We thank all reviewers for constructive comments. We added an ablation study on the SCAN length split to demonstrate its importance. For example, in the test set, there is a new pattern "jump around right thrice" that does not appear in the training set. Recursion and sequence manipulation supported by NeSS are critical to learn such parsing rules to generalize. NeSS is 100% in 2 runs, and 62.5% in 3 runs. When the model predicts the alternative translation, the exact match accuracy becomes lower.

We thank the reviewers for the useful feedback. We will add the accidentally missing legend to Figure 1. This is not an accurate description of the estimator. Our estimator is formally equivalent to the naive way to do it. The title and organization are aimed at making this clear.

We thank all reviewers for constructive comments. We added an ablation study on the SCAN length split to demonstrate its importance. For example, in the test set, there is a new pattern "jump around right thrice" that does not appear in the training set. Recursion and sequence manipulation supported by NeSS are critical to learn such parsing rules to generalize. NeSS is 100% in 2 runs, and 62.5% in 3 runs. When the model predicts the alternative translation, the exact match accuracy becomes lower.

Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, high-dimensional single-cell data. Neighbor embedding (NE) algorithms, such as t-SNE and UMAP, are widely used to embed high-dimensional single-cell data into low dimensions. But they often introduce undesirable distortions, resulting in misleading interpretations. Existing evaluation methods for NE algorithms primarily focus on separating discrete cell types rather than capturing continuous cell-state transitions, while dynamic modeling approaches rely on strong assumptions about cellular processes and specialized data. To address these challenges, we build on the Predictability-Computability-Stability (PCS) framework for reliable and reproducible data-driven discoveries. First, we systematically evaluate popular NE algorithms through empirical analysis, simulation, and theory, and reveal their key shortcomings, such as artifacts and instability. We then introduce NESS, a principled and interpretable machine learning approach to improve NE representations by leveraging algorithmic stability and to enable robust inference of smooth biological structures. NESS offers useful concepts, quantitative stability metrics, and efficient computational workflows to uncover developmental trajectories and cell-state transitions in single-cell data. Finally, we apply NESS to six single-cell datasets, spanning pluripotent stem cell differentiation, organoid development, and multiple tissue-specific lineage trajectories. Across these diverse contexts, NESS consistently yields useful biological insights, such as identification of transitional and stable cell states and quantification of transcriptional dynamics during development.

We present a framework for learning Node Embeddings from Static Subgraphs (NESS) using a graph autoencoder (GAE) in a transductive setting. NESS is based on two key ideas: i) Partitioning the training graph to multiple static, sparse subgraphs with non-overlapping edges using random edge split during data pre-processing, ii) Aggregating the node representations learned from each subgraph to obtain a joint representation of the graph at test time. Moreover, we propose an optional contrastive learning approach in transductive setting. We demonstrate that NESS gives a better node representation for link prediction tasks compared to current autoencoding methods that use either the whole graph or stochastic subgraphs. Our experiments also show that NESS improves the performance of a wide range of graph encoders and achieves state-of-the-art results for link prediction on multiple real-world datasets with edge homophily ratio ranging from strong heterophily to strong homophily.

What Sassoon had heard were the early results of a curious project at the University of Edinburgh in Scotland, where Ducceschi was a researcher at the time. The Next Generation Sound Synthesis, or NESS, team had pulled together mathematicians, physicists, and computer scientists to produce the most lifelike digital music ever created, by running hyper-realistic simulations of trumpets, guitars, violins, and more on a supercomputer. Sassoon, who works with both orchestral and digital music, "trying to smash the two together," was hooked. He became a resident composer with NESS, traveling back and forth between Milan and Edinburgh for the next few years. It was a steep learning curve.

Despite achieving tremendous success, existing deep learning models have exposed limitations in compositional generalization, the capability to learn compositional rules and apply them to unseen cases in a systematic manner. To tackle this issue, we propose the Neural-Symbolic Stack Machine (NeSS). It contains a neural network to generate traces, which are then executed by a symbolic stack machine enhanced with sequence manipulation operations. NeSS combines the expressive power of neural sequence models with the recursion supported by the symbolic stack machine. Without training supervision on execution traces, NeSS achieves 100% generalization performance in three domains: the SCAN benchmark of language-driven navigation tasks, the compositional machine translation benchmark, and context-free grammar parsing tasks.

Nonlinear equations systems (NESs) are widely used in real-world problems while they are also difficult to solve due to their characteristics of nonlinearity and multiple roots. Evolutionary algorithm (EA) is one of the methods for solving NESs, given their global search capability and an ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, the problem domain knowledge of NESs is particularly investigated in this study, using which we propose to incorporate the variable reduction strategy (VRS) into EAs to solve NESs. VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through the variable relationships existing in the equation systems. It enables to reduce partial variables and equations and shrink the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper integrates VRS into two existing state-of-the-art EA methods (i.e., MONES and DRJADE), respectively. Experimental results show that, with the assistance of VRS, the EA methods can significantly produce better results than the original methods and other compared methods.

A company that sells software that analyzes the human voice and touts the virtues of empathy, rapport and emotional intelligence is joining forces with West Point United States Military Academy in an effort to help cadets become better negotiators. Cogito Corp. is a Boston-based company that makes software that can analyze a person's voice in real-time. That information, the company says, can help customer service representatives show more empathy; the result is phone conversations that are more efficient and personalized, according to Cogito. Col. James Ness of West Point said that this kind of tech will help their students become better negotiators, a key skill for people in the military. "Cogito's behavioral analytics technology will systematically analyze communication patterns within negotiating sessions and provide insight into the cadet's psychological state," Ness, who directs the engineering psychology program at West Point, said in a statement.

Unsupervised discovery of synonymous phrases is useful in a variety of tasks ranging from text mining and search engines to semantic analysis and machine translation. This paper presents an unsupervised corpus-based conditional model: Near-Synonym System (NeSS) for finding phrasal synonyms and near synonyms that requires only a large monolingual corpus. The method is based on maximizing information-theoretic combinations of shared contexts and is parallelizable for large-scale processing. An evaluation framework with crowd-sourced judgments is proposed and results are compared with alternate methods, demonstrating considerably superior results to the literature and to thesaurus look up for multi-word phrases. Moreover, the results show that the statistical scoring functions and overall scalability of the system are more important than language specific NLP tools. The method is language-independent and practically useable due to accuracy and real-time performance via parallel decomposition.