Signature Sequence of Intersection Curve of Two Quadrics for Exact Morphological Classification

We present an efficient method for classifying the morphology of the intersection curve of two quadrics (QSIC) in PR3, 3D real projective space; here, the term morphology is used in a broad sense to mean the shape, topological, and algebraic properties of a QSIC, including singularity, reducibility, the number of connected components, and the degree of each irreducible component, etc. There are in total 35 different QSIC morphologies with non-degenerate quadric pencils. For each of these 35 QSIC morphologies, through a detailed study of the eigenvalue curve and the index function jump we establish a characterizing algebraic condition expressed in terms of the Segre characteristics and the signature sequence of a quadric pencil. We show how to compute a signature sequence with rational arithmetic so as to determine the morphology of the intersection curve of any two given quadrics. Two immediate applications of our results are the robust topological classification of QSIC in computing B-rep surface representation in solid modeling and the derivation of algebraic conditions for collision detection of quadric primitives

KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]

The volume of time series data has exploded due to the popularity of new applications, such as data center management and IoT. Subsequence matching is a fundamental task in mining time series data. All index-based approaches only consider raw subsequence matching (RSM) and do not support subsequence normalization. UCR Suite can deal with normalized subsequence match problem (NSM), but it needs to scan full time series. In this paper, we propose a novel problem, named constrained normalized subsequence matching problem (cNSM), which adds some constraints to NSM problem. The cNSM problem provides a knob to flexibly control the degree of offset shifting and amplitude scaling, which enables users to build the index to process the query. We propose a new index structure, KV-index, and the matching algorithm, KV-match. With a single index, our approach can support both RSM and cNSM problems under either ED or DTW distance. KV-index is a key-value structure, which can be easily implemented on local files or HBase tables. To support the query of arbitrary lengths, we extend KV-match to KV-match$_{DP}$, which utilizes multiple varied-length indexes to process the query. We conduct extensive experiments on synthetic and real-world datasets. The results verify the effectiveness and efficiency of our approach.Comment: 13 page

My3DGen: A Scalable Personalized 3D Generative Model

In recent years, generative 3D face models (e.g., EG3D) have been developed to tackle the problem of synthesizing photo-realistic faces. However, these models are often unable to capture facial features unique to each individual, highlighting the importance of personalization. Some prior works have shown promise in personalizing generative face models, but these studies primarily focus on 2D settings. Also, these methods require both fine-tuning and storing a large number of parameters for each user, posing a hindrance to achieving scalable personalization. Another challenge of personalization is the limited number of training images available for each individual, which often leads to overfitting when using full fine-tuning methods. Our proposed approach, My3DGen, generates a personalized 3D prior of an individual using as few as 50 training images. My3DGen allows for novel view synthesis, semantic editing of a given face (e.g. adding a smile), and synthesizing novel appearances, all while preserving the original person's identity. We decouple the 3D facial features into global features and personalized features by freezing the pre-trained EG3D and training additional personalized weights through low-rank decomposition. As a result, My3DGen introduces only $\textbf{240K}$ personalized parameters per individual, leading to a $\textbf{127}\times$ reduction in trainable parameters compared to the $\textbf{30.6M}$ required for fine-tuning the entire parameter space. Despite this significant reduction in storage, our model preserves identity features without compromising the quality of downstream applications.Comment: Project page: https://luchaoqi.com/my3dgen

An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies

Inconsistency handling is an important issue in knowledge management. Especially in ontology engineering, logical inconsistencies may occur during ontology construction. A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology. However, previous studies on selecting maximum consistent subsets have rarely considered the semantics of the axioms, which may result in irrational inference. In this paper, we propose a novel approach to reasoning with inconsistent ontologies in description logics based on the embeddings of axioms. We first give a method for turning axioms into distributed semantic vectors to compute the semantic connections between the axioms. We then define an embedding-based method for selecting the maximum consistent subsets and use it to define an inconsistency-tolerant inference relation. We show the rationality of our inference relation by considering some logical properties. Finally, we conduct experiments on several ontologies to evaluate the reasoning power of our inference relation. The experimental results show that our embedding-based method can outperform existing inconsistency-tolerant reasoning methods based on maximal consistent subsets.Comment: 9 pages,1 figur

Targeting the complex I and III of mitochondrial electron transport chain as a potentially viable option in liver cancer management

Abstract Liver cancer is one of the most common and lethal types of oncological disease in the world, with limited treatment options. New treatment modalities are desperately needed, but their development is hampered by a lack of insight into the underlying molecular mechanisms of disease. It is clear that metabolic reprogramming in mitochondrial function is intimately linked to the liver cancer process, prompting the possibility to explore mitochondrial biochemistry as a potential therapeutic target. Here we report that depletion of mitochondrial DNA, pharmacologic inhibition of mitochondrial electron transport chain (mETC) complex I/complex III, or genetic of mETC complex I restricts cancer cell growth and clonogenicity in various preclinical models of liver cancer, including cell lines, mouse liver organoids, and murine xenografts. The restriction is linked to the production of reactive oxygen species, apoptosis induction and reduced ATP generation. As a result, our findings suggest that the mETC compartment of mitochondria could be a potential therapeutic target in liver cancer

Solving the mystery of vanishing rivers in China

A major controversy was sparked worldwide by a recent national water census claiming that the number of Chinese rivers with watersheds ≥100 km2 was less than half the previous estimate of 50 000 rivers, which also stimulates debates on the potential causes and consequences. Here, we estimated the number of rivers in terms of stream-segmentation characteristics described by Horton, Strahler and Shreve stream-order rules, as well as their mixed mode for named rivers recorded in the Encyclopedia of Rivers and Lakes in China. As a result, the number of 'vanishing rivers' has been found to be highly relevant to statistical specifications in addition to the erroneous inclusion of pseudo-rivers primarily generated in arid or frost-thaw areas. The modified Horton stream-order scheme reasonably depicts the configuration of complete natural streams from headwater to destination, while the Strahler largely projects the fragmentation of the named river networks associated with human aggregation to the hierarchical river systems

On Uni-Modal Feature Learning in Supervised Multi-Modal Learning

We abstract the features (i.e. learned representations) of multi-modal data into 1) uni-modal features, which can be learned from uni-modal training, and 2) paired features, which can only be learned from cross-modal interactions. Multi-modal models are expected to benefit from cross-modal interactions on the basis of ensuring uni-modal feature learning. However, recent supervised multi-modal late-fusion training approaches still suffer from insufficient learning of uni-modal features on each modality. We prove that this phenomenon does hurt the model's generalization ability. To this end, we propose to choose a targeted late-fusion learning method for the given supervised multi-modal task from Uni-Modal Ensemble(UME) and the proposed Uni-Modal Teacher(UMT), according to the distribution of uni-modal and paired features. We demonstrate that, under a simple guiding strategy, we can achieve comparable results to other complex late-fusion or intermediate-fusion methods on various multi-modal datasets, including VGG-Sound, Kinetics-400, UCF101, and ModelNet40

Automatic Frequency-Based Flood Forecast From Numerical Weather Prediction Using A Service-Oriented Architecture

Destructive floods occurred more frequently in mountainous regions in China in recent years. However, the meteorological and hydrological station network in such regions is usually poor, and no long-series observations are available. Therefore, it is difficult to determine the hydrological parameters for flood discharge and stage forecast. This paper aims to propose an automatic frequency-based flood forecast framework from numerical weather prediction (NWP) using a Service Oriented Architecture (SOA). The proposed framework has 4 main steps. First, historical flood discharge is simulated by using a distributed hydrological model and satellite-derived rainfall dataset (e.g., the CMORPH and the TRMM), and the relationship between flood frequency and simulated flood discharge (i.e., the frequency curve) is established for each river reach. Second, by taking the advantages of the highly automatic SOA technology, the predicted rainfall data from the NWP (e.g., the TIGGE ensemble) are downloaded and interpreted automatically in real time. Third, a distributed hydrological model is automatically executed in the SOA environment to predict flow discharges of each river reach. And finally, the flood frequency is obtained from the simulated flow discharges by looking up the frequency curves, and warning information of possible floods is generated for potential sufferers. By using Web service in a social network, users can be informed such warning information at any time, and can make better preparation for the possible floods. Along with the real-time updates of the NWP, the latest warning information will always be available for users. From a sample demonstration, it can be concluded that the frequency-based flood forecast from the NWP is highly useful to enhance user awareness of flood risk, and the SOA and social network techniques are regarded as a feasible way for developing the automatic system

Direct determination of band-gap renormalization in degenerately doped ultrawide band gap β-Ga_{2}O_{3} semiconductor

Ga2O3 is emerging as a promising wide band-gap semiconductor for high-power electronics and deep ultraviolet optoelectronics. It is highly desirable to dope it with controllable carrier concentrations for different device applications. This work reports a combined photoemission spectroscopy and theoretical calculation study on the electronic structure of Si doped Ga_{2}O_{3} films with carrier concentration varying from 4.6×10^{18} cm^{−3} to 2.6×10^{20} cm^{−3}. Hard x-ray photoelectron spectroscopy was used to directly measure the widening of the band gap as a result of occupation of conduction band and band-gap renormalization associated with many-body interactions. A large band-gap renormalization of 0.3 eV was directly observed in heavily doped Ga_{2}O_{3}. Supplemented with hybrid density functional theory calculations, we demonstrated that the band-gap renormalization results from the decrease in energy of the conduction band edge driven by the mutual electrostatic interaction between added electrons. Moreover, our work reveals that Si is a superior dopant over Ge and Sn, because Si 3s forms a resonant donor state above the conduction band minimum, leaving the host conduction band mostly unperturbed and a high mobility is maintained though the doping level is high. Insights of the present work have significant implications in doping optimization of Ga_{2}O_{3} and realization of optoelectronic devices