Miracle-dz/UniFAR-FTUs huggingface dataset:
CS 234 Project Final Report : Approaches to Hierarchical Reinforcement Learning

Vezhnevets et al. recently introduced the Feudal Network, a Hierarchical Reinforcement Learning architecture capable of learning options without the manual specification of subtasks. The approach represents sub-goals as changes to a learned state representation, and in doing so converts the traditionally challenging task of subgoal discovery into a representation learning problem that can be solved effectively using neural networks. In this paper, we implement the Feudal Network and test its performance against competitive baseline agents. We present the details of our implementation with emphasis on describing certain design decisions in the implementation of the model. We then explore some of the challenging aspects of training the Feudal network, certain subcomponents of the model, and finally discuss conclusions as to the general applicability of the approach.
S0021859621001076jra 623..635

Frequent freezing injury greatly influences winter wheat production; thus, effective prevention and a command of agricultural production are vital. The freezing injury monitoring method integrated with ‘3S’ (geographic information systems (GIS), global positioning system (GPS) and remote sensing (RS)) technology has an unparalleled advantage. Using HuanJing (HJ)-1A/1B satellite images of a winter wheat field in Shanxi Province, China plus a field survey, crop types and winter wheat planting area were identified through repeated visual interpretations of image information and spatial analyses conducted in GIS. Six vegetation indices were extracted from processed HJ-1A/1B satellite images to determine whether the winter wheat suffered from freezing injury and its degree of severity and recovery, using change vector analysis (CVA), the freeze injury representative vegetation index and the combination of the two methods, respectively. Accuracy of the freezing damage classification results was verified by determining the impact of freezing damage on yield and quantitative analysis. The CVA and the change of normalized difference vegetation index (ΔNDVI) monitoring results were different so a comprehensive analysis of the combination of CVA and ΔNDVI was performed. The area with serious freezing injury covered 0.9% of the total study area, followed by the area of no freezing injury (3.5%), moderate freezing injury (10.2%) and light freezing injury (85.4%). Of the moderate and serious freezing injury areas, 0.2% did not recover; 1.2% of the no freezing injury and light freezing injury areas showed optimal recovery, 15.6% of the light freezing injury and moderate freezing injury areas showed poor recovery, and the remaining areas exhibited general recovery.
Parallel AI Planning on the SCC

We present in this paper a parallelized version of an existing Artificial Intelligence automated planner, implemented with standard programming models and tools (hybrid OpenMP/MPI). We then evaluate this planner with respect to its sequential version through extensive experiments over a wide range of academic benchmarks, on two different target architectures: a small standard cluster and the research processor SCC (“Single-chip Cloud Computer”) developed by Intel Labs and made available to the research community through the MARC program (“Many-core Applications Research Community”). We obtain interesting speedups (super-linear in some cases) on both architectures. Interestingly enough, these experiments also reveal different behaviors between the cluster and the SCC.
Pervasive Visualization in Immersive Augmented Reality for Software Performance Monitoring

Developers are usually unaware of the impact of code changes to the performance of software systems. Although developers can analyze the performance of a system by executing, for instance, a performance test to compare the performance of two consecutive versions of the system, changing from a programming task to a testing task would disrupt the development flow. Most performance visualization tools provide the user with a detailed view, which can be overwhelming and cause a high cognitive load. In this thesis, we propose the use of a city visualization that dynamically provides developers a pervasive view of the continuous performance of a system and lessens the cognitive load required to monitor it. We use an immersive augmented reality device (Microsoft HoloLens) to display our visualization and extend the integrated development environment on a computer screen to use the physical space. We report on technical details of the design and implementation of our visualization tool. Our effort explores a new visual metaphor to support the exploration and analysis of possibly very large and multidimensional performance data. Our initial result indicates that the city metaphor can be effectually employed to analyze dynamic performance data on a large and non-trivial software system. Additionally, we conducted an initial user study with ten participants, comparing performance and user experience in immersive augmented reality to that with a standard computer screen, and we report on the results. We asked participants to complete a set of ten tasks inspired by questions that arise from performance issues. To measure performance, we collected correctness, completion time and recollection of users. We measured user experience by collecting emotions that participants experienced during experiments, and we also measured perceived cognitive load. We observe that participants achieve comparable performance in immersive augmented reality and with a computer screen. We found that developers felt: (i) interested, (ii) open but also (iii) confused and (iv) angry when working in immersive augmented reality while achieving comparable performance to working with a standard computer screen.
Linkage disequilibrium screening for multiple sclerosis implicates JAG 1 and POU 2 AF 1 as susceptibility genes in Europeans The GAMES Collaborative Group ⁎

By combining all the data available from the Genetic Analysis of Multiple sclerosis in EuropeanS (GAMES) project, we have been able to identify 17 microsatellite markers showing consistent evidence for apparent association. As might be expected five of these markers map within the Major Histocompatibility Complex (MHC) and are in LD with HLA-DRB1. Individual genotyping of the 12 non-MHC markers confirmed association for three of them — D11S1986, D19S552 and D20S894. Association mapping across the candidate genes implicated by these markers in 937 UK trio families revealed modestly associated haplotypes in JAG1 (p=0.019) on chromosome 20p12.2 and POU2AF1 (p=0.003) on chromosome 11q23.1. © 2006 Elsevier B.V. All rights reserved.
NEURO-FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS 1

In practice, we meet with a frequent necessity for modelling dynamic multistage processes that depend on several time-varying factors, which can be measured with accuracy. This requires using a model that combines properties of the difference, switched, and neuro-fuzzy models with a large number of inputs and a big memory depth of each input. Such model will take into account all uncertainties, converting input actions into fuzzy processes by means of fuzzification, and will precisely reflect the multistage nature and dynamics of the object being studied. However, a large number of parameters can significantly complicate its configuration. The purpose of this work is to improve efficiency of structural and parametric modelling of multistage dynamic processes due to the development of a class of difference neuro-fuzzy switched models, as well as to the research and development of approaches to fuzzification of discrete processes on model inputs in order to simplify the process of its configuration. The authors have introduced a new class of difference neuro-fuzzy switched models that are characterized by a combination of structures of difference fuzzy models, neural networks, and systems with switchings enabling to model complex multistage processes, which are characterized by abrupt changes in structure or parameters. The authors have proposed a mechanism for fuzzification of input actions of a difference neuro-fuzzy switched model, which is characterized by the ability to convert input actions into discrete fuzzy processes using twodimensional fuzzy sets, and allows reducing the number of configurable parameters of the model.
The spatial spillover effect of technical efficiency and its influencing factors for China’s mariculture — based on the partial differential decomposition of a spatial durbin model in the coastal provinces of China

Using mariculture data from China’s nine coastal provinces from 2003 to 2015, this paper first evaluates the technical efficiency through data envelopment analysis (DEA). Then, this paper analyses the spatial spillover effect of technical efficiency and its influencing factors with a spatial panel Durbin model (SDM). The results indicate that there are obvious regional differences in the technical efficiency of China’s mariculture, and there is a great room for improvements in the technical efficiency of most areas. The results evidence a significant positive spatial correlation in the technical efficiency of China's mariculture. The technology promotion factor is beneficial for improving technical efficiency, and at the same time, this factor can produce a positive spatial spillover effect on the surrounding areas. The level of economic development clearly has a positive effect on the technical efficiency of the region, and the surrounding area has a negative spatial spillover effect. Moreover, the labor input has a negative effect on technology efficiency. The negative impact of the degree of disaster on technical efficiency is not significant.
SFB 649 Discussion Paper 2017-020 Pricing Green Financial Products

With increasing wind power penetration more and more volatile and weather dependent energy is fed into the German electricity system. To manage the risk of windless days and transfer revenue risk from wind turbine owners to investors wind power derivatives were introduced. These insurance-like securities (ILS) allow to hedge the risk of unstable wind power production on exchanges like Nasdaq and European Energy Exchange. These products have been priced before using risk neutral pricing techniques. We present a modern and powerful methodology to model weather derivatives with very skewed underlyings incorporating techniques from extreme event modelling to tune seasonal volatility and compare transformed Gaussian and non-Gaussian CARMA(p, q) models. Our results indicate that the transformed Gaussian CARMA(p, q) model is preferred over the non-Gaussian alternative with Lévy increments. Out-of-sample backtesting results show good performance wrt burn analysis employing smooth Market Price of Risk (MPR) estimates based on NASDAQ weekly and monthly German wind power futures prices and German wind power utilisation as underlying. A seasonal MPR of a smile-shape is observed, with positive values in times of high volatility, e.g. winter months, and negative values, in times of low volatility and production, e.g. in summer months. We conclude that producers pay premiums to insure stable revenue steams, while investors pay premiums when weather risk is high.
A Validation Framework for the Service-Oriented Process Designing

—In the service-oriented software systems, the services composition process is modeled using the service orchestration languages whose fault-handling and compensation mechanisms are crucial to guarantee the process running successfully. In this paper we propose to extend the syntax of BPEL to improve these two mechanisms. In order to validate their correctness, the composition is transformed to the planning graph. Then the validation of the fault-handling mechanism is regarded as a problem of seeking solution from the solution sets gained from the planning graph. We analyze the services composition structures and construct a relationship matrix to complete the validation of the compensation mechanism. A validation framework is proposed and an experiment is implemented to show our method effectiveness.
S-HRVM: Smart Watch-based Heart Rate Variability Monitoring System

Continuous heart rate variability (HRV) monitoring can monitor a user’s health and help them make adjustments and treatments. The current methods of using ECG or externally mounted sensors are accurate, but inconvenient for the user. Other methods of using computer vision rely on ambient lighting conditions, and there are issues that may violate user privacy. Mobile devices such as smart watches and smartphones hold the promise of providing a more convenient, practical, and non-invasive method of detection. In this paper, we propose S-HRVM, a smart watch-based heart rate variability monitoring system. The basic idea behind S-HRVM is to combine the physiological representation and motion state of user. In physiological representation, we propose SP-HR method to process heart rate data and extract HRV features. The motion state, which can be derived by using accelerometer data of smart watch, is used to reduce the power consumption. In addition, the HRV features are used as calculation parameters of G-MSPC (the general health monitoring model based on MSPC), which can be used to detect mental states and diseases associated with autonomic function. Extensive experimental results demonstrate the effectiveness of the proposed methods.
Plasma for burn shock resuscitation: is it time to go back to the future?

Patients with burn shock can be challenging to resuscitate. Burn shock produces a variety of physiologic derangements: Patients are hypovolemic from volume loss, have a increased systemic vascular resistance, and may have a depressed cardiac output depending on the extent of the thermal injury. Additionally, the burn wound produces a significant inflammatory cascade of events that contributes to the shock state. Fluid resuscitation is foundational for the initial treatment of burn shock. Typical resuscitation is with intravenous lactated Ringer's in accordance with well‐established formulas based on burn wound size.
Hierarchical Network Security Measurement and Optimal Proactive Defense in Cloud Computing Environments

This paper presents an in-depth study and analysis of hierarchical network security measurement and optimal active defense in the cloud computing environment. All the cloud platform security-related data collected through cloud platform monitoring is collected, and then the relevant security data is summarized and analyzed, so that the specific security posture index of the cloud platform can be derived, thus providing a reference for cloud platform managers to judge the security risks of the cloud platform. It provides a reference for cloud platform managers to judge the security risks of cloud platforms. Through the cloud platform security situation awareness system, we mainly study the construction of cloud platform, the construction of security situation awareness system, and the calculation of security situation value and use, thus greatly improving the stability, security, and reliability of the cloud platform. The application of the method avoids the drawbacks of traditional network security management, which relies entirely on past data and cannot sense changes in the security state of the system in real time. At the same time, the predicted results are added to the input of the fuzzy decision-making system, improving the accuracy of the assessment. The method improves the real-time and effectiveness of network security posture prediction, increases the convergence speed and prediction accuracy of the algorithm, and avoids the occurrence of overfitting. Simulation experiments based on the internet network security posture dataset show that this research method has less prediction error than the traditional machine learning methods and other deep learning methods, has higher learning efficiency, and is more rapid, accurate, and effective in predicting the trend of network security posture in the big data environment in the future period.
Competition: Low-Power Wireless Bus Baseline

The Low-Power Wireless Bus (LWB) is a communication protocol for low-power wireless multi-hop networks that was published in 2012. It provides a shared-bus abstraction for higher layer protocols and hides the complexity of the underlying network. Internally, LWB uses fast and reliable Glossy ﬂoods to exchange information within the network, thus supporting arbitrary trafﬁc patterns. The original code base was never ofﬁcially released, so we are participating in this competition with two goals: 1) to provide a baseline against which other protocols can be compared, and 2) to make a clean and refactored version of the code base publicly available.
LoSee: Long-Range Shared Bike Communication System Based on LoRaWAN Protocol

The development of LPWAN technology is gradually becoming an evolution of IoT (Internet of Things) applications, for its significant improvements of signal sensitivity and noise tolerance. At present, however, many IoT applications, such as shared-bike systems in China, are still using the communication technology of traditional mobile network, which consumes considerable power and suffers from high communication cost. In this paper, we present LoSee, a long-range shared-bike communication system, based on the LoRaWAN protocol. We clarify the system parameters of LoSee and determine its communication range. LoSee prototype system is implemented to track the bike route in real time. With the data collected from the prototype system, the relationship between the Packet Delivery Rate(PDR) and Signal to Noise Ratio(SNR) is built. Considering the impact of signal contention, a model is theoretically verified to decide the PDR under different node count and duty cycle. Finally, LoSee communication range is concluded and a solution is proposed for setting up a shared-bike system in the campus by LoRaWAN, which reduces power consumption and eases gateway deployment.
ALIGNER: Make the Utmost of Transmission Concurrency for Low Power Wireless Networks

Concurrent transmission (CT) is widely used to increase the throughput of various data transmissions in wireless networks, such as bulk data dissemination and high-rate data collection. In CT, besides the possible data frame collision at receivers, we observe that acknowledgement (ACK) collision at senders can signiﬁcantly diminish concurrency opportunities. Inthispaper, to avoid the potential ACK collision in CT, we propose ALIGNER which develops a new transmission pattern to coordinate concurrent senders in distributed manner. To avoid the collision between ACKs and data frames, we align the end of the data frames concurrently transmitted by several senders. ALIGNER adopts a tailor-made metrics to analyze the throughput beneﬁt of concurrent transmission for data collection protocols. We have implemented ALIGNER in TinyOS and conducted extensive experiments on a real testbed. Experimental results show that ALIGNER can signiﬁcantly increase the concurrency opportunities compared with the state-of-the-art CT methods.
Effect of the bacterial growth phase and coculture conditions on the interaction of Acanthamoeba castellanii with Shigella dysenteriae, Shigella flexneri, and Shigella sonnei

Shigella species and Acanthamoeba castellanii share the same ecological niches, and their interaction has been addressed in a limited number of research. However, there are still uncertain aspects and discrepant findings of this interaction. In the present study, the effects of the bacterial growth phase, cocultivation temperature and the type of culture media on the interaction of A. castellanii with Shigella dysenteriae, Shigella sonnei and Shigella flexneri were evaluated. In nutrient‐poor page's amoeba saline (PAS) medium, the number of recovered bacteria and the uptake rates were significantly higher in stationary phase cells than logarithmic phase cells. However, no significant differences were observed in the number of recovered bacteria and the uptake rates between logarithmic and stationary phase cells in nutrient‐rich peptone–yeast extract–glucose (PYG) medium. While the number of recovered bacteria was significantly higher in nutrient‐rich than nutrient‐poor media, in all the three Shigella species, the bacterial uptake rates were significantly higher in nutrient‐poor than nutrient‐rich media at both cocultivation temperatures. In both nutrient‐poor and nutrient‐rich media and at both cocultivation temperatures, the number of viable Shigella species after 24 h incubation were not influenced by the presence of A. castellanii. Although Shigella species did not proliferate in A. castellanii trophozoites, a considerable number of bacteria were survived in the trophozoites up to 15 days. From the public health perspective, the results of this study are important for further understanding of the nature of the interaction of these organisms and to deal with Shigella species in the environment.
FlatMagic: Improving Flat Colorization through AI-driven Design for Digital Comic Professionals

Creating digital comics involves multiple stages, some creative and some menial. For example, coloring a comic requires a labor-intensive stage known as ‘flatting,’ or masking segments of continuous color, as well as creative shading, lighting, and stylization stages. The use of AI can automate the colorization process, but early efforts have revealed limitations—technical and UX—to full automation. Via a formative study of professionals, we identify flatting as a bottleneck and key target of opportunity for human-guided AI-driven automation. Based on this insight, we built FlatMagic, an interactive, AI-driven flat colorization support tool for Photoshop. Our user studies found that using FlatMagic significantly reduced professionals’ real and perceived effort versus their current practice. While participants effectively used FlatMagic, we also identified potential constraints in interactions with AI and partially automated workflows. We reflect on implications for comic-focused tools and the benefits and pitfalls of intermediate representations and partial automation in designing human-AI collaboration tools for professionals.
Instant: A TSCH Schedule for Data Collection from Mobile Nodes

Low-power wearable devices are becoming increasingly important for fitness and healthcare applications. However, existing protocols based on the IEEE 802.15. 4 low-power wireless standard are not optimized for data collection from mobile devices. This paper presents Instant: a schedule for the IEEE 802.15. 4 TSCH protocol tailored for this application. We evaluate the data collection speed, energy consumption, and fairness of Instant, and show that Instant achieves several times higher data collection speed from mobile nodes compared with the state-of-the-art Orchestra schedule.