Miracle-dz/UniFAR-FTUs huggingface dataset:
Autonomous Charging of Electric Vehicle Fleets to Enhance Renewable Generation Dispatchability

A total 19% of generation capacity in California is offered by PV units and over some months, more than 10% of this energy is curtailed. In this research, a novel approach to reduce renewable generation curtailments and increasing system flexibility by means of electric vehicles' charging coordination is represented. The presented problem is a sequential decision making process, and is solved by fitted Q-iteration algorithm which unlike other reinforcement learning methods, needs fewer episodes of learning. Three case studies are presented to validate the effectiveness of the proposed approach. These cases include aggregator load following, ramp service and utilization of non-deterministic PV generation. The results suggest that through this framework, EVs successfully learn how to adjust their charging schedule in stochastic scenarios where their trip times, as well as solar power generation are unknown beforehand.
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems

Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access. This challenge can be separated into three subtasks, (1) knowledge-seeking turn detection, (2) knowledge selection, and (3) knowledge-grounded response generation. We use pre-trained language models, ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1 and 2, the coarse-grained information like domain and entity are used to enhance knowledge usage. For subtask 3, we use a latent variable to encode dialog history and selected knowledge better and generate responses combined with copy mechanism. Meanwhile, some useful post-processing strategies are performed on the model's final output to make further knowledge usage in the generation task. As shown in released evaluation results, our proposed system ranks second under objective metrics and ranks fourth under human metrics.
Query Answering via Decentralized Search

Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network. In such networks, when a query reaches an expert who does not have sufficient expertise, this query needs to be routed to other experts for further processing until it is completely solved; therefore, query answering efficiency is sensitive to the underlying query routing mechanism being used. Among all possible query routing mechanisms, decentralized search, operating purely on each expert’s local information without any knowledge of network global structure, represents the most basic and scalable routing mechanism, which is applicable to any network scenarios even in dynamic networks. However, there is still a lack of fundamental understanding of the efficiency of decentralized search in expert networks. In this regard, we investigate decentralized search by quantifying its performance under a variety of network settings. Our key findings reveal the existence of network conditions, under which decentralized search can achieve significantly short query routing paths (i.e., betweenO(log n) andO(log n) hops, n: total number of experts in the network). Based on such theoretical foundation, we further study how the unique properties of decentralized search in expert networks is related to the anecdotal small-world phenomenon. In addition, we demonstrate that decentralized search is robust against estimation errors introduced by misinterpreting the required expertise levels. To the best of our knowledge, this is the first work studying fundamental behaviors of decentralized search in expert networks. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.
Human Action Recognition From Various Data Modalities: A Review

Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios. Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities. In this article, we present a comprehensive survey of recent progress in deep learning methods for HAR based on the type of input data modality. Specifically, we review the current mainstream deep learning methods for single data modalities and multiple data modalities, including the fusion-based and the co-learning-based frameworks. We also present comparative results on several benchmark datasets for HAR, together with insightful observations and inspiring future research directions.
Estimating the Impact of Weather on Agriculture

We quantify the significance and magnitude of the effect of measurement error in satellite weather data on modeling agricultural production, agricultural productivity, and resilience outcomes. In order to provide rigor to our approach, we combine geospatial weather data from a variety of satellite sources with the geo-referenced household survey data from four sub-Saharan African countries that are part of the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) initiative. Our goal is to provide systematic evidence on which weather metrics have strong predictive power over a large set of crops and countries and which metrics are only useful in highly specific settings. JEL Classification: C83, O13, Q12
Design and development of a high-speed visible pyramid wavefront sensor for the MMT AO system

MAPS, MMT Adaptive optics exoPlanet characterization System, is the upgrade of the adaptive optics system for 6.5-m MMT. It is an NSF MSIP-funded project that includes developing an adaptive-secondary mirror, visible and near-infrared pyramid wavefront sensors, and the upgrade of Arizona infrared imager and echelle spectrograph (ARIES) and MMT High Precision Imaging Polarimeter (MMTPol) science cameras. This paper will present the design and development of the visible pyramid wavefront sensor, VPWFS. It consists of an acquisition camera, a fast-steering tip-tilt modulation mirror, a pyramid, a pupil imaging triplet lens, and a low noise and high-speed frame rate based CCID75 camera. We will report on hardware and software, present the laboratory characterization results of individual subsystems, and outline the on-sky commissioning plan.
Quantum-state estimation problem via optimal design of experiments

In this paper, we study the quantum-state estimation problem in the framework of optimal design of experiments. We first find the optimal designs about arbitrary qubit models for popular optimality criteria such as [Formula: see text]-, [Formula: see text]-, and [Formula: see text]-optimal designs. We also give the one-parameter family of optimality criteria which includes these criteria. We then extend a classical result in the design problem, the Kiefer–Wolfowitz theorem, to a qubit system showing the [Formula: see text]-optimal design which is equivalent to a certain type of the [Formula: see text]-optimal design. We next compare and analyze several optimal designs based on the efficiency. We explicitly demonstrate that an optimal design for a certain criterion can be highly inefficient for other optimality criteria.
Recent Developments in Modulation Spectroscopy for Trace Gas Detection Using Tunable Diode Lasers

Abstract The need for higher sensitive detection technology for trace gas samples, either in the laboratory setup or in the atmospheric remote sensing has been a goal for several decades. The development of the tunable diode laser has propelled the progress of trace detection technology, and modulation technology enables the improvement of the detection sensitivity. As a result, the detection of 10−7 or 10−8 absorbance is possible for some applications, and modulation technology has been applied to the ultraviolet as well as mid-infra red wavelength range. In this review, recent progress of tunable diode lasers and diode laser-based modulation technologies are presented. Wavelength modulation, frequency modulation, and two-tone frequency modulation techniques are mainly described along with the actual application of the techniques. In addition, the state-of-the-art of diode laser development which can be adopted for the trace detection is presented.
BKT-LSTM: Efficient Student Modeling for knowledge tracing and student performance prediction

Recently, we have seen a rapid rise in usage of online educational platforms. The personalized education became crucially important in future learning environments. Knowledge tracing (KT) refers to the detection of students' knowledge states and predict future performance given their past outcomes for providing adaptive solution to Intelligent Tutoring Systems (ITS). Bayesian Knowledge Tracing (BKT) is a model to capture mastery level of each skill with psychologically meaningful parameters and widely used in successful tutoring systems. However, it is unable to detect learning transfer across skills because each skill model is learned independently and shows lower efficiency in student performance prediction. While recent KT models based on deep neural networks shows impressive predictive power but it came with a price. Ten of thousands of parameters in neural networks are unable to provide psychologically meaningful interpretation that reflect to cognitive theory. In this paper, we proposed an efficient student model called BKT-LSTM. It contains three meaningful components: individual \textit{skill mastery} assessed by BKT, \textit{ability profile} (learning transfer across skills) detected by k-means clustering and \textit{problem difficulty}. All these components are taken into account in student's future performance prediction by leveraging predictive power of LSTM. BKT-LSTM outperforms state-of-the-art student models in student's performance prediction by considering these meaningful features instead of using binary values of student's past interaction in DKT. We also conduct ablation studies on each of BKT-LSTM model components to examine their value and each component shows significant contribution in student's performance prediction. Thus, it has potential for providing adaptive and personalized instruction in real-world educational systems.
Gravitational dynamics: A novel shift in the Hamiltonian paradigm

It is well known that Einstein's equations assume a simple polynomial form in the Hamiltonian framework based on a Yang-Mills phase space. We re-examine the gravitational dynamics in this framework and show that {\em time} evolution of the gravitational field can be re-expressed as (a gauge covariant generalization of) the Lie derivative along a novel shift vector field in {\em spatial} directions. Thus, the canonical transformation generated by the Hamiltonian constraint acquires a geometrical interpretation on the Yang-Mills phase space, similar to that generated by the diffeomorphism constraint. In classical general relativity this geometrical interpretation significantly simplifies calculations and also illuminates the relation between dynamics in the `integrable' (anti)self-dual sector and in the full theory. For quantum gravity, it provides a point of departure to complete the Dirac quantization program for general relativity in a more satisfactory fashion. This gauge theory perspective may also be helpful in extending the `double copy' ideas relating the Einstein and Yang-Mills dynamics to a non-perturbative regime. Finally, the notion of generalized, gauge covariant Lie derivative may also be of interest to the mathematical physics community as it hints at some potentially rich structures that have not been explored.
Salient Bundle Adjustment for Visual SLAM

Recently, the philosophy of visual saliency and attention has started to gain popularity in the robotics community. Therefore, this paper aims to mimic this mechanism in SLAM framework by using saliency prediction model. Comparing with traditional SLAM that treated all feature points as equal important in optimization process, we think that the salient feature points should play more important role in optimization process. Therefore, we proposed a saliency model to predict the saliency map, which can capture both scene semantic and geometric information. Then, we proposed Salient Bundle Adjustment by using the value of saliency map as the weight of the feature points in traditional Bundle Adjustment approach. Exhaustive experiments conducted with the state-of-the-art algorithm in KITTI and EuRoc datasets show that our proposed algorithm outperforms existing algorithms in both indoor and outdoor environments. Finally, we will make our saliency dataset and relevant source code open-source for enabling future research.
Rotational invariance in critical planar lattice models

In this paper, we prove that the large scale properties of a number of twodimensional lattice models are rotationally invariant. More precisely, we prove that the random-cluster model on the square lattice with cluster-weight 1 ≤ q ≤ 4 exhibits rotational invariance at large scales. This covers the case of Bernoulli percolation on the square lattice as an important example. We deduce from this result that the correlations of the Potts models with q ∈ {2, 3, 4} colors and of the six-vertex height function with ∆ ∈ [−1,−1/2] are rotationally invariant at large scales.
Emergent Hand Morphology and Control from Optimizing Robust Grasps of Diverse Objects

Evolution in nature illustrates that the creatures’ biological structure and their sensorimotor skills adapt to the environmental changes for survival. Likewise, the ability to morph and acquire new skills can facilitate an embodied agent to solve tasks of varying complexities. In this work, we introduce a data-driven approach where effective hand designs naturally emerge for the purpose of grasping diverse objects. Jointly optimizing morphology and control imposes computational challenges since it requires constant evaluation of a black-box function that measures the performance of a combination of embodiment and behavior. We develop a novel Bayesian Optimization algorithm that efficiently co-designs the morphology and grasping skills through learned latent-space representations. We design the grasping tasks based on a taxonomy of human grasp types: power grasp, pinch grasp, and lateral grasp. Through experimentation and comparative study, we demonstrate that our approach discovers robust and cost-efficient hand morphologies for grasping novel objects. Additional videos and results at https://xinleipan.github. io/emergent_morphology
Sensitivity analysis in the infinite dimensional Heston model

We consider the infinite dimensional Heston stochastic volatility model proposed in [5]. The price of a forward contract on a non-storable commodity is modelled by a generalized Ornstein-Uhlenbeck process in the Filipović space with this volatility. We prove different representation formulas for the forward price. Then we consider prices of options written on these forward contracts and we study sensitivity analysis with computation of the Greeks with respect to different parameters in the model. Since these parameters are infinite dimensional, we need to reinterpret the meaning of the Greeks. For this we use infinite dimensional Malliavin calculus and a randomization technique.
Tomographic imaging of complete quantum state of matter by ultrafast diffraction

Ming Zhang*, Shuqiao Zhang*, Haitan Xu*, Hankai Zhang, Xiangxu Mu, R. J. Dwayne Miller, Anatoly A. Ischenko, Oriol Vendrell, and Zheng Li ∗ State Key Laboratory for Mesoscopic Physics and Collaborative Innovation Center of Quantum Matter, School of Physics, Peking University, Beijing 10087, China Institute for Quantum Science and Engineering and Department of Physics, Southern University of Science and Technology, Shenzhen, 518055, China Departments of Chemistry and Physics, University of Toronto, Toronto, Ontario M5S 3H6, Canada Lomonosov Institute of Fine Chemical Technologies, RTU-MIREA Russian Technological University, Vernadskii Avenue 86, 119571 Moscow, Russia Physikalisch-Chemisches Institut, Universität Heidelberg, Im Neuenheimer Feld 229, D-69120 Heidelberg, Germany (Dated: December 23, 2020)
Resilience in Times of Global Pandemic: Steering Recovery and Thriving Trajectories

Disasters, including earthquakes, wildfires, terrorist attacks, and infectious disease outbreaks, are catastrophic events that expose individuals to stress, disrupt community routines and dynamics, undermine infrastructure and businesses, and result in economic losses for a significant period (Bader et al., 2019; Bakić, 2019). The World Health Organization declared the COVID-19 outbreak a pandemic in the early months of 2020 and classed it as a major disaster. This pandemic is unique, one of few catastrophic events in recent history to affect the entire global population, and its severity and long-term consequences will test individuals, organisations, communities, and nations in unprecedented ways.
Simulating the trade effects of the COVID‐19 pandemic

Abstract The WTO Global Trade Model, a quantitative trade model, is employed to project the impact on the global economy of the COVID‐19 pandemic. Because of the profound uncertainty about the duration of the pandemic and the containment measures, three scenarios are constructed, V‐shaped, U‐shaped and L‐shaped recovery, corresponding with a duration of the pandemic of 3 months, 6 months and more than a year. The pandemic and containment measures are assumed to lead to a general reduction of labour supply, a rise in trade costs, and reductions in both demand and supply in sectors most affected by the containment measures. GDP and trade are projected to fall by, respectively, 5% and 11% in the V‐shaped and L‐shaped scenarios and trade by, respectively, 8% and 20%. The response of trade to the reduction in GDP, measured by the trade‐to‐GDP elasticity, is projected to rise as the crisis lasts longer. The reason is that a longer duration will lead to a larger drop in spending on durables which are highly tradable.
The complete mitogenome of Lasioglossum affine (Hymenoptera: Halictidae) and phylogenetic analysis

Abstract The complete mitogenome of Lasioglossum affine (Hymenoptera: Halictidae) was sequenced and analyzed. The whole mitogenome is 17,352 bp (AT%=84.1%) and encodes 37 typical eukaryotic mitochondrial genes, including 13 protein-coding genes (PCGs), 22 tRNAs, two rRNAs, and an AT-rich region. Further analysis found three gene rearrangements, where trn I-Q-M → trn M-I-Q, trn W-C-Y → trn C-W-Y, and trn K-D → trn D-K were shuffled. The phylogenetic relationships of 19 species of Hymenoptera were established using maximum-likelihood method based on 13 concatenated PCGs. The result showed that Lasioglossum affine is a sister of Lasioglossum sp. SJW-2017.
Public Perception Regarding COVID-19, Nature of the Disease, Susceptibility to Complications, and Relationship to Influenza: A Study from Jordan Using Google Forms

Background COVID-19 was declared by the World Health Organization (WHO) as a pandemic and had spread to most of the globe. In the current study, perception regarding the nature of the COVID-19 disease, susceptibility to its complications, and its relationship to seasonal influenza was investigated among the Jordanian population. Methods This was a survey-based cross-sectional study among public in Jordan. Results A total of 1863 participants agreed to participate in this questionnaire-based study. The results showed that more than half (55%) of the sample believed that COVID-19 is a naturally occurring virus which causes a serious and possibly fatal disease (50.7%). The majority (62.9%) did not accept that this disease is actually caused by bacteria, or is similar to seasonal influenza. Interestingly, 45.0% of the sampled population felt that the COVID-19 virus was engineered in the lab. Most of the participants (95%) agreed that the elderly or individuals with chronic illnesses such as diabetes and heart disease were more susceptible to severe COVID-19 infections. With respect to symptoms, about two-thirds of the participants (65.2%) felt that the symptoms of COVID-19 are to a large extent similar to those of seasonal influenza. In addition, the majority (82.7%) believed that only some COVID-19 patients develop symptoms that can be described as severe. Factors such as age, gender, and education were found to modulate some of the perceptions of Jordanians regarding different aspects of COVID-19 disease. Conclusion The majority of Jordanians have a good perception about the nature, cause and symptoms of COVID-19 disease.
Navigated transcranial magnetic stimulation following awake craniotomy for resection of glioma: Description of two cases

Background: Although transcranial magnetic stimulation (TMS) has been indicated as a potential therapy for several neurologic conditions, there is little known regarding its use during the postoperative rehabilitation period in patients with brain tumors. Furthermore, seizures, a common presentation in these patients, are regarded as a major contraindication for TMS therapy. Case Description: We demonstrate that postoperative continuous theta burst stimulation (cTBS), a patterned form of repetitive TMS, was safely tolerated in addition to current neurorehabilitation techniques in two brain tumor patients, including one patient with a history of tumor-related epilepsy. We administered navigated 5 Hz cTBS to two patients within 48 h following awake craniotomy for tumor resection. Active motor thresholds were measured in both patients before TBS administration to determine stimulus intensity. We used resting-state fMRI to identify likely damaged networks based on postoperative deficits. This aided in TMS planning and allowed deficit targeted therapy contralateral to the lesioned network node. Both patients tolerated TBS therapy well and had no adverse effects, including posttreatment seizures, despite one patient having a history of tumor-related epilepsy. Conclusion: TBS may be safe in the immediate postoperative period for patients following brain tumor resection. Additional studies are needed to quantify the efficacy of TMS in improving neurologic deficits following tumor resection.