Universal Self-Consistency for Large Language Model Generation

Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs). However, self-consistency relies on the answer extraction process to aggregate multiple solutions, which is not applicable to free-form answers. In this work, we propose Universal Self-Consistency (USC), which leverages LLMs themselves to select the most consistent answer among multiple candidates. We evaluate USC on a variety of benchmarks, including mathematical reasoning, code generation, long-context summarization, and open-ended question answering. On open-ended generation tasks where the original self-consistency method is not applicable, USC effectively utilizes multiple samples and improves the performance. For mathematical reasoning, USC matches the standard self-consistency performance without requiring the answer formats to be similar. Finally, without access to execution results, USC also matches the execution-based voting performance on code generation

From waste to protein: a new strategy of converting composted distilled grain wastes into animal feed

Distilled grain waste (DGW) is rich in nutrients and can be a potential resource as animal feed. However, DGW contains as much as 14% lignin, dramatically reducing the feeding value. White-rot fungi such as Pleurotus ostreatus could preferentially degrade lignin with high efficiency. However, lignin derivatives generated during alcohol distillation inhibit P. ostreatus growth. Thus, finding a new strategy to adjust the DGW properties to facilitate P. ostreatus growth is critical for animal feed preparation and DGW recycling. In this study, three dominant indigenous bacteria, including Sphingobacterium thermophilum X1, Pseudoxanthomonas byssovorax X3, and Bacillus velezensis 15F were chosen to generate single and compound microbial inoculums for DGW composting to prepare substrates for P. ostreatus growth. Compared with non-inoculated control or single microbial inoculation, all composite inoculations, especially the three-microbial compound, led to faster organic metabolism, shorter composting process, and improved physicochemical properties of DGW. P. ostreatus growth assays showed the fastest mycelial colonization (20.43 μg·g−1 ergosterol) and extension (9 mm/d), the highest ligninolytic enzyme activities (Lac, 152.68 U·g−1; Lip, 15.56 U·g−1; MnP, 0.34 U·g−1; Xylanase, 10.98 U·g−1; FPase, 0.71 U·g−1), and the highest lignin degradation ratio (30.77%) in the DGW sample after 12 h of composting with the three-microbial compound inoculation when compared to other groups. This sample was relatively abundant in bacteria playing critical roles in amino acid, carbohydrate, energy metabolism, and xenobiotic biodegradation, as suggested by metagenomic analysis. The feed value analysis revealed that P. ostreatus mycelia full colonization in composted DGW led to high fiber content retention and decreased lignin content (final ratio of 5% lignin) but elevated protein concentrations (about 130 g·kg−1 DM). An additional daily weight gain of 0.4 kg/d was shown in cattle feeding experiments by replacing 60% of regular feed with it. These findings demonstrate that compound inoculant consisting of three indigenous microorganisms is efficient to compost DGW and facilitate P. ostreatus growth. P. ostreatus decreased the lignin content of composted DGW during its mycelial growth, improving the quality of DGW for feeding cattle

PaLM 2 Technical Report

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 is a Transformer-based model trained using a mixture of objectives. Through extensive evaluations on English and multilingual language, and reasoning tasks, we demonstrate that PaLM 2 has significantly improved quality on downstream tasks across different model sizes, while simultaneously exhibiting faster and more efficient inference compared to PaLM. This improved efficiency enables broader deployment while also allowing the model to respond faster, for a more natural pace of interaction. PaLM 2 demonstrates robust reasoning capabilities exemplified by large improvements over PaLM on BIG-Bench and other reasoning tasks. PaLM 2 exhibits stable performance on a suite of responsible AI evaluations, and enables inference-time control over toxicity without additional overhead or impact on other capabilities. Overall, PaLM 2 achieves state-of-the-art performance across a diverse set of tasks and capabilities. When discussing the PaLM 2 family, it is important to distinguish between pre-trained models (of various sizes), fine-tuned variants of these models, and the user-facing products that use these models. In particular, user-facing products typically include additional pre- and post-processing steps. Additionally, the underlying models may evolve over time. Therefore, one should not expect the performance of user-facing products to exactly match the results reported in this report

Multiscale Optimized Segmentation of Urban Green Cover in High Resolution Remote Sensing Image

The urban green cover in high-spatial resolution (HR) remote sensing images have obvious multiscale characteristics, it is thus not possible to properly segment all features using a single segmentation scale because over-segmentation or under-segmentation often occurs. In this study, an unsupervised cross-scale optimization method specifically for urban green cover segmentation is proposed. A global optimal segmentation is first selected from multiscale segmentation results by using an optimization indicator. The regions in the global optimal segmentation are then isolated into under- and fine-segmentation parts. The under-segmentation regions are further locally refined by using the same indicator as that in global optimization. Finally, the fine-segmentation part and the refined under-segmentation part are combined to obtain the final cross-scale optimized result. The green cover objects can be segmented at their specific optimal segmentation scales in the optimized segmentation result to reduce both under- and over-segmentation errors. Experimental results on two test HR datasets verify the effectiveness of the proposed method

Scene Flow Estimation Based on Adaptive Anisotropic Total Variation Flow-Driven Method

Scene flow estimation based on disparity and optical flow is a challenging task. We present a novel method based on adaptive anisotropic total variation flow-driven method for scene flow estimation from a calibrated stereo image sequence. The basic idea is that diffusion of flow field in different directions has different rates, which can be used to calculate total variation and anisotropic diffusion automatically. Brightness consistency and gradient consistency constraint are employed to establish the data term, and adaptive anisotropic flow-driven penalty constraint is employed to establish the smoothness term. Similar to the optical flow estimation, there are also large displacement problems in the estimation of the scene flow, which is solved by introducing a hierarchical computing optimization. The proposed method is verified by using the synthetic dataset and the real scene image sequences. The experimental results show the effectiveness of the proposed algorithm