MATERIALS AND METHODS We designed a hierarchical neural network ( denoted as CNN-Highway-LSTM or LSTM-Highway-LSTM ) for the track 1 of the national natural language processing ( NLP ) clinical challenge ( n2c2 ) on cohort selection for clinical trials in 2018 . The neural network is composed of 5 components : ( 1 ) sentence representation using convolutional neural network ( CNN ) or long short-term memory ( LSTM ) network ; ( 2 ) a highway network to adjust information flow ; ( 3 ) a self-attention neural network to reweight sentences ; ( 4 ) document representation using LSTM , which takes sentence representations in chronological order as input ; ( 5 ) a fully connected neural network to determine whether each criterion is met or not . We compared the proposed method with its variants , including the methods only using the first component to represent documents directly and the fully connected neural network for classification ( denoted as CNN-only or LSTM-only ) and the methods without using the highway network ( denoted as CNN-LSTM or LSTM-LSTM ) . Extensive experiments on CIFAR-10 , CIFAR-100 , Fashion-MNIST and USPS datasets are performed to validate the effectiveness of the proposed approach . Particularly , we outperform the original highway network and many state-of-the-art results . To the best our knowledge , on the Fashion-MNIST and USPS datasets , the achieved results are the best reported in literature . A major responsibility of highway engineers is to provide for the public a highway system capable of accommodating vehicle and pedestrian travel in a safe , efficient , and economic manner . In developing this highway system , the engineer is responsible for the planning , design , construction , operation , and maintenance of that system . In many instances the maintenance function is relegated to a minor position . Abstract Engineering designs for mountainous highways emphasize compliance checking to ensure safety . However , relying solely on compliance checking may lead designers to minimize costs at the expense of high risk indicators , since the overall risk level of the highway design is unknown to the designers . This paper describes a method for the simultaneous consideration of traffic safety risks and the associated cost burden related to the appropriate planning and design of a mountainous highway . When designed , horizontal and vertical highway alignment components are parametrically described geometric objects ( e.g. , smooth curves and straight lines ) . However , surface models resulting from highway design and for use in construction by automated machine guidance ( AMG ) are not geometrically smooth . Rather , they are triangulated irregular networks containing straight line segments that serve as edges of contiguous triangular facets . This work investigates the performance of deep networks on statistical parametric speech synthesis , particularly the question of whether different acoustic features can be better generated by a deeper network . To answer this question , this work examines a multi-stream highway network that separately generates spectral and F0 acoustic features based on the highway architecture . Experiments on the Blizzard Challenge 2011 corpus show that the accuracy of the generated spectral features consistently improves as the depth of the network increases from 2 to 40 , but the F0 trajectory can be generated equally well by either a deep or a shallow network . Research also indicates that when platooning allows maneuvers such as merging and splitting of platoons , these maneuvers present reduced benefits in traffic dynamics and consequently its impact on the environment . In light of these observations , this study presents a highway architecture that implements and controls lane and destination based fixed-routed platoons of automated vehicles with minimal lane changes . The study demonstrates that confining platoons to vehicles of same off-ramp destinations reduces the need for lane changes to a predetermined number , allows for more stable and longer strings of vehicle platoons of up to 24 vehicles per platoon . This traditional method results in the identification of locations that are not truly hazardous from a road-safety authority perspective and consequently may lead to a misapplication of safety improvement funding . Therefore , accidents should be categorized into one or a combination of the three highway system components : the driver , the vehicle , and the road . In previous work , a procedure has been described that allows each accident to have a membership in each of the three highway system components . This paper describes the application of this procedure for the identification of accident-prone locations . To address this issue , we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions : ( 1 ) Implicitly modelling by learning to predict node pair relations ( 2 ) Explicitly modelling by adding edges between nodes that potentially have the same label . To combine our two solutions , we propose a model-agnostic training framework named HighwayGraph , which overcomes the challenge of insufficient labeled nodes by sampling node pairs from the training set and adopting the self-training method . Extensive experimental results show that our HighwayGraph achieves consistent and significant improvements over four representative GNNs on three benchmark datasets . Geometric design is an important phase in the highway design process that is directly related to traffic safety . Highway elements should be jointly designed to account for such design criteria as sight distance , vehicle stability , driver comfort , drainage , and aesthetics . Intuitively , such a design should be based on a three-dimensional ( 3-D ) analysis . The computer simulation technology is adopted to simulate drivers’track decision behavior in driving in order to study the influence of highway geometric alignment on the path of vehicles under free flow traffic state . For this purpose , the driver directional control model taking highway alignment conditions into consideration is developed on the basis of preview optimal curvature model which is put forward by Academician Guo Konghui with the introduction of SK highway geometric model . The main conclusions obtained are as follows : When the driver ’s track decision behavior is influenced only by the condition of the road itself , the path has a tendency to shift inward of the curves as vehicles entered the curves and tended to shift outward of the curves as vehicles exit the curves . Apart from utilizing pre-trained character embeddings and trigram features , we incorporate neural language model and conduct multi-task training . Highway layers are applied to tackle the discordance issue of the naive co-training . Experimental results on CTB5 , CTB7 , and PPD datasets show the effectiveness of the proposed method . Prime reasons for additionally requiring background flows were twofold i ) to provide a rigorous basis for the economic evaluation , II ) within the adjacent urban and rural areas , to be able to show the influence of the scheme in the context of the full flows on the local roads . Typically , in economic terms , the area of inluence of a highway scheme will extend outside the traffic model validation area . It has been shown that use of partial flows only can cause an under-estimate of user benefits when applied to a COBA assessment . We observe that deeper networks often suffer from gradients vanishing and become hard to train — a problem that is well addressed in the literature . To address the above two problems , inspired by highway networks and deep residual networks , we add skip connections between two corresponding convolutional and deconvolutional layers as shown in Figure [ reference ] . A building block is shown in Figure [ reference ] . Recent follow - up work explored the order of activations in residual networks , presenting identity mappings in residual blocks and improving training of very deep networks . Successful training of very deep networks was also shown to be possible through the use of highway networks , which is an architecture that had been proposed prior to residual networks . The essential difference between residual and highway networks is that in the latter residual links are gated and weights of these gates are learned . Hyperparameters of each model were tuned on the development set of LDC2015E86 . For the GCN components , we use two layers , activations , and highway layers . We use single layer LSTMs . Second , proceed from the first to the final layer , normalizing the variance of the output of each layer to be equal to one . Experiment with different activation functions ( maxout , ReLU - family , tanh ) show that the proposed initialization leads to learning of very deep nets that ( i ) produces networks with test accuracy better or equal to standard methods and ( ii ) is at least as fast as the complex schemes proposed specifically for very deep nets such as FitNets ( ) and Highway ( ) . Performance is evaluated on GoogLeNet , CaffeNet , FitNets and Residual nets and the state - of - the - art , or very close to it , is achieved on the MNIST , CIFAR - 10 / 100 and ImageNet datasets . Language is the main tool of man communication , it has an impact on grammar in miscellaneous approaches . The rising of systemic grammar offers the foundation for the setting up of pragmatics . Abstract The ECOPATH II microcomputer software is presented as an approach for balancing ecosystem models . It includes ( i ) routines for balancing the flow in a steady-state ecosystem from estimation of a missing parameter for all groups in the system , ( ii ) routines for estimating network flow indices , and ( iii ) miscellaneous routines for deriving additional indices such as food selection indices and omnivory indices . The use of ECOPATH II is exemplified through presentation of a model of the Schlei Fjord ecosystem ( Western Baltic ) . This chapter describes the user-visible routines in the Raster Graphics Tool Kit . These routines can be classified broadly into five categories ( described in the order in which they are presented ) : initialization and finalization routines ( InitRGToolKit and FinalRGToolKit ) , creation and destruction routines ( MakeRaster , ExtractRaster and DeleteRaster ) , assignment and access routines ( AssignBit , AssignPel , GetBit , GetPel , InsertRaster , RasterPelFormat , RasterHeight and Raste rWidth ) , input and output routines ( ReadMemRaster , ReadRaster , WriteMemRaster and WriteRaster ) and miscellaneous routines ( HoldRaster , ReleaseRaster and RasterStatus ) . The pedagogical approach is improved through a more systematized presentation of machining processes along with frequent assessments . Student completed five modules – turning , milling , drilling and tapping , CNC introduction , and a miscellaneous operations module . Each module included specific instructions , supervised machining , and a quiz . The values determined by ROSFIT are essentially the same as those found using the BMDPAR , BMDP3R , and NONLIN programs on a Univac 90 60 mainframe computer . A typical run for competitive inhibition with 46 data points took a total time of about 18.5 min , not including dat entry time , 14 min for the graph generation , 2 min for the three required iterations , and 2.5 min for miscellaneous operations . The kernel data structure is a combination of engineering information and product-oriented definitions . The data from miscellaneous information systems , such as CAD/CAM , MRP/MRPII , ERP , and PDM , in the de sign and manufacturing phase of a product 's life cycle can be exchanged using this system . This paper also employs STEP to present an integrated product-oriented data structure to manage engineering information from different information systems . Over time , these techniques have been refined and have achieved limited success . They are concentrated into three categories : statistical methods , these mainly relate to the journey to crime , age of offending and offending behaviour ; techniques using geographical information systems that identify crime hot spots , repeat victimisation , crime attractors and crime generators ; a miscellaneous group which includes machine learning techniques to identify patterns in criminal behaviour and studies involving reoffending . The majority of current techniques involve the prediction of either a single offender ’s criminality or a single crimetype ’s next offence . This paper discusses review of literature of open shop scheduling problems . First , the problem is classified as per different measures of performance , viz . , minimization of makespan , minimization of sum of completion times of jobs , minimization of sum of weighted completion times of all jobs , minimization of total tardiness of all jobs , minimization of sum of weighted tardiness of all jobs , minimization of weighted sum of tardy jobs , and miscellaneous measures of the open shop scheduling problem . In each category , the literature is further classified based on approaches used and then the contributions of researchers in the respective categories are presented . Boundary value problems in polar coordinates 15 . Eigenfunction methods on arbitrary domains Part V. Miscellaneous Solution Methods : 16 . Separation of variables 17 . Prioritized Scheduling . Miscellaneous Status Methods . Methods These demonstrations have three implications for memory modelling : ( 1 ) the relationship between MINERVA and other memory models is clarified , suggesting that MIN- ERVA may be suitable as a basis for all memory modelling , ( 2 ) MINERVA is scalable to arbitrarily long-term learning , and ( 3 ) MINERVA is neurally plausible . Keywords : memory ; cognitive modelling ; MINERVA 2 ; vectors ; tensors ; holographic reduced representations ; HRRs . Introduction Most memory phenomena can be explained by unified , computational memory models ( e.g. , Franklin & Mewhort , 2013 ; Hintzman , 1984 ; Humphreys , Bain , & Pike , 1989 ; Jamieson & Mewhort , 2011 ) . More specifically , given any signature grid on G=(V , E ) over a set of symmetric functions , we completely classify the complexity to be in P or # P-hard , according to , of $ $ \sum_{\sigma : E \rightarrow \{0,1\}}\prod_{v\in V } f_v(\sigma \vert _ {E(v)}),$$ where ( 0 , 1 are the unary constant 0 , 1 functions ) . Not only is holographic reduction the main tool , but also the final dichotomy can be only naturally stated in the language of holographic transformations . The proof goes through another dichotomy theorem on Boolean complex weighted # CSP . In the study of counting complexity , such as # CSP , there are problems which are # P-hard over general graphs but P-time solvable over planar graphs . A recurring theme has been that a holographic reduction [ 36 ] to FKT precisely captures these problems . This dichotomy answers the question : This paper presents a new model of semantic role change that addresses this problem . The model uses an existing technique , Holographic Reduced Representation ( HRR ) for representing roles and their fillers . Starting with specific roles , the model learns to generalize roles through exposure to language data . According to Thagard and Stewart ( Cogn Sci 35(1):1–33 , 2011 ) , creativity results from the combination of neural representations ( an idea which Thagard calls ‘ the combinatorial conjecture ’ ) , and combination results from convolution , an operation on vectors defined in the holographic reduced representation ( HRR ) framework ( Plate , Holographic reduced representation : distributed representation for cognitive structures , 2003 ) . They use these ideas to understand creativity as it occurs in many domains , and in particular in science . Dynamically Structured Holographic Memory ( DSHM ) is a cognitive model of associative memory that can be applied to the problem of recommendation . DSHM uses holographically reduced representations to encode the associations between objects that it learns about to generate recommendations . We compare the recommendations from this holographic recommender to a user-based collaborative filtering algorithm on several dataset , including MovieLens , and two bibliographic datasets from a scientific digital library . As the compression ratio of applications dynamically changes so does the real memory size that is managed by the OS . In this paper , we describe and evaluate the operating system techniques developed for compressed memory systems that can deal with such dynamically changing memory size conditions . The method is used to calculate S parameters of passive components such as microstrip filters and transitions , and is proved to be accurate and reliable compared with the reported data . In the algorithm , non uniform meshes and three kinds of ABCs are adopted to reduce the order of the final sparse matrix equation , and compressed storage and fast iterative solver are applied to solve the sparse matrix equation . In order to save the storage space , we need to compress storage for such matrix . The main purpose of the compressed storage is to make more of the same nonzero elements share the same storage unit according to the distribution of matrix element , while the zero elements do n’t allocate storage space . In this paper , we studied the single vector compressed storage and the multi vectors compressed storage of the uniform block diagonal matrix , and obtained the corresponding storage address mapping function , so as to help the scientific research worker . However , they are ineffective on object-based programs because objects do not fall neatly into fixed-size blocks and have a more irregular layout . We present the first compressed memory hierarchy designed for object-based applications . We observe that ( i ) objects , not cache lines , are the natural unit of compression for these programs , as they traverse and operate on object pointers ; and ( ii ) though redundancy within each object is limited , redundancy across objects of the same type is plentiful . The resulting compressed graph fits in less than 100 GB of RAM , corresponding to a hardware cost of less than 300 U.S. dollars . We show that the compressed in-memory representation of the full corpus can be accessed with excellent performances , with edge lookup times close to memory random access . As a sample exploitation experiment we show that the compressed graph can be used to conduct clone detection at this scale , benefiting from main memory access speed . In the field of OLAP and Data Warehousing , column stores and compressed main-memory data storage technology have successfully been implemented in products that enable a significant speed improvement of analytical queries with special performance requirements . We could soon see the majority of analytical workloads move to such main-memory based systems . Further , mechanisms that focus on the capacity metric also require complex logic to locate the requested data in memory . In this paper , we design a highly simple compressed memory architecture that does not target the capacity metric . Instead , it focuses on complexity , energy , bandwidth , and reliability . An overview of a set of algorithms and data structures developed for compressed-memory machines is given . These include 1 ) very fast compression and decompression algorithms , for relatively small fixed-size lines , that are suitable for hardware implementation ; 2 ) methods for storing variable-size compressed lines in main memory that minimize overheads due to directory size and storage fragmentation , but that are simple enough for implementation as part of a system memory controller ; 3 ) a number of operating system modifications required to ensure that a compressed-memory machine never runs out of memory as the compression ratio changes dynamically . This research was done to explore the feasibility of computer architectures in which data are decompressed/compressed on cache misses/writebacks . PURPOSE : A reuse space reserve of compression memory system is provided to have free space for storing the contents being held in a compressed form and organized as pages . CONSTITUTION : The system includes a control apparatus for managing performance of input-output operation of the compressed contents between memory storage devices . We also introduce a new data mapping policy that is able to maximize read/write parallelism and NVM endurance , when dealing with compressed blocks . Combined , our proposals are able to eliminate OS involvement and improve performance by 7 % ( DRAM ) and 8 % ( NVM ) , and system energy by 12 % ( DRAM ) and 14 % ( NVM ) , relative to an uncompressed memory system .
MATERIALS AND METHODS We designed a hierarchical neural network ( denoted as CNN-Highway-LSTM or LSTM-Highway-LSTM ) for the track 1 of the national natural language processing ( NLP ) clinical challenge ( n2c2 ) on cohort selection for clinical trials in 2018 . The neural network is composed of 5 components : ( 1 ) sentence representation using convolutional neural network ( CNN ) or long short-term memory ( LSTM ) network ; ( 2 ) a highway network to adjust information flow ; ( 3 ) a self-attention neural network to reweight sentences ; ( 4 ) document representation using LSTM , which takes sentence representations in chronological order as input ; ( 5 ) a fully connected neural network to determine whether each criterion is met or not . We compared the proposed method with its variants , including the methods only using the first component to represent documents directly and the fully connected neural network for classification ( denoted as CNN-only or LSTM-only ) and the methods without using the highway network ( denoted as CNN-LSTM or LSTM-LSTM ) .
Extensive experiments on CIFAR-10 , CIFAR-100 , Fashion-MNIST and USPS datasets are performed to validate the effectiveness of the proposed approach . Particularly , we outperform the original highway network and many state-of-the-art results . To the best our knowledge , on the Fashion-MNIST and USPS datasets , the achieved results are the best reported in literature .
A major responsibility of highway engineers is to provide for the public a highway system capable of accommodating vehicle and pedestrian travel in a safe , efficient , and economic manner . In developing this highway system , the engineer is responsible for the planning , design , construction , operation , and maintenance of that system . In many instances the maintenance function is relegated to a minor position .
Abstract Engineering designs for mountainous highways emphasize compliance checking to ensure safety . However , relying solely on compliance checking may lead designers to minimize costs at the expense of high risk indicators , since the overall risk level of the highway design is unknown to the designers . This paper describes a method for the simultaneous consideration of traffic safety risks and the associated cost burden related to the appropriate planning and design of a mountainous highway .
When designed , horizontal and vertical highway alignment components are parametrically described geometric objects ( e.g. , smooth curves and straight lines ) . However , surface models resulting from highway design and for use in construction by automated machine guidance ( AMG ) are not geometrically smooth . Rather , they are triangulated irregular networks containing straight line segments that serve as edges of contiguous triangular facets .
This work investigates the performance of deep networks on statistical parametric speech synthesis , particularly the question of whether different acoustic features can be better generated by a deeper network . To answer this question , this work examines a multi-stream highway network that separately generates spectral and F0 acoustic features based on the highway architecture . Experiments on the Blizzard Challenge 2011 corpus show that the accuracy of the generated spectral features consistently improves as the depth of the network increases from 2 to 40 , but the F0 trajectory can be generated equally well by either a deep or a shallow network .
Research also indicates that when platooning allows maneuvers such as merging and splitting of platoons , these maneuvers present reduced benefits in traffic dynamics and consequently its impact on the environment . In light of these observations , this study presents a highway architecture that implements and controls lane and destination based fixed-routed platoons of automated vehicles with minimal lane changes . The study demonstrates that confining platoons to vehicles of same off-ramp destinations reduces the need for lane changes to a predetermined number , allows for more stable and longer strings of vehicle platoons of up to 24 vehicles per platoon .
This traditional method results in the identification of locations that are not truly hazardous from a road-safety authority perspective and consequently may lead to a misapplication of safety improvement funding . Therefore , accidents should be categorized into one or a combination of the three highway system components : the driver , the vehicle , and the road . In previous work , a procedure has been described that allows each accident to have a membership in each of the three highway system components . This paper describes the application of this procedure for the identification of accident-prone locations .
To address this issue , we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions : ( 1 ) Implicitly modelling by learning to predict node pair relations ( 2 ) Explicitly modelling by adding edges between nodes that potentially have the same label . To combine our two solutions , we propose a model-agnostic training framework named HighwayGraph , which overcomes the challenge of insufficient labeled nodes by sampling node pairs from the training set and adopting the self-training method . Extensive experimental results show that our HighwayGraph achieves consistent and significant improvements over four representative GNNs on three benchmark datasets .
Geometric design is an important phase in the highway design process that is directly related to traffic safety . Highway elements should be jointly designed to account for such design criteria as sight distance , vehicle stability , driver comfort , drainage , and aesthetics . Intuitively , such a design should be based on a three-dimensional ( 3-D ) analysis .
The computer simulation technology is adopted to simulate drivers’track decision behavior in driving in order to study the influence of highway geometric alignment on the path of vehicles under free flow traffic state . For this purpose , the driver directional control model taking highway alignment conditions into consideration is developed on the basis of preview optimal curvature model which is put forward by Academician Guo Konghui with the introduction of SK highway geometric model . The main conclusions obtained are as follows : When the driver ’s track decision behavior is influenced only by the condition of the road itself , the path has a tendency to shift inward of the curves as vehicles entered the curves and tended to shift outward of the curves as vehicles exit the curves .
Apart from utilizing pre-trained character embeddings and trigram features , we incorporate neural language model and conduct multi-task training . Highway layers are applied to tackle the discordance issue of the naive co-training . Experimental results on CTB5 , CTB7 , and PPD datasets show the effectiveness of the proposed method .
Prime reasons for additionally requiring background flows were twofold i ) to provide a rigorous basis for the economic evaluation , II ) within the adjacent urban and rural areas , to be able to show the influence of the scheme in the context of the full flows on the local roads . Typically , in economic terms , the area of inluence of a highway scheme will extend outside the traffic model validation area . It has been shown that use of partial flows only can cause an under-estimate of user benefits when applied to a COBA assessment .
We observe that deeper networks often suffer from gradients vanishing and become hard to train — a problem that is well addressed in the literature . To address the above two problems , inspired by highway networks and deep residual networks , we add skip connections between two corresponding convolutional and deconvolutional layers as shown in Figure [ reference ] . A building block is shown in Figure [ reference ] .
Recent follow - up work explored the order of activations in residual networks , presenting identity mappings in residual blocks and improving training of very deep networks . Successful training of very deep networks was also shown to be possible through the use of highway networks , which is an architecture that had been proposed prior to residual networks . The essential difference between residual and highway networks is that in the latter residual links are gated and weights of these gates are learned .
Hyperparameters of each model were tuned on the development set of LDC2015E86 . For the GCN components , we use two layers , activations , and highway layers . We use single layer LSTMs .
Second , proceed from the first to the final layer , normalizing the variance of the output of each layer to be equal to one . Experiment with different activation functions ( maxout , ReLU - family , tanh ) show that the proposed initialization leads to learning of very deep nets that ( i ) produces networks with test accuracy better or equal to standard methods and ( ii ) is at least as fast as the complex schemes proposed specifically for very deep nets such as FitNets ( ) and Highway ( ) . Performance is evaluated on GoogLeNet , CaffeNet , FitNets and Residual nets and the state - of - the - art , or very close to it , is achieved on the MNIST , CIFAR - 10 / 100 and ImageNet datasets .
Language is the main tool of man communication , it has an impact on grammar in miscellaneous approaches . The rising of systemic grammar offers the foundation for the setting up of pragmatics .
Abstract The ECOPATH II microcomputer software is presented as an approach for balancing ecosystem models . It includes ( i ) routines for balancing the flow in a steady-state ecosystem from estimation of a missing parameter for all groups in the system , ( ii ) routines for estimating network flow indices , and ( iii ) miscellaneous routines for deriving additional indices such as food selection indices and omnivory indices . The use of ECOPATH II is exemplified through presentation of a model of the Schlei Fjord ecosystem ( Western Baltic ) .
This chapter describes the user-visible routines in the Raster Graphics Tool Kit . These routines can be classified broadly into five categories ( described in the order in which they are presented ) : initialization and finalization routines ( InitRGToolKit and FinalRGToolKit ) , creation and destruction routines ( MakeRaster , ExtractRaster and DeleteRaster ) , assignment and access routines ( AssignBit , AssignPel , GetBit , GetPel , InsertRaster , RasterPelFormat , RasterHeight and Raste rWidth ) , input and output routines ( ReadMemRaster , ReadRaster , WriteMemRaster and WriteRaster ) and miscellaneous routines ( HoldRaster , ReleaseRaster and RasterStatus ) .
The pedagogical approach is improved through a more systematized presentation of machining processes along with frequent assessments . Student completed five modules – turning , milling , drilling and tapping , CNC introduction , and a miscellaneous operations module . Each module included specific instructions , supervised machining , and a quiz .
The values determined by ROSFIT are essentially the same as those found using the BMDPAR , BMDP3R , and NONLIN programs on a Univac 90 60 mainframe computer . A typical run for competitive inhibition with 46 data points took a total time of about 18.5 min , not including dat entry time , 14 min for the graph generation , 2 min for the three required iterations , and 2.5 min for miscellaneous operations .
The kernel data structure is a combination of engineering information and product-oriented definitions . The data from miscellaneous information systems , such as CAD/CAM , MRP/MRPII , ERP , and PDM , in the de sign and manufacturing phase of a product 's life cycle can be exchanged using this system . This paper also employs STEP to present an integrated product-oriented data structure to manage engineering information from different information systems .
Over time , these techniques have been refined and have achieved limited success . They are concentrated into three categories : statistical methods , these mainly relate to the journey to crime , age of offending and offending behaviour ; techniques using geographical information systems that identify crime hot spots , repeat victimisation , crime attractors and crime generators ; a miscellaneous group which includes machine learning techniques to identify patterns in criminal behaviour and studies involving reoffending . The majority of current techniques involve the prediction of either a single offender ’s criminality or a single crimetype ’s next offence .
This paper discusses review of literature of open shop scheduling problems . First , the problem is classified as per different measures of performance , viz . , minimization of makespan , minimization of sum of completion times of jobs , minimization of sum of weighted completion times of all jobs , minimization of total tardiness of all jobs , minimization of sum of weighted tardiness of all jobs , minimization of weighted sum of tardy jobs , and miscellaneous measures of the open shop scheduling problem . In each category , the literature is further classified based on approaches used and then the contributions of researchers in the respective categories are presented .
Boundary value problems in polar coordinates 15 . Eigenfunction methods on arbitrary domains Part V. Miscellaneous Solution Methods : 16 . Separation of variables 17 .
Prioritized Scheduling . Miscellaneous Status Methods . Methods
These demonstrations have three implications for memory modelling : ( 1 ) the relationship between MINERVA and other memory models is clarified , suggesting that MIN- ERVA may be suitable as a basis for all memory modelling , ( 2 ) MINERVA is scalable to arbitrarily long-term learning , and ( 3 ) MINERVA is neurally plausible . Keywords : memory ; cognitive modelling ; MINERVA 2 ; vectors ; tensors ; holographic reduced representations ; HRRs . Introduction Most memory phenomena can be explained by unified , computational memory models ( e.g. , Franklin & Mewhort , 2013 ; Hintzman , 1984 ; Humphreys , Bain , & Pike , 1989 ; Jamieson & Mewhort , 2011 ) .
More specifically , given any signature grid on G=(V , E ) over a set of symmetric functions , we completely classify the complexity to be in P or # P-hard , according to , of $ $ \sum_{\sigma : E \rightarrow \{0,1\}}\prod_{v\in V } f_v(\sigma \vert _ {E(v)}),$$ where ( 0 , 1 are the unary constant 0 , 1 functions ) . Not only is holographic reduction the main tool , but also the final dichotomy can be only naturally stated in the language of holographic transformations . The proof goes through another dichotomy theorem on Boolean complex weighted # CSP .
In the study of counting complexity , such as # CSP , there are problems which are # P-hard over general graphs but P-time solvable over planar graphs . A recurring theme has been that a holographic reduction [ 36 ] to FKT precisely captures these problems . This dichotomy answers the question :
This paper presents a new model of semantic role change that addresses this problem . The model uses an existing technique , Holographic Reduced Representation ( HRR ) for representing roles and their fillers . Starting with specific roles , the model learns to generalize roles through exposure to language data .
According to Thagard and Stewart ( Cogn Sci 35(1):1–33 , 2011 ) , creativity results from the combination of neural representations ( an idea which Thagard calls ‘ the combinatorial conjecture ’ ) , and combination results from convolution , an operation on vectors defined in the holographic reduced representation ( HRR ) framework ( Plate , Holographic reduced representation : distributed representation for cognitive structures , 2003 ) . They use these ideas to understand creativity as it occurs in many domains , and in particular in science .
Dynamically Structured Holographic Memory ( DSHM ) is a cognitive model of associative memory that can be applied to the problem of recommendation . DSHM uses holographically reduced representations to encode the associations between objects that it learns about to generate recommendations . We compare the recommendations from this holographic recommender to a user-based collaborative filtering algorithm on several dataset , including MovieLens , and two bibliographic datasets from a scientific digital library .
As the compression ratio of applications dynamically changes so does the real memory size that is managed by the OS . In this paper , we describe and evaluate the operating system techniques developed for compressed memory systems that can deal with such dynamically changing memory size conditions .
The method is used to calculate S parameters of passive components such as microstrip filters and transitions , and is proved to be accurate and reliable compared with the reported data . In the algorithm , non uniform meshes and three kinds of ABCs are adopted to reduce the order of the final sparse matrix equation , and compressed storage and fast iterative solver are applied to solve the sparse matrix equation .
In order to save the storage space , we need to compress storage for such matrix . The main purpose of the compressed storage is to make more of the same nonzero elements share the same storage unit according to the distribution of matrix element , while the zero elements do n’t allocate storage space . In this paper , we studied the single vector compressed storage and the multi vectors compressed storage of the uniform block diagonal matrix , and obtained the corresponding storage address mapping function , so as to help the scientific research worker .
However , they are ineffective on object-based programs because objects do not fall neatly into fixed-size blocks and have a more irregular layout . We present the first compressed memory hierarchy designed for object-based applications . We observe that ( i ) objects , not cache lines , are the natural unit of compression for these programs , as they traverse and operate on object pointers ; and ( ii ) though redundancy within each object is limited , redundancy across objects of the same type is plentiful .
The resulting compressed graph fits in less than 100 GB of RAM , corresponding to a hardware cost of less than 300 U.S. dollars . We show that the compressed in-memory representation of the full corpus can be accessed with excellent performances , with edge lookup times close to memory random access . As a sample exploitation experiment we show that the compressed graph can be used to conduct clone detection at this scale , benefiting from main memory access speed .
In the field of OLAP and Data Warehousing , column stores and compressed main-memory data storage technology have successfully been implemented in products that enable a significant speed improvement of analytical queries with special performance requirements . We could soon see the majority of analytical workloads move to such main-memory based systems .
Further , mechanisms that focus on the capacity metric also require complex logic to locate the requested data in memory . In this paper , we design a highly simple compressed memory architecture that does not target the capacity metric . Instead , it focuses on complexity , energy , bandwidth , and reliability .
An overview of a set of algorithms and data structures developed for compressed-memory machines is given . These include 1 ) very fast compression and decompression algorithms , for relatively small fixed-size lines , that are suitable for hardware implementation ; 2 ) methods for storing variable-size compressed lines in main memory that minimize overheads due to directory size and storage fragmentation , but that are simple enough for implementation as part of a system memory controller ; 3 ) a number of operating system modifications required to ensure that a compressed-memory machine never runs out of memory as the compression ratio changes dynamically . This research was done to explore the feasibility of computer architectures in which data are decompressed/compressed on cache misses/writebacks .
PURPOSE : A reuse space reserve of compression memory system is provided to have free space for storing the contents being held in a compressed form and organized as pages . CONSTITUTION : The system includes a control apparatus for managing performance of input-output operation of the compressed contents between memory storage devices .
We also introduce a new data mapping policy that is able to maximize read/write parallelism and NVM endurance , when dealing with compressed blocks . Combined , our proposals are able to eliminate OS involvement and improve performance by 7 % ( DRAM ) and 8 % ( NVM ) , and system energy by 12 % ( DRAM ) and 14 % ( NVM ) , relative to an uncompressed memory system .
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