MCF-VC: Mitigate Catastrophic Forgetting in Class-Incremental Learning for Multimodal Video Captioning

To address the problem of catastrophic forgetting due to the invisibility of old categories in sequential input, existing work based on relatively simple categorization tasks has made some progress. In contrast, video captioning is a more complex task in multimodal scenario, which has not been explored in the field of incremental learning. After identifying this stability-plasticity problem when analyzing video with sequential input, we originally propose a method to Mitigate Catastrophic Forgetting in class-incremental learning for multimodal Video Captioning (MCF-VC). As for effectively maintaining good performance on old tasks at the macro level, we design Fine-grained Sensitivity Selection (FgSS) based on the Mask of Linear's Parameters and Fisher Sensitivity to pick useful knowledge from old tasks. Further, in order to better constrain the knowledge characteristics of old and new tasks at the specific feature level, we have created the Two-stage Knowledge Distillation (TsKD), which is able to learn the new task well while weighing the old task. Specifically, we design two distillation losses, which constrain the cross modal semantic information of semantic attention feature map and the textual information of the final outputs respectively, so that the inter-model and intra-model stylized knowledge of the old class is retained while learning the new class. In order to illustrate the ability of our model to resist forgetting, we designed a metric CIDER_t to detect the stage forgetting rate. Our experiments on the public dataset MSR-VTT show that the proposed method significantly resists the forgetting of previous tasks without replaying old samples, and performs well on the new task.Comment: 13 page

Nonlinear vibrations of beams with spring and damping delayed feedback control

The primary, subharmonic, and superharmonic resonances of an Euler–Bernoulli beam subjected to harmonic excitations are studied with damping and spring delayed-feedback controllers. By method of multiple scales, the non-linear governing partial differential equation is transformed into linear differential equations directly. Effects of the feedback gains and time-delays on the steady state responses are investigated. The velocity and displacement delayed-feedback controllers are employed to suppress the primary and superharmonic resonances of the forced nonlinear oscillator. The stable vibration regions of the feedback gains and time-delays are worked out based on stablility conditions of the resonances. It is found that proper selection of feedback gains and time-delays can enhance the control performance of beam’s nonlinear vibration. Position of the bifurcation point can be changed or the bifurcation can be eliminated

Spleen-Yang-deficiency patients with polycystic ovary syndrome have higher levels of visfatin

AbstractObjectiveTo study serum visfatin levels in women with polycystic ovary syndrome (PCOS) grouped by Traditional Chinese Medicine (TCM) patterns. To study the correlations of serum visfatin levels with homeostatic model assessment insulin resistance (HOMA-IR), fasting plasma glucose (FPG), fasting insulin (FINS), body mass index (BMI), testosterone (T), total cholesterol (TC), and triglycerides (TG).MethodsTwo hundred and twelve PCOS patients were placed into the following TCM pattern subgroups: Kidney-Yang deficiency (KYD) group, Spleen-Yang deficiency (SYD) group, stagnant Liver-Qi transforming into heat (SLQTH) group, and Kidney-Yin deficiency (KYIND) group. The correlations between serum visfatin levels and HOMA-IR, FPG, FINS, BMI, T, TC, and TG were analyzed.ResultsOf all patients with PCOS, there were 82 in the KYD group (38.6%), 67 in the SYD group (31.6%), 37 in the SLQTH group (17.5%), and 26 in the KYIND group (12.3%). Visfatin levels in all PCOS subgroups were higher than those in the control group (P<0.01 or P<0.05). Among these subgroups, the visfatin levels in the SYD group were significantly higher than those in the other three TCM pattern groups (P<0.05). There were no statistical differences among the remaining three pattern groups. The levels of BMI, FINS, HOMA-IR, T, and TG were significantly higher in all subgroups than those in the control group (P<0.05). There were no significant differences in FPG and TC between all PCOS subgroups and the control group (P>0.05). The SYD group had higher levels of FINS and HOMA-IR compared with the KYD, SLQTH, and KYIND groups (P<0.05). In all subgroups, after controlling for BMI, TG, TC, and age, visfatin was positively correlated with FINS (r= 0.197, P=0.015) and HOMA-IR (r=0.173, P=0.033), and was not correlated with T.ConclusionKYD and SYD patterns are most common in PCOS patients. Increased visfatin is a common pathophysiologic manifestation in PCOS patients. The SYD group had the highest levels of visfatin, and visfatin was positively correlated with FINS and HOMA-IR

A panther chameleon skin-inspired core@shell supramolecular hydrogel with spatially organized multi-luminogens enables programmable color change

Organization of different iridophores into a core@shell structure constitutes an evolutionary novelty for panther chameleons that allows their skins to display diverse color change. Inspired by this natural color-changing design, we present a responsive core@shell-structured multi-luminogen supramolecular hydrogel system that generates a programmable multi-color fluorescent change. Specifically, red Eu$^{3+}$-amidopicolinate (R) luminogen is incorporated into the core hydrogel, while blue naphthalimide (B) and green perylene-tetracarboxylic acid (G) luminogens are grown into two supramolecular shell hydrogels. The intensities of G/B luminogens could then be controlled independently, which enables its emission color to be programmed easily from red to blue or green, nearly covering the full visible spectrum. Because of the differential excitation energies between these luminogens, a desirable excitation wavelength-dependent fluorescence is also achieved. Colorful materials with a patterned core@shell structure are also demonstrated for anti-counterfeiting, opening up the possibility of utilizing a bioinspired core@shell structure to develop an efficient multi-color fluorescent system with versatile uses

Classification, replication, and transcription of Nidovirales

Nidovirales is one order of RNA virus, with the largest single-stranded positive sense RNA genome enwrapped with membrane envelope. It comprises four families (Arterividae, Mesoniviridae, Roniviridae, and Coronaviridae) and has been circulating in humans and animals for almost one century, posing great threat to livestock and poultry，as well as to public health. Nidovirales shares similar life cycle: attachment to cell surface, entry, primary translation of replicases, viral RNA replication in cytoplasm, translation of viral proteins, virion assembly, budding, and release. The viral RNA synthesis is the critical step during infection, including genomic RNA (gRNA) replication and subgenomic mRNAs (sg mRNAs) transcription. gRNA replication requires the synthesis of a negative sense full-length RNA intermediate, while the sg mRNAs transcription involves the synthesis of a nested set of negative sense subgenomic intermediates by a discontinuous strategy. This RNA synthesis process is mediated by the viral replication/transcription complex (RTC), which consists of several enzymatic replicases derived from the polyprotein 1a and polyprotein 1ab and several cellular proteins. These replicases and host factors represent the optimal potential therapeutic targets. Hereby, we summarize the Nidovirales classification, associated diseases, “replication organelle,” replication and transcription mechanisms, as well as related regulatory factors

Artificial intelligence : A powerful paradigm for scientific research

Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe

Attitudes on the donation of human embryos for stem cell research among Chinese IVF patients and students

Bioethical debates on the use of human embryos and oocytes for stem cell research have often been criticized for the lack of empirical insights into the perceptions and experiences of the women and couples who are asked to donate these tissues in the IVF clinic. Empirical studies that have investigated the attitudes of IVF patients and citizens on the (potential) donation of their embryos and oocytes have been scarce and have focused predominantly on the situation in Europe and Australia. This article examines the viewpoints on the donation of embryos for stem cell research among IVF patients and students in China. Research into the perceptions of patients is based on in-depth interviews with IVF patients and IVF clinicians. Research into the attitudes of students is based on a quantitative survey study (n=427). The empirical findings in this paper indicate that perceptions of the donation of human embryos for stem cell research in China are far more diverse and complex than has commonly been suggested. Claims that ethical concerns regarding the donation and use of embryos and oocytes for stem cell research are typical for Western societies but absent in China cannot be upheld. The article shows that research into the situated perceptions and cultural specificities of human tissue donation can play a crucial role in the deconstruction of politicized bioethical argumentation and the (often ill-informed) assumptions about “others” that underlie socio-ethical debates on the moral dilemmas of technology developments in the life sciences

Numerical and experimental investigation of flow characteristics in natural gas pipe

To measure the natural gas flow in the natural gas pipeline, a flow measurement method based on a laser Doppler velocimeter (LDV) is proposed, and the flow field in the natural gas pipeline is accurately measured. The flow laws of the flow field in the pipeline are obtained. In this paper, the influence of the jet flow on the flow field in the main pipe during the injection of tracer particles is analyzed by numerical calculations, and the reliability of the calculation is verified by the LDV test. The results show that the influence of the jet on the flow field in the main pipe weakens rapidly within a short distance, which provides good conditions for measuring the flow in the LDV test pipeline. The error between the flow measured by LDV and the turbine standard device is within 0.47%. Meanwhile, the uncertainty of the LDV measurement test system of a natural gas pipeline is evaluated. The system uncertainty is less than 2%, which satisfies the uncertainty requirements of the metering system, verifying the feasibility of the LDV metering flow and providing a reliable basis for the accurate metering of natural gas

Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user&rsquo;s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances&rsquo; operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household&rsquo;s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods

Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods