Miracle-dz/UniFAR-FTUs huggingface dataset:
Frozen 2: Effects Vegetation Pipeline

Walt Disney Animation Studios’ ”Frozen 2” takes place in the Enchanted Forest, which is full of vegetation (e.g. distinctive leaves and foliage) that is manipulated by other characters, including the wind character, Gale. ”Frozen 2” also has multiple scenes where a large portion of the forest is on fire. The quantity and scale of vegetation effects in ”Frozen 2” presented a challenge to our Effects department. We developed two workflows, the Vegetation Asset workflow and the Fire Tree workflow, to help us achieve high quality artistic performance of procedural tree animation and fire tree simulations on ”Frozen 2”. Using the new workflows we not only saw an order of magnitude improvement in the work efficiency of our Effects artists, but also saw an increase in work satisfaction and overall artistic quality since the workflows handled the data management of various assets in the shot, allowing artists to concentrate more on their craft.
The role of the chaperone FimC in the formation of type 1 pili from Escherichia coli

Type 1 pili of uropathogenic Escherichia coli belong to a class of filamentous surface organelles expressed by various pathogenic bacteria. These appendages consist exclusively of protein subunits and they are associated with the capability of the bacteria to attach to the sites of infection. The assembly of bacterial pili by a dedicated periplasmic pilus chaperone and a specialized outer membrane usher is one of the most thoroughly studied examples of macromolar assembly. Extensive studies in vivo and in vitro investigated the interactions between periplasmic pilus chaperones and subunits or the binding of such chaperone·subunit complexes to the outer membrane usher. In contrast, little is known about the incorporation of subunits into the growing pilus, i.e. the establishment of subunit·subunit contacts. Here we present a model system derived from the type 1 pilus assembly-machinery that we used for a quantitative analysis of this central event of pilus assembly. Our results show that the intrinsic affinities of different subunits for each other correlate with their position in the final pilus structure. We further found that subunit·subunit complexes are thermodynamically more stable than the periplasmic chaperone·subunit complexes. Consequently, the incorporation of a subunit into the pilus is energetically favourable which in turn explains why pilus assembly does not require cellular energy. The formation of subunit·subunit complexes in the absence of the outer membrane usher was extremely slow with apparent halftimes of several hours. We provide evidence that the outer membrane usher acts as the catalysts of this reaction, ensuring that the formation of subunit·subunit interactions is coupled to secretion of the assembled subunits into the extracellular space. RESULTS Intrinsic affinities of pilus subunits 64
Hardness and approximation of the Probabilistic p-Center problem under Pressure

The Probabilistic $p$-Center problem under Pressure ({\tt Min P$p$CP}) is a variant of the usual {\tt Min $p$-Center} problem we recently introduced in the context of wildfire management. The problem is %basically to locate $p$ shelters minimizing the maximum distance people will have to cover %in order to reach one of these shelters to reach the closest accessible shelter in case of fire. The landscape is divided in zones and is modeled as an edge-weighted graph with vertices corresponding to zones and edges corresponding to direct connections between two adjacent zones. The uncertainty associated with fire outbreaks is modeled using a finite set of fire scenarios. Each scenario %defines corresponds to a fire outbreak on a single zone (i.e., on a vertex) with the main consequence of modifying evacuation paths in two ways. First, an evacuation path cannot pass through the vertex on fire. Second, the fact that %somebody someone close to the fire may not take rational decisions when selecting a direction to escape is modeled using new kinds of evacuation paths. In this paper, for a given instance of {\tt Min P$p$CP} defined by an edge-weighted graph $G=(V,E,L)$ and an integer $p$, we characterize the set of feasible solutions of {\tt Min P$p$CP}. We prove that {\tt Min P$p$CP} cannot be approximated with a ratio less than $\frac{56}{55}$ on subgrids (subgraphs of grids) of degree at most 3. Then, we propose some approximation results for {\tt Min P$p$CP}. These results require approximation results for two variants of the (deterministic) {\tt Min $p$-Center} problem called {\tt Min MAC $p$-Center} and {\tt Min Partial $p$-Center}.
BugsInPy: a database of existing bugs in Python programs to enable controlled testing and debugging studies

The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers. One factor that contributed to the rapid growth of Java testing and debugging tools is the availability of benchmarks. A popular benchmark is the Defects4J benchmark; its initial version contained 357 real bugs from 5 real-world Java programs. Each bug comes with a test suite that can expose the bug. Defects4J has been used by hundreds of testing and debugging studies and has helped to push the frontier of research in these directions. In this project, inspired by Defects4J, we create another benchmark database and tool that contain 493 real bugs from 17 real-world Python programs. We hope our benchmark can help catalyze future work on testing and debugging tools that work on Python programs.
Crystal structure of tris(2,2'-bipyridine)iron(II) bis(hexafluorophosphate), (C10H8N2)3Fe(PF6)2

Source of material: The title compound was obtained as a by-product during the reaction of [Fe4C)2(bpy)2(OAc)7](PF6)2 with Nmethyl-/V,^-bis(2-pyridyl methyl)amine (see ref. 1), and purified by recrystallization from methanol. It is isotypic with the analogous Ru-compound (see ref. 2). Due to the centrosymmetric space group the cell contains both stereoisomers of the cation. The view along [010] shows homochiral layers of cations changing their chirality along [(Ю1]. Reasonable P-F bond lengths can be obtained after correction for librational effects.
An Adaptive-MCMC Scheme for Setting Trajectory Lengths in Hamiltonian Monte Carlo

Hamiltonian Monte Carlo (HMC) is a powerful MCMC algorithm based on simulating Hamiltonian dynamics. Its performance depends strongly on choosing appropriate values for two parameters: the step size used in the simulation, and how long the simulation runs for. The step-size parameter can be tuned using standard adaptive-MCMC strategies, but it is less obvious how to tune the simulation-length parameter. The no-U-turn sampler (NUTS) eliminates this problematic simulation-length parameter, but NUTS’s relatively complex control flow makes it difficult to efficiently run many parallel chains on accelerators such as GPUs. NUTS also spends some extra gradient evaluations relative to HMC in order to decide how long to run each iteration without violating detailed balance. We propose ChEES-HMC, a simple adaptive-MCMC scheme for automatically tuning HMC’s simulation-length parameter, which minimizes a proxy for the autocorrelation of the state’s second moments. We evaluate ChEES-HMC and NUTS on many tasks, and find that ChEES-HMC typically yields larger effective sample sizes per gradient evaluation than NUTS does. When running many chains on a GPU, ChEES-HMC can also run significantly more gradient evaluations per second than NUTS, allowing it to quickly provide accurate estimates of posterior expectations. Proceedings of the 24 International Conference on Artificial Intelligence and Statistics (AISTATS) 2021, San Diego, California, USA. PMLR: Volume 130. Copyright 2021 by the author(s).
A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.
The traceback method in child language acquisition research: identifying patterns in early speech

abstractThis paper discusses the traceback method, which has been the basis of some influential papers on first language acquisition. The method sets out to demonstrate that many or even all utterances in a test corpus (usually the last two sessions of recording) can be accounted for with the help of recurrent fixed strings (like What’s that? ) or frame-and-slot patterns (like [What’s X? ]) that can also be identified in the remaining dataset (i.e., the previous sessions of recording). This is taken as evidence that language learning is much more item-based than previously assumed. In the present paper we sketch the development of the method over the last two decades, and discuss its relation to usage-based theory, as well as the cognitive plausibility of its components, and we highlight both its potential and its limitations.
Phosphate Complexing of NpO2+2

The equilibrium constant values for the formation of Np02H2P04, Np02HP04 and Νρ02(ΗΡ04)Γ have been determined at 1.0 Μ ionic strength. The solvent extraction technique employed either dinonylnaphthalene sulphonic acid or a mixture of dibenzoylmethane and tri-n-octylphosphine oxide as the organic phase extractants. The values obtained are: log /? ,„ = 4.54 ± 0.13; log β,22 = 7.49 ± 0.13 and log ßl2t = 2.52 ± 0.07.
Reviews zur Bildung im digitalen Wandel. Eine Einführung in Kontext und Methodik

Wilmers, Annika [Hrsg. ]; Anda, Carolin [Hrsg. ]; Keller, Carolin [Hrsg. ]; Rittberger, Marc [Hrsg. ]: Bildung im digitalen Wandel. Die Bedeutung fur das padagogische Personal und fur die Aus- und Fortbildung. Munster ; New York : Waxmann 2020, S. 7-29. - (Digitalisierung in der Bildung; 1) Padagogische Teildisziplin: Empirische Bildungsforschung; Medienpadagogik;
Preparedness or repeated short-term relief aid? Building drought resilience through early warning in southern Africa

Southern Africa is highly vulnerable to drought because of its dependence on climate-sensitive sectors of agriculture, hydroenergy and fisheries. Recurring droughts continue to impact rural livelihoods and degrade the environment. Drought severity in southern Africa is exacerbated by poor levels of preparedness and low adaptive capacity. Whilst weather extremes and hazards are inevitable, the preparedness to manage such hazards determines their impact and whether they become disasters. Southern Africa is often caught unprepared by drought as existing early warning systems lack the drought forecastingcomponent, which often results in reactionary interventions as opposed to well-planned and proactive response mechanisms. This study assesses the spatio-temporal changes of rainfall and aridity in southern Africa through an analysis of long-term precipitation and evaporation trends from 1960 to 2007. Stakeholder consultation was conducted in Madagascar, Malawi, Zambia and Zimbabwe during the peak of the 2015/16 drought, focusing on overall drought impacts, current water resource availability, existing early warning systems, adaptation mechanisms and institutional capacity to mitigate and managedroughts as part of overall disaster risk reduction strategies. Average rainfall has decreased by 26% in the region between 1960 and 2007, and aridity has increased by 11% between 1980 and 2007. The absence of drought forecasting and lack of institutional capacity to mitigate drought impede regional drought risk reduction initiatives. Existing multi-hazard early warning systems in the region focus on flooding and drought monitoring and assessment. Drought forecasting is often not given due consideration, yet it is a key component of early warning and resilience building. We propose a regional drought early warning framework, emphasising the importance of both monitoring and forecasting as being integral to a drought early warning system and building resilience to drought.
Designing metal grid contacts for four-terminal tandem solar cells

Flat-panel tandem solar cells have demonstrated the potential to exceed the efficiencies of their single-junction constituents. However, robust rules for tandem solar cell design are currently lacking, slowing the development of cost-effective implementations of this technology. The four-terminal (4T) architecture stacks electrically independent sub-cells and avoids current-matching losses, resulting in two main advantages over the conventional integrated twoterminal (2T) architecture: a higher energy yield and a loosened constraint on material bandgap combinations. Because both sub-cells are contacted independently in a 4T tandem, multiple stacked semi-transparent contacts are needed, causing significant shading and series resistance losses. Moreover, for stationary flat panel tandems that do not use tracking, contacts need to be optimized for a varying direction of incident sunlight. In this study, we develop a framework for optimizing metal grid contacts for 4T tandem solar cells and quantify the electrical and optical loss associated with these contacts. We also examine the question of under which circumstances it is beneficial to align metal grid contact fingers to reduce shading. We find that, for most applications, the front and back contacts of the top cell should be aligned, resulting in an increase in energy yield by 1 2 %. We also find that aligning the contacts of top and bottom cells is not beneficial and may even result in a reduced energy yield. This additionally highlights the importance of using energy yield, rather than standard test condition efficiency, as a figure of merit when considering device design. Thesis Supervisor: Tonio Buonassisi Title: Associate Professor of Mechanical Engineering
Dynamic Coordination-Based Reinforcement Learning for Driving Policy

With the development of communication technology and artificial intelligence technology, intelligent vehicle has become a very important part of Internet of Things technology. At present, the single-vehicle intelligence is gradually improved, and more and more unmanned vehicles appear on the road. In the future, these individual intelligence applications need to be transformed into collective intelligence to give full play to the greater advantages of unmanned driving. For example, individual intelligence is self-interest. If there is no collective cooperation, it may affect the whole traffic flow for its own speed. Although the vehicle ad hoc network technology provides a guarantee for the communication between vehicles and makes cooperation between vehicles possible, there are still challenges in how to adapt to coordination learning. Coordination reinforcement learning is one of the most promising methods to solve the multiagent coordination optimization problems. However, existing coordinative learning approaches that usually rely on static topologies cannot be easily adopted to solve the vehicle coordination problems in the dynamic environment. We propose a dynamic coordination reinforcement learning to help vehicles make their driving decisions. First, we apply driving safety field theory to construct the dynamic coordination graph (DCG), representing the dynamic coordination behaviors among vehicles. Second, we design reinforcement learning techniques on our DCG model to implement the joint optimal action reasoning for the multivehicle system and eventually derive the optimal driving policy for each vehicle. Finally, compared with other multiagent learning methods, our method has a significant improvement in security and speed, which is about 1% higher than other multiagent learning methods, but its training speed is also significantly improved about 8%.
Motility Resilience of Molecular Shuttles Against Defective Motors

Myosin and kinesin are biomolecular motors found in living cells. By propelling their associated cytoskeletal filaments, these biomolecular motors facilitate force generation and material transport in the cells. When extracted, the biomolecular motors are promising candidates for in vitro applications such as biosensor devices, on account of their high operating efficiency and nanoscale size. However, during integration into these devices, some of the motors become defective due to unfavorable adhesion to the substrate surface. These defective motors inhibit the motility of the cytoskeletal filaments which make up the molecular shuttles used in the devices. Difficulties in controlling the fraction of active and defective motors in experiments discourage systematic studies concerning the resilience of the molecular shuttle motility against the impedance of defective motors. Here, we used mathematical modelling to systematically examine the resilience of the propulsion by these molecular shuttles against the impedance of the defective motors. The model showed that the fraction of active motors on the substrate is the essential factor determining the resilience of the molecular shuttle motility. Approximately 40% of active kinesin or 80% of active myosin motors are required to constitute continuous gliding of molecular shuttles in their respective substrates. The simplicity of the mathematical model in describing motility behavior offers utility in elucidating the mechanisms of the motility resilience of molecular shuttles.
An Effective and Trustable Spatial Service Recommendation Algorithm for Spatial Query Retrieval In Geo-Social Network

The spatial information (e.g., restaurants/hotels) is related with the keyword(s) to indicate their businesses, services and features. The main issue of relevant information retrieval is to query an entity which includes a set of spatial query keywords and have the smallest amount of inter-object distance. The spatial queries with keywords have not been extensively explored. Still, the traditional method was focused on the multidimensional data. Previous works mostly targeted to predict the top-k Nearest Neighbors keyword query, where every keyword should be equivalent to the whole querying keywords. However, the mechanism does not consider the density of data entities in the spatial space. To overcome the above issues, An Effective and Trustable Spatial Service Recommendation (ETSSR) algorithm focuses on the most relevant information retrieval with the enhanced accuracy and minimal retrieval time for spatial information services. The main goal of work is to provide best spatial information retrieval with an accurateness of location prediction and minimal information retrieval time. The system minimizes the classification issue and visualization problem for spatial information in Geospatial Social network. The system improves the spatial information retrieval with an accuracy of location prediction and minimizes the information retrieval time compare than existing methods. Based on Experimental estimations, proposed ETSSR+KNN enhanced 0.48 P (Precision) and 0.49 R (Recall) and minimized 28 milliseconds query retrieval time.
Enhanced Evolutionary Automated Program Repair by Finer-Granularity Ingredients and Better Search Algorithms

Bug repair is time-consuming and tedious, which hampers software maintenance. To alleviate the burden, automated program repair (APR) is proposed and has been fruitful in the last decade. Evolutionary repair is the seminal work of this field and proliferated a family of approaches. The performance of evolutionary repair approaches is affected by two main factors: (1) search space, which defines all possible patches, and (2) search algorithms, which navigates the space. Although recent approaches have achieved remarkable progress, the main challenges of the two factors still remain. On one hand, the different kinds of search space are very coarse for containing correct patches. On the other hand, the search process guided by genetic algorithms is inefficient to find the correct patches in an appropriate time budget. In this paper, we propose MicroRepair, a new evolutionary repair approach to address the two challenges. Rather than finding statement-level patches like existing genetic repair approaches, MicroRepair enlarges the search space by breaking the statements into finer-granularity ingredients that are consist of AST leaves. As the search space grows exponentially, the former search algorithms may become inefficient to navigate in the larger space. We utilize the best multi-objective search algorithm selected from our empirical comparison on a set of search algorithms. We evaluated MicroRepair on 224 bugs of real-world from the benchmark Defects4J and compare it with several state-of-the-art repair approaches. The evaluation results show that MicroRepair correctly repaired 26 bugs with a precision of 62%, which significantly outperforms the state-of-the-art evolutionary APR approaches in terms of precision.