Miracle-dz/UniFAR-FTUs huggingface dataset:
LiDAR-based Recurrent 3D Semantic Segmentation with Temporal Memory Alignment

Understanding and interpreting a 3d environment is a key challenge for autonomous vehicles. Semantic segmentation of 3d point clouds combines 3d information with semantics and thereby provides a valuable contribution to this task. In many real-world applications, point clouds are generated by lidar sensors in a consecutive fashion. Working with a time series instead of single and independent frames enables the exploitation of temporal information. We therefore propose a recurrent segmentation architecture (RNN), which takes a single range image frame as input and exploits recursively aggregated temporal information. An alignment strategy, which we call Temporal Memory Alignment, uses ego motion to temporally align the memory between consecutive frames in feature space. A Residual Network and ConvGRU are investigated for the memory update. We demonstrate the benefits of the presented approach on two large-scale datasets and compare it to several state of-the-art methods. Our approach ranks first on the SemanticKITTI [4] multiple scan benchmark and achieves state-of-the-art performance on the single scan benchmark. In addition, the evaluation shows that the exploitation of temporal information significantly improves segmentation results compared to a single frame approach.
Confinement of knotted polymers in a slit

We investigate the effect of knot type on the properties of a ring polymer confined to a slit. For relatively wide slits, the more complex the knot, the more the force exerted by the polymer on the walls is decreased compared to an unknotted polymer of the same length. For more narrow slits the opposite is true. The crossover between these two regimes is, to first order, at smaller slit width for more complex knots. However, knot topology can affect these trends in subtle ways. Besides the force exerted by the polymers, we also study other quantities such as the monomer-density distribution across the slit and the anisotropic radius of gyration.
Production and characterization of compost made from garden and other waste

Different types of compost were made in a pilot plant, from pruning remains (percentages in volume ranging between 40-60%), leaf litter (20-30%), sewage sludge (0-10%), and biomass ash (0-20%). The aim of our study was to promote the utilization of plant remains to produce compost that, once stabilized and sanitized, could be used as an organic amendment and/or substrate. After six and a half months, all of the composts produced were stable, sanitized, and did not contain phytotoxic substances. However, the composts containing sludge and ash became stabilized and sanitized more rapidly than the others, and generally contained higher quantities of nutrients. The highest quality compost was produced by mixing 20% leaf litter, 10% sludge, 10% ash, and 60% pruning remains (% volume), and supplied highest quantities of phosphorus, calcium, magnesium, and potassium. This compost was categorized as class B, on the basis of the contents of chromium and zinc, i.e. it can be used as potting compost or mixed with other materials to lower the contents of chromium and zinc.
DRL-GAN: Dual-Stream Representation Learning GAN for Low-Resolution Image Classification in UAV Applications

Identifying tiny objects from extremely low-resolution (LR) unmanned-aerial-vehicle-based remote sensing images is generally considered as a very challenging task, because of very limited information in the object areas. In recent years, there have been very limited attempts to approach this problem. These attempts intend to deal with LR image classification by enhancing either the poor image quality or image representations. In this article, we argue that the performance improvement in LR image classification is affected by the inconsistency of the information loss and learning priority on low-frequency (LF) components and high-frequency (HF) components. To address this LF–HF inconsistency problem, we propose a dual-stream representation learning generative adversarial network (DRL-GAN). The core idea is to produce enhanced image representations optimal for LR recognition by simultaneously recovering the missing information in LF and HF components, respectively, under the guidance of high-resolution (HR) images. We evaluate the performance of DRL-GAN on the challenging task of LR image classification. A comparison of the experimental results on the LR benchmark, namely HRSC and CIFAR-10, and our newly collected ‘WIDER-SHIP’ dataset demonstrates the effectiveness of our DRL-GAN, which significantly improves the classification performance, with up to 10% gain on average.
Self-Secure Inverters Against Malicious Setpoints

The next generation of grid-interactive inverters is a cyber-physical device, which can receive setpoints in real-time from a utility operator or a third-party aggregator. This feature enhances the controllability of grid-interactive inverters to provide services beyond just pumping power to the grid. Being a cyber-physical device makes an inverter vulnerable to cyberattacks. In this paper, a model/knowledge-based technique is proposed for developing self-secure smart inverters. The reference model/knowledge is built based on the normal operating region of the inverter and its reduced-order dynamic model. The inverter can autonomously examine the incoming setpoints before engaging them to the local controller. The inverter must learn about the input and output circuits to determine its normal operating region through estimating system parameters. The estimation of the grid parameters is accomplished by injecting current at a different frequency than the power frequency. In this paper, the feasibility of realizing self-secure inverters is examined using a laboratory setup including a Powerflex 755 three-phase inverter and a 12 kW NHR 9410 power grid emulator. The results confirm that inverters can be programmed to autonomously accept or reject the incoming commands and protect themselves from malicious setpoints.
Jujubecake: An Extension of LSTM Considering Correlation among Input Blocks

This paper proposes a new neural network unit for sequential input, called Jujubecake Unit or Jujubecake Cell. In the chain structure formed by this unit, not only the correlation among individual inputs but also the correlation among blocks consist of several inputs is considered so that that performance can be improved. In this paper, Jujubecake model and the multi-layer LSTM model are evaluated in two experiments: language modelling and audio event detection. The experimental results show that Jujubecake Unit proposed in this paper outperforms the traditional LSTM.
Plug-and-Play Synthetic Aperture Radar Image Formation Using Deep Priors

The reconstruction of synthetic aperture radar (SAR) images from phase history data is an ill-posed inverse problem which, in several lines of recent work, is solved by minimizing a cost function. Existing reconstruction methods use regularization to tackle the ill-posed nature of the imaging task. However, in general, these regularizers are either too simple to capture complex spatial patterns and can only promote fixed, predefined features, or lead to non-quadratic cost functions that are nontrivial to minimize. Recently emerging plug-and-play (PnP) priors technique is a flexible framework that allows forward models of imaging systems to be integrated with state-of-the-art regularizers. Inspired by this, we propose a novel PnP SAR image reconstruction framework for spotlight-mode SAR. SAR involves complex-valued reflectivities with spatial structure on the reflectivity magnitudes that can be learned and imposed as priors. This distinguishing aspect is formulated into our proposed framework. We demonstrate the use and effectiveness of a convolutional neural network (CNN) based prior model for the reconstruction of synthetic and real SAR scenes and compare the results with FFT-based, non-quadratic regularization-based, and dictionary learning-based reconstruction methods as well as a PnP framework with BM3D regularizer. Our results suggest that these deep priors enable the learning and incorporation of complicated spatial patterns more effectively than existing methods, and produce significantly improved images especially from limited observations.
PL-GM:RGB-D SLAM With a Novel 2D and 3D Geometric Constraint Model of Point and Line Features

In the study of RGB-D SLAM (Simultaneous Localization and Mapping), two types of primary visual features, point and line features, have been widely utilized to calculate the camera pose. As an RGB-D camera can capture RGB and depth information simultaneously, most RGB-D SLAM methods only utilize the 2D information within the point and line features. To obtain a higher accuracy camera pose and utilize the 2D and 3D information within points and lines better, a novel geometric constraint model of points and lines (PL-GM) using an RGB-D camera is proposed in this paper. Our contributions are threefold. Firstly, the 3D points and lines generated by an RGB-D camera combining with 2D point and line features are utilized to establish the PL-GM, which is different from most models of point-line SLAM (PL-SLAM). Secondly, in addition to the 2D re-projection error of point and line features, the constraint errors of 3D points and lines are constructed and minimized likewise, and then a unified optimization model based on PL-GM is extended to the bundle adjustment model (BA). Finally, extensive experiments have been performed on two public benchmark RGB-D datasets and a real scenario sequence. These experimental results demonstrate that our method achieves a comparable or better performance than the state-of-the-art SLAM methods based on point and line features, and point features.
Modifications to the electronic structure of carbon nanotubes with symmetric and random vacancies

The influence of different types of atomic vacancies on the electronic structure of carbon nanotubes (20, 0) and (12, 12) was investigated using an empirical tight-binding method. The models with vacancies randomly and symmetrically arranged in an extended unit cell were constructed. The symmetrical distribution of defects was obtained by rotating a pore, obtained by removing a hexagonal ring from a graphitic shell around the tube axis. The random defects were generated by a specially developed algorithm, which eliminates a given number of atoms from the unit cell so that the remaining carbon atoms have no more than one dangling bond. The presence of vacancies in carbon nanotube causes an enhancement of density of occupied states that is near the Fermi level and whose localization increases with the number of two-coordinated atoms at the pore boundaries. It was found that the occurrence of atomic vacancies in the carbon nanotube walls might result in the narrow-gap semiconductors and ferromagnetic materials. © 2003 Wiley Periodicals, Inc. Int J Quantum Chem, 2004
Smart Multiple Attendance System through Single Image

Attendance marking is a common activity to keep track of the presence of students daily in all academic institutions at all grades. Traditional approaches for marking attendance were manual. These approaches are accurate without a chance of marking fake attendance but these are time-consuming and laborsome for a large number of students. To overcome the drawbacks of manual systems, automated systems are developed using radio frequency identification-based scanning, fingerprint scanning, Face-recognition, and Iris scanning based biometric systems. Each system has its pros and cons. Besides, all of these systems suffer from the limitation of human intervention to mark the attendance one by one at a time. To overcome the limitations of existing manual and automated attendance systems, in this work, we propose a robust and efficient attendance marking system from a single group image using face detection and recognition algorithms. In this system, a group image is captured from a high-resolution camera mounted at a fixed location to capture the group image for all the students sitting in a classroom. Next, the face images are extracted from the group image using a popular Viola-Jones algorithm followed by recognition using a convolutional neural network trained on the face database of students. We tested our system for different types of group images and types of databases. Our experimental results show that the proposed framework outperforms other attendance marking systems in terms of efficiency and ease of use and implementation. The proposed system is an autonomous attendance system that requires less human-machine interaction, making it possible to easily incorporate in a smart classroom.
Modeling one-choice discrete-continuous dual task

Performing two tasks simultaneously has been always a challenging issue. The effect of difficulty factor has investigated on discrete-continuous motor-cognitive dual-task (DT) using manual tracking task and auditory discrete stimuli. 25 participants that took part in this experiment were asked to track the target while responding to auditory stimuli at the same time. Two different levels of difficulty for tracking tasks were applied to the experiment. The effect of different difficulties was investigated via several factors as error rate, the response time (RT) using the Drift Diffusion Model (DDM) method. The results show that with different drift rates the model is consistent with RT of different difficulty levels.
Seniors’ ability to decode differently aged facial emotional expressions

The present investigation aims at assessing elders’ ability to decode facial emotional expressions conveyed by differently aged people in order to confirm (or disconfirm) the appropriateness of the “own age bias” theory, as well as investigate effects of different ages and different emotional categories. The study, involves 44 healthy elders (23 females), aged 65+ (mean age=75.09; SD=±7.9) which were requested to label 76 pictures depicting elders, middle-aged and young women and men displaying the six facial emotional expressions of disgust, anger, fear, sadness, happiness and neutrality. Results show a complex pattern of influences that calls for more deep investigations on the features to be accounted by providing socially and emotionally believable interfaces of effective and efficient algorithms to detect and decode their users’ emotional facial expressions.
Automated License Plate Recognition: A Survey on Methods and Techniques

With the explosive growth in the number of vehicles in use, automated license plate recognition (ALPR) systems are required for a wide range of tasks such as law enforcement, surveillance, and toll booth operations. The operational specifications of these systems are diverse due to the differences in the intended application. For instance, they may need to run on handheld devices or cloud servers, or operate in low light and adverse weather conditions. In order to meet these requirements, a variety of techniques have been developed for license plate recognition. Even though there has been a notable improvement in the current ALPR methods, there is a requirement to be filled in ALPR techniques for a complex environment. Thus, many approaches are sensitive to the changes in illumination and operate mostly in daylight. This study explores the methods and techniques used in ALPR in recent literature. We present a critical and constructive analysis of related studies in the field of ALPR and identify the open challenge faced by researchers and developers. Further, we provide future research directions and recommendations to optimize the current solutions to work under extreme conditions.
Promoting Teaching Self-efficacy in Computational Thinking of School Teachers

Computational Thinking (CT), one of the necessary and fundamental skills for students in the 21st century, is gaining more attention in K-12. The premise of integrating CT with other subjects is to promote school teachers with high self-efficacy in CT. Towards this, we developed a three-week Scratch online professional development(PD) course based on MIT CT framework, then implemented an online training among 84 primary and secondary school teachers from different subjects including Chinese, Mathematics, English, and Computer Science. To examine the effect of its impact on teachers’ self-efficacy in CT, a quasi-experiment method of a single group pretest-posttest was conducted, and quantitative and qualitative data were collected from scales, artifacts, and interviews. The results revealed that the online PD course significantly improved teaching self-efficacy in CT of these teachers. The findings indicated that online PD integrated with MIT CT framework through block-based programming was a potential approach to effectively promote teaching self-efficacy in CT for primary and secondary school teachers.
Formation mechanism of black LiTaO3 single crystals through chemical reduction

Lithium tantalate (LiTaO3, LT) wafers of different colors were prepared through chemical reduction of regular congruent LT wafers. Samples with different colors corresponding to different annealing temperatures were characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and measurements of the Curie temperature and density. It was found that chemical reduction does not influence the basic LT structure. The Ta charge state change due to chemical reduction was found to be the main reason for the formation of black LT wafers.
A Contemporary Survey of Unimodal Liveness Detection Techniques: Challenges & Opportunities

Biometric recognition is an extensive and energizing field of research but spoofing attacks, "a deceiver pretense as an authorized user by reproducing the biometrics of the original user", is still the main problem of this field. Liveness detection is one prime countermeasure to spoofing attacks and is based on the principle that some additional information can be obtained or refined to verify that the produced data is authentic or not by a standard verification system. It utilized the physiological or anatomical signs of life, for example, change in, movement of the mouth, facial expression, blinking of eyes, etc. This paper presents an extensive analysis of the various unimodal biometric system based liveness/vitality detection techniques, in which physiological properties are utilized to discriminate between live and fake biometric traits. Further, these techniques are categorized based on static, dynamic, behavioral, and image quality based methods, recently proposed by various researchers. A new classification is also developed for these liveness detection techniques. The work covers theories, methodology, and evaluation datasets and also aims at future work in this field of research.
Cloud Computing Inhibitors Among Small and Medium Enterprises

Cloud computing is an emerging information technology (IT) paradigm which undoubtedly has received significant attention from different spheres of influence. The cloud computing concept entails the provisioning of computing resources from remote locations to individuals or organizations over the internet at anytime. The technology is associated with several benefits but the reduction in capital and operational expenditure is the major one. The importance of IT innovations to Small and Medium Enterprises (SMEs) and the role of SMEs in economic growth have been demonstrated empirically. However, literature indicate that SMEs in developing economies are not taking the advantages of emerging technologies like cloud computing to improve operational excellence within their organizations. An in-depth review of academic publications, technical reports, and industry white papers on cloud computing has confirmed security and privacy is-sues as some of the key inhibitors to cloud computing adoption among SMEs. This is probably because the customer data is released to remote locations, which are invisible, and data is accessed over the internet, which is already over-whelmed with malicious attackers. This study explores the reasons for the lack of cloud computing adoption among SMEs in developing economies. When the rea-sons for this lag are identified, cloud computing service providers, technology policy makers, entrepreneurs and SMEs executives will be provided with the opportunity to develop appropriate solutions and strategies that will specifically meet the needs of SMEs in developing economies. This will promote and accelerate the acceptance rate of cloud services. When SMEs utilize cloud computing as an IT strategy, business growth becomes inevitable and the country’s economic growth is witnessed.
Taylor Rules and the Prospect of Indeterminacy: A Bayesian Econometric Investigation∗

Using a Bayesian estimation method allowing for indeterminacy, we show that a policy rule responding to the output gap is a key factor, combined with a high level of trend inflation, that led to indeterminacy during the pre-Volcker years. Our evidence also suggests that a policy rule targeting output growth can potentially achieve determinacy with high trend inflation. Estimation results for the Great Moderation confirm the stabilizing powers of a policy rule targeting output growth. A more aggressive stance against inflation, combined with a stronger policy response to output growth, helped lowering trend inflation and restore determinacy during the Great Moderation. Our findings hence suggest that the Fed made a switch from a policy rule targeting output gap to one targeting output growth from the pre-Volcker years to the Great Moderation. Assuming a single policy rule to address important macroeconomic questions such as the causes of indeterminacy and the sources of the Great Moderation may be misleading.
Source Zone Natural Attenuation at Petroleum Hydrocarbon Spill Sites—I: Site‐Specific Assessment Approach

This work focuses on the site‐specific assessment of source zone natural attenuation (SZNA) at petroleum spill sites, including the confirmation that SZNA is occurring, estimation of current SZNA rates, and anticipation of SZNA impact on future ground water quality. The approach anticipates that decision makers will be interested in answers to the following questions: (1) Is SZNA occurring and what processes are contributing to SZNA? (2) What are the current rates of mass removal associated with SZNA? (3) What are the longer‐term implications of SZNA for ground water impacts? and (4) Are the SZNA processes and rates sustainable? This approach is a data‐driven, macroscopic, multiple‐lines‐of‐evidence approach and is therefore consistent with the 2000 National Research Council’s recommendations and complementary to existing dissolved plume natural attenuation protocols and recent modeling work published by others. While this work is easily generalized, the discussion emphasizes SZNA assessment at petroleum hydrocarbon spill sites. The approach includes three basic levels of data collection and data reduction (Group I, Group II, and Group III). Group I measurements provide evidence that SZNA is occurring. Group II measurements include additional information necessary to estimate current SZNA rates, and group III measurements are focused on evaluating the long‐term implications of SZNA for source zone characteristics and ground water quality. This paper presents the generalized site‐specific SZNA assessment approach and then focuses on the interpretation of Group II data. Companion papers illustrate its application to source zones at a former oil field in California.