Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Guidance and Control for Lunar Landing using a Stabilized Seeker

We develop an integrated guidance and control system that in conjunction with a stabilized seeker and landing site detection software can achieve precise and safe planetary landing. The seeker tracks the designated landing site by adjusting seeker elevation and azimuth angles to center the designated landing site in the sensor field of view. The seeker angles, closing speed, and range to the designated landing site are used to formulate a velocity field that is used by the guidance and control system to achieve a safe landing at the designated landing site. The guidance and control system maps this velocity field, attitude, and rotational velocity directly to a commanded thrust vector for the lander’s four engines. The guidance and control system is implemented as a policy optimized using reinforcement meta learning. We demonstrate that the guidance and control system is compatible with multiple diverts during the powered descent phase, and is robust to seeker lag, actuator lag and degradation, and center of mass variation induced by fuel consumption. We outline several concepts of operations, including an approach using a preplaced landing beacon.
Commonsense Knowledge-Augmented Pretrained Language Models for Causal Reasoning Classification

Commonsense knowledge can be leveraged 001 for identifying causal relations in text. In this 002 work, we convert triples in ATOMIC 2020 , a wide 003 coverage commonsense reasoning knowledge 004 graph, to natural language text and continually 005 pretrain a BERT pretrained language model. 006 We evaluate the resulting model on answer-007 ing commonsense reasoning questions. Our 008 results show that a continually pretrained lan-009 guage model augmented with commonsense 010 reasoning knowledge outperforms our base-011 line on two commonsense causal reasoning 012 benchmarks, COPA and BCOPA-CE, without 013 additional improvement on the base model or 014 using quality-enhanced data for ﬁne-tuning. 015
Developing regionally specific environmental building tools for China

Despite the creation of a variety of environmental building tools, it remains difficult to select existing tools or to develop new tools to address specific needs in many regions. This is largely due to insufficient transparency and complexity surrounding existing tools as well as the vague understanding of regionally specific conditions and requirements. An approach is proposed for developing regionally specific environmental building tools for China. Two new conceptual frameworks are established to classify environmental building tools and their related factors. Based on the new frameworks, a survey was conducted to understand local Chinese architects' perceptions about the relative importance of environmental building factors, and their expectations about the character, orientation, function and styles of environmental building tools in China. Based on these results, a suite approach to developing regionally specific environmental building tools is proposed and the implications of their applications in China are considered. En dépit de la création de divers outils de construction environnementaux, il reste difficile de choisir entre la sélection d'outils existants et le développement de nouveaux outils pour répondre aux besoins spécifiques de nombreuses régions. Cela est largement dû au manque de transparence, à la complexité des outils existants concernant l'environnement et à la connaissance insuffisante des conditions et des exigences propres aux régions. L'auteur propose une méthode de développement d'outils de construction environnementaux propres aux régions chinoises. Deux nouveaux cadres conceptuels sont définis afin de classer les outils de construction environnementaux et leurs facteurs associés. Sur la base de ces nouveaux cadres, une enquête a été menée visant à comprendre la perception par les architectes chinois de l'importance relative des facteurs de construction environnementaux et leurs attentes concernant le caractère, l'orientation, la fonction et les styles des outils de construction environnementaux en Chine. Sur la base des résultats de cette étude, il est proposé une méthode de développement d'outils de construction environnementaux propres aux régions et d'évaluation des conséquences de leur application en Chine. Mots clés: outils d'évaluation évaluation environnementale régionalisme construction durable développement durable Chine
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning

Deep reinforcement learning (Deep RL) has recently seen signiﬁcant progress in developing algorithms for generalization. However, most algorithms target a single type of generalization setting. In this work, we study generalization across three disparate task structures: (a) tasks composed of spatial and temporal compositions of regularly occurring object motions; (b) tasks composed of active perception of and navigation towards regularly occurring 3D objects; and (c) tasks composed of remembering goal-information over sequences of regularly occurring object-conﬁgurations. These diverse task structures all share an underlying idea of compositionality: task completion always involves combining reccurring segments of task-oriented perception and behavior. We hypothesize that an agent can generalize within a task structure if it can discover representations that capture these reccurring task-segments. For our tasks, this corresponds to representations for recognizing individual object motions, for navigation towards 3D objects, and for navigating through object-conﬁgurations. Taking inspiration from cognitive science, we term representations for reccurring segments of an agent’s experience, “perceptual schemas”. We propose Feature Attending Recurrent Modules (FARM) , which learns a state representation where perceptual schemas are distributed across multiple, relatively small recurrent modules. We compare FARM to recurrent architectures that leverage spatial attention, which reduces observation features to a weighted average over spatial positions. Our experiments indicate that our feature-attention mechanism better enables FARM to generalize across the diverse object-centric domains we study.
Anisotropic Young-Laplace-equation provides insight into tissue growth

Anisotropic Young-Laplace-equation provides insight into tissue growth. Peter Fratzl1, F. Dieter Fischer2, Gerald A. Zickler2, John W.C. Dunlop3 1Max Planck Institute of Colloids and Interfaces, Department of Biomaterials, Potsdam Science Park, 14476 Potsdam-Golm, Germany 2Montanuniversität Leoben, Institute of Mechanics, 8700 Leoben, Austria 3Morphophysics Group, Department of the Chemistry and Physics of Materials, University of Salzburg, 5020 Salzburg, Austria
Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce

In the retail industry, electronic commerce (e-commerce) has grown quickly in the last decade and has further accelerated as a result of movement restrictions during the pandemic. While working with logistics and retail industry business collaborators, we found that the cost of delivery of products from the most opportune node in the supply chain (a quantity called the cost-to-serve or CTS) is a key challenge. In this paper, we formally define CTS as a decision-making problem. We then focus on the specific subproblem of delivering multiple products in arbitrary quantities from any warehouse to the customer doorstep. We find that a reinforcement learning (RL) formulation is able to exceed the performance of the state of the art rule based policies, while being significantly faster than traditional optimisation approaches such as mixed-integer linear programming. We hypothesise that scaling up the RL based methodology will have a significant impact on the operating margins of retailers in the ‘new normal’.
Unsupervised Reinforcement Learning in Multiple Environments

Several recent works have been dedicated to unsupervised reinforcement learning in a single environment, in which a policy is first pre-trained with unsupervised interactions, and then fine-tuned towards the optimal policy for several downstream supervised tasks defined over the same environment. Along this line, we address the problem of unsupervised reinforcement learning in a class of multiple environments, in which the policy is pre-trained with interactions from the whole class, and then fine-tuned for several tasks in any environment of the class. Notably, the problem is inherently multi-objective as we can trade off the pre-training objective between environments in many ways. In this work, we foster an exploration strategy that is sensitive to the most adverse cases within the class. Hence, we cast the exploration problem as the maximization of the mean of a critical percentile of the state visitation entropy induced by the exploration strategy over the class of environments. Then, we present a policy gradient algorithm, alphaMEPOL, to optimize the introduced objective through mediated interactions with the class. Finally, we empirically demonstrate the ability of the algorithm in learning to explore challenging classes of continuous environments and we show that reinforcement learning greatly benefits from the pre-trained exploration strategy w.r. t. learning from scratch.
Ultra-fast switches- a new element for medium voltage fault current limiting switchgear

In the past many uneconomic solutions for fault current limiting circuit breakers (FCLCB) have been introduced. Among them, a promising hybrid system has been published, wherein a combination of fast switches, power semiconductors and a resistor forms a very effective FCLCB. This paper presents a significantly improved and simplified approach that replaces the semiconductors with ultra-fast switching elements. Laboratory tests and computer simulations verify the feasibility of this approach. Its elegance lies in the simplicity of the switches and in the improved use of the characteristics of the switching arc over many series breaks, namely cathode-fall and instantaneous recovery. In addition, an ultra-high speed repulsion drive provides an extremely short breaking time (a few 100 microseconds) and the contact separation scatter is within microseconds. These features are the basis for a completely new approach to creating medium-voltage switchgear, and are particularly suited for fault-current limitation.
Novel PWM-controlled resonant converter

A novel PWM-controlled resonant converter is proposed. The combination of a resonant converter and an active-clamp circuit makes it possible to control the output voltage of the resonant converter by PWM method. The good controllability and other characteristics of this new resonant converter are analyzed, and are confirmed experimentally.
Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter

COVID-19 has ruptured routines and caused breakdowns in what had been conventional practice and custom: everything from going to work and school and shopping in the supermarket to socializing with friends and taking holidays. Nonetheless, COVID-19 does provide an opportunity to study how people make sense of radically changing circumstances over time. In this paper we demonstrate how Twitter affords this opportunity by providing data in real time, and over time. In the present research, we collect a large pool of COVID-19 related tweets posted by New Zealanders–citizens of a country successful in containing the coronavirus–from the moment COVID-19 became evident to the world in the last days of 2019 until 19 August 2020. We undertake topic modeling on the tweets to foster understanding and sensemaking of the COVID-19 tweet landscape in New Zealand and its temporal development and evolution over time. This information can be valuable for those interested in how people react to emergent events, including researchers, governments, and policy makers.
The Schistosoma mansoni nuclear receptor FTZ-F1 maintains esophageal gland function via transcriptional regulation of meg-8.3

Schistosomes infect over 200 million of the world’s poorest people, but unfortunately treatment relies on a single drug. Nuclear hormone receptors are ligand-activated transcription factors that regulate diverse processes in metazoans, yet few have been functionally characterized in schistosomes. During a systematic analysis of nuclear receptor function, we found that an FTZ-F1-like receptor was essential for parasite survival. Using a combination of transcriptional profiling and chromatin immunoprecipitation (ChIP), we discovered that the micro-exon gene meg-8.3 is a transcriptional target of SmFTZ-F1. We found that both Smftz-f1 and meg-8.3 are required for esophageal gland maintenance as well as integrity of the worm’s head. Together, these studies define a new role for micro-exon gene function in the parasite and suggest that factors associated with the esophageal gland could represent viable therapeutic targets.
NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics

The dominant paradigm for neural text generation is left-to-right decoding from autoregressive language models. Constrained or controllable generation under complex lexical constraints, however, requires foresight to plan ahead feasible future paths. Drawing inspiration from the A^* search algorithm, we propose NeuroLogic A*esque, a decoding algorithm that incorporates heuristic estimates of future cost. We develop lookahead heuristics that are efficient for large-scale language models, making our method a drop-in replacement for common techniques such as beam search and top-k sampling. To enable constrained generation, we build on NeuroLogic decoding (Lu et al., 2021), combining its flexibility in incorporating logical constraints with A*esque estimates of future constraint satisfaction. Our approach outperforms competitive baselines on five generation tasks, and achieves new state-of-the-art performance on table-to-text generation, constrained machine translation, and keyword-constrained generation. The improvements are particularly notable on tasks that require complex constraint satisfaction or in few-shot or zero-shot settings. NeuroLogic A*esque illustrates the power of decoding for improving and enabling new capabilities of large-scale language models.
Low 1/f noise CMOS active mixers for direct conversion

The analysis of direct conversion CMOS active mixers tailored to ReFlex standard is presented. To minimize 1/f noise, the switching stage pMOS devices have large area, low biasing current. pMOS and nMOS transconductors shunted together form the input stage. A 0.35-/spl mu/m prototype performs at 900 MHz as follows: 18 dB SSB NF averaged in the 100 Hz-3 kHz band, 18 dB gain, -4-dBm IIP3, 30-dBm IIP2 with 6 mA from 2.7-V supply.
HampDTI: a heterogeneous graph automatic meta-path learning method for drug-target interaction prediction

Predicting drug-target interactions (DTIs) is crucial at many phases of drug discovery and repositioning. Many computational methods based on heterogeneous networks (HNs) have proved their potential to predict DTIs by capturing extensive biological knowledge and semantic information from meta-paths. However, existing methods manually customize meta-paths, which is overly dependent on some specific expertise. Such strategy heavily limits the scalability and flexibility of these models, and even affects their predictive performance. To alleviate this limitation, we propose a novel HN-based method with attentive meta-path extraction for DTI prediction, named HampDTI, which is capable of automatically extracting useful meta-paths through a learnable attention mechanism instead of pre-definition based on domain knowledge. Specifically, by scoring multi-hop connections across various relations in the HN with each relation assigned an attention weight, HampDTI constructs a new trainable graph structure, called meta-path graph. Such meta-path graph implicitly measures the importance of every possible meta-path between drugs and targets. To enable HampDTI to extract more diverse meta-paths, we adopt a multi-channel mechanism to generate multiple meta-path graphs. Then, a graph neural network is deployed on the generated meta-path graphs to yield the multi-channel embeddings of drugs and targets. Finally, HampDTI fuses all embeddings from different channels for predicting DTIs. The meta-path graphs are optimized along with the model training such that HampDTI can adaptively extract valuable meta-paths for DTI prediction. The experiments on benchmark datasets not only show the superiority of HampDTI in DTI prediction over several baseline methods, but also, more importantly, demonstrate the effectiveness of the model discovering important meta-paths.
A first mathematical runtime analysis of the non-dominated sorting genetic algorithm II (NSGA-II): (hot-off-the-press track at GECCO 2022)

The non-dominated sorting genetic algorithm II (NSGA-II) is the most intensively used multi-objective evolutionary algorithm (MOEA) in real-world applications. However, in contrast to several simple MOEAs analyzed also via mathematical means, no such study exists for the NSGA-II so far. In this work, we show that mathematical runtime analyses are feasible also for the NSGA-II. As particular results, we prove that with a population size larger than the Pareto front size by a constant factor, the NSGA-II with two classic mutation operators and three different ways to select the parents satisfies the same asymptotic runtime guarantees as the SEMO and GSEMO algorithms on the basic OneMinMax and LOTZ benchmark functions. However, if the population size is only equal to the size of the Pareto front, then the NSGA-II cannot efficiently compute the full Pareto front (for an exponential number of iterations, the population will always miss a constant fraction of the Pareto front). Our experiments confirm the above findings. This paper for the Hot-off-the-Press track at GECCO 2022 summarizes the work Weijie Zheng, Yufei Liu, Benjamin Doerr: A First Mathematical Runtime Analysis of the Non-dominated Sorting Genetic Algorithm II (NSGA-II). AAAI2022, accepted [17].
GIMIRec: Global Interaction Information Aware Multi-Interest Framework for Sequential Recommendation

Sequential recommendation based on multi-interest framework models the user's recent interaction sequence into multiple different interest vectors, since a single low-dimensional vector cannot fully represent the diversity of user interests. However, most existing models only intercept users' recent interaction behaviors as training data, discarding a large amount of historical interaction sequences. This may raise two issues. On the one hand, data reflecting multiple interests of users is missing; on the other hand, the co-occurrence between items in historical user-item interactions is not fully explored. To tackle the two issues, this paper proposes a novel sequential recommendation model called"Global Interaction Aware Multi-Interest Framework for Sequential Recommendation (GIMIRec)". Specifically, a global context extraction module is firstly proposed without introducing any external information, which calculates a weighted co-occurrence matrix based on the constrained co-occurrence number of each item pair and their time interval from the historical interaction sequences of all users and then obtains the global context embedding of each item by using a simplified graph convolution. Secondly, the time interval of each item pair in the recent interaction sequence of each user is captured and combined with the global context item embedding to get the personalized item embedding. Finally, a self-attention based multi-interest framework is applied to learn the diverse interests of users for sequential recommendation. Extensive experiments on the three real-world datasets of Amazon-Books, Taobao-Buy and Amazon-Hybrid show that the performance of GIMIRec on the Recall, NDCG and Hit Rate indicators is significantly superior to that of the state-of-the-art methods. Moreover, the proposed global context extraction module can be easily transplanted to most sequential recommendation models.
Isotope effect on the anomalies of water: a corresponding state analysis

Light and heavy water show similar anomalies in thermodynamic and dynamic properties, with a consistent trend of anomalies occurring at higher temperature in heavy water. Viscosity also increases faster upon cooling in heavy water, causing a giant isotope effect, with a viscosity ratio near 2.4 at 244 K. While a simple temperature shift apparently helps in collapsing experimental data for both isotopes, it lacks a clear justification, changes value with the property considered, and requires additional ad hoc scaling factors. Here we use a corresponding state analysis based on the possible existence of a liquid-liquid critical point in supercooled water. This provides a coherent framework which leads to collapse of thermodynamic data. The ratio between dynamic properties of the isotopes is strongly reduced. In particular, the decoupling between viscosity $\eta$ and self-diffusion $D$, measured as a function of temperature $T$ by the Stokes-Einstein ratio $D\eta/T$, is found to collapse after applying the corresponding state analysis. Our results are consistent with simulations and suggest that the various isotope effects mirror that on the liquid-liquid transition.
A universal model for lossy and dispersive transmission lines for time domain CAD of circuits

A universal equivalent circuit for lossy and dispersive transmission lines is presented. Existing CAD (computer-aided design) packages, such as SPICE, can be used for its implementation. The starting points for obtaining the models are the analog filters which approximate the forward impulse response and characteristic impedance. The equivalent circuit is used to simulate the effects of pulse propagation on microstrip transmission lines. An examination of the validity of the model is carried out, analyzing the response for an example case in the time and frequency domains. <<ETX>>
GRAM: Generative Radiance Manifolds for 3D-Aware Image Generation

3D-aware image generative modeling aims to generate 3D-consistent images with explicitly controllable camera poses. Recent works have shown promising results by training neural radiance field (NeRF) generators on unstructured 2D images, but still cannot generate highly-realistic images with fine details. A critical reason is that the high memory and computation cost of volumetric representation learning greatly restricts the number of point samples for radiance integration during training. Deficient sampling not only limits the expressive power of the generator to handle fine details but also impedes effective GAN training due to the noise caused by unstable Monte Carlo sampling. We propose a novel approach that regulates point sampling and radiance field learning on 2D manifolds, embodied as a set of learned implicit surfaces in the 3D volume. For each viewing ray, we calculate ray-surface intersections and accumulate their radiance generated by the network. By training and rendering such radiance mani folds, our generator can produce high quality images with realistic fine details and strong visual 3D consistency. 11Project page: https://yudeng.github. io/GRAM/