Semantic Communications for Image Recovery and Classification via Deep Joint Source and Channel Coding

With the recent advancements in edge artificial intelligence (AI), future sixth-generation (6G) networks need to support new AI tasks such as classification and clustering apart from data recovery. Motivated by the success of deep learning, the semantic-aware and task-oriented communications with deep joint source and channel coding (JSCC) have emerged as new paradigm shifts in 6G from the conventional data-oriented communications with separate source and channel coding (SSCC). However, most existing works focused on the deep JSCC designs for one task of data recovery or AI task execution independently, which cannot be transferred to other unintended tasks. Differently, this paper investigates the JSCC semantic communications to support multi-task services, by performing the image data recovery and classification task execution simultaneously. First, we propose a new end-to-end deep JSCC framework by unifying the coding rate reduction maximization and the mean square error (MSE) minimization in the loss function. Here, the coding rate reduction maximization facilitates the learning of discriminative features for enabling to perform classification tasks directly in the feature space, and the MSE minimization helps the learning of informative features for high-quality image data recovery. Next, to further improve the robustness against variational wireless channels, we propose a new gated deep JSCC design, in which a gated net is incorporated for adaptively pruning the output features to adjust their dimensions based on channel conditions. Finally, we present extensive numerical experiments to validate the performance of our proposed deep JSCC designs as compared to various benchmark schemes

Novel silica filled deep eutectic solvent based nanofluids for energy transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2- butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

Novel Silica Filled Deep Eutectic Solvent Based Nanofluids for Energy Transportation

Liquid range of nanofluids is a crucial parameter as it intensively determines their application temperature scope. Meanwhile, improved thermal conductivity and stability are of great significances and comprise the main fundamental research topics of nanofluids. In this work, 2-butoxy-3,4-dihydropyran (DP), produced from a convenient one-pot three-component reaction in water, was employed as dual lipophilic brusher and metal nanoparticle anchor. It was found that DP was able to enhance the dispersing ability and thermal conductivity of SiO2 nanoparticle filled deep eutectic solvent (DES) based nanofluids simultaneously. The key to the success of this protocol mainly relies on the electrophilic property and acetylacetonate moiety of DP, which ensures the formation of DP surficial modified and copper nanoparticle coated silica. Molecular dynamics simulation revealed that the hydrogen bonding effect between base solvent and alkane chain of nanoparticle was responsible for the enhanced affinity, which thus resulted in an improved stability. Viscosities of the nanofluids dropped within a certain range owing to the ruin of hydrogen bonding association among solvent molecules resulted by the hydrogen bonding effect between nanoparticle and solvent. Thermal conductivity of the copper modified silica filled DES nanofluids exhibits a maximum 13.6% enhancement, which demonstrated the advantages of this chemical covalent protocol. Additionally, study upon viscosity and convective heat transfer coefficient of the nanofluids with varies types of silica nanoparticle and DES base solvents indicated that a 24.9% heat transfer coefficient enhancement was gained that further revealed the superiority of this protocol

Improvement of Printing Quality for Laser-induced Forward Transfer based Laser-Assisted Bioprinting Process using a CFD-based numerical model

As one of the three-dimensional (3D) bioprinting techniques with great application potential, laser-induced-forward-transfer (LIFT) based laser assisted bioprinting (LAB) transfers the bioink through a developed jet flow, and the printing quality highly depends on the stability of jet flow regime. To understand the connection between the jet flow and printing outcomes, a Computational Fluid Dynamic (CFD) model was developed for the first time to accurately describe the jet flow regime and provide a guidance for optimal printing process planning. By adopting the printing parameters recommended by the CFD model, the printing quality was greatly improved by forming stable jet regime and organized printing patterns on the substrate, and the size of printed droplet can also be accurately predicted through a static equilibrium model. The ultimate goal of this research is to direct the LIFT-based LAB process and eventually improve the quality of bioprinting

UniMa at SemEval-2018 Task 7 : semantic relation extraction and classification from scientific publications

Large repositories of scientific literature call for the development of robust methods to extract information from scholarly papers. This problem is addressed by the SemEval 2018 Task 7 on extracting and classifying relations found within scientific publications. In this paper, we present a feature-based and a deep learning-based approach to the task and discuss the results of the system runs that we submitted for evaluation

SongMASS: Automatic Song Writing with Pre-training and Alignment Constraint

Automatic song writing aims to compose a song (lyric and/or melody) by machine, which is an interesting topic in both academia and industry. In automatic song writing, lyric-to-melody generation and melody-to-lyric generation are two important tasks, both of which usually suffer from the following challenges: 1) the paired lyric and melody data are limited, which affects the generation quality of the two tasks, considering a lot of paired training data are needed due to the weak correlation between lyric and melody; 2) Strict alignments are required between lyric and melody, which relies on specific alignment modeling. In this paper, we propose SongMASS to address the above challenges, which leverages masked sequence to sequence (MASS) pre-training and attention based alignment modeling for lyric-to-melody and melody-to-lyric generation. Specifically, 1) we extend the original sentence-level MASS pre-training to song level to better capture long contextual information in music, and use a separate encoder and decoder for each modality (lyric or melody); 2) we leverage sentence-level attention mask and token-level attention constraint during training to enhance the alignment between lyric and melody. During inference, we use a dynamic programming strategy to obtain the alignment between each word/syllable in lyric and note in melody. We pre-train SongMASS on unpaired lyric and melody datasets, and both objective and subjective evaluations demonstrate that SongMASS generates lyric and melody with significantly better quality than the baseline method without pre-training or alignment constraint

Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G

Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the huge amount of data scattered at wireless network edge. In general, realizing edge intelligence corresponds to the process of sensing, communication, and computation, which are coupled ingredients for data generation, exchanging, and processing, respectively. However, conventional wireless networks design the sensing, communication, and computation separately in a task-agnostic manner, which encounters difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications such as auto-driving. This thus prompts a new design paradigm of seamless integrated sensing, communication, and computation (ISCC) in a task-oriented manner, which comprehensively accounts for the use of the data in the downstream AI applications. In view of its growing interest, this article provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art development, and shedding light on the road ahead