Conformal Prediction for Deep Classifier via Label Ranking

Conformal prediction is a statistical framework that generates prediction sets containing ground-truth labels with a desired coverage guarantee. The predicted probabilities produced by machine learning models are generally miscalibrated, leading to large prediction sets in conformal prediction. In this paper, we empirically and theoretically show that disregarding the probabilities' value will mitigate the undesirable effect of miscalibrated probability values. Then, we propose a novel algorithm named $\textit{Sorted Adaptive prediction sets}$ (SAPS), which discards all the probability values except for the maximum softmax probability. The key idea behind SAPS is to minimize the dependence of the non-conformity score on the probability values while retaining the uncertainty information. In this manner, SAPS can produce sets of small size and communicate instance-wise uncertainty. Theoretically, we provide a finite-sample coverage guarantee of SAPS and show that the expected value of set size from SAPS is always smaller than APS. Extensive experiments validate that SAPS not only lessens the prediction sets but also broadly enhances the conditional coverage rate and adaptation of prediction sets

Inference with Reference: Lossless Acceleration of Large Language Models

We propose LLMA, an LLM accelerator to losslessly speed up Large Language Model (LLM) inference with references. LLMA is motivated by the observation that there are abundant identical text spans between the decoding result by an LLM and the reference that is available in many real world scenarios (e.g., retrieved documents). LLMA first selects a text span from the reference and copies its tokens to the decoder and then efficiently checks the tokens' appropriateness as the decoding result in parallel within one decoding step. The improved computational parallelism allows LLMA to achieve over 2x speed-up for LLMs with identical generation results as greedy decoding in many practical generation scenarios where significant overlap between in-context reference and outputs exists (e.g., search engines and multi-turn conversations).Comment: 9 page

Deciphering exciton-generation processes in quantum-dot electroluminescence

Funder: the National Key Research and Development Program of China (2016YFB0401600, 2018YFB2200401), the National Natural Science Foundation of China (51522209, 21975220, 91833303, 51911530155, 91733302, 61635009 and 61975180), the Fundamental Research Funds for the Central Universities (2017XZZX001-03A, 2019QNA5005), and Zhejiang University Education Foundation Global Partnership Fund.Abstract: Electroluminescence of colloidal nanocrystals promises a new generation of high-performance and solution-processable light-emitting diodes. The operation of nanocrystal-based light-emitting diodes relies on the radiative recombination of electrically generated excitons. However, a fundamental question—how excitons are electrically generated in individual nanocrystals—remains unanswered. Here, we reveal a nanoscopic mechanism of sequential electron-hole injection for exciton generation in nanocrystal-based electroluminescent devices. To decipher the corresponding elementary processes, we develop electrically-pumped single-nanocrystal spectroscopy. While hole injection into neutral quantum dots is generally considered to be inefficient, we find that the intermediate negatively charged state of quantum dots triggers confinement-enhanced Coulomb interactions, which simultaneously accelerate hole injection and hinder excessive electron injection. In-situ/operando spectroscopy on state-of-the-art quantum-dot light-emitting diodes demonstrates that exciton generation at the ensemble level is consistent with the charge-confinement-enhanced sequential electron-hole injection mechanism probed at the single-nanocrystal level. Our findings provide a universal mechanism for enhancing charge balance in nanocrystal-based electroluminescent devices

Chromosome-scale genomics, metabolomics, and transcriptomics provide insight into the synthesis and regulation of phenols in Vitis adenoclada grapes

Vitis adenoclada is a wild grape unique to China. It exhibits well resistance to heat, humidity, fungal disease, drought, and soil infertility. Here, we report the high-quality, chromosome-level genome assembly of GH6 (V. adenoclada). The 498.27 Mb genome contained 221.78 Mb of transposable elements, 28,660 protein-coding genes, and 481.44 Mb of sequences associated with 19 chromosomes. GH6 shares a common ancestor with PN40024 (Vitis vinifera) from approximately 4.26–9.01 million years ago, whose divergence occurred later than Vitis rotundifolia and Vitis riparia. Widely-targeted metabolome and transcriptome analysis revealed that the profiles and metabolism of phenolic compounds in V. adenoclada varieties significantly were differed from other grape varieties. Specifically, V. adenoclada varieties were rich in phenolic acids and flavonols, whereas the flavan-3-ol and anthocyanin content was lower compared with other varieties that have V. vinifera consanguinity in this study. In addition, ferulic acid and stilbenes content were associated with higher expressions of COMT and STSs in V. adenoclada varieties. Furthermore, MYB2, MYB73-1, and MYB73-2 were presumably responsible for the high expression level of COMT in V. adenoclada berries. MYB12 (MYBF1) was positively correlated with PAL, CHS, FLS and UFGT.Meanwhile, MYB4 and MYBC2-L1 may inhibit the synthesis of flavan-3-ols and anthocyanins in two V. adenoclada varieties (YN2 and GH6). The publication of the V. adenoclada grape genome provides a molecular foundation for further revealing its flavor and quality characteristics, is also important for identifying favorable genes of the East Asian species for future breeding

Study on Modelling Standardization of Double-fed Wind Turbine and Its Application

Based on the standardized modelling of the International Modelling Team, study on double-fed induction generator (DFIG) wind turbine is processed in this paper, aiming at capability of universally and reasonably reflecting key performance related to large scale system analysis. The standardized model proposed is of high degree of structural modularity, easy functional extension and universalization of control strategy and signal. Moreover, it is applicable for wind turbines produced by different manufacturers through model parameter adjustment. The complexity of the model can meet both needs of grid-connected characteristic simulation of wind turbine and large scale power system simulation

Study on Modelling Standardization of Double-fed Wind Turbine and Its Application

Based on the standardized modelling of the International Modelling Team, study on double-fed induction generator (DFIG) wind turbine is processed in this paper, aiming at capability of universally and reasonably reflecting key performance related to large scale system analysis. The standardized model proposed is of high degree of structural modularity, easy functional extension and universalization of control strategy and signal. Moreover, it is applicable for wind turbines produced by different manufacturers through model parameter adjustment. The complexity of the model can meet both needs of grid-connected characteristic simulation of wind turbine and large scale power system simulation

Evaluation of Geological Disaster Sensitivity in Shuicheng District Based on the WOE-RF Model

To improve the prevention and control of geological disasters in Shuicheng District, 10 environmental factors—slope, slope direction, curvature, NDVI, stratum lithology, distance from fault, distance from river system, annual average rainfall, distance from road and land use—were selected as evaluation indicators by integrating factors such as landform, basic geology, hydrometeorology and engineering activities. Based on the weight of evidence, random forest, support vector machine and BP neural network algorithms were introduced to build WOE-RF, WOE-SVM and WOE-BPNN models. The sensitivity of Shuicheng District to geological disasters was evaluated using the GIS platform, and the region was divided into areas of extremely high, high, medium, low and extremely low sensitivity to geological disasters. By comparing and analyzing the ROC curve and the distribution law of the sensitivity index, the AUC evaluation accuracy of the WOE-RF, WOE-SVM and WOE-BPNN models was 0.836, 0.807 and 0.753, respectively; the WOE-RF model was shown to be the most effective. In the WOE-RF model, the extremely high-, high-, medium-, low- and extremely low-sensitivity areas accounted for 15.9%, 16.9%, 19.3%, 21.0% and 26.9% of the study area, respectively. The extremely high- and high-sensitivity areas are mainly concentrated in areas with large slopes, broken rock masses, river systems and intensive human engineering activity. These research results are consistent with the actual situation and can provide a reference for the prevention and control of geological disasters in this and similar mountainous areas