Role of inter-particle friction in granular materials under three dimensional conditions

The inter-particle friction is known to be an important contributor to the strength and deformation characteristics in granular materials. The mechanism of inter-particle friction to the macroscopic responses can be explained by microscopic investigations. Based on the discrete element method (DEM), a series of true triaxial tests for the cubic granular assembly are carried out and the effects of inter-particle friction coefficient (μ) on the evolutions of macro- and micromechanical parameters of granular materials are studied. The macroscopic stress, the distribution of coordination numbers and contact force with regard to strong and weak contact networks are concerned, as well as the corresponding fabric tensor and anisotropies. Findings indicate that increasing inter-particle friction sharpens the peak value of deviatoric stress and enhances the degree of dilatancy of the granular assembly at the macroscopic level. From the microscopic perspective, the distribution of the coordination number of the weak contact system varies dramatically, while the number of particles with smaller coordination number in the strong contact system changes little with different μ. Besides, the difference between strong and weak contact networks is enlarged, and anisotropy indicators are significantly enhanced, which strengthen the bearing ability of anisotropic stresses in granular materials

Dynamic Contrastive Distillation for Image-Text Retrieval

Although the vision-and-language pretraining (VLP) equipped cross-modal image-text retrieval (ITR) has achieved remarkable progress in the past two years, it suffers from a major drawback: the ever-increasing size of VLP models restrict its deployment to real-world search scenarios (where the high latency is unacceptable). To alleviate this problem, we present a novel plug-in dynamic contrastive distillation (DCD) framework to compress the large VLP models for the ITR task. Technically, we face the following two challenges: 1) the typical uni-modal metric learning approach is difficult to directly apply to cross-modal task, due to the limited GPU memory to optimize too many negative samples during handling cross-modal fusion features. 2) it is inefficient to static optimize the student network from different hard samples, which have different effects on distillation learning and student network optimization. We try to overcome these challenges from two points. First, to achieve multi-modal contrastive learning, and balance the training costs and effects, we propose to use a teacher network to estimate the difficult samples for students, making the students absorb the powerful knowledge from pre-trained teachers, and master the knowledge from hard samples. Second, to dynamic learn from hard sample pairs, we propose dynamic distillation to dynamically learn samples of different difficulties, from the perspective of better balancing the difficulty of knowledge and students' self-learning ability. We successfully apply our proposed DCD strategy on two state-of-the-art vision-language pretrained models, i.e. ViLT and METER. Extensive experiments on MS-COCO and Flickr 30 K benchmarks show the effectiveness and efficiency of our DCD framework. Encouragingly, we can speed up the inference at least 129 × compared to the existing ITR models. We further provide in-depth analyses and discussions that explain where the performance improvement comes from. We hope our work can shed light on other tasks that require distillation and contrastive learning

Hypoxic acclimatization training improves the resistance to motion sickness

ObjectiveVestibular provocation is one of the main causes of flight illusions, and its occurrence is closely related to the susceptibility of motion sickness (MS). However, existing training programs have limited effect in improving the resistance to motion sickness. In this study, we investigated the effects of hypoxia acclimatization training (HAT) on the resistance to motion sickness.MethodsHealthy military college students were identified as subjects according to the criteria. MS model was induced by a rotary chair. Experimental groups included control, HAT, 3D roller training (3DRT), and combined training.ResultsThe Graybiel scores were decreased in the HAT group and the 3DRT group and further decreased in the combined training group in MS induced by the rotary chair. Participants had a significant increase in blood pressure after the rotary chair test and a significant increase in the heart rate during the rotary chair test, but these changes disappeared in all three training groups. Additionally, LFn was increased, HFn was decreased, and LF/HF was increased accordingly during the rotary chair test in the control group, but the changes of these three parameters were completely opposite in the three training groups during the rotary chair test. Compared with the control group, the decreasing changes in pupillary contraction velocity (PCV) and pupillary minimum diameter (PMD) of the three training groups were smaller. In particular, the binocular PCV changes were further attenuated in the combined training group.ConclusionOur research provides a possible candidate solution for training military pilots in the resistance to motion sickness

Novel Evolved Immunoglobulin (Ig)-Binding Molecules Enhance the Detection of IgM against Hepatitis C Virus

Detection of specific antibodies against hepatitis C virus (HCV) is the most widely available test for viral diagnosis and monitoring of HCV infections. However, narrowing the serologic window of anti-HCV detection by enhancing anti-HCV IgM detection has remained to be a problem. Herein, we used LD5, a novel evolved immunoglobulin-binding molecule (NEIBM) with a high affinity for IgM, to develop a new anti-HCV enzyme-linked immunosorbent assay (ELISA) using horseradish peroxidase-labeled LD5 (HRP-LD5) as the conjugated enzyme complex. The HRP-LD5 assay showed detection efficacy that is comparable with two kinds of domestic diagnostic kits and the Abbott 3.0 kit when tested against the national reference panel. Moreover, the HRP-LD5 assay showed a higher detection rate (55.9%, 95% confidence intervals (95% CI) 0.489, 0.629) than that of a domestic diagnostic ELISA kit (Chang Zheng) (53.3%, 95% CI 0.463, 0.603) in 195 hemodialysis patient serum samples. Five serum samples that were positive using the HRP-LD5 assay and negative with the conventional anti-HCV diagnostic ELISA kits were all positive for HCV RNA, and 4 of them had detectable antibodies when tested with the established anti-HCV IgM assay. An IgM confirmation study revealed the IgM reaction nature of these five serum samples. These results demonstrate that HRP-LD5 improved anti-HCV detection by enhancing the detection of anti-HCV IgM, which may have potential value for the early diagnosis and screening of hepatitis C and other infectious diseases

Role of inter-particle friction in granular materials under three dimensional conditions

The inter-particle friction is known to be an important contributor to the strength and deformation characteristics in granular materials. The mechanism of inter-particle friction to the macroscopic responses can be explained by microscopic investigations. Based on the discrete element method (DEM), a series of true triaxial tests for the cubic granular assembly are carried out and the effects of inter-particle friction coefficient (μ) on the evolutions of macro- and micromechanical parameters of granular materials are studied. The macroscopic stress, the distribution of coordination numbers and contact force with regard to strong and weak contact networks are concerned, as well as the corresponding fabric tensor and anisotropies. Findings indicate that increasing inter-particle friction sharpens the peak value of deviatoric stress and enhances the degree of dilatancy of the granular assembly at the macroscopic level. From the microscopic perspective, the distribution of the coordination number of the weak contact system varies dramatically, while the number of particles with smaller coordination number in the strong contact system changes little with different μ. Besides, the difference between strong and weak contact networks is enlarged, and anisotropy indicators are significantly enhanced, which strengthen the bearing ability of anisotropic stresses in granular materials

Parameter-Efficient and Student-Friendly Knowledge Distillation

Knowledge distillation (KD) has been extensively employed to transfer the knowledge from a large teacher model to the smaller students, where the parameters of the teacher are fixed (or partially) during training. Recent studies show that this mode may cause difficulties in knowledge transfer due to the mismatched model capacities. To alleviate the mismatch problem, teacher-student joint training methods, e.g., online distillation, have been proposed, but it always requires expensive computational cost. In this paper, we present a parameter-efficient and student-friendly knowledge distillation method, namely PESF-KD, to achieve efficient and sufficient knowledge transfer by updating relatively few partial parameters. Technically, we first mathematically formulate the mismatch as the sharpness gap between their predictive distributions, where we show such a gap can be narrowed with the appropriate smoothness of the soft label. Then, we introduce an adapter module for the teacher and only update the adapter to obtain soft labels with appropriate smoothness. Experiments on a variety of benchmarks show that PESF-KD can significantly reduce the training cost while obtaining competitive results compared to advanced online distillation methods. Code will be released upon acceptance

Efficient Distributed Framework for Collaborative Multi-Agent Reinforcement Learning

Multi-agent reinforcement learning for incomplete information environments has attracted extensive attention from researchers. However, due to the slow sample collection and poor sample exploration, there are still some problems in multi-agent reinforcement learning, such as unstable model iteration and low training efficiency. Moreover, most of the existing distributed framework are proposed for single-agent reinforcement learning and not suitable for multi-agent. In this paper, we design an distributed MARL framework based on the actor-work-learner architecture. In this framework, multiple asynchronous environment interaction modules can be deployed simultaneously, which greatly improves the sample collection speed and sample diversity. Meanwhile, to make full use of computing resources, we decouple the model iteration from environment interaction, and thus accelerate the policy iteration. Finally, we verified the effectiveness of propose framework in MaCA military simulation environment and the SMAC 3D realtime strategy gaming environment with imcomplete information characteristics.Comment: 9 pages, 20 figure

Optimal Design of Power-On Downshift Control of Series-Parallel Hybrid Transmission Based on Motor Active Speed Regulation

Multi-speed transmission is the main development direction of hybrid transmission, which has brought higher shift quality requirements than traditional fuel vehicle transmission. However, there is less research on the shifting control of hybrid transmission, especially for the shifting control of dedicated hybrid transmission (DHT), which uses the wet clutch as a shift element. This paper studies the power-on downshift process of a two-speed series-parallel hybrid transmission, proposes a shifting control strategy based on motor active speed regulation, and deeply analyzes the causes of maximum impact during the shifting process. The results show that the reverse torque produced in the process of eliminating the remaining slip is the root cause of the maximum impact. On this basis, two optimization strategies are proposed to reduce the shift impact and improve the shift quality. The simulation results show that the proposed optimization strategies can effectively suppress the shift impact. In the meanwhile, for any control pressure of the OG (off-going) clutch in the speed phase within the range of (2.44–2.53 bar), a high shift quality in which the maximum impact is controlled lower than 10 m/s3 can be achieved, which has high engineering value and practical significance