146 research outputs found
Tab-CoT: Zero-shot Tabular Chain of Thought
The chain-of-though (CoT) prompting methods were successful in various
natural language processing (NLP) tasks thanks to their ability to unveil the
underlying complex reasoning processes. Such reasoning processes typically
exhibit implicitly structured steps. Recent efforts also started investigating
methods to encourage more explicitly structured reasoning procedures to be
captured. In this work, we propose Tab-CoT, a novel tabular-format CoT
prompting method, which allows the complex reasoning process to be explicitly
modelled in a highly structured manner. Despite its simplicity, we show that
our approach is capable of performing reasoning across multiple dimensions
(i.e., both rows and columns). We demonstrate our approach's strong zero-shot
and few-shot capabilities through extensive experiments on a range of reasoning
tasks.Comment: accepted by ACL 2023 Findin
Reduction of Coil-Crack Angle Sensitivity Effect Using a Novel Flux Feature of ACFM Technique
Alternating current field measurement (ACFM) testing is one of the promising techniques in the field of non-destructive testing with advantages of the non-contact capability and the reduction of lift-off effects. In this paper, a novel crack detection approach was proposed to reduce the effect of the angled crack (cack orientation) by using rotated ACFM techniques. The sensor probe is composed of an excitation coil and two receiving coils. Two receiving coils are orthogonally placed in the center of the excitation coil where the magnetic field is measured. It was found that the change of the x component and the peak value of the z component of the magnetic field when the sensor probe rotates around a crack followed a sine wave shape. A customized accelerated finite element method solver programmed in MATLAB was adopted to simulate the performance of the designed sensor probe which could significantly improve the computation efficiency due to the small crack perturbation. The experiments were also carried out to validate the simulations. It was found that the ratio between the z and x components of the magnetic field remained stable under various rotation angles. It showed the potential to estimate the depth of the crack from the ratio detected by combining the magnetic fields from both receiving coils (i.e., the x and z components of the magnetic field) using the rotated ACFM technique
Big-model Driven Few-shot Continual Learning
Few-shot continual learning (FSCL) has attracted intensive attention and
achieved some advances in recent years, but now it is difficult to again make a
big stride in accuracy due to the limitation of only few-shot incremental
samples. Inspired by distinctive human cognition ability in life learning, in
this work, we propose a novel Big-model driven Few-shot Continual Learning
(B-FSCL) framework to gradually evolve the model under the traction of the
world's big-models (like human accumulative knowledge). Specifically, we
perform the big-model driven transfer learning to leverage the powerful
encoding capability of these existing big-models, which can adapt the continual
model to a few of newly added samples while avoiding the over-fitting problem.
Considering that the big-model and the continual model may have different
perceived results for the identical images, we introduce an instance-level
adaptive decision mechanism to provide the high-level flexibility cognitive
support adjusted to varying samples. In turn, the adaptive decision can be
further adopted to optimize the parameters of the continual model, performing
the adaptive distillation of big-model's knowledge information. Experimental
results of our proposed B-FSCL on three popular datasets (including CIFAR100,
minilmageNet and CUB200) completely surpass all state-of-the-art FSCL methods.Comment: 9 pages 6 figure
The inversion modeling and aboveground biomass mapping of withered grass changes in the western grassland of Northeast China
The aboveground biomass (AGB) of withered grass is an important early-warning indicator for grassland fire risk. Most grassland fires occur during the dry-grass season. In order to improve the fire-warning efficiency of withered AGB, it is essential to rapidly acquire the amount of withered-grass biomass. Remote-sensing data has been widely used in monitoring and estimating grassland yields during the growing season. However, applying remote sensing to the estimation of withered grass is still in need of exploration. The aim of this work was to try to establish a remote-sensing estimation model for withered AGB in the dry-grass season. The estimation of aboveground biomass can effectively prevent the occurrence of fire, protect the environment, facilitate local management and reduce economic losses. Our approach was to, first, calculate a dry-grass index based on Sentinel-2 image data and using ENVI, SNAP, and ArcGIS software. Second, a model to estimate the fuel quantity during the dry-grass season was established by regression analysis combined with field-measured data. Finally, the estimation model was used to predict the amount of fuel in different months of the dry-grass season, followed by the fire-defense elements, which were quantified and mapped in the Longzhao Marsh wetlands. It was found that: 1) the two indices were significantly correlated (0.678) with the amount of fuel; 2) the established model could accurately estimate the amount of fuel in the study area during the dry season, and accurate test results demonstrated that the correlation between the estimated results of the best model and the measured values was 0.863, indicating high accuracy; 3) the spatiotemporal variation of withered grass in the study area was obviously different, and the quantities of fuel predicted for the other months were more accurate, which may reflect monthly dynamic changes in actual fuel quantities; and 4) the establishment of a remote-sensing estimation model for fuel quantity in the Longzhao Marsh during the dry-grass season could provide important parameters for fire-risk warning in the western grassland of Jilin Province and Northeast China
Enable Language Models to Implicitly Learn Self-Improvement From Data
Large Language Models (LLMs) have demonstrated remarkable capabilities in
open-ended text generation tasks. However, the inherent open-ended nature of
these tasks implies that there is always room for improvement in the quality of
model responses. To address this challenge, various approaches have been
proposed to enhance the performance of LLMs. There has been a growing focus on
enabling LLMs to self-improve their response quality, thereby reducing the
reliance on extensive human annotation efforts for collecting diverse and
high-quality training data. Recently, prompting-based methods have been widely
explored among self-improvement methods owing to their effectiveness,
efficiency, and convenience. However, those methods usually require explicitly
and thoroughly written rubrics as inputs to LLMs. It is expensive and
challenging to manually derive and provide all necessary rubrics with a
real-world complex goal for improvement (e.g., being more helpful and less
harmful). To this end, we propose an ImPlicit Self-ImprovemenT (PIT) framework
that implicitly learns the improvement goal from human preference data. PIT
only requires preference data that are used to train reward models without
extra human efforts. Specifically, we reformulate the training objective of
reinforcement learning from human feedback (RLHF) -- instead of maximizing
response quality for a given input, we maximize the quality gap of the response
conditioned on a reference response. In this way, PIT is implicitly trained
with the improvement goal of better aligning with human preferences.
Experiments on two real-world datasets and one synthetic dataset show that our
method significantly outperforms prompting-based methods.Comment: 28 pages, 5 figures, 4 table
Duet: efficient and scalable hybriD neUral rElation undersTanding
Learned cardinality estimation methods have achieved high precision compared
to traditional methods. Among learned methods, query-driven approaches face the
data and workload drift problem for a long time. Although both query-driven and
hybrid methods are proposed to avoid this problem, even the state-of-the-art of
them suffer from high training and estimation costs, limited scalability,
instability, and long-tailed distribution problem on high cardinality and
high-dimensional tables, which seriously affects the practical application of
learned cardinality estimators. In this paper, we prove that most of these
problems are directly caused by the widely used progressive sampling. We solve
this problem by introducing predicates information into the autoregressive
model and propose Duet, a stable, efficient, and scalable hybrid method to
estimate cardinality directly without sampling or any non-differentiable
process, which can not only reduces the inference complexity from O(n) to O(1)
compared to Naru and UAE but also achieve higher accuracy on high cardinality
and high-dimensional tables. Experimental results show that Duet can achieve
all the design goals above and be much more practical and even has a lower
inference cost on CPU than that of most learned methods on GPU
Low-loss optical waveguides in β-BBO crystal fabricated by femtosecond-laser writing
We report on the fabrication and characterization of β-BBO depressed cladding waveguides fabricated by femtosecond-laser writing with no significant changes in the waveguide lattice microstructure. The waveguiding properties and the propagation losses of the cladding structures are investigated, showing good transmission properties at wavelengths of 400 and 800 nm along TM polarization. The minimum propagation losses are measured to be as low as 0.19 dB/cm at wavelength of 800 nm. The well-preserved waveguide lattice microstructure and good guiding performances with low propagation losses suggest the potential applications of the cladding waveguides in β-BBO crystal as novel integrated photonic devices.The work is supported by the 111 Project of China (No. B13029), Junta de Castilla y León (Project SA116U13), and Ministerio de EconomÃa y Competitividad (MINECO) (FIS2013-44174-P)
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