Miracle-dz/UniFAR-FTUs huggingface dataset:
Intercomparison of Multiple UV-LIF Spectrometers Using the Aerosol Challenge Simulator

Measurements of primary biological aerosol particles (PBAPs) have been conducted worldwide using ultraviolet light-induced fluorescence (UV-LIF) spectrometers. However, how these instruments detect and respond to known biological and non-biological particles, and how they compare, remains uncertain due to limited laboratory intercomparisons. Using the Defence Science and Technology Laboratory, Aerosol Challenge Simulator (ACS), controlled concentrations of biological and non-biological aerosol particles, singly or as mixtures, were produced for testing and intercomparison of multiple versions of the Wideband Integrated Bioaerosol Spectrometer (WIBS) and Multiparameter Bioaerosol Spectrometer (MBS). Although the results suggest some challenges in discriminating biological particle types across different versions of the same UV-LIF instrument, a difference in fluorescence intensity between the non-biological and biological samples could be identified for most instruments. While lower concentrations of fluorescent particles were detected by the MBS, the MBS demonstrates the potential to discriminate between pollen and other biological particles. This study presents the first published technical summary and use of the ACS for instrument intercomparisons. Within this work a clear overview of the data pre-processing is also presented, and documentation of instrument version/model numbers is suggested to assess potential instrument variations between different versions of the same instrument. Further laboratory studies sampling different particle types are suggested before use in quantifying impact on ambient classification.
AJoin: Ad-hoc Stream Joins at Scale

The processing model of state-of-the-art stream processing engines is designed to execute long-running queries one at a time. However, with the advance of cloud technologies and multi-tenant systems, multiple users share the same cloud for stream query processing. This results in many ad-hoc stream queries sharing common stream sources. Many of these queries include joins. There are two main limitations that hinder performing ad-hoc stream join processing. The first limitation is missed optimization potential both in stream data processing and query optimization layers. The second limitation is the lack of dynamicity in query execution plans. We present AJoin, a dynamic and incremental ad-hoc stream join framework. AJoin consists of an optimization layer and a stream data processing layer. The optimization layer periodically reoptimizes the query execution plan, performing join reordering and vertical and horizontal scaling at run-time without stopping the execution. The data processing layer implements pipelineparallel join architecture. This layer enables incremental and consistent query processing supporting all the actions triggered by the optimizer. We implement AJoin on top of Apache Flink, an open-source data processing framework. AJoin outperforms Flink not only at ad-hoc multi-query workloads but also at single-query workloads.
Optimizing Databases by Learning Hidden Parameters of Solid State Drives

Solid State Drives (SSDs) are complex devices with varying internal implementations, resulting in subtle differences in behavior between devices. In this paper, we demonstrate how a database engine can be optimized for a particular device by learning its hidden parameters. This can not only improve an application's performance, but also potentially increase the lifetime of the SSD. Our approach for optimizing a database for a given SSD consists of three steps: learning the hidden parameters of the device, proposing rules to analyze the I/O behavior of the database, and optimizing the database by eliminating violations of these rules. We obtain two different characteristics of an SSD, namely the request size profile and the location profile , from which we learn multiple internal parameters. Based on these parameters, we propose rules to analyze the I/O behavior of a database engine. Using these rules, we uncover sub-optimal I/O patterns in SQLite3 and MariaDB when running on our experimental SSDs. Finally, we present three techniques to optimize these database engines: (1) use-hot-locations on SSD-S, which improves the SELECT operation throughput of SQLite3 and MariaDB by 29% and 27% respectively; it also improves the performance of YCSB on MariaDB by 1%-22% depending on the workload mix, (2) write-aligned-stripes on SSD-T, reduces the wear-out caused by SQLite3 write-ahead log (WAL) file by 3.1%, and (3) contain-write-in-flash-page on SSD-T, which reduces the wear-out caused by the MariaDB binary log file by 6.7%.
Learning to Play Connect Four from Game Images

We design a system to play Connect Four given access to an image of a game board. We experiment with three different deep-learning architectures: expert policy estimation, state value estimation, and a hybrid architecture that uses classical planning (MCTS). We compare our trained networks with a variety of standard players and discuss their relative strengths.
Learning to Rank Using Implicit Feedback and Active Exploration

Learning to rank using implicit feedback is a relatively new supervised machine learning method, in which training data is used in order to automatically calculate search engine document ranking parameters. This thesis describes the implementation and evaluation of a rank learning algorithm with active learning. The relevance judgments are collected from users in the form of click-through data, and the ranking parameters are learned incrementally over time. The online learner is implemented as a faculty search engine at the Royal Institute of Technology, and is evaluated both via human usage and user simulation experiments. The Kendall Tau Ranking Correlation Coefficient is used in order to compare the learned ranks with the corresponding true ranks of the experiments. Results of the evaluation show that the online learner fares marginally better in situations where users hold a common view of the relevance values of the documents in the document corpus. The results are not entirely conclusive, however. Hence, improvements of both the learner and the evaluation techniques are proposed for future researchers.
Electrically Small Water-Based Hemispherical Dielectric Resonator Antenna

Recently, water has been proposed as an interesting candidate for use in applications such as tunable microwave metamaterials and dielectric resonator antennas due to its high and temperature-dependent permittivity. In the present work, we considered an electrically small water-based dielectric resonator antenna made of a short monopole encapsulated by a hemispherical water cavity. The fundamental dipole resonances supported by the water cavity were used to match the short monopole to its feed line as well as the surrounding free space. Specifically, a magnetic (electric) dipole resonance was exploited for antenna designs with a total efficiency of 29.5% (15.6%) and a reflection coefficient of −24.1 dB (−10.9 dB) at 300 MHz. The dipole resonances were effectively excited with different monopole lengths and positions as well as different cavity sizes or different frequencies in the same cavity. The overall size of the optimum design was 18 times smaller than the free-space wavelength, representing the smallest water-based antenna to date. A prototype antenna was characterized, with an excellent agreement achieved between the numerical and experimental results. The proposed water-based antennas may serve as cheap and easy-to-fabricate tunable alternatives for use in very high frequency (VHF) and the low end of ultrahigh frequency (UHF) bands for a great variety of applications.
Oceanological and Hydrobiological Studies

Grazing of zooplankton on phytoplankton may contribute to a reduction of harmful cyanobacteria in eutrophic waters. However, the feeding capacity and interaction between zooplankton and toxic cyanobacteria vary among grazer species. In this study, laboratory feeding experiments were designed to measure the grazing rate of the copepod Cyclops vicinus on Microcystis aeruginosa and the potential microcystin (MC) accumulation in the grazer. Copepods were fed a mixed diet of the edible green alga Ankistrodesmus falcatus and toxic M. aeruginosa for 10 days. The results showed that C. vicinus e ciently ingested toxic Microcystis cells with high grazing rates, varying during the feeding period (68.9–606.3 Microcystis cells animal−1 d−1) along with Microcystis cell density. Microcystis cells exhibited a remarkable induction in MC production under grazing conditions with concentrations 1.67–12.5 times higher than those in control cultures. Furthermore, C. vicinus was found to accumulate MCs in its body with concentrations increasing during the experiment (0.05–3.21 μg MC animal−1). Further in situ studies are needed to investigate the ability of Cyclops and other copepods to assimilate and detoxify MCs at environmentally relevant concentrations before deciding on the biocontrol of Microcystis blooms by copepods.
Resource Allocation for Energy Harvesting-Aided Device-to-Device Communications: A Matching Game Approach

In this paper, we propose an optimization problem of joint spectrum resource matching, power allocation and transmission time duration in energy harvesting (EH) aided device-to-device (D2D) communications underlying cellular network. Particularly, to increase the spectral efficiency (SE), multiple D2D links are permitted to multiplex the same resource block (RB) of cellular user (CU), and multiple cellular RBs are allowed to be reused by one D2D link. Taking into account the Quality of Service (QoS) of cellular users and the harvested energy of D2D links, our objective is to maximize the sum SE of D2D communications. Specifically, the joint problem is a mixed-integer nonlinear programming problem, which has been proved to be NP-hard. In view of this observation, a novel match-based resource allocation algorithm is designed, which contains two key sub-methods with a progressive relationship. First, a many-to-many matching game with externalities is developed, which produces spectrum reusing during each iteration. Second, on the basis of the spectrum matching, a resource allocation algorithm is presented by employing the Lagrange multiplier and Bisection search method. Finally, comprehensive simulation results are provided to demonstrate the performance of the proposed algorithm with different network parameter settings. Numerical results validate that the proposed algorithm can achieve near-optimal performance and significantly outperform the other matching strategies in aspects of SE.
Research on Lane-Change Online-Planning for Automotive Vehicles based on Model Predictive Control

Lane change control is one of the key issues in the field of autonomous driving. Existing works have developed path-tracking controllers based on the pre-designed or off-line trajectory, which cannot adapt to the changes during the lane-change process. This paper adopts the real-time trajectory generation module and control module constructing the framework of lane change decision when there are at least 2 lanes on the left of the original lane. If the traffic environment changes, the vehicle will automatically update the trajectory to the left lane, redesign a new trajectory back into the original lane, or design a new lane into the 2nd left lane based on the real-time judgement of stability and safety criteria. It enables the vehicle to be more adaptable to unfavorable changes such as simultaneous lane changing maneuver of other vehicles. The online-planning module and the framework of the judgement criteria are the innovative points. Besides, this paper analyzes the influence of the prediction and control domains of Model Predictive Control. 20 steps and 10 steps were finally chosen as the prediction domain and control domain at the sample rate of 20Hz due to higher robustness and smaller hysteresis. The control module is based on the 3-degrees non-linear kinematic model. The discretization and linearization produce a linear model for model predictive control method. The co-simulation of Carsim and Matlab is conducted to verify the performance of proposed controller. In comparison with off-line trajectory planning methods, this method is characterized by shorter response time and stronger robustness in emergent conditions.
Model and Study of Clamping Force for Micro-gripper with PZT-driven

Micro-gripper is the key equipment in the micromanipulator field. Specially, the PZT-driven micro-gripper has been widely concerned by scientists. But it is the difficult to control the clamping force when the gripper is working under the micro/nano scale. So it requires models of the interaction forces and adhesion forces dedicated to this scale. This paper focuses on the studies of clamping force including the adhesion force, contact force and so on. The force model of the gripper will be established, and the process of the ”clip-hold-release” will be studied. The experimental results indicate that the gripping force are different for different materials.
A Robot 3C Assembly Skill Learning Method by Intuitive Human Assembly Demonstration

With the development of the Internet and computer technology, the demand for 3C products such as mobile phones has surged. At the same time, due to the continuous improvement of labor costs, it is urgent to automate the 3C assembly lines with industrial robots. The motivation of this paper is to propose an efficient off-line programming by demonstration method to automate 3C assembly lines. This process consists of two phases. In the first phase, the optical motion capture device is used to capture the position and orientation information of human hands during assembly process. In the second phase, those information of a couple of demonstrations are learned to derive a robot control policy. To do so, the local outlier factor based anomalous point detection algorithm as well as the trajectory segmentation algorithm inspired by density-based spatial clustering is utilized in advance to pre-process the demonstration data. Then the human assembly skill represented by a probabilistic policy is learned from those data to drive the robot to accomplish the same assembly task under new environment. The Gaussian Mixture Model is utilized to pre-structure the policy. The offline programming by demonstration method efficiently transfers human experience to the robot. Experimental results demonstrate the effectiveness of the method.
Features Found in Twitter Data and Examination of Retweeting Behavior

Various phenomena observed on Twitter help us understand the rules and characteristics of human behavior when they receive information about real-world events. With this motivation, we analyze a large number of tweets collected using the keyword search function of the Twitter API, and find the following nontrivial features. 1) Most tweets collected using keywords related to a significant real-world event are retweets (RTs). 2) The number of RTs of an original tweet, which is retweeted many times, gradually decreases, with day/night fluctuations, after reaching a sharp peak. 3) The number of RTs of an original tweet follows a power-law distribution. 4) The correlation coefficient between the number of RTs of an original tweet and the number of followers of the user posting the original tweet is small. Among these features, we examine the third feature (the power-law distribution of the number of RTs) in detail. As a result, we find that the power-law distribution can be explained by simple mathematical models based on the assumption that people select the original tweet for posting a RT depending on the number of RTs that it has received so far.
Random Forest Location Prediction from Social Networks during Disaster Events

Rapid location and classification of data posted on social networks during time-critical situations such as natural disasters, crowd movement and terrorism is very useful way to gain situational awareness and to plan response efforts. Twitter as successful real time micro-blogging social media, is increasingly used to improve resilience during extreme weather events/emergency management situations, including earthquake. It being used during crises by communicating potential risks and their impacts by informing agencies and officials. The geographical location information of such events are vital to rescue people in danger, or need assistance. However, only few messages contains there native geographical coordinates (GPS). So identifying location is a real challenge with Twitter data during critical situations. Identification of Tweets and their precise location are still inaccurate. In this work, we propose to use semi-supervised technique to utilize unlabeled data, which is often abundant at the onset of a crisis event, along with fewer labeled data. Specifically, we adopt an iterative Random Forest fitting-prediction framework to learn the semi-supervised model.
Orientation Correction for Hector SLAM at Starting Stage

Hector simultaneous localisation and mapping(SLAM) is a popular approach for mapping a space. It requires only a Light Detection and Ranging (LiDAR) sensor to perform the mapping. It uses previous scan results to estimate the current state of the system. However, Hector SLAM suffers from serious drifting in the starting stage. This does not affect the mapping during the process but will significantly interfere the future pose estimation of the robot. Because the future pose is an estimation from the previous pose, the drift from the beginning will be recorded and results in a random rotation and translation of the map frame against other ground truth frames. This research uses a reference frame to locate the robot and correct its orientation and position during the starting period of Hector SLAMing using Point-Line Iterative Closest Point (PL-ICP). By compare the trajectory from the reference frame and the trajectory generated by the Hector SLAM, the translations and rotations caused by the joggling from the beginning can be estimated. Map and current poses of the Hector node are rotated and translated according to this translation and rotation to re-align the mapping frame to the ground truth frame.
3-D Printed Circularly-Polarized Lens Antenna Operating at Terahertz Frequencies

A circularly-polarized (CP) lens antenna operating at 300 GHz is realized using a low-cost 3-D printer with high temperature resin. The CP antenna comprises three parts, a phase compensation lens, a dielectric grating providing a 90° phase difference between two orthogonal field components and a linearly-polarized feed horn. The antenna is measured using a near-field scanning system. Excellent agree between the simulated and measured radiation patterns is obtained.
Classification of Lung Nodules Using Textural Features

In this study, a computer-aided detection system was developed for detection and investigation of lung nodules in computed tomography images using image processing techniques. The computer aided detection system consists of four stages. First and second stages are preprocessing stages. First preprocessing stage is two-dimensional preprocessing stage and second preprocessing stage is three-dimensional preprocessing stage. Third stage of the developed system is the feature extraction stage. Five different groups of features are extracted from volume of interests in this stage. Last stage is the the nodule detection stage. The support vector machine algorithm is optimized using evolutionary algorithms to classify volume of interests using the features. The computer aided detection system achieves 93.76% sensitivity, 81.69% selectivity, 84.52% accuracy and 3.63 false positive per scan using only morphologic, statistical and histogram based features. After the inclusion of groups of outer surface statistical and outer surface GLCM and Gabor filter based textural features, performance rates of the computer aided detection system reaches 98.35% sensitivity, 90.42% selectivity, 92.28% accuracy and 2.33 false positive per scan. Results of experiments reveal that outer surface statistical and textural features are useful to increase sensitivity of the system. These features also decrease the number of false positives of the developed systemi
A Novel Semi-Analytical Mode-Matching Technique for Modeling Realistic Electromagnetic Well-Logging Sensors Immersed in Dissipative Inhomogeneous Media

This paper presents a novel formulation to model electromagnetic logging while drilling (LWD) sensors immersed in dissipative inhomogeneous geophysical formations. We analyze the electromagnetic boundary problem via the mode-matching technique and then use such results as a basis for modeling the complex-valued input impedance of the antennas employed in realistic LWD tools. Numerical simulations are presented to validate this approach.
Matrix-Based Information Sharing Approach for Dynamic Robot Teams

In wireless connected robot swarms, information sharing is necessary for robots to cooperatively achieve joint goals under a partially observable environment. Despite significant progress on this problem, existing researchers have not adequately dealt with the case that robots broadcast messages to multiple members with a dynamically changing communication medium. In this paper, we propose an effective information sharing approach for dynamic robot teams. By using a matrix representation for information distribution, the network structure and information needs, this model allows quick reasoning through a single computation. Following our theoretical analysis, this approach can perform as the MDP model for small teams when states are fully observable. In addition, we designed heuristic algorithms to help robots adapt to dynamic network connections and information distribution in large teams. Empirical simulation results manifest that the approach performs well in uncertain environments to effectively balance sharing information with minimizing communication costs.
A Generalized Mode-Matching Method for Glide-symmetric Structures

The new discovery of the extraordinary properties of meta-surfaces possessing higher symmetries is encouraging new studies to better understand the fundamental role of the symmetry, as well as to produce efficient numerical methods for enabling a fast optimization. Here, we propose a mode matching technique to explain the operation of glide-symmetric fully-metallic surface made of holes of arbitrary shape. The mode-matching is formulated to take into account the glide symmetry of the unit cell and isolate its impact. This formulation leads to a reduction of the computational domain to a half cell, and to derive the symmetry properties of the modal fields propagating in the metasurface. Numerical results from commercial software are employed to validate our method.
High Speed Comparator Design for the Implementation of Successive Approximation Register ADC

In this work, a comparator operating at 400MHz frequency with a resolution of 10 bits is designed. In order to eliminate the effect of offset voltage in the designed comparator circuit, auto-zero technique is used. The fully differential operational amplifier structure is used in the comparator circuit as it prevents common mode distortion. Clock signals and CMOS structures of switch, latch, inverter and full differential operational amplifier structures of the comparator circuit are given in detail. The performance parameters of the designed circuit are realized in Cadence environment with 0. 18µm technology.