETs with field-effect mobility (μFET) of 3.5 cm 2 V -1 s -1 , subthreshold-swing (SS) of 110 mV decade -1 , on-off ratio of 10 4 , and widely-tunable threshold voltage (V t ) are realized at a low voltage below 2 V in the open air

Gradient throwing characteristics of oscillating slat shovel for rhizome crop harvesters

An oscillating slat shovel has presented a promising application potential in the energy-saving and efficient harvesting of deep rhizome crops. This new type of shovel slat integrated harvesting device was developed by gradient amplitude and gradient vibration technology. This study aims to clarify the working characteristics of oscillating slat shovels and the mechanism of throwing separation. The throwing coefficient was selected to characterize the throwing separation ability of the slat shovel work-plane. A motion analysis was made to calculate the swing acceleration of the slat shovel work-plane. An analytical equation of the throwing coefficient was then established to combine with the working process, the periodic variation of the throwing coefficient, and the influence of parameters, including the amplitude, vibration frequency, and working length. The results showed that the throwing coefficient gradually increased at each point of the slat shovel work-plane, indicating outstanding gradient throwing characteristics and strong throwing ability. The maximum throwing coefficient was 9.98-19.72 in the separation area. After that, an investigation was made to determine the influence of the structure and working parameters of the oscillating slat shovel on the soil throwing separation performance. The EDEM-MBD coupling simulation model of the single pendulum shovel gate was established to simplify the structural model and the interaction between the rhizome, soil, and working components, where the indicators were set as the traction resistance, driving torque, the maximum separation distance between the soil and the slat (separation distance), and the ratio of the separated soil quality of each functional area of the work-plane to the total soil mass (separation ratio). A single-factor test was carried out with the amplitude, vibration frequency, and forward speed as factors. The results indicated that: 1) There were outstanding strong-weak cycles in the traction resistance and driving torque, due to the gradient throwing characteristics of oscillating slat shovel, soil viscosity, and plasticity. In the strong period, there was a large interaction force between shovel slat and soil, where the maximum separation distance occurred at the middle point of the separation area at the end point of the cutting stroke. 2) The amplitude was negatively correlated to the traction resistance, but positively correlated with the driving torque and separation distance. The vibration frequency was negatively correlated to the traction resistance, driving torque, and separation spacing. The forward velocity was positively correlated to traction resistance, driving torque, but negatively correlated to the separation distance. 3) There was a small influence of amplitude and vibration frequency on the separation ratio. There was a low separation of oscillating slat shovel, with the increase of forward velocity. 4) A combination of parameters was achieved when the amplitude was 7-11 mm in the strong period, where the average traction resistance was about 1 580.93-2 019.9 N, the maximum driving torque was about 224.04-322.11 N·m , and the maximum separation distance was about 59.58-98.3 mm. 5) The average traction resistance was about 1 416.43-1 866.38 N, the maximum driving torque was about 315.28-364.19 N·m, and the maximum separation distance was about 78.43-94.67 mm when the vibration frequency was 6.67-10.67 Hz. 6) The average traction resistance was about 1 429.43-2 110.48 N, the maximum driving torque was about 241.27-387.78 N·m, and the maximum separation distance was about 62.5-102.5 mm when the forward speed was 0.2-0.4 m/s.An optimal combination of parameters was selected for the field experiment: the amplitude was 9 mm, vibration frequency was 9.4 Hz, working speed was 0.32 m/s. The licorice harvesting test indicated that the traction resistance was about 32.17 kN, the driving torque was about 802.02 N·m, the excavation depth was about 468 mm, and the cleaning rate was about 96.42 %.Consequently, the oscillating slat shovel harvesting device can be feasible for the smooth and orderly operation, as well as the higher separation performance of rhizome-soil, where all the operation indexes met the national standards. This finding can provide a new method and design reference for the energy-saving and efficient harvesting of rhizomes, especially deep rhizome crops

Multi-Objective Personalized Product Retrieval in Taobao Search

In large-scale e-commerce platforms like Taobao, it is a big challenge to retrieve products that satisfy users from billions of candidates. This has been a common concern of academia and industry. Recently, plenty of works in this domain have achieved significant improvements by enhancing embedding-based retrieval (EBR) methods, including the Multi-Grained Deep Semantic Product Retrieval (MGDSPR) model [16] in Taobao search engine. However, we find that MGDSPR still has problems of poor relevance and weak personalization compared to other retrieval methods in our online system, such as lexical matching and collaborative filtering. These problems promote us to further strengthen the capabilities of our EBR model in both relevance estimation and personalized retrieval. In this paper, we propose a novel Multi-Objective Personalized Product Retrieval (MOPPR) model with four hierarchical optimization objectives: relevance, exposure, click and purchase. We construct entire-space multi-positive samples to train MOPPR, rather than the single-positive samples for existing EBR models.We adopt a modified softmax loss for optimizing multiple objectives. Results of extensive offline and online experiments show that MOPPR outperforms the baseline MGDSPR on evaluation metrics of relevance estimation and personalized retrieval. MOPPR achieves 0.96% transaction and 1.29% GMV improvements in a 28-day online A/B test. Since the Double-11 shopping festival of 2021, MOPPR has been fully deployed in mobile Taobao search, replacing the previous MGDSPR. Finally, we discuss several advanced topics of our deeper explorations on multi-objective retrieval and ranking to contribute to the community.Comment: 9 pages, 4 figures, submitted to the 28th ACM SIGKDD Conference on Knowledge Discovery & Data Minin

Experimental investigation on dynamic behaviour of heavy-haul railway track induced by heavy axle load

The damage to the track structure and the influence to the line deformation have greatly deteriorated with the increase of the axle load compared with that of the ordinary trains. However, there is a paucity of experimental research on the dynamic influence of the heavier haul freight trains on the railway tracks. The objective of this study is to investigate the dynamic behaviour of heavy-haul railway track induced by heavy axle load by field experimental tests. The wheel–rail dynamic force, the track structure dynamic deformation and the track vibration behaviour are measured and analysed when the train operates in the speed range from 10 to 75 km/h and the axle load of vehicles varies from 21 to 30 t. Comparisons between the results for the axle conditions of 25 and 30 t are made in this paper to reveal the axle load effects. It is demonstrated that part of the indicators reflecting the dynamic behaviour of the railway track increases approximately linearly with the train running speed and axle load, while others are influenced negligibly