Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretable Differencing of Machine Learning Models

Understanding the differences between machine learning (ML) models is of interest in scenarios ranging from choosing amongst a set of competing models, to updating a deployed model with new training data. In these cases, we wish to go beyond differences in overall metrics such as accuracy to identify where in the feature space do the differences occur. We formalize this problem of model differencing as one of predicting a dissimilarity function of two ML models' outputs, subject to the representation of the differences being human-interpretable. Our solution is to learn a Joint Surrogate Tree (JST), which is composed of two conjoined decision tree surrogates for the two models. A JST provides an intuitive representation of differences and places the changes in the context of the models' decision logic. Context is important as it helps users to map differences to an underlying mental model of an AI system. We also propose a refinement procedure to increase the precision of a JST. We demonstrate, through an empirical evaluation, that such contextual differencing is concise and can be achieved with no loss in fidelity over naive approaches.
Hidden symmetries of ReLU networks

The parameter space for any fixed architecture of feedforward ReLU neural networks serves as a proxy during training for the associated class of functions - but how faithful is this representation? It is known that many different parameter settings can determine the same function. Moreover, the degree of this redundancy is inhomogeneous: for some networks, the only symmetries are permutation of neurons in a layer and positive scaling of parameters at a neuron, while other networks admit additional hidden symmetries. In this work, we prove that, for any network architecture where no layer is narrower than the input, there exist parameter settings with no hidden symmetries. We also describe a number of mechanisms through which hidden symmetries can arise, and empirically approximate the functional dimension of different network architectures at initialization. These experiments indicate that the probability that a network has no hidden symmetries decreases towards 0 as depth increases, while increasing towards 1 as width and input dimension increase.
Precise and Generalized Robustness Certification for Neural Networks

The objective of neural network (NN) robustness certification is to determine if a NN changes its predictions when mutations are made to its inputs. While most certification research studies pixel-level or a few geometrical-level and blurring operations over images, this paper proposes a novel framework, GCERT, which certifies NN robustness under a precise and unified form of diverse semantic-level image mutations. We formulate a comprehensive set of semantic-level image mutations uniformly as certain directions in the latent space of generative models. We identify two key properties, independence and continuity, that convert the latent space into a precise and analysis-friendly input space representation for certification. GCERT can be smoothly integrated with de facto complete, incomplete, or quantitative certification frameworks. With its precise input space representation, GCERT enables for the first time complete NN robustness certification with moderate cost under diverse semantic-level input mutations, such as weather-filter, style transfer, and perceptual changes (e.g., opening/closing eyes). We show that GCERT enables certifying NN robustness under various common and security-sensitive scenarios like autonomous driving.
A decade of near-infrared variability in NGC4388: insights into the AGN structure

Variability studies have proven to be a powerful diagnostic tool for understanding the physics and properties of active galactic nuclei (AGNs). They provide insights into the spatial and temporal distribution of the emitting regions, the structure and dynamics of the accretion disc, and the properties of the central black hole. Here, we have analysed the K-band spectral variability of the Seyfert 1.9/2 galaxy NGC 4388 spanning five epochs over a period of 10 yr. We have performed spectral synthesis of the nuclear region and found that the contribution of warm dust (T ∼ 800 K) declined by 88 per cent during these 10 yr. In the same period, the [Ca viii] coronal line decreased 61 per cent, whereas Br γ emission declined 35 per cent. For the He i and H2, we did not detect any significant variation beyond their uncertainties. Based on the time span of these changes, we estimate that the region where the warm dust is produced is smaller than 0.6 pc, which suggests that this spectral feature comes from the innermost part of the region sampled, directly from the AGN torus. On the other hand, the bulk of [Ca viii] is produced in the inner ∼2 pc and the nuclear Br γ region is more extended, spanning a region larger than 3 pc. Lastly, He i and H2 are even more external, with most of the emission probably being produced in the host galaxy rather than in the AGN. This is the first spectroscopic variability study in the near-infrared for an AGN where the central source is not directly visible.
Characterizing the effect of retractions on scientific careers

Retracting academic papers is a fundamental tool of quality control when the validity of papers or the integrity of authors is questioned post-publication. While retractions do not eliminate papers from the record, they have far-reaching consequences for retracted authors and their careers, serving as a visible and permanent signal of potential transgressions. Previous studies have highlighted the adverse effects of retractions on citation counts and coauthors' citations; however, the broader impacts beyond these have not been fully explored. We address this gap leveraging Retraction Watch, the most extensive data set on retractions and link it to Microsoft Academic Graph, a comprehensive data set of scientific publications and their citation networks, and Altmetric that monitors online attention to scientific output. Our investigation focuses on: 1) the likelihood of authors exiting scientific publishing following a retraction, and 2) the evolution of collaboration networks among authors who continue publishing after a retraction. Our empirical analysis reveals that retracted authors, particularly those with less experience, tend to leave scientific publishing in the aftermath of retraction, particularly if their retractions attract widespread attention. We also uncover that retracted authors who remain active in publishing maintain and establish more collaborations compared to their similar non-retracted counterparts. Nevertheless, retracted authors with less than a decade of publishing experience retain less senior, less productive and less impactful coauthors, and gain less senior coauthors post-retraction. Taken together, notwithstanding the indispensable role of retractions in upholding the integrity of the academic community, our findings shed light on the disproportionate impact that retractions impose on early-career authors.
Astrophysically sourced quantum coherent photonic signals

Stimulated emission is shown to be robust in stars. Through Bose enhancement this produces quantum states of aligned, monochromatic photons similar to a laser. The probability of creating such states is computed. We show that from the solar corona such quantum states would propagate outside of the solar region and through the Solar System without decoherence. For a $1 {\rm m}^2$ detector at the distance of the Earth from the Sun we estimate rates of such quantum states in the few per second thus potentially detectable. The same process should lead to such quantum states also arriving from stars at interstellar distances.
Search for the decay modes D 0 ! e þ e , D 0 ! þ , and D 0 ! e

We present searches for the rare decay modes D 0 → e + e − , D 0 → µ + µ − , and D 0 → e ± µ ∓ in continuum e + e − → c ¯ c events recorded by the BABAR detector in a data sample that corresponds to an integrated luminosity of 468 fb − 1 . These decays are highly GIM suppressed but may be enhanced in several extensions of the Standard Model. Our observed event yields are consistent with the expected backgrounds. An excess is seen in the D 0 → µ + µ − channel, although the observed yield is consistent with an upward background ﬂuctuation at the 5% level. Using the Feldman-Cousins method, we set the following 90% conﬁdence level intervals on the branching fractions: B ( D 0 → e + e − ) < 1 . 7 × 10 − 7 , B ( D 0 → µ + µ − ) within [0 . 6 , 8 . 1] × 10 − 7 , and B ( D 0 → e ± µ ∓ ) < 3 . 3 × 10 − 7 .
Energy efficient cell-free massive MIMO on 5G deployments: sleep modes strategies and user stream management

This paper proposes the utilization of cell-free massive MIMO (CF-M-MIMO) processing on top of the regular micro/macrocellular deployments typically found in current 5G networks. Towards this end, it contemplates the connection of all base stations to a central processing unit (CPU) through fronthaul links, thus enabling the joint processing of all serviced user equipment (UE), yet avoiding the expensive deployment and maintenance of dozens of randomly scattered access points (APs). Moreover, it allows the implementation of centralized strategies to exploit the sleep mode capabilities of current baseband/RF hardware to (de)activate selected Base Stations (BSs) in order to maximize the network energy efficiency and to react to changes in UE behaviour and/or operator requirements. In line with current cellular network deployments, it considers the use of multiple antennas at the UE side that unavoidably introduces the need to effectively manage the number of streams to be directed to each UE in order to balance multiplexing gains and increased pilot contamination.
A Batch-to-Online Transformation under Random-Order Model

We introduce a transformation framework that can be utilized to develop online algorithms with low $\epsilon$-approximate regret in the random-order model from offline approximation algorithms. We first give a general reduction theorem that transforms an offline approximation algorithm with low average sensitivity to an online algorithm with low $\epsilon$-approximate regret. We then demonstrate that offline approximation algorithms can be transformed into a low-sensitivity version using a coreset construction method. To showcase the versatility of our approach, we apply it to various problems, including online $(k,z)$-clustering, online matrix approximation, and online regression, and successfully achieve polylogarithmic $\epsilon$-approximate regret for each problem. Moreover, we show that in all three cases, our algorithm also enjoys low inconsistency, which may be desired in some online applications.
Movement bias in asymmetric landscapes and its impact on population distribution and critical habitat size

Ecologists have long investigated how demographic and movement parameters determine the spatial distribution and critical habitat size of a population. However, most models oversimplify movement behavior, neglecting how landscape heterogeneity influences individual movement. We relax this assumption and introduce a reaction-advection-diffusion equation that describes population dynamics when individuals exhibit space-dependent movement bias toward preferred regions. Our model incorporates two types of these preferred regions: a high-quality habitat patch, termed `habitat', which is included to model avoidance of degraded habitats like deforested regions; and a preferred location, such as a chemoattractant source or a watering hole, that we allow to be asymmetrically located with respect to habitat edges. In this scenario, the critical habitat size depends on both the relative position of the preferred location and the movement bias intensities. When preferred locations are near habitat edges, the critical habitat size can decrease when diffusion increases, a phenomenon called the drift paradox. Also, ecological traps arise when the habitat overcrowds due to excessive attractiveness or the preferred location is near a low-quality region. Our results highlight the importance of species-specific movement behavior and habitat preference as drivers of population dynamics in fragmented landscapes and, therefore, in the design of protected areas.
Open Brain AI. Automatic Language Assessment

Language assessment plays a crucial role in diagnosing and treating individuals with speech, language, and communication disorders caused by neurogenic conditions, whether developmental or acquired. However, current assessment methods are manual, laborious, and time-consuming to administer and score, causing additional patient stress. To address these challenges, we developed Open Brain AI (https://openbrainai.com). This computational platform harnesses innovative AI techniques, namely machine learning, natural language processing, large language models, and automatic speech-to-text transcription, to automatically analyze multilingual spoken and written speech productions. This paper discusses the development of Open Brain AI, the AI language processing modules, and the linguistic measurements of discourse macro-structure and micro-structure. The fast and automatic analysis of language alleviates the burden on clinicians, enabling them to streamline their workflow and allocate more time and resources to direct patient care. Open Brain AI is freely accessible, empowering clinicians to conduct critical data analyses and give more attention and resources to other critical aspects of therapy and treatment.
GuP: Fast Subgraph Matching by Guard-based Pruning

Subgraph matching, which finds subgraphs isomorphic to a query, is crucial for information retrieval from data represented as a graph. To avoid redundant explorations in the data, existing methods restrict the search space by extracting candidate vertices and edges that may constitute isomorphic subgraphs. However, expensive computation is still required because candidate vertices induce many non-isomorphic subgraphs. In this paper, we propose GuP, a subgraph matching algorithm with pruning based on guards. A guard represents a pattern of intermediate search states that never lead to isomorphic subgraphs. By attaching a guard to each candidate vertex and edge, GuP adaptively filters out unnecessary candidates depending on the search state at each step. The experimental results show that GuP effectively reduces the search space and can answer difficult queries that state-of-the-art methods cannot answer within a practical time frame.
AerialFormer: Multi-resolution Transformer for Aerial Image Segmentation

Aerial Image Segmentation is a top-down perspective semantic segmentation and has several challenging characteristics such as strong imbalance in the foreground-background distribution, complex background, intra-class heterogeneity, inter-class homogeneity, and tiny objects. To handle these problems, we inherit the advantages of Transformers and propose AerialFormer, which unifies Transformers at the contracting path with lightweight Multi-Dilated Convolutional Neural Networks (MD-CNNs) at the expanding path. Our AerialFormer is designed as a hierarchical structure, in which Transformer encoder outputs multi-scale features and MD-CNNs decoder aggregates information from the multi-scales. Thus, it takes both local and global contexts into consideration to render powerful representations and high-resolution segmentation. We have benchmarked AerialFormer on three common datasets including iSAID, LoveDA, and Potsdam. Comprehensive experiments and extensive ablation studies show that our proposed AerialFormer outperforms previous state-of-the-art methods with remarkable performance. Our source code will be publicly available upon acceptance.
Frequency shifts during whistling occurs as transition between two phase synchronised limit cycles via a state of intermittency or abruptly

Self-sustained oscillations arising from the interactions between the hydrodynamic and the acoustic field are disastrous in engineering systems such as segmented solid rocket motors and large gas pipelines. These self-sustained oscillations (limit cycle oscillations) are also referred to as aeroacoustic instabilities, which can be heard as a whistle. Understanding the change in dynamical state by altering the control parameter in an aeroacoustic system is critical in designing control strategies for aeroacoustic instabilities. In this study, as the control parameter Reynolds number ($Re$) is varied, we hear a change in the whistling frequency. We show that this change in frequency occurs via the state of intermittency, which has bursts of periodic fluctuations amidst the regime of the aperiodic fluctuations in acoustic pressure fluctuations. At a higher Reynolds number, we observe an abrupt transition from one limit cycle oscillation (LCO) to another limit cycle oscillation during the shift in whistling frequency. Further, we use synchronisation theory to investigate the coupled behaviour of the acoustic and the hydrodynamic fields. The acoustic pressure ($p'$) and hydrodynamic ($v'$) fluctuations during LCO exhibit phase synchronisation. Thus, we conclude that the shift in whistling frequency is a transition between the two phase synchronised limit cycle oscillations that occurs either through the state of intermittency or abruptly. The periodic bursts of intermittency correspond to the phase-synchronised periodic $p'$ and $v'$, and the aperiodic epochs correspond to the desynchronised aperiodic $p'$ and $v'$.
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues

With the growing importance of detecting misinformation, many studies have focused on verifying factual claims by retrieving evidence. However, canonical fact verification tasks do not apply to catching subtle differences in factually consistent claims, which might still bias the readers, especially on contentious political or economic issues. Our underlying assumption is that among the trusted sources, one's argument is not necessarily more true than the other, requiring comparison rather than verification. In this study, we propose ClaimDiff, a novel dataset that primarily focuses on comparing the nuance between claim pairs. In ClaimDiff, we provide 2,941 annotated claim pairs from 268 news articles. We observe that while humans are capable of detecting the nuances between claims, strong baselines struggle to detect them, showing over a 19% absolute gap with the humans. We hope this initial study could help readers to gain an unbiased grasp of contentious issues through machine-aided comparison.
Atmospheric recycling of volatiles by pebble-accreting planets

Planets, embedded in their natal discs, harbour hot envelopes. When pebbles are accreted by these planets, the contained volatile components may sublimate, enriching the envelope and potentially changing its thermodynamical properties. However, the envelopes of embedded planets actively exchange material with the disc, which would limit the buildup of a vapour-rich atmosphere. To properly investigate these processes, we have developed a new phase change module to treat the sublimation process with hydrodynamical simulations. Combined with the recently developed multidust fluid approach, we conduct 2D self-consistent hydrodynamic simulations to study how pebble sublimation influences the water content of super-Earths and sub-Neptunes. We find the extent and the amount of vapour that a planet is able to hold on to is determined by the relative size of the sublimation front and the atmosphere. When the sublimation front lies far inside the atmosphere, vapour tends to be locked deep in the atmosphere and keeps accumulating through a positive feedback mechanism. On the other hand, when the sublimation front exceeds the (bound) atmosphere, the ice component of incoming pebbles can be fully recycled and the vapour content reaches a low, steady value. Low disc temperature, small planet mass, and high pebble flux (omitting accretion heating by pebbles) render the planet atmosphere vapour-rich while the reverse changes render it vapour-poor. The phase change module introduced here can in future studies also be employed to model the chemical composition of the gas in the vicinity of accreting planets and around snowlines.
Enabling Spatial Digital Twins: Technologies, Challenges, and Future Research Directions

A Digital Twin (DT) is a virtual replica of a physical object or system, created to monitor, analyze, and optimize its behavior and characteristics. A Spatial Digital Twin (SDT) is a specific type of digital twin that emphasizes the geospatial aspects of the physical entity, incorporating precise location and dimensional attributes for a comprehensive understanding within its spatial environment. The current body of research on SDTs primarily concentrates on analyzing their potential impact and opportunities within various application domains. As building an SDT is a complex process and requires a variety of spatial computing technologies, it is not straightforward for practitioners and researchers of this multi-disciplinary domain to grasp the underlying details of enabling technologies of the SDT. In this paper, we are the first to systematically analyze different spatial technologies relevant to building an SDT in layered approach (starting from data acquisition to visualization). More specifically, we present the key components of SDTs into four layers of technologies: (i) data acquisition; (ii) spatial database management \&big data analytics systems; (iii) GIS middleware software, maps \&APIs; and (iv) key functional components such as visualizing, querying, mining, simulation and prediction. Moreover, we discuss how modern technologies such as AI/ML, blockchains, and cloud computing can be effectively utilized in enabling and enhancing SDTs. Finally, we identify a number of research challenges and opportunities in SDTs. This work serves as an important resource for SDT researchers and practitioners as it explicitly distinguishes SDTs from traditional DTs, identifies unique applications, outlines the essential technological components of SDTs, and presents a vision for their future development along with the challenges that lie ahead.
Turbulent relative dispersion

▪ Abstract This review begins with the classical foundations of relative dispersion in Kolmogorov's similarity scaling. Analysis of the special cases of isotropic and homogeneous scalar fields is then used to establish most simply the connection with turbulent mixing. The importance of the two-particle acceleration covariance in relative dispersion is demonstrated from the kinematics of the motion of particle-pairs. A summary of the development of two-particle Lagrangian stochastic models is given, with emphasis on the assumptions and constraints involved, and on predictions of the scalar variance field for inhomogeneous sources. Two-point closures and kinematic simulation are also reviewed in the context of their prediction of the Richardson constant and other fundamental constants. In the absence of reliable field data, direct numerical simulations and laboratory measurements seem most likely to provide suitable data with which to test the assumptions and predictions of these theories.
Fourier Mukai transforms and applications to string theory

erez Abstract. We give an introductory review of Fourier-Mukai transforms and their application to various aspects of moduli problems, string theory and mirror symmetry. We develop the necessary mathematical background for Fourier-Mukai transforms such as aspects of derived categories and integral functors as well as their relative version which becomes important for making precise the notion of fiberwise T- duality on elliptic Calabi-Yau threefolds. We discuss various applications of the Fourier-Mukai transform to D-branes on Calabi-Yau manifolds as well as homological mirror symmetry and the construction of vector bundles for heterotic string theory. Transformaciones de Fourier-Mukai y aplicaciones a la teor´ia de cuerdas