Miracle-dz/UniFAR-FTUs huggingface dataset:
The GHP scaling limit of uniform spanning trees of dense graphs

We consider dense graph sequences that converge to a connected graphon and prove that the GHP scaling limit of their uniform spanning trees (USTs) is Aldous' Brownian CRT. Furthermore, we are able to extract the precise scaling constant from the limiting graphon. As an example, we can apply this to the scaling limit of the USTs of the Erdös–Rényi sequence for any fixed , and sequences of dense expanders. A consequence of GHP convergence is that several associated quantities of the spanning trees also converge, such as the height, diameter and law of a simple random walk.
Rethinking with Retrieval: Faithful Large Language Model Inference

Despite the success of large language models (LLMs) in various natural language processing (NLP) tasks, the stored knowledge in these models may inevitably be incomplete, out-of-date, or incorrect. This motivates the need to utilize external knowledge to assist LLMs. Unfortunately, current methods for incorporating external knowledge often require additional training or fine-tuning, which can be costly and may not be feasible for LLMs. To address this issue, we propose a novel post-processing approach, rethinking with retrieval (RR), which retrieves relevant external knowledge based on the decomposed reasoning steps obtained from the chain-of-thought (CoT) prompting. This lightweight approach does not require additional training or fine-tuning and is not limited by the input length of LLMs. We evaluate the effectiveness of RR through extensive experiments with GPT-3 on three complex reasoning tasks: commonsense reasoning, temporal reasoning, and tabular reasoning. Our results show that RR can produce more faithful explanations and improve the performance of LLMs.
Environmental-induced work extraction

A local measurement extracts work as a backaction, e.g., in a system of two entangled cavities: first cavity, $a$, comprises a piston and the measurement is carried out on the second cavity, $b$. When no one makes a measurement on the cavity $b$, i.e., it is simply placed in vacuum; environmental monitoring results in the coherent states as the einselected pointer states (the measurement basis) [PRL 70, 1187 (1993)]. This makes the measurement, that nature itself performs, a Gaussian one with a fixed strength $\lambda=1$. We show that this makes nature assign a \textit{fixed} amount of work to a particular entanglement degree $0\leq \xi(r) \leq 1$, i.e., $W=\xi(r)\times(\bar{n}\hbar\omega_a)$, nothing that the term in parenthesis is the entire thermal energy. Afterwards, we show that this phenomenon applies quite generally, i.e, not restricted to a two-cavities system. We also touch on the influence of inherited symmterization entanglement in this context. We can arrive an additional phenomenon by considering that work is simply the process of converting randomly moving microscopic ingredients~(vanishing mean-velocity) into a directional one, i.e, with a nonzero mean-velocity. We show that such a change in the character of the motion introduces curvature in spacetime according to general relativity. This phenomenon is the first demonstration of a quantitative relation between entanglement and curvature using solely the quantum optics arguments.
Temporally Layered Architecture for Adaptive, Distributed and Continuous Control

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to"act-or-not"at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.
Preparation of ultrafine jet-milled powders for Nd-Fe-B sintered magnets using hydrogenation–disproportionation–desorption–recombination and hydrogen decrepitation processes

Dy addition is used to increase the coercivity of Nd-Fe-B sintered magnets. Given that Dy is rare and expensive, a method is needed for reducing the Dy content in such magnets without decreasing their coercivity. Refining Nd2Fe14B grains is a prospective method for increasing the coercivity of Nd-Fe-B magnets. Conventional jet milling, however, cannot crush strip-casted Nd-Fe-B alloys into powders less than 1 μm in size. We report a process for preparing ultrafine jet-milled powders with an average size of 0.33 μm for Nd-Fe-B sintered magnets: a combination of hydrogenation–disproportionation–desorption–recombination, hydrogen decrepitation, and He jet milling.
Slumping regime in lock-release turbidity currents

Abstract Most gravitational currents occur on sloping topographies, often in the presence of particles that can settle during the current propagation. Yet an exhaustive exploration of associated parameters in experimental studies is still lacking. Here, we present an extensive experimental investigation of the slumping regime of turbidity (particle-laden) currents in two lock-release (dam-break) systems with inclined bottoms. We identify three regimes controlled by the ratio between settling and current inertia. (i) For negligible settling, the turbidity current morphodynamics corresponds to that of saline homogeneous gravity currents, in terms of velocity, slumping (constant-velocity) regime duration and current morphology. (ii) For intermediate settling, the slumping regime duration decreases to become fully controlled by a particle settling characteristic time. (iii) When settling overcomes the current initial inertia, the slumping (constant-velocity) regime is no longer detected. In the first two regimes, the current velocity increases with the bottom slope, of approximately $35\,\%$ between $0^\circ$ and $15^\circ$. Finally, our experiments show that the current propagates during the slumping regime with the same shape in the frame of the moving front. Strikingly, the current head is found to be independent of all experimental parameters covered in the present study. We also quantify water entrainment coefficients $E$ and compare them with previous literature, hence finding that $E$ increases rather linearly with the current Reynolds number.
Theoretical Characterization of How Neural Network Pruning Aﬀects its Generalization

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsiﬁed networks can not only retain the testing performance of the original dense models, but also sometimes even slightly boost the generalization performance. Theoretical understanding for such experimental observations are yet to be developed. This work makes the ﬁrst attempt to study how diﬀerent pruning fractions aﬀect the model’s gradient descent dynamics and generalization. Speciﬁcally, this work considers a classiﬁcation task for overparameterized two-layer neural networks, where the network is randomly pruned according to diﬀerent rates at the initialization. It is shown that as long as the pruning fraction is below a certain threshold, gradient descent can drive the training loss toward zero and the network exhibits good generalization performance. More surprisingly, the generalization bound gets better as the pruning fraction gets larger. To complement this positive result, this work further shows a negative result: there exists a large pruning fraction such that while gradient descent is still able to drive the training loss toward zero (by memorizing noise), the generalization performance is no better than random guessing. This further suggests that pruning can change the feature learning process, which leads to the performance drop of the pruned neural network. Up to our knowledge, this is the ﬁrst generalization result for pruned neural networks, suggesting that pruning can improve the neural network’s generalization.
High-dimensional latent Gaussian count time series: Concentration results for autocovariances and applications

This work considers stationary vector count time series models defined via deterministic functions of a latent stationary vector Gaussian series. The construction is very general and ensures a pre-specified marginal distribution for the counts in each dimension, depending on unknown parameters that can be marginally estimated. The vector Gaussian series injects flexibility into the model's temporal and cross-dimensional dependencies, perhaps through a parametric model akin to a vector autoregression. We show that the latent Gaussian model can be estimated by relating the covariances of the counts and the latent Gaussian series. In a possibly high-dimensional setting, concentration bounds are established for the differences between the estimated and true latent Gaussian autocovariances, in terms of those for the observed count series and the estimated marginal parameters. The results are applied to the case where the latent Gaussian series is a vector autoregression, and its parameters are estimated sparsely through a LASSO-type procedure.
A map-based model predictive control approach for train operation

Trains are a corner stone of public transport and play an important role in daily life. A challenging task in train operation is to avoid skidding and sliding, i.e. spinning or blocking of the wheels, during fast changes of traction conditions, which can, for example, occur due to changing weather conditions, crossings, tunnels or forest entries. The latter depends on local track conditions and can be recorded in a map together with other location-dependent information like speed limits and inclination. In this paper, a model predictive control (MPC) approach is developed. Thanks to the knowledge of future changes of traction conditions, the approach is able to avoid short-term skidding and sliding even under fast changes of traction conditions. In a first step, an optimal reference trajectory is determined by a multiple-shooting approach. In a second step, the reference trajectory is tracked by an MPC setup. The developed method is simulated along a track with fast-changing traction conditions for different scenarios, like changing weather conditions and unexpected delays. In all cases, skidding and sliding is avoided.
Recovering Top-Two Answers and Confusion Probability in Multi-Choice Crowdsourcing

Crowdsourcing has emerged as an effective platform for labeling large amounts of data in a cost- and time-efficient manner. Most previous work has focused on designing an efficient algorithm to recover only the ground-truth labels of the data. In this paper, we consider multi-choice crowdsourcing tasks with the goal of recovering not only the ground truth, but also the most confusing answer and the confusion probability. The most confusing answer provides useful information about the task by revealing the most plausible answer other than the ground truth and how plausible it is. To theoretically analyze such scenarios, we propose a model in which there are the top two plausible answers for each task, distinguished from the rest of the choices. Task difficulty is quantified by the probability of confusion between the top two, and worker reliability is quantified by the probability of giving an answer among the top two. Under this model, we propose a two-stage inference algorithm to infer both the top two answers and the confusion probability. We show that our algorithm achieves the minimax optimal convergence rate. We conduct both synthetic and real data experiments and demonstrate that our algorithm outperforms other recent algorithms. We also show the applicability of our algorithms in inferring the difficulty of tasks and in training neural networks with top-two soft labels.
Relative Roles of Land–Sea Distribution and Orography in Asian Monsoon Intensity

Abstract The relative impacts of various land–sea distributions (LSDs) and mountains on Asian monsoon extent and intensity are assessed using a series of AGCM simulations. The air–sea coupling effects are not considered in this study. All simulations were integrated with zonal mean SST, globally uniform vegetation, soil color, and, except several simulations, soil texture. The results show that the LSD plays a more fundamental role than orography in determining the extent of Asian and African monsoons. The tropical zonal LSD and Asian mountains both play a crucial role for establishing summer monsoon convection over the South Asian region. The monsoon circulation index (MCI1) defined by the difference of zonal wind between 850 and 200 hPa is used to measure the intensity of the South Asian summer monsoon. The large-scale meridional land–sea thermal contrast between the Eurasian continent and the Indian Ocean only induces a 1.8 m s−1 increase of MCI1. The presence of the Indian subcontinent and Indochina p.
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis

Learning-based methods have recently enabled performance leaps in analysis of high-dimensional functional MRI (fMRI) time series. Deep learning models that receive as input functional connectivity (FC) features among brain regions have been commonly adopted in the literature. However, many models focus on temporally static FC features across a scan, reducing sensitivity to dynamic features of brain activity. Here, we describe a plug-in graph neural network that can be flexibly integrated into a main learning-based fMRI model to boost its temporal sensitivity. Receiving brain regions as nodes and blood-oxygen-level-dependent (BOLD) signals as node inputs, the proposed GraphCorr method leverages a node embedder module based on a transformer encoder to capture temporally-windowed latent representations of BOLD signals. GraphCorr also leverages a lag filter module to account for delayed interactions across nodes by computing cross-correlation of windowed BOLD signals across a range of time lags. Information captured by the two modules is fused via a message passing algorithm executed on the graph, and enhanced node features are then computed at the output. These enhanced features are used to drive a subsequent learning-based model to analyze fMRI time series with elevated sensitivity. Comprehensive demonstrations on two public datasets indicate improved classification performance and interpretability for several state-of-the-art graphical and convolutional methods that employ GraphCorr-derived feature representations of fMRI time series as their input.
Emotion in Cognitive Architecture: Emergent Properties from Interactions with Human Emotion

This document presents endeavors to represent emotion in a computational cognitive architecture. The first part introduces research organizing with two axes of emotional affect: pleasantness and arousal. Following this basic of emotional components, the document discusses an aspect of emergent properties of emotion, showing interaction studies with human users. With these past author's studies, the document concludes that the advantage of the cognitive human-agent interaction approach is in representing human internal states and processes.
Vector dark matter in supercooled Higgs portal models

We consider extensions of the Standard Model by a hidden sector consisting of a gauge field coupled with a scalar field. Assuming the absence of dimensionful parameters in the tree level potential, radiative symmetry breaking will make the hidden sector gauge field massive and induce the electroweak scale of the Standard Model. We consider separately dark sector gauge groups $U(1)_{\rm{D}}$ and $SU(2)_{\rm{D}}$, and focus on probing the models with a combination of direct detection experiments and gravitational wave observatories. We find that recent dark matter direct detection results significantly constrain the parameter space of the models where they can account for the observed dark matter relic density via freeze-out. The gravitational wave signals originating from strongly first order electroweak phase transition in these models can be probed in future gravitational wave observatories such as the Laser Interferometer Space Antenna. We show how the projected results compliment direct detection experiments and can help probe parameter space near the neutrino floor of direct detection.
PCA-based Data Reduction and Signal Separation Techniques for James-Webb Space Telescope Data Processing

Principal Component Analysis (PCA)-based techniques can separate data into diﬀerent uncorrelated components and facilitate the statistical analysis as a pre-processing step. Independent Component Analysis (ICA) can separate statistically independent signal sources through a non-parametric and iterative algorithm. Non-negative matrix factorization is another PCA-similar approach to categorizing dimensions in physically-interpretable groups. Singular spectrum analysis (SSA) is a time-series-related PCA-like algorithm. After an introduction and a literature review on processing JWST data from the Near-Infrared Camera (NIRCam) and Mid-Infrared Instrument (MIRI), potential parts to intervene in the James Webb Space Telescope imaging data reduction pipeline will be discussed
Bubble in the Whale: Identifying the Optical Counterparts and Extended Nebula for the Ultraluminous X-Ray Sources in NGC 4631

We present a deep optical imaging campaign on the starburst galaxy NGC 4631 with CFHT/MegaCam. By supplementing the HST/ACS and Chandra/ACIS archival data, we search for the optical counterpart candidates of the five brightest X-ray sources in this galaxy, four of which are identified as ultraluminous X-ray sources (ULXs). The stellar environments of the X-ray sources are analyzed using the extinction-corrected color–magnitude diagrams and the isochrone models. We discover a highly asymmetric bubble nebula around X4 that exhibits different morphology in the Hα and [O iii] images. The [O iii]/Hα ratio map shows that the Hα-bright bubble may be formed mainly via the shock ionization by the one-sided jet/outflow, while the more compact [O iii] structure is photoionized by the ULX. We constrain the bubble expansion velocity and interstellar medium density with the MAPPINGS V code and hence estimate the mechanical power injected into the bubble as P w ∼ 5 × 1040 erg s−1 and the corresponding bubble age as ∼7 × 105 yr. Relativistic jets are needed to provide such a level of mechanical power with a mass-loss rate of ∼10−7 M ⊙ yr−1. Besides the accretion, the black hole spin is likely an additional energy source for the super-Eddington jet power.
TIGGE: Preliminary results on comparing and combining ensembles

TIGGE, the THORPEX Interactive Grand Global Ensemble, is a World Weather Research Programme project to accelerate the improvements in the accuracy of 1‐day to 2‐week high‐impact weather forecasts. This report discusses some preliminary results from predictability studies based on the ensemble data exchanged within TIGGE, and available at the TIGGE archive centres. In the first part of this work, the key characteristics of the eight ensemble systems available in the TIGGE database at the time of writing (December 2007) are compared, and the strengths and weaknesses of each system are highlighted. Then, issues related to the generation of multi‐model/multi‐analysis ensemble products are discussed, and some preliminary results on the potential value of combining different ensembles to generate medium‐range products with a grand multi‐model/multi‐analysis global ensemble are presented.
Intelligent Prediction of Turbulent Flow over Backward-Facing Step using Direct Numerical Simulation Data

Abstract In turbulent flows, there are very complicated nonlinear behaviors that are influenced by many indeterminate factors. Currently, Direct Numerical Simulation (DNS) plays a significant role in turbulent flow simulation modeling due to the improvement of computer technology, but it can still be too expensive and time-consuming. In this study, a multivariate Levenberg-Marquardt (LM) neural network is developed to predict the turbulent flow over backward-facing step using information from a DNS. A multi-task learning technique is used to improve generalization performance and the prediction accuracy of flow field properties with low computational cost. A novel approach is applied to determine the optimum number of hidden units in order to prevent overfitting. Radial Basis Function (RBF) networking is employed to evaluate the effectiveness of the proposed approach in predicting hidden units. The excellent performance of the Levenberg-Marquardt Artificial Neural Network is shown based on the estimation of the flow fields with a large amount of data, such as in the case of the backward-facing step flow. The results of the LM neural network demonstrate that the proposed method provides a practical guideline for the estimation of the number of hidden units essentially needed for Artificial Neural network (ANN) simulations.
Exploring the Use of Data-Driven Approaches for Anomaly Detection in the Internet of Things (IoT) Environment

—The Internet of Things (IoT) is a system that connects physical computing devices, sensors, software, and other technologies. Data can be collected, transferred, and exchanged with other devices over the network without requiring human interactions. One challenge the development of IoT faces is the existence of anomaly data in the network. Therefore, research on anomaly detection in the IoT environment has become popular and necessary in recent years. This survey provides an overview to understand the current progress of the different anomaly detection algorithms and how they can be applied in the context of the Internet of Things. In this survey, we categorize the widely used anomaly detection machine learning and deep learning techniques in IoT into three types: clustering-based, classification-based, and deep learning-based. For each category, we introduce some state-of-the-art anomaly detection methods and evaluate the advantages and limitations of each technique.