Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive astrocytes associated with prion disease impair the blood brain barrier

Background Impairment of the blood-brain barrier (BBB) is considered to be a common feature among neurodegenerative diseases, including Alzheimer’s, Parkinson’s and prion diseases. In prion disease, increased BBB permeability was reported 40 years ago, yet the mechanisms behind the loss of BBB integrity have never been explored. Recently, we showed that reactive astrocytes associated with prion diseases are neurotoxic. The current work examines the potential link between astrocyte reactivity and BBB breakdown. Results In prion-infected mice, the loss of BBB integrity and aberrant localization of aquaporin 4 (AQP4), a sign of retraction of astrocytic endfeet from blood vessels, were noticeable prior to disease onset. Gaps in cell-to-cell junctions along blood vessels, together with downregulation of Occludin, Claudin-5 and VE-cadherin, which constitute tight and adherens junctions, suggested that loss of BBB integrity is linked with degeneration of vascular endothelial cells. In contrast to cells isolated from non-infected adult mice, endothelial cells originating from prion-infected mice displayed disease-associated changes, including lower levels of Occludin, Claudin-5 and VE-cadherin expression, impaired tight and adherens junctions, and reduced trans-endothelial electrical resistance (TEER). Endothelial cells isolated from non-infected mice, when co-cultured with reactive astrocytes isolated from prion-infected animals or treated with media conditioned by the reactive astrocytes, developed the disease-associated phenotype observed in the endothelial cells from prion-infected mice. Reactive astrocytes were found to produce high levels of secreted IL-6, and treatment of endothelial monolayers originating from non-infected animals with recombinant IL-6 alone reduced their TEER. Remarkably, treatment with extracellular vesicles produced by normal astrocytes partially reversed the disease phenotype of endothelial cells isolated from prion-infected animals. Conclusions To our knowledge, the current work is the first to illustrate early BBB breakdown in prion disease and to document that reactive astrocytes associated with prion disease are detrimental to BBB integrity. Moreover, our findings suggest that the harmful effects are linked to proinflammatory factors secreted by reactive astrocytes.
Multi-Task Pseudo-Label Learning for Non-Intrusive Speech Quality Assessment Model

This study proposes a multi-task pseudo-label learning (MPL)-based non-intrusive speech quality assessment model called MTQ-Net. MPL consists of two stages: obtaining pseudo-label scores from a pretrained model and performing multi-task learning. The 3QUEST metrics, namely Speech-MOS (S-MOS), Noise-MOS (N-MOS), and General-MOS (G-MOS), are the assessment targets. The pretrained MOSA-Net model is utilized to estimate three pseudo labels: perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (SDI). Multi-task learning is then employed to train MTQ-Net by combining a supervised loss (derived from the difference between the estimated score and the ground-truth label) and a semi-supervised loss (derived from the difference between the estimated score and the pseudo label), where the Huber loss is employed as the loss function. Experimental results first demonstrate the advantages of MPL compared to training a model from scratch and using a direct knowledge transfer mechanism. Second, the benefit of the Huber loss for improving the predictive ability of MTQ-Net is verified. Finally, the MTQ-Net with the MPL approach exhibits higher overall predictive power compared to other SSL-based speech assessment models.
Interpretable Graph Neural Networks for Tabular Data

Data in tabular format is frequently occurring in real-world applications. Graph Neural Networks (GNNs) have recently been extended to effectively handle such data, allowing feature interactions to be captured through representation learning. However, these approaches essentially produce black-box models, in the form of deep neural networks, precluding users from following the logic behind the model predictions. We propose an approach, called IGNNet (Interpretable Graph Neural Network for tabular data), which constrains the learning algorithm to produce an interpretable model, where the model shows how the predictions are exactly computed from the original input features. A large-scale empirical investigation is presented, showing that IGNNet is performing on par with state-of-the-art machine-learning algorithms that target tabular data, including XGBoost, Random Forests, and TabNet. At the same time, the results show that the explanations obtained from IGNNet are aligned with the true Shapley values of the features without incurring any additional computational overhead.
Bures geodesics and quantum metrology

We study the geodesics on the manifold of mixed quantum states for the Bures metric. It is shown that these geodesics correspond to physical non-Markovian evolutions of the system coupled to an ancilla. Furthermore, we argue that geodesics lead to optimal precision in single-parameter estimation in quantum metrology. More precisely, if the unknown parameter is a phase shift proportional to the time parametrizing the geodesic, the estimation error obtained by processing the data of measurements on the system is equal to the smallest error that can be achieved from joint detections on the system and ancilla, meaning that the ancilla does not carry any information on this parameter. The error can saturate the Heisenberg bound. In addition, the measurement on the system bringing most information on the parameter is parameter-independent and can be determined in terms of the intersections of the geodesic with the boundary of quantum states. These results show that geodesic evolutions are of interest for high-precision detections in systems coupled to an ancilla in the absence of measurements on the ancilla.
Simple predictors of 
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At optimal doping, different cuprate compounds can exhibit vastly different critical temperatures for superconductivity ($T_c$), ranging from about 20 K to about 135 K. The precise properties of the lattice that determine the magnitude of the $T_c$ are currently unknown. In this paper, we investigate the dependence of the optimal doping $T_c$ on the parameters of the Emery ($d-p$) model for the CuO$_2$ planes in the cuprates. We show that the best scaling is obtained not with the parameters of the model written in the real ($d/p$-orbital) space, but rather written in the space of effective Wannier orbitals. In this basis, one obtains a model of three sublattices coupled through all possible 4-point interactions. We identify multiple predictor variables that fit the experimental $T_c$ to about $\pm4-5$ K and that remarkably depend on the leading attractive coupling constants in the transformed Hamiltonian.
Latent State Models of Training Dynamics

The impact of randomness on model training is poorly understood. How do differences in data order and initialization actually manifest in the model, such that some training runs outperform others or converge faster? Furthermore, how can we interpret the resulting training dynamics and the phase transitions that characterize different trajectories? To understand the effect of randomness on the dynamics and outcomes of neural network training, we train models multiple times with different random seeds and compute a variety of metrics throughout training, such as the $L_2$ norm, mean, and variance of the neural network's weights. We then fit a hidden Markov model (HMM) over the resulting sequences of metrics. The HMM represents training as a stochastic process of transitions between latent states, providing an intuitive overview of significant changes during training. Using our method, we produce a low-dimensional, discrete representation of training dynamics on grokking tasks, image classification, and masked language modeling. We use the HMM representation to study phase transitions and identify latent"detour"states that slow down convergence.
Effectively Using and Interpreting Stability Tests

Determining the likelihood of an avalanche occurring on a given slope is a critical decision faced by both avalanche professionals and backcountry enthusiasts. An important part of that decision-making process is using stability tests and interpreting their results. Though a variety of stability tests are available, we use the rutschblock and stuffblock tests since they both identify weak layers in the snowpack and, to a limited extent, quantify the stresses necessary for weak layer failure. However, there are several limitations to these stability tests, including site selection and interpretation of results, both of which require experience. While site selection may be the most important limitation, we focus on the interpretation of results. Such interpretations can be problematic, since those without the benefit of avalanche experience often latch onto stability test values as an absolute indicator of stability. However, our experience indicates that occasionally dangerous avalanche conditions exist when stability tests show a stable snowpack and that sometimes the snowpack is more stable than , stability tests indicate. In order to partially address the difficulties in test interpretation, we have begun to qualitatively define the quality of the shear failure into three separate categories. This paper will focus on combining the interpretation of rutschblock and stuffblock tests, and describing the shear failure quality, thereby giving useful qualitative field information about the avalanche conditions in a given location. In the end, users must realize that stability tests do not provide a numerical description of the snow stability, but are instead just a piece of the puzzle for avalanche prediction.
On Lifting Integrality Gaps to SSEH Hardness for Globally Constrained CSPs

A $\mu$-constrained Boolean MAX-CSP $(\psi)$ instance is a Boolean Max-CSP instance on predicate $\psi:\{0,1\}^{r} \rightarrow\{0,1\}$ where the objective is to find a labeling of relative weight exactly $\mu$ that maximizes the fraction of satisfied constraints. In this work, we study the approximability of constrained Boolean Max-CSPs via SDP hierarchies by relating the integrality gap of $\operatorname{Max}-\operatorname{CSP}(\psi)$ to its $\mu$-dependent approximation curve. Formally, assuming the Small-Set Expansion Hypothesis, we show that it is NP-hard to approximate $\mu$-constrained instances of $\operatorname{MAX}-\operatorname{CSP}(\psi)$ up to factor $\operatorname{Gap}_{\ell, \mu}(\psi) / \log (1 / \mu)^{2}$ (ignoring factors depending on r) for any $\ell \geq \ell(\mu, r)$. Here, $\operatorname{Gap}_{\ell, \mu}(\psi)$ is the optimal integrality gap of $\ell$-round Lasserre relaxation for $\mu$-constrained $\operatorname{MAX}-\operatorname{CSP}(\psi)$ instances. Our results are derived by combining the framework of Raghavendra [STOC 2008] along with more recent advances in rounding Lasserre relaxations and reductions from the Small-Set Expansion (SSE) problem. A crucial component of our reduction is a novel way of composing generic bias-dependent dictatorship tests with SSE, which could be of independent interest.
On the $2$-Selmer group of Jacobians of hyperelliptic curves

Let $\mathcal{C}$ be a hyperelliptic curve $y^2 = p(x)$ defined over a number field $K$ with $p(x)$ integral of odd degree. The purpose of the present article is to prove lower and upper bounds for the $2$-Selmer group of the Jacobian of $\mathcal{C}$ in terms of the class group of the $K$-algebra $K[x]/(p(x))$. Our main result is a formula relating these two quantities under some mild hypothesis. We provide some examples that prove that our lower and upper bounds are as sharp as possible. As a first application, we study the rank distribution of the $2$-Selmer group in families of quadratic twists. Under some extra hypothesis we prove that among prime quadratic twists, a positive proportion has fixed $2$-Selmer group. As a second application, we study the family of octic twists of the genus $2$ curve $y^2 = x^5 + x$.
Non-equilibrium fluctuations for SEP($\alpha$) with open boundary

We analyze the non-equilibrium fluctuations of the partial symmetric simple exclusion process, SEP($\alpha$), which allows at most $\alpha \in \mathbb{N}$ particles per site, and we put it in contact with stochastic reservoirs whose strength is regulated by a parameter $\theta \in \mathbb{R}$. Setting $\alpha = 1$, we find the results of [22, 16, 17] and extend the known results to cover all range of $\theta$.
Language-enhanced RNR-Map: Querying Renderable Neural Radiance Field maps with natural language

We present Le-RNR-Map, a Language-enhanced Renderable Neural Radiance map for Visual Navigation with natural language query prompts. The recently proposed RNR-Map employs a grid structure comprising latent codes positioned at each pixel. These latent codes, which are derived from image observation, enable: i) image rendering given a camera pose, since they are converted to Neural Radiance Field; ii) image navigation and localization with astonishing accuracy. On top of this, we enhance RNR-Map with CLIP-based embedding latent codes, allowing natural language search without additional label data. We evaluate the effectiveness of this map in single and multi-object searches. We also investigate its compatibility with a Large Language Model as an "affordance query resolver". Code and videos are available at the link https://intelligolabs.github. io/Le-RNR-Map/.
Bayesian Variational Time-lapse Full-waveform Inversion

Time-lapse seismic full-waveform inversion (FWI) provides estimates of dynamic changes in the subsurface by performing multiple seismic surveys at different times. Since FWI problems are highly non-linear and non-unique, it is important to quantify uncertainties in such estimates to allow robust decision making. Markov chain Monte Carlo (McMC) methods have been used for this purpose, but due to their high computational cost, those studies often require an accurate baseline model and estimates of the locations of potential velocity changes, and neglect uncertainty in the baseline velocity model. Such detailed and accurate prior information is not always available in practice. In this study we use an efficient optimization method called stochastic Stein variational gradient descent (sSVGD) to solve time-lapse FWI problems without assuming such prior knowledge, and to estimate uncertainty both in the baseline velocity model and the velocity change. We test two Bayesian strategies: separate Bayesian inversions for each seismic survey, and a single join inversion for baseline and repeat surveys, and compare the methods with the standard linearised double difference inversion. The results demonstrate that all three methods can produce accurate velocity change estimates in the case of having fixed (exactly repeatable) acquisition geometries, but that the two Bayesian methods generate more accurate results when the acquisition geometry changes between surveys. Furthermore the joint inversion provides the most accurate velocity change and uncertainty estimates in all cases. We therefore conclude that Bayesian time-lapse inversion, especially adopting a joint inversion strategy, may be useful to image and monitor the subsurface changes, in particular where uncertainty in the results might lead to significantly different decisions.
Global well-posedness and refined regularity criterion for the uni-directional Euler-alignment system

We investigate global solutions to the Euler-alignment system in $d$ dimensions with unidirectional flows and strongly singular communication protocols $\phi(x) = |x|^{-(d+\alpha)}$ for $\alpha \in (0,2)$. Our paper establishes global regularity results in both the subcritical regime $1<\alpha<2$ and the critical regime $\alpha=1$. Notably, when $\alpha=1$, the system exhibits a critical scaling similar to the critical quasi-geostrophic equation. To achieve global well-posedness, we employ a novel method based on propagating the modulus of continuity. Our approach introduces the concept of simultaneously propagating multiple moduli of continuity, which allows us to effectively handle the system of two equations with critical scaling. Additionally, we improve the regularity criteria for solutions to this system in the supercritical regime $0<\alpha<1$.
Never-ending Learning of User Interfaces

Machine learning models have been trained to predict semantic information about user interfaces (UIs) to make apps more accessible, easier to test, and to automate. Currently, most models rely on datasets of static screenshots that are labeled by human annotators, a process that is costly and surprisingly error-prone for certain tasks. For example, workers labeling whether a UI element is “tappable” from a screenshot must guess using visual signifiers, and do not have the benefit of tapping on the UI element in the running app and observing the effects. In this paper, we present the Never-ending UI Learner, an app crawler that automatically installs real apps from a mobile app store and crawls them to infer semantic properties of UIs by interacting with UI elements, discovering new and challenging training examples to learn from, and continually updating machine learning models designed to predict these semantics. The Never-ending UI Learner so far has crawled for more than 5,000 device-hours, performing over half a million actions on 6,000 apps to train three computer vision models for i) tappability prediction, ii) draggability prediction, and iii) screen similarity.
Magnetoresistance anomaly during the electrical triggering of a metal-insulator transition

Phase separation naturally occurs in a variety of magnetic materials and it often has a major impact on both electric and magnetotransport properties. In resistive switching systems, phase separation can be created on demand by inducing local switching, which provides an opportunity to tune the electronic and magnetic state of the device by applying voltage. Here we explore the magnetotransport properties in the ferromagnetic oxide (La,Sr)MnO3 (LSMO) during the electrical triggering of an intrinsic metal-insulator transition (MIT) that produces volatile resistive switching. This switching occurs in a characteristic spatial pattern, i.e., the formation of an insulating barrier perpendicular to the current flow, enabling an electrically actuated ferromagnetic-paramagnetic-ferromagnetic phase separation. At the threshold voltage of the MIT triggering, both anisotropic and colossal magnetoresistances exhibit anomalies including a large increase in magnitude and a sign flip. Computational analysis revealed that these anomalies originate from the coupling between the switching-induced phase separation state and the intrinsic magnetoresistance of LSMO. This work demonstrates that driving the MIT material into an out-of-equilibrium resistive switching state provides the means to electrically control of the magnetotransport phenomena.
Anomalous shift and optical vorticity in the steady photovoltaic current

Steady illumination of a non-centrosymmetric semiconductor results in a bulk photovoltaic current, which is contributed by real-space displacements (`shifts') of charged quasiparticles as they transit between Bloch states. The shift induced by interband excitation via absorption of photons has received the prevailing attention. However, this excitation-induced shift can be far outweighed ($\ll$) by the shift induced by intraband relaxation, or by the shift induced by radiative recombination of electron-hole pairs. This outweighing ($\ll$) is attributed to (i) time-reversal-symmetric, intraband Berry curvature, which results in an anomalous shift of quasiparticles as they scatter with phonons, as well as to (ii) topological singularities in the interband Berry phase (`optical vortices'), which makes the photovoltaic current extraordinarily sensitive to the linear polarization vector of the light source. Both (i-ii) potentially lead to nonlinear conductivities of order $mAV^{-2}$, without finetuning of the incident radiation frequency, band gap, or joint density of states.
Self-Supervised Single-Image Deconvolution with Siamese Neural Networks

Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-off between sharpness and grain. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. Recently, self-supervised blind-spot neural networks were successfully adopted for image deconvolution by including a known point-spread function in the end-to-end training. However, their practical application has been limited to 2D images in the biomedical domain because it implies large kernels that are poorly optimized. We tackle this problem with Fast Fourier Transform convolutions that provide training speed-up in 3D microscopy deconvolution tasks. Further, we propose to adopt a Siamese invariance loss for deconvolution and empirically identify its optimal position in the neural network between blind-spot and full image branches. The experimental results show that our improved framework outperforms the previous state-of-the-art deconvolution methods with a known point spread function.
On the Value of Information Structures in Stochastic Games

This paper studies how improved monitoring affects the limit equilibrium payoff set for stochastic games with imperfect public monitoring. We introduce a simple generalization of Blackwell garbling called weighted garbling in order to compare different information structures for this class of games. Our main result is the monotonicity of the limit perfect public equilibrium (PPE) payoff set with respect to this information order: we show that the limit PPE payoff sets with one information structure is larger than the limit PPE payoff sets with another information structure state by state if the latter information structure is a weighted garbling of the former. We show that this monotonicity result also holds for the class of strongly symmetric equilibrium. Finally, we introduce and discuss another weaker sufficient condition for the expansion of limit PPE payoff set. It is more complex and difficult to verify, but useful in some special cases.
Yielding to percolation: a universal scale

A theoretical and computational study analysing the initiation of yield-stress fluid percolation in porous media is presented. Yield-stress fluid flows through porous media are complicated due to the nonlinear rheological behaviour of this type of fluid, rendering the conventional Darcy type approach invalid. A critical pressure gradient must be exceeded to commence the flow of a yield-stress fluid in a porous medium. As the first step in generalising the Darcy law for yield-stress fluids, a universal scale based on the variational formulation of the energy equation is derived for the critical pressure gradient which reduces to the purely geometrical feature of the porous media. The presented scaling is then validated by both exhaustive numerical simulations (using an adaptive finite element approach based on the augmented Lagrangian method), and also the previously published data. The considered porous media are constructed by randomised obstacles with various topologies; namely square, circular and alternatively polygonal obstacles which are mimicked based on Voronoi tessellation of circular cases. Moreover, computations for the bidispersed obstacle cases are performed which further demonstrate the validity of the proposed universal scaling.