Federated learning algorithm based on knowledge distillation

Federated learning is a new scheme of distributed machine learning, which enables a large number of edge computing devices to jointly learn a shared model without private data sharing. Federated learning allows nodes to synchronize only the locally trained models instead of their own private data, which provides a guarantee for privacy and security. However, due to the challenges of heterogeneity in federated learning, which are: (1) heterogeneous model architecture among devices; (2) statistical heterogeneity in real federated dataset, which do not obey independent-identical-distribution, resulting in poor performance of traditional federated learning algorithms. To solve the problems above, this paper proposes FedDistill, a new distributed training method based on knowledge distillation. By introducing personalized model on each device, the personalized model aims to improve the local performance even in a situation that global model fails to adapt to the local dataset, thereby improving the ability and robustness of the global model. The improvement of the performance of local device benefits from the effect of knowledge distillation, which can guide the improvement of global model by knowledge transfer between heterogeneous networks. Experiments show that FedDistill can significantly improve the accuracy of classification tasks and meet the needs of heterogeneous users

Realistic Rainy Weather Simulation for LiDARs in CARLA Simulator

Employing data augmentation methods to enhance perception performance in adverse weather has attracted considerable attention recently. Most of the LiDAR augmentation methods post-process the existing dataset by physics-based models or machine-learning methods. However, due to the limited environmental annotations and the fixed vehicle trajectories in the existing dataset, it is challenging to edit the scene and expand the diversity of traffic flow and scenario. To this end, we propose a simulator-based physical modeling approach to augment LiDAR data in rainy weather in order to improve the perception performance of LiDAR in this scenario. We complete the modeling task of the rainy weather in the CARLA simulator and establish a pipeline for LiDAR data collection. In particular, we pay special attention to the spray and splash rolled up by the wheels of surrounding vehicles in rain and complete the simulation of this special scenario through the Spray Emitter method we developed. In addition, we examine the influence of different weather conditions on the intensity of the LiDAR echo, develop a prediction network for the intensity of the LiDAR echo, and complete the simulation of 4-feat LiDAR point cloud data. In the experiment, we observe that the model augmented by the synthetic data improves the object detection task's performance in the rainy sequence of the Waymo Open Dataset. Both the code and the dataset will be made publicly available at https://github.com/PJLab-ADG/PCSim#rainypcsim

Wavefield separation using the f–ξ domain for vertical seismic profiling data

Wavefield separation is an essential and critical step in data processing of vertical seismic profiles (VSPs). The τ–p transform is one of the effective approaches for separating the VSP wavefield. To further improve the efficiency of the τ–p transform, we developed an f–ξ domain τ–p transform based on the high-resolution Radon transform. The introduction of a new variable ξ removes the frequency dependence of the transform operator. As a result, the transform operator needs to be computed only once for all frequency components, which significantly improves the computational efficiency without decreasing accuracy. Furthermore, because the events of upgoing and downgoing waves are distributed in the vicinity of straight lines with different slopes through the origin in the f–ξ domain, we can easily define a filter to separate the upgoing and downgoing waves. Model tests showed that the wavefields are well separated and that the new method is a factor of ~3 faster than its counterpart in the τ–p domain. The field data example further demonstrates the effectiveness and feasibility of the approach

Dietary menthol-induced TRPM8 activation enhances WAT “browning” and ameliorates diet-induced obesity

Beige adipocytes are a new type of recruitable brownish adipocytes, with highly mitochondrial membrane uncoupling protein 1 expression and thermogenesis. Beige adipocytes were found among white adipocytes, especially in subcutaneous white adipose tissue (sWAT). Therefore, beige adipocytes may be involved in the regulation of energy metabolism and fat deposition. Transient receptor potential melastatin 8 (TRPM8), a Ca2+-permeable non-selective cation channel, plays vital roles in the regulation of various cellular functions. It has been reported that TRPM8 activation enhanced the thermogenic function of brown adiposytes. However, the involvement of TRPM8 in the thermogenic function of WAT remains unexplored. Our data revealed that TRPM8 was expressed in mouse white adipocytes at mRNA, protein and functional levels. The mRNA expression of Trpm8 was significantly increased in the differentiated white adipocytes than pre-adipocytes. Moreover, activation of TRPM8 by menthol enhanced the expression of thermogenic genes in cultured white aidpocytes. And menthol-induced increases of the thermogenic genes in white adipocytes was inhibited by either KT5720 (a protein kinase A inhibitor) or BAPTA-AM. In addition, high fat diet (HFD)-induced obesity in mice was significantly recovered by co-treatment with menthol. Dietary menthol enhanced WAT "browning" and improved glucose metabolism in HFD-induced obesity mice as well. Therefore, we concluded that TRPM8 might be involved in WAT "browning" by increasing the expression levels of genes related to thermogenesis and energy metabolism. And dietary menthol could be a novel approach for combating human obesity and related metabolic diseases

Two-dimensional sp2 carbon–conjugated covalent organic frameworks

We synthesized a two-dimensional (2D) crystalline covalent organic framework (sp2c-COF) that was designed to be fully π-conjugated and constructed from all sp2-carbons by C=C condensation reactions of tetrakis(4-formylphenyl)pyrene and 1,4-phenylenediacetonitrile. The C=C linkages topologically connect pyrene knots at regular intervals into a 2D lattice with π-conjugations extended along both x and y directions, and develop an eclipsed layer framework rather than the more conventionally obtained disordered structures. The sp2c-COF is a semiconductor with a discrete band gap of 1.9 eV and can be chemically oxidized to enhance conductivity by 12 orders of magnitude. The generated radicals are confined on the pyrene knots, enabling the formation of a paramagnetic carbon structure with high spin density. The sp2-carbon framework induces ferromagnetic phase transition to develop spin-spin coherence and align spins unidirectionally across the material

High‐precision size recognition and separation in synthetic 1D nanochannels

Covalent organic frameworks (COFs) allow elaborate manufacture of ordered one‐dimensional channels in the crystal. We defined a superlattice of COFs by engineering channels with a persistent triangular shape and discrete pore size. We observed a size‐recognition regime that is different from the characteristic adsorption of COFs, whereby pore windows and walls were cooperative so that triangular apertures sorted molecules of one‐atom difference and notch nanogrooves confined them into single‐file molecular chains. The recognition and confinement were accurately described by sensitive spectroscopy and femtosecond dynamic simulations. The resulting COFs enabled instantaneous separation of mixtures at ambient temperature and pressure. This study offers an approach to merge precise recognition, selective transport, and instant separation in synthetic 1D channels

A Counterfactual P -Value Approach for Benefit-Risk Assessment in Clinical Trials

Clinical trials generally allow various efficacy and safety outcomes to be collected for health interventions. Benefit-risk assessment is an important issue when evaluating a new drug. Currently, there is a lack of standardized and validated benefit-risk assessment approaches in drug development due to various challenges. To quantify benefits and risks, we propose a counterfactual p-value (CP) approach. Our approach considers a spectrum of weights for weighting benefit-risk values and computes the extreme probabilities of observing the weighted benefit-risk value in one treatment group as if patients were treated in the other treatment group. The proposed approach is applicable to single benefit and single risk outcome as well as multiple benefit and risk outcomes assessment. In addition, the prior information in the weight schemes relevant to the importance of outcomes can be incorporated in the approach. The proposed counterfactual p-values plot is intuitive with a visualized weight pattern. The average area under CP (AUCP) and preferred probability over time are used for overall treatment comparison and a bootstrap approach is applied for statistical inference. We assess the proposed approach using simulated data with multiple efficacy and safety endpoints and compare its performance with a stochastic multi-criteria acceptability analysis (SMAA) approach

Bullous pemphigoid complicated with macroamylasemia: a case report

High amylase level is a typical sign of acute pancreatitis. However， it can also be found in other diseases. In the article， a case of persistent elevation of amylase level for over 1 year was reported. A 66-year-old male patient suffered from bullous pemphigoid complicated with diabetes mellitus for 3 years. After bullous pemphigoid treatment 8 months ago， he presented with elevated amylase level ranging from 556 to 1106 U/L， serum lipase level between 62 U/L and 73 U/L and urine amylase level between 553 U/L and 1162 U/L， respectively. The renal amylase clearance rate /creatinine clearance ratio was 0.8% （<1%）. The patient had no gastrointestinal discomforts. Abdominal color Doppler ultrasound （color ultrasound） and enhanced CT scan revealed that the pancreas was normal. Color ultrasound showed no abnormality in the thyroid and salivary gland. The possibility of salivary gland disease was excluded by stomatologist consultation. The patient was diagnosed with macroamylasemia. The patient reported no discomfort during the 8-month follow-up. The serum amylase level was gradually decreased but still higher than the normal range. This case suggested macroamylasemia should be considered when a patient present with persistent elevation of amylase level， without dynamic changes， gastrointestinal symptoms or elevated serum lipase level