Miracle-dz/UniFAR-FTUs huggingface dataset:
Policy Learning with Deep Koopman Operator Networks

In this paper, a deep policy learning method based on Koopman operator networks is proposed for reinforcement learning tasks. Specifically, a deep neural network based on the Koopman operator is designed for nonlinear systems in reinforcement learning, and the environment dynamics are embedded in a high-dimensional space to obtain a highly approximate linear model. An iterative linear quadratic regulator method based on a value function is also proposed to complete the learning and solution of the optimal policy. The proposed model-based policy learning algorithm makes the policy in the reinforcement learning task easy to solve and improves the data efficiency and algorithm performance. The results of robot control experiments conducted at OpenAI Gym show that the proposed control algorithm has better control performance than the representative reinforcement learning and data-driven methods.
Application of Multi-Task Learning for Abnormal Diagnosis and Trip Variable Prediction in Nuclear Power Plants

A nuclear power plant is a large facility composed of many components, and abnormal states occasionally occur in which components fail. In the event of abnormal states, if appropriate measures are not taken, the abnormal states can worsen and cause an unexpected reactor trip. Therefore, in order to provide the operator with key state information in case of abnormal states, abnormal diagnosis and trip variable prediction were performed based on multi-task learning (MTL). The MTL is a method of performing multiple tasks through a single model. Specifically, the progressive layered extraction method, one of the MTL structures, was applied. It efficiently transmits information between tasks through a gating network and progressive routing mechanism. The proposed model showed higher diagnosis accuracy and lower prediction error than the basic MTL model. If the key state information is provided to the operator through the proposed model, it will be able to contribute to reducing human error and preventing the aggravation of abnormal states.
Intellibot: A Personalized Behavioral Analysis Chatbot Framework Powered by GPT-3

This paper is focused on helping individuals with autism, paranoid personality disorder (PPD), and other mental illnesses practice and improve their communication skills by offering them a friendly, intelligent, and comfortable environment in them to express their needs and overcome their daily communication struggles by practicing. The research aims to construct a personalized Chatbot based on GPT-3 technology to provide professional help to individuals with psychological issues. We proposed implementing an advanced system that chats with the user, detects their emotion, and replies accordingly; it also sees negative thoughts during user sessions and automatically terminates the session while referring the individual to a professional psychiatrist if needed. The paper discusses the whole process of developing the Chatbot from the preprocessing steps for the training data to an overview of GPT-3 and its capabilities. Our top priority is to guarantee the safety and well-being of our users; therefore, the paper addresses the challenges faced during the development of the Chatbot, including the unintentional encouragement of negative thoughts and how we overcame these challenges by implementing proactive measures. Additionally, the paper describes text classification using RoBERTa, voice classification using the "asapp/sew-d-base-plus-400k-ft-ls100h" model, and a Hidden Markov Model to analyze user emotions and provide appropriate responses. Ultimately, the proposed Chatbot aims to create a friendly and comfortable environment for users while offering professional support, enabling fulfilling connections, and improving their quality of life.
Metamorphism, Metastability, Mechanical Strength, and the Support of Mountain Belts

Exhumed high-pressure rocks from the Caledonian root zone in Norway provide analogues for processes occurring today under southern Tibet, allowing large-scale geophysical observations, from gravity and earthquakes, to be linked with the mechanical properties of metastable rocks under high mountains. Metastability is essential for the survival of thick mountain roots and, hence, of high mountains and is in turn controlled by water. If water is absent, dry granulite, formed from earlier melting episodes, is both stable and strong and likely to survive in the eclogite stability field of deep root zones for hundreds of million years. But if hydrous fluid is introduced, the transformation of granulite to eclogite is relatively rapid and accompanied by a dramatic loss of strength. In Norway, this transformation was initiated by water infiltration along fractures formed by earthquakes. The same process, as marked by deep earthquakes, may be occurring today beneath southern Tibet.
Deep Reinforcement Learning with semi-expert distillation for autonomous UAV cinematography

Unmanned Aerial Vehicles (UAVs, or drones) have revolutionized modern media production. Being rapidly deployable "flying cameras", they can easily capture aesthetically pleasing aerial footage of static or moving filming targets/subjects. Current approaches rely either on manual UAV/gimbal control by human experts, or on a combination of complex computer vision algorithms and hardware configurations for automating the flight+filming process. This paper explores an efficient Deep Reinforcement Learning (DRL) alternative, which implicitly merges the target detection and path planning steps into a single algorithm. To achieve this, a baseline DRL approach is augmented with a novel policy distillation component, which transfers knowledge from a suitable, semi-expert Model Predictive Control (MPC) controller into the DRL agent. Thus, the latter is able to autonomously execute a specific UAV cinematography task with purely visual input. Unlike the MPC controller, the proposed DRL agent does not need to know the 3D world position of the filming target during inference. Experiments conducted in a photorealistic simulator showcase superior performance and training speed compared to the baseline agent, while surpassing the MPC controller in terms of visual occlusion avoidance.
Farm Parcel Extraction in Remote-Sensing Images Based on Semantic Segmentation

Vectorized and structured data extracted from farm parcel segmentation and classification provides basic information for the research on pest control of large areas, crop yield forecasts, and crop varieties classification. On one hand, we investigate the effectiveness of an automated image segmentation method based on DeeplabV3+ architecture to enable it to automate the task of farm parcel delineation that originally relied on high labor costs. On the other hand, we detail an efficient post-processing on vectoring binary segmentation image to get more optimized image segmentation results. Overall, on the 0.8m resolution image dataset, the precision reaches 85.22%, the recall reached 83.78%, the F1-S was 81.08%, the accuracy was 82.54%, and IoU was 82.87%. Meanwhile, without losing too much precision, we train and test the model with 3.2m resolution images, which has the advantage of processing speed over 0.8m resolution. The method can be effectively applied to the task of segmentation of agricultural land. Extensive experiments show the effectiveness of using the DeeplabV3+ architecture and post-processing by vectoring binary segmentation for farm parcel segmentation in high remote sensing images.
Multi-Scale Hybrid Fusion Network for Mandarin Audio-Visual Speech Recognition

Compared to feature or decision fusion, hybrid fusion can beneficially improve audio-visual speech recognition accuracy. Existing works are mainly prone to design the multi-modality feature extraction process, interaction, and prediction, neglecting useful information on the multi-modality and the optimal combination of different predicted results. In this paper, we propose a multi-scale hybrid fusion network (MSHF) for mandarin audio-visual speech recognition. Our MSHF consists of a feature extraction subnetwork to exploit the proposed multi-scale feature extraction module (MSFE) to obtain multi-scale features and a hybrid fusion subnetwork to integrate the intrinsic correlation of different modality information, optimizing the weights of prediction results for different modalities to achieve the best classification. We further design a feature recognition module (FRM) for accurate audio-visual speech recognition. We conducted experiments on the CAS-VSR-W1k dataset. The experimental results show that the proposed method outperforms the selected competitive baselines and the state-of-the-art, indicating the superiority of our proposed modules.
Predictive Control of Robot Arm Trajectory Tracking Based on Control Lyapunov Function

Modern robotic systems are endowed with superior mobility and mechanical skills, making them suitable for using in special real-world scenarios, such as disaster scenarios where robots can be used to remove hazardous substances. Therefore, it is crucial to develop intelligent control algorithms that enable complex robots to accurately perform their movements. Firstly, considering optimization and input constraints, this article designs a robotic arm model predictive control (MPC) scheme based on control Lyapunov functions for the first time. It combines the stability guarantee provided by control Lyapunov functions (CLFs) with the optimality provided by MPC, and constructs a CLFs-MPC framework based on the premise of ensuring actuator performance. Secondly, by designing the arctangent function and further introducing the contraction constraint of the bounded controller, the recursive feasibility and stability of the robot control system are ensured. The stability of bounded controllers was analyzed using Lyapunov theory and LaSalle invariance principle. Finally, numerical simulation verifies the validity and superiority of the CLFs-MPC controller.
Time-Optimal and Smooth Trajectory Planning Based On Continuous Pseudo-Acceleration

The current time-optimal trajectory planning based on dynamics does not take into account the continuity of joint torque. Although the time obtained by the solution of the original time-optimal path parameterization (TOPP) algorithm is optimal, the joint torque obtained by the solution is not continuous, and the discontinuous joint torque will cause the manipulator to resonate, which will reduce the accuracy of the trajectory, making the obtained trajectory in practical engineering applications cannot be effectively applied. Therefore, this paper improves the problem of discontinuous torque on the basis of the TOPP algorithm, and proposes to use a quadratic polynomial curve to deal with the discontinuous part of the pseudo-acceleration in the phase plane composed of position and pseudo-acceleration, which not only ensures the integrity of the path, but also makes the joint torque continuous and smoothes the joint torque trajectory to some extent. This method is verified by the simulation experiment of the two-link manipulator.
A Graph-Based SLAM Method Assisted by Visual Marker in the Degenerate Scenes

In the post-COVID-19 pandemic era, hospitals and other places have an urgent need for mobile robots with autonomous disinfection ability, and robots need to complete SLAM tasks to realize autonomous navigation. Lidar is widely used for indoor SLAM. However, due to the lack of geometric structure in the indoor environment, two-dimensional lidar information degrades, rendering the robot unable to obtain effective positioning. Therefore, we leverage the easy identification and high robustness of ARTag to fuse vision and range sensor information. We introduce ARTag as visual marker to assist positioning, establish observation window to screen the acquired ARTag pose. We employ the pose graph optimization method to optimize the visual markers and laser scanning results in the back end. This reduces the positioning errors caused by the degradation of lidar information and reduces the frequency of optimization by improving the back end optimization strategy. This method is applied in a UltraViolet C (UVC) Disinfection Robot experiment. Experimental results show that our method effectively improves the positioning accuracy and robustness of the robot in the environments with degraded laser information.
Identifying Key Features in Aggregated Real-Life AIS-Messages for Classification of Maritime Vessels by Random Forests

Classification of vessels is a major task in many duties of maritime surveillance. In previous work, ad hoc approaches for classifying maritime vessels were applied to large sets of single AIS messages. The resulting performance has been explored based on real-life datasets in different maritime areas. The approaches were used in a particular application scenario of detecting AIS spoofing in fishery scenarios. Seeing good classification results, in particular by Random Forests, the questions arise whether these at least partly resulted from overfitting phenomena and which of the feature of classification in this problem are most relevant. To address these questions in the current contribution, AIS messages are aggregated per vessel and by the hour by statistical means providing a new AIS feature representation as a basis for classification. By providing a new data representation to the Random Forest classifier, we evaluate classification performance. Then we restrict the presented data to the semantic feature subgroups of the vessel’s positional, course, speed, and dimensions data individually and identify the key features of the classification. The results show that in our exemplary maritime experiments, classification relies mostly on vessels’ positional and dimensional information.
A Magnetic Soft Device for Tactile Haptic Actuation of the Fingertip

In this work, we introduce a novel haptic device composed of a wearable fingertip sheath, fabricated using an oleogel loaded with magnetic particles, and an external electromagnet. The sheath is actuated using the external magnetic field provided by the electromagnet, which is equipped with a field-focusing pole piece. The oleogel composite used in this device has been optimized for the transfer of the magnetic force from the material to the skin to provide perceptible forces to the wearer. We compare our composite to composites created with materials commonly used in the literature and find the force transfer from our material, as measured by a force sensor, to be much greater when actuated under the same range of input voltages to the electromagnet. We also present a psychophysical user study that shows a linear relationship between this range of input voltages and perceptual magnitude. This result indicates that the device provides a range of tactile feedback that can be driven to a desired intensity of sensation through proportional voltage control.
A Paleoclimate Modeling Experiment to Calculate the Soil Carbon Respiration Flux for the Paleocene-Eocene Thermal Maximum

Follow this and additional works at: https://scholarworks.umass. edu/theses Part of the Atmospheric Sciences Commons, Biogeochemistry Commons, Climate Commons, Geology Commons, Hydrology Commons, Organic Chemistry Commons, Other Computer Sciences Commons, Other Oceanography and Atmospheric Sciences and Meteorology Commons, Other Physical Sciences and Mathematics Commons, Soil Science Commons, and the Tectonics and Structure Commons
Multi-Tier 3D Trajectory Planning for Cellular-Connected UAVs in Complex Urban Environments

Cellular-connected unmanned aerial vehicles (UAVs) present a viable solution to address communication and navigation limitations by leveraging base stations for air–ground communication. However, in complex urban scenarios with stringent communication requirements, achieving asymmetrical control becomes crucial to strike a balance between communication reliability and flight safety. Moreover, existing cellular-connected UAV trajectory planning algorithms often struggle to handle real scenes with sudden and intricate obstacles. To address the aforementioned challenges, this paper presents the multi-tier trajectory planning method (MTTP), which takes into account air–ground communication service assurance and collision avoidance in intricate urban environments. The proposed approach establishes a flight risk model that accounts for both the outage probability of UAV-ground base station (GBS) communication and the complexity of flight environments, and transforms the inherently complex three-dimensional (3D) trajectory optimization problem into a risk distance minimization model. To optimize the flight trajectory, a hierarchical progressive solution approach is proposed, which combines the strengths of the heuristic search algorithm (HSA) and deep reinforcement learning (DRL) algorithm. This innovative fusion of techniques empowers MTTP to efficiently navigate complex scenarios with sudden obstacles and communication challenges. Simulations show that the proposed MTTP method achieves a more superior performance of trajectory planning than the conventional communication-based solution, which yields a substantial reduction in flight distance of at least 8.49% and an impressive 10% increase in the mission success rate. Furthermore, a real-world scenario is chosen from the Yuhang District, Hangzhou (a southern Chinese city), to validate the practical applicability of the MTTP method in highly complex operating scenarios.
Functional Ecology

1. Specialized insect herbivores commonly co-opt plant defences for protection against predators, but the costs, benefits, and mechanisms of sequestration are poorly understood. 2. We quantified sequestration of toxic cardenolides by the specialist aphid Aphis nerii when reared on four closely related milkweed ( Asclepias ) species with >20-fold variation in cardenolide content, and in the presence or absence of generalist ladybug predators.
Master’s Project Report A Federated Public-Document Private-Query Search System

Encrypted search systems are on the cutting edge of privacy-preserving technologies, allowing users to both gain insights into their data and allowing cloud storage providers to offer a valuable service, all the while keeping users’ sensitive data private. In the recent past, operating on encrypted data meant a choice between privacy and functionality, but new systems like Coeus[1] and DORY promise to give us both. This report describes our improved design of a Coeus-like system for privately searching and accessing documents from a public archive that, unlike Coeus, requires two (or more) noncollud-ing compute parties, where distributed trust allows for several key optimizations which would not be possible in a single-server system that uses homomorphic encryption. We conclude that the federated threat model allows our system to achieve significant performance improvements over the original Coeus system, plus realistic scalability, with costs in the order of a few hundred megabytes per search query and sent between servers —not users—as well as only light computation and bandwidth required on the user’s own device.
Comparative Effectiveness of New Oral Anticoagulants and Standard Thromboprophylaxis in Patients Having Total Hip or Knee Replacement A Systematic Review

Data Synthesis: Six good-quality systematic reviews compared NOACs with low-molecular-weight heparin (LMWH) for thromboprophylaxis after THR or TKR. Risk for symptomatic deep venous thrombosis, but not risk for death or nonfatal pulmonary embolism, was reduced with factor Xa inhibitors compared with LMWH (4 fewer events per 1000 patients). Conversely, the risk for major bleeding increased (2 more events per 1000 patients). Outcomes of dabigatran did not significantly differ from those of LMWH. Indirect evaluation of NOACs by common comparison with LMWH showed nonsignificantly reduced risks for venous thromboembolism with rivaroxaban compared with dabigatran (risk ratio [RR], 0.68 [95% CI, 0.21 to 2.23]) and apixaban (RR, 0.59 [CI, 0.26 to 1.33]) but increased major bleeding. New oral anticoagulants have not been compared with warfarin, aspirin, or unfractionated heparin.
Model Predictive Control for the Operation of Building Cooling Systems

This brief presents a model-based predictive control (MPC) approach to building cooling systems with thermal energy storage. We focus on buildings equipped with a water tank used for actively storing cold water produced by a series of chillers. First, simplified models of chillers, cooling towers, thermal storage tanks, and buildings are developed and validated for the purpose of model-based control design. Then an MPC for the chilling system operation is proposed to optimally store the thermal energy in the tank by using predictive knowledge of building loads and weather conditions. This brief addresses real-time implementation and feasibility issues of the MPC scheme by using a simplified hybrid model of the system, a periodic robust invariant set as terminal constraints, and a moving window blocking strategy. The controller is experimentally validated at the University of California, Merced. The experiments show a reduction in the central plant electricity cost and an improvement of its efficiency.
Hierarchical Image‐Space Radiosity for Interactive Global Illumination

We introduce image‐space radiosity and a hierarchical variant as a method for interactively approximating diffuse indirect illumination in fully dynamic scenes. As oft observed, diffuse indirect illumination contains mainly low‐frequency details that do not require independent computations at every pixel. Prior work leverages this to reduce computation costs by clustering and caching samples in world or object space. This often involves scene preprocessing, complex data structures for caching, or wasted computations outside the view frustum. We instead propose clustering computations in image space, allowing the use of cheap hardware mipmapping and implicit quadtrees to allow coarser illumination computations. We build on a recently introduced multiresolution splatting technique combined with an image‐space lightcut algorithm to intelligently choose virtual point lights for an interactive, one‐bounce instant radiosity solution. Intelligently selecting point lights from our reflective shadow map enables temporally coherent illumination similar to results using more than 4096 regularly‐sampled VPLs.
Convolutional Neural Network Measurement of 1 Non-Fiducial Cosmic Ray Electrons with the DAMPE 2 Experiment

The Dark Matter Particle Explorer (DAMPE) is a space-based Cosmic Ray (CR) observatory with the aim, among others, to study Cosmic Ray Electrons (CREs) up to 10 TeV. Due to the low CRE rate at multi-TeV range, we aim at increasing the acceptance by selecting events outside the fiducial volume. The complex topology of non-fiducial events require special treatment with sophisticated analysis tools. Therefore, we propose a Convolutional Neural Network (CNN) to identify non-fiducial CREs and reject background events, based on their interaction in DAMPE’s calorimeter. In the following, we will present the aforementioned method in order to precisely identify such events.