Distance Estimation Research Articles

RabbitTClust: enabling fast clustering analysis of millions of bacteria genomes with MinHash sketches

We present RabbitTClust, a fast and memory-efficient genome clustering tool based on sketch-based distance estimation. Our approach enables efficient processing of large-scale datasets by combining dimensionality reduction techniques with streaming and parallelization on modern multi-core platforms. 113,674 complete bacterial genome sequences from RefSeq, 455 GB in FASTA format, can be clustered within less than 6 min and 1,009,738 GenBank assembled bacterial genomes, 4.0 TB in FASTA format, within only 34 min on a 128-core workstation. Our results further identify 1269 redundant genomes, with identical nucleotide content, in the RefSeq bacterial genomes database.

Molecular and morphological approaches redefine the limits among polymorphic species in the Neotropical longhorn beetle genus, Myzomorphus Sallé (Coleoptera: Cerambycidae: Prioninae).

Myzomorphus Sallé is a charismatic genus of prionine longhorn beetles (Cerambycidae) composed of nine species. Myzomorphus species are found from Costa Rica to southern Brazil, but only two species have wide distributions across this range: M. scutellatus Sallé from Costa Rica to northern Brazil, and M. quadripunctatus (Gray) from Colombia to southern Brazil. These species are highly polymorphic and their limits are difficult to determine due to their strong morphological similarities--males are only distinguishable by subtle size variations and females by color patterns. Here, we used mitochondrial DNA (cox1 and 12S) to reconstruct the first phylogeny of Myzomorphus and, in combination with morphological data, assess the taxonomic limits between M. scutellatus and M. quadripunctatus. Our phylogenetic results confirm the monophyly of Myzomorphus and reveal a close relationship among M. birai, M. quadripunctatus and M. scutellatus. Using pairwise distance estimations, we found that the intraspecific variation of M. quadripunctatus is remarkably high (K2P: 0-11.7%; p-distances: 0-9.7%) and the interspecific distances of M. quadripunctatus in relation to M. birai and M. scutellatus (K2P: 14.8-20.1%; p-distances: 12-15%) are close to the intraspecific distances of M. quadripunctatus. We further analyzed the diagnostic characters of these species and found that their morphological intraspecific variations largely overlap. Altogether, our results demonstrate that the variability of M. birai, M. scutellatus and M. quadripunctatus represent polymorphisms of a single species. We thus argue for the synonymy of M. birai and M. scutellatus under M. quadripunctatus (syn. nov.) and highlight the need for multiple lines of evidence to solve the taxonomic problems in polymorphic species of Cerambycidae.

Analyzing Accurate Egocentric Distance Estimates of University Students in Virtual Environments with a Desktop Display and Gear VR Display

The perception of distances is crucial in both the real world and virtual environments. However, distances can be incorrectly estimated in the latter one, and they can be affected by technological and human factors. We created a virtual environment to take a step toward understanding this phenomenon. We assessed the egocentric distance estimation skills of 239 university students at 10 various distances between 25 cm and 160 cm at 15 cm intervals. A desktop display was used by 157 students, while the Gear VR display was used by 72 students. The effects of the following factors were analyzed: gender, height, dominant arm, previous VR experience, gaming hours per week, whether the participants wore glasses, their field of study, and display device. Logistic regression analysis was performed to assess their influences on the probabilities of accurate distance estimates, while linear regression analysis was conducted to examine their effects on estimation times. The results show that except for the factors of whether the participants wore glasses and their field of study, the probabilities of accurate distance estimates can be affected along with estimation times themselves.

Refraining from interaction can decrease fear of physical closeness during COVID-19

Perception of peripersonal space (PPS) and interpersonal distance (IPD) has been shown to be modified by external factors such as perceived danger, the use of tools, and social factors. Especially in times of social distancing in the context of the COVID-19 pandemic, it is vital to study factors that modify PPS and IPD. The present work addresses the question of whether wearing a face mask as a protection tool and social interaction impact the perception of IPD. We tested estimated IPD in pictures at three distances: 50 cm, 90 cm, and 150 cm in both social interaction (shaking hands) and without interaction and when the two people in the pictures wore a face mask or not. Data from 60 subjects were analyzed in a linear mixed model (on both difference in distance estimation to the depicted distance and in absolute distance estimation) and in a 3 (distance: 50, 90, 150) × 2 (interaction: no interaction, shake hands), × 2 face mask (no mask, mask) rmANOVA on distance estimation difference. All analyses showed that at a distance of 50 and 90 cm, participants generally underestimated the IPD while at an IPD of 150 cm, participants overestimated the distance. This could be grounded in perceived danger and avoidance behavior at closer distances, while the wider distance between persons was not perceived as dangerous. Our findings at an IPD of 90 cm show that social interaction has the largest effect at the border of our PPS, while the face mask did not affect social interaction at either distance. In addition, the ANOVA results indicate that when no social interaction was displayed, participants felt less unsafe when depicted persons wore a face mask at distances of 90 and 150 cm. This shows that participants are on the one hand aware of the given safety measures and internalized them; on the other hand, that refraining from physical social interaction helps to get close to other persons.

Monocular Based Navigation System for Autonomous Ground Robots Using Multiple Deep Learning Models

In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation.Graphical

A queueing model of visual search

Understanding how attentional resources are deployed in visual processing is a fundamental and highly debated topic. As an alternative to theoretical models of visual search that propose sequences of separate serial or parallel stages of processing, we suggest a queueing processing structure that entails a serial transition between parallel processing stages. We develop a continuous-time queueing model for standard visual search tasks to formalize and implement this notion. Specified as a finite-time, single-line, multiserver queueing system, the model accounts for both accuracy and response time (RT) data in visual search on a distributional level. It assumes two stages of processing. Visual stimuli first go through a massively parallel preattentive stage of feature encoding. They wait if necessary and then enter a limited-capacity attentive stage serially where multiple processing channels (“servers”) integrate features of several stimuli in parallel. A core feature of our model is the serial transition from the unlimited-capacity preattentive processing stage to the limited-capacity attentive processing stage. It enables asynchronous attentive processing of multiple stimuli in parallel and is more efficient than a simple chain of two successive, strictly parallel processing stages. The model accounts for response errors by means of two underlying mechanisms, namely, imperfect processing of the servers and, in addition, incomplete search adopted by the observer to maximize search efficiency under an accuracy constraint. For statistical inference, we develop a Monte-Carlo-based parameter estimation procedure, using maximum likelihood (ML) estimation for accuracy-related parameters and minimum distance (MD) estimation for RT-related parameters. We fit the model to two large empirical data sets from two types of visual search tasks. The model captures the accuracy rates almost perfectly and the observed RT distributions quite well, indicating a high explanatory power. The number of independent parallel processing channels that explain both data sets best was five. We also perform a Monte-Carlo model uncertainty analysis and show that the model with the correct number of parallel channels is selected for more than 90% of the simulated samples.

Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.

Multitarget positioning technology, such as FMCW millimeter-wave radar, has broad application prospects in autonomous driving and related mobile scenarios. However, it is difficult for existing correlation algorithms to balance high resolution and low complexity, and it is also difficult to ensure the robustness of the positioning algorithm using an aging antenna. This paper proposes a super-resolution and low-complexity positioning algorithm based on the orthogonal matching pursuit algorithm that can achieve more accurate distance and angle estimation for multiple objects in a low-SNR environment. The algorithm proposed in this paper improves the resolving power by two and one orders of magnitude, respectively, compared to the classical FFT and MUSIC algorithms in the same signal-to-noise environment, and the complexity of the algorithm can be reduced by about 25-30%, with the same resolving power as the OMP algorithm. Based on the positioning algorithm proposed in our paper, we use the PSO algorithm to optimize the arrangement of an aging antenna array so that its angle estimation accuracy is equivalent to that observed when the antenna is intact, improving the positioning algorithm's robustness. This paper also further realizes the use of the proposed algorithm and a single-frame intermediate frequency signal to estimate the position angle information of the object and obtain its motion trajectory and velocity, verifying the proposed algorithm's estimation ability when it comes to these qualities in a moving scene. Furthermore, this paper designs and carries out simulations and experiments. The experimental results verify that the positioning algorithm proposed in this paper can achieve accuracy, robustness, and real-time performance in autonomous driving scenarios.

Socio-Economic Effects of HIV Stigmatization: Experience of Qualitative Research

The phenomenon of stigmatization as well as its mechanisms are being discussed in this article. The author has discovered how the stigmatization of HIV-positive people affects both the economic and social spheres in the society. A sociological study was conducted by the method of in-depth interviews from March to May 2022. With the usage of qualitative analysis, the following stigmatizing attitudes amongst students and professors of Russian universities have been detected based on the typology of Yu. S. Smirnova. The analysis regarding the estimate of the acceptable social distance between the respondents and HIV-positive persons is also provided in this paper. A causal relationship between the stigmatization of HIV persons by the respondents and how knowledgeable they were on the topic of HIV is found.

Optimal Performance Evaluation of Localization of Sensor Nodes in Wireless Sensor Networks

Wireless Sensor Networks are a collection of nodes which contain tiny devices having low power and work with minimal cost. In such a network, the functioning of these nodes plays a very important role to sense data and also forward the sensed data to the target observer. For segregation of nodes in effective locations, there is a need to localize these nodes by combining location information with the sensed data for tracking and monitoring of malicious nodes, goods tracking etc. The various approaches towards localization may be classified as range-free or range-based, both having their own pros and cons. This paper presents a discussion on different localization approaches, followed by simulation results of localization using lognormal shadowing model for distance estimation and trilateration for location computation. The results depict the individual impacts of node density and area of sensed region on the localization error. It is observed that with a fixed node count, the errors in location estimates increase with the increase in node density. It is also depicted that the localization error increases towards the boundary of a network. Since the approach discussed in the paper uses loss in received power for distance computation, there is no cost overhead for additional hardware involved in the implementation. Thus the approach used is cost effective.

Submerged single-photon LiDAR imaging sensor used for real-time 3D scene reconstruction in scattering underwater environments.

We demonstrate a fully submerged underwater LiDAR transceiver system based on single-photon detection technologies. The LiDAR imaging system used a silicon single-photon avalanche diode (SPAD) detector array fabricated in complementary metal-oxide semiconductor (CMOS) technology to measure photon time-of-flight using picosecond resolution time-correlated single-photon counting. The SPAD detector array was directly interfaced to a Graphics Processing Unit (GPU) for real-time image reconstruction capability. Experiments were performed with the transceiver system and target objects immersed in a water tank at a depth of 1.8 meters, with the targets placed at a stand-off distance of approximately 3 meters. The transceiver used a picosecond pulsed laser source with a central wavelength of 532 nm, operating at a repetition rate of 20 MHz and average optical power of up to 52 mW, dependent on scattering conditions. Three-dimensional imaging was demonstrated by implementing a joint surface detection and distance estimation algorithm for real-time processing and visualization, which achieved images of stationary targets with up to 7.5 attenuation lengths between the transceiver and the target. The average processing time per frame was approximately 33 ms, allowing real-time three-dimensional video demonstrations of moving targets at ten frames per second at up to 5.5 attenuation lengths between transceiver and target.

Acquisition and retention of spatial knowledge through virtual reality experiences: Effects of VR setup and locomotion technique

The fidelity level of virtual reality (VR) setups can affect different aspects of virtual experiences, but its effects on knowledge acquisition and retention need clarification. The two studies in this paper focus on spatial knowledge. The first study compared three VR setups, one using a VR headset and two using a tablet, differing in display and interaction fidelity. Since the type of virtual environment (VE), and the locomotion technique employed to explore it, might affect spatial knowledge acquisition, we studied each setup in an indoor and an outdoor VE with two widely used locomotion techniques (teleport and steering). Results showed that setups offering higher display and interaction fidelity can improve acquisition of spatial knowledge in terms of distance estimations (with teleport in the indoor VE, and with steering in the outdoor VE) and object-to-object spatial relations (with steering in the outdoor VE), but not in terms of object locations. This can provide guidance about how to choose the appropriate combination of setup and locomotion technique based on the type of VE and the types of spatial knowledge to acquire. Moreover, sickness and usability results showed that VR headset was more usable than tablet, and suggest using teleport on VR headset and steering on tablet. Therefore, the second study focused on VR headset with teleport and tablet with steering, and extended the assessment of their effects to spatial knowledge retention after two weeks. Results showed that spatial knowledge decreased in both conditions, but the VR headset with teleport led to better acquisition and 2-weeks retention of distance estimations.

Systematic KMTNet Planetary Anomaly Search. VIII. Complete Sample of 2019 Subprime Field Planets

We complete the publication of all microlensing planets (and “possible planets”) identified by the uniform approach of the KMT AnomalyFinder system in the 21 KMT subprime fields during the 2019 observing season, namely, KMT-2019-BLG-0298, KMT-2019-BLG-1216, KMT-2019-BLG-2783, OGLE-2019-BLG-0249, and OGLE-2019-BLG-0679 (planets), as well as OGLE-2019-BLG-0344 and KMT-2019-BLG-0304 (possible planets). The five planets have mean log mass ratio measurements of (−2.6, −3.6, −2.5, −2.2, −2.3), median mass estimates of (1.81, 0.094, 1.16, 7.12, 3.34) M Jup, and median distance estimates of (6.7, 2.7, 5.9, 6.4, 5.6) kpc, respectively. The main scientific interest of these planets is that they complete the AnomalyFinder sample for 2019, which has a total of 25 planets that are likely to enter the statistical sample. We find statistical consistency with the previously published 33 planets from the 2018 AnomalyFinder analysis according to an ensemble of five tests. Of the 58 planets from 2018–2019, 23 were newly discovered by AnomalyFinder. Within statistical precision, half of the planets have caustic crossings, while half do not; an equal number of detected planets result from major- and minor-image light-curve perturbations; and an equal number come from KMT prime fields versus subprime fields.

Unsupervised out-of-distribution detection for safer robotically guided retinal microsurgery

PurposeA fundamental problem in designing safe machine learning systems is identifying when samples presented to a deployed model differ from those observed at training time. Detecting so-called out-of-distribution (OoD) samples is crucial in safety-critical applications such as robotically guided retinal microsurgery, where distances between the instrument and the retina are derived from sequences of 1D images that are acquired by an instrument-integrated optical coherence tomography (iiOCT) probe.MethodsThis work investigates the feasibility of using an OoD detector to identify when images from the iiOCT probe are inappropriate for subsequent machine learning-based distance estimation. We show how a simple OoD detector based on the Mahalanobis distance can successfully reject corrupted samples coming from real-world ex vivo porcine eyes.ResultsOur results demonstrate that the proposed approach can successfully detect OoD samples and help maintain the performance of the downstream task within reasonable levels. MahaAD outperformed a supervised approach trained on the same kind of corruptions and achieved the best performance in detecting OoD cases from a collection of iiOCT samples with real-world corruptions.ConclusionThe results indicate that detecting corrupted iiOCT data through OoD detection is feasible and does not need prior knowledge of possible corruptions. Consequently, MahaAD could aid in ensuring patient safety during robotically guided microsurgery by preventing deployed prediction models from estimating distances that put the patient at risk.

Research on the Estimation Method of Target Motion Parameters Based on Ternary Array

The ternary array passive positioning technique performs the estimation of target distance and bearing by measuring the time delay difference between array elements. The estimates are limited by the geometric model and time delay measurement errors, and the positioning accuracy and reliability are poor. In this paper, we propose an orientation matching-based motion target localization model. By introducing the target motion model, and establish a matching objective function for the time delay difference obtained by calculating the predefined target motion trajectory and the actual received data of the ternary array separately. The matching objective function is optimally solved by using Burg algorithm in the AR spectrum estimation method, and a highly accurate and reliable target parameter estimation can be obtained in a short time. The results of computer simulation experiments verify the effectiveness of the method.

Investigation of average crosslink distance and physicochemical properties of gummy candy during storage: Effect of formulation and storage temperature

Texture is linked to food structure at micro- and macro-levels. The most effective way to manipulate gel texture is to change the number of crosslinks per unit volume. Polymer concentration and type can be changed or the amount of evaporated water can be adjusted to achieve this. Gummy candy is a good food model to investigate texture due to the presence of versatile polymer gelling agents. The estimation of average cross-link distance is a good approach to monitor candy texture. Physicochemical properties are useful indicators showing storage stability. In this study, gummy candies were prepared with different glucose sirup:sucrose ratios (1.1 and 1.5), starch (0% and 1.5%), and gelatin (3% and 6%) concentrations. Texture (hardness), moisture content, water activity (aw), total soluble solids (TSS), and pH were measured. Candies were stored at 10, 20, and 30 °C for 12 weeks and at ambient temperatures of 15–22 °C for 52 weeks. Gelatin concentration, moisture content, storage time, and temperature affected hardness. At 1.5 glucose sirup:sucrose ratio, average cross-link distance decreased after 10 °C storage for 3% gelatin level and it increased after 20 and 30 °C storage for 6% gelatin and 0% starch level; however, it did not change for 15–22 °C storage. Moisture content, aw, TSS, and pH were affected by glucose sirup:sucrose ratio. A Weibullian model was used to express quality changes, and physicochemical indicators did not have an integer kinetic order. Tracking of physicochemical properties and texture with average cross-link distance calculation and modeling is useful for storage stability studies showing the quality of food gel products.

Development of real-time monitoring BLE-LoRa positioning system based on RSSI for non-line-of-sight condition

Indoor positioning has become popular in this decade and is used to locate users or objects in indoor environments. This is because global positioning system (GPS) is not efficient for indoor use due to the multipath fading effect. This research is about development bluetooth low energy (BLE) indoor positioning system with the aid of long range (LoRa) network and guideline on selection of the BLE beacons. Next, positioning systems are developed consisting of BLE beacons, a transceiver of hybrid BLE-LoRa module, a LoRa receiver and Raspberry Pi as real-time monitoring. The received signal strength indicator (RSSI) and BLE Mac address from BLE beacons received via LoRa network are analyzed using the positioning algorithm designed in MATLAB. The positioning algorithm incorporates distance estimation, filter implementation and trilateration technique. The estimated location is analyzed with the root mean square error (RMSE) and cumulative distribution function (CDF). According to the results, implementing the filter reduces the positioning accuracy error, achieving 90% accuracy of positioning error less than 1.20 meters for the whole testbed. Finally, the algorithm is embedded into Raspberry Pi to view the location via desktop.

Signal Loss Detection and Correction for Improved Distance Estimation in Indoor Environments Using Path Loss Models

Signal Loss Detection and Correction for Improved Distance Estimation in Indoor Environments Using Path Loss Models

Visual perception of travel distance for self-motion through crowds.

Humans can use visual motion to estimate the distance they have traveled. In static environments, optic flow generated by self-motion provides a pattern of expanding motion that is used for the estimation of travel distance. When the environment is populated by other people, their biological motion destroys the one-to-on correspondence between optic flow and travel distance. We investigated how observers estimate travel distance in a crowded environment. In three conditions, we simulated self-motion through a crowd of standing, approaching, or leading point-light walkers. For a standing crowd, optic flow is a veridical signal for distance perception. For an approaching crowd, the visual motion is the sum of the self-motion-induced optic flow and the optic flow produced by the approaching walkers. If only optic flow were to be used, travel distance estimates would be too high because of the approaching direction of the crowd toward the observer. If, on the other hand, cues from biological motion could be used to estimate the speed of the crowd, then the excessive optic from the approaching crowd flow might be compensated. In the leading crowd condition, in which walkers of the crowd keep their distance from the observer as they walk along with the observer, no optic flow is produced. In this condition, travel distance estimation would have to rely solely on biological motion information. We found that distance estimation was quite similar across these three conditions. This suggests that biological motion information can be used (a) to compensate for excessive optic flow in the approaching crowd condition and (b) to generate distance information in the leading crowd condition.

Low-frequency distributed acoustic sensing shape factors for fracture front detection

Accurate knowledge of fracture extents generated in multistage unconventional completions remains elusive. Crosswell low-frequency distributed acoustic sensing (LF-DAS) measurements can determine the time and location of a frac hit. Knowing where and when a frac hit occurs constrains the fracture extent but does not estimate it quantitatively. A recent study on crosswell LF-DAS demonstrated a simple method to rapidly determine the instantaneous fracture propagation rate when a frac hit occurs. This method, the zero strain-rate location method (ZSRLM), is based on laboratory experiments and numerical modeling assuming a radial fracture geometry. The method estimates a fracture propagation velocity that is used to extrapolate the final fracture extent. The propagation rate is calculated based on dynamic estimates of the nearest distance from the fiber to the front of a propagating fracture. The ZSRLM is adapted to estimate the distance to the fracture front based on rectangular fracture geometries. A 3D displacement discontinuity method program generates crosswell LF-DAS strain-rate waterfall plots considering a single, rectangular fracture of constant height. Over 150 different simulations were conducted varying formation mechanical properties, fracture height, and the vertical and horizontal offset between the treatment and monitor well. For each simulated case, the ZSRLM is used to estimate the distance to the fracture front based on the simulated waterfall plots. The difference between the estimated and actual distance to the front is corrected by a shape factor. The relationship among the shape factor, fracture height ratio, and vertical offset ratio is determined. Using a shape factor improves the performance of the ZSRLM by up to a factor of two for rectangular fractures. The updated ZSRLM is applied to extrapolate final fracture extents in two field cases: a single cluster stage in the Montney Formation and a multicluster stage of an Austin Chalk completion.

Improved Spatial Knowledge Acquisition through Sensory Augmentation.

Sensory augmentation provides novel opportunities to broaden our knowledge of human perception through external sensors that record and transmit information beyond natural perception. To assess whether such augmented senses affect the acquisition of spatial knowledge during navigation, we trained a group of 27 participants for six weeks with an augmented sense for cardinal directions called the feelSpace belt. Then, we recruited a control group that did not receive the augmented sense and the corresponding training. All 53 participants first explored the Westbrook virtual reality environment for two and a half hours spread over five sessions before assessing their spatial knowledge in four immersive virtual reality tasks measuring cardinal, route, and survey knowledge. We found that the belt group acquired significantly more accurate cardinal and survey knowledge, which was measured in pointing accuracy, distance, and rotation estimates. Interestingly, the augmented sense also positively affected route knowledge, although to a lesser degree. Finally, the belt group reported a significant increase in the use of spatial strategies after training, while the groups' ratings were comparable at baseline. The results suggest that six weeks of training with the feelSpace belt led to improved survey and route knowledge acquisition. Moreover, the findings of our study could inform the development of assistive technologies for individuals with visual or navigational impairments, which may lead to enhanced navigation skills and quality of life.