A Stochastic Geometry Analysis of Energy Harvesting in Large Scale Wireless Networks

In this paper, the theoretical sustainable capacity of wireless networks with radio frequency (RF) energy harvesting is analytically studied. Specifically, we consider a large scale wireless network where base stations (BSs) and low power wireless devices are deployed by homogeneous Poisson point process (PPP) with different spatial densities. Wireless devices exploit the downlink transmissions from the BSs for either information delivery or energy harvesting. Generally, a BS schedules downlink transmission to wireless devices. The scheduled device receives the data information while other devices harvest energy from the downlink signals. The data information can be successfully received by the scheduled device only if the device has sufficient energy for data processing, i.e., the harvested energy is larger than a threshold. Given the densities of BSs and users, we apply stochastic geometry to analyze the expected number of users per cell and the successful information delivery probability of a wireless device, based on which the total network throughput can be derived. It is shown that the maximum network throughput per cell can be achieved under the optimal density of BSs. Extensive simulations validate the analysis.Comment: This paper has been accepted by Greencom 201

Exploring Chain-of-Thought Style Prompting for Text-to-SQL

In-context learning with large language models (LLMs) has recently caught increasing attention due to its superior few-shot performance on various tasks. However, its performance on text-to-SQL parsing still has much room for improvement. In this paper, we hypothesize that a crucial aspect of LLMs to improve for text-to-SQL parsing is their multi-step reasoning ability. Thus, we systematically study how to enhance LLMs' reasoning ability through chain of thought (CoT) style prompting, including the original chain-of-thought prompting (Wei et al., 2022b) and least-to-most prompting (Zhou et al., 2023). Our experiments demonstrate that iterative prompting as in Zhou et al. (2023) may be unnecessary for text-to-SQL parsing, and using detailed reasoning steps tends to have more error propagation issues. Based on these findings, we propose a new CoT-style prompting method for text-to-SQL parsing. It brings 5.2 and 6.5 point absolute gains on the Spider development set and the Spider Realistic set, respectively, compared to the standard prompting method without reasoning steps; 2.4 and 1.5 point absolute gains, compared to the least-to-most prompting method.Comment: EMNLP 2023 main; long pape

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

Text-to-SQL Error Correction with Language Models of Code

Despite recent progress in text-to-SQL parsing, current semantic parsers are still not accurate enough for practical use. In this paper, we investigate how to build automatic text-to-SQL error correction models. Noticing that token-level edits are out of context and sometimes ambiguous, we propose building clause-level edit models instead. Besides, while most language models of code are not specifically pre-trained for SQL, they know common data structures and their operations in programming languages such as Python. Thus, we propose a novel representation for SQL queries and their edits that adheres more closely to the pre-training corpora of language models of code. Our error correction model improves the exact set match accuracy of different parsers by 2.4-6.5 and obtains up to 4.3 point absolute improvement over two strong baselines. Our code and data are available at https://github.com/OSU-NLP-Group/Auto-SQL-Correction.Comment: ACL 2023 Short Pape

Can acoustic indices reflect the characteristics of public recreational behavioral in urban green spaces?

Acoustic indicators serve as an effective means of assessing the quality of urban green space soundscape. The informative, easy accessibility and non-invasive nature of acoustic monitoring renders it an excellent tool for studying the interaction among the natural environment, wildlife, and human activities. Urban green space is essential in the urban ecosystem and constitutes the primary location for public outdoor recreation. However, the existing methods for monitoring public recreational behavior, such as on-site observation, drone observation, or questionnaire interviews, require significant labor or professional expertise. All of these methods have their limitations, so there is still much to be researched in the acoustic indices and recreational behavior. As a result, the potential for using acoustic characteristics to monitor public recreational behavior remains underexplored. To address this gap, this study investigates the potential of 5 widely used acoustic indices and acoustic intensity for monitoring public recreational behavior: Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Richness (AR), Normalized Difference Soundscape Index (NDSI), and Power Spectral Density (PSD). Data were collected from 35 monitoring points in urban green spaces during the opening hours (6:00–22:00) to analyze the relationship between these indices and public recreational behavior. The findings indicate that (1) ACI, ADI, and AR daily exhibited multi-peak daily variation characteristics similar to those of public recreational behavior, displaying a “W” shape, while NDSI exhibits opposite variation characteristics; (2) the spatial variation characteristics of ACI, ADI, and AR change in response to the green space, and these changes align with public recreational behavior; (3) the correlation analysis and generalized linear mixed model construction further demonstrate that acoustic indices are effective in capturing the dynamic activities of visitor behavior; and (4) PSD undergoes significant temporal dynamic changes along the frequency gradient, with different frequency intervals reflecting the activity information of different recreational behaviors. In conclusion, this research highlights the effectiveness of using acoustic indices to analyze both the spatial and temporal variation characteristics of public recreational behavior in urban green spaces. The results can provide valuable data support for the enhancement and renovation of urban green spaces

OsHAC1;1 and OsHAC1;2 function as arsenate reductases and regulate arsenic accumulation

Rice is a major dietary source of the toxic metalloid arsenic (As). Reducing its accumulation in rice (Oryza sativa) grain is of critical importance to food safety. Rice roots take up arsenate and arsenite depending on the prevailing soil conditions. The first step of arsenate detoxification is its reduction to arsenite, but the enzyme(s) catalyzing this reaction in rice remains unknown. Here, we identify OsHAC1;1 and OsHAC1;2 as arsenate reductases in rice. OsHAC1;1 and OsHAC1;2 are able to complement an Escherichia coli mutant lacking the endogenous arsenate reductase and to reduce arsenate to arsenite. OsHAC1:1 and OsHAC1;2 are predominantly expressed in roots, with OsHAC1;1 being abundant in the epidermis, root hairs, and pericycle cells while OsHAC1;2 is abundant in the epidermis, outer layers of cortex, and endodermis cells. Expression of the two genes was induced by arsenate exposure. Knocking out OsHAC1;1 or OsHAC1;2 decreased the reduction of arsenate to arsenite in roots, reducing arsenite efflux to the external medium. Loss of arsenite efflux was also associated with increased As accumulation in shoots. Greater effects were observed in a double mutant of the two genes. In contrast, overexpression of either OsHAC1;1 or OsHAC1;2 increased arsenite efflux, reduced As accumulation, and enhanced arsenate tolerance. When grown under aerobic soil conditions, overexpression of either OsHAC1;1 or OsHAC1;2 also decreased As accumulation in rice grain, whereas grain As increased in the knockout mutants. We conclude that OsHAC1;1 and OsHAC1;2 are arsenate reductases that play an important role in restricting As accumulation in rice shoots and grain