Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring irreversibility from learned representations of biological patterns

Thermodynamic irreversibility is a crucial property of living matter. Irreversible processes maintain spatiotemporally complex structures and functions characteristic of living systems. In high-dimensional biological dynamics, robust and general quantification of irreversibility remains a challenging task due to experimental noise and nonlinear interactions coupling many degrees of freedom. Here we use deep learning to identify tractable, low-dimensional representations of phase-field patterns in a canonical protein signaling process -- the Rho-GTPase system -- as well as complex Ginzburg-Landau dynamics. We show that factorizing variational autoencoder neural networks learn informative pattern features robustly to noise. Resulting neural-network representations reveal signatures of mesoscopic broken detailed balance and time-reversal asymmetry in Rho-GTPase and complex Ginzburg-Landau wave dynamics. Applying the compression-based Ziv-Merhav estimator of irreversibility to representations, we recover irreversibility trends across complex Ginzburg-Landau patterns varying widely in spatiotemporal frequency and noise level. Irreversibility estimates from representations similarly recapitulate cell-activity trends in a Rho-GTPase patterning system undergoing metabolic inhibition. Additionally, we find that our irreversibility estimates serve as a dynamical order parameter, distinguishing stable and chaotic dynamics in these nonlinear systems. Our framework leverages advances in deep learning to offer robust, model-free measurements of nonequilibrium and nonlinear behavior in complex living processes.
Quality In / Quality Out: Assessing Data quality in an Anomaly Detection Benchmark

Autonomous or self-driving networks are expected to provide a solution to the myriad of extremely demanding new applications in the Future Internet. The key to handle complexity is to perform tasks like network optimization and failure recovery with minimal human supervision. For this purpose, the community relies on the development of new Machine Learning (ML) models and techniques. However, ML can only be as good as the data it is fitted with. Datasets provided to the community as benchmarks for research purposes, which have a relevant impact in research findings and directions, are often assumed to be of good quality by default. In this paper, we show that relatively minor modifications on the same benchmark dataset (UGR'16, a flow-based real-traffic dataset for anomaly detection) cause significantly more impact on model performance than the specific ML technique considered. To understand this finding, we contribute a methodology to investigate the root causes for those differences, and to assess the quality of the data labelling. Our findings illustrate the need to devote more attention into (automatic) data quality assessment and optimization techniques in the context of autonomous networks.
Dynamic Factor Models for Binary Data in Circular Spaces: An Application to the U.S. Supreme Court

Latent factor models are widely used in the social and behavioral science as scaling tools to map discrete multivariate outcomes into low dimensional, continuous scales. In political science, dynamic versions of classical factor models have been widely used to study the evolution of justices' preferences in multi-judge courts. In this paper, we discuss a new dynamic factor model that relies on a latent circular space that can accommodate voting behaviors in which justices commonly understood to be on opposite ends of the ideological spectrum vote together on a substantial number of otherwise closely-divided opinions. We apply this model to data on non-unanimous decisions made by the U.S. Supreme Court between 1937 and 2021, and show that, for most of this period, voting patterns can be better described by a circular latent space.
Near-Atomic Scale Perspective on the Oxidation of Ti3 C2 Tx MXenes: Insights from Atom Probe Tomography.

MXenes are a family of 2D transition metal carbides and nitrides with remarkable properties and great potential for energy storage and catalysis applications. However, their oxidation behavior is not yet fully understood, and there are still open questions regarding the spatial distribution and precise quantification of surface terminations, intercalated ions, and possible uncontrolled impurities incorporated during synthesis and processing. Here, atom probe tomography analysis of as-synthesized Ti3 C2 Tx MXenes reveals the presence of alkali (Li, Na) and halogen (Cl, F) elements as well as unetched Al. Following oxidation of the colloidal solution of MXenes, it is observed that the alkalies enriched in TiO2 nanowires. Although these elements are tolerated through the incorporation by wet chemical synthesis, they are often overlooked when the activity of these materials is considered, particularly during catalytic testing. This work demonstrates how the capability of atom probe tomography to image these elements in 3D at the near-atomic scale can help to better understand the activity and degradation of MXenes, in order to guide their synthesis for superior functional properties. This article is protected by copyright. All rights reserved.
Quantifying Overfitting: Evaluating Neural Network Performance through Analysis of Null Space

Machine learning models that are overfitted/overtrained are more vulnerable to knowledge leakage, which poses a risk to privacy. Suppose we download or receive a model from a third-party collaborator without knowing its training accuracy. How can we determine if it has been overfitted or overtrained on its training data? It's possible that the model was intentionally over-trained to make it vulnerable during testing. While an overfitted or overtrained model may perform well on testing data and even some generalization tests, we can't be sure it's not over-fitted. Conducting a comprehensive generalization test is also expensive. The goal of this paper is to address these issues and ensure the privacy and generalization of our method using only testing data. To achieve this, we analyze the null space in the last layer of neural networks, which enables us to quantify overfitting without access to training data or knowledge of the accuracy of those data. We evaluated our approach on various architectures and datasets and observed a distinct pattern in the angle of null space when models are overfitted. Furthermore, we show that models with poor generalization exhibit specific characteristics in this space. Our work represents the first attempt to quantify overfitting without access to training data or knowing any knowledge about the training samples.
Solving Unit Commitment by Chemotactic PSO-DE Optimization Algorithm Combined With Lagrangian Relaxation

Abstract This paper presents Chemotactic PSO-DE (CPSO-DE) optimization algorithm combined with Lagrange Relaxation (LR) method for solving Unit Commitment (UC) problem. The proposed approach employs CPSO-DE algorithm for optimal settings of Lagrange multipliers in LR method. It is a hybrid heuristic algorithm based on Bacterial Foraging Optimization (BFO), Particle Swarm Optimization (PSO) and Differential Evolution (DE). The feasibility of the proposed method is demonstrated for 10–100 unit systems.
Neuron to Graph: Interpreting Language Model Neurons at Scale

Advances in Large Language Models (LLMs) have led to remarkable capabilities, yet their inner mechanisms remain largely unknown. To understand these models, we need to unravel the functions of individual neurons and their contribution to the network. This paper introduces a novel automated approach designed to scale interpretability techniques across a vast array of neurons within LLMs, to make them more interpretable and ultimately safe. Conventional methods require examination of examples with strong neuron activation and manual identification of patterns to decipher the concepts a neuron responds to. We propose Neuron to Graph (N2G), an innovative tool that automatically extracts a neuron's behaviour from the dataset it was trained on and translates it into an interpretable graph. N2G uses truncation and saliency methods to emphasise only the most pertinent tokens to a neuron while enriching dataset examples with diverse samples to better encompass the full spectrum of neuron behaviour. These graphs can be visualised to aid researchers' manual interpretation, and can generate token activations on text for automatic validation by comparison with the neuron's ground truth activations, which we use to show that the model is better at predicting neuron activation than two baseline methods. We also demonstrate how the generated graph representations can be flexibly used to facilitate further automation of interpretability research, by searching for neurons with particular properties, or programmatically comparing neurons to each other to identify similar neurons. Our method easily scales to build graph representations for all neurons in a 6-layer Transformer model using a single Tesla T4 GPU, allowing for wide usability. We release the code and instructions for use at https://github.com/alexjfoote/Neuron2Graph.
Parametric Shape Holomorphy of Boundary Integral Operators with Applications

We consider a family of boundary integral operators supported on a collection of parametrically defined bounded Lipschitz boundaries. Thus, the boundary integral operators themselves also depend on the parametric variables, leading to a parameter-to-operator map. One of the main results of this article is to establish the analytic or holomorphic dependence of said boundary integral operators upon the parametric variables, i.e., of the parameter-to-operator map. As a direct consequence we also establish holomorphic dependence of solutions to boundary integral equations, i.e., holomorphy of the parameter-to-solution map. To this end, we construct a holomorphic extension to complex-valued boundary deformations and investigate the complex Fr\'echet differentiability of boundary integral operators with respect to each parametric variable. The established parametric holomorphy results have been identified as a key property to derive best $N$-term approximation rates to overcome the so-called curse of dimensionality in the approximation of parametric maps with distributed, high-dimensional inputs. To demonstrate the applicability of the derived results, we consider as a concrete example the sound-soft Helmholtz acoustic scattering problem and its frequency-robust boundary integral formulations. For this particular application, we explore the consequences of our results in reduced order modelling, Bayesian shape inversion, and the construction of efficient surrogates using artificial neural networks.
Degenerations of Sheaves on Fibered Surfaces

We construct moduli stacks of stable sheaves for surfaces fibered over marked nodal curves by using expanded degenerations. These moduli stacks carry a virtual class and therefore give rise to enumerative invariants. In the case of a surface with two irreducible components glued along a smooth divisor, we prove a degeneration formula that relates the moduli space associated to the surface with the relative spaces associated to the two components. For a smooth surface and no markings, our notion of stability agrees with slope stability with respect to a suitable choice of polarization. We apply our results to compute elliptic genera of moduli spaces of stable sheaves on some elliptic surfaces.
Human Control: Definitions and Algorithms

How can humans stay in control of advanced artificial intelligence systems? One proposal is corrigibility, which requires the agent to follow the instructions of a human overseer, without inappropriately influencing them. In this paper, we formally define a variant of corrigibility called shutdown instructability, and show that it implies appropriate shutdown behavior, retention of human autonomy, and avoidance of user harm. We also analyse the related concepts of non-obstruction and shutdown alignment, three previously proposed algorithms for human control, and one new algorithm.
Flat limit of massless scalar scattering in $\mathrm{AdS}_2$

We delineate the flat limit of massless scalar scattering in $\mathrm{AdS}_2$. We derive the 1 $\to$ 1 $\mathcal{S}$-matrix from the CFT $2$-point function, which is proportional to the momentum-conserving delta function. We prove a kinematical characteristic of the $2 \to 2$ $\mathcal{S}$-matrix in $2d$, elucidating the presence of product of two delta functions arising from the $\phi^4$ contact interaction within the realm of the flat limit of AdS/CFT. We also show that the factorization of the $n \to n ~\mathcal{S}$-matrix for integrable models in the flat limit, employing a focused analysis on contact interaction, which play a pivotal role as fundamental constituents in the construction of the non-perturbative $\mathcal{S}$-matrix within integrable models. Although the factorization of the $\mathcal{S}$-matrix in integrable models is commonly perceived as an intrinsically non-perturbative notion, we effectively demonstrate its manifestation at the tree level in the flat limit. We calculate the $\mathcal{S}$-matrix by making use of the mapping between the CFT operator on the AdS boundary, and the scattering state in flat space. We adopt the bulk operator reconstruction to examine massless scalar scattering in the flat limit. We solve the Klein-Gordon equation in the global $\mathrm{AdS}_2$ for the massless scalar field. Notably, the solution is remarkably simple, characterized by a pure phase in global time and a sinusoidal function in the radial coordinate. This simplicity extends to the smearing function, enabling a mapping between the scattering state and CFT operator taking AdS corrections into account.
Melt-growth bulk superconductors and application to an axial-gap-type rotating machine

The present manuscript addresses key issues in the course of our study of materials processing of bulk high-temperature superconductors, trapped flux and its application to a prototype axial-gap-type rotating machine. The TUMSAT group has conducted a series of studies since 2003 on the growth of GdBa2Cu3O7−δ bulk material and its application in a compact low-speed high-torque rotating machine. In the stage of material growth, gaining the advantage of a large motive torque density requires large integrated flux in the motor/generators. A large grain surface might be required with sophisticated techniques for the melt-growth texture in the bulk with optimal flux pinning. In the second stage, the in situ magnetization procedure for bulk superconductors in the applied machine is a crucial part of the technology. Pulsed current excitation by using an armature copper winding has magnetized field pole bulks on the rotor. The axial-gap flux synchronous machine studied in the past decade is a condensed technology and indicates that further scientific development is required for a future compact machine to be superior to conventional ones in accordance with the cryogenic periphery and flux stabilization.
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning

A number of recent benchmarks seek to assess how well models handle natural language negation. However, these benchmarks lack the controlled example paradigms that would allow us to infer whether a model had truly learned how negation morphemes semantically scope. To fill these analytical gaps, we present the Scoped Negation NLI (ScoNe-NLI) benchmark, which contains contrast sets of six examples with up to two negations where either zero, one, or both negative morphemes affect the NLI label. We use ScoNe-NLI to assess fine-tuning and in-context learning strategies. We find that RoBERTa and DeBERTa models solve ScoNe-NLI after many shot fine-tuning. For in-context learning, we test the latest InstructGPT models and find that most prompt strategies are not successful, including those using step-by-step reasoning. To better understand this result, we extend ScoNe with ScoNe-NLG, a sentence completion test set that embeds negation reasoning in short narratives. Here, InstructGPT is successful, which reveals the model can correctly reason about negation, but struggles to do so on NLI examples outside of its core pretraining regime.
State-dependent potentials for the 
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We present measurements of three distinctive state-(in)dependent wavelengths for the $^1\text{S}_{0}-^3\text{P}_{0}$ clock transition in $^{174}\text{Yb}$ atoms. Specifically, we determine two magic wavelengths at $652.281(21)\,$THz and $542.50205(19)\,$THz, where the differential light shift on the $^1\text{S}_{0}-^3\text{P}_{0}$ clock transition vanishes, and one tune-out wavelength at $541.8325(5)\,$THz, where the polarizability of the $^1\text{S}_{0}$ ground state exhibits a zero crossing. The two new magic wavelengths are identified by spectroscopically interrogating cold $^{174}\text{Yb}$ atoms on the clock transition in a one-dimensional optical lattice. The ground-state tune-out wavelength is determined via a parametric heating scheme. With a simple empirical model, we then extrapolate the ground and excited state polarizability over a broad range of wavelengths in the visible spectrum.
Tracking Power System Events with Accuracy-Based PMU Adaptive Reporting Rate

Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 60255-118-1 standard) but also to the PMU reporting rate (RR). High RRs allow tracking fast dynamics, but produce many redundant measurement data in normal conditions. In view of an effective tradeoff, the present paper proposes an adaptive RR mechanism based on a real-time selection of the measurements, with the target of preserving the information content while reducing the data rate. The proposed method has been tested considering real-world datasets and applied to four different PMU algorithms. The results prove the method effectiveness in reducing the average data throughput as well as its scalability at PMU concentrator or storage level.
Mildly Overparameterized ReLU Networks Have a Favorable Loss Landscape

We study the loss landscape of both shallow and deep, mildly overparameterized ReLU neural networks on a generic finite input dataset for the squared error loss. We show both by count and volume that most activation patterns correspond to parameter regions with no bad local minima. Furthermore, for one-dimensional input data, we show most activation regions realizable by the network contain a high dimensional set of global minima and no bad local minima. We experimentally confirm these results by finding a phase transition from most regions having full rank Jacobian to many regions having deficient rank depending on the amount of overparameterization.
Characterization of flip process rules with the same trajectories

Garbe, Hladk\'y, \v{S}ileikis and Skerman recently introduced a general class of random graph processes called flip processes and proved that the typical evolution of these discrete-time random graph processes correspond to certain continuous-time deterministic graphon trajectories. We obtain a complete characterization of the equivalence classes of flip process rules with the same graphon trajectories. As an application, we characterize the flip process rules which are unique in their equivalence classes. These include several natural families of rules such as the complementing rules, the component completion rules, the extremist rules, and the clique removal rules.
Data and Knowledge for Overtaking Scenarios in Autonomous Driving

Autonomous driving has become one of the most popular research topics within Artificial Intelligence. An autonomous vehicle is understood as a system that combines perception, decision-making, planning, and control. All of those tasks require that the vehicle collects surrounding data in order to make a good decision and action. In particular, the overtaking maneuver is one of the most critical actions of driving. The process involves lane changes, acceleration and deceleration actions, and estimation of the speed and distance of the vehicle in front or in the lane in which it is moving. Despite the amount of work available in the literature, just a few handle overtaking maneuver and, because overtaking can be risky, no real-world dataset is available. This work contributes in this area by presenting a new synthetic dataset whose focus is the overtaking maneuver. We start by performing a thorough review of the state of the art in autonomous driving and then explore the main datasets found in the literature (public and private, synthetic and real), highlighting their limitations, and suggesting a new set of features whose focus is the overtaking maneuver.
Offline Meta Reinforcement Learning with In-Distribution Online Adaptation

Recent offline meta-reinforcement learning (meta-RL) methods typically utilize task-dependent behavior policies (e.g., training RL agents on each individual task) to collect a multi-task dataset. However, these methods always require extra information for fast adaptation, such as offline context for testing tasks. To address this problem, we first formally characterize a unique challenge in offline meta-RL: transition-reward distribution shift between offline datasets and online adaptation. Our theory finds that out-of-distribution adaptation episodes may lead to unreliable policy evaluation and that online adaptation with in-distribution episodes can ensure adaptation performance guarantee. Based on these theoretical insights, we propose a novel adaptation framework, called In-Distribution online Adaptation with uncertainty Quantification (IDAQ), which generates in-distribution context using a given uncertainty quantification and performs effective task belief inference to address new tasks. We find a return-based uncertainty quantification for IDAQ that performs effectively. Experiments show that IDAQ achieves state-of-the-art performance on the Meta-World ML1 benchmark compared to baselines with/without offline adaptation.