Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-world challenges for reinforcement learning in building control

Building upon prior research that highlighted the need for stan-dardizing environments for building control research, and inspired by recently introducedbenchmarks forreal life reinforcement learning control, here we propose a non-exhaustive nine real world challenges for reinforcement learning building controller. We argue that building control research should be expressed in this framework in addition to providing a standardized environment for re-peatability. Advanced controllers such as model predictive control and reinforcement learning control have both advantages and disadvantages thatprevent themfrom being implemented in real world buildings. Comparisons between the two are seldom, and often biased. By focusing on the benchmark problems and challenges, we can investigate the performance of the controllers under a variety of situations and generate a fair comparison. Lastly, we call for a more interdisciplinary eﬀort of the research community to address the real world challenges, and unlock the potentials of advanced building controllers.
Developing an empirical model for ducted tidal turbine performance using numerical simulation results

This article studies the effects of viscous loss, flow separation, and base pressure for ducted turbine designs using computational fluid dynamics (CFD) simulations. Analytical model coefficients for inlet and diffuser efficiency and base pressure coefficient parameterize these effects and have been identified from CFD results. General trends are that the inlet efficiency is nearly unity for the simulated designs; the diffuser efficiency has a significant impact on performance and is degraded by flow separation; and that the base pressure effect can provide a significant performance enhancement. Geometric features influencing each of the aforementioned parameters are identified and a regression-based model has been developed to predict ducted turbine performance.
ASCEND: A Spontaneous Chinese-English Dataset for Code-switching in Multi-turn Conversation

Code-switching is a speech phenomenon occurring when a speaker switches language during a conversation. Despite the spontaneous nature of code-switching in conversational spoken language, most existing works collect code-switching data from read speech instead of spontaneous speech. ASCEND (A Spontaneous Chinese-English Dataset) is a high-quality Mandarin Chinese-English code-switching corpus built on spontaneous multi-turn conversational dialogue sources collected in Hong Kong. We report ASCEND’s design and procedure for collecting the speech data, including annotations. ASCEND consists of 10.62 hours of clean speech, collected from 23 bilingual speakers of Chinese and English. Furthermore, we conduct baseline experiments using pre-trained wav2vec 2.0 models, achieving a best performance of 22.69% character error rate and 27.05% mixed error rate.
Joint Device Association, Resource Allocation, and Computation Offloading in Ultradense Multidevice and Multitask IoT Networks

With the emergence of more and more applications of Internet of Things (IoT) mobile devices (IMDs), a contradiction between mobile energy demand and limited battery capacity becomes increasingly prominent. In addition, in ultradense IoT networks, the ultradensely deployed small base stations (SBSs) will consume a large amount of energy. To reduce the network-wide energy consumption and prolong the standby time of IMDs and SBSs, under the proportional computation resource allocation and devices’ latency constraints, we jointly perform the device association, computation offloading, and resource allocation to minimize the network-wide energy consumption for ultradense multidevice and multitask IoT networks. To further balance the network loads and fully utilize the computation resources, we take account of multistep computation offloading. Considering that the finally formulated problem is in a nonlinear and mixed-integer form, we develop an improved hierarchical adaptive search (IHAS) algorithm to find its solution. Then, we give the convergence, computational complexity, and parallel implementation analyses for such an algorithm. By comparing with other algorithms, we can easily find that such an algorithm can greatly reduce the network-wide energy consumption under devices’ latency constraints.
Efficient Spatio-temporal Weather Forecasting Using U-Net

Weather forecast plays an essential role in multiple aspects of the daily life of human beings. Currently, physics based numerical weather prediction is used to predict the weather and requires enormous amount of computational resources. In recent years, deep learning based models have seen wide success in many weather-prediction related tasks. In this paper we describe our experiments for the Weather4cast 2021 Challenge, where 8 hours of spatio-temporal weather data is predicted based on an initial one hour of spatio-temporal data. We focus on SmaAt-UNet, an efficient U-Net based autoencoder. With this model we achieve competent results whilst maintaining low computational resources. Furthermore, several approaches and possible future work is discussed at the end of the paper.
Contact-Rich Manipulation of a Flexible Object based on Deep Predictive Learning using Vision and Tactility

We achieved contact-rich flexible object manipulation, which was difficult to control with vision alone. In the unzipping task we chose as a validation task, the gripper grasps the puller, which hides the bag state such as the direction and amount of deformation behind it, making it difficult to obtain information to perform the task by vision alone. Additionally, the flexible fabric bag state constantly changes during operation, so the robot needs to dynamically respond to the change. However, the appropriate robot behavior for all bag states is difficult to prepare in advance. To solve this problem, we developed a model that can perform contact-rich flexible object manipulation by real-time prediction of vision with tactility. We introduced a point-based attention mechanism for extracting image features, softmax transformation for predicting motions, and convolutional neural network for extracting tactile features. The results of experiments using a real robot arm revealed that our method can realize motions responding to the deformation of the bag while reducing the load on the zipper. Furthermore, using tactility improved the success rate from 56.7% to 93.3% compared with vision alone, demonstrating the effectiveness and high performance of our method.
Perceptual Loss with Recognition Model for Single-Channel Enhancement and Robust ASR

Single-channel speech enhancement approaches do not always improve automatic recognition rates in the presence of noise, because they can introduce distortions unhelpful for recognition. Following a trend towards end-to-end training of sequential neural network models, several research groups have addressed this problem with joint training of front-end enhancement module with back-end recognition module. While this approach ensures enhancement outputs are helpful for recognition, the enhancement model can overfit to the training data, weakening the recognition model in the presence of unseen noise. To address this, we used a pre-trained acoustic model to generate a perceptual loss that makes speech enhancement more aware of the phonetic properties of the signal. This approach keeps some benefits of joint training, while alleviating the overfitting problem. Experiments on Voicebank + DEMAND dataset for enhancement show that this approach achieves a new state of the art for some objective enhancement scores. In combination with distortion-independent training, our approach gets a WER of 2.80\% on the test set, which is more than 20\% relative better recognition performance than joint training, and 14\% relative better than distortion-independent mask training.
Dependency Learning for Legal Judgment Prediction with a Unified Text-to-Text Transformer

Given the fact of a case, Legal Judgment Prediction (LJP) involves a series of sub-tasks such as predicting violated law articles, charges and term of penalty. We propose leveraging a unified text-to-text Transformer for LJP, where the dependencies among sub-tasks can be naturally established within the auto-regressive decoder. Compared with previous works, it has three advantages: (1) it fits in the pretraining pattern of masked language models, and thereby can benefit from the semantic prompts of each sub-task rather than treating them as atomic labels, (2) it utilizes a single unified architecture, enabling full parameter sharing across all sub-tasks, and (3) it can incorporate both classification and generative sub-tasks. We show that this unified transformer, albeit pretrained on general-domain text, outperforms pretrained models tailored specifically for the legal domain. Through an extensive set of experiments, we find that the best order to capture dependencies is different from human intuitions, and the most reasonable logical order for humans can be sub-optimal for the model. We further include two more auxiliary tasks: court view generation and article content prediction, showing they can not only improve the prediction accuracy, but also provide interpretable explanations for model outputs even when an error is made. With the best configuration, our model outperforms both previous SOTA and a single-tasked version of the unified transformer by a large margin.
The relationship between size and power dissipation in a 1-10 MHz transformer

Consideration is given to the trade-off between size and power dissipation in the design of low-profile 1-10 MHz transformers with planar spiral windings. For a fixed power loss and number of conductor layers, the transformer with the smallest possible footprint can be designed by a trade-off of core and copper loss. A discussion is also presented of the relationship between the marginal reduction of the transformer footprint and the associated marginal increase in power dissipation and/or number of conductor layers for the design of the smallest possible transformer. <<ETX>>
Novel directional comparison protection based on travelling waves

This paper proposes a novel directional comparison protection. At its core is a directional element based on travelling waves. The directional element determines fault direction by the ratio of initial voltage and current travelling waves. In theory, if the ratio is equal to the surge admittance of the protected line, the fault is a reverse one; if the ratio is not equal to surge admittance of protected line, the fault is a forward one. In practice, appropriate thresholds are required and can also be set according to the practical situation. Using two directional elements and a proper communication channel, a new protection system can be constructed.
Top-heavy stellar mass distribution in galactic nuclei inferred from the universally high abundance ratio of [Fe/Mg]

Recent observations of active galactic nuclei (AGNs) have shown a high Fe ii/Mg ii line-flux ratio in their broad-line regions, nearly independent of redshift up to z ≳ 6. The high flux ratio requires rapid production of iron in galactic nuclei to reach an abundance ratio of [Fe/Mg] ≳ 0.2 as high as those observed in matured galaxies in the local universe. We propose a possible explanation of rapid iron enrichment in AGNs by massive star formation that follows a top-heavy initial mass function (IMF) with a power-law index of Γ larger than the canonical value of Γ = −2.35 for a Salpeter IMF. Taking into account metal production channels from different types of SNe, we find that the high value of [Fe/Mg] ≳ 0.2 requires the IMF to be characterized with Γ ≳ −1 (Γ ≳ 0) and a high-mass cutoff at Mmax ≃ 100–150 M⊙ (Mmax ≳ 250 M⊙). Given the conditions, core-collapse SNe with M* ≳ 70 M⊙ and pair-instability SNe give a major contribution for iron enrichment. Such top-heavy stellar IMFs would be a natural consequence from mass growth of stars formed in dense AGN disks under Bondi-like gas accretion that is regulated by feedback at M* ≳ 10 M⊙. The massive stellar population formed in AGN disks also leave stellar-mass black hole remnants, whose mergers associated with gravitational-wave emission account for at most 10 per cent of the merger rate inferred from LIGO/Virgo observations to simultaneously explain the high [Fe/Mg] ratio with metal ejection.
Injecting Numerical Reasoning Skills into Knowledge Base Question Answering Models

Embedding-based methods are popular for Knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. To enable better training, we propose two pre-training tasks with explicit numerical-oriented loss functions on two generated training datasets and a template-based data augmentation method for enriching ordinal constrained QA dataset. Extensive experiments on KBQA benchmarks demonstrate that with the help of our training algorithm, NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions.
Semi-supervised teacher-student deep neural network for materials discovery

Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and accurate machine learning models to filter out stable, synthesizable materials with desired properties. However, such efforts to build supervised regression or classification screening models have been severely hindered by the lack of unstable or unsynthesizable samples, which usually are not collected and deposited in materials databases such as ICSD and Materials Project (MP). At the same time, there are a significant amount of unlabelled data available in these databases. Here we propose a semi-supervised deep neural network (TSDNN) model for high-performance formation energy and synthesizability prediction, which is achieved via its unique teacher-student dual network architecture and its effective exploitation of the large amount of unlabeled data. For formation energy based stability screening, our semi-supervised classifier achieves an absolute 10.3\% accuracy improvement compared to the baseline CGCNN regression model. For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87.9\% to 97.9\% using 1/49 model parameters. To further prove the effectiveness of our models, we combined our TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to discover novel stable cubic structures. Out of 1000 recommended candidate samples by our models, 512 of them have negative formation energies as validated by our DFT formation energy calculations. Our experimental results show that our semi-supervised deep neural networks can significantly improve the screening accuracy in large-scale generative materials design.
A Tale of Two Circularization Periods

We reanalyze the exquisite eclipsing binary data from the Kepler and TESS missions, focusing on eccentricity measurements at short orbital periods to empirically constrain tidal circularization. We calculate a circularization period of ∼6 days due to nearly circular binaries with long orbital periods (“cold core”) but find many binaries with moderate eccentricities that circularize interior to only ∼3 days (“eccentricity envelope”). We show that these features are present in previous spectroscopic surveys. We also reaffirm the statistically significant difference between the eccentricity distributions of young (<1 Gyr) and old (>3 Gyr) binaries. Our work introduces new methods that have the potential to reconcile theoretical predictions with observations to empirically constrain tidal circularization.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images

Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To collect sufficient positive/negative samples for heuristic training, most object detectors preset region anchors in order to calculate Intersection-over-Union (IoU) against the ground-truthed data. In this case, small objects are frequently abandoned or mislabeled. In this paper, we present an effective Dynamic Enhancement Anchor (DEA) network to construct a novel training sample generator. Different from the other state-of-the-art techniques, the proposed network leverages a sample discriminator to realize interactive sample screening between an anchor-based unit and an anchor-free unit to generate eligible samples. Besides, multi-task joint training with a conservative anchor-based inference scheme enhances the performance of the proposed model while reducing computational complexity. The proposed scheme supports both oriented and horizontal object detection tasks. Extensive experiments on two challenging aerial benchmarks (i.e., DOTA and HRSC2016) indicate that our method achieves state-of-the-art performance in accuracy with moderate inference speed and computational overhead for training. On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0.40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3.08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone. Besides, our DEA-Net which integrated with the baseline of ReDet achieves the state-of-the-art performance by 80.37%. On HRSC2016, it surpasses the previous best model by 1.1% using only 3 horizontal anchors. The source code and the training set are made publicly available at: https://github.com/QxGeng/DEA-Net.
Analysis of stability and instability for standing waves of the double power one dimensional nonlinear Schr{\"o}dinger equation

For the double power one dimensional nonlinear Schrödinger equation, we establish a complete classification of the stability or instability of standing waves with positive frequencies. In particular, we fill out the gaps left open by previous studies. Stability or instability follows from the analysis of the slope criterion of Grillakis, Shatah and Strauss. The main new ingredients in our approach are a reformulation of the slope and the explicit calculation of the slope value in the zero-frequency case. Our theoretical results are complemented with numerical experiments.
Operational characteristics, parameters, and history of a (13T) Nb/sub 3/Sn dipole

The early design and test results have been previously reported. During the subsequent operation of "D20" the accelerator prototype dipole has provided both additional and more detailed data as to its characteristics and performance. D20's use as a test facility for high field critical current measurements has provided operational experience and history pertaining to accelerator required characteristics. There has been recently obtained data related to field quality, operational reproducibility and reliability, which will be presented. This prototype "D20" has attained the highest magnetic field of any accelerator prototype dipole constructed and tested to date. The magnet has continued to operate routinely.
Study of Factors Influencing the Performance of Students with the Analysis of Variance (ANOVA): Case of Morocco

In this paper, we investigate a statistical study to highlight some pertinent indicators contributing to the performance of students in Moroccan universities with open access. Such problem has attracted a great attention in higher education and many studies are devoted to this problematic to get some insights about the factors influencing directly or indirectly the efficiency of students’ productivity. Our approach is based on real data collected from many national different universities. From a sample of 901 students (S3 & S5) belonging to these institutions, we have performed ANOVA test by using the SPSS software to examine the impact of each variable which can be taken into account for explaining the dependent variable (student performance). It is worth noting that our methodology can be improved by other advanced statistic methods which will be the subject of our future work.
Deep Translation Prior: Test-time Training for Photorealistic Style Transfer

Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods.
Unbonded Posttensioned Concrete Bridge Piers. I: Monotonic and Cyclic Analyses

The monotonic and cyclic behavior of a proposed unbonded, posttensioned concrete bridge pier system is studied using finite-element analyses. A procedure to evaluate seismic capacities based on results from the monotonic and cyclic analyses is described in the framework of a two-level approach considering functional- and survival-performance limits. A set of criteria to define functional- and survival-level displacement capacities for the system is developed. The proposed criteria represent improvements over existing criteria in that they are applicable to both conventional reinforced concrete structures and unbonded posttensioned structures. The monotonic and cyclic behavior of prototype single-column pier and two-column bent designs is presented. Monotonic analyses are performed to characterize the stiffness, strength, ductility, and limit-state behavior of these systems. Cyclic analyses are carried out to estimate energy dissipation capacity, residual displacements, and general hysteretic behavior. The influence of the degree of unbonded posttensioning on bridge pier behavior is examined. Using the finite-element results and the proposed criteria, seismic capacities of the prototype bridge pier systems are established.