MicroRNA-like RNAs from the same miRNA precursors play a role in cassava chilling responses

Abstract MicroRNAs (miRNAs) are known to play important roles in various cellular processes and stress responses. MiRNAs can be identified by analyzing reads from high-throughput deep sequencing. The reads realigned to miRNA precursors besides canonical miRNAs were initially considered as sequencing noise and ignored from further analysis. Here we reported a small-RNA species of phased and half-phased miRNA-like RNAs different from canonical miRNAs from cassava miRNA precursors detected under four distinct chilling conditions. They can form abundant multiple small RNAs arranged along precursors in a tandem and phased or half-phased fashion. Some of these miRNA-like RNAs were experimentally confirmed by re-amplification and re-sequencing, and have a similar qRT-PCR detection ratio as their cognate canonical miRNAs. The target genes of those phased and half-phased miRNA-like RNAs function in process of cell growth metabolism and play roles in protein kinase. Half-phased miR171d.3 was confirmed to have cleavage activities on its target gene P-glycoprotein 11, a broad substrate efflux pump across cellular membranes, which is thought to provide protection for tropical cassava during sharp temperature decease. Our results showed that the RNAs from miRNA precursors are miRNA-like small RNAs that are viable negative gene regulators and may have potential functions in cassava chilling responses

Domestic Activities Classification from Audio Recordings Using Multi-scale Dilated Depthwise Separable Convolutional Network

Domestic activities classification (DAC) from audio recordings aims at classifying audio recordings into pre-defined categories of domestic activities, which is an effective way for estimation of daily activities performed in home environment. In this paper, we propose a method for DAC from audio recordings using a multi-scale dilated depthwise separable convolutional network (DSCN). The DSCN is a lightweight neural network with small size of parameters and thus suitable to be deployed in portable terminals with limited computing resources. To expand the receptive field with the same size of DSCN's parameters, dilated convolution, instead of normal convolution, is used in the DSCN for further improving the DSCN's performance. In addition, the embeddings of various scales learned by the dilated DSCN are concatenated as a multi-scale embedding for representing property differences among various classes of domestic activities. Evaluated on a public dataset of the Task 5 of the 2018 challenge on Detection and Classification of Acoustic Scenes and Events (DCASE-2018), the results show that: both dilated convolution and multi-scale embedding contribute to the performance improvement of the proposed method; and the proposed method outperforms the methods based on state-of-the-art lightweight network in terms of classification accuracy.Comment: 5 pages, 2 figures, 4 tables. Accepted for publication in IEEE MMSP202

Load Frequency Control in Isolated Micro-Grids with Electrical Vehicles Based on Multivariable Generalized Predictive Theory

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances

Multicluster-Coordination Industrial Internet of Things: The Era of Nonorthogonal Transmission

The imminent industrial Internet of Things (IIoT) aims to provide massive device connectivity and support ever-increasing data demands, putting today's production environment on the edge of a new era of innovations and changes. In a multicluster IIoT, devices may suffer severe intercluster interference due to the intensive frequency reuse among adjacent access points (APs), thus deteriorating their quality of service (QoS). To address this issue, conventional multicluster coordination in the IIoT provides orthogonal code-, frequency-, time- or spatial-domain multiple access for interference management, but this results in a waste of resources, especially in the context of the explosively increased number of devices

UAV-Ground BS Coordinated NOMA with Joint User Scheduling, Power Allocation and Trajectory Design

We propose an unmanned aerial vehicle (UAV) ground base station (GBS) coordinated NOMA scheme where UAV and GBS jointly serve the cell-edge users. To the best of our knowledge, this is the first work to investigate air-ground BSs coordination for UAV-assisted NOMA systems, by taking advantage of the interference between UAV and GBS. Therefore, the proposed UAV-GBS coordinated NOMA scheme achieves much higher sum rate of cell-edge users than the non-coordinated UAV-assisted NOMA schemes where interference is suppressed as much as possible. The proposed scheme also outperforms GBSs coordinated NOMA due to more flexible and cost-effective interference management, thanks to the deployment of low-cost UAV BS. We conduct joint optimization of power allocation, user scheduling and UAV trajectory for the UAV-GBS coordinated system. A closed-form optimal solution to power allocation is derived. In addition, a dedicated successive interference cancellation (SIC) ordering approach is proposed. It is proven that the selection of a cell-center user with higher SIC order contributes to a higher rate of cell-edge user, based on which an SIC order based user scheduling algorithm for both cell-center and cell-edge users is presented

Joint Resource Allocation for Adaptive Fuzzy Logic Based Coordinated Multi-Cell NOMA Systems

We investigate a downlink multi-cell non-orthogonal multiple access (NOMA) system with coordinated base stations (BSs) and propose a joint resource allocation (RA) scheme alongside adaptive user association to green the system. To the best of our knowledge, this is the first work to investigate joint allocation of subchannels and power for coordinated NOMA systems, while the previous work on RA for coordinated orthogonal multiple access (OMA) systems is not applicable. A serving channel gain based joint RA (SCG-JRA) algorithm is proposed, based on the theoretical proof that the total transmission power is mono-decreasing with respect to the SCGs of non-coordinated users. As for user association, an adaptive fuzzy logic (FL) based multi-criterion approach is proposed to achieve higher robustness against the combined effect of shadowing, fading and inter-cell interference, compared to the previous single-criterion based approaches. Numerical results show that the proposed SCG-JRA with adaptive FL based user association significantly outperforms the previous RA schemes assisted by single-criterion user association, in terms of energy efficiency (EE) and total transmission power, enabling a greener system

M4LE: A Multi-Ability Multi-Range Multi-Task Multi-Domain Long-Context Evaluation Benchmark for Large Language Models

Managing long sequences has become an important and necessary feature for large language models (LLMs). However, it is still an open question of how to comprehensively and systematically evaluate the long-sequence capability of LLMs. One of the reasons is that conventional and widely-used benchmarks mainly consist of short sequences. In this paper, we propose M4LE, a Multi-ability, Multi-range, Multi-task, Multi-domain benchmark for Long-context Evaluation. M4LE is based on a diverse NLP task pool comprising 36 NLP datasets, 11 task types and 12 domains. To alleviate the scarcity of tasks with naturally long sequences and incorporate multiple-ability assessment, we propose an automatic approach (but with negligible human annotations) to convert short-sequence tasks into a unified long-sequence scenario where LLMs have to identify single or multiple relevant spans in long contexts based on explicit or semantic hints. Specifically, the scenario includes five different types of abilities: (1) explicit single-span; (2) semantic single-span; (3) explicit multiple-span; (4) semantic multiple-span; and (5) global context understanding. The resulting samples in M4LE are evenly distributed from 1k to 8k input length. We conducted a systematic evaluation on 11 well-established LLMs, especially those optimized for long-sequence inputs. Our results reveal that: 1) Current LLMs struggle to understand long context, particularly when tasks require multiple-span attention. 2) Semantic retrieval task is more difficult for competent LLMs. 3) Models fine-tuned on longer text with position interpolation have comparable performance to those using Neural Tangent Kernel (NTK) aware scaling methods without fine-tuning. We make our benchmark publicly available to encourage future research in this challenging area.Comment: Code and data are available at https://github.com/KwanWaiChung/M4L

Aligning Large Language Models with Human: A Survey

Large Language Models (LLMs) trained on extensive textual corpora have emerged as leading solutions for a broad array of Natural Language Processing (NLP) tasks. Despite their notable performance, these models are prone to certain limitations such as misunderstanding human instructions, generating potentially biased content, or factually incorrect (hallucinated) information. Hence, aligning LLMs with human expectations has become an active area of interest within the research community. This survey presents a comprehensive overview of these alignment technologies, including the following aspects. (1) Data collection: the methods for effectively collecting high-quality instructions for LLM alignment, including the use of NLP benchmarks, human annotations, and leveraging strong LLMs. (2) Training methodologies: a detailed review of the prevailing training methods employed for LLM alignment. Our exploration encompasses Supervised Fine-tuning, both Online and Offline human preference training, along with parameter-efficient training mechanisms. (3) Model Evaluation: the methods for evaluating the effectiveness of these human-aligned LLMs, presenting a multifaceted approach towards their assessment. In conclusion, we collate and distill our findings, shedding light on several promising future research avenues in the field. This survey, therefore, serves as a valuable resource for anyone invested in understanding and advancing the alignment of LLMs to better suit human-oriented tasks and expectations. An associated GitHub link collecting the latest papers is available at https://github.com/GaryYufei/AlignLLMHumanSurvey.Comment: work in progres