Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading

In this paper, we propose a novel method for speaker adaptation in lip reading, motivated by two observations. Firstly, a speaker's own characteristics can always be portrayed well by his/her few facial images or even a single image with shallow networks, while the fine-grained dynamic features associated with speech content expressed by the talking face always need deep sequential networks to represent accurately. Therefore, we treat the shallow and deep layers differently for speaker adaptive lip reading. Secondly, we observe that a speaker's unique characteristics ( e.g. prominent oral cavity and mandible) have varied effects on lip reading performance for different words and pronunciations, necessitating adaptive enhancement or suppression of the features for robust lip reading. Based on these two observations, we propose to take advantage of the speaker's own characteristics to automatically learn separable hidden unit contributions with different targets for shallow layers and deep layers respectively. For shallow layers where features related to the speaker's characteristics are stronger than the speech content related features, we introduce speaker-adaptive features to learn for enhancing the speech content features. For deep layers where both the speaker's features and the speech content features are all expressed well, we introduce the speaker-adaptive features to learn for suppressing the speech content irrelevant noise for robust lip reading. Our approach consistently outperforms existing methods, as confirmed by comprehensive analysis and comparison across different settings. Besides the evaluation on the popular LRW-ID and GRID datasets, we also release a new dataset for evaluation, CAS-VSR-S68h, to further assess the performance in an extreme setting where just a few speakers are available but the speech content covers a large and diversified range.
Reduced-order models of endocardial shear stress patterns in the left atrial appendage from a data-augmented patient-specific database.

BACKGROUND Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA), and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized. AIM Our goal was to develop reduced-order data-driven models of LAA flow patterns to discriminate between AF and non-AF flow phenotypes and discover explainable determinants of LAA blood stasis. METHODS We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on the universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF patients. RESULTS We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns between patients with and without AF, which correlated with blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could help evaluate each patient's current risk of LAA thrombosis and enable prediction of how atrial remodeling could modulate this future stroke risk. Such information could potentially be used to personalize anticoagulation therapy in AF patients.
La concentration géographique des industries françaises

[fre] La concentration geographique des industries francaises par Francoise Maurel et Beatrice Sedillot . L'objet de cet article est d'apprecier empiriquement la concentration geographique des industries francaises, a partir d'une modelisation des choix de localisation des etablissements suggeree par Ellison-Glaeser ( 1994). L' indicateur de concentration geographique utilise mesure la correlation entre les decisions d'implantation de deux etablissements d'un meme secteur et permet ainsi de cerner les externalites de localisation propres a une industrie. A cote des industries extractives et traditionnelles, certaines industries de haute technologie apparaissent fortement localisees. Par ailleurs, pour certaines industries, les avantages de l'agglomeration depassent les frontieres du departement et se mesurent a l'echelon de la region. [eng] Geographical Concentration of French Industries by Francoise Maurel and Beatrice Sedillot . This article uses a model of production location choices suggested by Ellison Glaeser (1994) to produce an empirical assessment of the geographical concentration of French industries. The geographical concentration indicator used measures the correlation between the location choices of two businesses in the same sector, thus enabling the specific externalities of location choices for a given industry to be identified. Like mining and traditional industries, some high-tech industries seem to be densely clustered in certain areas. The advantages of clustering for some industries reach beyond local administrative boundaries and extend to entire regions.
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution

Compared to CNN-based methods, Transformer-based methods achieve impressive image restoration outcomes due to their abilities to model remote dependencies. However, how to apply Transformer-based methods to the field of blind super-resolution (SR) and further make an SR network adaptive to degradation information is still an open problem. In this paper, we propose a new degradation-aware self-attention-based Transformer model, where we incorporate contrastive learning into the Transformer network for learning the degradation representations of input images with unknown noise. In particular, we integrate both CNN and Transformer components into the SR network, where we first use the CNN modulated by the degradation information to extract local features, and then employ the degradation-aware Transformer to extract global semantic features. We apply our proposed model to several popular large-scale benchmark datasets for testing, and achieve the state-of-the-art performance compared to existing methods. In particular, our method yields a PSNR of 32.43 dB on the Urban100 dataset at $\times$2 scale, 0.94 dB higher than DASR, and 26.62 dB on the Urban100 dataset at $\times$4 scale, 0.26 dB improvement over KDSR, setting a new benchmark in this area. Source code is available at: https://github.com/I2-Multimedia-Lab/DSAT/tree/main.
Secondary frequency control of islanded microgrid considering wind and solar stochastics

As the high penetration of wind and photovoltaic distributed generation (DG) in the microgrid, the stochastic and low inertia emerge, bringing more challenges especially when the microgrid operates in isolated islands. Nevertheless, the reserve power of DGs in deloading control mode can be utilized for frequency regulation and mitigating frequency excursion. This paper proposed a model predictive control (MPC) secondary frequency control method considering wind and solar power generation stochastics. The extended state-space matrix including unknown stochastic power disturbance is established, and a Kalman filter is used to observe the unknown disturbance. The maximum available power of wind and solar DGs is estimated for establishing real-time variable constraints that prevent DGs output power from exceeding the limits. Through setting proper weight coefficients, wind and photovoltaic DGs are given priority to participate in secondary frequency control. The distributed restorative power of each DG is obtained by solving the quadratic programming(QP) optimal problem with variable constraints. Finally, a microgrid simulation model including multiple PV and wind DGs is built and performed in various scenarios compared to the traditional secondary frequency control method. The simulation results validated that the proposed method can enhance the frequency recovery speed and reDGce the frequency deviation, especially in severe photovoltaic and wind fluctuations scenarios.
FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports

Dream11 takes pride in being a unique platform that enables over 190 million fantasy sports users to demonstrate their skills and connect deeper with their favorite sports. While managing such a scale, one issue we are faced with is duplicate/multiple account creation in the system. This is done by some users with the intent of abusing the platform, typically for bonus offers. The challenge is to detect these multiple accounts before it is too late. We propose a graph-based solution to solve this problem in which we first predict edges/associations between users. Using the edge information we highlight clusters of colluding multiple accounts. In this paper, we talk about our distributed ML system which is deployed to serve and support the inferences from our detection models. The challenge is to do this in real-time in order to take corrective actions. A core part of this setup also involves human-in-the-loop components for validation, feedback, and ground-truth labeling.
Cloning, deleting, and hiding in modal quantum theory

We examine the toy model of modal quantum theory (MQT), an analogue of actual quantum theory based on finite fields. In particular, we investigate how several essential ``no-go'' results (for cloning, deleting and hiding processes) work in MQT. Cloning and deleting are still forbidden in MQT, though the details of these results are somewhat different in the new context. However, the information of a modal qubit can be completely hidden in the correlations between two entangled modal qubits.
Climate forcing on avian life history

In the 21 century, human-induced global climate change has been highlighted as one of the most serious threats to ecosystems worldwide. According to global climate scenarios, the mean temperature in Finland is expected to increase by 1.8–4.0°C by the end of the century. The regional and seasonal change in temperature has predicted to be spatially and temporally asymmetric, where the High-Arctic and Antarctic areas and winter and spring seasons have been projected to face the highest temperature increase. To understand how species respond to the ongoing climate change, we need to study how climate affects species in different phases of their life cycle. The impact of climate on breeding and migration of eight large-sized bird species was studied in this thesis, taking food availability into account. The findings show that climatic variables have considerable impact on the life-history traits of large-sized birds in northern Europe. The magnitude of climatic effects on migration and breeding was comparable with that of food supply, conventionally regarded as the main factor affecting these life-history traits. Based on the results of this thesis and the current climate scenarios, the following not mutually exclusive responses are possible in the near future. Firstly, asymmetric climate change may result in a mistiming of breeding because mild winters and early spring may lead to earlier breeding, whereas offspring are hatching into colder conditions which elevate mortality. Secondly, climate induced responses can differ between species with different breeding tactics (income vs. capital breeding), so that especially capital breeders can gain advantage on global warming as they can sustain higher energy resources. Thirdly, increasing precipitation has the potential to reduce the breeding success of many species by exposing nestlings to more severe post-hatching conditions and hampering the hunting conditions of parents. Fourthly, decreasing ice cover and earlier ice-break in the Baltic Sea will allow earlier spring migration in waterfowl. In eiders, this can potentially lead to more productive breeding. Fifthly, warming temperatures can favour parents preparing for breeding and increase nestling survival. Lastly, the climate-induced phenological changes in life history events will likely continue. Furthermore, interactions between climate and food resources can be complex and interact with each other. Eiders provide an illustrative example of this complexity, being caught in the crossfire between more benign ice conditions and lower salinity negatively affecting their prime food resource. The general conclusion is that climate is controlling not only the phenology of the species but also their reproductive output, thus affecting the entire population dynamics.
Fair Classifiers that Abstain without Harm

In critical applications, it is vital for classifiers to defer decision-making to humans. We propose a post-hoc method that makes existing classifiers selectively abstain from predicting certain samples. Our abstaining classifier is incentivized to maintain the original accuracy for each sub-population (i.e. no harm) while achieving a set of group fairness definitions to a user specified degree. To this end, we design an Integer Programming (IP) procedure that assigns abstention decisions for each training sample to satisfy a set of constraints. To generalize the abstaining decisions to test samples, we then train a surrogate model to learn the abstaining decisions based on the IP solutions in an end-to-end manner. We analyze the feasibility of the IP procedure to determine the possible abstention rate for different levels of unfairness tolerance and accuracy constraint for achieving no harm. To the best of our knowledge, this work is the first to identify the theoretical relationships between the constraint parameters and the required abstention rate. Our theoretical results are important since a high abstention rate is often infeasible in practice due to a lack of human resources. Our framework outperforms existing methods in terms of fairness disparity without sacrificing accuracy at similar abstention rates.
Multi-wavelength observations of multiple eruptions of the recurrent nova M31N 2008-12a

We report the optical, UV, and soft X-ray observations of the $2017-2022$ eruptions of the recurrent nova M31N 2008-12a. We infer a steady decrease in the accretion rate over the years based on the inter-eruption recurrence period. We find a ``cusp'' feature in the $r'$ and $i'$ band light curves close to the peak, which could be associated to jets. Spectral modelling indicates a mass ejection of 10$^{-7}$ to 10$^{-8}$ M$_{\odot}$ during each eruption, and an enhanced Helium abundance of He/He$_{\odot}$ $\approx$ 3. The super-soft source (SSS) phase shows significant variability, which is anti-correlated to the UV emission, indicating a common origin. The variability could be due to the reformation of the accretion disk. A comparison of the accretion rate with different models on the $\rm M_{WD}$$-\dot{M}$ plane yields the mass of a CO WD, powering the ``H-shell flashes'' every $\sim$ 1 year to be $>1.36$ M$_{\odot}$ and growing with time, making M31N 2008-12a a strong candidate for the single degenerate scenario of Type Ia supernovae progenitor.
Categorical probability spaces, ergodic decompositions, and transitions to equilibrium

We study a category of probability spaces and measure-preserving Markov kernels up to almost sure equality. This category contains, among its isomorphisms, mod-zero isomorphisms of probability spaces. It also gives an isomorphism between the space of values of a random variable and the sigma-algebra that it generates on the outcome space, reflecting the standard mathematical practice of using the two interchangeably, for example when taking conditional expectations. We show that a number of constructions and results from classical probability theory, mostly involving notions of equilibrium, can be expressed and proven in terms of this category. In particular: - Given a stochastic dynamical system acting on a standard Borel space, we show that the almost surely invariant sigma-algebra can be obtained as a limit and as a colimit; - In the setting above, the almost surely invariant sigma-algebra gives rise, up to isomorphism of our category, to a standard Borel space; - As a corollary, we give a categorical version of the ergodic decomposition theorem for stochastic actions; - As an example, we show how de Finetti's theorem and the Hewitt-Savage and Kolmogorov zero-one laws fit in this limit-colimit picture. This work uses the tools of categorical probability, in particular Markov categories, as well as the theory of dagger categories.
Metadata-Conditioned Generative Models to Synthesize Anatomically-Plausible 3D Brain MRIs

Generative AI models hold great potential in creating synthetic brain MRIs that advance neuroimaging studies by, for example, enriching data diversity. However, the mainstay of AI research only focuses on optimizing the visual quality (such as signal-to-noise ratio) of the synthetic MRIs while lacking insights into their relevance to neuroscience. To gain these insights with respect to T1-weighted MRIs, we first propose a new generative model, BrainSynth, to synthesize metadata-conditioned (e.g., age- and sex-specific) MRIs that achieve state-of-the-art visual quality. We then extend our evaluation with a novel procedure to quantify anatomical plausibility, i.e., how well the synthetic MRIs capture macrostructural properties of brain regions, and how accurately they encode the effects of age and sex. Results indicate that more than half of the brain regions in our synthetic MRIs are anatomically accurate, i.e., with a small effect size between real and synthetic MRIs. Moreover, the anatomical plausibility varies across cortical regions according to their geometric complexity. As is, our synthetic MRIs can significantly improve the training of a Convolutional Neural Network to identify accelerated aging effects in an independent study. These results highlight the opportunities of using generative AI to aid neuroimaging research and point to areas for further improvement.
High Order Mimetic Symplectic Methods For Hamiltonian Systems

Hamiltonian systems are known to conserve the Hamiltonian function, which describes the energy evolution over time. Obtaining a numerical spatio-temporal scheme that accurately preserves the discretized Hamiltonian function is often a challenge. In this paper, the use of high order mimetic spatial schemes is investigated for the numerical solution of Hamiltonian equations. The mimetic operators are based on developing high order discrete analogs of the vector calculus quantities divergence and gradient. The resulting high order operators preserve the properties of their continuum ones, and are therefore said to mimic properties of conservation laws and symmetries. Symplectic fourth order schemes are implemented in this paper for the time integration of Hamiltonian systems. A theoretical framework for the energy preserving nature of the resulting schemes is also presented, followed by numerical examples.
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions

The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. Currently, only a few comments explicitly mention those policies -- 20% of the English ones, but as few as 2% of the German and Turkish comments. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. The dataset contains the stances of the editors (keep, delete, merge, comment), along with the stated reason, and a content moderation policy, for each edit decision. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process. We release both our joint prediction models and the multilingual content moderation dataset for further research on automated transparent content moderation.
Testing Higher-order Clusterability on graphs

Analysis of higher-order organizations, usually small connected subgraphs called motifs, is a fundamental task on complex networks. This paper studies a new problem of testing higher-order clusterability: given query access to an undirected graph, can we judge whether this graph can be partitioned into a few clusters of highly-connected motifs? This problem is an extension of the former work proposed by Czumaj et al. (STOC' 15), who recognized cluster structure on graphs using the framework of property testing. In this paper, a good graph cluster on high dimensions is first defined for higher-order clustering. Then, query lower bound is given for testing whether this kind of good cluster exists. Finally, an optimal sublinear-time algorithm is developed for testing clusterability based on triangles.
A Hall-type condition for path covers in bipartite graphs

Let $G$ be a bipartite graph with bipartition $(X,Y)$. Inspired by a hypergraph problem, we seek an upper bound on the number of disjoint paths needed to cover all the vertices of $X$. We conjecture that a Hall-type sufficient condition holds based on the maximum value of $|S|-|\mathsf\Lambda(S)|$, where $S\subseteq X$ and $\mathsf\Lambda(S)$ is the set of all vertices in $Y$ with at least two neighbors in $S$. This condition is also a necessary one for a hereditary version of the problem, where we delete vertices from $X$ and try to cover the remaining vertices by disjoint paths. The conjecture holds when $G$ is a forest, has maximum degree $3$, or is regular with high girth, and we prove those results in this paper.
A Corrected Expected Improvement Acquisition Function Under Noisy Observations

Sequential maximization of expected improvement (EI) is one of the most widely used policies in Bayesian optimization because of its simplicity and ability to handle noisy observations. In particular, the improvement function often uses the best posterior mean as the best incumbent in noisy settings. However, the uncertainty associated with the incumbent solution is often neglected in many analytic EI-type methods: a closed-form acquisition function is derived in the noise-free setting, but then applied to the setting with noisy observations. To address this limitation, we propose a modification of EI that corrects its closed-form expression by incorporating the covariance information provided by the Gaussian Process (GP) model. This acquisition function specializes to the classical noise-free result, and we argue should replace that formula in Bayesian optimization software packages, tutorials, and textbooks. This enhanced acquisition provides good generality for noisy and noiseless settings. We show that our method achieves a sublinear convergence rate on the cumulative regret bound under heteroscedastic observation noise. Our empirical results demonstrate that our proposed acquisition function can outperform EI in the presence of noisy observations on benchmark functions for black-box optimization, as well as on parameter search for neural network model compression.