Miracle-dz/UniFAR-FTUs huggingface dataset:
Few-shot Image Generation via Masked Discrimination

Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained on only a few images. The discriminator can easily remember all the training samples and guide the generator to replicate them, leading to severe diversity degradation. Several methods have been proposed to relieve overfitting by adapting GANs pre-trained on large source domains to target domains using limited real samples. This work presents a novel approach to realize few-shot GAN adaptation via masked discrimination. Random masks are applied to features extracted by the discriminator from input images. We aim to encourage the discriminator to judge various images which share partially common features with training samples as realistic. Correspondingly, the generator is guided to generate diverse images instead of replicating training samples. In addition, we employ a cross-domain consistency loss for the discriminator to keep relative distances between generated samples in its feature space. It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality. The effectiveness of our approach is demonstrated both qualitatively and quantitatively with higher quality and greater diversity on a series of few-shot image generation tasks than prior methods.
The formal verification of the ctm approach to forcing

We discuss some highlights of our computer-verified proof of the construction, given a countable transitive set-model $M$ of $\mathit{ZFC}$, of generic extensions satisfying $\mathit{ZFC}+\neg\mathit{CH}$ and $\mathit{ZFC}+\mathit{CH}$. Moreover, let $\mathcal{R}$ be the set of instances of the Axiom of Replacement. We isolated a 21-element subset $\Omega\subseteq\mathcal{R}$ and defined $\mathcal{F}:\mathcal{R}\to\mathcal{R}$ such that for every $\Phi\subseteq\mathcal{R}$ and $M$-generic $G$, $M\models \mathit{ZC} \cup \mathcal{F}\text{``}\Phi \cup \Omega$ implies $M[G]\models \mathit{ZC} \cup \Phi \cup \{ \neg \mathit{CH} \}$, where $\mathit{ZC}$ is Zermelo set theory with Choice. To achieve this, we worked in the proof assistant Isabelle, basing our development on the Isabelle/ZF library by L. Paulson and others.
Global-to-local Expression-aware Embeddings for Facial Action Unit Detection

Expressions and facial action units (AUs) are two levels of facial behavior descriptors. Expression auxiliary information has been widely used to improve the AU detection performance. However, most existing expression representations can only describe pre-determined discrete categories (e.g., Angry, Disgust, Happy, Sad, etc.) and cannot capture subtle expression transformations like AUs. In this paper, we propose a novel fine-grained \textsl{Global Expression representation Encoder} to capture subtle and continuous facial movements, to promote AU detection. To obtain such a global expression representation, we propose to train an expression embedding model on a large-scale expression dataset according to global expression similarity. Moreover, considering the local definition of AUs, it is essential to extract local AU features. Therefore, we design a \textsl{Local AU Features Module} to generate local facial features for each AU. Specifically, it consists of an AU feature map extractor and a corresponding AU mask extractor. First, the two extractors transform the global expression representation into AU feature maps and masks, respectively. Then, AU feature maps and their corresponding AU masks are multiplied to generate AU masked features focusing on local facial region. Finally, the AU masked features are fed into an AU classifier for judging the AU occurrence. Extensive experiment results demonstrate the superiority of our proposed method. Our method validly outperforms previous works and achieves state-of-the-art performances on widely-used face datasets, including BP4D, DISFA, and BP4D+.
Procontext: Exploring Progressive Context Transformer for Tracking

Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as it cannot account for changes in object appearance between frames. To this end, we revamped the tracking framework with Progressive Context Encoding Transformer Tracker (ProContEXT), which coherently exploits spatial and temporal contexts to predict object motion trajectories. Specifically, ProContEXT leverages a context-aware self-attention module to encode the spatial and temporal context, refining and updating the multi-scale static and dynamic templates to progressively perform accurately tracking. It explores the complementary between spatial and temporal context, raising a new pathway to multi-context modeling for transformer-based trackers. In addition, ProContEXT revised the token pruning technique to reduce computational complexity. Extensive experiments on popular benchmark datasets such as GOT-10k and TrackingNet demonstrate that the proposed ProContEXT achieves state-of-the-art performance1.
Risk-Averse Model Predictive Control for Priced Timed Automata

In this paper, we propose a Risk-Averse Priced Timed Automata (PTA) Model Predictive Control (MPC) framework to increase flexibility of cyber-physical systems. To improve flexibility in these systems, our risk-averse framework solves a multi-objective optimization problem to minimize the cost and risk, simultaneously. While minimizing cost ensures the least effort to achieve a task, minimizing risk provides guarantees on the feasibility of the task even during uncertainty. Our framework explores the trade-off between these two qualities to obtain risk-averse control actions. The solution of risk-averse PTA MPC dynamic decision-making algorithm reacts relatively better to PTA changes compared to PTA MPC without risk-averse feature. An example from manufacturing systems is presented to show the application of the proposed control strategy.
Multimodal Transformer Distillation for Audio-Visual Synchronization

Audio-visual synchronization aims to determine whether the mouth movements and speech in the video are synchronized. VocaLiST reaches state-of-the-art performance by incorporating multimodal Transformers to model audio-visual interact information. However, it requires high computing resources, making it impractical for real-world applications. This paper proposed an MTDVocaLiST model, which is trained by our proposed multimodal Transformer distillation (MTD) loss. MTD loss enables MTDVocaLiST model to deeply mimic the cross-attention distribution and value-relation in the Transformer of VocaLiST. Additionally, we harness uncertainty weighting to fully exploit the interaction information across all layers. Our proposed method is effective in two aspects: From the distillation method perspective, MTD loss outperforms other strong distillation baselines. From the distilled model's performance perspective: 1) MTDVocaLiST outperforms similar-size SOTA models, SyncNet, and Perfect Match models by 15.65% and 3.35%; 2) MTDVocaLiST reduces the model size of VocaLiST by 83.52%, yet still maintaining similar performance.
The pseudogap: friend or foe of high T c ?

Although nineteen years have passed since the discovery of high temperature cuprate superconductivity 1, there is still no consensus on its physical origin. This is in large part because of a lack of understanding of the state of matter out of which the superconductivity arises. In optimally and underdoped materials, this state exhibits a pseudogap at temperatures large compared to the superconducting transition temperature 2, 3. Although discovered only three years after the pioneering work of Bednorz and Müller, the physical origin of this pseudogap behavior and whether it constitutes a distinct phase of matter is still shrouded in mystery. In the summer of 2004, a band of physicists gathered for five weeks at the Aspen Center for Physics to discuss the pseudogap. In this perspective, we would like to summarize some of the results presented there and discuss the importance of the pseudogap phase in the context of strongly correlated electron systems. Contents PAGE Acknowledgments 730 References 730
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts

Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and text relevancy. In this paper, we propose ERNIE-ViLG 2.0, a large-scale Chinese text-to-image diffusion model, to progressively upgrade the quality of generated images by: (1) incorporating fine-grained textual and visual knowledge of key elements in the scene, and (2) utilizing different denoising experts at different denoising stages. With the proposed mechanisms, ERNIE-ViLG 2.01 not only achieves a new state-of-the-art on MS-COCO with zero-shot FID-30k score of 6.75, but also significantly outperforms recent models in terms of image fidelity and image-text alignment, with side-by-side human evaluation on the bilingual prompt set ViLG-300.
Courbure totale des hypersurfaces algébriques réelles et patchwork

Cette these est consacree a l'etude des hypersurfaces algebriques reelles plongees dans l'espace euclidien de dimension m. Risler, inspire de la borne d'Harnack sur le nombre de composantes connexes d'une courbe algebrique reelle de degre n, a montre que la courbure totale des hypersurfaces alggbriques reelles est borne en fonction de son degre ou plus, generalement en fonction de son polygone de Newton. Plus tard, il a generalise cette borne au cas des hypersurfaces algebriques de dimension quelconque. En utilisant la methode de Viro, on construit des familles a un parametre d'hyperersurfaces algebriquesreelles telles que la courbure totale de ces hypersurfaces tend versla borne de Risler quand le parametre tend vers zero. En particulier, on voit les cas des T-hypersurfaces qui sont des hypersurfaces construites avec la methode de Viro pour lesquelles la decomposition utilisee est simpliciale et on montre que d'une certaine facon les T-hypersurfaces sont qeneriquement « maximales » par rapport a la Courbure totale. La maniere de les construire est inspiree de la geometrie integrale: on introduit ainsi une version de la theorie de Morse dans la methode de Viro, et on fait l'etude des points critiques de l' hypersurface par rapport au pinceau d'hyperplans paralleles a un hyperplan de coordonnees. On montre en particulier que la borne de Risler est optimale, pour tout dimension et tout degre, non seulement pour les hypersurfaces algebriques reelles mais aussi pour les T-hypersurfaces et que les hypersurfaces algebriques reelles "maximales" par rapport a la Courbure totale ont moins de restrictionstopologiques que ce qu'on attendait.
Neural Augmented Kalman Filtering with Bollinger Bands for Pairs Trading

Pairs trading is a family of trading techniques that determine their policies based on monitoring the relationships between pairs of assets. A common pairs trading approach relies on describing the pair-wise relationship as a linear Space State (SS) model with Gaussian noise. This representation facilitates extracting financial indicators with low complexity and latency using a Kalman Filter (KF), that are then processed using classic policies such as Bollinger Bands (BB). However, such SS models are inherently approximated and mismatched, often degrading the revenue. In this work, we propose KalmenNet-aided Bollinger bands Pairs Trading (KBPT), a deep learning aided policy that augments the operation of KF-aided BB trading. KBPT is designed by formulating an extended SS model for pairs trading that approximates their relationship as holding partial co-integration. This SS model is utilized by a trading policy that augments KF-BB trading with a dedicated neural network based on the KalmanNet architecture. The resulting KBPT is trained in a two-stage manner which first tunes the tracking algorithm in an unsupervised manner independently of the trading task, followed by its adaptation to track the financial indicators to maximize revenue while approximating BB with a differentiable mapping. KBPT thus leverages data to overcome the approximated nature of the SS model, converting the KF-BB policy into a trainable model. We empirically demonstrate that our proposed KBPT systematically yields improved revenue compared with model-based and data-driven benchmarks over various different assets.
Effective field theory for gravitational radiation in scalar-tensor gravity

A light scalar degree of freedom, as the one possibly responsible for the accelerated expansion of the Universe, could leave observable traces in the inspiral gravitational wave signal of binary systems. In order to study these effects, we extend the effective field theory formalism of Goldberger and Rothstein to minimal scalar-tensor theories of gravity. This class of models is still very broad, because the couplings of the scalar to matter are far less constrained than those a massless spin-2 field. In most of the paper we focus on conformal couplings. Using the effective field theory approach, we discuss the emergence of violations of the strong equivalence principle even in models that exhibit universality of free fall at the microscopic level. Our results on the conservative dynamics of the binary and its power emitted in gravitational and scalar radiation agree with those obtained with the standard post-Newtonian formalism. We also compare them to more recent work. Finally, we discuss the implications of considering a disformal coupling to matter.
A Type of Unifying Relations for Berends-Giele Currents in (A)dS

Unifying relations of amplitudes are elegant results in ﬂat spacetime, but the research on these in (A)dS case is not very rich. In this paper, we discuss a type of unifying relations in (A)dS by using Berends-Giele currents. By taking the ﬂat limit, we also get a semi-on-shell way to prove the unifying relations in the ﬂat case. We also discuss the applications of our results in cosmology.
Hessian spectrum at the global minimum and topology trivialization of locally isotropic Gaussian random fields

We study the energy landscape near the ground state of a model of a single particle in a random potential with trivial topology. More precisely, we find the large dimensional limit of the Hessian spectrum at the global minimum of the Hamiltonian $X_N(x) +\frac\mu2 \|x\|^2, x\in\rz^N,$ when $\mu$ is above the phase transition threshold so that the system has only one critical point. Here $X_N$ is a locally isotropic Gaussian random field. The same idea is also applied to study the more general model of elastic manifold. In the replica symmetric regime, our results confirm the predictions of Fyodorov and Le Doussal made in 2018 and 2020 using the replica method.
Painting the black box white: experimental findings from applying XAI to an ECG reading setting

The emergence of black-box, subsymbolic, and statistical AI systems has motivated a rapid increase in the interest regarding explainable AI (XAI), which encompasses both inherently explainable techniques, as well as approaches to make black-box AI systems explainable to human decision makers. Rather than always making black boxes transparent, these approaches are at risk of painting the black boxes white, thus failing to provide a level of transparency that would increase the system’s usability and comprehensibility, or even at risk of generating new errors (i.e., white-box paradox). To address these usability-related issues, in this work we focus on the cognitive dimension of users’ perception of explanations and XAI systems. We investigated these perceptions in light of their relationship with users’ characteristics (e.g., expertise) through a questionnaire-based user study involved 44 cardiology residents and specialists in an AI-supported ECG reading task. Our results point to the relevance and correlation of the dimensions of trust, perceived quality of explanations, and tendency to defer the decision process to automation (i.e., technology dominance). This contribution calls for the evaluation of AI-based support systems from a human–AI interaction-oriented perspective, laying the ground for further investigation of XAI and its effects on decision making and user experience.
Anharmonic multiphonon origin of the valence plasmon in SrTi1-xNbxO3

Doped SrTi 1 − x Nb x O 3 exhibits superconductivity and a mid-infrared optical response reminiscent of copper-oxide superconductors. Strangely, its plasma frequency, ω p , increases by a factor of ∼ 3 when cooling from 300K to 20K, without any accepted explanation. Here, we present momentum-resolved electron energy loss spectroscopy (M-EELS) measurements of SrTi 1 − x Nb x O 3 at nonzero momentum, q . We ﬁnd that the IR feature previously identiﬁed as a plasmon is present at large q in insulating SrTiO 3 , where it exhibits the same temperature dependence and may be identiﬁed as an anharmonic, multiphonon background. Doping with Nb increases its peak energy and total spectral weight, drawing this background to lower q where it becomes visible in IR optics experiments. We conclude that the “plasmon” in doped SrTi 1 − x Nb x O 3 is not a free-carrier mode, but a composite excitation that inherits its properties from the lattice anharmonicity of the insulator.
Towards Language-centric Scientiﬁc AI

Inspired by recent and revolutionary developments in AI, particularly in language understanding and generation, we set about designing AI systems that are able to address complex scientiﬁc tasks that challenge human capabilities to make new discoveries. Central to our approach is the notion of natural language as core representation, reasoning, and exchange format between sci-entiﬁc AI and human scientists. In this paper, we identify and discuss some of the main research challenges to accomplish such vision.
Strategic Geosteeering Workflow with Uncertainty Quantification and Deep Learning: A Case Study on the Goliat Field

The real-time interpretation of the logging-while-drilling data allows us to estimate the positions and properties of the geological layers in an anisotropic subsurface environment. Robust real-time estimations capturing uncertainty can be very useful for efficient geosteering operations. However, the model errors in the prior conceptual geological models and forward simulation of the measurements can be significant factors in the unreliable estimations of the profiles of the geological layers. The model errors are specifically pronounced when using a deep-neural-network (DNN) approximation which we use to accelerate and parallelize the simulation of the measurements. This paper presents a practical workflow consisting of offline and online phases. The offline phase includes DNN training and building of an uncertain prior near-well geo-model. The online phase uses the flexible iterative ensemble smoother (FlexIES) to perform real-time assimilation of extra-deep electromagnetic data accounting for the model errors in the approximate DNN model. We demonstrate the proposed workflow on a case study for a historic well in the Goliat Field (Barents Sea). The median of our probabilistic estimation is on-par with proprietary inversion despite the approximate DNN model and regardless of the number of layers in the chosen prior. By estimating the model errors, FlexIES automatically quantifies the uncertainty in the layers' boundaries and resistivities, which is not standard for proprietary inversion.
Exhuming nonnegative garrote from oblivion using suitable initial estimates- illustration in low and high-dimensional real data

Summary The nonnegative garrote (NNG) is among the first approaches that combine variable selection and shrinkage of regression estimates. When more than the derivation of a predictor is of interest, NNG has some conceptual advantages over the popular lasso. Nevertheless, NNG has received little attention. The original NNG relies on least-squares (OLS) estimates, which are highly variable in data with a high degree of multicollinearity (HDM) and do not exist in high-dimensional data (HDD). This might be the reason that NNG is not used in such data. Alternative initial estimates have been proposed but hardly used in practice. Analyzing three structurally different data sets, we demonstrated that NNG can also be applied in HDM and HDD and compared its performance with the lasso, adaptive lasso, relaxed lasso, and best subset selection in terms of variables selected, regression estimates, and prediction. Replacing OLS by ridge initial estimates in HDM and lasso initial estimates in HDD helped NNG select simpler models than competing approaches without much increase in prediction errors. Simpler models are easier to interpret, an important issue for descriptive modelling. Based on the limited experience from three datasets, we assume that the NNG can be a suitable alternative to the lasso and its extensions. Neutral comparison simulation studies are needed to better understand the properties of variable selection methods, compare them and derive guidance for practice.
Ca ii H and K Chromospheric Emission Lines in Late‐K and M Dwarfs

We have measured the profiles of the Ca ii H and K chromospheric emission lines in 147 main‐sequence stars of spectral type M5–K7 (masses 0.30–0.55 M⊙) using multiple high‐resolution spectra obtained during 6 years with the HIRES spectrometer on the Keck I telescope. Remarkably, the average FWHM, equivalent widths, and line luminosities of Ca ii H and K increase by a factor of 3 with increasing stellar mass over this small range of stellar masses. We fit the Ca ii H and K lines with a double‐Gaussian model to represent both the chromospheric emission and the non‐LTE central absorption. Most of the sample stars display a central absorption that is typically redshifted by ∼0.1 km s−1 relative to the emission. This implies that the higher level, lower density chromospheric material has a smaller outward velocity (or higher inward velocity) by 0.1 km s−1 than the lower level material in the chromosphere, but the nature of this velocity gradient remains unknown. The FWHM of the Ca ii H and K emission lines increase with stellar luminosity, reminiscent of the Wilson‐Bappu effect in FGK‐type stars. Both the equivalent widths and FWHM exhibit modest temporal variability in individual stars. At a given value of MV, stars exhibit a spread in both the equivalent width and FWHM of Ca ii H and K, due both to a spread in fundamental stellar parameters, including rotation rate, age, and possibly metallicity, and to the spread in stellar mass at a given MV. The K line is consistently wider than the H line, as expected, and its central absorption is more redshifted, indicating that the H and K lines form at slightly different heights in the chromosphere where the velocities are slightly different. The equivalent width of Hα correlates with Ca ii H and K only for stars having Ca ii equivalent widths above ∼2 Å, suggesting the existence of a magnetic threshold above which the lower and upper chromospheres become thermally coupled.
A stochastic analysis of subcritical Euclidean fermionic field theories

Building on previous work on the stochastic analysis for Grassmann random variables, we introduce a forward-backward stochastic diﬀerential equation (FBSDE) which provides a stochastic quantisation of Grassmann measures. Our method is inspired by the so-called continuous renormalisation group, but avoids the technical diﬃculties encountered in the direct study of the ﬂow equation for the eﬀective potentials. As an application, we construct a family of weakly coupled subcritical Euclidean fermionic ﬁeld theories and prove exponential decay of correlations.