Are adaptation challenges relevant to the location choices of internal migrants? Evidence from China

This paper highlights the relevance of adaptation challenges to the location choices of internal migrants, thereby adding to the recognition that they are newcomers to the host society. To achieve this, it presents an examination of how cultural, institutional and social differences between origin and destination regions, which internal migrants need to adapt to, impact their location choices, using labour migration within China as a case study. Competing-destination models show that these adaptation-related differences are indeed significant to internal migration, especially for younger and older women, more educated migrants, the self-employed, singles, and households moving together

IoTGAN: GAN Powered Camouflage Against Machine Learning Based IoT Device Identification

With the proliferation of IoT devices, researchers have developed a variety of IoT device identification methods with the assistance of machine learning. Nevertheless, the security of these identification methods mostly depends on collected training data. In this research, we propose a novel attack strategy named IoTGAN to manipulate an IoT device's traffic such that it can evade machine learning based IoT device identification. In the development of IoTGAN, we have two major technical challenges: (i) How to obtain the discriminative model in a black-box setting, and (ii) How to add perturbations to IoT traffic through the manipulative model, so as to evade the identification while not influencing the functionality of IoT devices. To address these challenges, a neural network based substitute model is used to fit the target model in black-box settings, it works as a discriminative model in IoTGAN. A manipulative model is trained to add adversarial perturbations into the IoT device's traffic to evade the substitute model. Experimental results show that IoTGAN can successfully achieve the attack goals. We also develop efficient countermeasures to protect machine learning based IoT device identification from been undermined by IoTGAN

Continual Learning with Strong Experience Replay

Continual Learning (CL) aims at incrementally learning new tasks without forgetting the knowledge acquired from old ones. Experience Replay (ER) is a simple and effective rehearsal-based strategy, which optimizes the model with current training data and a subset of old samples stored in a memory buffer. To further reduce forgetting, recent approaches extend ER with various techniques, such as model regularization and memory sampling. However, the prediction consistency between the new model and the old one on current training data has been seldom explored, resulting in less knowledge preserved when few previous samples are available. To address this issue, we propose a CL method with Strong Experience Replay (SER), which additionally utilizes future experiences mimicked on the current training data, besides distilling past experience from the memory buffer. In our method, the updated model will produce approximate outputs as its original ones, which can effectively preserve the acquired knowledge. Experimental results on multiple image classification datasets show that our SER method surpasses the state-of-the-art methods by a noticeable margin

Collective pharmaceutical procurement in China may have unintended consequences in supply and pricing

The collective pharmaceutical procurement was launched in China in 2018 to reduce the prices of selected drugs, by pooling the demands of member cities and granting the contract to the manufacturer with the lowest bid. We found the procurement significantly decreased the prices of most drugs. We also identified significant price increases on some drugs, indicating that manufacturers of these drugs may have strong market power to manipulate prices. The “winner-takes-all” principle applied in the procurement may further increase the market power of winning manufacturers by expanding their respective market shares. They may take the advantage of the market power to increase drug prices in the long-run. The continuously lowering price-caps may force the losing bidders to exit the market. A careful assessment of the unintended consequences of the collective procurement is warranted

Spinal Microglial Motility is Independent of Neuronal Activity and Plasticity in Adult Mice

Microglia are the resident macrophages in the central nervous system. In the spinal cord dorsal horn, microglia stay in resting condition during physiological sensory processing, and are activated under pathological conditions such as peripheral nerve injury. In cases such as this, the nearby resting microglia increase their motility and accumulate at the site of injury. However, direct evidence to support that nerve activity can enhance the motility of microglia has not yet to be reported. In this study we investigated whether the activation of spinal microglia under in vivo nerve injury may be mimicked by neuronal activity in the spinal cord slice preparation. We found that local application of spinal excitatory neurotransmitters, such as glutamate and substance P did not cause any change in the motility of microglial cells in the spinal cord dorsal horn. The motility of microglial cells is unlikely modulated by other transmitters, neuromodulators and chemokines, because similar applications such as GABA, serotonin, noradrenaline, carbachol, fractalkine or interleukin did not produce any obvious effect. Furthermore, low or high frequency stimulation of spinal dorsal root fibers at noxious intensities failed to cause any enhanced extension or retraction of the microglia processes. By contrast, focal application of ATP triggered rapid and robust activation of microglial cells in the spinal dorsal horn. Our results provide the first evidence that the activation of microglia in the spinal cord after nerve injury is unlikely due solely to neuronal activity, non-neuronal factors are likely responsible for the activation of nerve injury-related microglial cells in the spinal dorsal horn

Anisotropic magneto-resistance in MgO-based magnetic tunnel junctions induced by spin-orbit coupling

We performed a first-principles study of the tunneling anisotropic magneto-resistance (TAMR) in Ag(Ir,Pt)$/$MgO$/$Fe junctions. Enhanced TAMR with ideal and skewed fourfold angular dependence is found in-plane and out-of-plane TAMR of the system, respectively, which shows simple barrier thickness dependency with number around 10\% in some junctions. The complex angular dependency of the interfacial resonant states due to the spin-orbit coupling should be responsible to the complex and enhanced TAMR found in these junctions.Comment: 6 pages, 8 figure