Miracle-dz/UniFAR-FTUs huggingface dataset:
All-Fiber Optical Parametric Oscillator For Coherent Raman Imaging

Optical parametric oscillator (OPO) is a kind of light source similar to a laser but based on parametric gain from amplification in a nonlinear medium rather than from stimulated emission. It features wide frequency tunability and high power narrow linewidth output, which enables its application to laser spectroscopy and atom-light interaction. Although OPOs could provide two synchronized pulse trains for coherent anti-Stokes Raman spectroscopy (CARS) imaging, they remain bulky and sensitive tools requiring careful alignment, making these devices unpractical for surgical situations. Thus, all-fiber source for coherent Raman imaging have generated interest among clinical researchers and doctors. Thanks to recent advances in the understanding of nonlinear pulse evolution in optical fibers and engineering of PCF structures, fiber-based OPOs have achieved performance comparable to conventional solid-state devices. This thesis discussed the previous effort towards an all-fiber source of pulses for use in CARS imaging, as well as principles behind picosecond pulse generation and coherent Raman imaging. An all-fiber OPO pumped by commercial solid state laser and divided pulse amplifier is demonstrated based on frequency conversion of picosecond pulses through four-wave mixing process in customized photonic crystal fiber. This will be another step further towards an all-fiber device for coherent Raman microscopy.
A Bay Map Automatic Generation Method for Big Computer Centralized SCADA System in Unmanned Substations

In view of the fact that the static bay map is incapable of adapting to the future development of electric automation because of its maintenance difficulty and long delay response time of man-machine interface(MMI) operation,it is necessary for the electrical bay map in the big computer centralized SCADA system in the unmanned substations to be automatically generated without special parameters or file.Such key technologies for the automatic generation of the bay map as the bay map template and figure database auto association are analyzed at length.Two practical methods are presented for the automatic generation of the bay map,i.e.the method based on the Keywords of real-time database and the method based on scalable vector graphics(SVG).
Local Image Denoising using RAISR

Digital images are frequently degraded by Gaussian noise while capturing photos. This paper proposes a rapid and high accurate Gaussian noise removal method by applying the learned linear filter used in RAISR for super-resolution. The denoising methods are classified into local, nonlocal methods and deep-learning-based methods. The conventional local processing has a problem that high-frequency components of the original image are lost while reducing the noise. The nonlocal and deep-learning-based methods achieve higher denoising performance but take a long time for training and implementation. To solve these problems,we apply a super-resolution method to the local denoising method as post-processing because it can efficiently recover the high-frequency components. The super-resolution method uses a learned linear filter according to the feature of patches. The novelty of this paper is that the same processing as super-resolution is incorporated into denoising. The proposed algorithm is a rapid local denoising method and can achieve comparable performance to the high-accurate nonlocal denoising methods. Experimental results show that our proposed method provides accurate denoising performance with a low computational cost compared to nonlocal processing like BM3D.
Sparse frame acquisition toward fast volumetric dynamic optical coherence tomography imaging

In the biological and pharmaceutical research, there is a certain demand for label-free three-dimensional (3D) imaging of tissue function. Dynamic optical coherence tomography (OCT) has been demonstrated to fill this demand, but a very high-speed system is required for a volume acquisition of dynamic OCT. Here we demonstrate two new frame acquisition protocols which potentially enable the volumetric dynamic OCT by standard-speed OCT device with a reasonably short measurement time. One of the protocols takes multiple frames at a single location with a few hundred milliseconds interval. The other sequentially takes four frames at a single location, and then repeats this burst four-frame acquisition eight times with a few hundred milliseconds interval. These protocols were validated with in vitro and ex vivo samples. Both protocols gave reasonable dynamic OCT images.
Rapid assessment of abrupt urban mega-gully and landslide events with structure-from-motion photogrammetric techniques validates link to water resources infrastructure failures in an urban periphery

Abstract. Mass movement hazards in the form of mega-gullies and landslides pose significant risks in urbanizing areas, yet they are poorly documented. To obtain primary data on the size, frequency, and triggers of abrupt mega-gullies and landslides in urban areas, rapid assessment methods based on structure-from-motion (SfM) photogrammetric techniques and watershed models were developed and deployed in the Los Laureles Canyon, a rapidly urbanizing watershed in Tijuana, Mexico. Three abrupt earth surface hazards were observed over a 5-year study period including two mega-gullies and one landslide, and all were linked to a combination of rainfall and water resources infrastructure failures (WRIFs): (1) water main breaks resulted from rainfall-driven gully erosion that undermined supply lines, and the resulting water jets caused abrupt mega-gully formation; this represents one of the first studies to document this process in an urban environment; (2) antecedent saturation of a hillslope from a leaking water supply pipe contributed to an abrupt landslide during a storm event. The return period of the storms that triggered the WRIF-based earth surface hazards was ∼1–2 years, suggesting that such triggering events occur frequently. WRIF-based hazards were also a non-negligible contributor to sediment generation at the watershed scale. While the number of observed events is small, these results suggest that WRIF can, in some cases, be the single most important process generating abrupt and life-threatening earth surface hazards on the poor urban periphery. Future studies of the triggers and mechanisms of abrupt urban mega-gullies and landslides should consider the role of WRIFs in antecedent saturation and erosion by broken water supply lines.
A Hierarchical Motion Planning Framework for Autonomous Driving in Structured Highway Environments

This paper presents an efficient hierarchical motion planning framework with a long planning horizon for autonomous driving in structured environments. A 3D motion planning with time information is a non-convex problem because there exists more than one local minimum point and various constraints such as roads and obstacles. Accordingly, to deal with high computational complexity and the problem of falling into a local minimum in an enormous solution space, a decoupled method is utilized, that consists of two steps: Long-term planning and short-term planning. First, the long-term planner provides reasonable far-sighted behavior through two processes. In the first process, a rough path that includes a driving strategy is generated in the 2D spatial space. Then, the jump point search algorithm is utilized with time information on the path to reduce the computational burden of A*, giving an acceptable quality of solution at the same time. In this step, a safe, comfortable, and dynamically feasible trajectory is generated. Next, the short-term planner optimizes a short-sighted trajectory using particle swarm optimization. In this method, a steering angle set is encoded as a particle, resulting in a safe, comfortable, and kinodynamically feasible trajectory. The proposed algorithm is implemented and evaluated in a range of vehicle-in-the-loop simulation scenarios, which include various virtual static and dynamic obstacles generated by Intelligent Driver Model. In the evaluation results, the proposed method reduced the computation time by up to 0.696 s with increasing the step cost by up to about 3%. The proposed algorithm is executed every 100 ms for a planning horizon of 10 seconds, and the average computation time is 31 ms with the worst-case computation time of 94 ms.
Enhancing Sybil Detection via Social-Activity Networks: A Random Walk Approach

Detecting fake accounts (sybils) in online social networks (OSNs) is vital to protect OSN operators and their users from various malicious activities. Typical graph-based sybil detection (a mainstream methodology) assumes that sybils can make friends with only a limited (or small) number of honest users. However, recent evidences showed that this assumption does not hold in real-world OSNs, leading to low detection accuracy. To address this challenge, we explore users’ activities to assist sybil detection. The intuition is that honest users are much more selective in choosing who to interact with than to befriend with. We first develop the social and activity network (SAN), a two-layer hyper-graph that unifies users’ friendships and their activities, to fully utilize users’ activities. We also propose a more practical sybil attack model, where sybils can launch both friendship attacks and activity attacks. We then design Sybil_SAN to detect sybils via coupling three random walk-based algorithms on the SAN, and prove the convergence of Sybil_SAN. We develop an efficient iterative algorithm to compute the detection metric for Sybil_SAN, and derive the number of rounds needed to guarantee the convergence. We use “matrix perturbation theory” to bound the detection error when sybils launch many friendship attacks and activity attacks. Extensive experiments on both synthetic and real-world datasets show that Sybil_SAN is highly robust against sybil attacks, and can detect sybils accurately under practical scenarios, where current state-of-art sybil defenses have low accuracy. Lastly, we present two extensions of Sybil_SAN to further improve its accuracy.
A preliminary comparison of a songbird’s song repertoire size and other song measures between an urban and a rural site

Abstract Characteristics of birdsong, especially minimum frequency, have been shown to vary for some species between urban and rural populations and along urban–rural gradients. However, few urban–rural comparisons of song complexity—and none that we know of based on the number of distinct song types in repertoires—have occurred. Given the potential ability of song repertoire size to indicate bird condition, we primarily sought to determine if number of distinct song types displayed by Song Sparrows (Melospiza melodia) varied between an urban and a rural site. We determined song repertoire size of 24 individuals; 12 were at an urban (‘human‐dominated’) site and 12 were at a rural (‘agricultural’) site. Then, we compared song repertoire size, note rate, and peak frequency between these sites. Song repertoire size and note rate did not vary between our human‐dominated and agricultural sites. Peak frequency was greater at the agricultural site. Our finding that peak frequency was higher at the agricultural site compared to the human‐dominated site, contrary to many previous findings pertaining to frequency shifts in songbirds, warrants further investigation. Results of our pilot study suggest that song complexity may be less affected by anthropogenic factors in Song Sparrows than are frequency characteristics. Additional study, however, will be required to identify particular causal factors related to the trends that we report and to replicate, ideally via multiple urban–rural pairings, so that broader generalization is possible.
Interaction and introspection with tangible augmented objects

Most of our waking hours are now spent staring at a screen. While the advances in touch screens have enabled a more expressive interaction space with our devices, by using our fingers to interact with digital content, what we see and manipulate on screen is still being kept away from us, locked behind a glassy surface. The range of capabilities of the human senses is much richer than what screens can currently offer. In order to be sustainable in the future, interaction with the digital world should leverage these human capabilities instead of letting them atrophy. One way to provide richer interaction and visualization modalities is to rely on the physical world itself as a host for digital content. Spatial Augmented Reality provides a technical mean towards this idea, by using projectors to shed digitally controlled light onto real-world objects to augment them and their environment with features and content. This paves the way to a future where everyday objects will be embedded with rich and expressive capabilities, while still being anchored in the real world. In this thesis, we are interested in two main aspects related to these tangible augmented objects. In a first time, we are raising the question on how to interact with digital content when it is hosted on physical objects. As a basis for our investigation, we studied interaction modalities that leverage traditional input and output devices found in a typical desktop environment. Our rationale for this approach is to leverage the experience of users with traditional digital tools – tools which researchers and developers spent decades to make simpler and more efficient to use – while at the same time steering towards a physically enriched interaction space. In a second time, we go beyond theinteraction with the digital content of augmented objects and reflect on their potential as a humane medium support. We investigate how these augmented artifacts, combined with physiological computing, can be used to raise our awareness of the processes of our own bodies and minds and, eventually, foster introspection activities. This took the form of two different projects where we used tangible avatars to let users explore and customize real-time physiological feedback of their own inner states.
Assessing Numerical Model Skill at Simulating Coastal Flooding Using Field Observations of Deposited Debris and Photographic Evidence

Despite the growing range and availability of resources to support coastal flood hazard model development, there is often a scarcity of data to support critical assessment of the performance of community-scale coastal inundation models. Even where long-term tide gauge measurements are available in close proximity to the study area, the records provide little insight into the spatial distribution and limits of overland flooding, or the influence of topographic features and structures on flooding pathways. We present methods to support the assessment of model performance using field observations in lieu of, or supplementary to, conventional water-level records. A high-resolution, numerical coastal flood hazard model was developed to simulate storm surge-driven flooding in the Acadian Peninsula region of New Brunswick, Canada. Owing to the remoteness of the study area from tide gauge stations, model performance was assessed based on a comparison with field measurements of deposited wrack and debris, as well as photographic and video evidence of coastal flooding, for two significant storm surge events in recent history. Our research findings illustrate the value of observational and qualitative data for characterizing coastal flood hazards, lending gravity to the importance of non-conventional data sources, particularly in data-scarce regions.
Completion of User Preference based on CP-nets in Automated Negotiation

Automated negotiation is a process in which autonomous agents negotiate with the opponents to achieve some specific purposes for their users, such as maximising the users’ benefits. CP-net is one of the most important representations of user preferences in automated negotiation due to its ability and flexibility to express interdependent relationship among issues. In order to be able to negotiate better on behalf of users, a negotiating agent needs to fully understand its user’s preferences, so that it can adopt suitable negotiation strategies and obtain ideal negotiation results. However, the preference information provided to the negotiating agents by the users is often incomplete. Hence, based on partial preference information provided, this paper proposes a module in negotiation framework to complete user total preferences that are represented by CP-nets. The experimental results show the validity of CP-nets structure learning algorithm in the proposed module and confirm that the module can help users achieve better agreements in negotiation.
Transcriptomics Reveals the Effect of Thymol on the Growth and Toxin Production of Fusarium graminearum

Fusarium graminearum is a harmful pathogen causing head blight in cereals such as wheat and barley, and thymol has been proven to inhibit the growth of many pathogens. This study aims to explore the fungistatic effect of thymol on F. graminearum and its mechanism. Different concentrations of thymol were used to treat F. graminearum. The results showed that the EC50 concentration of thymol against F. graminearum was 40 μg/mL. Compared with the control group, 40 μg/mL of thymol reduced the production of Deoxynivalenol (DON) and 3-Ac-DON by 70.1% and 78.2%, respectively. Our results indicate that thymol can effectively inhibit the growth and toxin production of F. graminearum and cause an extensive transcriptome response. Transcriptome identified 16,727 non-redundant unigenes and 1653 unigenes that COG did not annotate. The correlation coefficients between samples were all >0.941. When FC was 2.0 times, a total of 3230 differential unigenes were identified, of which 1223 were up-regulated, and 2007 were down-regulated. Through the transcriptome, we confirmed that the expression of many genes involved in F. graminearum growth and synthesis of DON and other secondary metabolites were also changed. The gluconeogenesis/glycolysis pathway may be a potential and important way for thymol to affect the growth of F. graminearum hyphae and the production of DON simultaneously.
Particle swarm optimization for searching efficient experimental designs: A review

The class of nature‐inspired metaheuristic algorithms is increasingly used to tackle all kinds of optimization problems across disciplines. It also plays an important component in artificial intelligence and machine learning. Members in this class are general purpose optimization tools that virtually require no assumptions for them to be applicable. There are many such algorithms, and to fix ideas, we review one of its exemplary members called particle swarm optimization (PSO). We discuss the algorithm, its recent applications to find different types of efficient experimental designs, and provide resources, where codes for PSO and other metaheuristic algorithms and tutorials with examples are available.
impedance Analysis to Determine the Effect of Pressure Release on Limb Fluid Volume Change in Persons with Transtibial Limb Loss

Bioimpedance Analysis to Determine the Effect of Pressure Release on Limb Fluid Volume Change in Persons with Transtibial Limb Loss by Tyler Lee Hartley INTRODUCTION: Over 1 million Americans currently live with a lower limb amputation. Lower limb prostheses have been designed to produce a secure and comfortable fit between residual limb and prosthesis throughout the day. However, significant fluid volume fluctuations in the residual limb deteriorate socket fit and may cause decreased mobility due to discomfort or soft tissue injury from increased pressures and shear stresses. Extracellular fluid is thought to be the primary source of limb volume fluctuation and commonly decreases over the course of the day. Technologies designed to address limb fluid volume loss are ineffective and cumbersome. Methods to assess limb fluid volume change are also poor, lacking the capability to quantify fluid volume loss at high temporal resolution and while the prosthesis is donned. METHODS: Bioimpedance analysis provides tools to rapidly estimate extracellular fluid volume while a subject performs regular activity with the prosthesis donned. Using a custom bioimpedance analyzer, we tested 16 subjects each on three separate occasions in a repeated measures study. Each test session consisted of an activity sequence followed by a 30 minute period of calm sitting (dubbed the recovery sit). During the recovery sit, subjects sat with their prosthesis donned (ON), prosthesis doffed (OFF), or liner donned (LINER). An identical activity sequence followed the 30 minute sit. RESULTS: Volume changes during the OFF 30 minute recovery sit were significantly higher than during ON (p < 0.001). We observed that after the limb gained fluid volume during the OFF protocol, subsequent volume loss was negligible in 12 of 16 subjects during the following activity sequence. Conversely, volume lost during the 30 minute recovery period during ON was not recovered during three cycles of activity. During the LINER protocol, volume gains during the 30 minute sit were similar to those during OFF, but returned to baseline levels during subsequent activity. Gains in LINER during subsequent activity still remained elevated as compared to ON (p < 0.01). CONCLUSIONS: Decreasing applied pressure to the residual limb may function to recover limb fluid volume, allowing socket users to maintain a comfortable fit between limb and prosthesis. There exists a potential for development of socket technology that leverages pressure release to recover
A Novel Approach to Dining Bowl Reconstruction for Image-Based Food Volume Estimation

Knowing the amounts of energy and nutrients in an individual’s diet is important for maintaining health and preventing chronic diseases. As electronic and AI technologies advance rapidly, dietary assessment can now be performed using food images obtained from a smartphone or a wearable device. One of the challenges in this approach is to computationally measure the volume of food in a bowl from an image. This problem has not been studied systematically despite the bowl being the most utilized food container in many parts of the world, especially in Asia and Africa. In this paper, we present a new method to measure the size and shape of a bowl by adhering a paper ruler centrally across the bottom and sides of the bowl and then taking an image. When observed from the image, the distortions in the width of the paper ruler and the spacings between ruler markers completely encode the size and shape of the bowl. A computational algorithm is developed to reconstruct the three-dimensional bowl interior using the observed distortions. Our experiments using nine bowls, colored liquids, and amorphous foods demonstrate high accuracy of our method for food volume estimation involving round bowls as containers. A total of 228 images of amorphous foods were also used in a comparative experiment between our algorithm and an independent human estimator. The results showed that our algorithm overperformed the human estimator who utilized different types of reference information and two estimation methods, including direct volume estimation and indirect estimation through the fullness of the bowl.
Dynamic evolution of major accidents—concepts and rudiments

To carry further research on the generative and evolutionary machine of major accidents, on the base of comparative studies of typical major, special major accidents, the paper applied the theory of system dynamics to analyze the dynamic characteristics of inoculation, generation, development and qualitative change of major accidents, to explore the behavioral process and main influence factors of major accidents and to probe into governing rules of closed loop feedback system of major accidents. The author proposed to apply the theory and methodology of system dynamics on establishig a decision - making laboratory to elevate the risk control ability of major accidents.