Miracle-dz/UniFAR-FTUs huggingface dataset:
Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge

Modern deep learning-based 3D pose estimation approaches require plenty of 3D pose annotations. However, existing 3D datasets lack diversity, which limits the performance of current methods and their generalization ability. Although existing methods utilize 2D pose annotations to help 3D pose estimation, they mainly focus on extracting 2D structural constraints from 2D poses, ignoring the 3D information hidden in the images. In this paper, we propose a novel method to extract weak 3D information directly from 2D images without 3D pose supervision. Firstly, we utilize 2D pose annotations and perspective prior knowledge to generate the relationship of that keypoint is closer or farther from the camera, called relative depth. We collect a 2D pose dataset (MCPC) and generate relative depth labels. Based on MCPC, we propose a weakly-supervised pre-training (WSP) strategy to distinguish the depth relationship between two points in an image. WSP enables the learning of the relative depth of two keypoints on lots of in-the-wild images, which is more capable of predicting depth and generalization ability for 3D human pose estimation. After fine-tuning on 3D pose datasets, WSP achieves state-of-the-art results on two widely-used benchmarks.
Peer Effects in Active Labour Market Policies

This paper studies peer effects in the context of public sponsored vocational training for jobseekers in Germany. Using rich administrative data, I investigate how individual labour market outcomes of program participants are affected by the peer “quality” in the course, focusing on the employability of the peers. To identify a causal effect, I exploit quasi-random variation in the peer group composition within courses offered by the same training providers over time. I find strong evidence that the peer composition matters. Greater average exposure to highly-employable peers has a moderate positive impact on job stability after program participation. Peer effects on earnings are large and differ by program type. They are positive, and long-lasting in classic vocational training and negative but of short duration in retraining. Jobseekers with an individual employability below the median benefit comparatively more across all programs. Overall, the results suggest that peer effects depend on specific program features.
Sequential Analysis for Censored Regression Data

Abstract Motivated by Buckley and James' modification of the least squares procedure for censored regression data, we derive a score process that incorporates the time evolution and handles staggered entry data in a natural way. By censored regression data we mean that the responses are subject to a possible right censorship. Such cases often arise from clinical trials and industrial life tests. The score process can be interpreted as a weighted comparison of (transformed) survival times. This is especially suitable for the accelerated failure time regression model. By expressing it in an appropriate form so that the counting process and its associate martingale theory can be applied, we show that the score process is approximated by a mean 0 multidimensional Gaussian process. A consistent estimator of its covariance matrix function is provided. Based on the covariance matrix estimator and the sequential test of Slud and Wei, a repeated significance test is then proposed. Usefulness of the procedure is il.
Descent modulus and applications

The norm of the gradient k∇ f ( x ) k measures the maximum descent of a real-valued smooth function f at x . For (nonsmooth) convex functions, this is expressed by the distance dist(0 , ∂f ( x )) of the subdiﬀerential to the origin, while for general real-valued functions deﬁned on metric spaces by the notion of metric slope |∇ f | ( x ). In this work we propose an axiomatic deﬁnition of descent modulus T [ f ]( x ) of a real-valued function f at every point x , deﬁned on a general (not necessarily metric) space. The deﬁnition encompasses all above instances as well as average descents for functions deﬁned on probability spaces. We show that a large class of functions are completely determined by their descent modulus and corresponding critical values. This result is already surprising in the smooth case: a one-dimensional information (norm of the gradient) turns out to be almost as powerful as the knowledge of the full gradient mapping. In the nonsmooth case, the key element for this determination result is the break of symmetry induced by a downhill orientation, in the spirit of the deﬁnition of the metric slope. The particular case of functions deﬁned on ﬁnite spaces is studied in the last section. In this case, we obtain an explicit classiﬁcation of descent operators that are, in some sense, typical.
Knowledge Prompting for Few-shot Action Recognition

Few-shot action recognition in videos is challenging for its lack of supervision and difﬁculty in generalizing to unseen actions. To address this task, we propose a simple yet effec- tive method, called knowledge prompting, which leverages commonsense knowledge of actions from external resources to prompt a powerful pre-trained vision-language model for few-shot classiﬁcation. We ﬁrst collect large-scale language descriptions of actions, deﬁned as text proposals, to build an action knowledge base. The collection of text proposals is done by ﬁlling in handcraft sentence templates with external action-related corpus or by extracting action-related phrases from captions of Web instruction videos. Then we feed these text proposals into the pre-trained vision-language model along with video frames to generate matching scores of the proposals to each frame, and the scores can be treated as action semantics with strong generalization. Finally, we design a lightweight temporal modeling network to capture the temporal evolution of action semantics for classiﬁcation. Extensive experiments on six benchmark datasets demonstrate that our method generally achieves the state-of-the-art performance while reducing the training overhead to 1‰ of existing methods.
Two Constrasting Interpretations of Fisher's Fundamental Theorem of Natural Selection

Greenland Institute of Natural Resources,Nuuk, GreenlandThe modern interpretation of Fisher’s fundamental theorem of naturalselection is based on Price (1972). Like Fisher, he described the theoremas a balance between a partial increase in fitness caused by naturalselection and a partial decline caused by the deterioration of theenvironment. But to obtain generality, Price sacrificed fitness as aphenotypic trait with a well-defined genetic and environmental compo-nent. This contrasts with a more recent phenotypic interpretation (Witting,2000a), where fitness is treated as a well-defined phenotypic character.The two interpretations are compared, with some discussion of the factthat the generality of the modern interpretation may be in conflict withFisher. As predicted by the phenotypic interpretation, Fisher described thefundamental theorem as if it applies only to an ecological vacuum wherethere are no interactions among individuals.
PhysQ: A Physics Informed Reinforcement Learning Framework for Building Control

Large-scale integration of intermittent renewable energy sources calls for substantial demand side flexibility. Given that the built environment accounts for approximately 40% of total energy consumption in EU, unlocking its flexibility is a key step in the energy transition process. This paper focuses specifically on energy flexibility in residential buildings, leveraging their intrinsic thermal mass. Building on recent developments in the field of data-driven control, we propose PhysQ. As a physics-informed reinforcement learning framework for building control, PhysQ forms a step in bridging the gap between conventional model-based control and data-intensive control based on reinforcement learning. Through our experiments, we show that the proposed PhysQ framework can learn high quality control policies that outperform a business-as-usual, as well as a rudimentary model predictive controller. Our experiments indicate cost savings of about 9% compared to a business-as-usual controller. Further, we show that PhysQ efficiently leverages prior physics knowledge to learn such policies using fewer training samples than conventional reinforcement learning approaches, making PhysQ a scalable alternative for use in residential buildings. Additionally, the PhysQ control policy utilizes building state representations that are intuitive and based on conventional building models, that leads to better interpretation of the learnt policy over other data-driven controllers.
A spatial scan statistic

The scan statistic is commonly used to test if a one dimensional point process is purely random, or if any clusters can be detected. Here it is simultaneously extended in three directions:(i) a spatial scan statistic for the detection of clusters in a multi-dimensional point process is proposed, (ii) the area of the scanning window is allowed to vary, and (iii) the baseline process may be any inhomogeneous Poisson process or Bernoulli process with intensity pro-portional to some known function. The main interest is in detecting clusters not explained by the baseline process. These methods are illustrated on an epidemiological data set, but there are other potential areas of application as well.
Sensitivity study of heavy precipitation in Limited Area Model climate simulations : influence of the size of the domain and the use of the spectral nudging technique

We assess the impact of two sources of uncertainties in a Limited Area Model (LAM) on the representation of intense precipitation: the size of the domain of integration and the use of the spectral nudging technique (driving of the large scales within the domain of integration). We work in a perfect-model approach where the LAM is driven by a General Circulation Model (GCM) run at the same resolution and sharing the same physics and dynamics as the LAM. A set of three 50 km resolution simulations run over Western Europe with the LAM ALADINClimate and the GCM ARPEGE-Climate are performed to address this issue. Results are consistent with previous studies regarding the seaonal-mean fields. Furthermore, they show that neither the use of the spectral nudging nor the choice of a small domain are detrimental to the modeling of heavy precipitation in the present experiment. Corresponding Author. E-mail : jeanne.colin@cnrm.meteo. fr
Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic

It is expected that autonomous vehicles(AVs) and heterogeneous human-driven vehicles(HVs) will coexist on the same road. The safety and reliability of AVs will depend on their social awareness and their ability to engage in complex social interactions in a socially accepted manner. However, AVs are still ineﬃcient in terms of cooperating with HVs and struggle to understand and adapt to human behavior, which is particularly challenging in mixed autonomy. In a road shared by AVs and HVs, the social preferences or individual traits of HVs are unknown to the AVs and diﬀerent from AVs, which are expected to follow a policy, HVs are particularly diﬃcult to forecast since they do not necessarily follow a stationary policy. To address these challenges, we frame the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles’ interests, and safely adapt to other traﬃc situations. In contrast with existing works, we quantify AVs’ social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and inﬂuence the behavior of HVs.
Evaluating the Knowledge Dependency of Questions

The automatic generation of Multiple Choice Questions (MCQ) has the potential to reduce the time educators spend on student assessment significantly. However, existing evaluation metrics for MCQ generation, such as BLEU, ROUGE, and METEOR, focus on the n-gram based similarity of the generated MCQ to the gold sample in the dataset and disregard their educational value.They fail to evaluate the MCQ’s ability to assess the student’s knowledge of the corresponding target fact. To tackle this issue, we propose a novel automatic evaluation metric, coined Knowledge Dependent Answerability (KDA), which measures the MCQ’s answerability given knowledge of the target fact. Specifically, we first show how to measure KDA based on student responses from a human survey.Then, we propose two automatic evaluation metrics, KDA_disc and KDA_cont, that approximate KDA by leveraging pre-trained language models to imitate students’ problem-solving behavior.Through our human studies, we show that KDA_disc and KDA_soft have strong correlations with both (1) KDA and (2) usability in an actual classroom setting, labeled by experts. Furthermore, when combined with n-gram based similarity metrics, KDA_disc and KDA_cont are shown to have a strong predictive power for various expert-labeled MCQ quality measures.
Practice Makes Perfect: an iterative approach to achieve precise tracking for legged robots

Precise trajectory tracking for legged robots can be challenging due to their high degrees of freedom, unmodeled nonlinear dynamics, or random disturbances from the environment. A commonly adopted solution to overcome these challenges is to use optimization-based algorithms and approximate the system with a simplified, reduced-order model. Additionally, deep neural networks are becoming a more promising option for achieving agile and robust legged locomotion. These approaches, however, either require large amounts of onboard calculations or the collection of millions of data points from a single robot. To address these problems and improve tracking performance, this paper proposes a method based on iterative learning control. This method lets a robot learn from its own mistakes by exploiting the repetitive nature of legged locomotion within only a few trials. Then, a torque library is created as a lookup table so that the robot does not need to repeat calculations or learn the same skill over and over again. This process resembles how animals learn their muscle memories in nature. The proposed method is tested on the A1 robot in a simulated environment, and it allows the robot to pronk at different speeds while precisely following the reference trajectories without heavy calculations.
Uncertainty-Aware Vision-Based Metric Cross-View Geolocalization

This paper proposes a novel method for vision-based metric cross-view geolocalization (CVGL) that matches the camera images captured from a ground-based vehicle with an aerial image to determine the vehicle's geo-pose. Since aerial images are globally available at low cost, they represent a potential compromise between two established paradigms of autonomous driving, i.e. using expensive high-definition prior maps or relying entirely on the sensor data captured at runtime. We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We combine multiple vehicle datasets with aerial images from orthophoto providers on which we demonstrate the feasibility of our method. Since the ground truth poses are often inaccurate w.r. t. the aerial images, we implement a pseudo-label approach to produce more accurate ground truth poses and make them publicly available. While previous works require training data from the target region to achieve reasonable localization accuracy (i.e. same-area evaluation), our approach overcomes this limitation and outperforms previous results even in the strictly more challenging cross-area case. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region, which highlights the model's potential for global-scale application. We further integrate the uncertainty-aware predictions in a tracking framework to determine the vehicle's trajectory over time resulting in a mean position error on KITTI-360 of 0.78m.
A closer look at supernovae as seeds for galactic magnetization

Context. Explaining the currently observed magnetic fields in galaxies requires relatively strong seeding in the early Universe. One of the current theories proposes that magnetic seeds on the order of μG were expelled by supernova (SN) explosions after primordial fields of nG strength or weaker were amplified in stellar interiors. Aims. In this work, we take a closer look at this theory and calculate the maximum magnetic energy that can be injected in the interstellar medium by a stellar cluster of mass Mcl based on what is currently known about stellar magnetism. Methods. We consider early-type stars and adopt either a Salpeter or a top-heavy initial mass function. For their magnetic fields, we adopt either a Gaussian or a bimodal distribution. The Gaussian model assumes that all massive stars are magnetized with 103 < ⟨B*⟩< 104 G, while the bimodal, consistent with observations of Milky Way stars, assumes only 5 − 10% of OB stars have 103 < ⟨B*⟩< 104 G, while the rest have 10 < ⟨B*⟩< 102 G. We ignore the effect of magnetic diffusion and assume no losses of magnetic energy. Results. We find that the maximum magnetic energy that can be injected by a stellar population is between 10−10 and 10−7 times the total SN energy. The highest end of these estimates is about five orders of magnitude lower than what is usually employed in cosmological simulations, where about 10−2 of the SN energy is injected as magnetic. Conclusions. Pure advection of the stellar magnetic field by SN explosions is a good candidate for seeding a dynamo, but not enough to magnetize galaxies. Assuming SNe as the main mechanism for galactic magnetization, the magnetic field cannot exceed an intensity of 10−7 G in the best-case scenario for a population of 105 solar masses in a superbubble of 300 pc radius, while more typical values are between 10−10 and 10−9 G. Therefore, other scenarios for galactic magnetization at high redshift need to be explored.
Breaking Free From Fusion Rule: A Fully Semantic-Driven Infrared and Visible Image Fusion

Infrared and visible image fusion plays a vital role in the field of computer vision. Previous approaches make efforts to design various fusion rules in the loss functions. However, these experimental designed fusion rules make the methods more and more complex. Besides, most of them only focus on boosting the visual effects, thus showing unsatisfactory performance for the follow-up high-level vision tasks. To address these challenges, in this letter, we develop a semantic-level fusion network to sufficiently utilize the semantic guidance, emancipating the experimental designed fusion rules. In addition, to achieve a better semantic understanding of the feature fusion process, a fusion block based on the transformer is presented in a multi-scale manner. Moreover, we devise a regularization loss function, together with a training strategy, to fully use semantic guidance from the high-level vision tasks. Compared with state-of-the-art methods, our method does not depend on the hand-crafted fusion loss function. Still, it achieves superior performance on visual quality along with the follow-up high-level vision tasks.
Transport properties of polydisperse hard sphere fluid: Effect of distribution shape and mass scaling

A model polydisperse fluid represents many real fluids such as colloidal suspensions and polymer solutions. In this study, considering a concentrated size-polydisperse hard sphere fluid with size derived from two different distribution functions, namely, uniform and Gaussian and explore the effect of polydispersity and mass scaling on the transport properties in general. A simple analytical solution based on the Boltzmann transport equation is also presented (together with the solution using Chapman-Enskog (CE) method) using which various transport coefficients are obtained. The central idea of our approach is the realization that, in polydisperse system, the collision scattering cross section is proportional to a random variable \textit{z} which is equal to the sum of two random variables $\sigma_i$ and $\sigma_j$ (representing particle diameters), and the distribution of \textit{z} can be written as the convolution of the two distributions $P(\sigma_i)$ and $P(\sigma_j)$. The obtained transport coefficients are expressed as explicit function of polydispersity index, $\delta$, and their dependence on the nature of particle size distribution is explored. It is observed that in the low polydispersity limit, the transport coefficients are found to be insensitive to the type of size distribution functions considered. The analytical results (for diffusion coefficients and thermal conductivity) obtained using Chapman-Enskog method and our simple analytical approach agrees well with the simulation. However, for shear viscosity, our analyical approach agress for $\delta \le 20\%$, while it agrees upto $\delta \approx 40\%$ with the result obtained using CE-method (in the limit $\delta \rightarrow 0$). Interestingly, the effect of scaling mass (i.e., mass proportional to the particle size and thus a random variable) produces no significant qualitative difference.
Strong barriers for weighted quasilinear equations

In potential theory, use of barriers is one of the most important techniques. We construct strong barriers for weighted quasilinear elliptic operators. There are two applications: (i) solvability of Poisson-type equations with boundary singular data, and (ii) a geometric version of Hardy inequality. Our construction method can be applied to a general class of divergence form elliptic operators on domains with rough boundary.
Wake Measurements in ECN's Scaled Wind Farm

In ECN's scaled wind farm the wake evolution is studied in two different situations. A single wake is studied at two different locations downstream of a turbine and a single wake is studied in conjunction with a triple wake. Here, the wake is characterized by the relative wind speed, the turbulence intensity, the vertical wind speed and the turbulence (an)isotropy. Per situation all wake measurements are taken simultaneously together with the inflow conditions.
Event Causality Identification with Causal News Corpus - Shared Task 3, CASE 2022

The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence. This could be seen as a supervised sequence labeling task. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes the work of the 17 teams that submitted their results to our competition and 12 system description papers that were received. The best F1 scores achieved for Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing approaches involved pre-trained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants’ systems in this paper.