Miracle-dz/UniFAR-FTUs huggingface dataset:
Pool-Based Active Learning with Proper Topological Regions

Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning methods are there to detect, among a set of unlabeled data, the ones that are the most relevant for the training. We propose in this paper a meta-approach for pool-based active learning strategies in the context of multi-class classification tasks based on Proper Topological Regions. PTR, based on topological data analysis (TDA), are relevant regions used to sample cold-start points or within the active learning scheme. The proposed method is illustrated empirically on various benchmark datasets, being competitive to the classical methods from the literature.
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery

Time-series causal discovery (TSCD) is a fundamental problem of machine learning. However, existing synthetic datasets cannot properly evaluate or predict the algorithms' performance on real data. This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation. The pipeline starts from real observations in a specific scenario and produces a matching benchmark dataset. Firstly, we harness deep neural networks along with normalizing flow to accurately capture realistic dynamics. Secondly, we extract hypothesized causal graphs by performing importance analysis on the neural network or leveraging prior knowledge. Thirdly, we derive the ground truth causal graphs by splitting the causal model into causal term, residual term, and noise term. Lastly, using the fitted network and the derived causal graph, we generate corresponding versatile time-series proper for algorithm assessment. In the experiments, we validate the fidelity of the generated data through qualitative and quantitative experiments, followed by a benchmarking of existing TSCD algorithms using these generated datasets. CausalTime offers a feasible solution to evaluating TSCD algorithms in real applications and can be generalized to a wide range of fields. For easy use of the proposed approach, we also provide a user-friendly website, hosted on www.causaltime. cc.
Blending Imitation and Reinforcement Learning for Robust Policy Improvement

While reinforcement learning (RL) has shown promising performance, its sample complexity continues to be a substantial hurdle, restricting its broader application across a variety of domains. Imitation learning (IL) utilizes oracles to improve sample efficiency, yet it is often constrained by the quality of the oracles deployed. which actively interleaves between IL and RL based on an online estimate of their performance. RPI draws on the strengths of IL, using oracle queries to facilitate exploration, an aspect that is notably challenging in sparse-reward RL, particularly during the early stages of learning. As learning unfolds, RPI gradually transitions to RL, effectively treating the learned policy as an improved oracle. This algorithm is capable of learning from and improving upon a diverse set of black-box oracles. Integral to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient (RPG), both of which reason over whether to perform state-wise imitation from the oracles or learn from its own value function when the learner's performance surpasses that of the oracles in a specific state. Empirical evaluations and theoretical analysis validate that RPI excels in comparison to existing state-of-the-art methodologies, demonstrating superior performance across various benchmark domains.
The Feasibility of Electric Air Taxis: Balancing Time Savings and CO$_2$ Emissions -- A joint case study of respective plans in Paris

This paper evaluates the sustainability of Advanced Air Mobility (AAM) in urban and regional mobility, using Paris as a case study. Paris is committed to eco-friendly transportation and has introduced AAM, including electric Vertical Take-Off and Landing (eVTOL) air taxis for the 2024 Olympic Games. We assess eVTOL energy consumption and CO$_2$ emissions on urban and regional routes, comparing them with cars, public transport, and helicopters. Urban eVTOLs save around 23 minutes over cars and 22 minutes over public transport on 50 km routes. For regional routes (300 km), eVTOLs save 76 minutes over cars and 69 minutes over trains. However, eVTOLs' eco-friendliness depends on context. In urban areas, they consume more energy than electric cars, but beat traditional helicopters by 47%. For regional travel, eVTOLs outperform helicopters and some cars but lag behind electric vehicles and trains. To maximize AAM's sustainability in Paris, stakeholders must consider real-world operations and integrate eVTOLs into the broader transportation system. This approach can lead to greener urban and regional transportation.
Puddles: Application-Independent Recovery and Location-Independent Data for Persistent Memory

In this paper, we argue that current work has failed to provide a comprehensive and maintainable in-memory representation for persistent memory. PM data should be easily mappable into a process address space, shareable across processes, shippable between machines, consistent after a crash, and accessible to legacy code with fast, efficient pointers as first-class abstractions. While existing systems have provided niceties like mmap()-based load/store access, they have not been able to support all these necessary properties due to conflicting requirements. We propose Puddles, a new persistent memory abstraction, to solve these problems. Puddles provide application-independent recovery after a power outage; they make recovery from a system failure a system-level property of the stored data rather than the responsibility of the programs that access it. Puddles use native pointers, so they are compatible with existing code. Finally, Puddles implement support for sharing and shipping of PM data between processes and systems without expensive serialization and deserialization. Compared to existing systems, Puddles are at least as fast as and up to 1.34$\times$ faster than PMDK while being competitive with other PM libraries across YCSB workloads. Moreover, to demonstrate Puddles' ability to relocate data, we showcase a sensor network data-aggregation workload that results in a 4.7$\times$ speedup over PMDK.
Towards a Unified Framework for Sequential Decision Making

In recent years, the integration of Automated Planning (AP) and Reinforcement Learning (RL) has seen a surge of interest. To perform this integration, a general framework for Sequential Decision Making (SDM) would prove immensely useful, as it would help us understand how AP and RL fit together. In this preliminary work, we attempt to provide such a framework, suitable for any method ranging from Classical Planning to Deep RL, by drawing on concepts from Probability Theory and Bayesian inference. We formulate an SDM task as a set of training and test Markov Decision Processes (MDPs), to account for generalization. We provide a general algorithm for SDM which we hypothesize every SDM method is based on. According to it, every SDM algorithm can be seen as a procedure that iteratively improves its solution estimate by leveraging the task knowledge available. Finally, we derive a set of formulas and algorithms for calculating interesting properties of SDM tasks and methods, which make possible their empirical evaluation and comparison.
An exploratory study of the outdoor recreation participation of families who have a child under sixteen

Family recreation has been recognized as an effective way to nurture family customs and promote healthy youth development. In particular, outdoor-based recreation that includes inherent challenges often offers opportunities for a family to grow together and promotes family cohesion and youth development. Building on earlier research, this study attempts to further explore factors associated with motivation for participating in outdoor family recreation. A series of research questions was developed and analyzed with empirical data. The results indicated that travelers who have at least one child under 16 years of age were signifi cantly different in their motivations and preferences from those who travel without youth.
Constructing Image-Text Pair Dataset from Books

Digital archiving is becoming widespread owing to its effectiveness in protecting valuable books and providing knowledge to many people electronically. In this paper, we propose a novel approach to leverage digital archives for machine learning. If we can fully utilize such digitized data, machine learning has the potential to uncover unknown insights and ultimately acquire knowledge autonomously, just like humans read books. As a first step, we design a dataset construction pipeline comprising an optical character reader (OCR), an object detector, and a layout analyzer for the autonomous extraction of image-text pairs. In our experiments, we apply our pipeline on old photo books to construct an image-text pair dataset, showing its effectiveness in image-text retrieval and insight extraction.
Some geometric applications of the discrete heat flow

We present two geometric applications of heat flow methods on the discrete hypercube $\{-1,1\}^n$. First, we prove that if $X$ is a finite-dimensional normed space, then the bi-Lipschitz distortion required to embed $\{-1,1\}^n$ equipped with the Hamming metric into $X$ satisfies $$\mathsf{c}_X\big(\{-1,1\}^n\big) \gtrsim \sup_{p\in[1,2]} \frac{n}{\mathsf{T}_p(X) \min\{n,\mathrm{dim}(X)\}^{1/p}},$$ where $\mathsf{T}_p(X)$ is the Rademacher type $p$ constant of $X$. This estimate yields a mutual refinement of distortion lower bounds which follow from works of Oleszkiewicz (1996) and Ivanisvili, van Handel and Volberg (2020) for low-dimensional spaces $X$. The proof relies on an extension of an important inequality of Pisier (1986) on the biased hypercube combined with an application of the Borsuk-Ulam theorem from algebraic topology. Secondly, we introduce a new metric invariant called metric stable type as a functional inequality on the discrete hypercube and prove that it coincides with the classical linear notion of stable type for normed spaces. We also show that metric stable type yields bi-Lipschitz nonembeddability estimates for weighted hypercubes.
Periodic instabilities during compression or extension of the lithosphere 1. Deformation modes from an analytical perturbation method

We argue that the plastic rheology of the lithosphere, rather than its “recoverable” elastic properties, is responsible for the systematic development of periodic instabilities during compression or extension. We use a linear perturbation model with analytical solutions to calculate the instability modes for various rheologies. The growth of such periodic instabilities is enhanced by the highly nonlinear stress-strain rheologies encountered in the brittle layers of the lithosphere. On the contrary, ductile layers, deforming according to high temperature creep flow laws, tend to inhibit these instabilities. For oceanic domains, we assume that the only brittle layer is the upper part of the lithosphere. In compression, the only valid instability is a buckling whose wavelength is around 4 times the thickness of the brittle layer. Calculated wavelengths and growth rates are consistent with observations available for the Indian Ocean. For continental domains, a reasonable assumption is the existence of two plastic layers, one in the upper crust, the other in the upper mantle, for Moho temperatures between 450°C and 600°C. In compression and extension, two instabilities develop a long wavelength instability involving the whole lithosphere (coupling mode) and a short wavelength instability involving the crust and controlled by the upper brittle layer (intrinsic crustal mode). In compression, the coupling mode is a whole-lithosphere buckling, with a wavelength about 4 times the thickness of the active lithosphere (the two plastic layers plus the intermediate ductile layer). In extension, the coupling mode is a boudinage of opposite phase in the two plastic layers and a folding of the intermediate ductile layer. The intrinsic crustal mode is a crustal buckling in compression, crustal boudinage in extension. Neither deflects the Moho. The intrinsic crustal mode is favored by an increase in thermal gradient and by a decrease in strength of the ductile lower crust.
Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation

This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem. OS-UDA is the most challenging setting in Domain Adaptation, as only one single unlabeled target sample is assumed to be available for model adaptation. Driven by such single sample, our method LearnAug-UDA learns how to augment source data, making it perceptually similar to the target. As a result, a classifier trained on such augmented data will generalize well for the target domain. To achieve this, we designed an encoder-decoder architecture that exploits a perceptual loss and style transfer strategies to augment the source data. Our method achieves state-of-the-art performance on two well-known Domain Adaptation benchmarks, DomainNet and VisDA. The project code is available at https://github.com/IIT-PAVIS/LearnAug-UDA
Borges and AI

Many believe that Large Language Models (LLMs) open the era of Artificial Intelligence (AI). Some see opportunities while others see dangers. Yet both proponents and opponents grasp AI through the imagery popularised by science fiction. Will the machine become sentient and rebel against its creators? Will we experience a paperclip apocalypse? Before answering such questions, we should first ask whether this mental imagery provides a good description of the phenomenon at hand. Understanding weather patterns through the moods of the gods only goes so far. The present paper instead advocates understanding LLMs and their connection to AI through the imagery of Jorge Luis Borges, a master of 20th century literature, forerunner of magical realism, and precursor to postmodern literature. This exercise leads to a new perspective that illuminates the relation between language modelling and artificial intelligence.
Main sequence companions to white dwarfs II: the age-activity-rotation relation from a sample of Gaia common proper motion pairs

Magnetic activity and rotation are related to the age of low-mass main sequence stars. To further constrain these relations, we study a sample of 574 main sequence stars members of common proper motion pairs with white dwarfs, identified thanks to Gaia astrometry. We use the white dwarfs as age indicators, while the activity indexes and rotational velocities are obtained from the main sequence companions using standard procedures. We find that stars older than 5 Gyr do not display Hα nor Ca ii H&K emission unless they are fast rotators due to tidal locking from the presence of unseen companions and that the rotational velocities tend to decrease over time, thus supporting the so-called gyrochronology. However, we also find moderately old stars (≃2-6 Gyr) that are active presumably because they rotate faster than they should for their given ages. This indicates that they may be suffering from weakened magnetic braking or that they possibly evolved through wind accretion processes in the past. The activity fractions that we measure for all stars younger than 5 Gyr range between ≃10–40 per cent. This is line with the expectations, since our sample is composed of F, G, K and early M stars, which are thought to have short (<2 Gyr) activity lifetimes. Finally, we observe that the Hα fractional luminosities and the R’HK indexes for our sample of (slowly rotating) stars show a spread (−4 >log(LHα/Lbol); log(R’HK) > -5) typically found in inactive M stars or weakly active/inactive F, G, K stars.
A Strategy for Identifying Periodic Sources Contributing to the Galactic Center Excess

The origin of the Galactic Center gamma-ray excess has not been conclusively determined after over a decade of careful study. The two most widely discussed possibilities are a population of millisecond pulsars (MSPs), and annihilation of dark matter particles. In contrast with annihilating dark matter, MSPs are expected to produce periodic emission. We show that even though the number of photons contributing to the excess is small, there is potentially sufficient information in the data from Fermi to detect a periodic MSP signal. Such a detection would definitively prove that at least some fraction of the excess is due to MSPs. We argue that this conclusion is robust to potential timing perturbations of the gamma-ray photons, such as those due to Earth's orbit, even if the number of parameters that must be used to model the perturbations is $\sim 7$.
Particle pinning as a method to manipulate marginal stability

We study the critical behavior of low-frequency vibrations of packings with pinned particles near the jamming point. Soft modes form a plateau in the density of states and its frequency is controlled by the contact number as the ordinary jamming transition. The spatial structure of these modes is not largely affected by pins. Below the plateau, the non-Debye scaling predicted by mean-field theories and quasi-localized modes breaks down depending on the pinning procedures. We comprehensively explain these behaviors by the impact of pinning operations on the marginal stability of the packings.
Lightning Helmholtz Solver

In this dissertation we have applied Trefethen and Gopal's Lightning Method to solve the Helmholtz equation in the exterior of two dimensional piecewise smooth domains. The background theory motivating the method is presented, and we explore the optimal method implementation for the unit square, which is subsequently used to give a guide on parameter selection for a general region. The behaviour of the computed solutions is verified to act in accordance with our intuition and current understanding of wave propagation, and we show that the wave decays to approximately 0 in the shadow region.
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression

We conduct a comprehensive investigation into the dynamics of gradient descent using large-order constant step-sizes in the context of quadratic regression models. Within this framework, we reveal that the dynamics can be encapsulated by a specific cubic map, naturally parameterized by the step-size. Through a fine-grained bifurcation analysis concerning the step-size parameter, we delineate five distinct training phases: (1) monotonic, (2) catapult, (3) periodic, (4) chaotic, and (5) divergent, precisely demarcating the boundaries of each phase. As illustrations, we provide examples involving phase retrieval and two-layer neural networks employing quadratic activation functions and constant outer-layers, utilizing orthogonal training data. Our simulations indicate that these five phases also manifest with generic non-orthogonal data. We also empirically investigate the generalization performance when training in the various non-monotonic (and non-divergent) phases. In particular, we observe that performing an ergodic trajectory averaging stabilizes the test error in non-monotonic (and non-divergent) phases.
Ten tips for developing a more inviting syllabus

ABSTRACT In higher education, syllabi have traditionally served as written contracts between instructors and their students, providing first-hand information about the course and expectations. Reading the syllabus may provide students with first impressions or mental images of the instructor, thereby initiating a student-instructor relationship even before any interaction has occurred. Instructors can use syllabi to directly communicate values and practices of equity and inclusion, but students can perceive indirect messages through tone and language that may support or contradict stated values. Here, we share empirically derived recommendations for improving the tone of syllabi with inviting language and stylistic features that promote relationship-building with students.
Parental early life environments drive transgenerational plasticity of offspring metabolism in a freshwater fish (Danio rerio)

Parental experiences can lead to changes in offspring phenotypes through transgenerational plasticity (TGP). TGP is expected to play a role in improving the responses of offspring to changes in climate, but little is known about how the early lives of parents influence offspring TGP. Here, we use a model organism, zebrafish (Danio rerio), to contrast the effects of early and later life parental thermal environments on offspring routine metabolism. To accomplish this, we exposed both parents to either constant optimal (27°C) or environmentally realistic diel fluctuating (22–32°C) temperatures during early (embryonic and larval) and later (juvenile and adult) life in a factorial design. We found significant reduction of routine metabolic rates (greater than 20%) at stressful temperatures (22°C and 32°C) after biparental early life exposure to fluctuating temperatures, but little effect of later life parental temperatures on offspring metabolism. This reduction reflects metabolic compensation and is expected to enhance offspring body sizes under stressful temperatures. These changes occur over and above the effects of parental environments on egg size, suggesting alternate non-genetic mechanisms influenced offspring metabolic rates.
Parallel worlds and mixed economies: multi-proxy analysis reveals complex subsistence systems at the dawn of early farming in the northeast Baltic

The transition from foraging to farming was a key turning point in ancient socio-economies. Yet, the complexities and regional variations of this transformation are still poorly understood. This multi-proxy study provides a new understanding of the introduction and spread of early farming, challenging the notions of hierarchical economies. The most extensive biological and biomolecular dietary overview, combining zooarchaeological, archaeobotanical, dietary stable isotope and pottery lipid residue analyses is presented, to unravel the nature and extent of early farming in the 3rd millennium cal BCE in the northeast Baltic. Farming was introduced by incoming Corded Ware cultural groups (CWC), but some dietary segregation existed within these communities, with some having more access to domesticates, others incorporating more wild resources into their diet. The CWC groups coexisted in parallel with local hunter–fisher–gatherers (HFG) without any indication of the adoption of domesticates. There was no transition from foraging to farming in the 3rd millennium cal BCE in the NE Baltic. Instead, we see a complex system of parallel worlds with local HFGs continuing forager lifeways, and incoming farmers practising mixed economies, with the continuation of these subsistence strategies for at least a millennium after the first encounter with domesticated animals.