Dual-Branch Temperature Scaling Calibration for Long-Tailed Recognition

The calibration for deep neural networks is currently receiving widespread attention and research. Miscalibration usually leads to overconfidence of the model. While, under the condition of long-tailed distribution of data, the problem of miscalibration is more prominent due to the different confidence levels of samples in minority and majority categories, and it will result in more serious overconfidence. To address this problem, some current research have designed diverse temperature coefficients for different categories based on temperature scaling (TS) method. However, in the case of rare samples in minority classes, the temperature coefficient is not generalizable, and there is a large difference between the temperature coefficients of the training set and the validation set. To solve this challenge, this paper proposes a dual-branch temperature scaling calibration model (Dual-TS), which considers the diversities in temperature parameters of different categories and the non-generalizability of temperature parameters for rare samples in minority classes simultaneously. Moreover, we noticed that the traditional calibration evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to low-confidence samples in the minority classes, which leads to inaccurate evaluation of model calibration. Therefore, we also propose Equal Sample Bin Excepted Calibration Error (Esbin-ECE) as a new calibration evaluation metric. Through experiments, we demonstrate that our model yields state-of-the-art in both traditional ECE and Esbin-ECE metrics

centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate and long-tailed label shifts both exist across domains. To tackle the IDA problem, some current research focus on minimizing the distribution discrepancies of each corresponding class between source and target domains. Such methods rely much on the reliable pseudo labels' selection and the feature distributions estimation for target domain, and the minority classes with limited numbers makes the estimations more uncertainty, which influences the model's performance. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA (centroIDA). Firstly, class-based re-sampling strategy is used to obtain an unbiased classifier on source domain. Secondly, the accumulative class-centroids alignment loss is proposed for iterative class-centroids alignment across domains. Finally, class-wise feature alignment loss is used to optimize the feature representation for a robust classification boundary. A series of experiments have proved that our method outperforms other SOTA methods on IDA problem, especially with the increasing degree of label shift

The extraction of natural essential oils and terpenoids from plants by supercritical fluid

In order to provide guidance for the improvement of supercritical fluid extraction technology in the extraction of natural volatile oil and terpenoids from plants, SFE was compared with steam distillation, solvent extraction, Soxhlet extraction, pressure method and other traditional extraction processes, and the supercritical CO2 extraction conditions of SFE in the extraction of natural volatile oil and terpenoids were studied, including temperature, pressure, extraction time, extraction time, extraction time, extraction time, extraction time, extraction time and so on. The influence of entrainer or co extractant on the extraction effect was discussed to provide optimization parameters for the extraction process of natural volatile oil and terpenoids. SFE technology has advantages in the extraction of natural plant volatile oil and has broad application prospects in industrial production

Clinical Potential of an Enzyme-Based Novel Therapy for Cocaine Overdose

It is a grand challenge to develop a truly effective medication for treatment of cocaine overdose. The current available, practical emergence treatment for cocaine overdose includes administration of a benzodiazepine anticonvulsant agent (e.g. diazepam) and/or physical cooling with an aim to relieve the symptoms. The inherent difficulties of antagonizing physiological effects of drugs in the central nervous system have led to exploring protein-based pharmacokinetic approaches using biologics like vaccines, monoclonal antibodies, and enzymes. However, none of the pharmacokinetic agents has demonstrated convincing preclinical evidence of clinical potential for drug overdose treatment without a question mark on the timing used in the animal models. Here we report the use of animal models, including locomotor activity, protection, and rescue experiments in rats, of drug toxicity treatment with clinically relevant timing for the first time. It has been demonstrated that an efficient cocaine-metabolizing enzyme developed in our previous studies can rapidly reverse the cocaine toxicity whenever the enzyme is given to a living rat, demonstrating promising clinical potential of an enzyme-based novel therapy for cocaine overdose as a successful example in comparison with the commonly used diazepam

Machine-Learning-Assisted and Real-Time-Feedback-Controlled Growth of InAs/GaAs Quantum Dots

Self-assembled InAs/GaAs quantum dots (QDs) have properties highly valuable for developing various optoelectronic devices such as QD lasers and single photon sources. The applications strongly rely on the density and quality of these dots, which has motivated studies of the growth process control to realize high-quality epi-wafers and devices. Establishing the process parameters in molecular beam epitaxy (MBE) for a specific density of QDs is a multidimensional optimization challenge, usually addressed through time-consuming and iterative trial-and-error. Meanwhile, reflective high-energy electron diffraction (RHEED) has been widely used to capture a wealth of growth information in situ. However, it still faces the challenges of extracting information from noisy and overlapping images. Here, based on 3D ResNet, we developed a machine learning (ML) model specially designed for training RHEED videos instead of static images and providing real-time feedback on surface morphologies for process control. We demonstrated that ML from previous growth could predict the post-growth density of QDs, by successfully tuning the QD densities in near-real time from 1.5E10 cm-2 down to 3.8E8 cm-2 or up to 1.4 E11 cm-2. Compared to traditional methods, our approach, with in-situ tuning capabilities and excellent reliability, can dramatically expedite the material optimization process and improve the reproducibility of MBE growth, constituting significant progress for thin film growth techniques. The concepts and methodologies proved feasible in this work are promising to be applied to a variety of material growth processes, which will revolutionize semiconductor manufacturing for microelectronic and optoelectronic industries.Comment: 5 figure

A Cryophyte Transcription Factor, CbABF1, Confers Freezing, and Drought Tolerance in Tobacco

Abscisic acid responsive element binding factors (ABFs) play crucial roles in plant responses to abiotic stress. However, little is known about the roles of ABFs in alpine subnival plants, which can survive under extreme environmental conditions. Here, we cloned and characterized an ABF1 homolog, CbABF1, from the alpine subnival plant Chorispora bungeana. Expression of CbABF1 was induced by cold, drought, and abscisic acid. Subcellular localization analysis revealed that CbABF1 was located in the nucleus. Further, CbABF1 had transactivation activity, which was dependent on the N-terminal region containing 89 residues. A Snf1-related protein kinase, CbSnRK2.6, interacted with CbABF1 in yeast two-hybrid analysis and bimolecular fluorescence complementation assays. Transient expression assay revealed that CbSnRK2.6 enhanced the transactivation of CbABF1 on ABRE cis-element. We further found that heterologous expression of CbABF1 in tobacco improved plant tolerance to freezing and drought stress, in which the survival rates of the transgenic plants increased around 40 and 60%, respectively, compared with wild-type plants. Moreover, the transgenic plants accumulated less reactive oxygen species, accompanied by high activities of antioxidant enzymes and elevated expression of stress-responsive genes. Our results thus suggest that CbABF1 is a transcription factor that plays an important role in cold and drought tolerance and is a candidate gene in molecular breeding of stress-tolerant crops

Deubiquitinase PSMD14 enhances hepatocellular carcinoma growth and metastasis by stabilizing GRB2

Abstract(#br)Hepatocellular carcinoma (HCC) has emerged as one of the most common malignancies worldwide. It is associated with a high mortality rate, as evident from its increasing incidence and extremely poor prognosis. The deubiquitinating enzyme 26S proteasome non-ATPase regulatory subunit 14 (PSMD14) has been reported to act as an oncogene in several human cancers. The present study aimed to reveal the functional significance of PSMD14 in HCC progression and the underlying mechanisms. We found that PSMD14 was significantly upregulated in HCC tissues. Overexpression of PSMD14 correlated with vascular invasion, tumor number, tumor recurrence, and poor tumor-free and overall survival of patients with HCC. Knockdown and overexpression experiments demonstrated that PSMD14 promoted proliferation, migration, and invasion in HCC cells in vitro , and facilitated tumor growth and metastasis in vivo . Mechanistically, we identified PSMD14 as a novel post-translational regulator of GRB2. PSMD14 inhibits degradation of GRB2 via deubiquitinating this oncoprotein in HCC cells. Furthermore, pharmacological inhibition of PSMD14 with O-phenanthroline (OPA) suppressed the malignant behavior of HCC cells in vitro and in vivo . In conclusion, our findings suggest that PSMD14 could serve as a novel promising therapeutic candidate for HCC