Early morning East Lake is shrouded in ink-wash hues.A few wanderers stroll softly,stone bridges lie serene, gentle ripples caress the banks. Pavilions blend seamlessly with rolling hills,golden light shimmers softly,letting time flow gracefully in this exquisite Jiangnan garden.

One of China's four famous classical gardens, Lingering Garden was first built in 1593 during the Ming Dynasty (1368-1644). Its elegant layout combines graceful pavilions, winding covered walkways, tranquil waters, and striking rockeries. Architecture and nature blend seamlessly, revealing the timeless beauty of #Suzhou#'s classical gardens. #AcrossChina# #ChinaBound# #ChinaTravel#

We are being robbed blind All these spices at Walmart with the new digital price tags are wrong. They tell you the cost per ounce, but if you multiply that by the product’s weight, many of them are overcharging. By a lot Tone’s Restaurant Black Pepper: - Unit price on tag: $0.98 per ounce - Size: 8 oz - Unit price × size: $0.98 × 8 = $7.84 - Shelf price: $8.82 - Overcharging by $0.98 McCormick Italian Seasoning - Unit price on tag: $2.26 per ounce - Size: 2.25 oz - Unit price × size: $2.26 × 2.25 = $5.09 - Shelf price: $7.33 - Overcharging by $2.24 - McCormick lemon pepper is overcharging by $2.03 - Kinder’s Seasoning is overcharging by $0.46 - Kinder’s Butcher Blend Seasoning is overcharging by $0.57 There is something I want to point out to hopefully give you a new perspective on this At first you might thing this is just an accident or perhaps just some kind of system error But think of this, not one error has the price lower… every single one has the price higher This isn’t an error, charging more like this adds up to millions and millions of dollars over all their thousands of stores We are being robbed blind

Every useful model begins by measuring how far its guess sits from reality. Simple linear regression draws the straight line ŷ = wx + b and shrinks the average squared gap (1/n) ∑ (y_i − ŷ_i)². Polynomial and multiple versions keep the same loss while letting the features curve or multiply, so one equation still captures richer structure. Ridge, Lasso, and Elastic Net add penalties on the weights—λ ∑ w_i², λ ∑ |w_i|, or a blend—so the fit stays honest outside the training points. Logistic and softmax then convert the linear score into probabilities, mapping continuous inputs onto binary or multi-class chances. Regression is the disciplined act of turning error into a usable pattern.

Reconstruct a moving, deforming object in 4D (3D + time) from a single-camera video — even under heavy occlusion and large non-rigid motion 🎥 Title: Lift4D: Harmonizing Single-View 3D Estimation for 4D Reconstruction In-the-Wild URL: 🎥 Overview A test-time optimization framework combining single-view 3D prediction with deformable 3D Gaussian Splatting. It fuses learned geometry/appearance priors with the video to handle severe occlusion and non-rigid motion. ❓ Problem it solves ・Directly predicting 4D is limited by scarce training data and generalizes poorly ・Initializing 3D and refining with video alone breaks down on in-the-wild scenes with large deformation and occlusion 💡 Methodology Three components: ・Causal Latent Conditioning: aligns an image-to-3D model temporally without retraining, coupling adjacent latents at the ODE level, with t₀ trading off consistency vs per-frame fidelity ・Deformable Gaussians: sparse control nodes with time-varying SE(3) transforms + linear blend skinning deform a canonical representation across frames ・Occlusion-aware refinement: compares only visible pixels, while a view-conditioned diffusion prior completes unobserved surfaces 📊 Results ・Compared against STAG4D, PAD3R, L4GM, DreamMesh4D, V2M4, BANMo ・On Pexels data, point-tracking error EPE 0.072 (vs 0.119–0.211 for competitors) ・~30 min per 32-frame video on a single H200 GPU #3DReconstruction# #GaussianSplatting#

From ancient Buddhist grottoes carved into majestic cliffs to vibrant, otherworldly rock formations and rolling green grasslands, breathtaking nature and profound history blend seamlessly here. Discover this spectacular diversity of landscapes and 1,600-year-old cultural heritage in Sunan Yugur Autonomous County, Zhangye City, northwest China's Gansu Province.

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