Vector search powers transformers technology, but real-world use demands hybrid queries that combine vector similarity with attribute filters (e.g., "top document in category X, from 2023"). Current solutions trade off recall, speed, and flexibility, relying on fragile index hacks that don't scale. We introduce FusedANN (Fused Attribute-Vector Nearest Neighbor), a geometric framework that elevates filtering to ANN optimization constraints and introduces a convex fused space via a Lagrangian-like relaxation. Our method jointly embeds attributes and vectors through transformer-based convexification, turning hard filters into continuous, weighted penalties that preserve top-k semantics while enabling efficient approximate search. We prove that FusedANN reduces to exact filtering under high selectivity, gracefully relaxes to semantically nearest attributes when exact matches are insufficient, and preserves downstream ANN alpha-approximation guarantees. Empirically, FusedANN improves query throughput by eliminating brittle filtering stages, achieving superior recall-latency tradeoffs on standard hybrid benchmarks without specialized index hacks, delivering up to 3 times higher throughput and better recall than state-of-the-art hybrid and graph-based systems. Theoretically, we provide explicit error bounds and parameter selection rules that make FusedANN practical for production. This establishes a principled, scalable, and verifiable bridge between symbolic constraints and vector similarity, unlocking a new generation of filtered retrieval systems for large, hybrid, and dynamic NLP/ML workloads.

Geospatial data come from various sources, such as satellites, aircraft, and LiDAR. The variability of the source is not limited to the types of data acquisition techniques, as we have maps from different time periods. To incorporate these data for a coherent analysis, it is essential to first align different "styles" of geospatial data to its matching images that point to the same location on the surface of the Earth. In this paper, we approach the image registration as a two-step process of (1) extracting geospatial contents invariant to visual (and any other non-content-related) information, and (2) matching the data based on such (purely) geospatial contents. We hypothesize that a combination of $\beta$-VAE-like architecture [2] and adversarial training will achieve both the disentanglement of the geographic information and artistic styles and generation of new map tiles by composing the encoded geographic information with any artistic style.

For content recommender systems such as TikTok and YouTube, the platform's decision algorithm shapes the incentives of content producers, including how much effort the content producers invest in the quality of their content. Many platforms employ online learning, which creates intertemporal incentives, since content produced today affects recommendations of future content. In this paper, we study the incentives arising from online learning, analyzing the quality of content produced at a Nash equilibrium. We show that classical online learning algorithms, such as Hedge and EXP3, unfortunately incentivize producers to create low-quality content. In particular, the quality of content is upper bounded in terms of the learning rate and approaches zero for typical learning rate schedules. Motivated by this negative result, we design a different learning algorithm -- based on punishing producers who create low-quality content -- that correctly incentivizes producers to create high-quality content. At a conceptual level, our work illustrates the unintended impact that a platform's learning algorithm can have on content quality and opens the door towards designing platform learning algorithms that incentivize the creation of high-quality content.

We develop a theoretical framework for $S_n$-equivariant quantum convolutional circuits, building on and significantly generalizing Jordan's Permutational Quantum Computing (PQC) formalism. We show that quantum circuits are a natural choice for Fourier space neural architectures affording a super-exponential speedup in computing the matrix elements of $S_n$-Fourier coefficients compared to the best known classical Fast Fourier Transform (FFT) over the symmetric group. In particular, we utilize the Okounkov-Vershik approach to prove Harrow's statement (Ph.D. Thesis 2005 p.160) on the equivalence between $\operatorname{SU}(d)$- and $S_n$-irrep bases and to establish the $S_n$-equivariant Convolutional Quantum Alternating Ans{\"a}tze ($S_n$-CQA) using Young-Jucys-Murphy (YJM) elements. We prove that $S_n$-CQA are dense, thus expressible within each $S_n$-irrep block, which may serve as a universal model for potential future quantum machine learning and optimization applications. Our method provides another way to prove the universality of Quantum Approximate Optimization Algorithm (QAOA), from the representation-theoretical point of view. Our framework can be naturally applied to a wide array of problems with global $\operatorname{SU}(d)$ symmetry. We present numerical simulations to showcase the effectiveness of the ans{\"a}tze to find the sign structure of the ground state of the $J_1$--$J_2$ antiferromagnetic Heisenberg model on the rectangular and Kagome lattices. Our work identifies quantum advantage for a specific machine learning problem, and provides the first application of the celebrated Okounkov-Vershik's representation theory to machine learning and quantum physics.

Controlling the style of natural language by disentangling the latent space is an important step towards interpretable machine learning. After the latent space is disentangled, the style of a sentence can be transformed by tuning the style representation without affecting other features of the sentence. Previous works usually use adversarial training to guarantee that disentangled vectors do not affect each other. However, adversarial methods are difficult to train. Especially when there are multiple features (e.g., sentiment, or tense, which we call style types in this paper), each feature requires a separate discriminator for extracting a disentangled style vector corresponding to that feature. In this paper, we propose a unified distribution-controlling method, which provides each specific style value (the value of style types, e.g., positive sentiment, or past tense) with a unique representation. This method contributes a solid theoretical basis to avoid adversarial training in multi-type disentanglement. We also propose multiple loss functions to achieve a style-content disentanglement as well as a disentanglement among multiple style types. In addition, we observe that if two different style types always have some specific style values that occur together in the dataset, they will affect each other when transferring the style values. We call this phenomenon training bias, and we propose a loss function to alleviate such training bias while disentangling multiple types. We conduct experiments on two datasets (Yelp service reviews and Amazon product reviews) to evaluate the style-disentangling effect and the unsupervised style transfer performance on two style types: sentiment and tense. The experimental results show the effectiveness of our model.

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. Firstly, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Secondly, traditional text mining methods fail to effectively extract concepts through words and phrases. Thirdly, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using the complementary knowledge-bases makes the results biased to the content of the external database and deviates the understanding and interpretation away from the real nature of the given short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the tightly connected author subgraphs from microblog short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. Additionally, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.

Bio: Alexander Gude is currently a data scientist at Lab41 working on investigating recommender system algorithms. He holds a BA in physics from University of California, Berkeley, and a PhD in Elementary Particle Physics from University of Minnesota-Twin Cities. About: Lab41 is a "challenge lab" where the U.S. Intelligence Community comes together with their counterparts in academia, industry, and In-Q-Tel to tackle big data. It allows participants from diverse backgrounds to gain access to ideas, talent, and technology to explore what works and what doesn't in data analytics.

A content vector, that is an array of values that represents the information content suitable for the Vector space model matching, as explained in 4.; with paired justifications (They express the provenience of that element, if from initial interview or the current active stereotype, or feedback, etc.) Moreover, changes to the User Model are regulated using a fixed hierarchy, so that lower items in the hierarchy cannot overwrite the values set by higher items. For example an element modified by the user feedback is not affected by changes from the stereotypes, etc. Contexts are initializated all the same for every user that has a non-zero value in her/his user model content vector for each cluster having one or more non-null contexts. Then these contexts are copied in the user's model and evolves indipendently according to the single user history. A set of user keywords each weighted with a value representing its actual importance for the user.

Bio: Alexander Gude is currently a data scientist at Lab41 working on investigating recommender system algorithms. He holds a BA in physics from University of California, Berkeley, and a PhD in Elementary Particle Physics from University of Minnesota-Twin Cities. About: Lab41 is a "challenge lab" where the U.S. Intelligence Community comes together with their counterparts in academia, industry, and In-Q-Tel to tackle big data. It allows participants from diverse backgrounds to gain access to ideas, talent, and technology to explore what works and what doesn't in data analytics.

For two of the datasets we are using a small sample for testing. The OpenStreetMap data is limited to edits in Azerbaijan from 2012 and earlier, and the Git data is just from the Django GitHub repository. The datasets we have selected span a wide range of densities, user and item counts, and types of ratings. Additionally, they provide a wide variety of information about items and users allowing us to explore different methods of extracting content vectors from the datasets.