Miracle-dz/UniFAR-FTUs huggingface dataset:
Probabilistic Safety Regions Via Finite Families of Scalable Classifiers

Supervised classification recognizes patterns in the data to separate classes of behaviours. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning. The data analyst may minimize the classification error on a class at the expense of increasing the error of the other classes. The error control of such a design phase is often done in a heuristic manner. In this context, it is key to develop theoretical foundations capable of providing probabilistic certifications to the obtained classifiers. In this perspective, we introduce the concept of probabilistic safety region to describe a subset of the input space in which the number of misclassified instances is probabilistically controlled. The notion of scalable classifiers is then exploited to link the tuning of machine learning with error control. Several tests corroborate the approach. They are provided through synthetic data in order to highlight all the steps involved, as well as through a smart mobility application.
Relative representations for cognitive graphs

Although the latent spaces learned by distinct neural networks are not generally directly comparable, recent work in machine learning has shown that it is possible to use the similarities and differences among latent space vectors to derive"relative representations"with comparable representational power to their"absolute"counterparts, and which are nearly identical across models trained on similar data distributions. Apart from their intrinsic interest in revealing the underlying structure of learned latent spaces, relative representations are useful to compare representations across networks as a generic proxy for convergence, and for zero-shot model stitching. In this work we examine an extension of relative representations to discrete state-space models, using Clone-Structured Cognitive Graphs (CSCGs) for 2D spatial localization and navigation as a test case. Our work shows that the probability vectors computed during message passing can be used to define relative representations on CSCGs, enabling effective communication across agents trained using different random initializations and training sequences, and on only partially similar spaces. We introduce a technique for zero-shot model stitching that can be applied post hoc, without the need for using relative representations during training. This exploratory work is intended as a proof-of-concept for the application of relative representations to the study of cognitive maps in neuroscience and AI.
An adaptive Bayesian approach to gradient-free global optimization

Many problems in science and technology require finding global minima or maxima of complicated objective functions. The importance of global optimization has inspired the development of numerous heuristic algorithms based on analogies with physical, chemical or biological systems. Here we present a novel algorithm, SmartRunner, which employs a Bayesian probabilistic model informed by the history of accepted and rejected moves to make an informed decision about the next random trial. Thus, SmartRunner intelligently adapts its search strategy to a given objective function and moveset, with the goal of maximizing fitness gain (or energy loss) per function evaluation. Our approach is equivalent to adding a simple adaptive penalty to the original objective function, with SmartRunner performing hill ascent on the modified landscape. The adaptive penalty can be added to many other global optimization schemes, enhancing their ability to find high-quality solutions. We have explored SmartRunner's performance on a standard set of test functions, the Sherrington-Kirkpatrick (SK) spin glass model, and Kauffman's NK fitness model, finding that it compares favorably with several widely-used alternative approaches to gradient-free optimization.
Certified Vision-Based State Estimation for Autonomous Landing Systems Using Reachability Analysis

This paper studies the problem of designing a certified vision-based state estimator for autonomous landing systems. In such a system, a neural network (NN) processes images from a camera to estimate the aircraft's relative position with respect to the runway. We propose an algorithm to design such NNs with certified properties in terms of their ability to detect runways and provide accurate state estimation. At the heart of our approach is the use of geometric models of perspective cameras to obtain a mathematical model that captures the relation between the aircraft states and the inputs. We show that such geometric models enjoy mixed monotonicity properties that can be used to design state estimators with certifiable error bounds. We show the effectiveness of the proposed approach using an experimental testbed on data collected from event-based cameras.
SortedAP: Rethinking evaluation metrics for instance segmentation

Designing metrics for evaluating instance segmentation revolves around comprehensively considering object detection and segmentation accuracy. However, other important properties, such as sensitivity, continuity, and equality, are overlooked in the current study. In this paper, we reveal that most existing metrics have a limited resolution of segmentation quality. They are only conditionally sensitive to the change of masks or false predictions. For certain metrics, the score can change drastically in a narrow range which could provide a misleading indication of the quality gap between results. Therefore, we propose a new metric called sortedAP, which strictly decreases with both object- and pixel-level imperfections and has an uninterrupted penalization scale over the entire domain. We provide the evaluation toolkit and experiment code at https://www.github. com/looooongChen/sortedAP.
Connecting NTK and NNGP: A Unified Theoretical Framework for Neural Network Learning Dynamics in the Kernel Regime

Artificial neural networks have revolutionized machine learning in recent years, but a complete theoretical framework for their learning process is still lacking. Substantial progress has been made for infinitely wide networks. In this regime, two disparate theoretical frameworks have been used, in which the network's output is described using kernels: one framework is based on the Neural Tangent Kernel (NTK) which assumes linearized gradient descent dynamics, while the Neural Network Gaussian Process (NNGP) kernel assumes a Bayesian framework. However, the relation between these two frameworks has remained elusive. This work unifies these two distinct theories using a Markov proximal learning model for learning dynamics in an ensemble of randomly initialized infinitely wide deep networks. We derive an exact analytical expression for the network input-output function during and after learning, and introduce a new time-dependent Neural Dynamical Kernel (NDK) from which both NTK and NNGP kernels can be derived. We identify two learning phases characterized by different time scales: gradient-driven and diffusive learning. In the initial gradient-driven learning phase, the dynamics is dominated by deterministic gradient descent, and is described by the NTK theory. This phase is followed by the diffusive learning stage, during which the network parameters sample the solution space, ultimately approaching the equilibrium distribution corresponding to NNGP. Combined with numerical evaluations on synthetic and benchmark datasets, we provide novel insights into the different roles of initialization, regularization, and network depth, as well as phenomena such as early stopping and representational drift. This work closes the gap between the NTK and NNGP theories, providing a comprehensive framework for understanding the learning process of deep neural networks in the infinite width limit.
Effect of Adapting to Human Preferences on Trust in Human-Robot Teaming

We present the effect of adapting to human preferences on trust in a human-robot teaming task. The team performs a task in which the robot acts as an action recommender to the human. It is assumed that the behavior of the human and the robot is based on some reward function they try to optimize. We use a new human trust-behavior model that enables the robot to learn and adapt to the human's preferences in real-time during their interaction using Bayesian Inverse Reinforcement Learning. We present three strategies for the robot to interact with a human: a non-learner strategy, in which the robot assumes that the human's reward function is the same as the robot's, a non-adaptive learner strategy that learns the human's reward function for performance estimation, but still optimizes its own reward function, and an adaptive-learner strategy that learns the human's reward function for performance estimation and also optimizes this learned reward function. Results show that adapting to the human's reward function results in the highest trust in the robot.
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis

In many practical applications, 3D point cloud analysis requires rotation invariance. In this paper, we present a learnable descriptor invariant under 3D rotations and reflections, i.e., the O(3) actions, utilizing the recently introduced steerable 3D spherical neurons and vector neurons. Specifically, we propose an embedding of the 3D spherical neurons into 4D vector neurons, which leverages end-to-end training of the model. In our approach, we perform TetraTransform--an equivariant embedding of the 3D input into 4D, constructed from the steerable neurons--and extract deeper O(3)-equivariant features using vector neurons. This integration of the TetraTransform into the VN-DGCNN framework, termed TetraSphere, negligibly increases the number of parameters by less than 0.0002%. TetraSphere sets a new state-of-the-art performance classifying randomly rotated real-world object scans of the challenging subsets of ScanObjectNN. Additionally, TetraSphere outperforms all equivariant methods on randomly rotated synthetic data: classifying objects from ModelNet40 and segmenting parts of the ShapeNet shapes. Thus, our results reveal the practical value of steerable 3D spherical neurons for learning in 3D Euclidean space. The code is available at https://github.com/pavlo-melnyk/tetrasphere.
Insights into the Energy Transfers in Hydrodynamic Turbulence Using Field-theoretic Tools

Turbulent flows exhibit intriguing energy transfers. In this paper, we compute the renormalized viscosities, mode-to-mode energy transfers, energy fluxes, and shell-to-shell energy transfers for the two-dimensional (2D) and three-dimensional (3D) hydrodynamic turbulence (HDT) using field-theoretic methods. We employ Craya-Herring basis that provides separate renormalized viscosities and energy transfers for its two components. In addition, Craya-Herring basis eliminates complex tensor algebra and simplifies the calculations considerably. In the $k^{-5/3}$ spectral regime of 2D HDT, the energy transfers between neighbouring (local) wavenumbers are forward, but they are backwards for distant (nonlocal) wavenumbers. The individual transfers between the distant wavenumber shells are small, but their cumulative sum is significant and it overcomes the forward local transfer to yield a constant inverse energy cascade. For 3D HDT, the mode-to-mode and shell-to-shell energy transfers reveal forward energy transfers for both local and nonlocal wavenumbers. More importantly, using scale-by-scale energy transfers we show that the cumulative nonlocal and local energy transfers are of the same order, which is contrary to the assumption of local energy transfers in turbulence. For 3D HDT, the renormalized viscosity, $\nu_2(k)$, computed by us and other authors are in general agreement, and it varies as $k^{-4/3}$. For 2D HDT, our the renormalized viscosity, $\nu_1(k)>0$, but $\nu_1(k)$ reported in literature shows significant variations including a negative $\nu_1(k)$. In this paper, we argue that the inconsistencies in $\nu_1(k)$ indicate inadequacy of renormalization group analysis that takes into account only local interactions and excludes nonlocal ones. In 2D HDT, the opposing nature of local and nonlocal energy transfers amplifies the error in $\nu_1(k)$.
Growth of Subalpine Shrubs and Snowgrass Following a Rare Occurrence of Frost and Drought in Southeastern Australia

From March 1982 to March 1983, the subalpine tract of the Bogong High Plains in southeastern Australia was subject to a series of unusual climatic events. Relatively severe frosts occurred during autumn and winter, snow cover during winter was low, and the 1982/83 summer was particularly dry. This combination of frost and drought is the only one recorded over the past 50 yr. The net annual aboveground production of the dominant shrubs from open grassy-heath and closed heath communities ranged from 480 to 830 g m-2 yr-', with greater production by shrubs within more sheltered, closed heath communities. These values were considerably greater than those of snowgrass (Poa sp.) within grassy swards in these two community types, (135 g m-2 yr-'). Such a combination of climatic events may have longlasting effects, and in particular may favor the growth of the shrubs over snowgrass where these two different life forms are competing within the shrub-grass complex.
Data Augmentation for Conversational AI

Advancements in conversational systems have revolutionized information access, surpassing the limitations of single queries. However, developing dialogue systems requires a large amount of training data, which is a challenge in low-resource domains and languages. Traditional data collection methods like crowd-sourcing are labor-intensive and time-consuming, making them ineffective in this context. Data augmentation (DA) is an affective approach to alleviate the data scarcity problem in conversational systems. This tutorial provides a comprehensive and up-to-date overview of DA approaches in the context of conversational systems. It highlights recent advances in conversation augmentation, open domain and task-oriented conversation generation, and different paradigms of evaluating these models. We also discuss current challenges and future directions in order to help researchers and practitioners to further advance the field in this area.
Adaptive Address Family Selection for Latency-Sensitive Applications on Dual-stack Hosts

Latency is becoming a key factor of performance for Internet applications and has triggered a number of changes in its protocols. Our work revisits the impact on latency of address family selection in dual-stack hosts. Through RIPE Atlas measurements, we analyse the address families latency difference and establish two requirements based on our findings for a latency-focused selection mechanism. First, the address family should be chosen per destination. Second, the choice should be able to evolve over time dynamically. We propose and implement a solution formulated as an online learning problem balancing exploration and exploitation. We validate our solution in simulations based on RIPE Atlas measurements, implement and evaluate our prototype in four access networks using Chrome and popular web services. We demonstrate the ability of our solution to converge towards the lowest-latency address family and improve the latency of transport connections used by applications.
A Penny a Function: Towards Cost Transparent Cloud Programming

Understanding and managing monetary cost factors is crucial when developing cloud applications. However, the diverse range of factors influencing costs for computation, storage, and networking in cloud applications poses a challenge for developers who want to manage and minimize costs proactively. Existing tools for understanding cost factors are often detached from source code, causing opaqueness regarding the origin of costs. Moreover, existing cost models for cloud applications focus on specific factors such as compute resources and necessitate manual effort to create the models. This paper presents initial work toward a cost model based on a directed graph that allows deriving monetary cost estimations directly from code using static analysis. Leveraging the cost model, we explore visualizations embedded in a code editor that display costs close to the code causing them. This makes cost exploration an integrated part of the developer experience, thereby removing the overhead of external tooling for cost estimation of cloud applications at development time.
On correlation functions of higher-spin currents in arbitrary dimensions $d>3$

We revisit the problem of classification and explicit construction of the conformal three-point correlation functions of currents of arbitrary integer spin in arbitrary dimensions. For the conserved currents, we set up the equations for the conservation conditions and solve them completely for some values of spins, confirming the earlier counting of the number of independent structures matching them with the higher-spin cubic vertices in one higher dimension. The general solution for the correlators of conserved currents we delegate to a follow-up work.
Turn-ons and turn-offs: Causes and mechanisms of the initiation and termination of coral reef growth

The objective of this review is to develop and organize a conceptual framework for the discussion of the mechanisms of reef growth turn-on and turn-off. An improved understanding of the causes, mechanisms and short-term symptoms of reef turn-on/turn-off is essential if we are to deal with the challenge of understanding and managing reef resources under conditions of both immediate anthropogenic impacts and long-term climatic changes. The discussion attempts to provide a basis for identifying the future research and conceptual synthesis that can best improve both our understanding of the causes of past transitions in reef development and our abiltiy to predict with confidence the future outcome of presently changing conditions.
Resonance triplet dynamics in the quenched unitary Bose gas

The quenched unitary Bose gas is a paradigmatic example of a strongly interacting out-of-equilibrium quantum system, whose dynamics become difficult to describe theoretically due to the growth of non-Gaussian quantum correlations. We develop a conserving many-body theory capable of capturing these effects, allowing us to model the post-quench dynamics in the previously inaccessible time regime where the gas departs from the universal prethermal stage. Our results show that this departure is driven by the growth of strong lossless three-body correlations, rather than atomic losses, thus framing the heating of the gas in this regime as a fully coherent phenomenon. We uncover the specific few-body scattering processes that affect this heating, and show that the expected connection between the two-body and three-body contacts and the tail of the momentum distribution is obscured following the prethermal stage, explaining the absence of this connection in experiments. Our general framework, which reframes the dynamics of unitary quantum systems in terms of explicit connections to microscopic physics, can be broadly applied to any quantum system containing strong few-body correlations.
Does Writing with Language Models Reduce Content Diversity?

Large language models (LLMs) have led to a surge in collaborative writing with model assistance. As different users incorporate suggestions from the same model, there is a risk of decreased diversity in the produced content, potentially limiting diverse perspectives in public discourse. In this work, we measure the impact of co-writing on diversity via a controlled experiment, where users write argumentative essays in three setups -- using a base LLM (GPT3), a feedback-tuned LLM (InstructGPT), and writing without model help. We develop a set of diversity metrics and find that writing with InstructGPT (but not the GPT3) results in a statistically significant reduction in diversity. Specifically, it increases the similarity between the writings of different authors and reduces the overall lexical and content diversity. We additionally find that this effect is mainly attributable to InstructGPT contributing less diverse text to co-written essays. In contrast, the user-contributed text remains unaffected by model collaboration. This suggests that the recent improvement in generation quality from adapting models to human feedback might come at the cost of more homogeneous and less diverse content.
Distributed Optimization via Gradient Descent with Event-Triggered Zooming Over Quantized Communication

In this paper, we study unconstrained distributed optimization strongly convex problems, in which the exchange of information in the network is captured by a directed graph topology over digital channels that have limited capacity (and hence information should be quantized). Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor. However, it is not possible to determine in advance the optimal quantization level that ensures specific performance guarantees (such as achieving an error floor below a predefined threshold). Choosing a very small quantization level that would guarantee the desired performance, requires information packets of very large size, which is not desirable (could increase the probability of packet losses, increase delays, etc) and often not feasible due to the limited capacity of the channels available. In order to obtain a communication-efficient distributed solution and a sufficiently close proximity to the optimal solution, we propose a quantized distributed optimization algorithm that converges in a finite number of steps and is able to adjust the quantization level accordingly. The proposed solution uses a finite-time distributed optimization protocol to find a solution to the problem for a given quantization level in a finite number of steps and keeps refining the quantization level until the difference in the solution between two successive solutions with different quantization levels is below a certain pre-specified threshold. Therefore, the proposed algorithm progressively refines the quantization level, thus eventually achieving low error floor with a reduced communication burden. The performance gains of the proposed algorithm are demonstrated via illustrative examples.
A subspace constrained randomized Kaczmarz method for structure or external knowledge exploitation

We study a subspace constrained version of the randomized Kaczmarz algorithm for solving large linear systems in which the iterates are confined to the space of solutions of a selected subsystem. We show that the subspace constraint leads to an accelerated convergence rate, especially when the system has structure such as having coherent rows or being approximately low-rank. On Gaussian-like random data, it results in a form of dimension reduction that effectively improves the aspect ratio of the system. Furthermore, this method serves as a building block for a second, quantile-based algorithm for the problem of solving linear systems with arbitrary sparse corruptions, which is able to efficiently exploit partial external knowledge about uncorrupted equations and achieve convergence in difficult settings such as in almost-square systems. Numerical experiments on synthetic and real-world data support our theoretical results and demonstrate the validity of the proposed methods for even more general data models than guaranteed by the theory.