Giovanni Arrighi in Beijing: Rethinking the Transformation of the Labor Supply in Rural China During the Reform Era

This article uses a Marxian political economy perspective to analyze the formation of the reserve army of labor in China during the reform era, which began in 1978. Following the methodology in Giovanni Arrighi’s seminal 1970 article, we seek to show that the formation of an industrial reserve army is a historical process in which the state played an active role. The Marxian historical method is empirically more relevant than the Lewis model in understanding the surplus labor question in China

Regulated Market, Trapped Workers: The Impacts of the Tolerant and Prudent Policy on Labour Precarity in China’s Online Ridehailing Sector

Since 2017, China has adopted the tolerant and prudent policy in regulating emerging digital platform industries. The impacts of this policy on labour precarity have been rarely studied. Drawing on an original survey of over 600 ride-hailing drivers in two Chinese cities, Nanjing and Beijing, we conduct a political economy analysis for a three-party framework involving the municipal government, capital, and labour. We find that, in accordance with the tolerant and prudent principle, municipal governments stipulated regulations regarding the qualifications of ride-hailing vehicles and drivers. These regulations, although they can help reduce labour precarity in the marketplace for licensed drivers, have exacerbated precarity in the workplace. Specifically, in response to the regulations, the ride-hailing platforms aligned with third- party rental companies that provided licensed vehicles. This arrangement has effectively trapped many ride- hailing drivers in the industry: our quantitative analysis shows that drivers bounded by a rental or rent-to- own agreement worked significantly longer hours than counterparts who steered their own vehicles

Learning Deep Intensity Field for Extremely Sparse-View CBCT Reconstruction

Sparse-view cone-beam CT (CBCT) reconstruction is an important direction to reduce radiation dose and benefit clinical applications. Previous voxel-based generation methods represent the CT as discrete voxels, resulting in high memory requirements and limited spatial resolution due to the use of 3D decoders. In this paper, we formulate the CT volume as a continuous intensity field and develop a novel DIF-Net to perform high-quality CBCT reconstruction from extremely sparse (fewer than 10) projection views at an ultrafast speed. The intensity field of a CT can be regarded as a continuous function of 3D spatial points. Therefore, the reconstruction can be reformulated as regressing the intensity value of an arbitrary 3D point from given sparse projections. Specifically, for a point, DIF-Net extracts its view-specific features from different 2D projection views. These features are subsequently aggregated by a fusion module for intensity estimation. Notably, thousands of points can be processed in parallel to improve efficiency during training and testing. In practice, we collect a knee CBCT dataset to train and evaluate DIF-Net. Extensive experiments show that our approach can reconstruct CBCT with high image quality and high spatial resolution from extremely sparse views within 1.6 seconds, significantly outperforming state-of-the-art methods. Our code will be available at https://github.com/xmed-lab/DIF-Net.Comment: MICCAI'2

Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively

Large-scale pre-trained language models have achieved impressive results on a wide range of downstream tasks recently. However, fine-tuning an extremely large-scale pre-trained language model on limited target datasets is often plagued by overfitting and representation degradation. In this paper, we propose a Dynamic Parameter Selection (DPS) algorithm for the large-scale pre-trained models during fine-tuning, which adaptively selects a more promising subnetwork to perform staging updates based on gradients of back-propagation. Experiments on the GLUE benchmark show that DPS outperforms previous fine-tuning methods in terms of overall performance and stability, and consistently achieves better results with variable pre-trained language models. In addition, DPS brings a large magnitude of improvement in out-of-domain transferring experiments and low-resource scenarios, which shows that it can maintain stable general contextual features and reduce the representation collapse. We release our code at https://github.com/ZhangHaojie077/DPSComment: NeurIPS 202

Security and Energy-aware Collaborative Task Offloading in D2D communication

Device-to-device (D2D) communication technique is used to establish direct links among mobile devices (MDs) to reduce communication delay and increase network capacity over the underlying wireless networks. Existing D2D schemes for task offloading focus on system throughput, energy consumption, and delay without considering data security. This paper proposes a Security and Energy-aware Collaborative Task Offloading for D2D communication (Sec2D). Specifically, we first build a novel security model, in terms of the number of CPU cores, CPU frequency, and data size, for measuring the security workload on heterogeneous MDs. Then, we formulate the collaborative task offloading problem that minimizes the time-average delay and energy consumption of MDs while ensuring data security. In order to meet this goal, the Lyapunov optimization framework is applied to implement online decision-making. Two solutions, greedy approach and optimal approach, with different time complexities, are proposed to deal with the generated mixed-integer linear programming (MILP) problem. The theoretical proofs demonstrate that Sec2D follows a [O(1∕V),O(V)] energy-delay tradeoff. Simulation results show that Sec2D can guarantee both data security and system stability in the collaborative D2D communication environment

Photomodulating RNA cleavage using photolabile circular antisense oligodeoxynucleotides

Caged antisense oligodeoxynucleotides (asODNs) are synthesized by linking two ends of linear oligodeoxynucleotides using a photocleavable linker. Two of them (H30 and H40) have hairpin-like structures which show a large difference in thermal stability (ΔTm = 17.5°C and 11.6°C) comparing to uncaged ones. The other three (C20, C30 and C40) without stable secondary structures have the middle 20 deoxynucleotides complementary to 40-mer RNA. All caged asODNs have restricted opening which provides control over RNA/asODN interaction. RNase H assay results showed that 40-mer RNA digestion could be photo-modulated 2- to 3-fold upon light-activation with H30, H40, C30 and C40, while with C20, RNA digestion was almost not detectable; however, photo-activation triggered >20-fold increase of RNA digestion. And gel shift assays showed that it needed >0.04 μM H40 and 0.5 μM H30 to completely bind 0.02 μM 40-mer RNA, and for C40 and C30, it needed >0.2 μM and 0.5 μM for 0.02 μM 40-mer RNA binding. However, even 4 μM C20 was not able to fully bind the same concentration of 40-mer RNA. By simple adjustment of ring size of caged asODNs, we could successfully photoregulate their hybridization with mRNA and target RNA hydrolysis by RNase H with light activation

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

© 2020 Binbin Huang et al. Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme

Calcium Oxalate Induces Renal Injury through Calcium-Sensing Receptor

Objective. To investigate whether calcium-sensing receptor (CaSR) plays a role in calcium-oxalate-induced renal injury. Materials and Methods. HK-2 cells and rats were treated with calcium oxalate (CaOx) crystals with or without pretreatment with the CaSR-specific agonist gadolinium chloride (GdCl3) or the CaSR-specific antagonist NPS2390. Changes in oxidative stress (OS) in HK-2 cells and rat kidneys were assessed. In addition, CaSR, extracellular signal-regulated protein kinase (ERK), c-Jun N-terminal protein kinase (JNK), and p38 expression was determined. Further, crystal adhesion assay was performed in vitro, and the serum urea and creatinine levels and crystal deposition in the kidneys were also examined. Results. CaOx increased CaSR, ERK, JNK, and p38 protein expression and OS in vitro and in vivo. These deleterious changes were further enhanced upon pretreatment with the CaSR agonist GdCl3 but were attenuated by the specific CaSR inhibitor NPS2390 compared with CaOx treatment alone. Pretreatment with GdCl3 further increased in vitro and in vivo crystal adhesion and renal hypofunction. In contrast, pretreatment with NPS2390 decreased in vitro and in vivo crystal adhesion and renal hypofunction. Conclusions. CaOx-induced renal injury is related to CaSR-mediated OS and increased mitogen-activated protein kinase (MAPK) signaling, which subsequently leads to CaOx crystal adhesion

Real-time and dynamic fault-tolerant scheduling for scientific workflows in clouds

Cloud computing has become a popular technology for executing scientific workflows. However, with a large number of hosts and virtual machines (VMs) being deployed, the cloud resource failures, such as the permanent failure of hosts (HPF), the transient failure of hosts (HTF), and the transient failure of VMs (VMTF), bring the service reliability problem. Therefore, fault tolerance for time-consuming scientific workflows is highly essential in the cloud. However, existing fault-tolerant (FT) approaches consider only one or two above failure types and easily neglect the others, especially for the HTF. This paper proposes a Real-time and dynamic Fault-tolerant Scheduling (ReadyFS) algorithm for scientific workflow execution in a cloud, which guarantees deadline constraints and improves resource utilization even in the presence of any resource failure. Specifically, we first introduce two FT mechanisms, i.e., the replication with delay execution (RDE) and the checkpointing with delay execution (CDE), to cope with HPF and VMTF, simultaneously. Additionally, the rescheduling (ReSC) is devised to tackle the HTF that affects the resource availability of the entire cloud datacenter. Then, the resource adjustment (RA) strategy, including the resource scaling-up (RS-Up) and the resource scaling-down (RS-Down), is used to adjust resource demands and improve resource utilization dynamically. Finally, the ReadyFS algorithm is presented to schedule real-time scientific workflows by combining all the above FT mechanisms with RA strategy. We conduct the performance evaluation with real-world scientific workflows and compare ReadyFS with five vertical comparison algorithms and three horizontal comparison algorithms. Simulation results confirm that ReadyFS is indeed able to guarantee the fault tolerance of scientific workflow execution and improve cloud resource utilization