NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework

Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train. We propose a simple and efficient learning framework, TLM, that does not rely on large-scale pretraining. Given some labeled task data and a large general corpus, TLM uses task data as queries to retrieve a tiny subset of the general corpus and jointly optimizes the task objective and the language modeling objective from scratch. On eight classification datasets in four domains, TLM achieves results better than or similar to pretrained language models (e.g., RoBERTa-Large) while reducing the training FLOPs by two orders of magnitude. With high accuracy and efficiency, we hope TLM will contribute to democratizing NLP and expediting its development.Comment: 14 pages, 5 figure

Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment

Processing of digital images is continuously gaining in volume and relevance, with concomitant demands on data storage, transmission and processing power. Encoding the image information in quantum-mechanical systems instead of classical ones and replacing classical with quantum information processing may alleviate some of these challenges. By encoding and processing the image information in quantum-mechanical systems, we here demonstrate the framework of quantum image processing, where a pure quantum state encodes the image information: we encode the pixel values in the probability amplitudes and the pixel positions in the computational basis states. Our quantum image representation reduces the required number of qubits compared to existing implementations, and we present image processing algorithms that provide exponential speed-up over their classical counterparts. For the commonly used task of detecting the edge of an image, we propose and implement a quantum algorithm that completes the task with only one single-qubit operation, independent of the size of the image. This demonstrates the potential of quantum image processing for highly efficient image and video processing in the big data era.Comment: 13 pages, including 9 figures and 5 appendixe

Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

Paired Bid-Based Double Auction Mechanism for RAN Slicing in 5G-and-Beyond System

Network slicing has been widely deemed as a promising technology that enables the sharing of infrastructure resources for 5G-and-beyond mobile networks. Infrastructure Providers (InPs) abstract physical network into multiple isolated network slices, each of which can be operated as a virtual network by different Mobile Virtual Network Operators (MVNOs). However, the asymmetric information between resource supply of InP and usage requirement of MVNO challenges the resource allocation when enforcing slicing in the radio access network (RAN). In this paper, we propose a paired bid-based double-auction mechanism for a slicing-based RAN to improve resource allocation efficiency. We construct a market model in which the MVNOs and the InPs submit respective bids and ask Network Slice Broker (NSB) for slice transactions, and the NSB determines the winner pairs and corresponding payments to clear the slice market by maximizing social welfare. Numerical results validate the effectiveness of our proposed mechanism on improving the overall network resource allocation efficiency without collecting full information on the competitive strategies and utility functions of the MVNOs and InPs

Rubber Material Properties of Several Rubber Tree Strains

：The rubber from rubber tree strain reyan 8-79 (hainan), zhanshi 218-6 (guangdong), yunyan 73-46 and yunyan 75-11 (yunnan) were tested to determine the physical and chemical properties, processing properties of raw rubber and physical and mechanical properties of vulcanized rubber. The results showed that raw rubber from different tree strains had different physical and chemical properties, processing properties,andthe physical and mechanical properties of vulcanized rubber were different as well. Yunyan 75-11 had the highest mooney viscosity, Reyan 8-79 had the highest protein content, Zhanshi 218-6 had the best tensile and tearing strength, Yunyan 73-46 had small elastic modulus, large loss factor and good processing properties

Effects of Nano TiC Particles on Recrystallization and Mechanical Properties of Al-Zn-Mg-Cu Alloy

The recrystallization and mechanical properties of 7085 alloy and TiC/7085 composites with different nano TiC content (0.1, 0.3, 0.5, and 1 wt%) were investigated in this work. Results showed that as the TiC content increased from 0.1 to 1 wt%, dynamic recrystallization was promoted in which the composites proceeded by hot deformation; after T6 treatment, static recrystallization was hindered. In addition, the ultimate strength of composites first increased and then decreased with the increase of nano TiC particle content from 0.1 to 1 wt%. When the content of nano TiC particles reached 0.5 wt%, the tensile strength of the nanocomposites was improved to 608 MPa, 12% higher than that of 7085 alloy, via the reinforcing particle strengthening mechanism. Due to the grain coarsening and the TiC particle cluster, the ultimate tensile strength of 1 wt% TiC/7085 composite decreased to 585 MPa

Hydrochemical Characteristics and Genetic Mechanism of Geothermal Springs in the Aba Area, Western Sichuan Province, China

Geothermal resources have been a source of significant clean energy in the world. The Sichuan Province is famous for its abundant geothermal resources in China, especially in western Sichuan. The Aba area is a significant minority region in northwestern Sichuan with abundant geothermal resources. In this study, hydrochemical and D-O analyses were conducted on the eight collected geothermal springs to investigate the genetic mechanism of the geothermal resource in the Aba area. The exposed temperatures and pH values of the geothermal springs ranged from 23 °C to 48 °C and from 6.6 to 9.5, respectively. Based on the hydrochemical characteristics, the eight geothermal springs were classified into two types: class A and class B. The class A geothermal springs belonged to the hydrochemical type of Ca-Mg-HCO3-SO4 and Ca-Mg-HCO3 and were affected by the weathering and dissolution of carbonate and silicate. The class B hydrochemical type of geothermal spring was Na-HCO3, which was determined by the weathering and dissolution of evaporite and silicate. A Na-K-Mg triangle diagram revealed that the geothermal springs belonged to immature water. A chalcedony geothermometer indicated that the temperature of the class A shallow geothermal reservoir in the Aba area was 59.70–73.00 °C and 70.65–120.91 °C for class B. Silicon enthalpy approaches showed that the initial reservoir temperature for class A was 181.36–203.07 °C (mixed by 85.76–89.44% cold water) and 271.74–295.58 °C (mixed by 87.39–87.54% cold water) for class B. The recharge elevation of the geothermal spring was 3415–3495 m as calculated by the D-O isotopes. We have proposed these genetic models of the two typical geothermal springs. The achievements provide a vital reference for the further development of geothermal water and the sustainable utilization of geothermal resources in the Aba area

Hydrochemical Characteristics and Genetic Mechanism of Geothermal Springs in the Aba Area, Western Sichuan Province, China

Geothermal resources have been a source of significant clean energy in the world. The Sichuan Province is famous for its abundant geothermal resources in China, especially in western Sichuan. The Aba area is a significant minority region in northwestern Sichuan with abundant geothermal resources. In this study, hydrochemical and D-O analyses were conducted on the eight collected geothermal springs to investigate the genetic mechanism of the geothermal resource in the Aba area. The exposed temperatures and pH values of the geothermal springs ranged from 23 °C to 48 °C and from 6.6 to 9.5, respectively. Based on the hydrochemical characteristics, the eight geothermal springs were classified into two types: class A and class B. The class A geothermal springs belonged to the hydrochemical type of Ca-Mg-HCO3-SO4 and Ca-Mg-HCO3 and were affected by the weathering and dissolution of carbonate and silicate. The class B hydrochemical type of geothermal spring was Na-HCO3, which was determined by the weathering and dissolution of evaporite and silicate. A Na-K-Mg triangle diagram revealed that the geothermal springs belonged to immature water. A chalcedony geothermometer indicated that the temperature of the class A shallow geothermal reservoir in the Aba area was 59.70–73.00 °C and 70.65–120.91 °C for class B. Silicon enthalpy approaches showed that the initial reservoir temperature for class A was 181.36–203.07 °C (mixed by 85.76–89.44% cold water) and 271.74–295.58 °C (mixed by 87.39–87.54% cold water) for class B. The recharge elevation of the geothermal spring was 3415–3495 m as calculated by the D-O isotopes. We have proposed these genetic models of the two typical geothermal springs. The achievements provide a vital reference for the further development of geothermal water and the sustainable utilization of geothermal resources in the Aba area