Добірка наукової літератури з теми "Ground truth prior"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Ground truth prior".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Ground truth prior"
1
Chen, Jierun, Song Wen, and S. H. Gary Chan. "Joint Demosaicking and Denoising in the Wild: The Case of Training Under Ground Truth Uncertainty." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (May 18, 2021): 1018–26. http://dx.doi.org/10.1609/aaai.v35i2.16186.
Повний текст джерелаАнотація:
Image demosaicking and denoising are the two key fundamental steps in digital camera pipelines, aiming to reconstruct clean color images from noisy luminance readings. In this paper, we propose and study Wild-JDD, a novel learning framework for joint demosaicking and denoising in the wild. In contrast to previous works which generally assume the ground truth of training data is a perfect reflection of the reality, we consider here the more common imperfect case of ground truth uncertainty in the wild. We first illustrate its manifestation as various kinds of artifacts including zipper effect, color moire and residual noise. Then we formulate a two-stage data degradation process to capture such ground truth uncertainty, where a conjugate prior distribution is imposed upon a base distribution. After that, we derive an evidence lower bound (ELBO) loss to train a neural network that approximates the parameters of the conjugate prior distribution conditioned on the degraded input. Finally, to further enhance the performance for out-of-distribution input, we design a simple but effective fine-tuning strategy by taking the input as a weakly informative prior. Taking into account ground truth uncertainty, Wild-JDD enjoys good interpretability during optimization. Extensive experiments validate that it outperforms state-of-the-art schemes on joint demosaicking and denoising tasks on both synthetic and realistic raw datasets.
2
Prasanna, Shivika, Naveen Premnath, Suveen Angraal, Ramy Sedhom, Rohan Khera, Helen Parsons, Syed Hussaini, et al. "Sentiment analysis of tweets on prior authorization." Journal of Clinical Oncology 39, no. 28_suppl (October 1, 2021): 322. http://dx.doi.org/10.1200/jco.2020.39.28_suppl.322.
Повний текст джерелаАнотація:
322 Background: Natural language processing (NLP) algorithms can be leveraged to better understand prevailing themes in healthcare conversations. Sentiment analysis, an NLP technique to analyze and interpret sentiments from text, has been validated on Twitter in tracking natural disasters and disease outbreaks. To establish its role in healthcare discourse, we sought to explore the feasibility and accuracy of sentiment analysis on Twitter posts (‘’tweets’’) related to prior authorizations (PAs), a common occurrence in oncology built to curb payer-concerns about costs of cancer care, but which can obstruct timely and appropriate care and increase administrative burden and clinician frustration. Methods: We identified tweets related to PAs between 03/09/2021-04/29/2021 using pre-specified keywords [e.g., #priorauth etc.] and used Twarc, a command-line tool and Python library for archiving Twitter JavaScript Object Notation data. We performed sentiment analysis using two NLP models: (1) TextBlob (trained on movie reviews); and (2) VADER (trained on social media). These models provide results as polarity, a score between 0-1, and a sentiment as ‘’positive’’ (>0), ‘’neutral’’ (exactly 0), or ‘’negative’’ (<0). We (AG, NP) manually reviewed all tweets to give the ground truth (human interpretation of reality) including a notation for sarcasm since models are not trained to detect sarcasm. We calculated the precision (positive predictive value), recall (sensitivity), and the F1-Score (measure of accuracy, range 0-1, 0=failure, 1=perfect) for the models vs. the ground truth. Results: After preprocessing, 964 tweets (mean 137/ week) met our inclusion criteria for sentiment analysis. The two existing NLP models labeled 42.4%- 43.3% tweets as positive, as compared to the ground truth (5.6% tweets positive). F-1 scores of models across labels ranged from 0.18-0.54. We noted sarcasm in 2.8% of tweets. Detailed results in Table. Conclusions: We demonstrate the feasibility of performing sentiment analysis on a topic of high interest within clinical oncology and the deficiency of existing NLP models to capture sentiment within oncologic Twitter discourse. Ongoing iterations of this work further train these models through better identification of the tweeter (patient vs. health care worker) and other analytics from shared content.[Table: see text]
3
Witkowski, Jens, and David Parkes. "A Robust Bayesian Truth Serum for Small Populations." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 1492–98. http://dx.doi.org/10.1609/aaai.v26i1.8261.
Повний текст джерелаАнотація:
Peer prediction mechanisms allow the truthful elicitation of private signals (e.g., experiences, or opinions) in regard to a true world state when this ground truth is unobservable. The original peer prediction method is incentive compatible for any number of agents n >= 2, but relies on a common prior, shared by all agents and the mechanism. The Bayesian Truth Serum (BTS) relaxes this assumption. While BTS still assumes that agents share a common prior, this prior need not be known to the mechanism. However, BTS is only incentive compatible for a large enough number of agents, and the particular number of agents required is uncertain because it depends on this private prior. In this paper, we present a robust BTS for the elicitation of binary information which is incentive compatible for every n >= 3, taking advantage of a particularity of the quadratic scoring rule. The robust BTS is the first peer prediction mechanism to provide strict incentive compatibility for every n >= 3 without relying on knowledge of the common prior. Moreover, and in contrast to the original BTS, our mechanism is numerically robust and ex post individually rational.
4
KUMAR, P. V. HAREESH, P. MADHUSOODANAN, M. P. AJAI KUMAR, and A. RAGHUNADHA RAO. "Characteristics of Arabian Sea mini warm pool during May 2003." MAUSAM 56, no. 1 (January 19, 2022): 169–74. http://dx.doi.org/10.54302/mausam.v56i1.891.
Повний текст джерелаАнотація:
Oceanographic surveys were carried out onboard INS Sagardhwani as a part of ARMEX in the deep and coastal regions of the eastern Arabian Sea during May 2003 to study the mini warm pool characteristics. The observational period was characterized by typical pre-monsoon conditions, as indicated by weak winds and clear skies. TMI SST data showed very good agreement with the ground truth observations (root mean square departure of ~0.2oC). Both the satellite imagery and ground truth showed surface temperature (SST) in excess of 31° C in the eastern Arabian Sea. This mini warm pool attained its maximum dimension ~8 days prior to the onset of summer monsoon over Kerala and the dissipated stated prior to the onset date. This information can be used as an index for the prediction of summer monsoon onset. Alternate bands of cyclonic and anti-cyclonic circulation pattern were evident both in the ground truth and satellite imagery. In the regions of SST more than 31° C, surface salinity was found to be less than 34.75 PSU and its depth extent was limited to thin surface layer resulting highly stratified layer. The low saline water present in this region was due to the northward / northwestward advection of low saline waters of equatorial Indian Ocean origin and the re-circulation of Bay of Bengal water mass trapped in the central Arabian Sea during winter by the eddy type of circulation.
5
Tribble, Curt, Nick Teman, and Walter Merrill. "The Calm Before The Storm: The 4th Year of Medical School prior to a Surgery Residency." Heart Surgery Forum 24, no. 3 (May 24, 2021): E451—E455. http://dx.doi.org/10.1532/hsf.3919.
Повний текст джерелаАнотація:
Many medical students figure that their fourth year of medical school should be a time primarily focused on residency interviews and resting up for residency. While the interview part is necessary, the concept that one should be resting during that year is a myth. In fact, nothing could be further from the truth. Your top priority should be to prepare yourself to hit the ground running as a great surgical intern.
6
Tang, Tim Y., Daniele De Martini, Shangzhe Wu, and Paul Newman. "Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization." International Journal of Robotics Research 40, no. 12-14 (September 28, 2021): 1488–509. http://dx.doi.org/10.1177/02783649211045736.
Повний текст джерелаАнотація:
Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This work makes a different assumption. It assumes that an overhead image of the workspace is available and utilizes that as a map for use for range-based sensor localization by a vehicle. Here, range-based sensors are radars and lidars. Our motivation is simple, off-the-shelf, publicly available overhead imagery such as Google satellite images can be a ubiquitous, cheap, and powerful tool for vehicle localization when a usable prior sensor map is unavailable, inconvenient, or expensive. The challenge to be addressed is that overhead images are clearly not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localization method that not only handles the modality difference, but is also cheap to train, learning in a self-supervised fashion without requiring metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations in cross-modality localization, achieving localization errors on-par with a prior supervised approach while requiring no pixel-wise aligned ground truth for supervision at training. We pay particular attention to the use of millimeter-wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting conditions, makes for a compelling and valuable use case.
7
Davani, Aida Mostafazadeh, Mark Díaz, and Vinodkumar Prabhakaran. "Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations." Transactions of the Association for Computational Linguistics 10 (2022): 92–110. http://dx.doi.org/10.1162/tacl_a_00449.
Повний текст джерелаАнотація:
Abstract Majority voting and averaging are common approaches used to resolve annotator disagreements and derive single ground truth labels from multiple annotations. However, annotators may systematically disagree with one another, often reflecting their individual biases and values, especially in the case of subjective tasks such as detecting affect, aggression, and hate speech. Annotator disagreements may capture important nuances in such tasks that are often ignored while aggregating annotations to a single ground truth. In order to address this, we investigate the efficacy of multi-annotator models. In particular, our multi-task based approach treats predicting each annotators’ judgements as separate subtasks, while sharing a common learned representation of the task. We show that this approach yields same or better performance than aggregating labels in the data prior to training across seven different binary classification tasks. Our approach also provides a way to estimate uncertainty in predictions, which we demonstrate better correlate with annotation disagreements than traditional methods. Being able to model uncertainty is especially useful in deployment scenarios where knowing when not to make a prediction is important.
8
Brooks, Douglas A., and Ayanna M. Howard. "Quantifying Upper-Arm Rehabilitation Metrics for Children through Interaction with a Humanoid Robot." Applied Bionics and Biomechanics 9, no. 2 (2012): 157–72. http://dx.doi.org/10.1155/2012/978498.
Повний текст джерелаАнотація:
The objective of this research effort is to integrate therapy instruction with child-robot play interaction in order to better assess upper-arm rehabilitation. Using computer vision techniques such as Motion History Imaging (MHI), edge detection, and Random Sample Consensus (RANSAC), movements can be quantified through robot observation. In addition, incorporating prior knowledge regarding exercise data, physical therapeutic metrics, and novel approaches, a mapping to therapist instructions can be created allowing robotic feedback and intelligent interaction. The results are compared with ground truth data retrieved via the Trimble 5606 Robotic Total Station and visual experts for the purpose of assessing the efficiency of this approach. We performed a series of upper-arm exercises with two male subjects, which were captured via a simple webcam. The specific exercises involved adduction and abduction and lateral and medial movements. The analysis shows that our algorithmic results compare closely to the results obtain from the ground truth data, with an average algorithmic error is less than 9% for the range of motion and less than 8% for the peak angular velocity of each subject.
9
Zagheni, Emilio, and Ingmar Weber. "Demographic research with non-representative internet data." International Journal of Manpower 36, no. 1 (April 7, 2015): 13–25. http://dx.doi.org/10.1108/ijm-12-2014-0261.
Повний текст джерелаАнотація:
Purpose – Internet data hold many promises for demographic research, but come with severe drawbacks due to several types of bias. The purpose of this paper is to review the literature that uses internet data for demographic studies and presents a general framework for addressing the problem of selection bias in non-representative samples. Design/methodology/approach – The authors propose two main approaches to reduce bias. When ground truth data are available, the authors suggest a method that relies on calibration of the online data against reliable official statistics. When no ground truth data are available, the authors propose a difference in differences approach to evaluate relative trends. Findings – The authors offer a generalization of existing techniques. Although there is not a definite answer to the question of whether statistical inference can be made from non-representative samples, the authors show that, when certain assumptions are met, the authors can extract signal from noisy and biased data. Research limitations/implications – The methods are sensitive to a number of assumptions. These include some regularities in the way the bias changes across different locations, different demographic groups and between time steps. The assumptions that we discuss might not always hold. In particular, the scenario where bias varies in an unpredictable manner and, at the same time, there is no “ground truth” available to continuously calibrate the model, remains challenging and beyond the scope of this paper. Originality/value – The paper combines a critical review of existing substantive and methodological literature with a generalization of prior techniques. It intends to provide a fresh perspective on the issue and to stimulate the methodological discussion among social scientists.
10
Diaz, Antonio L., Andrew E. Ortega, Henry Tingle, Andres Pulido, Orlando Cordero, Marisa Nelson, Nicholas E. Cocoves, et al. "The Bathy-Drone: An Autonomous Unmanned Drone-Tethered Sonar System." Drones 6, no. 8 (August 22, 2022): 220. http://dx.doi.org/10.3390/drones6080220.
Повний текст джерелаАнотація:
A unique drone-based system for underwater mapping (bathymetry) was developed at the University of Florida. The system, called the “Bathy-drone”, comprises a drone that drags, via a tether, a small vessel on the water surface in a raster pattern. The vessel is equipped with a recreational commercial off-the-shelf (COTS) sonar unit that has down-scan, side-scan, and chirp capabilities and logs GPS-referenced sonar data onboard or transmitted in real time with a telemetry link. Data can then be retrieved post mission and plotted in various ways. The system provides both isobaths and contours of bottom hardness. Extensive testing of the system was conducted on a 5 acre pond located at the University of Florida Plant Science and Education Unit in Citra, FL. Prior to performing scans of the pond, ground-truth data were acquired with an RTK GNSS unit on a pole to precisely measure the location of the bottom at over 300 locations. An assessment of the accuracy and resolution of the system was performed by comparison to the ground-truth data. The pond ground truth had an average depth of 2.30 m while the Bathy-drone measured an average 21.6 cm deeper than the ground truth, repeatable to within 2.6 cm. The results justify integration of RTK and IMU corrections. During testing, it was found that there are numerous advantages of the Bathy-drone system compared to conventional methods including ease of implementation and the ability to initiate surveys from the land by flying the system to the water or placing the platform in the water. The system is also inexpensive, lightweight, and low-volume, thus making transport convenient. The Bathy-drone can collect data at speeds of 0–24 km/h (0–15 mph) and, thus, can be used in waters with swift currents. Additionally, there are no propellers or control surfaces underwater; hence, the vessel does not tend to snag on floating vegetation and can be dragged over sandbars. An area of more than 10 acres was surveyed using the Bathy-drone in one battery charge and in less than 25 min.
Дисертації з теми "Ground truth prior"
1
Samarasinghe, Devanarayanage Pradeepa. "Efficient methodologies for real-time image restoration." Phd thesis, 2011. http://hdl.handle.net/1885/9859.
Повний текст джерелаАнотація:
In this thesis we investigate the problem of image restoration. The main focus of
our research is to come up with novel algorithms and enhance existing techniques in order to deliver efficient and effective methodologies, applicable in real-time image restoration scenarios. Our research starts with a literature review, which identifies the gaps in existing techniques and helps us to come up with a novel classification on image restoration, which integrates and discusses more recent developments in the area of image restoration. With this novel classification, we identified three major areas which need our attention.
The first developments relate to non-blind image restoration. The two mostly used techniques, namely deterministic linear algorithms and stochastic nonlinear algorithms are compared and contrasted. Under deterministic linear algorithms, we develop a class of more effective novel quadratic linear regularization models, which outperform the existing linear regularization models. In addition, by looking in a new perspective, we evaluate and compare the performance of deterministic and stochastic restoration algorithms and explore the validity of the performance claims made so far on those algorithms. Further, we critically challenge the ne- cessity of some complex mechanisms in Maximum A Posteriori (MAP) technique under stochastic image deconvolution algorithms.
The next developments are focussed in blind image restoration, which is claimed to be more challenging. Constant Modulus Algorithm (CMA) is one of the most popular, computationally simple, tested and best performing blind equalization algorithms in the signal processing domain. In our research, we extend the use of CMA in image restoration and develop a broad class of blind image deconvolution algorithms, in particular algorithms for blurring kernels with a separable property. These algorithms show significantly faster convergence than conventional algorithms. Although CMA method has a proven record in signal processing applications related to data communications systems, no research has been carried out to the investigation of the applicability of CMA for image restoration in practice. In filling this gap and taking into account the differences of signal processing in im- age processing and data communications contexts, we extend our research on the applicability of CMA deconvolution under the assumptions on the ground truth image properties. Through analyzing the main assumptions of ground truth image properties being zero-mean, independent and uniformly distributed, which char- acterize the convergence of CMA deconvolution, we develop a novel technique to overcome the effects of image source correlation based on segmentation and higher order moments of the source. Multichannel image restoration techniques recently gained much attention over the single channel image restoration due to the benefits of diversity and redundancy of the information between the channels. Exploiting these benefits in real time applications is often restricted due to the unavailability of multiple copies of the same image. In order to overcome this limitation, as the last area of our research, we develop a novel multichannel blind restoration model with a single image, which eliminates the constraint of the necessity of multiple copies of the blurred image. We consider this as a major contribution which could be extended to wider areas of research integrated with multiple disciplines such as demosaicing.
Книги з теми "Ground truth prior"
1
Lange, Marc. Idealism and Incommensurability. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198746973.003.0016.
Повний текст джерелаАнотація:
Kuhn famously talks about the scientists after a scientific revolution living in a different ‘world’ from the scientists prior to the revolution. This talk could be understood in lots of different ways, but one way is certainly that the notion of truth is relative to a paradigm—a form of idealism. This chapter lays this out and argues against it by arguing against the strong form of incommensurability on which it relies. In particular, the chapter (i) argues that even in the course of a Kuhnian ‘crisis,’ arguments from neutral ground for or against some candidate paradigms can be mounted, and (ii) argues against Feyerabend’s contention that because the rivals in a ‘crisis’ disagree on the gold standards for reliable observations, there is no non-question-begging way to confirm or to disconfirm those rivals. The chapter draws upon Galilean examples to argue for each of these points.
Частини книг з теми "Ground truth prior"
1
Zhang, Yu, Fagui Liu, and Quan Tang. "Utilize Spatial Prior in Ground Truth: Spatial-Enhanced Loss for Semantic Segmentation." In Lecture Notes in Computer Science, 312–21. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-15934-3_26.
Повний текст джерела
2
Akhondi-Asl, Alireza, and Simon K. Warfield. "Estimation of the Prior Distribution of Ground Truth in the STAPLE Algorithm: An Empirical Bayesian Approach." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33415-3_73.
Повний текст джерела
3
Yu, Guorui, Guangliang Yang, Tongxin Li, Xinhui Han, Shijie Guan, Jialong Zhang, and Guofei Gu. "MinerGate: A Novel Generic and Accurate Defense Solution Against Web Based Cryptocurrency Mining Attacks." In Communications in Computer and Information Science, 50–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_5.
Повний текст джерелаАнотація:
AbstractWeb-based cryptocurrency mining attacks, also known as cryptojacking, become increasingly popular. A large number of diverse platforms (e.g., Windows, Linux, Android, and iOS) and devices (e.g., PC, smartphones, tablets, and even critical infrastructures) are widely impacted. Although a variety of detection approaches were recently proposed, it is challenging to apply these approaches to attack prevention directly.Instead, in this paper, we present a novel generic and accurate defense solution, called “MinerGate”, against cryptojacking attacks. To achieve the goal, MinerGate is designed as an extension of network gateways or proxies to protect all devices behind it. When attacks are identified, MinerGate can enforce security rules on victim devices, such as stopping the execution of related JavaScript code and alerting victims. Compared to prior approaches, MinerGate does not require any modification of browsers or apps to collect the runtime features. Instead, MinerGate focuses on the semantics of mining payloads (usually written in WebAssembly/asm.js), and semantic-based features.In our evaluation, we first verify the correctness of MinerGate by testing MinerGate in a real environment. Then, we check MinerGate’s performance and confirm MinerGate introduces relatively low overhead. Last, we verify the accuracy of MinerGate. For this purpose, we collect the largest WebAssembly/asm.js related code with ground truth to build our experiment dataset. By comparing prior approaches and MinerGate on the dataset, we find MinerGate achieves better accuracy and coverage (i.e., 99% accuracy and 98% recall). Our dataset will be available online, which should be helpful for more solid understanding of cryptojacking attacks.
4
Arzberger, Fabian, Jasper Zevering, Anton Bredenbeck, Dorit Borrmann, and Andreas Nüchter. "Unconventional Trajectories for Mobile 3D Scanning and Mapping." In Autonomous Mobile Mapping Robots [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108132.
Повний текст джерелаАнотація:
State-of-the-art LiDAR-based 3D scanning and mapping systems focus on scenarios where good sensing coverage is ensured, such as drones, wheeled robots, cars, or backpack-mounted systems. However, in some scenarios more unconventional sensor trajectories come naturally, e.g., rolling, descending, or oscillating back and forth, but the literature on these is relatively sparse. As a result, most implementations developed in the past are not able to solve the SLAM problem in such conditions. In this chapter, we propose a robust offline-batch SLAM system that is able to address more challenging trajectories, which are characterized by weak angles of incidence and limited FOV while scanning. The proposed SLAM system is an upgraded version of our previous work and takes as input the raw points and prior pose estimates, yet the latter are subject to large amounts of drift. Our approach is a two-staged algorithm where in the first stage coarse alignment is fast achieved by matching planar polygons. In the second stage, we utilize a graph-based SLAM algorithm for further refinement. We evaluate the mapping accuracy of the algorithm on our own recorded datasets using high-resolution ground truth maps, which are available from a TLS.
5
Henderson, David, and Terry Horgan. "On the Armchair Justification of Conceptually Grounded Necessary Truths." In The A Priori in Philosophy, 110–31. Oxford University Press, 2013. http://dx.doi.org/10.1093/acprof:oso/9780199695331.003.0006.
Повний текст джерела
6
Todd, Patrick. "Betting on the Open Future." In The Open Future, 119–47. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192897916.003.0007.
Повний текст джерелаАнотація:
A.N. Prior considered an objection to open future views, viz. that they are inconsistent with our ordinary practices of betting. Prior worried that, on open future views, if we bet on rain, and then it does rain, I could refuse to grant the payout on grounds that the proposition you bet was true was not true at the time of the bet. The author argues that this objection fails, by developing a picture of betting on which we are not betting on anything like current truth. He then considers the objection that his view is inconsistent with the idea that there are non-zero probabilities of future events; he argues that though our credence in a given future contingent proposition may be zero, the objective probability of the relevant event may nevertheless be high. The author develops a comparison between this view and parallel views about the probability of conditionals and probabilities in fictions.
7
Rosenkoetter, Timothy. "Kant on the Epistemology of the Obvious." In The Sensible and Intelligible Worlds, 132–57. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199688265.003.0007.
Повний текст джерелаАнотація:
Abstract The launching point for this chapter is Kant’s striking claim that all and only mathematical cognition is “evident.” After proposing the hypothesis that “evident” propositions are a proper subset of “obvious” propositions (namely, the subset of the obvious grounded in a priori intuition), the chapter fills a lacuna in the secondary literature by developing a systematic account of when and why Kant conceives of this or that proposition as “obvious.” The core idea is that certainty regarding an obvious truth is accessible to any subject who possesses “common human understanding.” The fact that there are two modes of access available to a subject who currently lacks that certainty (or is outright mistaken)—one through rule-based instruction, the other through measures that regain the healthy use of common human understanding—partitions the obvious into two classes, including most of mathematics in the former class and truths such as the causal principle, basic moral principles, and tautologies in the latter. Overall, the chapter argues that Kant’s theory of the obvious plays a previously unrecognized role rationalizing and tying together disparate Kantian positions that might otherwise seem unimportant or merely idiosyncratic.
8
Rosenberger, Christophe, Sébastien Chabrier, Hélène Laurent, and Bruno Emile. "Unsupervised and Supervised Image Segmentation Evaluation." In Advances in Image and Video Segmentation, 365–93. IGI Global, 2006. http://dx.doi.org/10.4018/978-1-59140-753-9.ch018.
Повний текст джерелаАнотація:
Segmentation is a fundamental step in image analysis and remains a complex problem. Many segmentation methods have been proposed in the literature but it is difficult to compare their efficiency. In order to contribute to the solution of this problem, some evaluation criteria have been proposed for the last decade to quantify the quality of a segmentation result. Supervised evaluation criteria use some a priori knowledge such as a ground truth while unsupervised ones compute some statistics in the segmentation result according to the original image. The main objective of this chapter is to first review both types of evaluation criteria from the literature. Second, a comparative study is proposed in order to identify the efficiency of these criteria for different types of images. Finally, some possible applications are presented.
9
Audi, Robert. "Apriority, Disputability, and Necessity." In Seeing, Knowing, and Doing, 144–64. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780197503508.003.0009.
Повний текст джерелаАнотація:
This chapter shows how the self-evident and, by extension, a priori propositions in general may plausibly be considered necessary. These propositions are best taken to have, as truthmakers, abstract objects and their interrelations. It is also argued that the a priori may be plausibly taken to extend to certain normative truths and to many propositions that, like some perceptual principles discussed in earlier chapters, belong to philosophy itself. As the case of philosophy well illustrates, when a priori propositions are substantive, there may be widespread rational disagreement on them. This is especially clear if, as argued here, beliefs can be rational even if not sufficiently well-grounded to be justified. This possibility implies that someone may rationally, though unjustifiedly, reject even certain self-evident propositions. How this happens is explained, and the chapter also shows both difficulties in identifying rational disagreements and some prospects for resolving them.
10
Ravallion, Martin. "Should the Randomistas (Continue to) Rule?" In Randomized Control Trials in the Field of Development, 47–78. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198865360.003.0003.
Повний текст джерелаАнотація:
The rising popularity of randomized controlled trials (RCTs) in development applications has come with continuing debates about the merits of this approach. The chapter takes stock of the issues. It argues that an unconditional preference for RCTs is questionable on three main counts. First, the case for such a preference is unclear on a priori grounds. For example, with a given budget, even a biased observational study can come closer to the truth than a costly RCT. Second, the ethical objections to RCTs have not been properly addressed by advocates. Third, there is a risk of distorting the evidence-base for informing policy-making, given that an insistence on RCTs generates selection bias in what gets evaluated. Going forward, pressing knowledge gaps should drive the questions asked and how they are answered, not the methodological preferences of some researchers. The gold standard is the best method for the question at hand.
Тези доповідей конференцій з теми "Ground truth prior"
1
Zhussip, Magauiya, Shakarim Soltanayev, and Se Young Chun. "Training Deep Learning Based Image Denoisers From Undersampled Measurements Without Ground Truth and Without Image Prior." In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2019. http://dx.doi.org/10.1109/cvpr.2019.01050.
Повний текст джерела
2
Taft, Brent S., and Sally M. Smith. "ASETS-II Oscillating Heat Pipe Space Flight Experiment: Ground Truth Results." In ASME 2017 Heat Transfer Summer Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/ht2017-4706.
Повний текст джерелаАнотація:
The ASETS-II experiment consists of three oscillating heat pipes (OHPs), an electronics box, and mounting structures that control boundary conditions. Each OHP consists of 34 channels in a typical single-layer closed loop design. Butane was selected as the working fluid for OHP #1 and #2 for its performance stability. R-134a was selected for OHP #3 in order to explore the Bond number limit’s influence on OHP operation in microgravity. The ASETS-II Flight and Flight Spare hardware were subjected to a comprehensive set of ground testing to baseline performance prior to flight testing. For most test conditions, the Flight and Flight Spare test results for OHP #1 and OHP #2 are within the margin of uncertainty in the measurements. OHP #3 on the Flight hardware performs similarly to OHP #3 on the Flight Spare hardware; however, the difference in performance is outside the margin of uncertainty in the measurements. This variation in performance may be attributable to the fact that OHP #3 is being pushed to operate near its Bond number limit.
3
Li, Xiang, Ben Aldridge, Robert Fisher, and Jonathan Rees. "Estimating the ground truth from multiple individual segmentations incorporating prior pattern analysis with application to skin lesion segmentation." In 2011 8th IEEE International Symposium on Biomedical Imaging (ISBI 2011). IEEE, 2011. http://dx.doi.org/10.1109/isbi.2011.5872670.
Повний текст джерела
4
Mishra, Mayank, Dhiraj Madan, Gaurav Pandey, and Danish Contractor. "Variational Learning for Unsupervised Knowledge Grounded Dialogs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/597.
Повний текст джерелаАнотація:
Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to be known during training and rely on the use of a retrieval system to fetch relevant documents from a large index. The documents used to generate the responses are modeled as latent variables whose prior probabilities need to be estimated. Models such as RAG, marginalize the document probabilities over the documents retrieved from the index to define the log-likelihood loss function which is optimized end-to-end. In this paper, we develop a variational approach to the above technique wherein, we instead maximize the Evidence Lower bound (ELBO). Using a collection of three publicly available open-conversation datasets, we demonstrate how the posterior distribution, which has information from the ground-truth response, allows for a better approximation of the objective function during training. To overcome the challenges associated with sampling over a large knowledge collection, we develop an efficient approach to approximate the ELBO. To the best of our knowledge, we are the first to apply variational training for open-scale unsupervised knowledge grounded dialog systems.
5
Xiong, Mingkang, Zhenghong Zhang, Weilin Zhong, Jinsheng Ji, Jiyuan Liu, and Huilin Xiong. "Self-supervised Monocular Depth and Visual Odometry Learning with Scale-consistent Geometric Constraints." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/134.
Повний текст джерелаАнотація:
The self-supervised learning-based depth and visual odometry (VO) estimators trained on monocular videos without ground truth have drawn significant attention recently. Prior works use photometric consistency as supervision, which is fragile under complex realistic environments due to illumination variations. More importantly, it suffers from scale inconsistency in the depth and pose estimation results. In this paper, robust geometric losses are proposed to deal with this problem. Specifically, we first align the scales of two reconstructed depth maps estimated from the adjacent image frames, and then enforce forward-backward relative pose consistency to formulate scale-consistent geometric constraints. Finally, a novel training framework is constructed to implement the proposed losses. Extensive evaluations on KITTI and Make3D datasets demonstrate that, i) by incorporating the proposed constraints as supervision, the depth estimation model can achieve state-of-the-art (SOTA) performance among the self-supervised methods, and ii) it is effective to use the proposed training framework to obtain a uniform global scale VO model.
6
Zhang, Yuxiang, Jiamei Fu, Dongyu She, Ying Zhang, Senzhang Wang, and Jufeng Yang. "Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/639.
Повний текст джерелаАнотація:
Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.
7
Su, Tianxiang. "Real-Time Inference and Uncertainty Quantification of Friction for Coiled Tubing Operations." In SPE/ICoTA Well Intervention Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/209022-ms.
Повний текст джерелаАнотація:
Abstract For many years, tubing force models (TFMs) have been used to predict coiled tubing (CT) reach in the wellbore, de-risk interventions, and track CT pipe life. Several input parameters, for example, the friction coefficient, the stripper friction, and the fluid viscosity, are difficult to obtain before an operation. They are typically fitted later during the operation or once the operation is completed. A method is presented here to infer those parameters iteratively in real time during the job. The method is based on the unscented Kalman filter (UKF). A TFM predicts the surface weight from input parameters. The UKF solves the "inverse problem" by observing the noisy surface weight measurement and infers the unknown parameters in a probabilistic way. Unlike the fitting-based optimization methods, UKF is an iterative method and makes an incremental update to the parameters from each measurement. Moreover, based on probabilistic theories, the UKF computes rigorously the uncertainty bounds of the inferred parameters. We implemented a UKF framework in Simulink. To test the model, a synthetic dataset with well-defined ground truth friction and well trajectory was first created. This synthetic well had a vertical section of 2 km (6,562 ft) below the surface, followed by a 2-km deviated section of constant dogleg severity where the well changes from vertical to horizontal, followed by a last horizontal section of 2 km. An analytic solution for the surface weight was derived for this specific well, and Gaussian noise was added to the solution to mimic the measurement noise. The noisy synthetic data were fed into the customized UKF framework with a wrong initial guess of the friction. The UKF framework incrementally and correctly adjusted the friction to the ground truth friction value in a few hundred iterations at 0.1 Hz. In real operations, if there is a sudden condition change, one expects the UKF to take similar steps to adapt to the change. Next, actual data from a previous job were replayed iteratively into the UKF framework. No ground-truth friction value existed for that dataset. But with the real-time inferred friction, the model was observed to provide a much better surface weight prediction compared to a legacy model without parameter inference. To the knowledge of the author, this is the first time UKF is reported to perform parameter inference for CT operations. It provides clear advantages compared to the legacy fitting-based method because (1) it can easily handle multiparameter inference; (2) it is straightforward to quantify the uncertainty of the inference; (3) the filter can incorporate any prior knowledge about the parameters; and (4) the method can be applied iteratively, continuously, and automatically.
8
Yang, Jufeng, Dongyu She, and Ming Sun. "Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/456.
Повний текст джерелаАнотація:
Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e., Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i.e., Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.
9
Paradis, Olivia P., Nathan T. Jessurun, Mark Tehranipoor, and Navid Asadizanjani. "Color Normalization for Robust Automatic Bill of Materials Generation and Visual Inspection of PCBs." In ISTFA 2020. ASM International, 2020. http://dx.doi.org/10.31399/asm.cp.istfa2020p0172.
Повний текст джерелаАнотація:
Abstract A Bill of Materials (BoM) is the list of all components present on a Printed Circuit Board (PCB). BoMs are useful for multiple forms of failure analysis and hardware assurance. In this paper, we build upon previous work and present an updated framework to automatically extract a BoM from optical images of PCBs in order to keep up to date with technological advancements. This is accomplished by revising the framework to emphasize the role of machine learning and by incorporating domain knowledge of PCB design and hardware Trojans. For accurate machine learning methods, it is critical that the input PCB images are normalized. Hence, we explore the effect of imaging conditions (e.g. camera type, lighting intensity, and lighting color) on component classification, before and after color correction. This is accomplished by collecting PCB images under a variety of imaging conditions and conducting a linear discriminant analysis before and after color checker profile correction, a method commonly used in photography. This paper shows color correction can effectively reduce the intraclass variance of different PCB components, which results in a higher component classification accuracy. This is extremely desirable for machine learning methods, as increased prior knowledge can decrease the number of ground truth images necessary for training. Finally, we detail the future work for data normalization for more accurate automatic BoM extraction. Index Terms – automatic visual inspection; PCB reverse engineering; PCB competitor analysis; hardware assurance; bill of materials
10
Korabelnikov, Alexandr N., Alexandr V. Kolsanov, Sergey S. Chaplygin, Pavel M. Zelter, Konstantin V. Bychenkov, and Artem V. Nikonorov. "LIVER TUMOR SEGMENTATION CT DATA BASED ON ALEXNET-LIKE CONVOLUTION NEURAL NETS." In Information Technology and Nanotechnology-2016. IP Zaitsev V.D., 2016. http://dx.doi.org/10.18287/1613-0073-2016-1638-348-356.
Повний текст джерелаАнотація:
Anatomical structure segmentation on computed tomography (CT) is the key stage in medical visualization and computer diagnosis. Tumors are one of types of internal structures, for which the problem of automatic segmentation today has no solution fully satisfying by quality. The reason is high variance of tumor’s density and inability of using a priori anatomical information about shape. In this paper we propose automatic method of liver tumors segmentation based on convolution neural nets (CNN). Studying and validation have been performed on set of CT with liver and tumors segmentation ground truth. Average error (VOE) by cross-validation is 17.3%. Also there were considered algorithms of pre- and post-processing which increase accuracy and performance of segmentation procedure. Particularly the acceleration of the segmentation procedure with negligible decrease of quality has been reached 6 times.
Звіти організацій з теми "Ground truth prior"
1
Ruiz, Pablo, Craig Perry, Alejando Garcia, Magali Guichardot, Michael Foguer, Joseph Ingram, Michelle Prats, Carlos Pulido, Robert Shamblin, and Kevin Whelan. The Everglades National Park and Big Cypress National Preserve vegetation mapping project: Interim report—Northwest Coastal Everglades (Region 4), Everglades National Park (revised with costs). National Park Service, November 2020. http://dx.doi.org/10.36967/nrr-2279586.
Повний текст джерелаАнотація:
The Everglades National Park and Big Cypress National Preserve vegetation mapping project is part of the Comprehensive Everglades Restoration Plan (CERP). It is a cooperative effort between the South Florida Water Management District (SFWMD), the United States Army Corps of Engineers (USACE), and the National Park Service’s (NPS) Vegetation Mapping Inventory Program (VMI). The goal of this project is to produce a spatially and thematically accurate vegetation map of Everglades National Park and Big Cypress National Preserve prior to the completion of restoration efforts associated with CERP. This spatial product will serve as a record of baseline vegetation conditions for the purpose of: (1) documenting changes to the spatial extent, pattern, and proportion of plant communities within these two federally-managed units as they respond to hydrologic modifications resulting from the implementation of the CERP; and (2) providing vegetation and land-cover information to NPS park managers and scientists for use in park management, resource management, research, and monitoring. This mapping project covers an area of approximately 7,400 square kilometers (1.84 million acres [ac]) and consists of seven mapping regions: four regions in Everglades National Park, Regions 1–4, and three in Big Cypress National Preserve, Regions 5–7. The report focuses on the mapping effort associated with the Northwest Coastal Everglades (NWCE), Region 4 , in Everglades National Park. The NWCE encompasses a total area of 1,278 square kilometers (493.7 square miles [sq mi], or 315,955 ac) and is geographically located to the south of Big Cypress National Preserve, west of Shark River Slough (Region 1), and north of the Southwest Coastal Everglades (Region 3). Photo-interpretation was performed by superimposing a 50 × 50-meter (164 × 164-feet [ft] or 0.25 hectare [0.61 ac]) grid cell vector matrix over stereoscopic, 30 centimeters (11.8 inches) spatial resolution, color-infrared aerial imagery on a digital photogrammetric workstation. Photo-interpreters identified the dominant community in each cell by applying majority-rule algorithms, recognizing community-specific spectral signatures, and referencing an extensive ground-truth database. The dominant vegetation community within each grid cell was classified using a hierarchical classification system developed specifically for this project. Additionally, photo-interpreters categorized the absolute cover of cattail (Typha sp.) and any invasive species detected as either: Sparse (10–49%), Dominant (50–89%), or Monotypic (90–100%). A total of 178 thematic classes were used to map the NWCE. The most common vegetation classes are Mixed Mangrove Forest-Mixed and Transitional Bayhead Shrubland. These two communities accounted for about 10%, each, of the mapping area. Other notable classes include Short Sawgrass Marsh-Dense (8.1% of the map area), Mixed Graminoid Freshwater Marsh (4.7% of the map area), and Black Mangrove Forest (4.5% of the map area). The NWCE vegetation map has a thematic class accuracy of 88.4% with a lower 90th Percentile Confidence Interval of 84.5%.
2
Anderson, Gerald L., and Kalman Peleg. Precision Cropping by Remotely Sensed Prorotype Plots and Calibration in the Complex Domain. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7585193.bard.
Повний текст джерелаАнотація:
This research report describes a methodology whereby multi-spectral and hyperspectral imagery from remote sensing, is used for deriving predicted field maps of selected plant growth attributes which are required for precision cropping. A major task in precision cropping is to establish areas of the field that differ from the rest of the field and share a common characteristic. Yield distribution f maps can be prepared by yield monitors, which are available for some harvester types. Other field attributes of interest in precision cropping, e.g. soil properties, leaf Nitrate, biomass etc. are obtained by manual sampling of the filed in a grid pattern. Maps of various field attributes are then prepared from these samples by the "Inverse Distance" interpolation method or by Kriging. An improved interpolation method was developed which is based on minimizing the overall curvature of the resulting map. Such maps are the ground truth reference, used for training the algorithm that generates the predicted field maps from remote sensing imagery. Both the reference and the predicted maps are stratified into "Prototype Plots", e.g. 15xl5 blocks of 2m pixels whereby the block size is 30x30m. This averaging reduces the datasets to manageable size and significantly improves the typically poor repeatability of remote sensing imaging systems. In the first two years of the project we used the Normalized Difference Vegetation Index (NDVI), for generating predicted yield maps of sugar beets and com. The NDVI was computed from image cubes of three spectral bands, generated by an optically filtered three camera video imaging system. A two dimensional FFT based regression model Y=f(X), was used wherein Y was the reference map and X=NDVI was the predictor. The FFT regression method applies the "Wavelet Based", "Pixel Block" and "Image Rotation" transforms to the reference and remote images, prior to the Fast - Fourier Transform (FFT) Regression method with the "Phase Lock" option. A complex domain based map Yfft is derived by least squares minimization between the amplitude matrices of X and Y, via the 2D FFT. For one time predictions, the phase matrix of Y is combined with the amplitude matrix ofYfft, whereby an improved predicted map Yplock is formed. Usually, the residuals of Y plock versus Y are about half of the values of Yfft versus Y. For long term predictions, the phase matrix of a "field mask" is combined with the amplitude matrices of the reference image Y and the predicted image Yfft. The field mask is a binary image of a pre-selected region of interest in X and Y. The resultant maps Ypref and Ypred aremodified versions of Y and Yfft respectively. The residuals of Ypred versus Ypref are even lower than the residuals of Yplock versus Y. The maps, Ypref and Ypred represent a close consensus of two independent imaging methods which "view" the same target. In the last two years of the project our remote sensing capability was expanded by addition of a CASI II airborne hyperspectral imaging system and an ASD hyperspectral radiometer. Unfortunately, the cross-noice and poor repeatability problem we had in multi-spectral imaging was exasperated in hyperspectral imaging. We have been able to overcome this problem by over-flying each field twice in rapid succession and developing the Repeatability Index (RI). The RI quantifies the repeatability of each spectral band in the hyperspectral image cube. Thereby, it is possible to select the bands of higher repeatability for inclusion in the prediction model while bands of low repeatability are excluded. Further segregation of high and low repeatability bands takes place in the prediction model algorithm, which is based on a combination of a "Genetic Algorithm" and Partial Least Squares", (PLS-GA). In summary, modus operandi was developed, for deriving important plant growth attribute maps (yield, leaf nitrate, biomass and sugar percent in beets), from remote sensing imagery, with sufficient accuracy for precision cropping applications. This achievement is remarkable, given the inherently high cross-noice between the reference and remote imagery as well as the highly non-repeatable nature of remote sensing systems. The above methodologies may be readily adopted by commercial companies, which specialize in proving remotely sensed data to farmers.