We study the problem of minimizing the average of a large number of smooth convex functions penalized with a strongly convex regularizer. We propose and analyze a novel primal-dual method (Quartz) which at every iteration samples and updates a random subset of the dual variables, chosen according to an arbitrary distribution. In contrast to typical analysis, we directly bound the decrease of the primal-dual error (in expectation), without the need to first analyze the dual error. Depending on the choice of the sampling, we obtain efficient serial and mini-batch variants of the method. In the serial case, our bounds match the best known bounds for SDCA (both with uniform and importance sampling). With standard mini-batching, our bounds predict initial data-independent speedup as well as additional data-driven speedup which depends on spectral and sparsity properties of the data. Keywords: empirical risk minimization, dual coordinate ascent, arbitrary sampling, data-driven speedup.

Neural Information Processing Systems http://nips.cc/

Dialogue summarization aims to distill the core meaning of a conversation into a concise text. This is crucial for reducing the complexity and noise inherent in dialogue-heavy applications. While recent approaches typically train language models to mimic human-written summaries, such supervision is costly and often results in outputs that lack task-specific focus limiting their effectiveness in downstream applications, such as medical tasks. In this paper, we propose \app, a framework for task-oriented utility-based dialogue summarization. \app starts by generating multiple summaries and task-oriented question-answer pairs from a dialogue in a zero-shot manner using a pool of large language models (LLMs). The quality of the generated summaries is evaluated by having LLMs answer task-related questions before \textit{(i)} selecting the best candidate answers and \textit{(ii)} identifying the most informative summary based on these answers. Finally, we fine-tune the best LLM on the selected summaries. When validated on multiple datasets, \app demonstrates its effectiveness by achieving competitive results in various zero-shot settings, rivaling fully-supervised State-of-the-Art (SotA) methods.

We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolution eightfold (8x) and addresses segmentation inaccuracies due to the overlapping X-ray attenuation in micro-CT measurement for different rock minerals and phases. The proposed generative model is a 3D Deep Convolutional Wasserstein Generative Adversarial Network with Gradient Penalty (3D DC WGAN-GP). The algorithm is trained on segmented 3D low-resolution micro-CT images and segmented unpaired complementary 2D high-resolution Laser Scanning Microscope (LSM) images. The algorithm was demonstrated on multiple samples of Berea sandstones. We achieved high-quality super-resolved 3D images with a resolution of 0.4375 micro-m/voxel and accurate segmentation for constituting minerals and pore space. The described procedure can significantly expand the modern capabilities of digital rock physics.

The expansion of a quartz mine is providing a breath of life to a small, desolate North Carolina town that is now set to become a powerhouse for the world's AI revolution. Sibelco's mines have been extracting quartz in Spruce Pine since the 1970s but its 500 million investment between now and 2027 will fuel the global demand for semiconductors. The expansion comes as Nvidia has become the leading global manufacturer of semiconductor chips, holding roughly 80 percent of the global market share as of last year. The global quartz market value reached 8.5 billion in 2022 and is expected to reach nearly 18.7 billion by 2031, with China topping other countries as the largest customer. The Sibelco mine is located in Spruce Pine, North Carolina and is the top producer of the world's high-purity quartz Sibelco's mining sites are located in the small town of Spruce Pine, about two hours northwest of Charlotte, and increasing its high-purity quartz output will make the US the leading supplier of the mineral.

We perform a set of flow and reactive transport simulations within three-dimensional fracture networks to learn the factors controlling mineral reactions. CO$_2$ mineralization requires CO$_2$-laden water, dissolution of a mineral that then leads to precipitation of a CO$_2$-bearing mineral. Our discrete fracture networks (DFN) are partially filled with quartz that gradually dissolves until it reaches a quasi-steady state. At the end of the simulation, we measure the quartz remaining in each fracture within the domain. We observe that a small backbone of fracture exists, where the quartz is fully dissolved which leads to increased flow and transport. However, depending on the DFN topology and the rate of dissolution, we observe a large variability of these changes, which indicates an interplay between the fracture network structure and the impact of geochemical dissolution. In this work, we developed a machine learning framework to extract the important features that support mineralization in the form of dissolution. In addition, we use structural and topological features of the fracture network to predict the remaining quartz volume in quasi-steady state conditions. As a first step to characterizing carbon mineralization, we study dissolution with this framework. We studied a variety of reaction and fracture parameters and their impact on the dissolution of quartz in fracture networks. We found that the dissolution reaction rate constant of quartz and the distance to the flowing backbone in the fracture network are the two most important features that control the amount of quartz left in the system. For the first time, we use a combination of a finite-volume reservoir model and graph-based approach to study reactive transport in a complex fracture network to determine the key features that control dissolution.

Optimizing quantum circuits is challenging due to the very large search space of functionally equivalent circuits and the necessity of applying transformations that temporarily decrease performance to achieve a final performance improvement. This paper presents Quarl, a learning-based quantum circuit optimizer. Applying reinforcement learning (RL) to quantum circuit optimization raises two main challenges: the large and varying action space and the non-uniform state representation. Quarl addresses these issues with a novel neural architecture and RL-training procedure. Our neural architecture decomposes the action space into two parts and leverages graph neural networks in its state representation, both of which are guided by the intuition that optimization decisions can be mostly guided by local reasoning while allowing global circuit-wide reasoning. Our evaluation shows that Quarl significantly outperforms existing circuit optimizers on almost all benchmark circuits. Surprisingly, Quarl can learn to perform rotation merging, a complex, non-local circuit optimization implemented as a separate pass in existing optimizers.

Abstract: We develop a new randomized iterative algorithm -- -stochastic dual ascent (SDA) -- -for finding the projection of a given vector onto the solution space of a linear system. The method is dual in nature: with the dual being a non-strongly concave quadratic maximization problem without constraints. In each iteration of SDA, a dual variable is updated by a carefully chosen point in a subspace spanned by the columns of a random matrix drawn independently from a fixed distribution. The distribution plays the role of a parameter of the method. Our complexity results hold for a wide family of distributions of random matrices, which opens the possibility to fine-tune the stochasticity of the method to particular applications.

I have never lived in Los Angeles, but I have probably spent more time thinking about L.A. than any other city that I haven't resided in. This is partly the fault of Hollywood, of Ice Cube and The White Album, of Curb Your Enthusiasm and Party Down, of the despised Lakers, but it's mostly the fault of Mike Davis. Davis, the historian and urban theorist who died on Tuesday, was probably my favorite writer about cities that I have ever read. He didn't only write about L.A., not by a long shot, but L.A. was his Beatrice, his Dark Lady. Every time I visit Los Angeles Davis' work floods through my brain, often down to specific words, phrases, and sentences.

An anonymous reader quotes a report from the Guardian: An artificial intelligence tool that scans eyes can accurately predict a person's risk of heart disease in less than a minute, researchers say. They used the tool to scan images from 88,052 UK Biobank participants aged 40 to 69. The researchers looked specifically at the width, vessel area and degree of curviness of the arteries and veins in the retina to develop prediction models for stroke, heart attack and death from circulatory disease. They subsequently applied the models to the retinal images of 7,411 participants, aged 48 to 92, of the European prospective investigation into cancer (Epic)-Norfolk study. The performance of Quartz was compared with the widely used Framingham risk scores framework.