Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning

Integrating robotic systems in architectural and construction processes is of core interest to increase the efficiency of the building industry. Automated planning for such systems enables design analysis tools and facilitates faster design iteration cycles for designers and engineers. However, generic task-and-motion planning (TAMP) for long-horizon construction processes is beyond the capabilities of current approaches. In this paper, we develop a multi-agent TAMP framework for long horizon problems such as constructing a full-scale building. To this end we extend the Logic-Geometric Programming framework by sampling-based motion planning, a limited horizon approach, and a task-specific structural stability optimization that allow an effective decomposition of the task. We show that our framework is capable of constructing a large pavilion built from several hundred geometrically unique building elements from start to end autonomously.
CycleISP: Real Image Restoration via Improved Data Synthesis

The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, capturing a real dataset is an unacceptably expensive and cumbersome procedure. Consequently, image denoising algorithms are mostly developed and evaluated on synthetic data that is usually generated with a widespread assumption of additive white Gaussian noise (AWGN). While the CNNs achieve impressive results on these synthetic datasets, they do not perform well when applied on real camera images, as reported in recent benchmark datasets. This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline. In this paper, we present a framework that models camera imaging pipeline in forward and reverse directions. It allows us to produce any number of realistic image pairs for denoising both in RAW and sRGB spaces. By training a new image denoising network on realistic synthetic data, we achieve the state-of-the-art performance on real camera benchmark datasets. The parameters in our models are ~5 times lesser than the previous best method for RAW denoising. Furthermore, we demonstrate that the proposed framework generalizes beyond image denoising problem e.g., for color matching in stereoscopic cinema. The source code and pre-trained models are available at https://github.com/swz30/CycleISP.
Are vector vortex beams endowed with any entanglement

Polarization of light beams is one of the most important physical phenomena. But up till now it was only described in the paraxial approximation in which it is considered to be a single degree of freedom that is characterized by the local Stokes parameters over the transverse plane. Based on such a description, vector vortex beams are considered to be entangled in polarization and spatial mode. Here we show that there is not any entanglement in a large class of representative vector vortex beams, including the well-known cylindrical-vector beams. This is achieved by developing an approach to exactly characterize the polarization of a general beam. It is found that the Stokes parameters, when generalized rigorously to a general beam in momentum space, are physical quantities with respect to a natural coordinate system. The so-called Stratton vector determining the natural coordinate system fixes a natural representation for the polarization in which the Pauli matrices represent the intrinsic degree of freedom of the polarization with respect to the natural coordinate system. As a result, the Stratton vector itself shows up as another degree of freedom of the polarization. From this point of view, the light beams specified by a Stratton vector parallel to the propagation axis as well as by the eigenvalues of the Pauli matrix $\hat{\sigma}_1$ are precisely vector vortex beams. They are not endowed with any entanglement.
Weakly-Supervised Salient Object Detection via Scribble Annotations

Compared with laborious pixel-wise dense labeling, it is much easier to label data by scribbles, which only costs 1~2 seconds to label one image. However, using scribble labels to learn salient object detection has not been explored. In this paper, we propose a weakly-supervised salient object detection model to learn saliency from such annotations. In doing so, we first relabel an existing large-scale salient object detection dataset with scribbles, namely S-DUTS dataset. Since object structure and detail information is not identified by scribbles, directly training with scribble labels will lead to saliency maps of poor boundary localization. To mitigate this problem, we propose an auxiliary edge detection task to localize object edges explicitly, and a gated structure-aware loss to place constraints on the scope of structure to be recovered. Moreover, we design a scribble boosting scheme to iteratively consolidate our scribble annotations, which are then employed as supervision to learn high-quality saliency maps. As existing saliency evaluation metrics neglect to measure structure alignment of the predictions, the saliency map ranking may not comply with human perception. We present a new metric, termed saliency structure measure, as a complementary metric to evaluate sharpness of the prediction. Extensive experiments on six benchmark datasets demonstrate that our method not only outperforms existing weakly-supervised/unsupervised methods, but also is on par with several fully-supervised state-of-the-art models (Our code and data is publicly available at: https://github.com/JingZhang617/Scribble_Saliency).
Short-term oscillation and falling dynamics for a water drop dripping in quiescent air

The short-term transient falling dynamics of a dripping water drop in quiescent air has been investigated through both simulation and experiment. The focus is on the short term behavior and the time range considered covers about eight dominant second-mode oscillations of the drop after it is formed. Due to the small fluid inertia the growth of the drop is quasi-static and is well captured by the static pendant drop theory. Nevertheless, the pinching dynamics and the resulting post-formation state of the drop trigger a nonlinear oscillation when the drop falls. The initial shape of the drop when it is just formed is decomposed into spherical harmonic modes. The pinching dynamics such as interface overturning introduces small-scale variation on the drop contour, which in turn contributes to the finite amplitudes of the higher-order modes. Furthermore, the initial kinetic energy when the droplet is just formed is as important as the initial surface energy contained in the drop shape, and is found to amplify the initial oscillation amplitude and to induce a phase shift in the oscillation of all the modes. By incorporating both the initial surface and kinetic energy, the linear model for a free drop oscillation yields very good predictions for the second and third modes. The mode amplitude spectra show both the primary frequencies that are consistent with the Lamb's theory and the secondary frequencies arising from different modes due to nonlinear inter-mode coupling. The complex transient flow inside and outside the drop is induced by the interaction between the falling motion and the nonlinear oscillation. The streamlines indicate that the internal flow is substantially different from the Hill vortex for a falling drop without oscillation. The temporal evolutions of both the internal flow and the wake morphology follow the dominant second oscillation mode.
07 48 8 v 1 [ he pph ] 1 7 M ar 2 02 0 P-wave bottom baryons of the SU ( 3 ) flavor 6

We investigate P -wave bottom baryons of the SU(3) flavor 6F , and systematically study their Dwave decays into ground-state bottom baryons and pseudoscalar mesons. Together with Refs. [1–4], a rather complete study is performed on both mass spectra and decay properties of P -wave bottom baryons, using the method of QCD sum rules and light-cone sum rules within the framework of heavy quark effective theory. Among all the possibilities, we find four Σb, four Ξ ′ b, and six Ωb baryons, with limited widths and so capable of being observed. Their masses, mass splittings within the same multiplets, and decay properties are extracted (summarized in Table VI) for future experimental searchings.
Tunable deconfined quantum criticality and interplay of different valence-bond solid phases

We use quantum Monte Carlo simulations to study an S = 1/2 spin model with competing multi-spin interactions. We find a quantum phase transition between a columnar valence-bond solid (cVBS) and a Néel antiferromagnet (AFM), as in the scenario of deconfined quantum-critical points, as well as a transition between the AFM and a staggered valence-bond solid (sVBS). By continuously varying a parameter, the sVBS–AFM and AFM–cVBS boundaries merge into a direct sVBS–cVBS transition. Unlike previous models with putative deconfined AFM–cVBS transitions, e.g., the standard J–Q model, in our extended J–Q model with competing cVBS and sVBS inducing terms the transition can be tuned from continuous to first-order. We find the expected emergent U(1) symmetry of the microscopically Z4 symmetric cVBS order parameter when the transition is continuous. In contrast, when the transition changes to first-order, the clock-like Z4 fluctuations are absent and there is no emergent higher symmetry. We argue that the confined spinons in the sVBS phase are fracton-like. We also present results for an SU(3) symmetric model with a similar phase diagram. The new family of models can serve as a useful tool for further investigating open questions related to deconfined quantum criticality and its associated emergent symmetries.
R M ] 1 7 M ar 2 02 0 Convex Risk Measures based on Divergence Special issue in honor of Terry Rockafellar

Risk measures connect probability theory or statistics to optimization, particularly to convex optimization. They are nowadays standard in applications of finance and in insurance involving risk aversion. This paper investigates a wide class of risk measures on Orlicz spaces. The characterizing function describes the decision maker’s risk assessment towards increasing losses. We link the risk measures to a crucial formula developed by Rockafellar for the Average Value-atRisk based on convex duality, which is fundamental in correspondingoptimization problems. We characterize the dual and provide complementary representations.
Directionally dependent multi-view clustering using copula model

Recent developments in high-throughput methods have resulted in the collection of high-dimensional data types from multiple sources and technologies that measure distinct yet complementary information. Integrated clustering of such multiple data types or multi-view clustering is critical for revealing pathological insights. However, multi-view clustering is challenging due to the complex dependence structure between multiple data types, including directional dependency. Specifically, genomics data types have pre-specified directional dependencies known as the central dogma that describes the process of information flow from DNA to messenger RNA (mRNA) and then from mRNA to protein. Most of the existing multi-view clustering approaches assume an independent structure or pair-wise (non-directional) dependence between data types, thereby ignoring their directional relationship. Motivated by this, we propose a biology-inspired Bayesian integrated multi-view clustering model that uses an asymmetric copula to accommodate the directional dependencies between the data types. Via extensive simulation experiments, we demonstrate the negative impact of ignoring directional dependency on clustering performance. We also present an application of our model to a real-world dataset of breast cancer tumor samples collected from The Cancer Genome Altas program and provide comparative results.
Optimal market making in the presence of latency

This paper studies optimal market making for large-tick assets in the presence of latency. We consider a random walk model for the asset price and formulate the market maker's optimization problem using Markov Decision Processes (MDP). We characterize the value of an order and show that it plays the role of one-period reward in the MDP model. Based on this characterization, we provide explicit criteria for assessing the profitability of market making when there is latency. Under our model, we show that a market maker can earn a positive expected profit if there are sufficient uninformed market orders hitting the market maker's limit orders compared with the rate of price jumps, and the trading horizon is sufficiently long. In addition, our theoretical and numerical results suggest that latency can be an additional source of risk and latency impacts negatively the performance of market makers.
1 7 M ar 2 02 0 Flag-dipole spinors : On the dual structure derivation and C , P and T symmetries

In this manuscript we report the flag-dipole spinors dual structure derivation, and analyse the properties of the corresponding operator generating such structure. This particular construction may be interesting to cosmological and phenomenological applications. In addition, we analyse the behaviour of the flag-dipole spinors under action of discrete symmetries, facing an unconventional property encoded on (CPT ). PACS numbers: 04.62. +v, 03.70. +k, 03.65. -w
Identification of Body Balance Deterioration of Gait in Women Using Accelerometers

This study presents a simple methodology for the evaluation of gait with accelerometers, for rapid and simple application, in which we employ current balance tests in clinical practice (Timed Up and Go, Chair Stand Test and Six-Minute Walk Test). The aim was to determine whether the accelerometric valuation of gait can detect alterations in balance. The sample of this cross-sectional research, made during the months of May and June 2018, was composed of 145 healthy adult women (x = 63.8 ± 8.41 years), from the city of Ourense (Spain). They walked with a triaxial accelerometer at the fourth lumbar vertebra for a distance of 20 m. The test was repeated three times, and the mean of the three measurements was used in the analysis. There was a reduction in the values of acceleration recorded along all three of the axes and in the root mean square as the age increased. This reduction was very significant for the minimum values registered along the vertical and transverse axes, and for the maximum values along the mediolateral axis. Only the maximum values of the vector module demonstrated significant differences among the three age groups. A regression model allowed us to identify the values that give more information on the Timed Up and Go Test, namely: the maximum values of the root mean square and the mediolateral axis. An exhaustive analysis of the vertical and mediolateral axes and the vector module allows for the detection of early alterations in the automatic gait pattern.
A method yielding comparable estimates of the fraternal birth order and female fecundity effects in male homosexuality

The fraternal birth order effect (FBOE) is the finding that older brothers increase the probability of homosexuality in later-born males, and the female fecundity effect (FFE) is the finding that the mothers of homosexual males produce more offspring than the mothers of heterosexual males. In a recent paper, Khovanova proposed a novel method for computing independent estimates of these effects on the same samples and expressing the magnitude and direction of the effects in the same metric. In her procedure, only families with one or two sons are examined, and daughters are ignored. The present study investigated the performance of Khovanova's method using archived data from 10 studies, comprising 14 samples totalling 5390 homosexual and heterosexual subjects. The effect estimate for the FBOE showed that an increase from zero older brothers to one older brother is associated with a 38% increase in the odds of homosexuality. By contrast, the effect estimate for the FFE showed that the increase from zero younger brothers to one younger brother is not associated with any increase in the odds of homosexuality. The former result supports the maternal immune hypothesis of male homosexuality; the latter result does not support the balancing selection hypothesis.
Direct and indirect effects of noise pollution alter biological communities in and near noise-exposed environments

Noise pollution is pervasive across every ecosystem on Earth. Although decades of research have documented a variety of negative impacts of noise to organisms, key gaps remain, such as how noise affects different taxa within a biological community and how effects of noise propagate across space. We experimentally applied traffic noise pollution to multiple roadless areas and quantified the impacts of noise on birds, grasshoppers and odonates. We show that acoustically oriented birds have reduced species richness and abundance and different community compositions in experimentally noise-exposed areas relative to comparable quiet locations. We also found both acoustically oriented grasshoppers and odonates without acoustic receptors to have reduced species richness and/or abundance in relatively quiet areas that abut noise-exposed areas. These results suggest that noise pollution not only affects acoustically oriented animals, but that noise may reverberate through biological communities through indirect effects to those with no clear links to the acoustic realm, even in adjacent quiet environments.
Evolution of fossorial locomotion in the transition from tetrapod to snake-like in lizards

Dramatic evolutionary transitions in morphology are often assumed to be adaptive in a new habitat. However, these assumptions are rarely tested because such tests require intermediate forms, which are often extinct. In vertebrates, the evolution of an elongate, limbless body is generally hypothesized to facilitate locomotion in fossorial and/or cluttered habitats. However, these hypotheses remain untested because few studies examine the locomotion of species ranging in body form from tetrapod to snake-like. Here, we address these functional hypotheses by testing whether trade-offs exist between locomotion in surface, fossorial and cluttered habitats in Australian Lerista lizards, which include multiple intermediate forms. We found that snake-like species penetrated sand substrates faster than more lizard-like species, representing the first direct support of the adaptation to fossoriality hypothesis. By contrast, body form did not affect surface locomotion or locomotion through cluttered leaf litter. Furthermore, all species with hindlimbs used them during both fossorial and surface locomotion. We found no evidence of a trade-off between fossorial and surface locomotion. This may be either because Lerista employed kinematic strategies that took advantage of both axial- and limb-based propulsion. This may have led to the differential occupation of their habitat, facilitating diversification of intermediate forms.
Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students

The rapid development of the technologies for online learning provides students with extensive resources for self-learning and brings new opportunities for data-driven research on educational management. An important issue of online learning is to diagnose the knowledge proficiency (i.e., the mastery level of a certain knowledge concept) of each student. Considering that it is a common case that students inevitably learn and forget knowledge from time to time, it is necessary to track the change of their knowledge proficiency during the learning process. Existing approaches either relied on static scenarios or ignored the interpretability of diagnosis results. To address these problems, in this article, we present a focused study on diagnosing the knowledge proficiency of students, where the goal is to track and explain their evolutions simultaneously. Specifically, we first devise an explanatory probabilistic matrix factorization model, Knowledge Proficiency Tracing (KPT), by leveraging educational priors. KPT model first associates each exercise with a knowledge vector in which each element represents a specific knowledge concept with the help of Q-matrix. Correspondingly, at each time, each student can be represented as a proficiency vector in the same knowledge space. Then, our KPT model jointly applies two classical educational theories (i.e., learning curve and forgetting curve) to capture the change of students’ proficiency level on concepts over time. Furthermore, for improving the predictive performance, we develop an improved version of KPT, named Exercise-correlated Knowledge Proficiency Tracing (EKPT), by considering the connectivity among exercises with the same knowledge concepts. Finally, we apply our KPT and EKPT models to three important diagnostic tasks, including knowledge estimation, score prediction, and diagnosis result visualization. Extensive experiments on four real-world datasets demonstrate that both of our models could track the knowledge proficiency of students effectively and interpretatively.
Emotion Dynamics of Public Opinions on Twitter

Recently, social media has been considered the fastest medium for information broadcasting and sharing. Considering the wide range of applications such as viral marketing, political campaigns, social advertisement, and so on, influencing characteristics of users or tweets have attracted several researchers. It is observed from various studies that influential messages or users create a high impact on a social ecosystem. In this study, we assume that public opinion on a social issue on Twitter carries a certain degree of emotion, and there is an emotion flow underneath the Twitter network. In this article, we investigate social dynamics of emotion present in users’ opinions and attempt to understand (i) changing characteristics of users’ emotions toward a social issue over time, (ii) influence of public emotions on individuals’ emotions, (iii) cause of changing opinion by social factors, and so on. We study users’ emotion dynamics over a collection of 17.65M tweets with 69.36K users and observe 63% of the users are likely to change their emotional state against the topic into their subsequent tweets. Tweets were coming from the member community shows higher influencing capability than the other community sources. It is also observed that retweets influence users more than hashtags, mentions, and replies.