How to Retrain Recommender System? A Sequential Meta-Learning Method

Practical recommender systems need be periodically retrained to refresh the model with new interaction data. To pursue high model fidelity, it is usually desirable to retrain the model on both historical and new data, since it can account for both long-term and short-term user preference. However, a full model retraining could be very time-consuming and memory-costly, especially when the scale of historical data is large. In this work, we study the model retraining mechanism for recommender systems, a topic of high practical values but has been relatively little explored in the research community. Our first belief is that retraining the model on historical data is unnecessary, since the model has been trained on it before. Nevertheless, normal training on new data only may easily cause overfitting and forgetting issues, since the new data is of a smaller scale and contains fewer information on long-term user preference. To address this dilemma, we propose a new training method, aiming to abandon the historical data during retraining through learning to transfer the past training experience. Specifically, we design a neural network-based transfer component, which transforms the old model to a new model that is tailored for future recommendations. To learn the transfer component well, we optimize the "future performance" -- i.e., the recommendation accuracy evaluated in the next time period. Our Sequential Meta-Learning(SML) method offers a general training paradigm that is applicable to any differentiable model. We demonstrate SML on matrix factorization and conduct experiments on two real-world datasets. Empirical results show that SML not only achieves significant speed-up, but also outperforms the full model retraining in recommendation accuracy, validating the effectiveness of our proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202

Secure smart metering based on LoRa technology

National Research Foundation (NRF) Singapore under Energy Programm

Accelerate Presolve in Large-Scale Linear Programming via Reinforcement Learning

Large-scale LP problems from industry usually contain much redundancy that severely hurts the efficiency and reliability of solving LPs, making presolve (i.e., the problem simplification module) one of the most critical components in modern LP solvers. However, how to design high-quality presolve routines -- that is, the program determining (P1) which presolvers to select, (P2) in what order to execute, and (P3) when to stop -- remains a highly challenging task due to the extensive requirements on expert knowledge and the large search space. Due to the sequential decision property of the task and the lack of expert demonstrations, we propose a simple and efficient reinforcement learning (RL) framework -- namely, reinforcement learning for presolve (RL4Presolve) -- to tackle (P1)-(P3) simultaneously. Specifically, we formulate the routine design task as a Markov decision process and propose an RL framework with adaptive action sequences to generate high-quality presolve routines efficiently. Note that adaptive action sequences help learn complex behaviors efficiently and adapt to various benchmarks. Experiments on two solvers (open-source and commercial) and eight benchmarks (real-world and synthetic) demonstrate that RL4Presolve significantly and consistently improves the efficiency of solving large-scale LPs, especially on benchmarks from industry. Furthermore, we optimize the hard-coded presolve routines in LP solvers by extracting rules from learned policies for simple and efficient deployment to Huawei's supply chain. The results show encouraging economic and academic potential for incorporating machine learning to modern solvers

A Novel Method of Wireless Micro Energy Transmission Based on MEMS Micro Coil

Based on current implantable devices, a battery’s rigidity and large size makes it prone to immune rejection and wound incisions. Additionally, it is limited by its finite lifespan, which hinders long-term usage. These limitations greatly restrict the development of implantable medical device systems towards miniaturization and minimally invasive approaches. Consequently, obtaining high-fidelity and stable biological signals from the target tissue area of the organism remains challenging. Therefore, there is a need to develop wireless power transmission technology. In this paper, we propose a wireless micro energy transfer method based on MEMS micro coils for charging implantable devices. Through simulation calculations, we first investigate the influence of coaxial distance, horizontal displacement, and rotation angle between the MEMS micro coil and the transmitting coil on power transmission. Subsequently, we utilize micro nanofabrication technology to create a MEMS micro spiral copper coil with a line width, thickness, and spacing of 50 µm and a total of five turns. Finally, we conduct wireless power transmission tests on the coil. The results show that, when the transmitting coil and the receiving coil are 10 mm apart and the operating frequency is 100 kHz, the power of the wireless power transmission system reaches 45 µW. This power level is sufficient to meet the power supply requirements of implantable pacemakers. Therefore, this technology holds great potential for applications in the field of wireless power transmission for implantable medical devices, including pacemakers and brain neurostimulators

The complete mitochondrial genome of Leuciscus merzbacheri (Cypriniformes: Cyprinidae)

Leuciscus merzbacheri (Zugmayer, 1912) is a cyprinid fish endemic to China, with a distribution range limited to Xinjiang Province. As a landmark species in the Junggar Basin, L. merzbacheri is of considerable significance regarding our understanding of the adaptive evolution of salt and alkali tolerance. In this study, the complete mitochondrial sequence of L. merzbacheri was obtained for the first time by high-throughput sequencing. The circular mitogenome is 16,609 bp in length and contains the standard 37 genes, including 13 protein-coding, 22 transfer RNA, and 2 ribosomal RNA genes, which is similar to that of other fish. The mitogenome contents of A, T, C, and G were 27.9, 26.3, 27.1, and 18.7%, respectively. Phylogenetically, L. merzbacheri was located on a new branch near the base of the phylogenetic tree, thereby suggesting an early origin

Research on Catastrophic Pillar Instability in Room and Pillar Gypsum Mining

Gypsum mines in China are mostly exploited through room and pillar mining. Due to backward mining technology and a long history of mining, a great number of pillars were left in gypsum mines. Many serious work safety accidents occurred as the result of goaf instability in history, which posed severe threats to the security of people’s lives and property. Based on the characteristics of surrounding rock damage, this research improved the constitutive equation of gypsum rock mass damage by establishing a damage evolution model and introducing a shape parameter. Meanwhile, the cusp catastrophe equation was deduced based on the catastrophe theory and the constitutive equation of gypsum rock mass damage, thus summarizing the criteria for pillar instability; the pillar safety factor was obtained by means of the interrelation between pillar load and pillar strength. Based on the criteria for pillar instability and the pillar safety factor obtained, the necessary and sufficient conditions for pillar stability were concluded. These conclusions are of significance in that they provide theoretic reference for the treatment of gypsum goaf, as well as for further mining

Conversion of recombinant human ferritin light chain inclusion bodies into uniform nanoparticles in Escherichia coli for facile production

Prokaryotic expression systems are widely used to produce many types of biologics because of their extreme conveniences and unmatchable cost. However, production of recombinant human ferritin light chain (rhFTL) protein is largely restrained because its expression in Escherichia coli tends to form inclusion bodies (IBs). In this study, a prokaryotic expression vector (FTL-pBV220) harboring the rhFTL gene was constructed using a pBV220 plasmid. The tag-free rhFTL was highly expressed and almost entirely converted to soluble form, and thus the rhFTL was successfully self-assembled into uniform nanoparticles in E. coli. To establish a simplified downstream process, a precipitation procedure based on the optimized incubation temperature, pH condition, and ionic strength was developed to remove impurities from the crude lysate supernatant. The rhFTL retained in the clarified supernatant was subsequently purified in a single step using Capto Butyl column resulting in a considerable recovery and high purity. The purified rhFTL was characterized and verified by mass spectrometry and spectral and morphological analyses. The results revealed that rhFTL exhibited highly ordered and fairly compact structures and the spherical structures were preserved

Enhanced Low-Velocity Impact Resistance of Helicoidal Composites by Fused Filament Fabrication (FFF)

Bioinspired composites, capable of tailoring mechanical properties by the strategy of making full use of their advantages and bypassing their drawbacks, are vital for numerous engineering applications such as lightweight ultrahigh-strength, enhanced toughness, improved low-/high- velocity impact resistance, wave filtering, and energy harvesting. Helicoidal composites are examples of them. However, how to optimize the geometric structure to maximize the low-velocity impact resistance of helicoidal composites has been ignored, which is vital to the lightweight and high strength for aerospace, defense, ship, bridge, dam, vessel, and textile industries. Here, we combined experiments and numerical simulations to report the dynamic response of helicoidal composites subjected under low-velocity impact (0–10 m/s). Our helicoidal structures, inspired by the Stomatopod Dactyl club, are fabricated using polylactic acid (PLA) by FFF in a single-phase way. The helicoidal strategy aims to exploit, to a maximum extent, the axial tensile strength of filaments and simultaneously make up the shortage of inter-filament contact strength. We demonstrate experimentally that the low-velocity impact resistance has been enhanced efficiently as the helicoidal angle varies, and that the 15° helicoidal plate is better than others, which has also been confirmed by the numerical simulations. The findings reported here provide a new routine to design composites systems with enhanced impact resistance, offering a method to improve impact performance and expand the application of 3D printing

Artif. Cells Blood Substit. Biotechnol.

Human plasma fraction IV is an intermediate precipitate during the production of human serum albumin using cold ethanol method. Haptoglobin locates in this fraction can be purified for various applications. A new process integration of polyethylene glycol (PEG) precipitation and ion-exchange chromatography (IEC) was developed for purification of haptoglobin, which could effectively purify the haptoglobin from 16.6% to 95%. The recovery of the new process was 58.2% in comparison to 30.3% of the conventional affinity chromatography. Furthermore, 175 mg haptoglobin production in a scaled-up process showed the method to be simple, fast, and low-cost.Human plasma fraction IV is an intermediate precipitate during the production of human serum albumin using cold ethanol method. Haptoglobin locates in this fraction can be purified for various applications. A new process integration of polyethylene glycol (PEG) precipitation and ion-exchange chromatography (IEC) was developed for purification of haptoglobin, which could effectively purify the haptoglobin from 16.6% to 95%. The recovery of the new process was 58.2% in comparison to 30.3% of the conventional affinity chromatography. Furthermore, 175 mg haptoglobin production in a scaled-up process showed the method to be simple, fast, and low-cost