Building a Cloud Storage Service System

AbstractCloud Storage services are increasingly noticed as they promise elastic capability and high reliability at low cost. In such services, you can store most of your files to authenticated Cloud Storage Service center, and you do not worry about your space being inadequate or wasted because the storage being able to be adjusted dynamically is the most important feature of the Cloud Storage. In this paper, we present a solution about how to build a Cloud Storage Service System based on the open-source distributed database, it follows a stratum design that includes Web service front-end, transformation processing layer and data storing layer. Terminal users can access their own data in this system through three Web service interfaces. More over, a complete prototype system based on this architecture is demonstrated

Implications of a Possible Spectral Structure of Cosmic-ray Protons Unveiled by the DAMPE

The recent observations revealed that the cosmic-ray (CR) proton spectrum showed a complex structure: the hardening at $\rm \sim 200~GeV$ and softening at $\rm \sim 10~TeV$. However, so far the physical origins of this spectral feature remain strongly debated. In this work, we simulate the acceleration of cosmic-ray protons in a nearby Supernova remnant (SNR) by solving numerically the hydrodynamic equations and the equation for the quasi-isotropic CR momentum distribution in the spherically symmetrical case to derive the spectrum of protons injected into the interstellar medium (ISM), and then simulate the propagation process of those accelerated CR particles to calculate the proton fluxes reaching the Earth. Besides, we use the DRAGON numerical code to calculate the large-scale cosmic-ray proton spectrum. Our simulated results are in good agreement with the observed data (including the observed data of proton fluxes and dipole anisotropy). We conclude that the spectral feature of cosmic-ray protons in this energy band may originate from the superposition of the distribution from the nearby SNR and background diffusive cosmic-ray component. We find that the release of particles from this nearby SNR has a time delay. Besides, it can be found that the nonlinear response of energetic particles, release time of CR protons, and age of the local SNR can leave strong signatures in the spectrum of the resulting CR proton fluxes.Comment: 11 pages,9 figures and 1 table. Accepted for publication in Ap

The Potential Use of DCE-MRI Texture Analysis to Predict HER2 2+ Status

Purpose: To evaluate the ability of texture analysis of breast dynamic contrast enhancement-magnetic resonance (DCE-MR) images in differentiating human epidermal growth factor receptor 2 (HER2) 2+ status of breast tumors.Methods: A total of 73 cases were retrospectively selected. HER2 2+ status was confirmed by fluorescence in situ hybridization. For each case, 279 textural features were derived. A student's t-test or Mann-Whitney U test was used to select features with statistically significant differences between HER2 2+ positive and negative groups. A principal component analysis was applied to eliminate feature correlation. Three machine learning classifiers, logistic regression (LR), quadratic discriminant analysis (QDA), and a support vector machine (SVM), were trained and tested using a leave-one-out cross-validation method. The area under a receiver operating characteristic curve (AUC) was measured to assess the classifier's performance.Results: The AUCs for the different classifiers were satisfactory, ranging from 0.808 to 0.865. The classification methods derived with LR and SVM demonstrated similarly high performances, and the accuracy levels were 81.06 and 81.18%, respectively. The AUC for the classifier derived with SVM was the highest (0.865), and a marked specificity (88.90%) was presented. For the classifier with LR, the AUC was 0.851, and the corresponding sensitivity (94.44%) was the highest.Conclusion: The texture analysis for breast DCE-MRI proposed in this study demonstrated potential utility in HER2 2+ status discrimination

Ada-TTA: Towards Adaptive High-Quality Text-to-Talking Avatar Synthesis

We are interested in a novel task, namely low-resource text-to-talking avatar. Given only a few-minute-long talking person video with the audio track as the training data and arbitrary texts as the driving input, we aim to synthesize high-quality talking portrait videos corresponding to the input text. This task has broad application prospects in the digital human industry but has not been technically achieved yet due to two challenges: (1) It is challenging to mimic the timbre from out-of-domain audio for a traditional multi-speaker Text-to-Speech system. (2) It is hard to render high-fidelity and lip-synchronized talking avatars with limited training data. In this paper, we introduce Adaptive Text-to-Talking Avatar (Ada-TTA), which (1) designs a generic zero-shot multi-speaker TTS model that well disentangles the text content, timbre, and prosody; and (2) embraces recent advances in neural rendering to achieve realistic audio-driven talking face video generation. With these designs, our method overcomes the aforementioned two challenges and achieves to generate identity-preserving speech and realistic talking person video. Experiments demonstrate that our method could synthesize realistic, identity-preserving, and audio-visual synchronized talking avatar videos.Comment: 6 pages, 3 figure

PEJL: A path-enhanced joint learning approach for knowledge graph completion

Knowledge graphs (KGs) often suffer from incompleteness. Knowledge graph completion (KGC) is proposed to complete missing components in a KG. Most KGC methods focus on direct relations and fail to leverage rich semantic information in multi-hop paths. In contrast, path-based embedding methods can capture path information and utilize extra semantics to improve KGC. However, most path-based methods cannot take advantage of full multi-hop information and neglect to capture multiple semantic associations between single and multi-hop triples. To bridge the gap, we propose a novel path-enhanced joint learning approach called PEJL for KGC. Rather than learning multi-hop representations, PEJL can recover multi-hop embeddings by encoding full multi-hop components. Meanwhile, PEJL extends the definition of translation energy functions and generates new semantic representations for each multi-hop component, which is rarely considered in path-based methods. Specifically, we first use the path constraint resource allocation (PCRA) algorithm to extract multi-hop triples. Then we use an embedding recovering module consisting of a bidirectional gated recurrent unit (GRU) layer and a fully connected layer to obtain multi-hop embeddings. Next, we employ a KG modeling module to leverage various semantic information and model the whole knowledge graph based on translation methods. Finally, we define a joint learning approach to train our proposed PEJL. We evaluate our model on two KGC datasets: FB15K-237 and NELL-995. Experiments show the effectiveness and superiority of PEJL

Mega-TTS 2: Zero-Shot Text-to-Speech with Arbitrary Length Speech Prompts

Zero-shot text-to-speech aims at synthesizing voices with unseen speech prompts. Previous large-scale multispeaker TTS models have successfully achieved this goal with an enrolled recording within 10 seconds. However, most of them are designed to utilize only short speech prompts. The limited information in short speech prompts significantly hinders the performance of fine-grained identity imitation. In this paper, we introduce Mega-TTS 2, a generic zero-shot multispeaker TTS model that is capable of synthesizing speech for unseen speakers with arbitrary-length prompts. Specifically, we 1) design a multi-reference timbre encoder to extract timbre information from multiple reference speeches; 2) and train a prosody language model with arbitrary-length speech prompts; With these designs, our model is suitable for prompts of different lengths, which extends the upper bound of speech quality for zero-shot text-to-speech. Besides arbitrary-length prompts, we introduce arbitrary-source prompts, which leverages the probabilities derived from multiple P-LLM outputs to produce expressive and controlled prosody. Furthermore, we propose a phoneme-level auto-regressive duration model to introduce in-context learning capabilities to duration modeling. Experiments demonstrate that our method could not only synthesize identity-preserving speech with a short prompt of an unseen speaker but also achieve improved performance with longer speech prompts. Audio samples can be found in https://mega-tts.github.io/mega2_demo/