Efficient Encoding of Graphics Primitives with Simplex-based Structures

Grid-based structures are commonly used to encode explicit features for graphics primitives such as images, signed distance functions (SDF), and neural radiance fields (NeRF) due to their simple implementation. However, in $n$-dimensional space, calculating the value of a sampled point requires interpolating the values of its $2^n$ neighboring vertices. The exponential scaling with dimension leads to significant computational overheads. To address this issue, we propose a simplex-based approach for encoding graphics primitives. The number of vertices in a simplex-based structure increases linearly with dimension, making it a more efficient and generalizable alternative to grid-based representations. Using the non-axis-aligned simplicial structure property, we derive and prove a coordinate transformation, simplicial subdivision, and barycentric interpolation scheme for efficient sampling, which resembles transformation procedures in the simplex noise algorithm. Finally, we use hash tables to store multiresolution features of all interest points in the simplicial grid, which are passed into a tiny fully connected neural network to parameterize graphics primitives. We implemented a detailed simplex-based structure encoding algorithm in C++ and CUDA using the methods outlined in our approach. In the 2D image fitting task, the proposed method is capable of fitting a giga-pixel image with 9.4% less time compared to the baseline method proposed by instant-ngp, while maintaining the same quality and compression rate. In the volumetric rendering setup, we observe a maximum 41.2% speedup when the samples are dense enough.Comment: 10 pages, 8 figure

Automatic generation of native ad styles using visual attributes of images

Native advertisements mimic the look and feel of a publisher’s content slots and are used to monetize their inventories. Currently, native ad styles are created manually by the publisher based on hand-written rules and heuristics. This can result in ad styles that do not consistently resemble the look-and-feel of the publisher’s pages or apps. Also, current techniques generally use the DOM structure or HTML source of the publisher’s page or app to generate the native ad. However, the DOM structure or HTML source is not always available, e.g., for apps. This disclosure describes the use of machine learning techniques to automatically generate native ad styles from key visual attributes of images. The images can be screenshots or design mockups. The techniques can generate native ad styles that match the publisher’s look-and-feel closely without recourse to the DOM structure or HTML source for the publisher’s page or app

Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method

Adversarial example detection is known to be an effective adversarial defense method. Black-box attack, which is a more realistic threat and has led to various black-box adversarial training-based defense methods, however, does not attract considerable attention in adversarial example detection. In this paper, we fill this gap by positioning the problem of black-box adversarial example detection (BAD). Data analysis under the introduced BAD settings demonstrates (1) the incapability of existing detectors in addressing the black-box scenario and (2) the potential of exploring BAD solutions from a data perspective. To tackle the BAD problem, we propose a data reconstruction-based adversarial example detection method. Specifically, we use variational auto-encoder (VAE) to capture both pixel and frequency representations of normal examples. Then we use reconstruction error to detect adversarial examples. Compared with existing detection methods, the proposed method achieves substantially better detection performance in BAD, which helps promote the deployment of adversarial example detection-based defense solutions in real-world models.Comment: 14 pages, 8 figures, 13 table

Promoting Open-domain Dialogue Generation through Learning Pattern Information between Contexts and Responses

Recently, utilizing deep neural networks to build the opendomain dialogue models has become a hot topic. However, the responses generated by these models suffer from many problems such as responses not being contextualized and tend to generate generic responses that lack information content, damaging the user's experience seriously. Therefore, many studies try introducing more information into the dialogue models to make the generated responses more vivid and informative. Unlike them, this paper improves the quality of generated responses by learning the implicit pattern information between contexts and responses in the training samples. In this paper, we first build an open-domain dialogue model based on the pre-trained language model (i.e., GPT-2). And then, an improved scheduled sampling method is proposed for pre-trained models, by which the responses can be used to guide the response generation in the training phase while avoiding the exposure bias problem. More importantly, we design a response-aware mechanism for mining the implicit pattern information between contexts and responses so that the generated replies are more diverse and approximate to human replies. Finally, we evaluate the proposed model (RAD) on the Persona-Chat and DailyDialog datasets; and the experimental results show that our model outperforms the baselines on most automatic and manual metrics

Pre-training also Transfers Non-Robustness

Pre-training has enabled state-of-the-art results on many tasks. In spite of its recognized contribution to generalization, we observed in this study that pre-training also transfers adversarial non-robustness from pre-trained model into fine-tuned model in the downstream tasks. Using image classification as an example, we first conducted experiments on various datasets and network backbones to uncover the adversarial non-robustness in fine-tuned model. Further analysis was conducted on examining the learned knowledge of fine-tuned model and standard model, and revealed that the reason leading to the non-robustness is the non-robust features transferred from pre-trained model. Finally, we analyzed the preference for feature learning of the pre-trained model, explored the factors influencing robustness, and introduced a simple robust pre-traning solution

High quality and wafer-scale cubic silicon carbide single crystals

Silicon carbide (SiC) is an important semiconductor material for fabricating power electronic devices that exhibit higher switch frequency, lower energy loss and substantial reduction both in size and weight in comparison with its Si-based counterparts1-4. Currently, most devices, such as metal-oxide-semiconductor field effect transistors, which are core devices used in electric vehicles, photovoltaic industry and other applications, are fabricated on a hexagonal polytype 4H-SiC because of its commercial availability5. Cubic silicon carbide (3C-SiC), the only cubic polytype, has a moderate band gap of 2.36 eV at room-temperature, but a superior mobility and thermal conduction than 4H-SiC4,6-11. Moreover, the much lower concentration of interfacial traps between insulating oxide gate and 3C-SiC helps fabricate reliable and long-life devices7-10,12-14. The growth of 3C-SiC crystals, however, has remained a challenge up to now despite of decades-long efforts by researchers because of its easy transformation into other polytypes during growth15-19, limiting the 3C-SiC based devices. Here, we report that 3C-SiC can be made thermodynamically favored from nucleation to growth on a 4H-SiC substrate by top-seeded solution growth technique(TSSG), beyond what's expected by classic nucleation theory. This enables the steady growth of quality and large sized 3C-SiC crystals (2~4-inch in diameter and 4.0~10.0 mm in thickness) sustainable. Our findings broaden the mechanism of hetero-seed crystal growth and provide a feasible route to mass production of 3C-SiC crystals,offering new opportunities to develop power electronic devices potentially with better performances than those based on 4H-SiC.Comment: 17 pages, 4 figure

Research on Thermosensitive Coatings for Thermal Runaway Warning in Energy Storage Power Station

[Introduction] Lithium iron phosphate battery storage power plants are an important basis for new power systems to consume large-scale new energy, however, the thermal runaway of battery cells seriously threatens the operational safety of storage power plants. It is important to conduct real-time monitoring and scientific warning of local overheating in storage power plants. [Method] In this work, a thermal microcapsule with the ability to sense overheating temperature and produce colour changes was prepared and added in appropriate amounts to an epoxy resin matrix to form a composite insulating material with the characteristics of sensing external overheating temperature fields. [Result] Test results show that the colour of the prepared thermosensitive microcapsule/epoxy insulating temperature indication coating can change sensitively with external temperature changes, with a sudden colour change occurring at around 60 °C. When the doping mass fraction of the thermosensitive microcapsules is 0.25%, the insulation strength and dielectric properties of the composite coating are comparable to those of the pure epoxy resin material, maintaining good intrinsic electrical properties. [Conclusion] The thermosensitive colour-changing composite insulation coating proposed in the study can visibly change the temperature of the external local overheating state, providing a new technical route for the application of thermal runaway warning in energy storage power plants, which has certain engineering application value