DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition

This paper presents our pioneering effort for emotion recognition in conversation (ERC) with pre-trained language models. Unlike regular documents, conversational utterances appear alternately from different parties and are usually organized as hierarchical structures in previous work. Such structures are not conducive to the application of pre-trained language models such as XLNet. To address this issue, we propose an all-in-one XLNet model, namely DialogXL, with enhanced memory to store longer historical context and dialog-aware self-attention to deal with the multi-party structures. Specifically, we first modify the recurrence mechanism of XLNet from segment-level to utterance-level in order to better model the conversational data. Second, we introduce dialog-aware self-attention in replacement of the vanilla self-attention in XLNet to capture useful intra- and inter-speaker dependencies. Extensive experiments are conducted on four ERC benchmarks with mainstream models presented for comparison. The experimental results show that the proposed model outperforms the baselines on all the datasets. Several other experiments such as ablation study and error analysis are also conducted and the results confirm the role of the critical modules of DialogXL.Comment: Accepted by AAAI 2021 main conferenc

Effects of temperature on photosynthetic performance and nitrate reductase activity in vivo assay in Gracilariopsis lemaneiformis (Rhodophyta)

Gracilariopsis lemaneiformis is an economically-valued species and widely cultured in China at present. After being acclimated to different growth temperatures (15, 20, 25, and 30 degrees C) for 7 days, the relative growth rate (RGR), nitrate reductase activity, soluble protein content and chlorophyll a fluorescence of G. lemaneiformis were examined. Results show that RGR was markedly affected by temperature especially at 20 degrees C at which G. lemaneiformis exhibited the highest effective quantum yield of PSII [Y(II)] and light-saturated electron transport rate (ETRmax), but the lowest non-photochemical quenching. Irrespective of growth temperature, the nitrate reductase activity increased with the incubation temperature from 15 to 30 degrees C. In addition, the greatest nitrate reductase activity was found in the thalli grown at 20 degrees C. The value of temperature coefficient Q10 of alga cultured in 15 degrees C was the greatest among those of other temperatures tested. Results indicate that the optimum temperature for nitrate reductase synthesis was relatively lower than that for nitrate reductase activity, and the relationship among growth, photosynthesis, and nitrate reductase activity showed that the optimum temperature for activity of nitrate reductase in vivo assay should be the same to the optimal growth temperature

UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View

In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods have system complexity while the accuracy of single-modal ones is relatively low, how to make a tradeoff between them is difficult. In this work, we propose a universal cross-modality knowledge distillation framework (UniDistill) to improve the performance of single-modality detectors. Specifically, during training, UniDistill projects the features of both the teacher and the student detector into Bird's-Eye-View (BEV), which is a friendly representation for different modalities. Then, three distillation losses are calculated to sparsely align the foreground features, helping the student learn from the teacher without introducing additional cost during inference. Taking advantage of the similar detection paradigm of different detectors in BEV, UniDistill easily supports LiDAR-to-camera, camera-to-LiDAR, fusion-to-LiDAR and fusion-to-camera distillation paths. Furthermore, the three distillation losses can filter the effect of misaligned background information and balance between objects of different sizes, improving the distillation effectiveness. Extensive experiments on nuScenes demonstrate that UniDistill effectively improves the mAP and NDS of student detectors by 2.0%~3.2%

Joint Generator-Ranker Learning for Natural Language Generation

Generate-then-rank is a widely used mechanism for text generation, where a generator produces multiple text candidates and a ranker chooses the best one among the text candidates. However, existing methods usually train the generator and the ranker individually, neglecting the mutual feedback that could further enhance the generation quality. To tackle this limitation, we propose JGR, a novel joint training algorithm that integrates the generator and the ranker in a single framework. JGR optimizes the generator with a hybrid objective that combines data likelihood and ranker reward, and trains the ranker with a contrastive loss that compares the generator outputs. By iteratively updating the generator and the ranker, JGR can effectively harmonize their learning and enhance their quality jointly. We evaluate JGR on various text generation tasks and demonstrate that it surpasses existing methods on four public datasets across three common generation scenarios. Our code and models are publicly available at https://github.com/microsoft/ProphetNet/tree/master/JGR

DESIGN AND EXPERIMENTS FOR SHOVEL-FINGER AND CYLINDER PEANUT-PICKING DEVICE

ABSTRACT Two-stage harvesting is the main method for performing the mechanized harvesting of peanuts, and the picking device is a core part of the combined harvester in China. In order to solve the problem of pod loss caused by the "stacking", "impact" and "throwing up" of peanut plants by a traditional cam-slide spring-finger cylinder picking device, the shovel-finger and cylinder peanut-picking device was developed and used in a picking-up performance test based on the study of peanut-plant strip-picking characteristics. The mathematical model of the mechanism was established by analyzing the structure of the mechanism and the peanut-plant strip-picking characteristics, and the parameters of the mechanism were optimized using the objective function method. The prototype was developed and tested. The experiments in which the prototype was used to collect peanut plants indicated that the phenomenon of peanut plants being stacked and thrown disappeared. Through a response surface analysis and prototype test, the optimal working parameters of the picking device were obtained: the forward speed V was 48.0 m/minute, the rotational speed N was 45.3 r/minute and the ground height H was -18 mm. The peanut-plant picking rate was 98.9% and the fruit loss rate was 2.8% under two different harvesting conditions for which the peanut-plant moisture content was 15~17%

ModelScope-Agent: Building Your Customizable Agent System with Open-source Large Language Models

Large language models (LLMs) have recently demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior. To further unleash the power of LLMs to accomplish complex tasks, there is a growing trend to build agent framework that equips LLMs, such as ChatGPT, with tool-use abilities to connect with massive external APIs. In this work, we introduce ModelScope-Agent, a general and customizable agent framework for real-world applications, based on open-source LLMs as controllers. It provides a user-friendly system library, with customizable engine design to support model training on multiple open-source LLMs, while also enabling seamless integration with both model APIs and common APIs in a unified way. To equip the LLMs with tool-use abilities, a comprehensive framework has been proposed spanning over tool-use data collection, tool retrieval, tool registration, memory control, customized model training, and evaluation for practical real-world applications. Finally, we showcase ModelScopeGPT, a real-world intelligent assistant of ModelScope Community based on the ModelScope-Agent framework, which is able to connect open-source LLMs with more than 1000 public AI models and localized community knowledge in ModelScope. The ModelScope-Agent library\footnote{https://github.com/modelscope/modelscope-agent} and online demo\footnote{https://modelscope.cn/studios/damo/ModelScopeGPT/summary} are now publicly available

Soil Moisture Availability at Early Growth Stages Strongly Affected Root Growth of Bothriochloa ischaemum When Mixed With Lespedeza davurica

Rainfall is the main resource of soil moisture in the semiarid areas, and the altered rainfall pattern would greatly affect plant growth and development. Root morphological traits are critical for plant adaptation to changeable soil moisture. This study aimed to clarify how root morphological traits of Bothriochloa ischaemum (a C4 herbaceous species) and Lespedeza davurica (a C3 leguminous species) in response to variable soil moisture in their mixtures. The two species were co-cultivated in pots at seven mixture ratios under three soil water regimes [80% (HW), 60% (MW), and 40% (LW) of soil moisture field capacity (FC)]. At the jointing, flowering, and filling stages of B. ischaemum, the LW and MW treatments were rewatered to MW or HW, respectively. At the end of growth season, root morphological traits of two species were evaluated. Results showed that the root morphological response of B. ischaemum was more sensitive than that of L. davurica under rewatering. The total root length (TRL) and root surface area (RSA) of both species increased as their mixture ratio decreased, which suggested that mixed plantation of the two species would be beneficial for their own root growth. Among all treatments, the increase of root biomass (RB), TRL, and RSA reached the highest levels when soil water content increased from 40 to 80% FC at the jointing stage. Our results implied that species-specific response in root morphological traits to alternated rainfall pattern would greatly affect community structure, and large rainfall occurring at early growth stages would greatly increase their root growth in the semiarid environments

Volatile Constituents, Inorganic Elements and Primary Screening of Bioactivity of Black Coral Cigarette Holders

Black corals (BC) have been used for a long time in Chinese medicine, and may have some pharmaceutical functions when used as material for cigarette holders in southeast China. This study is aimed to investigate the bioactivities of volatile constituents in BC and to explore the folklore behind the use of BC cigarette holders (BCCHs). We extracted the volatile constituents of BC by supercritical fluid extraction (SFE) with carbon dioxide (CO2-SFE), then identified and analyzed the constituents by gas chromatography-mass spectrometry (GC-MS). In total, 15 components were reliably identified in BC and found to be biologically active. These included triethyl phosphate, butylated hydroxytoluene, cedrol, n-hexadecanoic acid, squalene, and cholesterol. Meanwhile 13 inorganic elements (P, Ca, Mg, S, B, Si, Fe, Cu, Zn, Ba, etc.) were determined by inductively coupled plasma spectrometer (ICPS). In the bioactivity tests, the BC extract (BCE) showed a scavenging activity of 2,2-diphenyl-1-picrylhydrazyl free radicals and hydroxyl radicals by phenanthroline-Fe (II) oxidation and moderate inhibition of Gram-positive microorganisms. The antioxidant and antimicrobial activities of BC, which are related to the active chemical composition, may explain the perceived benefit for cigarette smokers who use BCCHs