Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesoscale Time and Space Wind Variability

Abstract The results of a program to study the time and space variability of the wind field below 5 km are presented. Wind data were collected using pilot balloons tracked with the METRAC positioning system. Balloons were launched at 10-30 min intervals from a single launch point and at 30 min intervals from pairs of launch points separated by 20 m, 4.415 km and 20.910 km. Winds were averaged over 100 m layers. Experiments were separated by cyclonic and anticyclonic weather patterns. It was found that the average time variability of the wind for anticyclonic weather patterns followed a 1/3 power law for time lags from 30 to 300 min. The result of frontal passages during the cyclonic experiments increased the power law relationship to about 1/2. The wind variability for a space separation of 4.415 km was found to correspond to a time lag of ∼ 17 min. The wind variability for a space separation of 20.910 km was found to correspond to a time lag of ∼90 min though this estimate is based upon relatively few ob.
“If I Had All the Time in the World”: Ophthalmologists’ Perceptions of Anchoring Bias Mitigation in Clinical AI Support

Clinical needs and technological advances have resulted in increased use of Artificial Intelligence (AI) in clinical decision support. However, such support can introduce new and amplify existing cognitive biases. Through contextual inquiry and interviews, we set out to understand the use of an existing AI support system by ophthalmologists. We identified concerns regarding anchoring bias and a misunderstanding of the AI’s capabilities. Following, we evaluated clinicians’ perceptions of three bias mitigation strategies as integrated into their existing decision support system. While clinicians recognised the danger of anchoring bias, we identified a concern around the impact of bias mitigation on procedure time. Our participants were divided in their expectations of any positive impact on diagnostic accuracy, stemming from varying reliance on the decision support. Our results provide insights into the challenges of integrating bias mitigation into AI decision support.
Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling

We propose a cross-lingual neural codec language model, VALL-E X, for cross-lingual speech synthesis. Specifically, we extend VALL-E and train a multi-lingual conditional codec language model to predict the acoustic token sequences of the target language speech by using both the source language speech and the target language text as prompts. VALL-E X inherits strong in-context learning capabilities and can be applied for zero-shot cross-lingual text-to-speech synthesis and zero-shot speech-to-speech translation tasks. Experimental results show that it can generate high-quality speech in the target language via just one speech utterance in the source language as a prompt while preserving the unseen speaker's voice, emotion, and acoustic environment. Moreover, VALL-E X effectively alleviates the foreign accent problems, which can be controlled by a language ID. Audio samples are available at \url{https://aka.ms/vallex}.
Investigating and modeling day-to-day route choices based on laboratory experiments. Part II: A route-dependent attraction-based stochastic process model

To explain day-to-day (DTD) route-choice behaviors and traffic dynamics observed in a series of lab experiments, Part I of this research proposed a discrete choice-based analytical dynamic model (Qi et al., 2023). Although the deterministic model could well reproduce the experimental observations, it converges to a stable equilibrium of route flow while the observed DTD evolution is apparently with random oscillations. To overcome the limitation, the paper proposes a route-dependent attraction-based stochastic process (RDAB-SP) model based on the same behavioral assumptions in Part I of this research. Through careful comparison between the model-based estimation and experimental observations, it is demonstrated that the proposed RDAB-SP model can accurately reproduce the random oscillations both in terms of flow switching and route flow evolution. To the best of our knowledge, this is the first attempt to explain and model experimental observations by using stochastic process DTD models, and it is interesting to find that the seemingly unanticipated phenomena (i.e., random route switching behavior) is actually dominated by simple rules, i.e., independent and probability-based route-choice behavior. Finally, an approximated model is developed to help simulate the stochastic process and evaluate the equilibrium distribution in a simple and efficient manner, making the proposed model a useful and practical tool in transportation policy design.
Multi-point and multi-interval availabilities for Markov repairable systems with history-dependent up and down states

A Markov repairable system with history-dependent up and down states is presented. The state space of the repairable system is divided into three sets, which are up states, down states and changeable states. Using aggregated stochastic processes, we can get the multi-interval availability and multi-point availability for the Markov repairable system. Finally, numerical examples are given to illustrate the obtained results.
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception

Semantic occupancy perception is essential for autonomous driving, as automated vehicles require a fine-grained perception of the 3D urban structures. However, existing relevant benchmarks lack diversity in urban scenes, and they only evaluate front-view predictions. Towards a comprehensive benchmarking of surrounding perception algorithms, we propose OpenOccupancy, which is the first surrounding semantic occupancy perception benchmark. In the OpenOccupancy benchmark, we extend the large-scale nuScenes dataset with dense semantic occupancy annotations. Previous annotations rely on LiDAR points superimposition, where some occupancy labels are missed due to sparse LiDAR channels. To mitigate the problem, we introduce the Augmenting And Purifying (AAP) pipeline to ~ 2× densify the annotations, where ∼4000 human hours are involved in the labeling process. Besides, camera-based, LiDAR-based and multi-modal baselines are established for the OpenOccupancy benchmark. Furthermore, considering the complexity of surrounding occupancy perception lies in the computational burden of high-resolution 3D predictions, we propose the Cascade Occupancy Network (CONet) to refine the coarse prediction, which relatively enhances the performance by ∼30% than the baseline. We hope the OpenOccupancy benchmark‡ will boost the development of surrounding occupancy perception algorithms.
Expressivity of Shallow and Deep Neural Networks for Polynomial Approximation

This study explores the number of neurons required for a Rectified Linear Unit (ReLU) neural network to approximate multivariate monomials. We establish an exponential lower bound on the complexity of any shallow network approximating the product function over a general compact domain. We also demonstrate this lower bound doesn't apply to normalized Lipschitz monomials over the unit cube. These findings suggest that shallow ReLU networks experience the curse of dimensionality when expressing functions with a Lipschitz parameter scaling with the dimension of the input, and that the expressive power of neural networks is more dependent on their depth rather than overall complexity.
Tidal deformability of fermion-boson stars: Neutron stars admixed with ultralight dark matter

In this work we investigate the tidal deformability of a neutron star admixed with dark matter, modeled as a massive, self-interacting, complex scalar field. We derive the equations to compute the tidal deformability of the full Einstein-Hilbert-Klein-Gordon system self-consistently, and probe the influence of the scalar field mass and self-interaction strength on the total mass and tidal properties of the combined system. We find that dark matter core-like configurations lead to more compact objects with smaller tidal deformability, and dark matter cloud-like configurations lead to larger tidal deformability. Electromagnetic observations of certain cloud-like configurations would appear to violate the Buchdahl limit. The self-interaction strength is found to have a significant effect on both mass and tidal deformability. We discuss observational constraints and the connection to anomalous detections. We also investigate how this model compares to those with an effective bosonic equation of state and find the interaction strength where they converge sufficiently.
On the Origin of Dust Structures in Protoplanetary Disks: Constraints from the Rossby Wave Instability

High-resolution submillimeter observations of protoplanetary disks with ALMA have revealed that dust rings are common in large, bright disks. The leading explanation for these structures is dust trapping in a local gas pressure maximum, caused by an embedded planet or other dynamical process. Independent of origin, such dust traps should be stable for many orbits to collect significant dust. However, ringlike perturbations in gas disks are also known to trigger the Rossby wave instability (RWI). We investigate whether axisymmetric pressure bumps can simultaneously trap dust and remain stable to the RWI. The answer depends on the thermodynamic properties of pressure bumps. For isothermal bumps, dust traps are RWI stable for widths from ∼1 to several gas scale heights. Adiabatic dust traps are stable over a smaller range of widths. For temperature bumps with no surface density component, however, all dust traps tend to be unstable. Smaller values of disk aspect ratio allow stable dust trapping at lower bump amplitudes and over a larger range of widths. We also report a new approximate criterion for RWI. Instability occurs when the radial oscillation frequency is ≲75% of the Keplerian frequency, which differs from the well-known Lovelace necessary (but not sufficient) criterion for instability. Our results can guide ALMA observations of molecular gas by constraining the resolution and sensitivity needed to identify the pressure bumps thought to be responsible for dust rings.
Foundation Models for Decision Making: Problems, Methods, and Opportunities

Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, language models are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and generalist interaction. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems that can interact effectively across a diverse range of applications such as dialogue, autonomous driving, healthcare, education, and robotics. In this manuscript, we examine the scope of foundation models for decision making, and provide conceptual tools and technical background for understanding the problem space and exploring new research directions. We review recent approaches that ground foundation models in practical decision making applications through a variety of methods such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning, and discuss common challenges and open problems in the field.
Small scale quasigeostrophic convective turbulence at large Rayleigh number

A numerical investigation of an asymptotically reduced model for quasi-geostrophic Rayleigh-B\'enard convection is conducted in which the depth-averaged flows are numerically suppressed by modifying the governing equations. The Reynolds number and Nusselt number show evidence of approaching the diffusion-free scalings of $Re \sim Ra E/Pr$ and $Nu \sim Pr^{-1/2} Ra^{3/2} E^2$, respectively, where $E$ is the Ekman number and $Pr$ is the Prandtl number. For large $Ra$, the presence of depth-invariant flows, such as large-scale vortices, yield heat and momentum transport scalings that exceed those of the diffusion-free scaling laws. The Taylor microscale does not vary significantly with increasing $Ra$, whereas the integral length scale grows weakly. The computed length scales remain $O(1)$ with respect to the linearly unstable critical wavenumber; we therefore conclude that these scales remain viscously controlled. We do not find a point-wise Coriolis-Inertia-Archimedean (CIA) force balance in the turbulent regime; interior dynamics are instead dominated by horizontal advection (inertia), vortex stretching (Coriolis) and the vertical pressure gradient. A secondary, sub-dominant balance between the buoyancy force and the viscous force occurs in the interior and the ratio of the rms of these two forces is found to approach unity with increasing $Ra$. This secondary balance is attributed to the turbulent fluid interior acting as the dominant control on the heat transport. These findings indicate that a pointwise CIA balance does not occur in the high Rayleigh number regime of quasi-geostrophic convection in the plane layer geometry. Instead, simulations are characterized by what may be termed a \textsl{non-local} CIA balance in which the buoyancy force is dominant within the thermal boundary layers and is spatially separated from the interior Coriolis and inertial forces.
Challenges of the Creation of a Dataset for Vision Based Human Hand Action Recognition in Industrial Assembly

This work presents the Industrial Hand Action Dataset V1, an industrial assembly dataset consisting of 12 classes with 459,180 images in the basic version and 2,295,900 images after spatial augmentation. Compared to other freely available datasets tested, it has an above-average duration and, in addition, meets the technical and legal requirements for industrial assembly lines. Furthermore, the dataset contains occlusions, hand-object interaction, and various fine-grained human hand actions for industrial assembly tasks that were not found in combination in examined datasets. The recorded ground truth assembly classes were selected after extensive observation of real-world use cases. A Gated Transformer Network, a state-of-the-art model from the transformer domain was adapted, and proved with a test accuracy of 86.25% before hyperparameter tuning by 18,269,959 trainable parameters, that it is possible to train sequential deep learning models with this dataset.
Towards Interpretable and Efficient Automatic Reference-Based Summarization Evaluation

Interpretability and efficiency are two important considerations for the adoption of neural automatic metrics. In this work, we develop strong-performing automatic metrics for reference-based summarization evaluation, based on a two-stage evaluation pipeline that first extracts basic information units from one text sequence and then checks the extracted units in another sequence. The metrics we developed include two-stage metrics that can provide high interpretability at both the fine-grained unit level and summary level, and one-stage metrics that achieve a balance between efficiency and interpretability. We make the developed tools publicly available at https://github.com/Yale-LILY/AutoACU.
Lipschitz Stability Estimate and Uniqueness for a Problem for the Mean Field Games System

A Lipschitz stability estimate is derived for the solution of the second order mean ﬁeld games system (MFGS). It is assumed that the initial condition for the value function is given, in addition to the two conventional ones: initial condition for the density function and terminal condition for the value function. That additional initial condition can be interpreted as a result of a measurement, e.g
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation

Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout. These techniques usually incur overhead by having to train multiple model instances or do not produce very diverse predictions. This comprehensive and extensive survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they aim to admit"what they don't know", and fall back onto a prior belief. Furthermore, they allow uncertainty estimation in a single model and forward pass by parameterizing distributions over distributions. This survey recapitulates existing works, focusing on the implementation in a classification setting, before surveying the application of the same paradigm to regression. We also reflect on the strengths and weaknesses compared to other existing methods and provide the most fundamental derivations using a unified notation to aid future research.
The Classification of Non-Euclidean Plane Crystallographic Groups

This paper deals with the algebraic classification of non-euclidean plane crystallographic groups (NEC groups, for short) with compact quotient space. The groups considered are the discrete groups of motions of the Lobatschewsky or hyperbolic plane, including those which contain orientation-reversing reflections and glide-reflections. The corresponding problem for Fuchsian groups, which contain only orientable transformations, is essentially solved in the work of Fricke and Klein (6).
Tight Certification of Adversarially Trained Neural Networks via Nonconvex Low-Rank Semidefinite Relaxations

Adversarial training is well-known to produce high-quality neural network models that are empirically robust against adversarial perturbations. Nevertheless, once a model has been adversarially trained, one often desires a certification that the model is truly robust against all future attacks. Unfortunately, when faced with adversarially trained models, all existing approaches have significant trouble making certifications that are strong enough to be practically useful. Linear programming (LP) techniques in particular face a"convex relaxation barrier"that prevent them from making high-quality certifications, even after refinement with mixed-integer linear programming (MILP) and branch-and-bound (BnB) techniques. In this paper, we propose a nonconvex certification technique, based on a low-rank restriction of a semidefinite programming (SDP) relaxation. The nonconvex relaxation makes strong certifications comparable to much more expensive SDP methods, while optimizing over dramatically fewer variables comparable to much weaker LP methods. Despite nonconvexity, we show how off-the-shelf local optimization algorithms can be used to achieve and to certify global optimality in polynomial time. Our experiments find that the nonconvex relaxation almost completely closes the gap towards exact certification of adversarially trained models.
Why Aggregate Long Memory Time Series?

This article shows that, for large samples, temporally aggregating a true long memory time series (in order to get an improved estimator) may make little or no sense, as the practitioner can get virtually the same estimates as those from the aggregated series by choosing the appropriate bandwidths on the original one, provided some fairly general conditions apply. Besides, the practitioner has a wider choice of bandwidths than she has of aggregating levels. However, these results apply only to two specific and commonly used estimators, and do not apply to the aggregation procedure undertaken to compute the realized volatility. Also, aggregating a time series in order to test true versus spurious long memory (as in Ohanissian et al., 2008) is a relevant issue, particularly regarding stochastic and/or realized volatility, as many nonlinear processes display spurious long memory where the above result does not apply.
Culture-to-Culture Image Translation and User Evaluation

The article introduces the concept of image"culturization,"which we define as the process of altering the ``brushstroke of cultural features"that make objects perceived as belonging to a given culture while preserving their functionalities. First, we defined a pipeline for translating objects' images from a source to a target cultural domain based on state-of-the-art Generative Adversarial Networks. Then, we gathered data through an online questionnaire to test four hypotheses concerning the impact of images belonging to different cultural domains on Italian participants. As expected, results depend on individual tastes and preferences: however, they align with our conjecture that some people, during the interaction with an intelligent system, will prefer to be shown images modified to match their cultural background. The study has two main limitations. First, we focussed on the culturization of individual objects instead of complete scenes. However, objects play a crucial role in conveying cultural meanings and can strongly influence how an image is perceived within a specific cultural context. Understanding and addressing object-level translation is a vital step toward achieving more comprehensive scene-level translation in future research. Second, we performed experiments with Italian participants only. We think that there are unique aspects of Italian culture that make it an interesting and relevant case study for exploring the impact of image culturization. Italy is a very culturally conservative society, and Italians have specific sensitivities and expectations regarding the accurate representation of their cultural identity and traditions, which can shape individuals' preferences and inclinations toward certain visual styles, aesthetics, and design choices. As a consequence, we think they are an ideal candidate for a preliminary investigation of image culturization.
A study of resonance structure in helium using metastable excitation by electron impact with high energy resolution

The cross section for excitation of metastable states of helium by electron impact has been studied as a function of incident energy up to the ionization potential. The energy scale is established by determining accurately the position on the metastable spectra of the 23S and 21S levels, whose energies are precisely known from spectroscopic data. The positions of the numerous resonance structures observed in the cross section are determined and the values obtained compared with the results of previous electron transmission and metastable excitation experiments. The energy of the 22S resonance, observed in an elastic scattering experiment performed simultaneously, is found to be 19.367+or-0.007 eV. A modified Rydberg formula is used to predict energies of the n=3 and 4 resonance states, and the correspondences between the predicted and observed energies are used to suggest classifications for the observed resonances.
The Reverend and the Ravens: Comment on Seawright

The purpose of this Comment is to put the current debate regarding testing necessary conditions into perspective and to point out a particularly troubling aspect of the “all cases” research design (Seawright 2002). Prior to the recent spate of books and articles in the social sciences (Ragin 1987; Dion 1988; Braumoeller and Goertz 2000), the debate over the testing or the confirmation of necessary conditions took place in the philosophical literature, mainly in terms of Hempel's (1945) paradox of the ravens. In what follows, I briefly review Hempel's paradox and the Bayesian solution to it. I argue that Seawright's account, while Bayesian in nature, relies on an assumption that no Bayesian would be willing to make.