Evolution of the tetragonal to rhombohedral transition in (1 − x)(Bi1/2Na1/2)TiO3 − xBaTiO3 (x ≤ 7%)

(1 − x)(Bi1/2Na1/2)TiO3 − xBaTiO3 has been the most studied Pb-free piezoelectric material in the last decade; however, puzzles still remain about its phase transitions, especially around the important morphotropic phase boundary (MPB). By introducing the strain glass transition concept from the ferroelastic field, it was found that the phase transition from tetragonal (T, P4bm) to rhombohedral (R, R3c) was affected by a strain glass transition at higher temperature for x ≥ 4%. In these compositions, the T–R transition was delayed or even totally suppressed and displayed huge thermal hysteresis upon cooling and heating. Also, isothermal phase transitions were predicted and realized successfully in the crossover region, where the interaction between the T–R transition and the strain glass transition was strong. Our results revealed the strain glass nature in compositions around the MPB in this important material, and also provide new clues for understanding the transition complexity in other (Bi1/2Na1/2)TiO3-based Pb-free piezoelectric materials

THash: A Practical Network Optimization Scheme for DHT-based P2P Applications

International audienceP2P platforms have been criticized because of the heavy strain that they can inflict on costly inter-domain links of network operators. It is therefore mandatory to develop network optimization schemes for controlling the load generated by a P2P platform on an operator network. While many research efforts exist on centralized tracker-based systems, in recent years multiple DHT-based P2P platforms have been widely deployed and considered as commercial services due to their scalability and fault tolerance. Finding network optimization for DHT-based P2P applications has thereby potential large practical impacts. In this paper, we present THash, a simple scheme that implements a distributed and effective network optimization for DHT systems. THash uses standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients to choose their sharing peers in proper domains. We have implemented THash in a major commercial P2P system (PPLive), using the standard ALTO/P4P protocol as the network information source. We conducted experiments over this network in real operation and observed that compared with Native DHT, THash reduced respectively by 47.4% and 67.7% the inter-PID and inter-AS traffic, while reducing the average downloading time by 14.6% to 24.5%

Network Optimization for DHT-based Applications

International audienceP2P platforms have been criticized because of the heavy strain that some P2P services can inflict on costly inter-domain links of network operators. It is therefore necessary to develop network optimization schemes for controlling the load generated by P2P platforms on an operator network. Previous focus on network optimization has been mostly on centralized tracker-based systems. However, in recent years multiple DHT-based P2P networks are widely deployed due to their scalability and fault tolerance, and these networks have even been considered as platforms for commercial services..Thereby, finding network optimization for DHT-based P2P applications has potentially large practical impacts. In this paper, we present THash, a simple scheme to implement an effective distributed network optimization for DHT systems. THash is based on standard DHT put/get semantics and utilizes a triple hash method to guide the DHT clients sharing resources with peers in proper domains. We have implemented THash in a major P2P application (PPLive) by using the standard ALTO/P4P protocol as the network information source. We conducted realistic experiments over the network and observed that compared with Native DHT, THash only generated 45.5\% and 35.7\% of inter-PID and inter-AS traffic, and at the same time shortened the average downloading time by 13.8\% to 22.1\%

Development of Channeled Nanofibrous Scaffolds for Oriented Tissue Engineering

A tissue‐engineering scaffold resembling the structure of the natural extracellular matrix can often facilitate tissue regeneration. Nerve and tendon are oriented micro‐scale tissue bundles. In this study, a method combining injection molding and thermally induced phase separation techniques is developed to create single‐ and multiple‐channeled nanofibrous poly( L ‐lactic acid) scaffolds. The overall shape, the number and spatial arrangement of channels, the channel wall matrix architecture, the porosity and mechanical properties of the scaffolds are all tunable. The porous NF channel wall matrix provides an excellent microenvironment for protein adsorption and the attachment of PC12 neuronal cells and tendon fibroblast cells, showing potential for neural and tendon tissue regeneration. A method combining injection molding and thermally induced phase separation is developed to create single‐ and multiple‐channeled nanofibrous polymer scaffolds. The porous nanofibrous channel wall provides an excellent microenvironment for protein adsorption and cell attachment, showing potential for nerve and tendon regeneration.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/92054/1/761_ftp.pd

Unraveling Feature Extraction Mechanisms in Neural Networks

The underlying mechanism of neural networks in capturing precise knowledge has been the subject of consistent research efforts. In this work, we propose a theoretical approach based on Neural Tangent Kernels (NTKs) to investigate such mechanisms. Specifically, considering the infinite network width, we hypothesize the learning dynamics of target models may intuitively unravel the features they acquire from training data, deepening our insights into their internal mechanisms. We apply our approach to several fundamental models and reveal how these models leverage statistical features during gradient descent and how they are integrated into final decisions. We also discovered that the choice of activation function can affect feature extraction. For instance, the use of the \textit{ReLU} activation function could potentially introduce a bias in features, providing a plausible explanation for its replacement with alternative functions in recent pre-trained language models. Additionally, we find that while self-attention and CNN models may exhibit limitations in learning n-grams, multiplication-based models seem to excel in this area. We verify these theoretical findings through experiments and find that they can be applied to analyze language modeling tasks, which can be regarded as a special variant of classification. Our contributions offer insights into the roles and capacities of fundamental components within large language models, thereby aiding the broader understanding of these complex systems.Comment: Accepted by EMNLP 202

Chord-Conditioned Melody Choralization with Controllable Harmonicity and Polyphonicity

Melody choralization, i.e. generating a four-part chorale based on a user-given melody, has long been closely associated with J.S. Bach chorales. Previous neural network-based systems rarely focus on chorale generation conditioned on a chord progression, and none of them realised controllable melody choralization. To enable neural networks to learn the general principles of counterpoint from Bach's chorales, we first design a music representation that encoded chord symbols for chord conditioning. We then propose DeepChoir, a melody choralization system, which can generate a four-part chorale for a given melody conditioned on a chord progression. Furthermore, with the improved density sampling, a user can control the extent of harmonicity and polyphonicity for the chorale generated by DeepChoir. Experimental results reveal the effectiveness of our data representation and the controllability of DeepChoir over harmonicity and polyphonicity. The code and generated samples (chorales, folk songs and a symphony) of DeepChoir, and the dataset we use now are available at https://github.com/sander-wood/deepchoir.Comment: 7 pages, 4 figures, 2 table

The Application of OCTA in Assessment of Anti-VEGF Therapy for Idiopathic Choroidal Neovascularization

Purpose. To assess the morphology of idiopathic choroidal neovascularization (ICNV) by optical coherence tomography angiography (OCTA) and determine the therapeutic effects of intravitreal antivascular endothelial growth factor (anti-VEGF). Method. Patients with naive ICNV were assessed by spectral domain optical coherence tomography (SD-OCT) and OCTA in this observational study. The timing of observation was before treatment, 1 day after treatment with intravitreal anti-VEGF injection, and 1 month after the treatment. The central retina thickness (CRT) on SD-OCT, selected CNV area, and flow area on OCTA were measured. Results. A total of 17 eyes from 17 patients with ICNV were included in this study. OCTA showed visible irregular choroidal neovascularization with “tree-in-bud” form on outer retinal layer. After treatment, as well as in the 1-day follow-up, CNV decreased in size from the periphery, and the vessel density was reduced. As shown on OCTA, the selected CNV area and flow area were significantly reduced compared to pretreatment. The rate of CNV vessel area changes was higher on OCTA than the changes in CRT on SD-OCT at 1-day and 1-month follow-up. Conclusion. Intravitreal injection of anti-VEGF is effective for idiopathic choroidal neovascularization, and the treatment outcomes are observable after 1 day. OCTA provides a useful approach for monitoring and evaluating the treatment of intravitreal anti-VEGF for CNV

TunesFormer: Forming Irish Tunes with Control Codes by Bar Patching

This paper introduces TunesFormer, an efficient Transformer-based dual-decoder model specifically designed for the generation of melodies that adhere to user-defined musical forms. Trained on 214,122 Irish tunes, TunesFormer utilizes techniques including bar patching and control codes. Bar patching reduces sequence length and generation time, while control codes guide TunesFormer in producing melodies that conform to desired musical forms. Our evaluation demonstrates TunesFormer's superior efficiency, being 3.22 times faster than GPT-2 and 1.79 times faster than a model with linear complexity of equal scale while offering comparable performance in controllability and other metrics. TunesFormer provides a novel tool for musicians, composers, and music enthusiasts alike to explore the vast landscape of Irish music. Our model and code are available at https://github.com/sander-wood/tunesformer.Comment: 5 pages, 3 figures, 1 tabl