Miracle-dz/UniFAR-FTUs huggingface dataset:
Practical Algorithms for Orientations of Partially Directed Graphical Models

In observational studies, the true causal model is typically unknown and needs to be estimated from available observational and limited experimental data. In such cases, the learned causal model is commonly represented as a partially directed acyclic graph (PDAG), which contains both directed and undirected edges indicating uncertainty of causal relations between random variables. The main focus of this paper is on the maximal orientation task, which, for a given PDAG, aims to orient the undirected edges maximally such that the resulting graph represents the same Markov equivalent DAGs as the input PDAG. This task is a subroutine used frequently in causal discovery, e. g. , as the final step of the celebrated PC algorithm. Utilizing connections to the problem of finding a consistent DAG extension of a PDAG, we derive faster algorithms for computing the maximal orientation by proposing two novel approaches for extending PDAGs, both constructed with an emphasis on simplicity and practical effectiveness.
Quasinormal modes of a holonomy corrected Schwarzschild black hole

We analyze the quasinormal modes (QNMs) of a recently obtained solution of a Schwarzschild black hole (BH) with corrections motivated by Loop Quantum Gravity (LQG). This spacetime is regular everywhere and presents the global structure of a wormhole, with a minimal surface whose radius depends on a LQG parameter. We focus on the investigation of massless scalar field perturbations over the spacetime. We compute the QNMs with the WKB approximation, as well as the continued fraction method. The QNM frequency orbits, for $l=0$ and $n>0$, where $l$ and $n$ are the multipole and overtone numbers, respectively, are self-intersecting, spiraling curves in the complex plane. These orbits accumulate to a fixed complex value corresponding to the QNMs of the extremal case. We obtain that, for small values of the LQG parameter, the overall damping decreases as we increase the LQG parameter. Moreover the spectrum of the quantum corrected black hole exhibits an oscillatory pattern, which might imply in the existence of QNMs with vanishing real part. This pattern suggests that the limit $n\rightarrow \infty$ for the real part of the QNMs is not well-defined, what differs from Schwarzschild's case. We also analyze the time-domain profiles for the scalar perturbations, showing that the LQG correction does not alter the Schwarzschild power-law tail. We compute the fundamental mode from the time profile by means of the Prony method, obtaining excellent agreement with the two previously mentioned methods.
Quiescent and Active Galactic Nuclei as Factories of Merging Compact Objects in the Era of Gravitational Wave Astronomy

Galactic nuclei harbouring a central supermassive black hole (SMBH), possibly surrounded by a dense nuclear cluster (NC), represent extreme environments that house a complex interplay of many physical processes that uniquely affect stellar formation, evolution, and dynamics. The discovery of gravitational waves (GWs) emitted by merging black holes (BHs) and neutron stars (NSs), funnelled a huge amount of work focused on understanding how compact object binaries (COBs) can pair up and merge together. Here, we review from a theoretical standpoint how different mechanisms concur with the formation, evolution, and merger of COBs around quiescent SMBHs and active galactic nuclei (AGNs), summarising the main predictions for current and future (GW) detections and outlining the possible features that can clearly mark a galactic nuclei origin.
Reconfigurable Intelligent Surfaces to Enable Energy-Efficient IoT Networks

In this article, we study the uplink (UL) channel of a cellular network of Internet of Things (IoT) devices assisted by a reconfigurable intelligent surface (RIS) with a limited number of reflecting angle configurations. Firstly, we derive an expression of the required transmit power for the machine-type devices (MTDs) to attain a target signal-to-noise ratio (SNR), considering a channel model that accounts for the RIS discretization into sub-wavelength reflecting elements. Such an expression demonstrates that the transmit power depends on the target SNR, the position of the MTD in the service area, and the RIS setup, which includes the number of reflecting elements and the available reflecting angle configurations. Secondly, we develop an expression for the expected battery lifetime (EBL) of the MTDs, which explicitly depends on the MTD transmit power. Numerical simulations on the energy efficiency (EE) evaluated via the EBL demonstrate the benefits of adopting RISs to enable energy-efficient IoT networks.
Varying Calcium Abundances in Solar Flares Seen by the Solar Maximum Mission

We report on calcium abundance A(Ca) estimates during the decay phases of 194 solar X-ray flares using archived data from the Bent Crystal Spectrometer (BCS) on the Solar Maximum Mission (operational 1980–1989). The abundances are derived from the ratio of the total calcium X-ray line emission in BCS channel 1 to that in neighboring continuum, with temperature from a satellite-to-resonance line ratio. Generally, the calcium abundance is found to be about 3 times the photospheric abundance, as previously found, indicating a “first ionization potential” (FIP) effect for calcium, which has a relatively low FIP value. The precision of the abundance estimates (referred to hydrogen on a logarithmic scale with A(H) = 12), is typically ∼± 0.01, enabling any time variations of A(Ca) during the flare decay to be examined. For a total of 270 short time segments with A(Ca) determined to better than 2.3% accuracy, many (106; 39%) showed variations in A(Ca) at the 3σ level. For the majority, in 74 (70%) of these 106 segments A(Ca) decreased with time, and for 32 (30%) A(Ca) increased with time. For 79 out of 270 (29%) we observed constant or nearly constant A(Ca), and the remaining 85 (31%) with irregular time behavior. A common feature was the presence of discontinuities in the time behavior of A(Ca). Relating these results to the ponderomotive force theory of Laming, we attribute the nature of varying A(Ca) to the emergence of loop structures in addition to the initial main loop, each with its characteristic calcium abundance.
Multi-Robot-Guided Crowd Evacuation: Two-Scale Modeling and Control Based on Mean-Field Hydrodynamic Models

Emergency evacuation describes a complex situation involving time-critical decision-making by evacuees. Mobile robots are being actively explored as a potential solution to provide timely guidance. In this work, we study a robot-guided crowd evacuation problem where a small group of robots is used to guide a large human crowd to safe locations. The challenge lies in how to use micro-level human-robot interactions to indirectly influence a population that significantly outnumbers the robots to achieve the collective evacuation objective. To address the challenge, we follow a two-scale modeling strategy and explore hydrodynamic models, which consist of a family of microscopic social force models that describe how human movements are locally affected by other humans, the environment, and robots, and associated macroscopic equations for the temporal and spatial evolution of the crowd density and flow velocity. We design controllers for the robots such that they not only automatically explore the environment (with unknown dynamic obstacles) to cover it as much as possible, but also dynamically adjust the directions of their local navigation force fields based on the real-time macrostates of the crowd to guide the crowd to a safe location. We prove the stability of the proposed evacuation algorithm and conduct extensive simulations to investigate the performance of the algorithm with different combinations of human numbers, robot numbers, and obstacle settings.
Memory-aided Contrastive Consensus Learning for Co-salient Object Detection

Co-salient object detection (CoSOD) aims at detecting common salient objects within a group of relevant source images. Most of the latest works employ the attention mechanism for finding common objects. To achieve accurate CoSOD results with high-quality maps and high efficiency, we propose a novel Memory-aided Contrastive Consensus Learning (MCCL) framework, which is capable of effectively detecting co-salient objects in real time (∼150 fps). To learn better group consensus, we propose the Group Consensus Aggregation Module (GCAM) to abstract the common features of each image group; meanwhile, to make the consensus representation more discriminative, we introduce the Memory-based Contrastive Module (MCM), which saves and updates the consensus of images from different groups in a queue of memories. Finally, to improve the quality and integrity of the predicted maps, we develop an Adversarial Integrity Learning (AIL) strategy to make the segmented regions more likely composed of complete objects with less surrounding noise. Extensive experiments on all the latest CoSOD benchmarks demonstrate that our lite MCCL outperforms 13 cutting-edge models, achieving the new state of the art (∼5.9% and ∼6.2% improvement in S-measure on CoSOD3k and CoSal2015, respectively). Our source codes, saliency maps, and online demos are publicly available at https://github.com/ZhengPeng7/MCCL.
UniFLG: Unified Facial Landmark Generator from Text or Speech

Talking face generation has been extensively investigated owing to its wide applicability. The two primary frameworks used for talking face generation comprise a text-driven framework, which generates synchronized speech and talking faces from text, and a speech-driven framework, which generates talking faces from speech. To integrate these frameworks, this paper proposes a unified facial landmark generator (UniFLG). The proposed system exploits end-to-end text-to-speech not only for synthesizing speech but also for extracting a series of latent representations that are common to text and speech, and feeds it to a landmark decoder to generate facial landmarks. We demonstrate that our system achieves higher naturalness in both speech synthesis and facial landmark generation compared to the state-of-the-art text-driven method. We further demonstrate that our system can generate facial landmarks from speech of speakers without facial video data or even speech data.
Hyperbolic Anderson model with L\'evy white noise: spatial ergodicity and fluctuation

In this paper, we study one-dimensional hyperbolic Anderson models (HAM) driven by space-time pure-jump L\'evy white noise in a finite-variance setting. Motivated by recent active research on limit theorems for stochastic partial differential equations driven by Gaussian noises, we present the first study in this L\'evy setting. In particular, we first establish the spatial ergodicity of the solution and then a quantitative central limit theorem (CLT) for the spatial averages of the solution to HAM in both Wasserstein distance and Kolmogorov distance, with the same rate of convergence. To achieve the first goal (i.e. spatial ergodicity), we exploit some basic properties of the solution and apply a Poincar\'e inequality in the Poisson setting, which requires delicate moment estimates on the Malliavin derivatives of the solution. Such moment estimates are obtained in a soft manner by observing a natural connection between the Malliavin derivatives of HAM and a HAM with Dirac delta velocity. To achieve the second goal (i.e. CLT), we need two key ingredients: (i) a univariate second-order Poincar\'e inequality in the Poisson setting that goes back to Last, Peccati, and Schulte (Probab. Theory Related Fields, 2016) and has been recently improved by Trauthwein (arXiv:2212.03782); (ii) aforementioned moment estimates of Malliavin derivatives up to second order. We also establish a corresponding functional central limit theorem by (a) showing the convergence in finite-dimensional distributions and (b) verifying Kolmogorov's tightness criterion. Part (a) is made possible by a linearization trick and the univariate second-order Poincar\'e inequality, while part (b) follows from a standard moment estimate with an application of Rosenthal's inequality.
From $t$-SNE to UMAP with contrastive learning

Neighbor embedding methods $t$-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions appear to be unrelated. In practice, they yield strongly differing embeddings and can suggest conflicting interpretations of the same data. The fundamental reasons for this and, more generally, the exact relationship between $t$-SNE and UMAP have remained unclear. In this work, we uncover their conceptual connection via a new insight into contrastive learning methods. Noise-contrastive estimation can be used to optimize $t$-SNE, while UMAP relies on negative sampling, another contrastive method. We find the precise relationship between these two contrastive methods and provide a mathematical characterization of the distortion introduced by negative sampling. Visually, this distortion results in UMAP generating more compact embeddings with tighter clusters compared to $t$-SNE. We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings. Moving along this spectrum of embeddings leads to a trade-off between discrete / local and continuous / global structures, mitigating the risk of over-interpreting ostensible features of any single embedding. We provide a PyTorch implementation.
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data

Semi-supervised learning aims to train a model using limited labels. State-of-the-art semi-supervised methods for image classification such as PAWS rely on self-supervised representations learned with large-scale unlabeled but curated data. However, PAWS is often less effective when using real-world unlabeled data that is uncurated, e.g., contains out-of-class data. We propose RoPAWS, a robust extension of PAWS that can work with real-world unlabeled data. We first reinterpret PAWS as a generative classifier that models densities using kernel density estimation. From this probabilistic perspective, we calibrate its prediction based on the densities of labeled and unlabeled data, which leads to a simple closed-form solution from the Bayes' rule. We demonstrate that RoPAWS significantly improves PAWS for uncurated Semi-iNat by +5.3% and curated ImageNet by +0.4%.
Membership Inference Attack for Beluga Whales Discrimination

To efficiently monitor the growth and evolution of a particular wildlife population, one of the main fundamental challenges to address in animal ecology is the re-identification of individuals that have been previously encountered but also the discrimination between known and unknown individuals (the so-called"open-set problem"), which is the first step to realize before re-identification. In particular, in this work, we are interested in the discrimination within digital photos of beluga whales, which are known to be among the most challenging marine species to discriminate due to their lack of distinctive features. To tackle this problem, we propose a novel approach based on the use of Membership Inference Attacks (MIAs), which are normally used to assess the privacy risks associated with releasing a particular machine learning model. More precisely, we demonstrate that the problem of discriminating between known and unknown individuals can be solved efficiently using state-of-the-art approaches for MIAs. Extensive experiments on three benchmark datasets related to whales, two different neural network architectures, and three MIA clearly demonstrate the performance of the approach. In addition, we have also designed a novel MIA strategy that we coined as ensemble MIA, which combines the outputs of different MIAs to increase the attack accuracy while diminishing the false positive rate. Overall, one of our main objectives is also to show that the research on privacy attacks can also be leveraged"for good"by helping to address practical challenges encountered in animal ecology.
Information-Restricted Neural Language Models Reveal Different Brain Regions’ Sensitivity to Semantics, Syntax, and Context

Abstract A fundamental question in neurolinguistics concerns the brain regions involved in syntactic and semantic processing during speech comprehension, both at the lexical (word processing) and supra-lexical levels (sentence and discourse processing). To what extent are these regions separated or intertwined? To address this question, we introduce a novel approach exploiting neural language models to generate high-dimensional feature sets that separately encode semantic and syntactic information. More precisely, we train a lexical language model, GloVe, and a supra-lexical language model, GPT-2, on a text corpus from which we selectively removed either syntactic or semantic information. We then assess to what extent the features derived from these information-restricted models are still able to predict the fMRI time courses of humans listening to naturalistic text. Furthermore, to determine the windows of integration of brain regions involved in supra-lexical processing, we manipulate the size of contextual information provided to GPT-2. The analyses show that, while most brain regions involved in language comprehension are sensitive to both syntactic and semantic features, the relative magnitudes of these effects vary across these regions. Moreover, regions that are best fitted by semantic or syntactic features are more spatially dissociated in the left hemisphere than in the right one, and the right hemisphere shows sensitivity to longer contexts than the left. The novelty of our approach lies in the ability to control for the information encoded in the models’ embeddings by manipulating the training set. These “information-restricted” models complement previous studies that used language models to probe the neural bases of language, and shed new light on its spatial organization.
Computational expressivity of (circular) proofs with fixed points

We study the computational expressivity of proof systems with fixed point operators, within the ‘proofs-as-programs’ paradigm. We start with a calculus μLJ (due to Clairambault) that extends intuitionistic logic by least and greatest positive fixed points. Based in the sequent calculus, μLJ admits a standard extension to a ‘circular’ calculus CμLJ. Our main result is that, perhaps surprisingly, both μLJ and CμLJ represent the same first-order functions: those provably total in $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$, a subsystem of second-order arithmetic beyond the ‘big five’ of reverse mathematics and one of the strongest theories for which we have an ordinal analysis (due to Rathjen). This solves various questions in the literature on the computational strength of (circular) proof systems with fixed points.For the lower bound we give a realisability interpretation from an extension of Peano Arithmetic by fixed points that has been shown to be arithmetically equivalent to $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$ (due to Möllerfeld). For the upper bound we construct a novel computability model in order to give a totality argument for circular proofs with fixed points. In fact we formalise this argument itself within $\Pi _2^1 - {\text{C}}{{\text{A}}_0}$ in order to obtain the tight bounds we are after. Along the way we develop some novel reverse mathematics for the Knaster-Tarski fixed point theorem.
Oxidized organic molecules in the tropical free troposphere over Amazonia

ABSTRACT New particle formation (NPF) in the tropical free troposphere (FT) is a globally important source of cloud condensation nuclei, affecting cloud properties and climate. Oxidized organic molecules (OOMs) produced from biogenic volatile organic compounds are believed to contribute to aerosol formation in the tropical FT, but without direct chemical observations. We performed in situ molecular-level OOMs measurements at the Bolivian station Chacaltaya at 5240 m above sea level, on the western edge of Amazonia. For the first time, we demonstrate the presence of OOMs, mainly with 4–5 carbon atoms, in both gas-phase and particle-phase (in terms of mass contribution) measurements in tropical FT air from Amazonia. These observations, combined with air mass history analyses, indicate that the observed OOMs are linked to isoprene emitted from the rainforests hundreds of kilometers away. Based on particle-phase measurements, we find that these compounds can contribute to NPF, at least the growth of newly formed nanoparticles, in the tropical FT on a continental scale. Thus, our study is a fundamental and significant step in understanding the aerosol formation process in the tropical FT.
Towards Human-Bot Collaborative Software Architecting with ChatGPT

Architecting software-intensive systems can be a complex process. It deals with the daunting tasks of unifying stakeholders’ perspectives, designers’ intellect, tool-based automation, pattern-driven reuse, and so on, to sketch a blueprint that guides software implementation and evaluation. Despite its benefits, architecture-centric software engineering (ACSE) suffers from a multitude of challenges. ACSE challenges could stem from a lack of standardized processes, socio-technical limitations, and scarcity of human expertise etc. that can impede the development of existing and emergent classes of software. Software Development Bots (DevBots) trained on large language models can help synergise architects’ knowledge with artificially intelligent decision support to enable rapid architecting in a human-bot collaborative ACSE. An emerging solution to enable this collaboration is ChatGPT, a disruptive technology not primarily introduced for software engineering, but is capable of articulating and refining architectural artifacts based on natural language processing. We detail a case study that involves collaboration between a novice software architect and ChatGPT to architect a service-based software. Future research focuses on harnessing empirical evidence about architects’ productivity and explores socio-technical aspects of architecting with ChatGPT to tackle challenges of ACSE.
Motion of air cavities in long horizontal ducts

This paper describes an experimental study of the flow associated with the intrusion of an air cavity into a long horizontal duct as water was allowed to drain from one end. Flows of this nature were discussed by Benjamin (1968), who showed that throttling of the flow of water from the end of the duct would cause both the celerity and the depth of the cavity to reduce. However, the experiments described in this paper revealed that the celerity of the cavity was not reduced from its unthrottled value until the water depth beneath the cavity was 0·78 of the duct depth. For values of this depth ratio between 0·5 and 0·78, the flow as a whole was unsteady. It is shown that Benjamin's model can be modified to allow for the unsteady nature of the flow. Benjamin's original model was found to describe accurately the form and behaviour of the cavity in the case of unthrottled flow, when the flow was steady, and also when the depth beneath the cavity exceeded 0·78 of the duct height, when the flow was again steady. Surface-tension effects were found to reduce the celerity of the cavity and to modify its shape as described by Gardner & Crow (1970).