Abstract Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal lingual injuries without occupying the hands. Collection interpretation lip challenging. Here, we propose concept a novel lip-language decoding system self-powered, low-cost, contact flexible triboelectric sensors well-trained dilated recurrent neural network model based on prototype learning. The structural principle electrical properties are measured...

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, mapping from continuous latent factors to observed signal parametrized by convolutional neural network. iterates following two steps: (1) Inferential back-propagation, which infers Langevin dynamics or gradient descent. (2) Learning updates parameters given inferred computations in both steps are powered and they...

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, mapping from continuous latent factors to observed signal parametrized by convolutional neural network. iterates following two steps: (1) Inferential back-propagation, which infers Langevin dynamics or gradient descent. (2) Learning updates parameters given inferred computations in both steps are powered and they...

This paper proposes the divergence triangle as a framework for joint training of generator model, energy-based model and inference model. The is compact symmetric (anti-symmetric) objective function that seamlessly integrates variational learning, adversarial wake-sleep algorithm, contrastive in unified probabilistic formulation. unification makes processes sampling, inference, energy evaluation readily available without need costly Markov chain Monte Carlo methods. Our experiments...

We propose to learn energy-based model (EBM) in the latent space of a generator model, so that EBM serves as prior stands on top-down network model. Both and can be learned jointly by maximum likelihood, which involves short-run MCMC sampling from both posterior distributions vector. Due low dimensionality expressiveness network, simple capture regularities data effectively, is efficient mixes well. show exhibits strong performances terms image text generation anomaly detection. The one-page...

Surface coal mining in semi-arid regions has detrimental impacts on the structure and function of surface ecosystems, thereby impeding attainment regional sustainable development goals. Moreover, impact climate change ecological restoration areas is an inevitable consideration. To elucidate response ecosystem services to change, topography, soil, vegetation socioeconomic development, this study selected six large-scale mines located China as research objects. In study, we aimed assess main...

Large language models (LLMs), while driving a new wave of interactive AI applications across numerous domains, suffer from high inference costs and heavy cloud dependency. Motivated by the redundancy phenomenon in linguistics, we propose progressive paradigm over edge, i.e., firstly generating sketch answer LLMs at cloud, then conducting parallel extension to fill details small (SLMs) edge. Progressive offers potential benefits improve throughput reduce latency facing key implementation...

Elastic computing enables dynamic scaling to meet workload demands, and Remote Direct Memory Access (RDMA) enhances this by providing high-throughput, low-latency network communication. However, integrating RDMA into elastic remains a challenge, particularly in control plane operations for connection setup. This paper revisits the assumptions of prior work on high-performance computing, reveals that extreme microsecond-level optimizations are often unnecessary. By challenging conventional...

Token filtering has been proposed to enhance utility of large language models (LLMs) by eliminating inconsequential tokens during training. While using fewer should reduce computational workloads, existing studies have not succeeded in achieving higher efficiency. This is primarily due the insufficient sparsity caused only output layers, as well inefficient sparse GEMM (General Matrix Multiplication), even when having sufficient sparsity. paper presents Collider, a system unleashing full...

Decades of research on Internet congestion control (CC) have produced a plethora algorithms that optimize for different performance objectives. Applications face the challenge choosing most suitable algorithm based their needs, and it takes tremendous efforts expertise to customize CC when new demands emerge. In this paper, we explore basic question: can design single satisfy objectives?

With the wide application of electronic health records (EHR) in healthcare facilities, event prediction with deep learning has gained more and attention. A common feature EHR data used for deep-learning-based predictions is historical diagnoses. Existing work mainly regards a diagnosis as an independent disease does not consider clinical relations among diseases visit. Many machine approaches assume representations are static different visits patient. However, real practice, multiple that...

Topography is one of the important factors influencing distribution rural settlements, as natural environment in plateau-mountain areas more complicated and harsher forces various influences are obvious. Analyzing correlation between settlement topography would help understand influence on human activities. This paper takes settlements Yunnan Province research object, introduces concept index, integrates methods GIS spatial analysis mathematical statistics to analyze them. The results show...

This paper proposes a joint training method to learn both the variational auto-encoder (VAE) and latent energy-based model (EBM). The of VAE EBM are based on an objective function that consists three Kullback-Leibler divergences between distributions vector image, is elegant symmetric anti-symmetric form divergence triangle seamlessly integrates adversarial learning. In this scheme, serves as critic generator model, while inference in serve approximate synthesis sampler EBM. Our experiments...

With the growing complexity of deep learning applications, users have started to delegate their data and models cloud. Among these online services, which involve both training inference procedures, are widely deployed. To ensure privacy guarantee on public cloud, researchers proposed a plethora privacy-preserving algorithms with different techniques, ranging from obfuscation mechanisms cryptographic tools. However, none them is applicable services. They either focus only or procedure while...

The binary split grammar is powerful to parse façade in a broad range of types, whose structure characterized by repetitive patterns with different layouts. We notice that, as far two labels are concerned, BSG parsing equivalent approximating matrix multiple rank-one patterns. Then, we propose an efficient algorithm decompose arbitrary into and residual matrix, magnitude small the sense l <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup>...

Due to the availability of references research papers and rich information contained in papers, various citation analysis approaches have been proposed identify similar documents for scholar recommendation. Despite success previous approaches, they are, however, based on co-occurrence items. Once there are no items available documents, will not work well. Inspired by distributed representations words literature natural language processing, we propose a novel approach measuring similarity...

Abstract Background Kidney transplantation is the optimal treatment to cure patients with end-stage renal disease (ESRD). However, infectious complication, especially pneumonia, main cause of mortality in early stage. Immune monitoring by relevant biomarkers provides direct evidence immune status. We aimed study association between and pneumonia kidney transplant through machine learning models. Methods A total 146 receiving panel our center, including 46 recipients 100 stable recipients,...