Brand marketing strategy (taking Xiaomi as an example)

This article mainly talks about Xiaomi's marketing strategy, including products, promotions, pricing, brand marketing offerings, emphasis on product innovation, and smart use of hungry marketing. This successful case can serve as an example for other brands and help them do better marketing and brand promotion

High order computation of optimal transport, mean field planning, and mean field games

Mean-field games (MFGs) have shown strong modeling capabilities for large systems in various fields, driving growth in computational methods for mean-field game problems. However, high order methods have not been thoroughly investigated. In this work, we explore applying general high-order numerical schemes with finite element methods in the space-time domain for computing the optimal transport (OT), mean-field planning (MFP), and MFG problems. We conduct several experiments to validate the convergence rate of the high order method numerically. Those numerical experiments also demonstrate the efficiency and effectiveness of our approach

A primal-dual approach for solving conservation laws with implicit in time approximations

In this work, we propose a novel framework for the numerical solution of time-dependent conservation laws with implicit schemes via primal-dual hybrid gradient methods. We solve an initial value problem (IVP) for the partial differential equation (PDE) by casting it as a saddle point of a min-max problem and using iterative optimization methods to find the saddle point. Our approach is flexible with the choice of both time and spatial discretization schemes. It benefits from the implicit structure and gains large regions of stability, and overcomes the restriction on the mesh size in time by explicit schemes from Courant--Friedrichs--Lewy (CFL) conditions (really via von Neumann stability analysis). Nevertheless, it is highly parallelizable and easy-to-implement. In particular, no nonlinear inversions are required! Specifically, we illustrate our approach using the finite difference scheme and discontinuous Galerkin method for the spatial scheme; backward Euler and backward differentiation formulas for implicit discretization in time. Numerical experiments illustrate the effectiveness and robustness of the approach. In future work, we will demonstrate that our idea of replacing an initial-value evolution equation with this primal-dual hybrid gradient approach has great advantages in many other situations

What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?

With the growing success of multi-modal learning, research on the robustness of multi-modal models, especially when facing situations with missing modalities, is receiving increased attention. Nevertheless, previous studies in this domain exhibit certain limitations, as they often lack theoretical insights or their methodologies are tied to specific network architectures or modalities. We model the scenarios of multi-modal models encountering missing modalities from an information-theoretic perspective and illustrate that the performance ceiling in such scenarios can be approached by efficiently utilizing the information inherent in non-missing modalities. In practice, there are two key aspects: (1) The encoder should be able to extract sufficiently good features from the non-missing modality; (2) The extracted features should be robust enough not to be influenced by noise during the fusion process across modalities. To this end, we introduce Uni-Modal Ensemble with Missing Modality Adaptation (UME-MMA). UME-MMA employs uni-modal pre-trained weights for the multi-modal model to enhance feature extraction and utilizes missing modality data augmentation techniques to better adapt to situations with missing modalities. Apart from that, UME-MMA, built on a late-fusion learning framework, allows for the plug-and-play use of various encoders, making it suitable for a wide range of modalities and enabling seamless integration of large-scale pre-trained encoders to further enhance performance. And we demonstrate UME-MMA's effectiveness in audio-visual datasets~(e.g., AV-MNIST, Kinetics-Sound, AVE) and vision-language datasets~(e.g., MM-IMDB, UPMC Food101)

Metabolic Engineering to Improve Docosahexaenoic Acid Production in Marine Protist Aurantiochytrium sp. by Disrupting 2,4-Dienoyl-CoA Reductase

Docosahexaenoic acid (DHA) has attracted attention from researchers because of its pharmacological and nutritional importance. Currently, DHA production costs are high due to fermentation inefficiency; however, improving DHA yield by metabolic engineering in thraustochytrids is one approach to reduce these costs. In this study, a high-yielding (53.97% of total fatty acids) DHA production strain was constructed by disrupting polyunsaturated fatty acid beta-oxidation via knockout of the 2,4-dienyl-CoA reductase (DECR) gene (KO strain) in Aurantiochytrium sp. Slight differences in cell growth was observed in the wild-type and transformants (OE and KO), with cell concentrations in stationary of 2.65×106, 2.36×106 and 2.56×106 cells mL-1 respectively. Impressively, the KO strain yielded 21.62% more neutral lipids and 57.34% greater DHA production; moreover, the opposite was observed when overexpressing DECR (OE strain), with significant decreases of 30.49% and 64.61%, respectively. Furthermore, the KO strain showed a prolonged DHA production period with a sustainable increase from 63 to 90 h (170.03 to 203.27 mg g−1 DCW), while that of the wildtype strain decreased significantly from 150.58 to 140.10 mg g−1 DCW. This new approach provides an advanced proxy for the construction of sustainable DHA production strains for industrial purposes and deepens our understanding of the metabolic pathways of Aurantiochytrium sp

Combination of MRO SHARAD and deep-learning-based DTM to search for subsurface features in Oxia Planum, Mars

Context. Oxia Planum is a mid-latitude region on Mars that attracts a great amount of interest worldwide. An orbiting radar provides an effective way to probe the Martian subsurface and detect buried layers or geomorphological features. The Shallow radar orbital radar system on board the NASA Mars reconnaissance orbiter transmits pulsed signals towards the nadir and receives returned echoes from dielectric boundaries. However, radar clutter can be induced by a higher topography of the off-nadir region than that at the nadir, which is then manifested as subsurface reflectors in the radar image. Aims. This study combines radar observations, terrain models, and surface images to investigate the subsurface features of the ExoMars landing site in Oxia Planum. Methods. Possible subsurface features are observed in radargrams. Radar clutter is simulated using the terrain models, and these are then compared to radar observations to exclude clutter and identify possible subsurface return echoes. Finally, the dielectric constant is estimated with measurements in both radargrams and surface imagery. Results. The resolution and quality of the terrain models greatly influence the clutter simulations. Higher resolution can produce finer cluttergrams, which assists in identifying possible subsurface features. One possible subsurface layering sequence is identified in one radargram. Conclusions. A combination of radar observations, terrain models, and surface images reveals the dielectric constant of the surface deposit in Oxia Planum to be 4.9–8.8, indicating that the surface-covering material is made up of clay-bearing units in this region