Incorrect Guidance Research Articles

Bias Mitigation and Representation Optimization for Noise-Robust Cross-modal Retrieval

The remarkable progress in cross-modal retrieval relies on accurately-annotated multimedia datasets. In practice, most existing datasets used for training cross-modal retrieval models are automatically collected from the Internet to reduce data collection costs. However, it inevitably contains mismatched pairs, i.e. , noisy correspondences, thus degrading the model performance. Recent advances utilize the predicted similarity distribution of individual samples for noise validation and correction, which easily faces two challenging dilemmas: 1) confirmation bias and 2) unstable performance with increasing noise. In light of the above, we propose a generalized B ias M itigation and R epresentation O ptimization framework (BMRO). Specifically, we propose a Bias Estimator (BE) to estimate the unbiased confidence factor of a sample by contrasting it against its nearest neighbors. Unbiased confidence factor can precisely adjust sample contribution and enhance accurate sample division. This facilitates the Adaptive Representation Optimizer (ARO) in providing tailored optimization strategies for clean and noisy samples. ARO performs contrastive learning between clean samples and generated hard samples, thus promoting the generalizability and robustness of the representation. Besides, it utilizes complementary learning to reduce incorrect guidance from noisy samples. Extensive experiments on five visual-text benchmarks verify that our BMRO can significantly improve the matching accuracy and performance stability against noisy correspondences.

An Assessment of Different Decision Support Software from the Perspective of Potential Drug-Drug Interactions in Patients with Chronic Kidney Diseases.

Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug-drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD and to ascertain the concordance among different Clinical Decision Support Software (CDSS). A cross-sectional study was conducted in a nephrology outpatient clinic at a university hospital. The pDDIs were identified and evaluated using Lexicomp® and Medscape®. The patients' characteristics, comorbidities, and medicines used were recorded. The concordance of different CDSS were evaluated using the Kendall W coefficient. An evaluation of 1121 prescribed medications for 137 patients was carried out. The mean age of the patients was 64.80 ± 14.59 years, and 41.60% of them were male. The average year with CKD was 6.48 ± 5.66. The mean number of comorbidities was 2.28 ± 1.14. The most common comorbidities were hypertension, diabetes, and coronary artery disease. According to Medscape, 679 pDDIs were identified; 1 of them was contraindicated (0.14%), 28 (4.12%) were serious-use alternative, and 650 (9.72%) were interventions that required closely monitoring. According to Lexicomp, there were 604 drug-drug interactions. Of these interactions, 9 (1.49%) were in the X category, 60 (9.93%) were in the D category, and 535 (88.57%) were in the C category. Two different CDSS systems exhibited statistically significant concordance with poor agreement (W = 0.073, p < 0.001). Different CDSS systems are commonly used in clinical practice to detect pDDIs. However, various factors such as the operating principles of these programs and patient characteristics can lead to incorrect guidance in clinical decision making. Therefore, instead of solely relying on programs with lower reliability and consistency scores, multidisciplinary healthcare teams, including clinical pharmacists, should take an active role in identifying and preventing pDDIs.

Depth Map Super-Resolution Reconstruction Based on Multi-Channel Progressive Attention Fusion Network

Depth maps captured by traditional consumer-grade depth cameras are often noisy and low-resolution. Especially when upsampling low-resolution depth maps with large upsampling factors, the resulting depth maps tend to suffer from vague edges. To address these issues, we propose a multi-channel progressive attention fusion network that utilizes a pyramid structure to progressively recover high-resolution depth maps. The inputs of the network are the low-resolution depth image and its corresponding color image. The color image is used as prior information in this network to fill in the missing high-frequency information of the depth image. Then, an attention-based multi-branch feature fusion module is employed to mitigate the texture replication issue caused by incorrect guidance from the color image and inconsistencies between the color image and the depth map. This module restores the HR depth map by effectively integrating the information from both inputs. Extensive experimental results demonstrate that our proposed method outperforms existing methods.

Improving Scene Text Image Super-resolution via Dual Prior Modulation Network

Scene text image super-resolution (STISR) aims to simultaneously increase the resolution and legibility of the text images, and the resulting images will significantly affect the performance of downstream tasks. Although numerous progress has been made, existing approaches raise two crucial issues: (1) They neglect the global structure of the text, which bounds the semantic determinism of the scene text. (2) The priors, e.g., text prior or stroke prior, employed in existing works, are extracted from pre-trained text recognizers. That said, such priors suffer from the domain gap including low resolution and blurriness caused by poor imaging conditions, leading to incorrect guidance. Our work addresses these gaps and proposes a plug-and-play module dubbed Dual Prior Modulation Network (DPMN), which leverages dual image-level priors to bring performance gain over existing approaches. Specifically, two types of prior-guided refinement modules, each using the text mask or graphic recognition result of the low-quality SR image from the preceding layer, are designed to improve the structural clarity and semantic accuracy of the text, respectively. The following attention mechanism hence modulates two quality-enhanced images to attain a superior SR result. Extensive experiments validate that our method improves the image quality and boosts the performance of downstream tasks over five typical approaches on the benchmark. Substantial visualizations and ablation studies demonstrate the advantages of the proposed DPMN. Code is available at: https://github.com/jdfxzzy/DPMN.

Intelligent scanning for optimal rock discontinuity sets considering multiple parameters based on manifold learning combined with UAV photogrammetry

The Sichuan-Tibet railway, which spans many alpine canyon regions, is being built in southwestern China. Investigating the characteristics of rock discontinuity sets is the basis for identifying dangerous rock masses above the tunnel portals. The traditional methods of identifying discontinuity sets usually consider orientations and ignore other parameters, which results in incorrect guidance for rock engineering. To this end, the affinity propagation (AP) algorithm based on modified isometric mapping (Isomap) is proposed for partitioning discontinuity sets based on orientation, trace length, and aperture. The new unsupervised algorithm (ISOAP) uses manifold learning to complete the transformation process for orientations from spherical vectors to scalars and avoids selecting the initial clustering center to achieve global optimization. The Silhouette Index is used to intelligently scan the optimal clustering results. The proposed algorithm is tested on a complex artificial data set and on Shanley and Mahtab's data set. Since accurately obtaining discontinuity information is impossible by traditional means (i.e., using geological compasses and measurement tapes) due to the existence of a mass of high and steep slopes, the ISOAP algorithm is combined with semiautomatic technology based on unmanned aerial vehicle (UAV) photogrammetry and applied to a rock slope located along the railway. The introduction of manifold learning is beneficial for quickly applying abundant unmodified clustering algorithms to rock engineering and searching the optimal algorithm suitable for analyzing the structural characteristics of a specific fractured rock mass. The proposed method can simplify rock engineering analyses and provide more reasonable results.

Tackling background ambiguities in multi-class few-shot point cloud semantic segmentation

Few-shot point cloud semantic segmentation learns to segment novel classes with scarce labeled samples. Within an episode, a novel target class is defined by a few support samples with corresponding binary masks, where only the points of this class are labeled as foreground and others are regarded as background. In the tasks involving multiple target classes, since the meanings of background are diverse for different target classes, background ambiguities appear: Some points labeled as background in one support sample may be of other target classes. It will result in incorrect guidance and damage model’s segmentation performance. However, previous methods in the literature do not consider this problem. In this paper, we propose a simple yet effective approach to tackle background ambiguities, which adopts the entropy of predictions on query samples to the training objective function as an additional regularization. Besides, we design a feature transformation operation to reduce the feature differences between support and query samples. With our proposed approach, fine-tuning, a weak baseline method for few-shot segmentation, gains significant performance improvement (e.g., 7.48% and 7.04% in 2-way-1-shot and 3-way-1-shot tasks of S3DIS, respectively) and outperforms current state-of-the-art methods in all the task settings of S3DIS and ScanNet benchmark datasets.

Feedback and Direction Sources Influence Navigation Decision Making on Experienced Routes.

When navigating in a new environment, it is typical for people to resort to external guidance such as Global Positioning System (GPS), or people. However, in the real world, even though navigators have learned the route, they may still prefer to travel with external guidance. We explored how the availability of feedback and the source of external guidance affect navigation decision-making on experienced routes in the presence of external guidance. In three experiments, participants navigated a simulated route three times and then verbally confirmed that they had learned it. They then traveled the same route again, accompanied with no, correct, or incorrect direction guidance, which latter two were provided by a GPS (Experiment 1), a stranger (Experiment 2), or a friend (Experiment 3). Half of the participants received immediate feedback on their navigation decisions, while the other half without feedback did not know if they had selected the correct directions. Generally, without feedback, participants relied on external guidance, regardless of the direction sources. Results also showed that participants trusted the GPS the most, but performed best with their friends as a direction source. With feedback, participants did not show differences in performance between the correct and incorrect guidance conditions, indicating that feedback plays a critical role in evaluating the reliability of external guidance. Our findings suggest that incorrect guidance without any feedback might disturb navigation decision-making, which was further moderated by the perceived credibility of direction sources. We discuss these results within the context of navigation decision-making theory and consider implications for wayfinding behaviors as a social activity.

SSDs Revisited: Part I-A Framework for Sample Size Guidance on Species Sensitivity Distribution Analysis.

We propose a framework on sample size for species sensitivity distribution (SSD) analyses, with perspectives on Bayesian, frequentist, and even nonparametric approaches to estimation. The intent of a statistical sample size analysis is to ensure that the implementation of a statistical model will satisfy a minimum performance standard when relevant conditions are met. It requires that a statistical model be fully specified and that the means of measuring its performance as a function of sample size be detailed. Defining the model conditions under which sample size is calculated is often the most difficult, and important, aspect of sample size analysis because if the model is not representative, then the sample size analysis will provide incorrect guidance. Definitive guidance on sample size requires general agreement on representative models and their performance from stakeholders in important domains such as chemical safety assessments involving government regulators and industry; the present study provides an initial framework that could be used to this end in the future. In addition, our analysis provides immediate value for understanding how well current SSD analyses perform under a few basic models, sample sizes, and quantitative performance criteria. The results confirm that many analyses are adequately sized to estimate hazardous concentration percentile values (typically the 5th percentile for chemical hazard assessments). However, on the low end of sizes seen in common practice, hazardous concentration estimates can be more than 1 order of magnitude greater than the model-defined value. Environ Toxicol Chem 2019;38:1514-1525. © 2019 SETAC.

Quality concerns over managers' quarterly earnings guidance

This paper investigates how often and to what extent quarterly earnings guidance is of poor quality, the causes of poor quality guidance, and the ultimate effect of such guidance on investors' earnings expectations. I operationalize poor quality guidance as that guidance which is directionally incorrect relative to the pre-guidance analyst consensus. Results show that 12% of the sample meets this definition. In terms of what causes directionally incorrect guidance, expectation management and forecast difficulty play an equally important role in determining directionally incorrect guidance. Both analysts and market participants are adversely impacted by directionally incorrect guidance, but substantially less so when the likelihood of directionally incorrect guidance increases. Finally, market participants appear to be more capable of using publicly observable cues to access the likelihood of directionally incorrect guidance increases.

Improved hierarchical conditional random field model for object segmentation

Although the hierarchical conditional random field (HCRF) model has been successfully applied to multi-class object segmentation, there is still room for improvement. Firstly, the pairwise potential in the HCRF model has the tendency to over-smooth boundaries of regions that are similar to their neighbors. Secondly, the higher-order potential associated with multiple unsupervised segments is prone to producing incorrect guidance to inference in the under-segmentation situation. Finally, the co-occurrence potential as a measure of inter-object relationships cannot completely suppress some uncommon combinations of object classes due to joint optimization of multi-potential cost function. To alleviate these problems, we propose an improved HCRF model that efficiently combines information from global, middle and local scales for object segmentation in this paper. At the global scale, scene categorization technique is adopted to recognize the scene category of an image. The scene consistency then enforces object segmentation to align with feasible labels in specific scenes at the local and middle scales. Furthermore, an improved pairwise potential and a segment-reliable consistency potential are developed at the local and middle scales, respectively. These potentials rectify the over-smoothness issues by propagating the believed labeling from the unary potential and perform coherent inference by ensuring reliable segment consistency. Experimental results on the MSRC-21 dataset demonstrate that the improved HCRF model achieves better subjective results, as well as state-of-the-art objective results in terms of both global accuracy of 87.98 % and average accuracy of 81.43 %.

Modeling and Simulation of the Active Jammer Effect in the Crossed Array Detectors Infrared Seeker

IR active jammers or the directed infrared counter measures(DIRCM)generate IR signals which attack the guidance signals passed to the sensors in IR-guided weapons. They provide IR signals with appropriate frequency, phase and intensity which will be superposed on those produced by the IR energy of the target passing through the reticle. When both the jamming signal and the modulated target energy signals are received by the missile‟s IR sensor, they cause the tracker to produce incorrect guidance commands and push the target out of the field of view (FOV). In this paper, the seeker signals of a crossed four slits or crossed array detectors (CAT) infrared seeker will be simulated. Then the active jammer signal is modeled and simulated. After that, the effect of all the active jammer parameters will be discussed and simulated. Finally, the conditions or the ranges of these parameters in which the active jammer can affect the seeking process will be stated.

A new clustering approach for partitioning directional data

The Sichuan-Tibet railway, which spans many alpine canyon regions, is being built in southwestern China. Investigating the characteristics of rock discontinuity sets is the basis for identifying dangerous rock masses above the tunnel portals. The traditional methods of identifying discontinuity sets usually consider orientations and ignore other parameters, which results in incorrect guidance for rock engineering. To this end, the affinity propagation (AP) algorithm based on modified isometric mapping (Isomap) is proposed for partitioning discontinuity sets based on orientation, trace length, and aperture. The new unsupervised algorithm (ISOAP) uses manifold learning to complete the transformation process for orientations from spherical vectors to scalars and avoids selecting the initial clustering center to achieve global optimization. The Silhouette Index is used to intelligently scan the optimal clustering results. The proposed algorithm is tested on a complex artificial data set and on Shanley and Mahtab's data set. Since accurately obtaining discontinuity information is impossible by traditional means (i.e., using geological compasses and measurement tapes) due to the existence of a mass of high and steep slopes, the ISOAP algorithm is combined with semiautomatic technology based on unmanned aerial vehicle (UAV) photogrammetry and applied to a rock slope located along the railway. The introduction of manifold learning is beneficial for quickly applying abundant unmodified clustering algorithms to rock engineering and searching the optimal algorithm suitable for analyzing the structural characteristics of a specific fractured rock mass. The proposed method can simplify rock engineering analyses and provide more reasonable results.

General Kinetic Rules for Rapid Thermal Processing

AbstractUp to now, kinetic effects in rapid thermal processing (RTP) have been assessed qualitatively and rather vaguely through the concept of thermal budget. We discuss the shortcomings associated with thermal budget and present an alternate conceptual framework that explicitly treats selectivity between desired and undesired physical phenomena. This framework is quite simple and is intended for rapidly assessing the qualitative effects of various heating programs. From a purely kinetic perspective, selecting the best program involves only comparing the activation energies for the desired and undesired rate phenomena. We cite examples where application of the thermal budget approach gives incorrect guidance for minimizing the undesired process. Such deficiencies are rectified by the framework presented here.

The effect of incorrect guidance upon human maze learning.

The effect of incorrect guidance upon human maze learning.