Yale University, founded in 1701 in New Haven, Connecticut, is a private Ivy League research university and one of the oldest institutions of higher education in the United States. It is renowned for its commitment to educating leaders and contributors across all sectors of society, with a rich history of academic excellence and public service. NEC Labs America collaborates with Yale University to study fairness in machine learning, robust language generation, and the development of ethical AI systems. Please read about our latest news and collaborative publications with Yale University.

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A Quantum Variational Autoencoder Utilizing Regularized Mixed-state Latent Representations

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A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. Inclassical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representationlearning for subsequent tasks, and novel data generation. However, no quantum model has been proposed thatexactly captures all of these features for direct application to quantum data on quantum computers. Some existingquantum models for data compression lack regularization of latent representations, thus preventing direct use forgeneration and control of generalization. Others are hybrid models with only some internal quantum components,impeding direct training on quantum data. To address this, we present a fully quantum framework, ?-QVAE,which encompasses all the capabilities of classical VAEs and can be directly applied to map both classicaland quantum data to a lower-dimensional space, while effectively reconstructing much of the original statefrom it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodatesvarious divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at everystage, it can utilize the full data density matrix and allow for a training objective defined on probabilisticmixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications forprivate and federated learning. In addition to exploring the theoretical properties of ?-QVAE, we demonstrateits performance on representative genomics and synthetic data. Our results indicate that ?-QVAE consistentlylearns representations that better utilize the capacity of the latent space and exhibits similar or better performancecompared with matched classical models.

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Top 10 Most Legendary College Pranks of All-Time for April Fools’ Day

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At NEC Labs America, we celebrate innovation in all forms—even the brilliantly engineered college prank. From MIT’s police car on the Great Dome to Caltech hacking the Rose Bowl, these legendary stunts showcase next-level planning, stealth, and technical genius. Our Top 10 list honors the creativity behind pranks that made history (and headlines). This April Fools’ Day, we salute the hackers, makers, and mischief-makers who prove that brilliance can be hilarious.

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A Variational Graph Partitioning Approach to Modeling Protein Liquid-liquid Phase Separation

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Graph neural networks (GNNs) have emerged as powerful tools for representation learning. Their efficacy depends on their having an optimal underlying graph. In many cases, the most relevant information comes from specific subgraphs. In this work, we introduce a GNN-based framework (graph-partitioned GNN [GP-GNN]) to partition the GNN graph to focus on the most relevant subgraphs. Our approach jointly learns task-dependent graph partitions and node representations, making it particularly effective when critical features reside within initially unidentified subgraphs. Protein liquid-liquid phase separation (LLPS) is a problem especially well-suited to GP-GNNs because intrinsically disordered regions (IDRs) are known to function as protein subdomains in it, playing a key role in the phase separation process. In this study, we demonstrate how GP-GNN accurately predicts LLPS by partitioning protein graphs into task-relevant subgraphs consistent with known IDRs. Our model achieves state-of-the-art accuracy in predicting LLPS and offers biological insights valuable for downstream investigation.

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Variational methods for Learning Multilevel Genetic Algorithms using the Kantorovich Monad

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Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms over arbitrarily many levels based on concepts from category theory and population genetics. We formulate a multilevel version of the Wright-Fisher process using this approach, and we show that this model can be analyzed to clarify key features of multilevel selection. Particularly, we derive an extended multilevel probabilistic version of Price’s Equation via the Kantorovich Monad, and we use this to characterize regimes of parameter space within which selection acts antagonistically or cooperatively across levels. Finally, we show how our framework can provide a unified setting for learning genetic algorithms (GAs), and we show how we can use a Variational Optimization and a multi-level analogue of coalescent analysis to fit multilevel GAs to simulated data.

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zeta-QVAE: A Quantum Variational Autoencoder utilizing Regularized Mixed-state Latent Representations

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A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves compressing the original data to manageable dimensions while still representing essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To bridge this gap, we present a fully quantum framework, ?-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied for both classical and quantum data compression. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full-data density matrix and allow for a “global” training objective. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ?-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results consistently indicate that ?-QVAE exhibits similar or better performance compared to matched classical models.

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Disentangled Wasserstein Autoencoder for T-Cell Receptor Engineering

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In protein biophysics, the separation between the functionally important residues (forming the active site or binding surface) and those that create the overall structure (the fold) is a well-established and fundamental concept. Identifying and modifying those functional sites is critical for protein engineering but computationally nontrivial, and requires significant domain knowledge. To automate this process from a data-driven perspective, we propose a disentangled Wasserstein autoencoder with an auxiliary classifier, which isolates the function-related patterns from the rest with theoretical guarantees. This enables one-pass protein sequence editing and improves the understanding of the resulting sequences and editing actionsinvolved. To demonstrate its effectiveness, we apply it to T-cell receptors (TCRs), a well-studied structure-function case. We show that our method can be used to alterthe function of TCRs without changing the structural backbone, outperforming several competing methods in generation quality and efficiency, and requiring only 10% of the running time needed by baseline models. To our knowledge, this is the first approach that utilizes disentangled representations for TCR engineering.

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Binding Peptide Generation for MHC Class I Proteins with Deep Reinforcement Learning

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Motivation: MHC Class I protein plays an important role in immunotherapy by presenting immunogenic peptides to anti-tumor immune cells. The repertoires of peptides for various MHC Class I proteins are distinct, which can be reflected by their diverse binding motifs. To characterize binding motifs for MHC Class I proteins, in vitro experiments have been conducted to screen peptides with high binding affinities to hundreds of given MHC Class I proteins. However, considering tens of thousands of known MHC Class I proteins, conducting in vitro experiments for extensive MHC proteins is infeasible, and thus a more efficient and scalable way to characterize binding motifs is needed.Results: We presented a de novo generation framework, coined PepPPO, to characterize binding motif for any given MHC Class I proteins via generating repertoires of peptides presented by them. PepPPO leverages a reinforcement learning agent with a mutation policy to mutate random input peptides into positive presented ones. Using PepPPO, we characterized binding motifs for around 10 000 known human MHC Class I proteins with and without experimental for the rapid screening of neoantigens at a much lower time cost than previous deep-learning methods.

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DECODE: A Deep-learning Framework for Condensing Enhancers and Refining Boundaries with Large-scale Functional Assays

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MotivationMapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches have implemented enhancer discovery as a binary classification problem without accurate boundary detection, producing low-resolution annotations with superfluous regions and reducing the statistical power for downstream analyses (e.g. causal variant mapping and functional validations). Here, we addressed these challenges via a two-step model called Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays (DECODE). First, we employed direct enhancer-activity readouts from novel functional characterization assays, such as STARR-seq, to train a deep neural network for accurate cell-type-specific enhancer prediction. Second, to improve the annotation resolution, we implemented a weakly supervised object detection framework for enhancer localization with precise boundary detection (to a 10 bp resolution) using Gradient-weighted Class Activation Mapping.ResultsOur DECODE binary classifier outperformed a state-of-the-art enhancer prediction method by 24% in transgenic mouse validation. Furthermore, the object detection framework can condense enhancer annotations to only 13% of their original size, and these compact annotations have significantly higher conservation scores and genome-wide association study variant enrichments than the original predictions. Overall, DECODE is an effective tool for enhancer classification and precise localization.

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