This document describes a method called SemanticSVD++ for improving recommendations by incorporating semantic categories. It begins by describing how movies were aligned to DBPedia URIs and categorized semantically. It then quantifies the "cold-start categories" problem, where many categories are unreviewed. It proposes transferring categories between items using vertex kernels on the semantic graph. User profiles track taste evolution over time for categories. SemanticSVD++ extends the SVD++ model to incorporate these semantic category biases and tastes into predictions. The method is evaluated for improving ratings predictions.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
There is increasing need for large-scale recommendation systems. Typical solutions rely on periodically retrained batch algorithms, but for massive amounts of data, training a new model could take hours. This is a problem when the model needs to be more up-to-date. For example, when recommending TV programs while they are being transmitted the model should take into consideration users who watch a program at that time.
The promise of online recommendation systems is fast adaptation to changes, but methods of online machine learning from streams is commonly believed to be more restricted and hence less accurate than batch trained models. Combining batch and online learning could lead to a quickly adapting recommendation system with increased accuracy. However, designing a scalable data system for uniting batch and online recommendation algorithms is a challenging task. In this talk we present our experiences in creating such a recommendation engine with Apache Flink and Apache Spark.
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Claudio Greco
Slides for the presentation of the paper "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
The solution of problem of parameterization of the proximity function in ace ...eSAT Journals
Abstract
In this work, a new approach for defining the value of the proximity function, which is carried out in the second step of the
Algorithms for Calculating Estimates (ACE) in the area of Pattern Recognition, is presented. The value of the proximity function
is defined as a part of corresponding features of two objects. The main attention is paid to essential features of the polytypic in a
given training set. One of the important problems of the ACE is to compare the values of fuzzy attributes. The main idea of this
approach is considering the proximity the corresponding quantitative and qualitative features together. Here a complexity of
comparing the qualitative features and an approach of overcoming such complexity are considered. Such features include the
features with fuzzy values. The membership function of fuzzy set theory is used for determining membership degrees of the feature
values describing with linguistic values for improve the quality of ACE. The steps of the algorithm for transfer the results is
obtained from the comparison of the two values of fuzzy feature by using membership function to the proximity function. The
membership function with two parameters (b and c) is used. For defining optimal values of these parameters evolutionary
algorithms for solving optimization problems are used, one of them is Genetic algorithm. By using genetic algorithm initial
parameters’ values of the membership function are generated and transmitted to the proximity function. The ACE is run and value
of functional quality is defined during the training process with given training set. If the value of the functional quality is not
sufficiently high than the values obtained by Genetic algorithm, these values are regenerated using special operators (selection,
crossover, mutation) of the Genetic algorithm. The algorithm for selection optimal values of the parameters of the membership
function using the Genetic algorithm is given.
Key Words: ACE, proximity function, Genetic algorithm, membership function, parameters, operators.
Discovering Your AI Super Powers - Tips and Tricks to Jumpstart your AI ProjectsWee Hyong Tok
In this session, we will share about cutting-edge deep learning innovations, and present emerging trends in the AI community. This session is for data scientists, developers who have a keen interest in getting started in an AI project, and wants to learn the tools of the trade. We will draw on practical experiences from working on various AI projects, and share the key learning, and pitfalls
Presented at the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011), August 26th, 2011 in Cluj-Napoca, Romania.
Publication: http://bit.ly/x1OpFL
Abstract:
In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc1 ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification methods based on AdaBoost and Random forest algorithms for identifying interest objects like: cars, pedestrians, poles in traffic and we derive a set of annotations for each traffic scene. These annotations are mapped to OpenCyc concepts and predicates, spatiotemporal rules for object classification and scene understanding are then asserted in the knowledge base. Finally, we show that the system performs well in understanding traffic scene situations and summarizing them. The novelty of the approach resides in the combination of stereo-based object detection and recognition methods with logic based spatio-temporal reasoning.
Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
#10 pydata warsaw object detection with dn nsAndrew Brozek
PyData Warsaw #10: Deep & Machine Learning
Object detection with Deep Learning
These are the references for the first part of the talk.
1) a Stanford lecture
http://vision.stanford.edu/teaching/cs231n/slides/2016/winter1516_lecture8.pdf
2) OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://arxiv.org/abs/1312.6229
3) Selective Search for Object Recognition https://www.koen.me/research/selectivesearch/
4) Rich feature hierarchies for accurate object detection and semantic segmentation
https://arxiv.org/abs/1311.2524
5) Fast R-CNN
https://arxiv.org/abs/1504.08083
6) Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
https://arxiv.org/pdf/1506.01497.pdf
7) A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
https://arxiv.org/abs/1704.03414
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Multimodal Residual Networks for Visual QAJin-Hwa Kim
Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.
k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
From User Needs to Community Health: Mining User Behaviour to Analyse Online ...Matthew Rowe
Invited keynote talk at the 1st Workshop of Quality, Motivation and Coordination of Open Collaboration @ the International Conference on Social Informatics 2013
More Related Content
Similar to Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Alessandro Suglia
Presentation for "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
See paper: http://ceur-ws.org/Vol-1653/paper_11.pdf
Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neur...Claudio Greco
Slides for the presentation of the paper "Ask Me Any Rating: A Content-based Recommender System based on Recurrent Neural Networks" at the 7th Italian Information Retrieval Workshop.
The solution of problem of parameterization of the proximity function in ace ...eSAT Journals
Abstract
In this work, a new approach for defining the value of the proximity function, which is carried out in the second step of the
Algorithms for Calculating Estimates (ACE) in the area of Pattern Recognition, is presented. The value of the proximity function
is defined as a part of corresponding features of two objects. The main attention is paid to essential features of the polytypic in a
given training set. One of the important problems of the ACE is to compare the values of fuzzy attributes. The main idea of this
approach is considering the proximity the corresponding quantitative and qualitative features together. Here a complexity of
comparing the qualitative features and an approach of overcoming such complexity are considered. Such features include the
features with fuzzy values. The membership function of fuzzy set theory is used for determining membership degrees of the feature
values describing with linguistic values for improve the quality of ACE. The steps of the algorithm for transfer the results is
obtained from the comparison of the two values of fuzzy feature by using membership function to the proximity function. The
membership function with two parameters (b and c) is used. For defining optimal values of these parameters evolutionary
algorithms for solving optimization problems are used, one of them is Genetic algorithm. By using genetic algorithm initial
parameters’ values of the membership function are generated and transmitted to the proximity function. The ACE is run and value
of functional quality is defined during the training process with given training set. If the value of the functional quality is not
sufficiently high than the values obtained by Genetic algorithm, these values are regenerated using special operators (selection,
crossover, mutation) of the Genetic algorithm. The algorithm for selection optimal values of the parameters of the membership
function using the Genetic algorithm is given.
Key Words: ACE, proximity function, Genetic algorithm, membership function, parameters, operators.
Discovering Your AI Super Powers - Tips and Tricks to Jumpstart your AI ProjectsWee Hyong Tok
In this session, we will share about cutting-edge deep learning innovations, and present emerging trends in the AI community. This session is for data scientists, developers who have a keen interest in getting started in an AI project, and wants to learn the tools of the trade. We will draw on practical experiences from working on various AI projects, and share the key learning, and pitfalls
Presented at the 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP 2011), August 26th, 2011 in Cluj-Napoca, Romania.
Publication: http://bit.ly/x1OpFL
Abstract:
In this paper we introduce a system for semantic understanding of traffic scenes. The system detects objects in video images captured in real vehicular traffic situations, classifies them, maps them to the OpenCyc1 ontology and finally generates descriptions of the traffic scene in CycL or cvasi-natural language. We employ meta-classification methods based on AdaBoost and Random forest algorithms for identifying interest objects like: cars, pedestrians, poles in traffic and we derive a set of annotations for each traffic scene. These annotations are mapped to OpenCyc concepts and predicates, spatiotemporal rules for object classification and scene understanding are then asserted in the knowledge base. Finally, we show that the system performs well in understanding traffic scene situations and summarizing them. The novelty of the approach resides in the combination of stereo-based object detection and recognition methods with logic based spatio-temporal reasoning.
Computer Vision Landscape : Present and FutureSanghamitra Deb
Millions of people all around the world Learn with Chegg. Education at Chegg is powered by the depth and diversity of the content that we have. A huge part of our content is in form of images. These images could be uploaded by students or by content creators. Images contain text that is extracted using a transcription service. Very often uploaded images are noisy. This leads to irrelevant characters or words in the transcribed text. Using object detection techniques we develop a service that extracts the relevant parts of the image and uses a transcription service to get clean text. In the first part of the presentation, I will talk about building an object detection model using YOLO for cropping and masking images to obtain a cleaner text from transcription. YOLO is a deep learning object detection and recognition modeling framework that is able to produce highly accurate results with low latency. In the next part of my presentation, I will talk about the building the Computer Vision landscape at Chegg. Starting from images on academic materials that are composed of elements such as text, equations, diagrams we create a pipeline for extracting these image elements. Using state of the art deep learning techniques we create embeddings for these elements to enhance downstream machine learning models such as content quality and similarity.
#10 pydata warsaw object detection with dn nsAndrew Brozek
PyData Warsaw #10: Deep & Machine Learning
Object detection with Deep Learning
These are the references for the first part of the talk.
1) a Stanford lecture
http://vision.stanford.edu/teaching/cs231n/slides/2016/winter1516_lecture8.pdf
2) OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
https://arxiv.org/abs/1312.6229
3) Selective Search for Object Recognition https://www.koen.me/research/selectivesearch/
4) Rich feature hierarchies for accurate object detection and semantic segmentation
https://arxiv.org/abs/1311.2524
5) Fast R-CNN
https://arxiv.org/abs/1504.08083
6) Faster R-CNN: Towards Real-Time Object
Detection with Region Proposal Networks
https://arxiv.org/pdf/1506.01497.pdf
7) A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection
https://arxiv.org/abs/1704.03414
[SOCRS2013]Differential Context Modeling in Collaborative FilteringYONG ZHENG
Abstract: Context-aware recommender systems (CARS) try to adapt their recommendations to users’ specific contextual situations. In many recommender systems, particularly those based on collaborative filtering (CF), the additional contextual constraints may lead to increased sparsity in the user preference data, thus fewer matches between the current user context and previous situations. Our earlier work proposed two approaches to deal with this problem – differential context relaxation (DCR) and differential context weighting (DCW) and we have successfully examined them using user-based collaborative filtering (UBCF). In this paper, we put DCR and DCW into one framework called differential context modeling (DCM). As a general framework, DCM is able to be applied to other recommendation algorithms other than UBCF. We expand the application of DCM to the other two CF approaches: item-based CF and slope one recommender. Predictive performances are evaluated based on two real-world data sets and experimental results demonstrate that applying DCM to those two algorithms is able to improve predictive accuracy compared with our baselines: context-free CF algorithms and contextual pre-filtering algorithms.
Slides were formed by referring to the text Machine Learning by Tom M Mitchelle (Mc Graw Hill, Indian Edition) and by referring to Video tutorials on NPTEL
Multimodal Residual Networks for Visual QAJin-Hwa Kim
Deep neural networks continue to advance the state-of-the-art of image recognition tasks with various methods. However, applications of these methods to multimodality remain limited. We present Multimodal Residual Networks (MRN) for the multimodal residual learning of visual question-answering, which extends the idea of the deep residual learning. Unlike the deep residual learning, MRN effectively learns the joint representation from vision and language information. The main idea is to use element-wise multiplication for the joint residual mappings exploiting the residual learning of the attentional models in recent studies. Various alternative models introduced by multimodality are explored based on our study. We achieve the state-of-the-art results on the Visual QA dataset for both Open-Ended and Multiple-Choice tasks. Moreover, we introduce a novel method to visualize the attention effect of the joint representations for each learning block using back-propagation algorithm, even though the visual features are collapsed without spatial information.
k-Means is a rather simple but well known algorithms for grouping objects, clustering. Again all objects need to be represented as a set of numerical features. In addition the user has to specify the number of groups (referred to as k) he wishes to identify. Each object can be thought of as being represented by some feature vector in an n dimensional space, n being the number of all features used to describe the objects to cluster. The algorithm then randomly chooses k points in that vector space, these point serve as the initial centers of the clusters. Afterwards all objects are each assigned to center they are closest to. Usually the distance measure is chosen by the user and determined by the learning task. After that, for each cluster a new center is computed by averaging the feature vectors of all objects assigned to it. The process of assigning objects and recomputing centers is repeated until the process converges. The algorithm can be proven to converge after a finite number of iterations. Several tweaks concerning distance measure, initial center choice and computation of new average centers have been explored, as well as the estimation of the number of clusters k. Yet the main principle always remains the same. In this project we will discuss about K-means clustering algorithm, implementation and its application to the problem of unsupervised learning
Similar to Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++ (20)
From User Needs to Community Health: Mining User Behaviour to Analyse Online ...Matthew Rowe
Invited keynote talk at the 1st Workshop of Quality, Motivation and Coordination of Open Collaboration @ the International Conference on Social Informatics 2013
Attention Economics in Social Web SystemsMatthew Rowe
Slides from a Highwire Digital Futures Seminar that I gave at Lancaster University on 25th October 2012 covering Attention Economics in Social Web Systems
Using Behaviour Analysis to Detect Cultural Aspects in Social Web SystemsMatthew Rowe
Presented at:
-Aston Business School, Birmingham, UK. 2011
-Keynote presentation at Detecting and Exploiting Cultural Diversity on the Social Web Workshop, 20th Annual Conference on Information and Knowledge Management 2011
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
1. TRANSFERRING SEMANTIC
CATEGORIES WITH VERTEX
KERNELS:
RECOMMENDATIONS WITH
SemanticSVD++
MATTHEW ROWE
SCHOOL OF COMPUTING AND COMMUNICATIONS
@MROWEBOT | M.ROWE@LANCASTER.AC.UK
International Semantic Web Conference 2014
Riva del Garda, Trento, Italy
3. Latent Factor Models: Factor Consistency Problem
1 … f
1
2
3
1 2 3
1 8* 8* 3*
2 9* ? 1*
3 9* 8* 1*
1 2 3
1
…
f
≈
F = #factors (a priori)
Time
?
?
?
?
• Cannot ‘accurately’ align latent factors
• Cannot tell how users’ taste have evolved
2
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
4. Solution: Semantic Categories
i <URI> {<SKOS_CATEGORY>}
1 … c
1
2
3
1 2 3
1 8* 8* 3*
2 9* ? 1*
3 9* 8* 1*
≈
Preference for
category c at
time s
√" √"
Time
S3e manticSVD++: Incorporating Semantic Taste Evolution for Predicting Ratings. M Rowe. In the proceedings of
the International Conference on Web Intelligence 2014. Warsaw, Poland. (2014)
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
8. ?category). If the year of the movie item appears within a mapped category
(?category) then we identified the given semantic URI as denoting the item.
This disambiguation was needed here as multiple films can share the same ti-tle
Dataset: MovieTweetings + URI Alignment
(i.e. film remakes). This approach achieved coverage (i.e. proportion of items
mapped) of 69%: this reduced coverage is explained by the recency of the movies
being reviewed and "the I rated Aliens title/lack tt0133093/ of coverage 8/10 http://#IMDb"this www.imdb.com/
!
on DBPedia at present. Table 1
presents the statistics of the dataset following URI alignment.
explicit factors. The web of linked data provided a resource
for such information, where movies appear within the linked
data cloud as Uniform Resource Identifiers (URIs) which, upon
dereferencing, return information about the movie: director,
year of release, actors, and the semantic categories in which
the film has been placed. For instance, for the movie ‘Alien’
released in 1979, which we shall now use as a running
example, the following categories are found:
<h t t p : / / dbpedia . org / r e sour c e / Al iens ( fi lm)>
https://github.com/sidooms/MovieTweetings
From this point on we use the following notations to aid comprehension: u, v
denote users, i, j denote SPARQL
items, r denotes a known rating value (where r [1, 10]),
r ˆdenotes a predicted Query for
Get Semantic
2 Candidate
rating value, Categories c denotes of
a semantic Disambiguate
based on
category that an item
has been mapped to, URIs and from
cats(i) is candidate
a each
convenience Movie’s function Year
that returns the set
of semantic categories Movie’s of title
item i.
dcterms : s u b j e c t c a t egory : Al ien ( f ranchi se ) f i l m s ;
dcterms : s u b j e c t c a t egory :1986 hor r o r f i lms .
In this work we use DBPedia URIs, given their relation
to semantic (SKOS) categories. In order to provide such
information, however, we require a link between a given item
within one of our recommendation datasets and the URI that
denotes that movie item, prompting the question: How can
items be aligned to their semantic web URIs? Our method for
semantic URI alignment functioned as follows: first, we used
who: and and that in the work The information evolved limited following the large approach the ratings items obscure Table 1. Statistics of the MovieTweetings dataset with the reduction from the original
dataset shown in parentheses.
7
#Users #Items #Ratings Ratings Period
14,749 (-22.5%) 7,913 (-30.8%) 86,779 (-25.9%) [28-03-2013,23-09-2013]
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
{(ItemID,<URI>)}
Training (first 80%), Validation (next 10%), Testing (final 10%)
11. Now, let C be the set of categories that a given user has rated previously,
mapping function :
Definition 2 (Vertex Kernel). Given graph vertices u and v from a linked
data graph G0, we define a vertex kernel (k) as a surjective function that maps
the product of two vertices’ attribute vectors into a real valued space, where (u)
is a convenience function that returns kernel-specific attributes to be used by the
function (i.e. an n-dimensional attribute vector of node u: (u) 2 Rn). Hence:
and D be the set of categories that a given item has been mapped to, then we
define the Category Transfer function as follows:
k : V ⇥ V ! R (2)
k((u), (v))7−! x Triple-Object Vectors of
(3)
Categories
Given this formulation, we can vary the kernel function (k(., .)) to measure
the similarity between arbitrary categories based on the topology k(φ(c2), φ(of c4))the !
linked
data graph that surrounds them. All the kernels considered in c2this ! paper c4!
function
over two nodes’ feature vectors. Therefore, to derive the feature vector for a given
category node (c), we include information about the objects that c is linked to
within the linked data graph. Let c ?p ?o define a triple where c appears
within the subject position. We can then populate a vector (x) based on the
object concepts that c links to over 1-hop: 1 2 Rn - where n denotes the
dimensionality of the vector space. This can also be extended to n hops away
from c by traversing edges away from c and collecting the objects within the
traversed triples. Each element in the vector is weighted by the out-degree of
Category Transfer Function
f(C,D) = {argmax
c2C
k
!
c, d
: d 2 D} (1)
The codomain of the Category Transfer function is therefore a subset of the
set of categories that the user has rated beforehand (i.e. C0 ⇢ C). In the above
dbpedia:c2!
dbpedia:c4!
dbpedia:_1!
dbpedia:_2!
dbpedia:_3!
…!
dbpedia:_n!
0
0.5
0.5
…
0
0
0
0.5
…
0.5
Vertex Kernel
Function
Rated Categories
Categories of the Item
10 Vary k(.,.): Cosine, Dice, Squared Euclidean, JS-Divergence
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
12. User Profiling with Semantic Categories
Split user’s
training ratings
into 5-stages
Derive the user’s
average rating
per semantic
category
Calculate the
probability of
the user rating
the category
highly
For each stage…
s
Pu
and how their tastes have evolved, at this
time. From this point onwards we reserve
characters for set notations, as follows:
users, and i, j denote items.
known rating value (where r 2 [1, 5] or
ˆr denotes a predicted rating value.
provided as quadruples of the form
where t denotes the time of the rating,
segmented into training (Dtrain), validation
test (Dtest) sets by the above mentioned
semantic category that an item has been
cats(i) is a convenience function that
of semantic categories of item i.
Profiles
profiles describe the preferences that a user
time for given semantic categories.
understanding how a profile at one point
profile at an earlier point in time,
taste evolution has taken place. In
McAuley and Leskovec [5] the assessment of
in the context 11
of review platforms (e.g.
Review) demonstrated the propensity
based on their own ‘personal clock’. This
to segment a user’s lifetime (i.e. time
From these definitions we then derived the discrete prob-ability
distribution of the user rating the category favourably
as follows, defining the set Cu,s
train as containing all unique
categories of items rated by u in stage s:
Pr(c|Du,s
train) =
avrating(Du,s,c
train) X
c02Cu,s
train
avrating(Du,s,c0
train )
(4)
When implementing this approach, we only consider the
categories that item URIs are directly mapped to; that is,
only those categories that are connected to the URI by the
dbterms:subject predicate. Prior work by Ostuni et al.
[8] performed a mapping where grandparent categories were
mapped to URIs, however we chose the parent categories in
this instance to open up the possibility of other mappings in
the future - i.e. via linked data node vertex kernels.
B. User Taste Evolution: From Prior Taste Profiles
1 2
5
We now turn to looking at the evolution of users’ tastes
…
over time in order to understand how their preferences change.
Given our use of probability distributions to model the lifecycle
stage specific taste profile of each user, we can apply infor-mation
Time
theoretic measures based on information entropy. One
such measure is conditional entropy, it enables one to assess
the information needed to describe the taste profile of a user
at one time step (Q) using his taste profile from the previous
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
13. Taste Evolution over Rated Categories
0.275 0.280 0.285 0.290
Lifecycle Stages
Conditional Entropy
●
●
●
●
1 2 3 4 5
Users diverge from prior tastes
(increase)
0.112 0.114 0.116
Lifecycle Stages
Transfer Entropy
● ●
●
●
1 2 3 4 5
Users are not influenced by global
tastes (increase)
12
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
14. stages (e.g.
95% con-fidence
Putting it all together: SemanticSVD++!
stage s.
describes the
current stage
previous stage
categories at the
entropy of
(6)
calculate
conditional prob-ability
variables
(7)
Fig. 3. Transfer entropy between consecutive lifecycle stages (e.g.
H(P2|P3)) across the datasets, together with the bounds of the 95% con-fidence
interval for the derived means.
Modified version of SVD++ with:
• User taste evolution captured in semantic category biases
• Semantic personalisation component
named SemanticSV D++, an extension of Koren et al.’s ear-lier
SV D++ model [2]. The predictive function of the model
is shown in full in Eq. 8, we now explain each component in
greater detail.
ˆrui =
zStatic}|Biases{
μ + bi + bu +
Category Biases z }| {
↵ibi,cats(i) + ↵ubu,cats(i)
+
Personalisation Component z }| {
q|i
pu + |R(u)|−12
X
j2R(u)
yj
+ |cats(R(u))|−12
X
c2cats(R(u))
zc
!
(8)
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
A. Static Biases
13
15. SV D++ model [2]. The predictive function of the model
shown in full in Eq. 8, we now explain each component in
greater detail.
such signals on a per-user basis by assessing the change in
transfer entropy for each user over time and modelling this as a
global influence factor u. We derive this as follows, based on
measuring the proportional change in transfer entropy starting
from lifecycle period k that produced a monotonic increase or
decrease in transfer entropy:
Transferring Categories using Vertex Kernels
ˆrui =
zStatic}|Biases{
μ + bi + bu +
Category Biases z }| {
↵ibi,cats(i) + ↵ubu,cats(i)
General Category
Biases
+
u =
1
4 − k
X4
Ts+1|s
Q!P − Ts|s−1
Q!P
z i
|Personalisation }| Component Ts|s−1
{
z Prior s=Rated k
}| Categories Q!P
{
qpu + |R(u)|−12X
By combining the average change rate (uc
|C cats(i)|
X
highly rating a given category c2{cats(c with i)C}
the global influence factor
(u), we then derived j2R(the conditional u)
probability of a user
rating a given category highly as follows, where Pu
yj
bu,cats(i) =
⇣
!k
⌘
1
+
⇣
1 − !k
⌘
) of the user
Transferred Categories z }| {
Vertex Kernel (k)
can be varied
+ |cats(R(u))|−12
X
Pr(+|c, u)
1
|fk(C, cats(i)/C)|
X
the taste profile of the user observed c2fk(for C,D)
the final lifecycle
stage (5):
c2cats(R(u))
5 denotes
Pr(+|c, u)
zc
!
User Biases to
Categories
(8)
Static Biases
The static 14
biases include the general bias of the given
dataset (μ), which is the mean rating score across all ratings
within the training segment; the item bias (bi), and the user bias
(13)
(16)
Here we have !k-weighted the influence of the transferred categories on the
bias in order Pr(+|assess the e↵ects of the transferred categories on recommendation
accuracy. In essence, c, u) !=
k forms one of our hyperparameters that we optimise
when tuning the model over the validation set for a given vertex kernel (k). As
!k 2 [0, 1] we can assess its e↵ect: a larger !k places more emphasis on known
information, while a lower !k places more emphasis on transferred categories by
the given kernel (k). As the category biases are, in essence, static features we
included two weights, one for each category bias, defined as ↵i and ↵u for the
item biases to categories and the user biases to categories respectively - these
weights are then learnt during the training phase of inducing the model.
Prior Rating z }| {
Pu
5 (c) +
Change Rate z }| {
!u
c Pu
5 (c) +
Glzobal}I|nflue{nce
uQ5(c) (14)
hyperparameter D. Model To learn (item and vectors) function, fitting using min
b⇤,↵⇤,p⇤,To learn (SGD) the order through Average change in Transfer
Entropy of the User
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
β controls the
transfer of
category ratings
16. Experiments
¨ Aim: Predict users’ ratings of items
¤ Minimise the Root Mean Square Error (RMSE)
1. How does a semantic model perform
against existing MF-based approaches?
2. Does transferring categories actually
reduce error?
15
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
17. Experimental Setup
¨ Tested four models (trained using Stochastic Gradient
Descent)
¤ SVD and SVD++ (baselines)
¤ SB-SVD++: SVD++ with Semantic Category Biases
¤ S-SVD++ (SB-SVD++ with personalisation component)
¨ Tuned hyperparameters over the validation split:
¤ All models: Learning rate, regularisation weight, #factors
¤ Semantic Models: transfer parameter β
¨ Model testing:
¤ Trained models using both training+validation splits
¤ Applied to held-out final 10% of reviews
¨ Evaluation measure: Root Mean Square Error
16
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
18. that the user has previously rated, rather than transferring in ratings to cover
the unreviewed categories. We find varying levels across the other kernels where,
aside from the JS-Divergence kernel, the optimised k places more emphasis on
using rated semantic categories that the item is aligned to.
Results: Ratings Prediction Error
Table 4. Root Mean Square Error (RMSE) results with each model’s best kernel is
highlighted in bold with the p-value of the Mann-Whitney with the baseline marked.
Model Kernel Tuned Parameters RMSE
SVD - = 0.001, ⌘ = 0.1, f = 50 1.786
SVD++ - = 0.01, ⌘ = 0.05, f = 100 1.591
SBSVD++ - = 105, ⌘ = 0.05, f = 100 1.590*
Cosine = 105, ⌘ = 0.05, f = 20, $k = 0.9 1.588***
Dice = 0.001, ⌘ = 0.05, f = 20, $k = 0.7 1.589**
Squared-Euclidean = 105, ⌘ = 0.05, f = 20, $k = 0.6 1.589**
JS-Divergence = 0.01, ⌘ = 0.05, f = 50, $k = 0.3 1.590*
SSVD++ - = 0.001, ⌘ = 0.05, f = 20 1.590*
Cosine = 0.01, ⌘ = 0.05, f = 5, $k = 0.8 1.588***
Dice = 0.001, ⌘ = 0.05, f = 20, $k = 0.9 1.590*
Squared-Euclidean = 0.05, ⌘ = 0.05, f = 5, $k = 0.7 1.590*
JS-Divergence = 104, ⌘ = 0.05, f = 10, $k = 0.8 1.589**
Significance codes: p-value 0.001 *** 0.01 ** 0.05 * 0.1 .
Tuned β gives preference of known rated categories over transferred ones
7 N.b. all tested models significantly outperformed SV D at p 0.001, so we do not
report the di↵erent p-values here.
17
Transferring Semantic Categories with Vertex Kernels: Recommendations with SemanticSVD++
19. Conclusions
¨ Users’ taste evolution can be tracked with semantic
categories
¨ Vertex kernels overcome the cold-start categories
problem
¨ Significant reduction in error:
¤ Over baselines + When transferring semantic
categories
¨ Future Work:
1. Vertex kernels based on linked data graph traversals
2. Ranked-loss objectives (i.e. top-k models)