Miracle-dz/UniFAR-FTUs huggingface dataset:
A Universal Theory of Spin Squeezing

We provide extensive numerical and analytic evidence for the following conjecture: Any Hamiltonian exhibiting finite temperature, easy-plane ferromagnetism (XY order) can be used to generate scalable spin squeezing, and thus to perform quantum-enhanced sensing. Our conjecture is guided by a deep connection between the quantum Fisher information of pure states and the spontaneous breaking of a continuous symmetry. We demonstrate that spin-squeezing exhibits a phase diagram with a sharp transition between scalable squeezing and non-squeezing. This transition coincides with the equilibrium phase boundary for XY order at a finite temperature. In the scalable squeezing phase, we predict a sensitivity scaling as $N^{-7/10}$, between the standard quantum limit, $N^{-1/2}$, and that achieved in all-to-all coupled easy-plane spin models, $N^{-5/6}$. Our results provide fundamental insight into the landscape of Hamiltonians that can be used to generate metrologically useful quantum states.
Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

To tackle the global climate challenge, it urgently needs to develop a collaborative platform for comprehensive weather forecasting on large-scale meteorological data. Despite urgency, heterogeneous meteorological sensors across countries and regions, inevitably causing multivariate heterogeneity and data exposure, become the main barrier. This paper develops a foundation model across regions capable of understanding complex meteorological data and providing weather forecasting. To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data. Moreover, a novel prompt learning mechanism has been adopted to satisfy low-resourced sensors' communication and computational constraints. The effectiveness of the proposed method has been demonstrated on classical weather forecasting tasks using three meteorological datasets with multivariate time series.
ECGAN: Self-supervised generative adversarial network for electrocardiography

High-quality synthetic data can support the development of effective predictive models for biomedical tasks, especially in rare diseases or when subject to compelling privacy constraints. These limitations, for instance, negatively impact open access to electrocardiography datasets about arrhythmias. This work introduces a self-supervised approach to the generation of synthetic electrocardiography time series which is shown to promote morphological plausibility. Our model (ECGAN) allows conditioning the generative process for specific rhythm abnormalities, enhancing synchronization and diversity across samples with respect to literature models. A dedicated sample quality assessment framework is also defined, leveraging arrhythmia classifiers. The empirical results highlight a substantial improvement against state-of-the-art generative models for sequences and audio synthesis.
Multifunctional nanostructures for intracellular delivery and sensing in electrogenic cells

In electrophysiology, multielectrode array devices (MEA) are the gold standard for the study of large ensambles of electrogenic cells. In the last decades, thanks to the adoption of nanotechnologies, the study of physiological and pathological conditions of electro-active cells in culture have becomes increasingly accurate. In parallel, studies exploited the integration of nanostructures with delivering capabilities with single-cell specificity and high throughput in biosensing platforms. Delivery and recording have independently led to great advances in neurobiology, however, their integration on a single chip would give complete insights into pathologies development and fundamental advancements in drug screening methods. In this work, we demonstrate how a microfluidic-MEA technology may be used to record both spontaneous and chemically induced activity in vitro. We propose a device that can deliver molecules to only a few chosen cells and detecting the response in cellular activity at multiple sites simultaneously. In addition, will be discussed how the adoption of nanoporous metamaterial in place of nanostructures might lower costs and speed up production. Furthermore, this same material, will be identified for the first time in this work as photoelectrical modulating material for eliciting electrogenic cells firing activity. Specifically, by converting NIR laser pulses into stimulatory currents, plasmonic metamaterials may be employed to induce action potentials. This method enables remote access to optical pacing with precise spatiotemporal control, allowing to be used as a valid alternative of the traditional genetic-based optical stimulation techniques. Therefore, in addition to pharmaceutical applications, these final characteristics may pave the way for a new generation of minimally invasive, cellular type-independent all-optical plasmonic pacemakers and muscle actuators.
On some gateways between sum rules

We present correspondences induced by some classical mappings between measures on an interval and measures on the unit circle. More pre- cisely, we link their sequences of orthogonal polynomial and their recursion coeﬃcients. We also deduce some correspondences between particular equi- librium measures of random matrix ensembles. Additionally, we show that these mappings open up gateways between the sum rules associated with some classical models, leading to new formulations of several sum rules.
Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of"getting closer to"the long-term fairness goal and present possibility and impossibility results accordingly.
Characterization and Learning of Causal Graphs with Small Conditioning Sets

Constraint-based causal discovery algorithms learn part of the causal graph structure by systematically testing conditional independences observed in the data. These algorithms, such as the PC algorithm and its variants, rely on graphical characterizations of the so-called equivalence class of causal graphs proposed by Pearl. However, constraint-based causal discovery algorithms struggle when data is limited since conditional independence tests quickly lose their statistical power, especially when the conditioning set is large. To address this, we propose using conditional independence tests where the size of the conditioning set is upper bounded by some integer $k$ for robust causal discovery. The existing graphical characterizations of the equivalence classes of causal graphs are not applicable when we cannot leverage all the conditional independence statements. We first define the notion of $k$-Markov equivalence: Two causal graphs are $k$-Markov equivalent if they entail the same conditional independence constraints where the conditioning set size is upper bounded by $k$. We propose a novel representation that allows us to graphically characterize $k$-Markov equivalence between two causal graphs. We propose a sound constraint-based algorithm called the $k$-PC algorithm for learning this equivalence class. Finally, we conduct synthetic, and semi-synthetic experiments to demonstrate that the $k$-PC algorithm enables more robust causal discovery in the small sample regime compared to the baseline algorithms.
Real-Time Simultaneous Localization and Mapping with LiDAR Intensity

We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method, which addresses the geometry degeneracy problem in un-structured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.
Explainable Multilayer Graph Neural Network for cancer gene prediction

Abstract Motivation The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene–gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene–gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Results Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision–recall curve over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research. Availability and implementation Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.
Recurrent Generic Contour-based Instance Segmentation with Progressive Learning

Contour-based instance segmentation has been actively studied, thanks to its flexibility and elegance in processing visual objects within complex backgrounds. In this work, we propose a novel deep network architecture, i.e., PolySnake, for generic contour-based instance segmentation. Motivated by the classic Snake algorithm, the proposed PolySnake achieves superior and robust segmentation performance with an iterative and progressive contour refinement strategy. Technically, PolySnake introduces a recurrent update operator to estimate the object contour iteratively. It maintains a single estimate of the contour that is progressively deformed toward the object boundary. At each iteration, PolySnake builds a semantic-rich representation for the current contour and feeds it to the recurrent operator for further contour adjustment. Through the iterative refinements, the contour progressively converges to a stable status that tightly encloses the object instance. Beyond the scope of general instance segmentation, extensive experiments are conducted to validate the effectiveness and generalizability of our PolySnake in two additional specific task scenarios, including scene text detection and lane detection. The results demonstrate that the proposed PolySnake outperforms the existing advanced methods on several multiple prevalent benchmarks across the three tasks. The codes and pre-trained models are available at https://github.com/fh2019ustc/PolySnake
Towards Flexibility and Interpretability of Gaussian Process State-Space Model

The Gaussian process state-space model (GPSSM) has garnered considerable attention over the past decade. However, the standard GP with a preliminary kernel, such as the squared exponential kernel or Mat\'{e}rn kernel, that is commonly used in GPSSM studies, limits the model's representation power and substantially restricts its applicability to complex scenarios. To address this issue, we propose a new class of probabilistic state-space models called TGPSSMs, which leverage a parametric normalizing flow to enrich the GP priors in the standard GPSSM, enabling greater flexibility and expressivity. Additionally, we present a scalable variational inference algorithm that offers a flexible and optimal structure for the variational distribution of latent states. The proposed algorithm is interpretable and computationally efficient due to the sparse GP representation and the bijective nature of normalizing flow. Moreover, we incorporate a constrained optimization framework into the algorithm to enhance the state-space representation capabilities and optimize the hyperparameters, leading to superior learning and inference performance. Experimental results on synthetic and real datasets corroborate that the proposed TGPSSM outperforms several state-of-the-art methods. The accompanying source code is available at \url{https://github.com/zhidilin/TGPSSM}.
Cosmic chronometers to calibrate the ladders and measure the curvature of the Universe. A model-independent study

We use the state-of-the-art data on cosmic chronometers (CCH) and the Pantheon+ compilation of supernovae of Type Ia (SNIa) to test the constancy of the SNIa absolute magnitude, M, and the robustness of the cosmological principle (CP) at z ≲ 2 with a model-agnostic approach. We do so by reconstructing M(z) and the curvature parameter Ωk(z) using Gaussian Processes. Moreover, we use CCH in combination with data on baryon acoustic oscillations (BAO) from various galaxy surveys (6dFGS, BOSS, eBOSS, WiggleZ, DES Y3) to measure the sound horizon at the baryon-drag epoch, rd, from each BAO data point and check their consistency. Given the precision allowed by the CCH, we find that M(z), Ωk(z) and rd(z) are fully compatible (at $<68~{{\%}}$ C.L.) with constant values. This justifies our final analyses, in which we put constraints on these constant parameters under the validity of the CP, the metric description of gravity and standard physics in the vicinity of the stellar objects, but otherwise in a model-independent way. If we exclude the SNIa contained in the host galaxies employed by SH0ES, our results read $M=(-19.314^{+0.086}_{-0.108})$ mag, rd = (142.3 ± 5.3) Mpc and $\Omega _k=-0.07^{+0.12}_{-0.15}$, with H0 = (71.5 ± 3.1) km s−1 Mpc−1 (68% C.L.). These values are independent from the main data sets involved in the H0 tension, namely, the cosmic microwave background and the first two rungs of the cosmic distance ladder. If, instead, we also consider the SNIa in the host galaxies, calibrated with Cepheids, we measure $M=(-19.252^{+0.024}_{-0.036})$ mag, $r_d=(141.9^{+5.6}_{-4.9})$ Mpc, $\Omega _k=-0.10^{+0.12}_{-0.15}$ and $H_0=(74.0^{+0.9}_{-1.0})$ km s−1 Mpc−1.
On Investigating the Conservative Property of Score-Based Generative Models

Existing Score-Based Models (SBMs) can be categorized into constrained SBMs (CSBMs) or unconstrained SBMs (USBMs) according to their parameterization approaches. CSBMs model probability density functions as Boltzmann distributions, and assign their predictions as the negative gradients of some scalar-valued energy functions. On the other hand, USBMs employ flexible architectures capable of directly estimating scores without the need to explicitly model energy functions. In this paper, we demonstrate that the architectural constraints of CSBMs may limit their modeling ability. In addition, we show that USBMs' inability to preserve the property of conservativeness may lead to degraded performance in practice. To address the above issues, we propose Quasi-Conservative Score-Based Models (QCSBMs) for keeping the advantages of both CSBMs and USBMs. Our theoretical derivations demonstrate that the training objective of QCSBMs can be efficiently integrated into the training processes by leveraging the Hutchinson's trace estimator. In addition, our experimental results on the CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets validate the effectiveness of QCSBMs. Finally, we justify the advantage of QCSBMs using an example of a one-layered autoencoder.
Multiscale mobility patterns and the restriction of human movement

From the perspective of human mobility, the COVID-19 pandemic constituted a natural experiment of enormous reach in space and time. Here, we analyse the inherent multiple scales of human mobility using Facebook Movement maps collected before and during the first UK lockdown. Firstly, we obtain the pre-lockdown UK mobility graph and employ multiscale community detection to extract, in an unsupervised manner, a set of robust partitions into flow communities at different levels of coarseness. The partitions so obtained capture intrinsic mobility scales with better coverage than nomenclature of territorial units for statistics (NUTS) regions, which suffer from mismatches between human mobility and administrative divisions. Furthermore, the flow communities in the fine-scale partition not only match well the UK travel to work areas but also capture mobility patterns beyond commuting to work. We also examine the evolution of mobility under lockdown and show that mobility first reverted towards fine-scale flow communities already found in the pre-lockdown data, and then expanded back towards coarser flow communities as restrictions were lifted. The improved coverage induced by lockdown is well captured by a linear decay shock model, which allows us to quantify regional differences in both the strength of the effect and the recovery time from the lockdown shock.
A multiview microwave imaging system for two-dimensional penetrable objects

A microwave imaging system is based on a multiview numerical solution to the integral equation of 2D transverse magnetic (TM) scattering is proposed. This solution is achieved by the moment method, and a pseudoinversion transformation is used to face ill-conditioning problems. All experimental setup is described that uses a scanning subsystem for measuring the values of the scattered electric field inside an observation domain located outside the investigation one (i.e., the area containing the cross sections of cylindrical dielectric scatters). Rotations of the investigation domain with respect to the scanning subsystem and the transmitting antenna allow a multiview imaging process. The imaging system does not require plane-wave illumination and does not use any first-order approximations; hence, it may be used even in the case of strong scatterers. The offline and once-and-for-all computation of the pseudoinverse matrix allows an inexpensive reconstruction in terms of computer resources. Some tests of the system were carried out, and the results are reported. >
Activity in regions sensitive to auditory speech is modified during speech production: fMRI evidence for an efference copy

Models of speech production postulate that, in order to facilitate rapid and precise control of articulation, the predicted auditory feedback is sent to the auditory system to be compared with incoming sensory data. If this is so, an ‘‘error’’ signal may be observed when the predicted auditory feedback and the sensory consequences of vocalization do not match. We used event‐related fMRI to look for the neural concomitants of such an error signal. In two conditions volunteers whispered ‘‘ted. ’’ In one of these, voice‐gated noise was used to mask the auditory feedback, which should result in an error signal. Two other conditions were yoked to the production conditions (either clearly heard or masked), but were listen‐only and therefore no error signal would be expected. We acquired whole‐brain EPI data from 21 subjects using a fast‐sparse design. Activity in the superior temporal gyrus bilaterally was significantly greater for clear than masked speech during the listen‐only trials and significantly higher f.
An approach to the probability distribution of cusum run length

The classical method of studying a cumulative sum control scheme of the decision interval type has been to regard the scheme as a sequence of sequential tests, to determine the average sample number for these component tests and hence to study the average run length for the scheme. A different approach in which the operation of the scheme is regarded as forming a Markov chain is set out. The transition probability matrix for this chain is obtained and then the properties of this matrix used to determine not only the average run lengths for the scheme, but also moments and percentage points of the run-length distribution and exact probabilities of run length. The method may be used with any discrete distribution and also, as ani accurate approximation, with any continuous distribution for the random variable which is to be controlled. Examples are given for the cases of a Poisson random variable and a normal random variable.
Fear, accessibility, and legitimacy: An examination of the effects of political violence on health security in Pakistan

How does political violence affect public health risks? While past research provides useful insights, it remains unclear how the effect of violence on health risks varies by perpetrator identity, target, and the intensity of attacks. We argue that indiscriminate attacks by militants will discourage civilians from accessing healthcare services. In contrast, clashes between militants and governments are likely to induce state actors to augment healthcare provision, while reinforced state control may facilitate civilians’ access. We test our arguments by employing multilevel modeling using subnational data on political violence events within Pakistan between 2005 and 2011, and immunization rates for under-5 infectious diseases.