Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance

Using an Entropy-GRA, TOPSIS, and PCA Method to Evaluate the Competitiveness of AFVs – The China Case

With the increase in severe environmental problems associated with fossil fuel vehicles, the development of Alternative Fuel Vehicles (AFVs) has led to their promotion and use in Chinese provinces and cities. The comprehensive evaluation of competitiveness of the AFV industry in Chinese cities is beneficial to analyse the effects and relationships of different factors to promote the sustainable development of the AFV industry and guide the growth paths of the cities. An industrial competitiveness evaluation index system is established based on the characteristics of AFVs, and the development of the AFV industry in ten typical cities in China is comprehensively evaluated based on the Grey Relative Analysis (GRA) Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) and Principal Component Analysis (PCA) methods. To evaluate the results, the entropy weighting method is used for the weight distribution, and the industrial competitiveness rankings of ten cities are obtained by the entropy-GRA, TOPSIS, PCA (EGTP) method. The results show that Beijing is ranked first, followed by Shanghai, and Qingdao is ranked last. By analysing the correlation between the evaluation methods and indicators, it is found that EGTP has a high correlation with the other three evaluation methods, which proves the rationality of the weighted linear combination of GRA and the other three methods. Indices C5 (pure electric car proportion) and C13 (average concentration of PM2.5) were outliers due to the small number of samples.</p

Multi-Trace Superpotentials vs. Matrix Models

We consider N = 1 supersymmetric U(N) field theories in four dimensions with adjoint chiral matter and a multi-trace tree-level superpotential. We show that the computation of the effective action as a function of the glueball superfield localizes to computing matrix integrals. Unlike the single-trace case, holomorphy and symmetries do not forbid non-planar contributions. Nevertheless, only a special subset of the planar diagrams contributes to the exact result. Some of the data of this subset can be computed from the large-N limit of an associated multi-trace Matrix model. However, the prescription differs in important respects from that of Dijkgraaf and Vafa for single-trace superpotentials in that the field theory effective action is not the derivative of a multi-trace matrix model free energy. The basic subtlety involves the correct identification of the field theory glueball as a variable in the Matrix model, as we show via an auxiliary construction involving a single-trace matrix model with additional singlet fields which are integrated out to compute the multi-trace results. Along the way we also describe a general technique for computing the large-N limits of multi-trace Matrix models and raise the challenge of finding the field theories whose effective actions they may compute. Since our models can be treated as N = 1 deformations of pure N =2 gauge theory, we show that the effective superpotential that we compute also follows from the N = 2 Seiberg-Witten solution. Finally, we observe an interesting connection between multi-trace local theories and non-local field theory.Comment: 35 pages, LaTeX, 6 EPS figures. v2: typos fixed, v3: typos fixed, references added, Sec. 5 added explaining how multi-trace theories can be linearized in traces by addition of singlet fields and the relation of this approach to matrix model

MHITNet: a minimize network with a hierarchical context-attentional filter for segmenting medical ct images

In the field of medical CT image processing, convolutional neural networks (CNNs) have been the dominant technique.Encoder-decoder CNNs utilise locality for efficiency, but they cannot simulate distant pixel interactions properly.Recent research indicates that self-attention or transformer layers can be stacked to efficiently learn long-range dependencies.By constructing and processing picture patches as embeddings, transformers have been applied to computer vision applications. However, transformer-based architectures lack global semantic information interaction and require a large-scale training dataset, making it challenging to train with small data samples. In order to solve these challenges, we present a hierarchical contextattention transformer network (MHITNet) that combines the multi-scale, transformer, and hierarchical context extraction modules in skip-connections. The multi-scale module captures deeper CT semantic information, enabling transformers to encode feature maps of tokenized picture patches from various CNN stages as input attention sequences more effectively. The hierarchical context attention module augments global data and reweights pixels to capture semantic context.Extensive trials on three datasets show that the proposed MHITNet beats current best practise

Pseudogap and its connection to particle-hole asymmetry electronic state and Fermi arcs in cuprate superconductors

The particle-hole asymmetry electronic state of cuprate superconductors and the related doping and temperature dependence of the Fermi arc length are studied based on the kinetic energy driven superconducting mechanism. By taking into account the interplay between the SC gap and normal-state pseudogap, the essential feature of the evolution of the Fermi arc length with doping and temperature is qualitatively reproduced. It is shown that the particle-hole asymmetry electronic state is a natural consequence due to the presence the normal-state pseudogap in the particle-hole channel. The Fermi arc length increases with increasing temperatures below the normal-state pseudogap crossover temperature $T^{*}$, and it covers the full length of the Fermi surface for $T>T^{*}$. In particular, in analogy to the temperature dependence of the Fermi arc length, the low-temperature Fermi arc length in the underdoped regime increases with increasing doping, and then it evolves into a continuous contour in momentum space near the end of the superconducting dome. The theory also predicts an almost linear doping dependence of the Fermi arc length.Comment: 9 pages, 8 figure