Data assimilation provides a mathematical solution to combine optimally observations and models . We define a dedicated Image Model consistent with the physical knowledge of ocean dynamics . Satellite images are then assimilated into this model using a variational data assimilation scheme . Very often , the spectral components are degraded by a blur modelled by a linear operator and an additive noise . In this paper , we address the problem of recovering the image components in a wavelet domain by adopting a variational approach . Our contribution is twofold . Furthermore , such systems do not fully support image content ( e.g. , photos of landscapes or living organisms ) management , a requirement of many BIS end-users . In order to meet their needs , these users — e.g . , biologists , environmental experts — often have to alternate between separate biodiversity and image information systems to combine information extracted from them . This hampers the addition of new data sources , as well as cooperation among scientists . We propose a template matching method for component inspection of SMD assembly system . To discriminate wrong assembled components , the input image of component is matched with its standard image by template matching algorithm . For a fast inspection system , the calculation time of matching algorithm should be reduced . Our approach also provides confidentiality and integrity to the code dissemination process by utilizing energy-efficient encryption and authentication mechanisms with RC4 and the CBC-MAC . We have evaluated SIMAGE in a network of real sensors and the results show that adjusting the packet size as a function of link quality reduces the retransmitted data by 93 % and the image transmission time by 35 % when compared to the existing code dissemination protocols . The trade-offs between reliability , security overhead , and overall transmission time for SIMAGE are also discussed . The invention relates to a method for accurately detecting surface quality on line in the production process of band steel , belongs to the technical field of on-line detection of the surface quality of the band steel , and in particular relates to an image type method for accurately detecting the surface quality on line in the production process of the band steel . By the method , the surface defects of the band steel can be detected in real time and classified accurately . To test our approach in different reverberation conditions , other contaminated versions of the latter training and test data are generated with different impulse responses . These simulations are based on the image method and correspond to four different reverberation times ( ranging from 250 to 1000 ms ) . More details on the realistic impulse responses adopted in this corpus can be found in . However , regardless of making networks deeper to improve accuracy , little effort has been made to build text classification models to constrained resources . It is a very different scenario compared to image approaches , where size and speed constrained models have been proposed . In real - world applications , size and speed are constraints to an efficient mobile and embedded deployment of deep models . We propose two testing methods , as well as analogs of one-sided and two-sided tests . In simulation , the methods have high power and compare favorably against existing interpretability methods . When applied to vision and language models , the framework selects features that intuitively explain model predictions . MethodsThe dataset used in this study contains information of 23,095 patients who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up . Five global interpretability techniques ( Feature Importance , Partial Dependence Plot , Individual Conditional Expectation , Feature Interaction , Global Surrogate Models ) and two local interpretability techniques ( Local Surrogate Models , Shapley Value ) have been applied to present the role of the interpretability techniques on assisting the clinical staff to get better understanding and more trust of the outcomes of the machine learning-based predictions . ResultsSeveral experiments have been conducted and reported . ConclusionsVarious interpretability techniques can vary in their explanations for the behavior of the machine learning model . The global interpretability techniques have the advantage that it can generalize over the entire population while local interpretability techniques focus on giving explanations at the level of instances . Both methods can be equally valid depending on the application need . Interpretability logic is a modal description of the interpretability predicate . The modal system IL is an extension of the provability logic GL ( Godel – Lob ) . This proceedings volume contains research papers in mathematical logic , especially in model theory and its applications to algebra and formal theories of arithmetic . Other papers address interpretability theory , computable analysis , modal logic , and the history of mathematical logic in Iran . The conference was held in Tehran , Iran , in October 2003 , with the expressed purpose of bringing together researchers with connections to Iranian logicians and promoting further research in mathematical logic in Iran . This framework tests the “ sensitivity ” of the algorithms and helps in evaluating “ implementation invariance ” , which are desired characteristics for any interpretability mechanism . We demonstrate our framework using two well-known algorithms namely “ Saliency Maps ” and “ Grad-CAM ” and introduce a new interpretability technique called “ Forward-Backward Interpretability algorithm ” that provides a systematic framework for visualizing information flow in deep networks . Finally , we also present visualization and interpretability results for an impactful scientific application involving microstructure-property mapping in material science . The proposed method achieves a significant positive return on investment ( ROI ) in all NDF markets under analysis , outperforming recurrent and classical baselines by a wide margin . Finally , we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy . To support their efforts , we utilized a dataset containing all criminal offenses charged within the state of Pennsylvania in 2016 - 17 in order to derive analytical insights , and perform a series of statistical tests to evaluate and quantify biases in the bail determination system in Pennsylvania . In addition to this , we also developed a predictive model and utilised model interpretability tools to evaluate the factors affecting bail type assignment . One of the key focus areas of our work was the determination of racial bias , particularly between the defendants identified as Black and White in the court records . We formulate rules ( principles ) for constructing fractional generalizations of standard models , which were described by differential equations of integer order . Important requirements to building fractional generalization of dynamical models ( the rules for “ fractional-dynamic generalizers ” ) are represented as the derivability principle , the multiplicity principle , the solvability and correspondence principles , and the interpretability principle . The characteristic properties of fractional derivatives of non-integer order are the violation of standard rules and properties that are fulfilled for derivatives of integer order . With this motivation , we present a production ML reproducibility solution , namely a comprehensive time ordered event sequence for machine learning applications . We demonstrate how our approach can bring this known common interpretability technique into production viability . We further present the system design and early performance characteristics of our reproducibility solution . Specifically , current defect prediction models still fail to explain why models make such a prediction and fail to uphold the privacy laws in terms of the requirement to explain any decision made by an algorithm . In this paper , we empirically evaluate three model-agnostic techniques , i.e. , two state-of-the-art Local Interpretability Model-agnostic Explanations technique ( LIME ) and BreakDown techniques , and our improvement of LIME with Hyper Parameter Optimisation ( LIME-HPO ) . Through a case study of 32 highly-curated defect datasets that span across 9 open-source software systems , we conclude that ( 1 ) model-agnostic techniques are needed to explain individual predictions of defect models ; ( 2 ) instance explanations generated by model-agnostic techniques are mostly overlapping ( but not exactly the same ) with the global explanation of defect models and reliable when they are re-generated ; ( 3 ) model-agnostic techniques take less than a minute to generate instance explanations ; and ( 4 ) more than half of the practitioners perceive that the contrastive explanations are necessary and useful to understand the predictions of defect models . Computational results obtained from CAB , AP , and a new dataset for Iranian road network ( IRN ) confirm the efficiency of the proposed preprocessing . In fact , averagely , the run times for CAB , AP , and IRN are reduced by 75.57 , 80.41 , and 82.96 % , respectively , when the model is solved by preprocessing . Moreover , on average , the number of nodes in the branch and cut tree is decreased by 90.87 , 95.67 , and 95.86 % for CAB , AP , and IRN , respectively , using the presented preprocessing . The credibility of the IR-AHP-MABAC model was demonstrated by comparing the results of different multi-criteria techniques and analyzing viability . The results of the IRN approach and fuzzy comparison have shown that the new approach to dealing with imprecision yields credible , reputable ranks . Am . 100 ( 5 ) , 3286 - 3294 ] . A generalised IRN algorithm is presented , in which multiple time varying temporal correlations can be defined . The resulting time varying pitches are perceptually very salient . The Illinois Resource Network ( IRN ) , a statewide , computerized information service , puts business , government and the public in touch with more than 6500 university faculty experts and federal research laboratory scientists . Since 1984 , IRN has been used nearly 4000 times by business , government and education . We propose a self-organized content-based Image Retrieval Network ( IRN ) that is inspired by a Metric Social Network ( MSN ) search system . The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries . We propose a simple but flexible method for solving the generalized vector-valued TV ( VTV ) functional , which includes both the ¿ 2-VTV and ¿ 1-VTV regularizations as special cases , to address the problems of deconvolution and denoising of vector-valued ( e.g. color ) images with Gaussian or salt-and-pepper noise . This algorithm is the vectorial extension of the Iteratively Reweighted Norm ( IRN ) algorithm [ 1 ] originally developed for scalar ( grayscale ) images . This method offers competitive computational performance for denoising and deconvolving vector-valued images corrupted with Gaussian ( ¿ 2-VTV case ) and salt-and-pepper noise ( ¿ 1-VTV case ) . Methods for identifying a nonlinear dynamic process with unknown and possibly variable dead times via an internal recurrent network ( IRN ) model are proposed . It is shown that an IRN with sufficient hidden nodes can be used directly for the identification of a nonlinear dynamic process with fixed or variable dead times . We create a three-layer network consisting of space of stations , space of stops and space of changes . We also compare the network characteristics of the space of stops and the space of station networks generated from the timetable of IRN . We show that the space of stops network is a useful representation for the passengers of IRN where as the space of stations is a useful representation for the operations team of IRN . Our method first generates an individual relationship network ( IRN ) for each user and item by developing a novel fitting algorithm of relationship networks to control the relationship propagation and contracting . We then fuse matrix factorization with social regularization and the neighborhood model using IRN 's to generate recommendations . Our approach is quite general , and can also be applied to the item-item relationship network by switching the roles of users and items . We show that the need for PFC is an artifact of current RoCE NIC designs rather than a fundamental requirement . We propose an improved RoCE NIC ( IRN ) design that makes a few simple changes to the RoCE NIC for better handling of packet losses . We show that IRN ( without PFC ) outperforms RoCE ( with PFC ) by 6 - 83 % for typical network scenarios . Hence , recent works have proposed approaches for injecting multi - step paths such as random walks through sequences of triples during training , further improving performance on KBC tasks . IRN and Neural LP explore multi - step relations by using an RNN controller with attention over an external memory . Compared to RL - based approaches , it is hard to interpret the traversal paths , and these models can be computationally expensive to access the entire graph in memory . In this work , we propose to solve this problem by modeling the downscaling and upscaling processes from a new perspective , i.e. an invertible bijective transformation , which can largely mitigate the ill-posed nature of image upscaling . We develop an Invertible Rescaling Net ( IRN ) with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process . In this way , upscaling is made tractable by inversely passing a randomly-drawn latent variable with the low-resolution image through the network .
Data assimilation provides a mathematical solution to combine optimally observations and models . We define a dedicated Image Model consistent with the physical knowledge of ocean dynamics . Satellite images are then assimilated into this model using a variational data assimilation scheme .
Very often , the spectral components are degraded by a blur modelled by a linear operator and an additive noise . In this paper , we address the problem of recovering the image components in a wavelet domain by adopting a variational approach . Our contribution is twofold .
Furthermore , such systems do not fully support image content ( e.g. , photos of landscapes or living organisms ) management , a requirement of many BIS end-users . In order to meet their needs , these users — e.g . , biologists , environmental experts — often have to alternate between separate biodiversity and image information systems to combine information extracted from them . This hampers the addition of new data sources , as well as cooperation among scientists .
We propose a template matching method for component inspection of SMD assembly system . To discriminate wrong assembled components , the input image of component is matched with its standard image by template matching algorithm . For a fast inspection system , the calculation time of matching algorithm should be reduced .
Our approach also provides confidentiality and integrity to the code dissemination process by utilizing energy-efficient encryption and authentication mechanisms with RC4 and the CBC-MAC . We have evaluated SIMAGE in a network of real sensors and the results show that adjusting the packet size as a function of link quality reduces the retransmitted data by 93 % and the image transmission time by 35 % when compared to the existing code dissemination protocols . The trade-offs between reliability , security overhead , and overall transmission time for SIMAGE are also discussed .
The invention relates to a method for accurately detecting surface quality on line in the production process of band steel , belongs to the technical field of on-line detection of the surface quality of the band steel , and in particular relates to an image type method for accurately detecting the surface quality on line in the production process of the band steel . By the method , the surface defects of the band steel can be detected in real time and classified accurately .
To test our approach in different reverberation conditions , other contaminated versions of the latter training and test data are generated with different impulse responses . These simulations are based on the image method and correspond to four different reverberation times ( ranging from 250 to 1000 ms ) . More details on the realistic impulse responses adopted in this corpus can be found in .
However , regardless of making networks deeper to improve accuracy , little effort has been made to build text classification models to constrained resources . It is a very different scenario compared to image approaches , where size and speed constrained models have been proposed . In real - world applications , size and speed are constraints to an efficient mobile and embedded deployment of deep models .
We propose two testing methods , as well as analogs of one-sided and two-sided tests . In simulation , the methods have high power and compare favorably against existing interpretability methods . When applied to vision and language models , the framework selects features that intuitively explain model predictions .
MethodsThe dataset used in this study contains information of 23,095 patients who underwent clinician-referred exercise treadmill stress testing at Henry Ford Health Systems between 1991 and 2009 and had a complete 10-year follow-up . Five global interpretability techniques ( Feature Importance , Partial Dependence Plot , Individual Conditional Expectation , Feature Interaction , Global Surrogate Models ) and two local interpretability techniques ( Local Surrogate Models , Shapley Value ) have been applied to present the role of the interpretability techniques on assisting the clinical staff to get better understanding and more trust of the outcomes of the machine learning-based predictions . ResultsSeveral experiments have been conducted and reported .
ConclusionsVarious interpretability techniques can vary in their explanations for the behavior of the machine learning model . The global interpretability techniques have the advantage that it can generalize over the entire population while local interpretability techniques focus on giving explanations at the level of instances . Both methods can be equally valid depending on the application need .
Interpretability logic is a modal description of the interpretability predicate . The modal system IL is an extension of the provability logic GL ( Godel – Lob ) .
This proceedings volume contains research papers in mathematical logic , especially in model theory and its applications to algebra and formal theories of arithmetic . Other papers address interpretability theory , computable analysis , modal logic , and the history of mathematical logic in Iran . The conference was held in Tehran , Iran , in October 2003 , with the expressed purpose of bringing together researchers with connections to Iranian logicians and promoting further research in mathematical logic in Iran .
This framework tests the “ sensitivity ” of the algorithms and helps in evaluating “ implementation invariance ” , which are desired characteristics for any interpretability mechanism . We demonstrate our framework using two well-known algorithms namely “ Saliency Maps ” and “ Grad-CAM ” and introduce a new interpretability technique called “ Forward-Backward Interpretability algorithm ” that provides a systematic framework for visualizing information flow in deep networks . Finally , we also present visualization and interpretability results for an impactful scientific application involving microstructure-property mapping in material science .
The proposed method achieves a significant positive return on investment ( ROI ) in all NDF markets under analysis , outperforming recurrent and classical baselines by a wide margin . Finally , we propose two orthogonal interpretability approaches to verify noise stability and detect the driving factors of the learned tenor selection strategy .
To support their efforts , we utilized a dataset containing all criminal offenses charged within the state of Pennsylvania in 2016 - 17 in order to derive analytical insights , and perform a series of statistical tests to evaluate and quantify biases in the bail determination system in Pennsylvania . In addition to this , we also developed a predictive model and utilised model interpretability tools to evaluate the factors affecting bail type assignment . One of the key focus areas of our work was the determination of racial bias , particularly between the defendants identified as Black and White in the court records .
We formulate rules ( principles ) for constructing fractional generalizations of standard models , which were described by differential equations of integer order . Important requirements to building fractional generalization of dynamical models ( the rules for “ fractional-dynamic generalizers ” ) are represented as the derivability principle , the multiplicity principle , the solvability and correspondence principles , and the interpretability principle . The characteristic properties of fractional derivatives of non-integer order are the violation of standard rules and properties that are fulfilled for derivatives of integer order .
With this motivation , we present a production ML reproducibility solution , namely a comprehensive time ordered event sequence for machine learning applications . We demonstrate how our approach can bring this known common interpretability technique into production viability . We further present the system design and early performance characteristics of our reproducibility solution .
Specifically , current defect prediction models still fail to explain why models make such a prediction and fail to uphold the privacy laws in terms of the requirement to explain any decision made by an algorithm . In this paper , we empirically evaluate three model-agnostic techniques , i.e. , two state-of-the-art Local Interpretability Model-agnostic Explanations technique ( LIME ) and BreakDown techniques , and our improvement of LIME with Hyper Parameter Optimisation ( LIME-HPO ) . Through a case study of 32 highly-curated defect datasets that span across 9 open-source software systems , we conclude that ( 1 ) model-agnostic techniques are needed to explain individual predictions of defect models ; ( 2 ) instance explanations generated by model-agnostic techniques are mostly overlapping ( but not exactly the same ) with the global explanation of defect models and reliable when they are re-generated ; ( 3 ) model-agnostic techniques take less than a minute to generate instance explanations ; and ( 4 ) more than half of the practitioners perceive that the contrastive explanations are necessary and useful to understand the predictions of defect models .
Computational results obtained from CAB , AP , and a new dataset for Iranian road network ( IRN ) confirm the efficiency of the proposed preprocessing . In fact , averagely , the run times for CAB , AP , and IRN are reduced by 75.57 , 80.41 , and 82.96 % , respectively , when the model is solved by preprocessing . Moreover , on average , the number of nodes in the branch and cut tree is decreased by 90.87 , 95.67 , and 95.86 % for CAB , AP , and IRN , respectively , using the presented preprocessing .
The credibility of the IR-AHP-MABAC model was demonstrated by comparing the results of different multi-criteria techniques and analyzing viability . The results of the IRN approach and fuzzy comparison have shown that the new approach to dealing with imprecision yields credible , reputable ranks .
Am . 100 ( 5 ) , 3286 - 3294 ] . A generalised IRN algorithm is presented , in which multiple time varying temporal correlations can be defined . The resulting time varying pitches are perceptually very salient .
The Illinois Resource Network ( IRN ) , a statewide , computerized information service , puts business , government and the public in touch with more than 6500 university faculty experts and federal research laboratory scientists . Since 1984 , IRN has been used nearly 4000 times by business , government and education .
We propose a self-organized content-based Image Retrieval Network ( IRN ) that is inspired by a Metric Social Network ( MSN ) search system . The proposed network model is strictly data-owner oriented so no data redistribution among peers is needed in order to efficiently process queries .
We propose a simple but flexible method for solving the generalized vector-valued TV ( VTV ) functional , which includes both the ¿ 2-VTV and ¿ 1-VTV regularizations as special cases , to address the problems of deconvolution and denoising of vector-valued ( e.g. color ) images with Gaussian or salt-and-pepper noise . This algorithm is the vectorial extension of the Iteratively Reweighted Norm ( IRN ) algorithm [ 1 ] originally developed for scalar ( grayscale ) images . This method offers competitive computational performance for denoising and deconvolving vector-valued images corrupted with Gaussian ( ¿ 2-VTV case ) and salt-and-pepper noise ( ¿ 1-VTV case ) .
Methods for identifying a nonlinear dynamic process with unknown and possibly variable dead times via an internal recurrent network ( IRN ) model are proposed . It is shown that an IRN with sufficient hidden nodes can be used directly for the identification of a nonlinear dynamic process with fixed or variable dead times .
We create a three-layer network consisting of space of stations , space of stops and space of changes . We also compare the network characteristics of the space of stops and the space of station networks generated from the timetable of IRN . We show that the space of stops network is a useful representation for the passengers of IRN where as the space of stations is a useful representation for the operations team of IRN .
Our method first generates an individual relationship network ( IRN ) for each user and item by developing a novel fitting algorithm of relationship networks to control the relationship propagation and contracting . We then fuse matrix factorization with social regularization and the neighborhood model using IRN 's to generate recommendations . Our approach is quite general , and can also be applied to the item-item relationship network by switching the roles of users and items .
We show that the need for PFC is an artifact of current RoCE NIC designs rather than a fundamental requirement . We propose an improved RoCE NIC ( IRN ) design that makes a few simple changes to the RoCE NIC for better handling of packet losses . We show that IRN ( without PFC ) outperforms RoCE ( with PFC ) by 6 - 83 % for typical network scenarios .
Hence , recent works have proposed approaches for injecting multi - step paths such as random walks through sequences of triples during training , further improving performance on KBC tasks . IRN and Neural LP explore multi - step relations by using an RNN controller with attention over an external memory . Compared to RL - based approaches , it is hard to interpret the traversal paths , and these models can be computationally expensive to access the entire graph in memory .
In this work , we propose to solve this problem by modeling the downscaling and upscaling processes from a new perspective , i.e. an invertible bijective transformation , which can largely mitigate the ill-posed nature of image upscaling . We develop an Invertible Rescaling Net ( IRN ) with deliberately designed framework and objectives to produce visually-pleasing low-resolution images and meanwhile capture the distribution of the lost information using a latent variable following a specified distribution in the downscaling process . In this way , upscaling is made tractable by inversely passing a randomly-drawn latent variable with the low-resolution image through the network .
Assimilation of SST Satellite Images for Estimation of Ocean Circulation Velocity Generalized multivariate exponential power prior for wavelet-based multichannel image restoration A digital library framework for biodiversity information systems 자동부품검사를 위한 웨이블렛 변환 기반 영상정합 SIMAGE: Secure and Link-Quality Cognizant Image Distribution for wireless sensor networks Method for accurately detecting surface quality on line in production process of band steel Light Gated Recurrent Units for Speech Recognition Squeezed Very Deep Convolutional Neural Networks for Text Classification Interpreting Black Box Models with Statistical Guarantees On the interpretability of machine learning-based model for predicting hypertension Bisimulation quotients of Veltman models Logic in Tehran : proceedings of the Workshop and Conference on Logic, Algebra and Arithmetic, held October 18-22, 2003 A study of interpretability mechanisms for deep networks WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series Investigating Cash Bail Patterns in Pennsylvania Rules for Fractional-Dynamic Generalizations: Difficulties of Constructing Fractional Dynamic Models Interpretability and Reproducability in Production Machine Learning Applications An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models Flow shipment scheduling in an incomplete hub location-routing network design problem Integration of interval rough AHP and interval rough MABAC methods for evaluating university web pages Pitch detection of dynamic iterated rippled noise by humans and a modified auditory model. The Illinois resource network - linking business and government with university and federal laboratory expertise Building self-organized image retrieval network A generalized vector-valued total variation algorithm Identification of Nonlinear Dynamic Processes with Unknown and Variable Dead Time Using an Internal Recurrent Neural Network Transit timetables as multi-layer networks An Approach for Building Efficient and Accurate Social Recommender Systems Using Individual Relationship Networks Revisiting network support for RDMA M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search Invertible Image Rescaling
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IGARSS (2) ICIP GLOBECOM IEEE Transactions on Emerging Topics in Computational Intelligence ICANN Logic in Tehran: Proceedings of theWorkshop and Conference on Logic, Algebra and Arithmetic, held October 18–22, 2003 WWW (Companion Volume) ICMLA FJCC LSDS-IR ICIP COMSNETS SIGCOMM NeurIPS
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