Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing strength and range of atom-atom interaction in a coupled-cavity array via parametric drives

Coherent long-range interactions between atoms are a prerequisite for numerous applications in the field of quantum information science, but they usually decrease exponentially with the increase in atomic separation. Here we present an appealing method to dramatically enhance the long-range atom-atom interaction mediated by a coupled-cavity array that is subjected to two-photon (parametric) drives. Our method allows one to greatly amplify both the localization length of the single-photon bound-state wavefunction and the effective atom-photon coupling strength, resulting in a significant improvement of photon-mediated coherent interaction between two distant atoms. Additionally, we illustrate this effect by analyzing how it facilitates the transfer of information and the creation of entanglement between the atoms.
An Improved Doubly Robust Estimator Using Partially Recovered Unmeasured Spatial Confounder

Studies in environmental and epidemiological sciences are often spatially varying and observational in nature with the aim of establishing cause and effect relationships. One of the major challenges with such studies is the presence of unmeasured spatial confounders. 'spatial confounding' is the phenomenon in which the spatial residuals are correlated to the spatial covariates in the model, when unaccounted for it can lead to biased causal effect estimates. This paper develops a novel method that adjusts for the spatial confounding bias under a spatial-causal inference framework when the treatment is binary. By combining tools from spatial statistics and causal inference literature, we propose a method that reduces the bias due to spatial confounding. We partially recover the unmeasured spatial confounder using the spatial residuals and propose an improved doubly robust estimator based on it. Through simulation studies, we demonstrate that the proposed doubly robust estimator outperforms the existing methods and has the lowest bias and close to nominal coverage in most scenarios. Finally, we implement our method to estimate the effect of installing SCR/SNCR NOx emission control technologies on ambient ozone concentrations.
Compsuite: A Dataset of Java Library Upgrade Incompatibility Issues

Modern software systems heavily rely on external libraries developed by third-parties to ensure efficient development. However, frequent library upgrades can lead to compatibility issues between the libraries and their client systems. In this paper, we introduce Compsuite, a dataset that includes 123 real-world Java client-library pairs where upgrading the library causes an incompatibility issue in the corresponding client. Each incompatibility issue in Compsuite is associated with a test case authored by the developers, which can be used to reproduce the issue. The dataset also provides a command-line interface that simplifies the execution and validation of each issue. With this infrastructure, users can perform an inspection of any incompatibility issue with the push of a button, or reproduce an issue step-by-step for a more detailed investigation. We make Compsuite publicly available to promote open science. We believe that various software analysis techniques, such as compatibility checking, debugging, and regression test selection, can benefit from Compsuite. The demonstration video of Compsuite is available at https://www.youtube. com/watch? v=7DQGsGs_65s.
Approximation and Progressive Display of Multiverse Analyses

A multiverse analysis evaluates all combinations of"reasonable"analytic decisions to promote robustness and transparency, but can lead to a combinatorial explosion of analyses to compute. Long delays before assessing results prevent users from diagnosing errors and iterating early. We contribute (1) approximation algorithms for estimating multiverse sensitivity and (2) monitoring visualizations for assessing progress and controlling execution on the fly. We evaluate how quickly three sampling-based algorithms converge to accurately rank sensitive decisions in both synthetic and real multiverse analyses. Compared to uniform random sampling, round robin and sketching approaches are 2 times faster in the best case, while on average estimating sensitivity accurately using 20% of the full multiverse. To enable analysts to stop early to fix errors or decide when results are"good enough"to move forward, we visualize both effect size and decision sensitivity estimates with confidence intervals, and surface potential issues including runtime warnings and model quality metrics.
TSGN: Temporal Scene Graph Neural Networks with Projected Vectorized Representation for Multi-Agent Motion Prediction

Predicting future motions of nearby agents is essential for an autonomous vehicle to take safe and effective actions. In this paper, we propose TSGN, a framework using Temporal Scene Graph Neural Networks with projected vectorized representations for multi-agent trajectory prediction. Projected vectorized representation models the traffic scene as a graph which is constructed by a set of vectors. These vectors represent agents, road network, and their spatial relative relationships. All relative features under this representation are both translation-and rotation-invariant. Based on this representation, TSGN captures the spatial-temporal features across agents, road network, interactions among them, and temporal dependencies of temporal traffic scenes. TSGN can predict multimodal future trajectories for all agents simultaneously, plausibly, and accurately. Mean-while, we propose a Hierarchical Lane Transformer for capturing interactions between agents and road network, which filters the surrounding road network and only keeps the most probable lane segments which could have an impact on the future behavior of the target agent. Without sacrificing the prediction performance, this greatly reduces the computational burden. Experiments show TSGN achieves state-of-the-art performance on the Argoverse motion forecasting benchmark.
Matching the 2HDM to the HEFT and the SMEFT: Decoupling and perturbativity

We consider the 2 Higgs Doublet Model (2HDM) and compare two effective field theory (EFT) approaches to it, according to whether the heavy degrees of freedom are integrated out before (SMEFT) or after (HEFT) spontaneous symmetry breaking. %We show that, in the HEFT, an inconsistent EFT is obtained if one considers an expansion simply in inverse powers of the heavy masses. %We consider a HEFT expansion consistent with perturbativity %We show that, if the HEFT approach is required to comply with perturbativity, it ends up obeying the same power counting as the SMEFT one. % By requiring decoupling and perturbativity in the 2HDM, we define a consistent EFT expansion in inverse powers of the heavy masses which is applied to both the SMEFT and the HEFT matchings to the 2HDM. We organize this expansion with a dimensionless parameter $\xi$, and investigate the tree-level scatterings $hh\rightarrow hh$ and $WW\rightarrow hh$ up to $\mathcal{O}(\xi^2)$. We find no differences between the HEFT and the SMEFT approaches at this order. We show scenarios where even including dimension-8 operators of the SMEFT is insufficient to obtain an accurate matching to the 2HDM.
Improved List Decoding for Polar-Coded Probabilistic Shaping

A modified successive cancellation list (SCL) decoder is proposed for polar-coded probabilistic shaping. The decoder exploits the deterministic encoding rule for shaping bits to rule out candidate code words that the encoder would not generate. This provides error detection and decreases error rates compared to standard SCL decoding while at the same time reducing the length of the outer cyclic redundancy check code.
Remarks on the A** algorithm

Abstract The A** algorithm is a famous heuristic path-finding algorithm. In this paper its different definitions will be analyzed firstly. Then its memory complexity is going to be investigated. On the one hand, the well-known concept of better-information will be extended to compare the different heuristics in the A** algorithm. On the other hand, a new proof will be given to show that there is no deterministic graph-search algorithm having better memory complexity than A**∗. At last the time complexity of A** will be discussed.
Logarithmic light cone, slow entanglement growth and scrambling, and quantum memory

Effective light cones may emerge in non-relativistic local quantum systems from the Lieb-Robinson bounds, resulting in exponentially decaying commutator norms of two space-time separated operators in the Heisenberg picture. Here, we derive a mechanism for the emergence and consequences of a logarithmic light cone (LLC). As a possible way, the LLC can emerge from a phenomenological model of many-body-localization. We show that the information scrambling is logarithmically slow in the regime of the LLC. We prove that the bipartite entanglement entropy grows logarithmically with time for arbitrary finite space dimensions and arbitrary initial pure states. As an application in quantum information processing, the LLC supports long-lived quantum memory after unitary time evolution: a quantum code with macroscopic code distance and exponentially long lifetime.
A Flow-Based Generative Model for Rare-Event Simulation

Solving decision problems in complex, stochastic environments is often achieved by estimating the expected outcome of decisions via Monte Carlo sampling. However, sampling may overlook rare, but important events, which can severely impact the decision making process. We present a method in which a Normalizing Flow generative model is trained to simulate samples directly from a conditional distribution given that a rare event occurs. By utilizing Coupling Flows, our model can, in principle, approximate any sampling distribution arbitrarily well. By combining the approximation method with Importance Sampling, highly accurate estimates of complicated integrals and expectations can be obtained. We include several examples to demonstrate how the method can be used for efficient sampling and estimation, even in high-dimensional and rare-event settings. We illustrate that by simulating directly from a rare-event distribution significant insight can be gained into the way rare events happen.
Creative Data Generation: A Review Focusing on Text and Poetry

The rapid advancement in machine learning has led to a surge in automatic data generation, making it increasingly challenging to differentiate between naturally or human-generated data and machine-generated data. Despite these advancements, the generation of creative data remains a challenge. This paper aims to investigate and comprehend the essence of creativity, both in general and within the context of natural language generation. We review various approaches to creative writing devices and tasks, with a specific focus on the generation of poetry. We aim to shed light on the challenges and opportunities in the field of creative data generation.
(Almost) complete characterization of stability of a discrete-time Hawkes process with inhibition and memory of length two

We consider a discrete-time version of a Hawkes process defined as a Poisson auto-regressive process whose parameters depend on the past of the trajectory. We allow these parameters to take on negative values, modelling inhibition. More precisely, the model is the stochastic process $(X_n)_{n\ge0}$ with parameters $a_1,\ldots,a_p \in \mathbb{R}$, $p\in\mathbb{N}$ and $\lambda \ge 0$, such that for all $n\ge p$, conditioned on $X_0,\ldots,X_{n-1}$, $X_n$ is Poisson distributed with parameter \[ \left(a_1 X_{n-1} + \cdots + a_p X_{n-p} + \lambda \right)_+ \] We consider specifically the case $p = 2$, for which we are able to classify the asymptotic behavior of the process for the whole range of parameters, except for boundary cases. In particular, we show that the process remains stochastically bounded whenever the linear recurrence equation $x_n = a_1x_{n-1} + a_2x_{n-1} + \lambda$ remains bounded, but the converse is not true. Relatedly, the criterion for stochastic boundedness is not symmetric in $a_1$ and $a_2$, in contrast to the case of non-negative parameters, illustrating the complex effects of inhibition.
Voxel-wise classification for porosity investigation of additive manufactured parts with 3D unsupervised and (deeply) supervised neural networks

Additive Manufacturing (AM) has emerged as a manufacturing process that allows the direct production of samples from digital models. To ensure that quality standards are met in all manufactured samples of a batch, X-ray computed tomography (X-CT) is often used combined with automated anomaly detection. For the latter, deep learning (DL) anomaly detection techniques are increasingly, as they can be trained to be robust to the material being analysed and resilient towards poor image quality. Unfortunately, most recent and popular DL models have been developed for 2D image processing, thereby disregarding valuable volumetric information. This study revisits recent supervised (UNet, UNet++, UNet 3+, MSS-UNet) and unsupervised (VAE, ceVAE, gmVAE, vqVAE) DL models for porosity analysis of AM samples from X-CT images and extends them to accept 3D input data with a 3D-patch pipeline for lower computational requirements, improved efficiency and generalisability. The supervised models were trained using the Focal Tversky loss to address class imbalance that arises from the low porosity in the training datasets. The output of the unsupervised models is post-processed to reduce misclassifications caused by their inability to adequately represent the object surface. The findings were cross-validated in a 5-fold fashion and include: a performance benchmark of the DL models, an evaluation of the post-processing algorithm, an evaluation of the effect of training supervised models with the output of unsupervised models. In a final performance benchmark on a test set with poor image quality, the best performing supervised model was UNet++ with an average precision of 0.751 $\pm$ 0.030, while the best unsupervised model was the post-processed ceVAE with 0.830 $\pm$ 0.003. The VAE/ceVAE models demonstrated superior capabilities, particularly when leveraging post-processing techniques.
Propagation of high-power fiber laser with high-order-mode content

Propagation properties of high-power fiber laser with high-order-mode (HOM) content are studied numerically for the first time to the best of our knowledge. The effect of HOM on the propagation property is evaluated by the power in the bucket (PIB) metric. It is shown that PIB is mainly dependent on HOM content rather than the relative phase between the fundamental mode and HOM. The PIB in vacuum is more than 80% when the power fraction of the HOM is controlled to be less than 50% at 5 km. The relative phase has an impact on the peak intensity position and concentration of the far-field intensity distribution. If an adaptive optics system is used to correct the peak intensity deviation, the results indicate that there exists a maximal value of PIB as relative phase increases. Such effect is weakened when propagating in turbulence. Compared to the laser beams without HOM, laser beams with HOM content are less influenced by the turbulence and can reduce average intensity fluctuation. The results may be useful in the design of a high-power fiber laser system.
Interference Cancellation Algorithms for Grant-Free Multiple Access With Massive MIMO

In next generation Internet-of-Things, the overhead introduced by grant-based multiple access protocols may engulf the access network as a consequence of the unprecedented number of connected devices. Grant-free access protocols are therefore gaining an increasing interest to support massive access from machine-type devices with intermittent activity. In this paper, coded random access (CRA) with massive multiple input multiple output (MIMO) is investigated as a solution to design highly-scalable massive multiple access protocols, taking into account stringent requirements on latency and reliability. With a focus on signal processing aspects at the physical layer and their impact on the overall system performance, critical issues of successive interference cancellation (SIC) over fading channels are first analyzed. Then, SIC algorithms and a scheduler are proposed that can overcome some of the limitations of the current access protocols. The effectiveness of the proposed processing algorithms is validated by Monte Carlo simulation, for different CRA protocols and by comparisons with developed benchmarks.
Accelerated Algorithms For Nonlinear Matrix Decomposition With The Relu Function

In this paper, we study the following nonlinear matrix decomposition (NMD) problem: given a sparse nonnegative matrix X, find a low-rank matrix $\Theta$ such that $X\approx f(\Theta)$, where f is an element-wise nonlinear function. We focus on the case where $f(\cdot)=\max(0,\cdot)$, the rectified linear unit (ReLU) nonlinear activation. We refer to the corresponding problem as ReLU-NMD. We first provide a brief overview of the existing approaches that were developed to tackle ReLU-NMD. Then we introduce two new algorithms: (1) aggressive accelerated NMD (A-NMD) which uses an adaptive Nesterov extrapolation to accelerate an existing algorithm, and (2) three-block NMD (3B-NMD) which parametrizes $\Theta=WH$ and leads to a significant reduction in the computational cost. We also propose an effective initialization strategy based on the nuclear norm as a proxy for the rank function. We illustrate the effectiveness of the proposed algorithms on synthetic and real-world data sets.
SHP2 participates in decidualization by activating ERK to maintain normal nuclear localization of progesterone receptor

In brief The establishment and maintenance of embryo implantation and pregnancy require decidualization of endometrial stromal cells. This paper reveals that SHP2 ensures the correct subcellular localization of progesterone receptor, thereby safeguarding the process of decidualization. Abstract Decidualization is the process of conversion of endometrial stromal cells into decidual stromal cells, which is caused by progesterone production that begins during the luteal phase of the menstrual cycle and then increases throughout pregnancy dedicated to support embryonic development. Decidualization deficiency is closely associated with various pregnancy complications, such as recurrent miscarriage (RM). Here, we reported that Src-homology-2-containing phospho-tyrosine phosphatase (SHP2), a key regulator in the signal transduction process downstream of various receptors, plays an indispensable role in decidualization. SHP2 expression was upregulated during decidualization. SHP2 inhibitor RMC-4550 and shRNA-mediated SHP2 reduction resulted in a decreased level of phosphorylation of ERK and aberrant cytoplasmic localization of progesterone receptor (PR), coinciding with reduced expression of IGFBP1 and various other target genes of decidualization. Solely inhibiting ERK activity recapitulated these observations. Administration of RMC-4550 led to decidualization deficiency and embryo absorption in mice. Moreover, reduced expression of SHP2 was detected in the decidua of RM patients. Our results revealed that SHP2 is key to PR's nuclear localization, thereby indispensable for decidualization and that reduced expression of SHP2 might be engaged in the pathogenesis of RM.
Enhancing Total Optical Throughput of Microscopy with Deep Learning for Intravital Observation

The significance of performing large‐depth dynamic microscopic imaging in vivo for life science research cannot be overstated. However, the optical throughput of the microscope limits the available information per unit of time, i.e., it is difficult to obtain both high spatial and temporal resolution at once. Here, a method is proposed to construct a kind of intravital microscopy with high optical throughput, by making near‐infrared‐II (NIR‐II, 900–1880 nm) wide‐field fluorescence microscopy learn from two‐photon fluorescence microscopy based on a scale‐recurrent network. Using this upgraded NIR‐II fluorescence microscope, vessels in the opaque brain of a rodent are reconstructed three‐dimensionally. Five‐fold axial and thirteen‐fold lateral resolution improvements are achieved without sacrificing temporal resolution and light utilization. Also, tiny cerebral vessel dilatations in early acute respiratory failure mice are observed, with this high optical throughput NIR‐II microscope at an imaging speed of 30 fps.
Blind Signal Separation Using an Adaptive Generalized Compound Gamma Distribution

We propose an independent component analysis (ICA) algorithm which can separate mixtures of sub- and super- Gaussian source signals with self-adaptive nonlinearities. The ICA algorithm in the framework of natural Riemannian gradient, is derived using the parameterized Generalized Compound Gamma Distribution density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Adaptive Generalized Compound Gamma Distribution density model. Computer simulation results confirm the validity and high performance of the proposed algorithm
Meeting the service needs of youth with and without a self-reported mental health diagnosis during COVID-19.

Background The COVID-19 pandemic catalyzed major changes in how youth mental health (MH) services are delivered. Understanding youth's MH, awareness and use of services since the pandemic, and differences between youth with and without a MH diagnosis, can help us optimize MH services during the pandemic and beyond. Objectives We investigated youth's MH and service use one year into the pandemic and explored differences between those with and without a self-reported MH diagnosis. Methods In February 2021, we administered a web-based survey to youth, 12-25 years, in Ontario. Data from 1373 out of 1497 (91.72%) participants were analyzed. We assessed differences in MH and service use between those with (N=623, 45.38%) and without (N=750, 54.62%) a self-reported MH diagnosis. Logistic regressions were used to explore MH diagnosis as a predictor of service use while controlling for confounders. Results 86.73% of participants reported worse MH since COVID-19, with no between-group differences. Participants with a MH diagnosis had higher rates of MH problems, service awareness and use, compared to those without a diagnosis. MH diagnosis was the strongest predictor of service use. Gender and affordability of basic needs also independently predicted use of distinct services. Conclusion Various services are required to mitigate the negative effects of the pandemic on youth MH and meet their service needs. Whether youth have a MH diagnosis may be important to understanding what services they are aware of and use. Sustaining pandemic-related service changes require increasing youth's awareness of digital interventions and overcoming other barriers to care.