Enhancing Black-Box Few-Shot Text Classification with Prompt-Based Data Augmentation

Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate how to optimize few-shot text classification without accessing the gradients of the LLMs. To achieve this, we treat the black-box model as a feature extractor and train a classifier with the augmented text data. Data augmentation is performed using prompt-based finetuning on an auxiliary language model with a much smaller parameter size than the black-box model. Through extensive experiments on eight text classification datasets, we show that our approach, dubbed BT-Classifier, significantly outperforms state-of-the-art black-box few-shot learners and performs on par with methods that rely on full-model tuning

Synthesis and Characterization of Nanostructured WC-Co/Al Powder Prepared by Mechanical Alloying

Nanostructured WC-Co/Al powder was synthesized from WC-12Co powder and pure Al powder by mechanical alloying (MA). The morphology and microstructural evolution of WC-Co/Al powder were investigated by a series of characterization methods. The results showed that the β-Co phase in the initial WC-12Co powder was replaced by the AlxCo phases (such as Al9Co2 and Al13Co4). As the ball milling time increased, the average grain size of WC in the WC-Co/Al powder decreased firstly and then remained at a constant value of around 40 nm. The deposition behavior of powders sprayed by high velocity oxygen fuel (HVOF) spraying was investigated. During spraying, the WC-Co/Al powder had a better flattening than the WC-12Co powder without ball milling, which is beneficial to fabricate compact coatings with lower porosity

Towards General and Efficient Online Tuning for Spark

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search. In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively

Response Properties of Interneurons and Pyramidal Neurons in Macaque MSTd and VPS Areas During Self-Motion

To perceive self-motion perception, the brain needs to integrate multi-modal sensory signals such as visual, vestibular and proprioceptive cues. Self-motion perception is very complex and involves multi candidate areas. Previous studies related to self-motion perception during passive motion have revealed that some of the areas show selective response to different directions for both visual (optic flow) and vestibular stimuli, such as the dorsal subdivision of the medial superior temporal area (MSTd) and the visual posterior sylvian fissure (VPS), although MSTd is dominated by visual signals and VPS is dominated by vestibular signals. However, none of studies related to self-motion perception have distinguished the different neuron types with distinct neuronal properties in cortical microcircuitry, which limited our understanding of the local circuits for self-motion perception. In the current study, we classified the recorded MSTd and VPS neurons into putative pyramidal neurons and putative interneurons based on the extracellular action potential waveforms and spontaneous firing rates. We found that: (1) the putative interneurons exhibited obviously broader direction tuning than putative pyramidal neurons in response to their dominant (visual for MSTd; vestibular for VPS) stimulation type; (2) either in visual or vestibular condition, the putative interneurons were more responsive but with larger variability than the putative pyramidal neurons for both MSTd and VPS areas; and (3) the timing of vestibular and visual peak directional tuning was earlier in the putative interneurons than that of the putative pyramidal neurons for both MSTd and VPS areas. Based on these findings we speculated that, within the microcircuitry, several adjacent putative interneurons with broad direction tuning receive earlier strong but variable signals, which might act feedforward input to shape the direction tuning of the target putative pyramidal neuron, but each interneuron may participate in several microcircuitries, targeting different output neurons

Progressive Research in the Molecular Mechanisms of Chronic Fluorosis

Long-term excessive intake of fluoride (F) leads to chronic fluorosis, resulting in dental fluorosis and skeletal fluorosis. Chronic exposure to high doses of fluoride can also cause damage to soft tissues, especially when it passes through the blood-brain, blood-testis, and blood-placenta barrier, causing damage to the corresponding tissues. Fluorosis has become a public health problem in some countries or regions around the world. Understanding the pathogenesis of fluorosis is very important. Although the exact mechanism of fluorosis has not been fully elucidated, various mechanisms of fluoride-induced toxicity have been proposed. In this chapter, we will introduce the research progress of the mechanism of fluorosis, focusing on dental fluorosis, skeletal fluorosis, nervous and reproductive system toxicity, and influential factors related to fluoride toxicity (i.e., genetic background, co-exposure with other element). In addition, the application of proteomics and metabolomics in the study of the pathogenesis of fluorosis is also introduced. Currently, there is still no specific treatment for fluorosis. However, since fluorosis is caused by excessive intake of fluoride, avoiding excessive fluoride intake is the critical measure to prevent the disease. In endemic regions, health education and supplement diet with vitamins C, D and E, and calcium and antioxidant compounds are important

Urania: Visualizing Data Analysis Pipelines for Natural Language-Based Data Exploration

Exploratory Data Analysis (EDA) is an essential yet tedious process for examining a new dataset. To facilitate it, natural language interfaces (NLIs) can help people intuitively explore the dataset via data-oriented questions. However, existing NLIs primarily focus on providing accurate answers to questions, with few offering explanations or presentations of the data analysis pipeline used to uncover the answer. Such presentations are crucial for EDA as they enhance the interpretability and reliability of the answer, while also helping users understand the analysis process and derive insights. To fill this gap, we introduce Urania, a natural language interactive system that is able to visualize the data analysis pipelines used to resolve input questions. It integrates a natural language interface that allows users to explore data via questions, and a novel data-aware question decomposition algorithm that resolves each input question into a data analysis pipeline. This pipeline is visualized in the form of a datamation, with animated presentations of analysis operations and their corresponding data changes. Through two quantitative experiments and expert interviews, we demonstrated that our data-aware question decomposition algorithm outperforms the state-of-the-art technique in terms of execution accuracy, and that Urania can help people explore datasets better. In the end, we discuss the observations from the studies and the potential future works