Yes, there’s open-source and free-to-use software for the fashion industry! When it comes to creating powerful digital tools for this purpose, there are many developers worldwide building programs available for download and use without needing a huge budget or a partnership with a famous brand name. Most of these projects include everything from 3D visioning, simulation of fabrics, managing production processes, and many more.  We have below a few examples of the innovations that have become accessible through […]

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Despite the sustained popularity of Q-learning as a practical tool for policy determination, a majority of relevant theoretical literature deals with either constant ($η_{t}equiv η$) or polynomially decaying ($η_{t} = ηt^{-α}$) learning schedules. However, it is well known that these choices suffer from either persistent bias or prohibitively slow convergence. In contrast, the recently proposed linear decay to zero (texttt{LD2Z}: $η_{t,n}=η(1-t/n)$) schedule has shown appreciable empirical performance, but its theoretical and statistical properties remain largely unexplored, especially in […]

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arXiv:2603.23581v1 Announce Type: new Abstract: In clustering, strong dominance in the size of a particular cluster is often undesirable, motivating a measure of cluster size uniformity that can be used to filter such partitions. A basic requirement of such a measure is stability: partitions that differ only slightly in their point assignments should receive similar uniformity scores. A difficulty arises because cluster labels are not fixed objects; algorithms may produce different numbers of labels even when the underlying […]

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In geophysics, hydrocarbon prospect risking involves assessing the risks associated with hydrocarbon exploration by integrating data from various sources. Machine learning-based classifiers trained on tabular data have been recently used to make faster decisions on these prospects. The lack of transparency in the decision-making processes of such models has led to the emergence of explainable AI (XAI). LIME and SHAP are two such examples of these XAI methods which try to generate explanations of a particular decision by […]

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The Flexible Job Shop Problem (FJSP) is a well-studied combinatorial optimization problem with extensive applications for manufacturing and production scheduling. It involves assigning jobs to various machines to optimize criteria, such as minimizing total completion time. Current learning-based methods in this domain often rely on localized feature extraction models, limiting their capacity to capture overarching dependencies spanning operations and machines. This paper introduces an innovative architecture that harnesses Mamba, a state-space model with linear computational complexity, to facilitate […]

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The previous post talked about doing perfect shuffles: divide a deck in half, and alternately let one card from each half fall. It matters which half lets a card fall first. If the top half’s bottom card falls first, this is called an in-shuffle. If the bottom half’s bottom card falls first, it’s called an out-shuffle. With an out-shuffle, the top and bottom cards don’t move. Presumably it’s called an out-shuffle because the outside cards remain in place. […]

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Fixed-horizon A/B tests are slow and prone to false positives when you peek at data. Sequential testing with mSPRT enables anytime-valid inference, letting teams monitor experiments continuously and stop early without inflating Type I errors. Techniques like linearization and guardrails handle ratio metrics and outliers, ensuring fast, reliable, and statistically rigorous B2C decision-making.

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We study the center problem for polynomial maps y=f(x)=−∑n=0∞anxn+1, arising from homogeneous algebraic curves x+y+∑k=0nαn−k,kxn−kyk=0. While explicit conditions were previously known only for low even degrees n = 2,4,6,8,10, their general structure remained conjectural. In this paper we resolve the case n = 12 and prove that the observed algebraic patterns completely characterize the center for all even degrees n. More precisely, we show that the center condition is equivalent to one of two explicit families of algebraic […]

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arXiv:2603.25758v1 Announce Type: new Abstract: Diffusion models have significantly reshaped the field of generative artificial intelligence and are now increasingly explored for their capacity in discriminative representation learning. Diffusion Transformer (DiT) has recently gained attention as a promising alternative to conventional U-Net-based diffusion models, demonstrating a promising avenue for downstream discriminative tasks via generative pre-training. However, its current training efficiency and representational capacity remain largely constrained due to the inadequate timestep searching and insufficient exploitation of DiT-specific feature […]

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