Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristic Impedance Adjustment of Thin-Metal Mesh Transmission Lines for mmWave Display-Integrated Antennas

This paper proposes four methods and empirical formulas of adjusting characteristic impedances for thin-metal mesh transmission lines. The characteristic impedances are discretely adjusted by changing the number and the size of unit meshes, which provides macro-tuning capability, and the discrete values can be tuned more precisely by varying the thin-metal line width and the aspect ratio of mesh geometry. The validity of proposed methods is confirmed by full-wave numerical simulations, and the simulated impedance variations are well-described by our empirical formulas. For further verifications, 26 distinguished samples of thin-metal mesh transmission lines and a 28-GHz thin-metal mesh antenna are fabricated, and their characteristics are measured in millimeter-wave spectrums. The measured results confirm that the proposed methods and empirical formulas can provide accurate and more flexible design rules for impedance adjustment, which allows potential advances in display-integrated antenna applications.
Online Learning in Unknown Markov Games

We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable. We show that in this challenging setting, achieving sublinear regret against the best response in hindsight is statistically hard. We then consider a weaker notion of regret by competing with the minimax value of the game, and present an algorithm that achieves a sublinear ˜ O ( K 2 / 3 ) regret after K episodes. This is the ﬁrst sublinear regret bound (to our knowledge) for online learning in unknown Markov games. Importantly, our regret bound is independent of the size of the opponents’ action spaces. As a result, even when the opponents’ actions are fully observable, our regret bound improves upon existing analysis (e.g., (Xie et al., 2020)) by an exponential factor in the number of opponents.
Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks

We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs. We show that any optimal policy necessarily satisfies the k-SP constraint. Notably, the k-SP constraint prevents the policy from exploring state-action pairs along the non-k-SP trajectories (e.g., going back and forth). However, in practice, excluding state-action pairs may hinder the convergence of RL algorithms. To overcome this, we propose a novel cost function that penalizes the policy violating SP constraint, instead of completely excluding it. Our numerical experiment in a tabular RL setting demonstrates that the SP constraint can significantly reduce the trajectory space of policy. As a result, our constraint enables more sample efficient learning by suppressing redundant exploration and exploitation. Our experiments on MiniGrid, DeepMind Lab, Atari, and Fetch show that the proposed method significantly improves proximal policy optimization (PPO) and outperforms existing novelty-seeking exploration methods including count-based exploration even in continuous control tasks, indicating that it improves the sample efficiency by preventing the agent from taking redundant actions.
Robust Generalized Labeled Multi-Bernoulli Filter for Multitarget Tracking With Unknown Non- Stationary Heavy-Tailed Measurement Noise

A robust generalized labeled multi-Bernoulli (GLMB) filter is presented to perform multitarget tracking (MTT) with unknown non-stationary heavy-tailed measurement noise (HTMN). The HTMN is modeled as a multivariate Student’s t-distribution with unknown and time-varying mean. The proposed filter relaxes the restrictive assumption that the mean of HTMN is zero, and can effectively deal with MTT under the condition that the mean of HTMN is unknown and time-varying. The variational Bayesian (VB) approximation is applied in the GLMB filtering framework with the augmented state. The marginal likelihood function is obtained via minimizing the Kullback-Leibler divergence by the variational lower bound. The simulation results demonstrate that the proposed filter can effectively track multiple targets in both linear and nonlinear scenarios when the mean of HTMN is unknown and time-varying.
Optimization of Intelligent Management and Monitoring System of Sports Training Hall Based on Internet of Things

This paper provides an indepth analysis and research on the intelligent management and monitoring system of sports training hall through the Internet of Things. Firstly, it introduces the project background of the sports training base stadium service system, analyses the significance of the system development, and describes the related technology and the method of development. The paper then analyses the requirements of the management business of the system and describes the main business processes of the system with activity diagrams; the paper then analyses the functional requirements and data requirements of the system and describes the system with use case diagrams and thumbnail class diagrams, respectively. Based on this, the thesis designs the overall aspect of the system, giving the corresponding total package diagram, and designs all functions one by one, giving the corresponding class diagram, the corresponding sequence diagram, and the corresponding processing flow diagram. The thesis gives the design of database tables by designing entity property diagrams and entity-to-entity relationship diagrams according to the data requirements and system functions. Finally, the research of the thesis is summarized accordingly, stating what has been done, what results have been achieved, what problems still exist, and what further work needs to be done to make the outlook of the work.
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?

In deep model compression, the recent finding"Lottery Ticket Hypothesis"(LTH) (Frankle&Carbin, 2018) pointed out that there could exist a winning ticket (i.e., a properly pruned sub-network together with original weight initialization) that can achieve competitive performance than the original dense network. However, it is not easy to observe such winning property in many scenarios, where for example, a relatively large learning rate is used even if it benefits training the original dense model. In this work, we investigate the underlying condition and rationale behind the winning property, and find that the underlying reason is largely attributed to the correlation between initialized weights and final-trained weights when the learning rate is not sufficiently large. Thus, the existence of winning property is correlated with an insufficient DNN pretraining, and is unlikely to occur for a well-trained DNN. To overcome this limitation, we propose the"pruning&fine-tuning"method that consistently outperforms lottery ticket sparse training under the same pruning algorithm and the same total training epochs. Extensive experiments over multiple deep models (VGG, ResNet, MobileNet-v2) on different datasets have been conducted to justify our proposals.
End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series

This paper presents a novel approach to forecasting of hierarchical time series that produces coherent, probabilistic forecasts without requiring any explicit post-processing step. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy and incorporates the reconciliation step as part of a single trainable model. This is achieved by applying the reparameterization trick and utilizing the observation that reconciliation can be cast as an optimization problem with a closed-form solution. These model features make end-to-end learning of hierarchical forecasts possible, while accomplishing the challenging task of generating forecasts that are both probabilistic and coherent. Impor-tantly, our approach also accommodates general aggregation constraints including grouped, temporal, and cross-temporal hierarchies. An extensive empirical evaluation on real-world hierarchical datasets demonstrates the advantages of the proposed approach over the state-of-the-art.
Effects of nitrogen forms on root characteristics in wheat

Pot experiments were carried out to study the differences of root characteristics and the effects of nitrogen forms on root characteristics in two wheat varieties with different nitrogen uptake efficiency(NUE). The results indicated that NUE of two wheat varieties were different,and the effects of nitrogen forms on NUE were different,the order of NUE in Qinmai 11 among N form treatments was CONH2-NNH+4-NNO-3-N,and the order of that in Yangnong 9817 was CONH2-NNO-3-NNH+4-N.The results also indicated that root volume,root dry weight,root activity,total absorbing area and actively absorbing area in Qinmai 11 were greater than those in Yangnong 9817.The effects of N forms on root characteristics in two varieties were different,the order of root volume and root dry weight in the whole growth period,root activity and actively absorbing area from elongation stage to anthesis in Qinmai 11 among N form treatments was CONH2-NNH+4-NNO-3-N,and the order of those in Yang9817 was CONH2-NNO-3-NNH+4-N.So,from elongation stage to anthesis,the effects of N forms on NUE in wheat were consisitent with those of N forms on root characteristics.
Transfer-Based Semantic Anomaly Detection

Detecting semantic anomalies is challenging due to the countless ways in which they may appear in real-world data. While enhancing the robustness of networks may be sufﬁcient for modeling simplistic anomalies, there is no good known way of preparing models for all potential and unseen anomalies that can potentially occur, such as the appearance of new object classes. In this paper, we show that a previously overlooked strategy for anomaly detection (AD) is to introduce an explicit inductive bias toward representations transferred over from some large and varied semantic task. We rigorously verify our hypothesis in controlled trials that utilize intervention, and show that it gives rise to surprisingly effective auxiliary objectives that outperform previous AD paradigms.
Design and Implementation of Intelligent Gateway System for Monitoring Livestock and Poultry Feeding Environment Based on Bluetooth Low Energy

This article proposes a gateway system design method applied to the monitoring of a breeding environment based on Bluetooth low energy consumption. Through the queue mechanism and buffer management method, the key information that has the greatest impact on the monitoring effect is sent first, and the feedback mechanism is used to adjust the sampling period of the node in conjunction with the abnormal degree of the monitoring parameter. Experimental results show that the system constructed in this article significantly shortens the transmission delay of key information, with an average packet loss rate of 1.09%. Compared with the traditional method, this method is more timely and effective in handling abnormal data. It effectively solves the short distance and low efficiency of Bluetooth “point-to-multipoint” communication in the case of high data concurrency, and improves the reliability and stability of communication. In turn, the losses caused by environmental mutations are reduced.
Cardiovascular disease and prevention among people living with HIV in South Florida

Supplemental Digital Content is available in the text Abstract Antiretroviral therapy (ART) has improved survival of patients living with HIV (PLWH); however, this has been accompanied by an increase in cardiovascular disease (CVD). Although preventative measures for CVD among the general population are well described, information is limited about CVD prevention among PLWH. The goal of this study was to characterize the prevalence of CVD in our population and to assess the use of primary and secondary prevention. We performed a retrospective review of PLWH receiving primary care at a large academic center in Miami, Florida. We characterized the prevalence of CVD, CVD risk, and the use of aspirin and statins for primary and secondary CVD prevention. A total of 985 charts were reviewed (45% women, 55% men). Average age was 52.2 years. Average CD4 count was 568 cells/microL. 92.9% were receiving ART, and 71% were virologically suppressed. The median 10-year ASCVD risk was 7.3%. The prevalence of CVD was 10.4% (N = 102). The odds of having CVD was lower in patients on ART (OR 0.47, 95% CI: 0.25–0.90, P = .02). The use of medications for primary and secondary prevention of CVD based on current guidelines was low: 15% and 37% for aspirin respectively, and 25% and 44% for statins. CVD risk and rates of CVD are high among PLWH and receiving ART could protect against CVD. However, the use of medications for primary and secondary prevention is low. Increased awareness of CVD risk-reduction strategies is needed among providers of PLWH to decrease the burden of CVD.
Clinical significance of YAP1 and TAZ in esophageal squamous cell carcinoma

Abstract Background Esophageal cancer is the eighth most frequent and sixth most fatal cancer worldwide. This study aimed to investigate the clinical characteristics and prognostic significance of yes related protein 1 (YAP1) and transcriptional co-activator with PDZ binding motif (TAZ) in patients with esophageal squamous cell carcinoma (ESCC). Methods A total of 306 ESCC pathological specimens and adjacent tissues (as control; tissues from the esophageal mucosa >5 cm from the edge of the tumor) were collected between January, 2008 and December, 2018. Immunohistochemical staining was used to assess the expression of YAP1 and TAZ proteins in the ESCC and adjacent tissues, and their relationship with clinicopathological parameters was evaluated using SPSS 21.0 software. Results YAP1 and TAZ proteins were highly expressed in ESCC, and their expression was closely related to TNM stage and lymph node metastasis. Expression of YAP1 was associated with tumor size (P = .029), differentiation (P = .000), depth of invasion (P = .001), and TNM stage (P = .000). Expression of TAZ was associated with tumor size (P = .034), differentiation (P = .000), depth of invasion (P = .029), lymph node metastasis (P = .006), and ethnicity (P < .001). The expression of YAP1 protein was positively correlated with the expression of TAZ protein (r = 0.257, P < .05). YAP1 and TAZ expression (P = .039 and .000, respectively), tumor size (P = .041), and lymph node metastasis (P = .001) significantly affected the overall survival of patients with ESCC, and represent independent factors for overall survival. Conclusion YAP1 and TAZ proteins are highly expressed in ESCC, and closely related to the clinical and pathological parameters such as the diameter of the tumor, degree of differentiation, and depth of invasion, indicating that YAP1 and TAZ may be involved in the development of ESCC. YAP1 and TAZ may be used as prognostic markers in ESCC.
Anticancer Drugs for Intra-Arterial Treatment of Colorectal Cancer Liver Metastases: In-Vitro Screening after Short Exposure Time

To treat colorectal liver metastases, intra-arterial chemotherapies may complete therapeutic arsenal. Drugs using intra-arterially are very heterogeneous. The aim of this study was to select the most efficient drug on a panel of colorectal cancer (CRC) cell lines (Caco-2, HCT 116, HT 29, SW 48, SW 480, SW 620) exposed for 30 min to 12 cytotoxic agents (doxorubicin, epirubicin, idarubicin, 5-FU, raltitrexed, gemcitabine, cisplatin, oxaliplatin, mitomycin C, irinotecan, streptozocin, paclitaxel) at different concentrations. The effect on cell viability was measured using the WST-1 cell viability assay. For each drug and cell line, the IC50 and IC90 were calculated, which respectively correspond to the drug concentration (mg/mL) required to obtain 50% and 90% of cell death. We also quantified the cytotoxic index (CyI90 = C Max/IC90) to compare drug efficacy. The main findings of this study are that idarubicin emerged as the most cytotoxic agent to most of the tested CRC cell lines (Caco-2, HT29, HCT116, SW620 and SW480). Gemcitabine seemed to be the most efficient chemotherapy for SW48. Interestingly, the most commonly used cytotoxic agents in the systemic and intra-arterial treatment of colorectal liver metastasis (CRLM) (oxaliplatin, 5-FU, irinotecan) showed very limited cytotoxicity to all the cell lines.
Towards a Framework for Definition of Enterprise Safety Indicators

Safety of enterprises, as well as socio-technical systems in general, is due to several factors and can be considered an emergent property, depending on non-linear and symbiotic interactions between humans and technical systems taking places in collaborative business processes. The safety-II perspective promotes assessing and gathering meaningful knowledge about normal work, and its effect on safety and productivity. We aim at providing a support to this activity, proposing a three-phases framework for the definition of indicators on enterprise safety performance. This framework includes collection of knowledge on work-asimagined (WAI) business processes, ontology-based modelling of work-as-done (WAD) business processes and some guidelines to define indicators taking into account the actual needs and implicit knowledge of sharp-end operators by means of the Functional Resonance Analysis Method (FRAM).
S0021859621000101jra 1..13

The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGROSunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.
Hydrogenation of Carbon Nanotubes: Roles of Symmetry and Strain

Carbon nanotube is excellent material for hydrogen storage and molecular sensory applications thanks to its quasi-one dimensional geometry and unique structure-property relationship. In this paper, hydrogenation of carbon nanotubes is discussed in the extent of binding geometry and mechanochemical coupling under structural deformation. Our first-principles calculations show that the atomic structures, mechanical and electronic properties of carbon nanotubes can be significantly modified by hydrogenation. Moreover, the hydrogenation process is controlled by strain loading. Under an axial compressive or tensile strain of 10 %, the binding energies of hydrogen on carbon nanotubes can be changed up to -0.24 and 0.74 eV respectively. Analysis on electronic structure, mechanical properties and charge density reveals the underline mechanisms. The results reported here offer a way not only to tune the binding strength of hydrogen on carbon nanotubes in a controllable and reversible manner, but also to engineer the properties of carbon nanotubes through a synergistic control on hydrogen binding and mechanical loading.
DepMiner: A software prototype for the extraction of signiﬁcant dependencies

We propose DepMiner a software prototype implementing a simple but effective model for the evaluation of itemsets, and in general for the evaluation of the dependencies between the variables on a domain of ﬁnite values. This method is based on ∆ , the departure of the observed probability of a set of variables in a database and a referential probability, estimated in the condition of maximum entropy. It is able to distinguish between dependencies intrinsic to the itemset and dependencies “inherited” from the subsets: thus it is suitable to evaluate the utility of an itemset w.r. t. its subsets and to reduce the volume of non signiﬁcant itemsets. The method is powerful: at the same time detects signiﬁcant positive dependencies as well as negative ones. Since ∆ is anti-monotonic it can be embedded efﬁciently in algorithms. The system returns itemsets ranked by a normalized version of ∆ and the histogram showing their distribution. It employs a statistical method for setting a threshold for ∆ .
OMICs, Epigenetics, and Genome Editing Techniques for Food and Nutritional Security

The incredible success of crop breeding and agricultural innovation in the last century greatly contributed to the Green Revolution, which significantly increased yields and ensures food security, despite the population explosion. However, new challenges such as rapid climate change, deteriorating soil, and the accumulation of pollutants require much faster responses and more effective solutions that cannot be achieved through traditional breeding. Further prospects for increasing the efficiency of agriculture are undoubtedly associated with the inclusion in the breeding strategy of new knowledge obtained using high-throughput technologies and new tools in the future to ensure the design of new plant genomes and predict the desired phenotype. This article provides an overview of the current state of research in these areas, as well as the study of soil and plant microbiomes, and the prospective use of their potential in a new field of microbiome engineering. In terms of genomic and phenomic predictions, we also propose an integrated approach that combines high-density genotyping and high-throughput phenotyping techniques, which can improve the prediction accuracy of quantitative traits in crop species.