A General Framework for Complex Network Applications

Complex network theory has been applied to solving practical problems from different domains. In this paper, we present a general framework for complex network applications. The keys of a successful application are a thorough understanding of the real system and a correct mapping of complex network theory to practical problems in the system. Despite of certain limitations discussed in this paper, complex network theory provides a foundation on which to develop powerful tools in analyzing and optimizing large interconnected systems.Comment: 8 page

Dynamic MLP for MRI Reconstruction

As convolutional neural networks (CNN) become the most successful reconstruction technique for accelerated Magnetic Resonance Imaging (MRI), CNN reaches its limit on image quality especially in sharpness. Further improvement on image quality often comes at massive computational costs, hindering their practicability in the clinic setting. MRI reconstruction is essentially a deconvolution problem, which demands long-distance information that is difficult to be captured by CNNs with small convolution kernels. The multi-layer perceptron (MLP) is able to model such long-distance information, but it restricts a fixed input size while the reconstruction of images in flexible resolutions is required in the clinic setting. In this paper, we proposed a hybrid CNN and MLP reconstruction strategy, featured by dynamic MLP (dMLP) that accepts arbitrary image sizes. Experiments were conducted using 3D multi-coil MRI. Our results suggested the proposed dMLP can improve image sharpness compared to its pure CNN counterpart, while costing minor additional GPU memory and computation time. We further compared the proposed dMLP with CNNs using large kernels and studied pure MLP-based reconstruction using a stack of 1D dMLPs, as well as its CNN counterpart using only 1D convolutions. We observed the enlarged receptive field has noticeably improved image quality, while simply using CNN with a large kernel leads to difficulties in training. Noticeably, the pure MLP-based method has been outperformed by CNN-involved methods, which matches the observations in other computer vision tasks for natural images

D-Unet: A Dual-encoder U-Net for Image Splicing Forgery Detection and Localization

Recently, many detection methods based on convolutional neural networks (CNNs) have been proposed for image splicing forgery detection. Most of these detection methods focus on the local patches or local objects. In fact, image splicing forgery detection is a global binary classification task that distinguishes the tampered and non-tampered regions by image fingerprints. However, some specific image contents are hardly retained by CNN-based detection networks, but if included, would improve the detection accuracy of the networks. To resolve these issues, we propose a novel network called dual-encoder U-Net (D-Unet) for image splicing forgery detection, which employs an unfixed encoder and a fixed encoder. The unfixed encoder autonomously learns the image fingerprints that differentiate between the tampered and non-tampered regions, whereas the fixed encoder intentionally provides the direction information that assists the learning and detection of the network. This dual-encoder is followed by a spatial pyramid global-feature extraction module that expands the global insight of D-Unet for classifying the tampered and non-tampered regions more accurately. In an experimental comparison study of D-Unet and state-of-the-art methods, D-Unet outperformed the other methods in image-level and pixel-level detection, without requiring pre-training or training on a large number of forgery images. Moreover, it was stably robust to different attacks.Comment: 13 pages, 13 figure

Monazite behaviour during isothermal decompression in pelitic granulites: a case study from Dinggye, Tibetan Himalaya

Monazite is a key accessory mineral for metamorphic geochronology, but interpretation of its complex chemical and age zoning acquired during high-temperature metamorphism and anatexis remains a challenge. We investigate the petrology, pressure–temperature and timing of metamorphism in pelitic and psammitic granulites that contain monazite from the Greater Himalayan Crystalline Complex (GHC) in Dinggye, southern Tibet. These rocks underwent isothermal decompression from pressure of >10 kbar to ~5 kbar at temperatures of 750–830 °C, and recorded three metamorphic stages at kyanite (M1), sillimanite (M2) and cordierite-spinel grade (M3). Monazite and zircon crystals were dated by microbeam techniques either as grain separates or in thin sections. U–Th–Pb ages are linked to specific conditions of mineral growth on the basis of zoning patterns, trace element signatures, index mineral inclusions (melt inclusions, sillimanite and K-feldspar) in dated domains and textural relationships with co-existing minerals. The results show that inherited domains (500–400 Ma) are preserved in monazite even at granulite-facies conditions. Few monazites or zircon yield ages related to the M1- stage (~30–29 Ma), possibly corresponding to prograde melting by muscovite dehydration. During the early stage of isothermal decompression, inherited or prograde monazites in most samples were dissolved in the melt produced by biotite dehydration-melting. Most monazite grains crystallized from melt toward the end of decompression (M3-stage, 21–19 Ma) and are chemically related to garnet breakdown reactions. Another peak of monazite growth occurred at final melt crystallization (~15 Ma), and these monazite grains are unzoned and are homogeneous in composition. In a regional context, our pressure–temperature–time data constrains peak high-pressure metamorphism within the GHC to ~30–29 Ma in Dinggye Himalaya. Our results are in line with a meltassisted exhumation of the GHC rocks

Quantum theory of light diffraction

At present, the theory of light diffraction only has the simple wave-optical approach. In this paper, we study light diffraction with the approach of relativistic quantum theory. We find that the slit length, slit width, slit thickness and wave-length of light have affected to the diffraction intensity and form of diffraction pattern. However, the effect of slit thickness on the diffraction pattern can not be explained by wave-optical approach, and it can be explained in quantum theory. We compare the theoretical results with single and multiple slits experiment data, and find the theoretical results are accordance with the experiment data. Otherwise, we give some theory prediction. We think all the new prediction will be tested by the light diffraction experiment.Comment: 10 page

Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses

Large language model (LLM) powered chatbots are primarily text-based today, and impose a large interactional cognitive load, especially for exploratory or sensemaking tasks such as planning a trip or learning about a new city. Because the interaction is textual, users have little scaffolding in the way of structure, informational "scent", or ability to specify high-level preferences or goals. We introduce ExploreLLM that allows users to structure thoughts, help explore different options, navigate through the choices and recommendations, and to more easily steer models to generate more personalized responses. We conduct a user study and show that users find it helpful to use ExploreLLM for exploratory or planning tasks, because it provides a useful schema-like structure to the task, and guides users in planning. The study also suggests that users can more easily personalize responses with high-level preferences with ExploreLLM. Together, ExploreLLM points to a future where users interact with LLMs beyond the form of chatbots, and instead designed to support complex user tasks with a tighter integration between natural language and graphical user interfaces.Comment: 19 pages, 11 figure

Bridging the Granularity Gap for Acoustic Modeling

While Transformer has become the de-facto standard for speech, modeling upon the fine-grained frame-level features remains an open challenge of capturing long-distance dependencies and distributing the attention weights. We propose \textit{Progressive Down-Sampling} (PDS) which gradually compresses the acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. In addition, we develop a representation fusion method to alleviate information loss that occurs inevitably during high compression. In this way, we compress the acoustic features into 1/32 of the initial length while achieving better or comparable performances on the speech recognition task. And as a bonus, it yields inference speedups ranging from 1.20$\times$ to 1.47$\times$. By reducing the modeling burden, we also achieve competitive results when training on the more challenging speech translation task.Comment: ACL 2023 Finding