Miracle-dz/UniFAR-FTUs huggingface dataset:
Extreme measures in continuous time Conic Finance

Dynamic spectral risk measures define a claim's valuation bounds as supremum and infimum of expectations of the claim's payoff over a dominated set of measures. The measures at which such extrema are attained are called extreme measures. We determine explicit expressions for their Radon-Nykodim derivatives with respect to the common dominating measure. Based on the formulas found, we estimate the extreme measures in two cases. First, the dominating measure is calibrated to mid prices of options and valuation bounds are given by options bid and ask prices. Second, the dominating measure is estimated from historical mid equity prices and valuation bounds are given by historical 5-day high and low prices. In both experiments, we find that the market determines upper bounds by testing scenarios in which losses are significantly lower than expected under the dominating measure, while lower bounds by ones in which gains are only slightly lower than in the base case.
Assessment of local isotropy, Kolmogorov constant, and modified eddy viscosity-based modeling for particle-laden turbulent channel flows

A large number of models which address the dynamics of particle-laden turbulent ﬂows have been developed based on the assumption of local isotropy and use the Kolmogorov constant that corre-lates the spectral distribution of turbulent kinetic energy with the turbulent dissipation rate. Many turbulence models (Stochastic and LES models) use the Kolmogorov constant in the formulation. Compilation of a large number of experimental data for diﬀerent ﬂow conﬁgurations has revealed that the Kolmogorov constant is independent of Reynolds number in the limit of high Reynolds number [1]. However, several numerical studies at low and intermediate Reynolds numbers which address the ﬂow situations of practical importance consider that the Kolmogorov constant remains unchanged irrespective of whether the ﬂow is single phase or multiphase. In the present work, we assess the variation of local isotropy of ﬂuid ﬂuctuations with the increase in particle loading in particle-laden turbulent channel ﬂows. We also estimate the Kolmogorov constant using second-order velocity structure functions and compensated spectra in case of low Reynolds number turbulent ﬂows. Our study reveals that the Kolmogorov constant decreases in the channel center with an increase in the particle volume fraction for the range of Reynolds number investigated here. The estimated variation of the Kolmogorov constant is used to express the Smagorinsky coeﬃcient as a function of solid loading in particle-laden ﬂows. Then, a new modeling technique is adopted using the large eddy simulation (LES) to predict the ﬂuid phase statistics without solving simultaneous particle phase equations. This new methodology also helps understand the drastic decrease in turbulence intensity at critical particle volume loading. turbulent channel ﬂows at two bulk Reynolds numbers and diﬀerent Stokes numbers over a range of particle volume fractions. It is observed that the local isotropy of small and large scales decreases with an increase in particle volume loading. We report the variation of the Kolmogorov constant with an increase in volume loading at low and moderate Reynolds numbers due to an increase in anisotropy of ﬂuid velocity ﬂuctuations. In the near-wall region ( y + = 15), a decrease in the Kolmogorov constant is observed when estimated via the second-order velocity structure
A Large (≈ 1 pc) Contracting Envelope Around the Prestellar Core L1544

Prestellar cores, the birthplace of Sun-like stars, form from the fragmentation of the filamentary structure that composes molecular clouds, from which they must inherit at least partially the kinematics. Furthermore, when they are on the verge of gravitational collapse, they show signs of subsonic infall motions. How extended these motions are, which depends on how the collapse occurs, remains largely unknown. We want to investigate the kinematics of the envelope that surrounds the prototypical prestellar core L1544, studying the cloud-core connection. To our aims, we observed the HCO+ (1–0) transition in a large map. HCO+ is expected to be abundant in the envelope, making it an ideal probe of the large-scale kinematics in the source. We modeled the spectrum at the dust peak by means of a nonlocal thermodynamical equilibrium radiative transfer. In order to reproduce the spectrum at the dust peak, a large (∼1 pc) envelope is needed, with low density (tens of cm−3 at most) and contraction motions, with an inward velocity of ≈ 0.05 km s−1. We fitted the data cube using the Hill5 model, which implements a simple model for the optical depth and excitation temperature profiles along the line of sight, in order to obtain a map of the infall velocity. This shows that the infall motions are extended, with typical values in the range 0.1–0.2 km s−1. Our results suggest that the contraction motions extend in the diffuse envelope surrounding the core, which is consistent with recent magnetic field measurements in the source, which showed that the envelope is magnetically supercritical.
The continuous-time pre-commitment KMM problem in incomplete markets

This paper studies the continuous-time pre-commitment KMM problem proposed by Klibanoff, Marinacci and Mukerji (2005) in incomplete financial markets, which concerns with the portfolio selection under smooth ambiguity. The decision maker (DM) is uncertain about the dominated priors of the financial market, which are characterized by a second-order distribution (SOD). The KMM model separates risk attitudes and ambiguity attitudes apart and the aim of the DM is to maximize the two-fold utility of terminal wealth, which does not belong to the classical subjective utility maximization problem. By constructing the efficient frontier, the original KMM problem is first simplified as an one-fold expected utility problem on the second-order space. In order to solve the equivalent simplified problem, this paper imposes an assumption and introduces a new distorted Legendre transformation to establish the bipolar relation and the distorted duality theorem. Then, under a further assumption that the asymptotic elasticity of the ambiguous attitude is less than 1, the uniqueness and existence of the solution to the KMM problem are shown and we obtain the semi-explicit forms of the optimal terminal wealth and the optimal strategy. Explicit forms of optimal strategies are presented for CRRA, CARA and HARA utilities in the case of Gaussian SOD in a Black-Scholes financial market, which show that DM with higher ambiguity aversion tends to be more concerned about extreme market conditions with larger bias. In the end of this work, numerical comparisons with the DMs ignoring ambiguity are revealed to illustrate the effects of ambiguity on the optimal strategies and value functions.
DeXtreme: Transfer of Agile In-hand Manipulation from Simulation to Reality

Recent work has demonstrated the ability of deep reinforcement learning (RL) algorithms to learn complex robotic behaviours in simulation, including in the domain of multi-fingered manipulation. However, such models can be challenging to transfer to the real world due to the gap between simulation and reality. In this paper, we present our techniques to train a) a policy that can perform robust dexterous manipulation on an anthropomorphic robot hand and b) a robust pose estimator suitable for providing reliable real-time information on the state of the object being manipulated. Our policies are trained to adapt to a wide range of conditions in simulation. Consequently, our vision-based policies significantly outperform the best vision policies in the literature on the same reorientation task and are competitive with policies that are given privileged state information via motion capture systems. Our work reaffirms the possibilities of sim-to-real transfer for dexterous manipulation in diverse kinds of hardware and simulator setups, and in our case, with the Allegro Hand and Isaac Gym GPU-based simulation. Furthermore, it opens up possibilities for researchers to achieve such results with commonly-available, affordable robot hands and cameras. Videos of the resulting policy and supplementary information, including experiments and demos, can be found on the website.
The Robustness Limits of SoTA Vision Models to Natural Variation

Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classification. While previous generations of vision models were shown to lack robustness to factors such as pose, it's unclear the extent to which this next generation of models are more robust. To study this question, we develop a dataset of more than 7 million images with controlled changes in pose, position, background, lighting, and size. We study not only how robust recent state-of-the-art models are, but also the extent to which models can generalize variation in factors when they're present during training. We consider a catalog of recent vision models, including vision transformers (ViT), self-supervised models such as masked autoencoders (MAE), and models trained on larger datasets such as CLIP. We find out-of-the-box, even today's best models are not robust to common changes in pose, size, and background. When some samples varied during training, we found models required a significant portion of diversity to generalize -- though eventually robustness did improve. When diversity is only seen for some classes however, we found models did not generalize to other classes, unless the classes were very similar to those seen varying during training. We hope our work will shed further light on the blind spots of SoTA models and spur the development of more robust vision models.
Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding

Dialogue understanding tasks often necessitate abundant annotated data to achieve good performance and that presents challenges in low-resource settings. To alleviate this barrier, we explore few-shot data augmentation for dialogue understanding by prompting large pre-trained language models and present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. Models fine-tuned on our augmented data mixed with few-shot ground truth data are able to approach or surpass existing state-of-the-art performance on both datasets. For DailyDialog specifically, using 10% of the ground truth data we outperform the current state-of-the-art model which uses 100% of the data.
Online Model Error Correction With Neural Networks in the Incremental 4D‐Var Framework

Recent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System.
Vitruvio: 3D Building Meshes via Single Perspective Sketches

Today's architectural engineering and construction (AEC) software require a learning curve to generate a three-dimension building representation. This limits the ability to quickly validate the volumetric implications of an initial design idea communicated via a single sketch. Allowing designers to translate a single sketch to a 3D building will enable owners to instantly visualize 3D project information without the cognitive load required. If previous state-of-the-art (SOTA) data-driven methods for single view reconstruction (SVR) showed outstanding results in the reconstruction process from a single image or sketch, they lacked specific applications, analysis, and experiments in the AEC. Therefore, this research addresses this gap, introducing the first deep learning method focused only on buildings that aim to convert a single sketch to a 3D building mesh: Vitruvio. Vitruvio adapts Occupancy Network for SVR tasks on a specific building dataset (Manhattan 1K). This adaptation brings two main improvements. First, it accelerates the inference process by more than 26% (from 0.5s to 0.37s). Second, it increases the reconstruction accuracy (measured by the Chamfer Distance) by 18%. During this adaptation in the AEC domain, we evaluate the effect of the building orientation in the learning procedure since it constitutes an important design factor. While aligning all the buildings to a canonical pose improved the overall quantitative metrics, it did not capture fine-grain details in more complex building shapes (as shown in our qualitative analysis). Finally, Vitruvio outputs a 3D-printable building mesh with arbitrary topology and genus from a single perspective sketch, providing a step forward to allow owners and designers to communicate 3D information via a 2D, effective, intuitive, and universal communication medium: the sketch.
Toward an Intelligent Tutoring System for Argument Mining in Legal Texts

We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our experiments focused on the feasibility of the proposed framework are promising. We show that the system is capable of identifying a potential error in the analysis with very low false positives rate (2.0-3.5%), as well as of predicting the key argument element type (e.g., an issue or a holding) with a reasonably high F1-score (0.74).
Microquasars as heating sources of the intergalactic medium during reionization of the Universe

The effect of the primeval sources of radiation and particles on the thermodynamical state of the intergalactic medium during the epoch of reionization is still unclear. In this work, we explore the contribution of electrons accelerated in the jets of high-redshift microquasars to heating and ionizing the intergalactic medium. We develop Monte Carlo simulations of the propagation and energy deposition of these electrons as they travel away from their sources. We find that microquasars contribute significantly to heating the intergalactic medium and are effective ionizers only near the galaxies. Their effect on heating is of the same order of magnitude as that of cosmic rays from supernovae.
A Molecular Gas Ring Hidden in the Sombrero Galaxy

We present Herschel, ALMA, and MUSE observations of the molecular ring of Messier 104, also known as the Sombrero galaxy. These previously unpublished archival data shed new light on the content of the interstellar medium of M104. In particular, molecular hydrogen measured by CO emission and dust measured by far-infrared light are uniformly distributed along the ring. The ionized gas revealed by Hα and [C ii] emission is distributed in knots along the ring. Despite being classified as an SAa galaxy, M104 displays features typical of early-type galaxies. We therefore compared its [C ii] and dust emission to a sample of early-type galaxies observed with Herschel and SOFIA. The [C ii]/FIR ratio of M104 is much lower than that of typical star-forming galaxies and is instead much more similar to that of early-type galaxies. By classifying regions using optical emission line diagnostics, we also find that regions classified as H ii lie closer to star-forming galaxies in the [C ii]/FIR diagram than those classified as low-ionization emission regions. The good match between [C ii] and Hα emission, in conjunction with the lack of correlation between CO emission and star formation, suggests that there is very limited active star formation along the ring and that most of the [C ii] emission is from ionized and neutral atomic gas rather than molecular gas. From the total intensity of the CO line, we estimate a molecular hydrogen mass of 0.9 × 109 M ⊙, a value intermediate between those of early-type galaxies and the content of the molecular ring of our galaxy.
Strong manipulation of the valley splitting upon twisting and gating in MoSe$_2$/CrI$_3$ and WSe$_2$/CrI$_3$ van der Waals heterostructures

We investigate the twist-angle and gate dependence of the proximity-induced exchange coupling in the monolayer transition-metal dichalcogenides (TMDCs) MoSe$_2$ and WSe$_2$ due to the vdW coupling to the ferromagnetic semiconductor CrI$_3$, from first-principles calculations. A model Hamiltonian, that captures the relevant band edges at the $K/K^{\prime}$ valleys of the proximitized TMDCs, is employed to quantify the proximity-induced exchange. Upon twisting from 0{\deg} to 30{\deg}, we find a transition of the TMDC valence band (VB) edge exchange splitting from about $-2$ to $2$ meV, while the conduction band (CB) edge exchange splitting remains nearly unchanged at around $-3$ meV. For the VB of WSe$_2$ (MoSe$_2$) on CrI$_3$, the exchange coupling changes sign at around 8{\deg} (16{\deg}). We find that even at the angles with almost zero spin splittings of the VB, the real-space spin polarization profile of holes at the band edge is highly non-uniform, with alternating spin up and spin down orbitals. Furthermore, a giant tunability of the proximity-induced exchange coupling is provided by a transverse electric field of a few V/nm. We complement our \textit{ab initio} results by calculating the excitonic valley splitting to provide experimentally verifiable optical signatures of the proximity exchange. Specifically, we predict that the valley splitting increases almost linearly as a function of the twist angle. Furthermore, the proximity exchange is highly tunable by gating, allowing to tailor the valley splitting in the range of 0 to 12 meV in WSe$_2$/CrI$_3$, which is equivalent to external magnetic fields of up to about 60 Tesla. Our results highlight the important impact of the twist angle and gating when employing magnetic vdW heterostructures in experimental geometries.
Massless Schwinger model with a 4-fermi-interaction at topological angle $\theta = \pi$

We study the massless Schwinger model with an additional 4-fermi interaction and a topological term. For topological angle θ = π charge conjugation symmetry is implemented in a non-trivial way and we study the possibility of its spontaneous breaking. For the lattice discretization we use staggered fermions and the Villain action for the gauge ﬁelds, where the topological term is an integer and charge conjugation at θ = π is an exact symmetry. The complex action problem is overcome by a suitable worldline/worldsheet representation. We ﬁnd that as a function of the 4-fermi coupling the system shows a critical point separating a weak coupling phase where charge conjugation symmetry is intact from a strong coupling phase with spontaneously broken charge conjugation symmetry.
Proximal Mean Field Learning in Shallow Neural Networks

We propose a custom learning algorithm for shallow over-parameterized neural networks, i.e., networks with single hidden layer having infinite width. The infinite width of the hidden layer serves as an abstraction for the over-parameterization. Building on the recent mean field interpretations of learning dynamics in shallow neural networks, we realize mean field learning as a computational algorithm, rather than as an analytical tool. Specifically, we design a Sinkhorn regularized proximal algorithm to approximate the distributional flow for the learning dynamics over weighted point clouds. In this setting, a contractive fixed point recursion computes the time-varying weights, numerically realizing the interacting Wasserstein gradient flow of the parameter distribution supported over the neuronal ensemble. An appealing aspect of the proposed algorithm is that the measure-valued recursions allow meshless computation. We demonstrate the proposed computational framework of interacting weighted particle evolution on binary and multi-class classification. Our algorithm performs gradient descent of the free energy associated with the risk functional.
Does Self-Rationalization Improve Robustness to Spurious Correlations?

Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been investigated for their interpretability and utility to end-users. However, the extent to which training with human-written rationales facilitates learning remains an under-explored question. We ask whether training models to self-rationalize can aid in their learning to solve tasks for the right reasons. Specifically, we evaluate how training self-rationalization models with free-text rationales affects robustness to spurious correlations in fine-tuned encoder-decoder and decoder-only models of six different sizes. We evaluate robustness to spurious correlations by measuring performance on 1) manually annotated challenge datasets and 2) subsets of original test sets where reliance on spurious correlations would fail to produce correct answers. We find that while self-rationalization can improve robustness to spurious correlations in low-resource settings, it tends to hurt robustness in higher-resource settings. Furthermore, these effects depend on model family and size, as well as on rationale content. Together, our results suggest that explainability can come at the cost of robustness; thus, appropriate care should be taken when training self-rationalizing models with the goal of creating more trustworthy models.