Miracle-dz/UniFAR-FTUs huggingface dataset:
Chest Pain Management Using Prehospital Point-of-Care Troponin and Paramedic Risk Assessment.

Importance Prehospital point-of-care troponin testing and paramedic risk stratification might improve the efficiency of chest pain care pathways compared with existing processes with equivalent health outcomes, but the association with health care costs is unclear. Objective To analyze whether prehospital point-of-care troponin testing and paramedic risk stratification could result in cost savings compared with existing chest pain care pathways. Design, Setting, and Participants In this economic evaluation of adults with acute chest pain without ST-segment elevation, cost-minimization analysis was used to assess linked ambulance, emergency, and hospital attendance in the state of Victoria, Australia, between January 1, 2015, and June 30, 2019. Interventions Paramedic risk stratification and point-of-care troponin testing. Main Outcomes and Measures The outcome was estimated mean annualized statewide costs for acute chest pain. Between May 17 and June 25, 2022, decision tree models were developed to estimate costs under 3 pathways: (1) existing care, (2) paramedic risk stratification and point-of-care troponin testing without prehospital discharge, or (3) prehospital discharge and referral to a virtual emergency department (ED) for low-risk patients. Probabilities for the prehospital pathways were derived from a review of the literature. Multivariable probabilistic sensitivity analysis with 50 000 Monte Carlo iterations was used to estimate mean costs and cost differences among pathways. Results A total of 188 551 patients attended by ambulance for chest pain (mean [SD] age, 61.9 [18.3] years; 50.5% female; 49.5% male; Indigenous Australian, 2.0%) were included in the model. Estimated annualized infrastructure and staffing costs for the point-of-care troponin pathways, assuming a 5-year device life span, was $2.27 million for the pathway without prehospital discharge and $4.60 million for the pathway with prehospital discharge (incorporating virtual ED costs). In the decision tree model, total annual cost using prehospital point-of-care troponin and paramedic risk stratification was lower compared with existing care both without prehospital discharge (cost savings, $6.45 million; 95% uncertainty interval [UI], $0.59-$16.52 million; lower in 94.1% of iterations) and with prehospital discharge (cost savings, $42.84 million; 95% UI, $19.35-$72.26 million; lower in 100% of iterations). Conclusions and Relevance Prehospital point-of-care troponin and paramedic risk stratification for patients with acute chest pain could result in substantial cost savings. These findings should be considered by policy makers in decisions surrounding the potential utility of prehospital chest pain risk stratification and point-of-care troponin models provided that safety is confirmed in prospective studies.
The Interference of Two-Dimensional Parallel Jets : 3rd Report, The Region near the Nozzles in Triple Jets

Flow patterns of three two-dimensional parallel jets were investigated experimentally. In order to examine the effect of varying the ratio of the velocities in the central and outside jets on flow pattern, detailed measurements of the flow by a hot-wire anemometer and flow visualization by oil film were carried out. The results obtained are as follows. (1) The flow fields near the nozzles can be classified in three patterns of the flow according to the velocity ratio. 82) There is a specific velocity ratio at which the flow is unstable. (3) The flow patterns by means of visualization agree well with those of the streamlines, qualitatively.
Local electronic properties of a molecular monolayer: C 60 on Ag(001)

We have spatially resolved the local electronic structure of a C 6 0 monolayer on Ag(001) using scanning tunneling spectroscopy at T=7 K. Our measurements resolve four band states derived from C 6 0 highest occupied molecular orbital, lowest unoccupied molecular orbital (LUMO), and LUMO + 1. We observe spatial inhomogeneity in the energy-resolved local density of states, which reflects C 6 0 internal structure. In addition, we are able to resolve the molecular orientation of coexisting bright and dim C 6 0 molecules in the monolayer. This combination of topographic and spectroscopic measurements provides further evidence that the appearance of bright and dim molecules originates from a Ag surface reconstruction.
Solving High-Dimensional PDEs with Latent Spectral Models

Deep models have achieved impressive progress in solving partial differential equations (PDEs). A burgeoning paradigm is learning neural operators to approximate the input-output mappings of PDEs. While previous deep models have explored the multiscale architectures and various operator designs, they are limited to learning the operators as a whole in the coordinate space. In real physical science problems, PDEs are complex coupled equations with numerical solvers relying on discretization into high-dimensional coordinate space, which cannot be precisely approximated by a single operator nor efficiently learned due to the curse of dimensionality. We present Latent Spectral Models (LSM) toward an efficient and precise solver for high-dimensional PDEs. Going beyond the coordinate space, LSM enables an attention-based hierarchical projection network to reduce the high-dimensional data into a compact latent space in linear time. Inspired by classical spectral methods in numerical analysis, we design a neural spectral block to solve PDEs in the latent space that approximates complex input-output mappings via learning multiple basis operators, enjoying nice theoretical guarantees for convergence and approximation. Experimentally, LSM achieves consistent state-of-the-art and yields a relative gain of 11.5% averaged on seven benchmarks covering both solid and fluid physics. Code is available at https://github.com/thuml/Latent-Spectral-Models.
A Survey on Approximate Multiplier Designs for Energy Efficiency: From Algorithms to Circuits

Given the stringent requirements of energy efficiency for Internet-of-Things edge devices, approximate multipliers, as a basic component of many processors and accelerators, have been constantly proposed and studied for decades, especially in error-resilient applications. The computation error and energy efficiency largely depend on how and where the approximation is introduced into a design. Thus, this article aims to provide a comprehensive review of the approximation techniques in multiplier designs ranging from algorithms and architectures to circuits. We have implemented representative approximate multiplier designs in each category to understand the impact of the design techniques on accuracy and efficiency. The designs can then be effectively deployed in high-level applications, such as machine learning, to gain energy efficiency at the cost of slight accuracy loss.
Identifying Expert Behavior in Offline Training Datasets Improves Behavioral Cloning of Robotic Manipulation Policies

This letter presents our solution for the Real Robot Challenge III1, aiming to address dexterous robotic manipulation tasks through learning from offline data. In this competition, participants were given two types of datasets for each task: expert and mixed. Each expert dataset is collected by a high-skill policy, whereas the mixed dataset is collected using both expert and non-expert policies. We found that the vanilla behavioural cloning (BC) can learn a very proficient policy with minimal human intervention when trained on expert datasets. Notably, BC outperformed even the most advanced offline reinforcement learning (RL) algorithms. However, when applied to mixed datasets, the performance of BC deteriorates; similarly, the performance of offline RL algorithms is also less than satisfactory. Upon examining the provided datasets, it was apparent that each mixed dataset contained a significant proportion of expert data, which should enable the training of a proficient BC agent. However, the expert data is not labelled in the datasets. As a result, we propose a classifier to identify the pattern of the expert behaviour within a mixed dataset and then utilize it to isolate the expert data. To further boost the BC performance, we take advantage of the geometric symmetry of the arena to augment the training dataset through mathematical transformations. Ultimately, our submission outperformed that of all other participants.
Temperature behavior of the Optical Absorption and Photoluminescence Spectra of InP/ZnS Nanocrystals with a Stabilizing Polyvinylpyrrolidone-based Coating

: The present paper deals with the results of a research work on the optical properties of colloidal InP/ZnS nanocrystals stabilized with a heterobifunctional polyvinylpyrrolidone polymer. We have analyzed the absorption and photoluminescence spectra of the samples as solutions with various concentrations and deposited films, as well as the patterns of their temperature changes in the range of 6.5–296 K. An inhomogeneous broadening of exciton optical bands has been observed to be related to a wide distribution of nanocrystals in size. A temperature shift of the exciton absorption and emission maxima has been found to be due to the interaction with acoustic phonons. It has been shown that the quenching of defect-related luminescence is carried out through local energy levels of dangling bonds of phosphorus atoms involved at the core-shell interface, and the temperature stability of exciton emission is determined by the thickness of the ZnS shell.
Diverse legal case search

In last decades, legal case search has received more and more attention. Legal practitioners need to work or enhance their efficiency by means of class case search. In the process of searching, legal practitioners often need the search results under several different causes of cases as reference. However, existing work tends to focus on the relevance of the judgments themselves, without considering the connection between the causes of action. Several well-established diversity search techniques already exist in open-field search efforts. However, these techniques do not take into account the specificity of legal search scenarios, e.g., the subtopic may not be independent of each other, but somehow connected. Therefore, we construct a diversity legal retrieval model. This model takes into account both diversity and relevance, and is well adapted to this scenario. At the same time, considering the lack of dataset with diversity labels, we constructed a diversity legal retrieval dataset and obtained labels by manual labeling. experiments confirmed that our model is effective.
Constrained Policy Optimization with Explicit Behavior Density For Offline Reinforcement Learning

Due to the inability to interact with the environment, offline reinforcement learning (RL) methods face the challenge of estimating the Out-of-Distribution (OOD) points. Existing methods for addressing this issue either control policy to exclude the OOD action or make the $Q$ function pessimistic. However, these methods can be overly conservative or fail to identify OOD areas accurately. To overcome this problem, we propose a Constrained Policy optimization with Explicit Behavior density (CPED) method that utilizes a flow-GAN model to explicitly estimate the density of behavior policy. By estimating the explicit density, CPED can accurately identify the safe region and enable optimization within the region, resulting in less conservative learning policies. We further provide theoretical results for both the flow-GAN estimator and performance guarantee for CPED by showing that CPED can find the optimal $Q$-function value. Empirically, CPED outperforms existing alternatives on various standard offline reinforcement learning tasks, yielding higher expected returns.
Cosmic background neutrinos deflected by gravity: DEMNUni simulation analysis

The local supercluster acts as a gravity deflection source for cosmic background neutrinos. This deflection by gravity changes the neutrino helicity and therefore has important consequences for ground based tritium capture experiments aimed at determining if the neutrino is Dirac or Majorana. Here we explore the deflection effect of the local supercluster using two simulations from the DEMNUni suite characterised by very different mass resolutions, as they are both filled with 20483 dark matter particles (and an equal number of massive neutrino particles) but have comoving volumes of (2 h -1Gpc)3 and (500 h -1Mpc)3, respectively. We reaffirm our previous results and show that the lightest neutrinos are ultra-relativistic enough to suffer little deflection by gravity and at the same time not relativistic enough to achieve the same capture rate for Dirac and Majorana cases. This means that the capture rate in Ptolemy-like experiments will be sensitive to the neutrino nature and that gravity deflection enlarges the difference between Majorana and Dirac rates. Moreover, using the relation between mass and momentum of the neutrinos frozen Fermi-Dirac distribution, but taking into consideration gravity corrections, we are able to calculate the deflection angle for different neutrino masses from the same set of neutrinos obtained from the simulation. Doing so, we provide a formula to compute the deflection angle for any neutrino mass, such that when cosmology detects an absolute neutrino mass, precise predictions can be made for tritium ground-based detectors on Earth aimed to determine neutrinos nature.
Epsilon-Identifiability of Causal Quantities

Identifying the effects of causes and causes of effects is vital in virtually every scientific field. Often, however, the needed probabilities may not be fully identifiable from the data sources available. This paper shows how partial identifiability is still possible for several probabilities of causation. We term this epsilon-identifiability and demonstrate its usefulness in cases where the behavior of certain subpopulations can be restricted to within some narrow bounds. In particular, we show how unidentifiable causal effects and counterfactual probabilities can be narrowly bounded when such allowances are made. Often those allowances are easily measured and reasonably assumed. Finally, epsilon-identifiability is applied to the unit selection problem.
Drift of ablated material after pellet injection in a tokamak

Pellet injection is used for fuelling and controlling discharges in tokamaks, and it is foreseen in ITER. During pellet injection, a movement of the ablated material towards the low-field side (or outward major radius direction) occurs because of the inhomogeneity of the magnetic field. Due to the complexity of the theoretical models, computer codes developed to simulate the cross-field drift are computationally expensive. Here, we present a one-dimensional semi-analytical model for the radial displacement of ablated material after pellet injection, taking into account both the Alfvén and ohmic currents which shortcircuit the charge separation creating the drift. The model is suitable for rapid calculation of the radial drift displacement, and can be useful for e.g. modelling of disruption mitigation via pellet injection.
Echoes of the hexagon: remnants of hexagonal packing inside regular polygons

Based on numerical simulations that we have carried out, we provide evidence that for regular polygons with $\sigma= 6j$ sides (with $j=2,3,\dots$), $N(k)=3 k (k+1)+1$ (with $k=1,2,\dots$) congruent disks of appropriate size can be nicely packed inside these polygons in highly symmetrical configurations which apparently have maximal density for $N$ sufficiently small. These configurations are invariant under rotations of $\pi/3$ and are closely related to the configurations with perfect hexagonal packing in the regular hexagon and to the configurations with {\sl curved hexagonal packing} (CHP) in the circle found long time ago by Graham and Lubachevsky~\cite{Graham97}. The packing fraction, i.e. the portion of accessible volume (area) occupied by multiple solid object has a role in determining the properties of granular materials and fluids.At the basis of our explorations are the algorithms that we have devised, which are very efficient in producing the CHP and more general configurations inside regular polygons. We have used these algorithms to generate a large number of CHP configurations for different regular polygons and numbers of disks; a careful study of these results has made possible to fully characterize the general properties of the CHP configurations and to devise a {\sl deterministic} algorithm that completely ensembles a given CHP configuration once an appropriate input is specified.
Delayed Stochastic Algorithms for Distributed Weakly Convex Optimization

This paper studies delayed stochastic algorithms for weakly convex optimization in a distributed network with workers connected to a master node. Recently, Xu et al. 2022 showed that an inertial stochastic subgradient method converges at a rate of $\mathcal{O}(\tau_{\text{max}}/\sqrt{K})$ which depends on the maximum information delay $\tau_{\text{max}}$. In this work, we show that the delayed stochastic subgradient method ($\texttt{DSGD}$) obtains a tighter convergence rate which depends on the expected delay $\bar{\tau}$. Furthermore, for an important class of composition weakly convex problems, we develop a new delayed stochastic prox-linear ($\texttt{DSPL}$) method in which the delays only affect the high-order term in the complexity rate and hence, are negligible after a certain number of $\texttt{DSPL}$ iterations. In addition, we demonstrate the robustness of our proposed algorithms against arbitrary delays. By incorporating a simple safeguarding step in both methods, we achieve convergence rates that depend solely on the number of workers, eliminating the effect of the delay. Our numerical experiments further confirm the empirical superiority of our proposed methods.
Quadratic Matrix Factorization with Applications to Manifold Learning

Matrix factorization is a popular framework for modeling low-rank data matrices. Motivated by manifold learning problems, this paper proposes a quadratic matrix factorization (QMF) framework to learn the curved manifold on which the dataset lies. Unlike local linear methods such as the local principal component analysis, QMF can better exploit the curved structure of the underlying manifold. Algorithmically, we propose an alternating minimization algorithm to optimize QMF and establish its theoretical convergence properties. Moreover, to avoid possible over-fitting, we then propose a regularized QMF algorithm and discuss how to tune its regularization parameter. Finally, we elaborate how to apply the regularized QMF to manifold learning problems. Experiments on a synthetic manifold learning dataset and three real datasets, including the MNIST handwritten dataset, a cryogenic electron microscopy dataset, and the Frey Face dataset, demonstrate the superiority of the proposed method over its competitors.
Robust Meta Learning for Image based tasks

A machine learning model that generalizes well should obtain low errors on unseen test examples. Thus, if we learn an optimal model in training data, it could have better generalization performance in testing tasks. However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown. To tackle this challenge, we propose a novel robust meta-learning method, which is more robust to the image-based testing tasks which is unknown and has distribution shifts with training tasks. Our robust meta-learning method can provide robust optimal models even when data from each distribution are scarce. In experiments, we demonstrate that our algorithm not only has better generalization performance but also robust to different unknown testing tasks.
Krylov Methods for Large-Scale Dynamical Systems: Application in Fluid Dynamics

The ability to predict and characterize bifurcations from the onset of unsteadiness to the transition to turbulence is of critical importance in both academic and industrial applications. Numerous tools from dynamical system theory can be employed for that purpose. In this review, we focus on the practical computation and stability analyses of steady and time-periodic solutions with a particular emphasis on very high-dimensional systems such as those resulting from the discrete Navier-Stokes equations. In addition to a didactically concise theoretical framework, we introduce nekStab, an open source and user-friendly toolbox dedicated to such analyses using the spectral element solver Nek5000. Relying on Krylov methods and a time-stepper formulation, nekStab inherits the flexibility and high performance capabilities of Nek5000 and can be used to study the stability properties of flows in complex three-dimensional geometries. The performances and accuracy of nekStab are presented on the basis of standard benchmarks from the literature. For the sake of pedagogy and clarity, most of the algorithms implemented in nekStab are presented herein using Python pseudocode. Because of its flexibility and domain-agnostic nature, the methodology presented in this work can be applied to develop similar toolboxes for other solvers, most importantly outside the field of fluid dynamics.
Hierarchical Imitation Learning with Vector Quantized Models

The ability to plan actions on multiple levels of abstraction enables intelligent agents to solve complex tasks effectively. However, learning the models for both low and high-level planning from demonstrations has proven challenging, especially with higher-dimensional inputs. To address this issue, we propose to use reinforcement learning to identify subgoals in expert trajectories by associating the magnitude of the rewards with the predictability of low-level actions given the state and the chosen subgoal. We build a vector-quantized generative model for the identified subgoals to perform subgoal-level planning. In experiments, the algorithm excels at solving complex, long-horizon decision-making problems outperforming state-of-the-art. Because of its ability to plan, our algorithm can find better trajectories than the ones in the training set
Prediction of Customer Churn in Banking Industry

With the growing competition in banking industry, banks are required to follow customer retention strategies while they are trying to increase their market share by acquiring new customers. This study compares the performance of six supervised classification techniques to suggest an efficient model to predict customer churn in banking industry, given 10 demographic and personal attributes from 10000 customers of European banks. The effect of feature selection, class imbalance, and outliers will be discussed for ANN and random forest as the two competing models. As shown, unlike random forest, ANN doesn’t reveal any serious concern regarding overfitting and is also robust to noise. Therefore, ANN structure with five nodes in a single hidden layer is recognized as the best performing classifier.
Incentives to Offer Algorithmic Recourse

Due to the importance of artificial intelligence (AI) in a variety of high-stakes decisions, such as loan approval, job hiring, and criminal bail, researchers in Explainable AI (XAI) have developed algorithms to provide users with recourse for an unfavorable outcome. We analyze the incentives for a decision-maker to offer recourse to a set of applicants. Does the decision-maker have the incentive to offer recourse to all rejected applicants? We show that the decision-maker only offers recourse to all applicants in extreme cases, such as when the recourse process is impossible to manipulate. Some applicants may be worse off when the decision-maker can offer recourse.