Contract work has evolved to touch privacy, security, revenue recognition, data residency, vendor risk, renewals and numerous internal approvals. At the same time, teams are expected to turn agreements around faster and keep every signed obligation visible after signature. Artificial intelligence is becoming a practical layer in this process. It can read language at scale, pull key terms into structured fields, highlight unusual clauses, and support quicker hand-offs between legal departments and the business. Five tools stand out […]

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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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Fruits are an integral part of our diet. Various types of proteins and vitamins are obtained through fruits. Apple is a major fruit that is consumed globally. This is a multipurpose fruit that is used in the preparation of various food products and also in medicines. Therefore, it is important to analyze its future prices. India is the largest producer of apples, thus it is very important to analyze the Apple prices of Indian agricultural markets. Machine learning […]

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arXiv:2507.22095v3 Announce Type: replace Abstract: We consider fully connected and feedforward deep neural networks with dependent and possibly heavy-tailed weights, as introduced in Lee et al. 2023, to address limitations of the standard Gaussian prior. It has been proved in Lee et al. 2023 that, as the number of nodes in the hidden layers grows large, according to a sequential and ordered limit, the law of the output converges weakly to a Gaussian mixture. Among our results, we […]

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In the framework of (fuzzy) Multi-Criteria Decision-Making, we propose a method that1 allows the decision maker to subjectively approach the problem by suitably modifying the decision matrix. We consider a decision problem related to a random quantity X with set of values {x1, x2, . . . , xn}, and a set of properties {C1, C2, . . . , Cm}of X. In this setting, the properties Cj are the criteria of the decision problem, the alternatives represent […]

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arXiv:2601.02451v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test. We adapt Manifold-Constrained Hyper-Connections (mhc)~citep{xie2025mhc}, recently proposed for Transformers, to graph neural networks. Our method, mHC-GNN, expands node representations across $n$ parallel streams and constrains stream-mixing matrices to the Birkhoff polytope via Sinkhorn-Knopp normalization. We prove that mHC-GNN exhibits exponentially slower over-smoothing (rate $(1-gamma)^{L/n}$ vs. $(1-gamma)^L$) and can distinguish graphs beyond 1-WL. Experiments on 10 […]

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