Miracle-dz/UniFAR-FTUs huggingface dataset:
Populating 3D Scenes by Learning Human-Scene Interaction

Humans live within a 3D space and constantly interact with it to perform tasks. Such interactions involve physical contact between surfaces that is semantically meaningful. Our goal is to learn how humans interact with scenes and leverage this to enable virtual characters to do the same. To that end, we introduce a novel Human-Scene Interaction (HSI) model that encodes proximal relationships, called POSA for "Pose with prOximitieS and contActs". The representation of interaction is body-centric, which enables it to generalize to new scenes. Specifically, POSA augments the SMPL-X parametric human body model such that, for every mesh vertex, it encodes (a) the contact probability with the scene surface and (b) the corresponding semantic scene label. We learn POSA with a VAE conditioned on the SMPL-X vertices, and train on the PROX dataset, which contains SMPL-X meshes of people interacting with 3D scenes, and the corresponding scene semantics from the PROX-E dataset. We demonstrate the value of POSA with two applications. First, we automatically place 3D scans of people in scenes. We use a SMPL-X model fit to the scan as a proxy and then find its most likely placement in 3D. POSA provides an effective representation to search for "affordances" in the scene that match the likely contact relationships for that pose. We perform a perceptual study that shows significant improvement over the state of the art on this task. Second, we show that POSA’s learned representation of body-scene interaction supports monocular human pose estimation that is consistent with a 3D scene, improving on the state of the art. Our model and code are available for research purposes at https://posa.is. tue.mpg. de.
Necessary condition for sparse optimal control problem with intermediate constraints

This article treats optimal sparse control problems with multiple constraints defined at intermediate points of the time domain. For such problems with intermediate constraints, we first establish a new Pontryagin maximum principle that provides first order necessary conditions for optimality in such problems. Then we announce and employ a new numerical algorithm to arrive at, in a computationally tractable fashion, optimal stateaction trajectories from the necessary conditions given by our maximum principle. Several detailed illustrative examples are included.
Quantum reinforcement learning in continuous action space

Quantum reinforcement learning (QRL) is one promising algorithm proposed for near-term quantum devices. Early QRL proposals are eﬀective at solving problems in discrete action space, but often suﬀer from the curse of dimensionality in the continuous domain due to discretization. To address this problem, we propose a quantum Deep Deterministic Policy Gradient algorithm that is eﬃcient at solving both classical and quantum sequential decision problems in the continuous domain. As an application, our method can solve the quantum state-generation problem in a single shot: it only requires a one-shot optimization to generate a model that outputs the desired control sequence for arbitrary target state. In comparison, the standard quantum control method requires optimizing for each target state.
Direct observation of ultrafast dark exciton formation in monolayer WS2

The exciton landscape in transition metal dichalcogenides is crucial for their optical properties and difficult to measure experimentally, since many exciton states are not accessible to optical interband spectroscopy. Here, we combine a tunable pump, high-harmonic probe laser source with a 3D momentum imaging technique to map photoemitted electrons within the entire first Brillouin zone of monolayer WS2. This gives us momentum-, energyand time-resolved access to excited states that are forming during and after optical excitation on an ultrafast timescale. The high temporal resolution of our experimental setup allows us to follow the formation of a momentumforbidden dark KΣ exciton a few tens of femtoseconds after optical excitation. The experimental results are in excellent agreement to a fully microscopic theory, resolving the temporal and spectral dynamics of bright and dark excitons in WS2. 1 ar X iv :2 01 2. 11 38 5v 1 [ co nd -m at . m tr lsc i] 2 1 D ec 2 02 0
Optimal Ratcheting of Dividends in a Brownian Risk Model

We study the problem of optimal dividend payout from a surplus process governed by Brownian motion with drift under the additional constraint of ratcheting, i.e. the dividend rate can never decrease. We solve the resulting two-dimensional optimal control problem, identifying the value function to be the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. For finitely many admissible dividend rates we prove that threshold strategies are optimal, and for any finite continuum of admissible dividend rates we establish the $\varepsilon$-optimality of curve strategies. This work is a counterpart of Albrecher et al. (2020), where the ratcheting problem was studied for a compound Poisson surplus process with drift. In the present Brownian setup, calculus of variation techniques allow to obtain a much more explicit analysis and description of the optimal dividend strategies. We also give some numerical illustrations of the optimality results.
Detectability of biosignatures on LHS 1140 b

<p>Rocky extrasolar planets orbiting M dwarfs are prime targets in the search for habitable surface conditions and biosignatures with near-future telescopes like the James Webb Space Telescope (JWST) and the Extremely Large Telescope (ELT). Even for the closest known targets the capabilities to characterize Earth-like or CO<sub>2</sub>-dominated atmospheres with JWST or ELT might still be limited to a few molecules such as CO<sub>2</sub> or CH<sub>4</sub>. Hence it would be difficult to draw conclusions on the surface conditions and potential habitability of these planets. In clear H<sub>2</sub>-He atmospheres the molecular features in transmission spectra could be much larger and hence potential biosignatures might be detectable. </p><p>In this study, we investigate the detectability of the potential biosignatures NH<sub>3</sub>, PH<sub>3</sub>, CH<sub>3</sub>Cl, and N<sub>2</sub>O, assuming different H<sub>2</sub>-He atmospheres for the habitable zone super-Earth LHS 1140 b. Recent observations of the atmosphere of LHS 1140 b suggest that the planet might hold a clear H<sub>2</sub>-dominated atmosphere and might show an absorption feature around 1.4 &#181;m due to H<sub>2</sub>O or CH<sub>4</sub> absorption. Here we use the coupled convective-climate-photochemistry model 1D-TERRA to simulate H<sub>2</sub> atmospheres of LHS 1140 b with different amounts of CH<sub>4</sub> and assuming that the planet has an ocean and a biosphere. </p><p>The destruction of the potential biosignatures NH<sub>3</sub>, PH<sub>3</sub>, CH<sub>3</sub>Cl, and N<sub>2</sub>O shows a weak dependence on the concentrations of CH<sub>4</sub>. For weak abundances of CH<sub>4</sub> only 5 to 10 transits are required to detect these molecules with JWST or ELT. However, for CH<sub>4</sub> surface mixing ratios of a few percent only NH<sub>3</sub> and N<sub>2</sub>O might be detectable with less than 10 transits. A scenario with large abundances of CH<sub>4</sub> is consistent with the spectral feature at 1.4 &#181;m and such an atmosphere might allow habitable surface temperatures. If this spectral feature at 1.4 &#181;m originates from H<sub>2</sub>O absorption, the planet is likely not habitable at the surface. </p>
THE CHESAPEAKE BAY INSTITUTE TECHNIQUE FOR THE WINKLER DISSOLVED OXYGEN METHOD

A modified technique for the Winkler determination of dissolved oxygen has been used at the Chesapeake Bay Institute for several years. The modifications result in increased simplicity and speed and reduction in the errors due to manipulation. The unusual features of the technique are the elimination of the volumetric transfer of the final iodine solution and the reagent concentrations. The sources of errors in the various techniques for the Winkler method are discussed in detail by Carpenter (1965) and by Carritt ( 1964)) who also reports the results of an intercomparison experiment.
Reinforcement Learning for Test Case Prioritization

In modern software engineering, Continuous Integration (CI) has become an indispensable step towards systematically managing the life cycles of software development. Large companies struggle with keeping the pipeline updated and operational, in useful time, due to the large amount of changes and addition of features, that build on top of each other and have several developers, working on different platforms. Associated with such software changes, there is always a strong component of Testing. As teams and projects grow, exhaustive testing quickly becomes inhibitive, becoming adamant to select the most relevant test cases earlier, without compromising software quality. This paper extends recent studies on applying Reinforcement Learning to optimize testing strategies. We test its ability to adapt to new environments, by testing it on novel data extracted from a financial institution, yielding a Normalized percentage of Fault Detection (NAPFD) of over $0.6$ using the Network Approximator and Test Case Failure Reward. Additionally, we studied the impact of using Decision Tree (DT) Approximator as a model for memory representation, which failed to produce significant improvements relative to Artificial Neural Networks.
Determination of carbonyl compounds in waters usingtriazine-based hydrazine reagents and liquid chromatography

Triazine-based hydrazine reagents have been developed and applied for the determination of aldehydes in waters. N-Methyl-4-N′,N′-dimethylamino-6-(4 ′-methoxy-1′-naphthyl)-1,3,5-triazine-2-hydrazine (MDMNTH) and 4-N,N-dimethylamino-6-(4′-methoxy-1′-naphthyl)-1,3,5- triazine-2-hydrazine (DMNTH) react with these carbonyl compounds with very high yields under the formation of the respective hydrazones, which are separated by reversed-phase liquid chromatography with binary gradients of acetonitrile and water. Organic modifiers are added to obtain improved peak shapes. UV/vis as well as fluorescence detection has been applied successfully to quantify the hydrazones. The derivatization of aqueous solutions of the aldehydes has been optimized. Limits of detection are in the range between 0.1 μM and 1.0 μM depending on the analyte and the reagent used for derivatization. Although calibration via external standards is possible for higher analyte concentrations, best results are obtained by calibration via the derivatization reaction. Compared to the US EPA method 8315A, the newly developed method is characterized by very easy sample preparation, lower limits of detection without enrichment and excellent reproducibility with an average RSD of 1.5% with DMNTH as reagent and 3.5% with MDMNTH as reagent for a formaldehyde concentration of 1.0 ×10−5 mol L−1. Aldehyde-spiked mineral water samples were analyzed successfully with the new method.
Offline Reinforcement Learning from Images with Latent Space Models

Offline reinforcement learning (RL) refers to the problem of learning policies from a static dataset of environment interactions. Offline RL enables extensive use and re-use of historical datasets, while also alleviating safety concerns associated with online exploration, thereby expanding the real-world applicability of RL. Most prior work in offline RL has focused on tasks with compact state representations. However, the ability to learn directly from rich observation spaces like images is critical for real-world applications such as robotics. In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces. Model-based offline RL algorithms have achieved state of the art results in state based tasks and have strong theoretical guarantees. However, they rely crucially on the ability to quantify uncertainty in the model predictions, which is particularly challenging with image observations. To overcome this challenge, we propose to learn a latent-state dynamics model, and represent the uncertainty in the latent space. Our approach is both tractable in practice and corresponds to maximizing a lower bound of the ELBO in the unknown POMDP. In experiments on a range of challenging image-based locomotion and manipulation tasks, we find that our algorithm significantly outperforms previous offline model-free RL methods as well as state-of-the-art online visual model-based RL methods. Moreover, we also find that our approach excels on an image-based drawer closing task on a real robot using a pre-existing dataset. All results including videos can be found online at https://sites.google. com/view/lompo/ .
A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets. A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field. This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.
Object-Centric Diagnosis of Visual Reasoning

When answering questions about an image, it not only needs knowing what -- understanding the fine-grained contents (e.g., objects, relationships) in the image, but also telling why -- reasoning over grounding visual cues to derive the answer for a question. Over the last few years, we have seen significant progress on visual question answering. Though impressive as the accuracy grows, it still lags behind to get knowing whether these models are undertaking grounding visual reasoning or just leveraging spurious correlations in the training data. Recently, a number of works have attempted to answer this question from perspectives such as grounding and robustness. However, most of them are either focusing on the language side or coarsely studying the pixel-level attention maps. In this paper, by leveraging the step-wise object grounding annotations provided in the GQA dataset, we first present a systematical object-centric diagnosis of visual reasoning on grounding and robustness, particularly on the vision side. According to the extensive comparisons across different models, we find that even models with high accuracy are not good at grounding objects precisely, nor robust to visual content perturbations. In contrast, symbolic and modular models have a relatively better grounding and robustness, though at the cost of accuracy. To reconcile these different aspects, we further develop a diagnostic model, namely Graph Reasoning Machine. Our model replaces purely symbolic visual representation with probabilistic scene graph and then applies teacher-forcing training for the visual reasoning module. The designed model improves the performance on all three metrics over the vanilla neural-symbolic model while inheriting the transparency. Further ablation studies suggest that this improvement is mainly due to more accurate image understanding and proper intermediate reasoning supervisions.
An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations

Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging. Challenges include the deep structure of document-level discourse trees, the requirement of subtle semantic judgments, and the lack of large-scale training corpora. To address such challenges, we propose to exploit robust representations derived from multiple levels of granularity across syntax and semantics, and in turn incorporate such representations in an end-to-end encoder-decoder neural architecture for more resourceful discourse processing. In particular, we first use a pre-trained contextual language model that embodies high-order and long-range dependency to enable finer-grain semantic, syntactic, and organizational representations. We further encode such representations with boundary and hierarchical information to obtain more refined modeling for document-level discourse processing. Experimental results show that our parser achieves the state-of-the-art performance, approaching human-level performance on the benchmarked RST dataset.
Syntactic categories for dependent type theory: sketching and adequacy

We argue that locally Cartesian closed categories form a suitable doctrine for defining dependent type theories, including non-extensional ones. Using the theory of sketches [KPT99], one may define syntactic categories for type theories in a style that resembles the use of Martin-Löf’s Logical Framework [NPS90], following the “judgments as types” principle [HHP93; Mar87]. The concentration of type theories into their locally Cartesian closed categories of judgments is particularly convenient for proving syntactic metatheorems by semantic means (canonicity, normalization, etc.). Perhaps surprisingly, the notion of a context plays no role in the definitions of type theories in this sense, but the structure of a class of display maps can be imposed on a theory post facto wherever needed, as advocated by the Edinburgh school and realized by the %worlds declarations of the Twelf proof assistant [HHP93; PS99; HL07]. Uemura [Uem19] has proposed representable map categories together with a stratified logical framework for similar purposes. The stratification in Uemura’s framework restricts the use of dependent products to be strictly positive, in contrast to the tradition of Martin-Löf’s logical framework [Mar87; NPS90] and Schroeder-Heister’s analysis of higher-level deductions [Sch87]. We prove a semantic adequacy result for locally Cartesian closed categories relative to Uemura’s representable map categories: if a theory is definable in the framework of Uemura, the locally Cartesian closed category that it generates is a conservative (fully faithful) extension of its syntactic representable map category. On this basis, we argue for the use of locally Cartesian closed categories as a simpler alternative to Uemura’s representable map categories.
Spatial Monte Carlo Integration with Annealed Importance Sampling

Evaluating expectations on an Ising model (or Boltzmann machine) is essential for various applications, including statistical machine learning. However, in general, the evaluation is computationally difficult because it involves intractable multiple summations or integrations; therefore, it requires approximation. Monte Carlo integration (MCI) is a well-known approximation method; a more effective MCI-like approximation method was proposed recently, called spatial Monte Carlo integration (SMCI). However, the estimations obtained using SMCI (and MCI) exhibit a low accuracy in Ising models under a low temperature owing to degradation of the sampling quality. Annealed importance sampling (AIS) is a type of importance sampling based on Markov chain Monte Carlo methods that can suppress performance degradation in low-temperature regions with the force of importance weights. In this study, a method is proposed to evaluate the expectations on Ising models combining AIS and SMCI. The proposed method performs efficiently in both high- and low-temperature regions, which is demonstrated theoretically and numerically.
CARLA Real Traffic Scenarios - novel training ground and benchmark for autonomous driving

This work introduces interactive traffic scenarios in the CARLA simulator, which are based on real-world traffic. We concentrate on tactical tasks lasting several seconds, which are especially challenging for current control methods. The CARLA Real Traffic Scenarios (CRTS) is intended to be a training and testing ground for autonomous driving systems. To this end, we open-source the code under a permissive license and present a set of baseline policies. CRTS combines the realism of traffic scenarios and the flexibility of simulation. We use it to train agents using a reinforcement learning algorithm. We show how to obtain competitive polices and evaluate experimentally how observation types and reward schemes affect the training process and the resulting agent's behavior.
Robust Alignment of Multi-Exposed Images With Saturated Regions

It is challenging to align multi-exposed images due to large illumination variations, especially in presence of saturated regions. In this paper, a novel image alignment algorithm is proposed to cope with the multi-exposed images with saturated regions. Specifically, the multi-exposed images are first normalized by using intensity mapping functions (IMFs) in consideration of saturated pixels. Then, the normalized images are coded by using the local binary pattern (LBP). Finally, the coded images are aligned by formulating an optimization problem by using a differentiable Hamming distance. Experimental results show that the proposed algorithm outperforms state-of-the-art alignment methods for multi-exposed images in terms of alignment accuracy and robustness to exposure values.