Causality in collective filtering

In signal processinga causal filter is a linear and time-invariant causal system. The word causal indicates that the filter output depends only on past and present inputs. A filter whose output also depends on future inputs is non-causalwhereas a filter whose output depends only on future inputs is anti-causal.

Systems including filters that are realizable i. If shortening is necessary, it is often accomplished as the product of the impulse-response with a window function. An example of an anti-causal filter is a maximum phase filter, which can be defined as a stableanti-causal filter whose inverse is also stable and anti-causal.

A realizable output is. Any linear filter such as a moving average can be characterized by a function h t called its impulse response. Its output is the convolution. Define the function. We now have the following relation. This means that the Fourier transforms of h t and g t are related as follows.

Taking the Hilbert transform of the above equation yields this relation between "H" and its Hilbert transform:. From Wikipedia, the free encyclopedia. Categories : Signal processing Filter theory.

causality in collective filtering

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causality in collective filtering

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I accept. Polski English Login or register account. Enhancing Collective Filtering with Causal Representation. Paolucci, MarioPicascia, Stefano. Abstract In this paper, we propose to enhance the practice of web-based collective filtering with the addition of a causality linking module. Causality lies at the foundations of human understanding, when presented in visual form, is especially suited to the task as it is intuitive to understand and to use.

But in its simplicity, causality could provide a semantic network over the filtering tool, connecting representations of real world facts. Authors Close. Assign yourself or invite other person as author. It allow to create list of users contirbution.

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Assignment does not change access privileges to resource content. Wrong email address. You're going to remove this assignment. Are you sure? Yes No. Additional information Data set: ieee. Publisher IEEE. You have to log in to notify your friend by e-mail Login or register account. Download to disc. High contrast On Off.Enter your mobile number or email address below and we'll send you a link to download the free Kindle App.

Causal filter

Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. To get the free app, enter your mobile phone number. This book is a strikingly new exploration of the fundamentals of Maxwell's electromagnetic theory and of Newton's theory of gravitation. Starting with an analysis of causality in the phenomenon of electromagnetic induction, the author discovers a series of heretofore unknown or overlooked electromagnetic interdependencies and equations.

causality in collective filtering

One of the most notable new results is the discovery that Maxwell's equations do not depict cause and effect relations between electromagnetic phenomena: causal dependencies in electromagnetic phenomena are found to be described by solutions of Maxwell's equations in the form of retarded electric and magnetic field integrals. A consequence of this discovery is that, contrary to the generally accepted view, time-variable electric and magnetic fields cannot cause each other and that both fields are simultaneously created by their true causative sources -- time-dependent electric charges and currents.

Another similarly important discovery is that Lenz's law of electromagnetic induction is a manifestation of the previously ignored electric force produced by the time-dependent electric currents. These discoveries lead to important new methods of calculations of various electromagnetic effects in time- depended electromagnetic systems.

The new methods are demonstrated by a variety of illustrative examples. Continuing his analysis of causal electromagnetic relations, the author finds that these relations are closely associated with the law of momentum conservation, and that with the help of the law of momentum conservation one can analyze causal relations not only in electromagnetic but also in gravitational systems.

This leads to the discovery that in the time-dependent gravitational systems the momentum cannot be conserved without a second gravitational force field, which the author calls the "cogravitational, or Heaviside's, field.

The author then generalizes Newton's gravitational theory to time-dependent systems and derives causal gravitational equations in the form of two retarded integrals similar to the retarded integrals for the electric and magnetic fields introduced previously. One of the most important consequences of the causal gravitational equations is that a gravitational interaction between two bodies involves not one force as in Newton's theory but as many as five different forces corresponding to the five terms in the two retarded gravitational and cogravitational field integrals.

These forces depend not only on the masses and separation of the interacting bodies, but also on their velocity and acceleration and even on the rate of change of their masses.

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A series of illustrative examples on the calculation of these new forces is provided and a graphical representation of these forces is given. The book concludes with a discussion of the possibility of antigravitation as a consequence of the negative equivalent mass of the gravitational field energy. The book is written in the style and format of a textbook. The clear presentation, the detailed derivations of all the basic formulas and equations, and the many illustrative examples make this book well suitable not only for independent studies but also as a supplementary textbook in courses on electromagnetic theory and gravitation.

The second edition of the book refines and improves the first edition, especially in the presentation and development of Newton's gravitational theory generalized to time-dependent gravitational systems. The book has been augmented by several new Appendixes. Particularly notable are Appendixes 5, 6, and 8. Appendixes 5 and 6 present novel "dynamic" electric and gravitational field maps of rapidly moving charges and masses.

Appendix 8 contains the little-known but extremely important Heaviside's article on the generalization of Newton's gravitational theory. Read more Read less. Kindle Cloud Reader Read instantly in your browser. Frequently bought together. Add all three to Cart Add all three to List. Some of these items ship sooner than the others. Show details. Ships from and sold by Amazon. FREE Shipping. Customers who viewed this item also viewed.In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals.

In frequency domain analyses power or coherence of neural data, it is common to preprocess the time series by filtering or decimating. However, in other fields, it has been shown theoretically that filtering in combination with Granger causality may lead to spurious or missed causalities. We investigated whether this result translates to multivariate causality methods derived from Granger causality with a a simulation study and b an application to magnetoencephalographic data.

To this end, we performed extensive simulations of the effect of applying different filtering techniques and evaluated the performance of five different multivariate causality measures in combination with two numerical significance measures random permutation and leave one out method. The analysis included three of the most widely used filters high-pass, low-pass, notch filterfour different filter types Butterworth, Chebyshev I and II, elliptic filtervariation of filter order, decimating and interpolation.

The effect of filtering on Granger causality based multivariate causality measures

The simulation results suggest that preprocessing without a strong prior about the artifact to be removed disturbs the information content and time ordering of the data and leads to spurious and missed causalities. Only if apparent artifacts like a current or movement artifact are present, filtering out the respective disturbance seems advisable.

While oversampling poses no problem, decimation by a factor greater than the minimum time shift between the time series may lead to wrong inferences. In general, the multivariate causality measures are very sensitive to data preprocessing.

Abstract In the past, causality measures based on Granger causality have been suggested for assessing directionality in neural signals. Publication types Research Support, Non-U.To browse Academia. Skip to main content. Log In Sign Up. Download Free PDF. Causality in collective filtering Mario Paolucci. Stefano Picascia. Causality in collective filtering. In this paper, we describe a proposal for improving the prac- tice of web-based collective filtering, in particular for what regards dis- cussions and selection of issues about policy, based on the intuitive con- cept of causality.

We give some examples of the suggested system workflow and we present guidelines for its im- plementation. They implement the principles of rating and ranking to present information in a hierarchical way, ordered according measures of relevance and community appreciation. They exploit the participatory practices typical of Web 2.

The basic principles that inform collective filters have been proposed for deliberative platforms, ICT systems aimed at facilitating debate and policy monitoring. Web 2. Tagging con- sists of user generated keywords aimed to describe a resource, or some aspects of a resource, as perceived from the cognitive perspective of the individual. The tag approach is used for several kind of resources, for example, technical documentation, entertainment resources as movie or music description, and so on.

In the following, we focus on a particular kind of resource, that is, social news sites, where users select and discuss upon "interesting" and valuable news headlines proposed through a process of collaborative content filtering and dis- played as a list. The process of selection is quite straightforward and typical of the Web 2. A basic reputation mechanism [7] is included: users submitting popular i.

Sometimes the voice of high-karma users counts more towards the reaching of the threshold needed for a post to get on the frontpage, or a comment to be displayed.

However, the difference is in the semantics of a vote. PageRank is unable to discern between different semantics of a web link. For example, when linking to a fraudulent web page as a warning to other users, PageRank will count the link as a positive vote. Social reputation systems operate on a semantic layer above PageRank, by allowing people to consciously vote for pages, and even to express negative ratings.

With several millions of users collectively filtering and discussing news items, sites like digg. For what regards resources connected to entertainment, popularity may result from surprise, shock, aesthetic appeal or novelty, and that is in accord with the purposes of the system; much less so when there is a level of reality to be defended - where one is promoting or burying items whose objective reality gets masked from the perception of the collective, or, even worse, facts that have an effect on decisions, on policy, that would be prioritized better without the shock value.

For a decision to make sense, it must be based on real facts; even more so for decisions that involve the attribution of representative power. We must suppose that electors are correctly informed on the matters on which they are called to vote.

Asking people that are uninformed amounts to throwing a dice; even worse, calling to decide people that have been mystified, fed with purposely inaccurate information, and manipulated through the over-exposition to emotionally rele- vant episodic narrative, amounts to covering decisions taken elsewhere with a false blanket of popular consent.

Freedom alone, however, is not enough; it should be accompanied by a sincere research of truth on the part of the pro- fessionals of the press - the journalists. While the first can only exist if protected by law, the second is probably harder to maintain; some kind of ethical code and the passion of the readers for truth can contribute. How is the current situation with regards to that?

Is the principle of inde- pendence of press upheld? Experts - journalists, intellectuals, technicians - used to play the role of information selectors. The functioning of this information selection mechanism works in two phases; a first selection process individuates a number of individuals; these are then attributed a social power, that of selecting relevant and truthful information, and in turn present this to the general public.

We could call this mechanism centralized selection.In control theorya causal system also known as a physical or nonanticipative system is a system where the output depends on past and current inputs but not future inputs—i. The idea that the output of a function at any time depends only on past and present values of input is defined by the property commonly referred to as causality.

A system that has some dependence on input values from the future in addition to possible dependence on past or current input values is termed a non-causal or acausal systemand a system that depends solely on future input values is an anticausal system. Note that some authors have defined an anticausal system as one that depends solely on future and present input values or, more simply, as a system that does not depend on past input values.

Classically, nature or physical reality has been considered to be a causal system. Physics involving special relativity or general relativity require more careful definitions of causality, as described elaborately in Causality physics. The causality of systems also plays an important role in digital signal processingwhere filters are constructed so that they are causal, sometimes by altering a non-causal formulation to remove the lack of causality so that it is realizable.

For more information, see causal filter. For a causal system, the impulse response of the system must use only the present and past values of the input to determine the output.

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This requirement is a necessary and sufficient condition for a system to be causal, regardless of linearity. Note that similar rules apply to either discrete or continuous cases. By this definition of requiring no future input values, systems must be causal to process signals in real time. From Wikipedia, the free encyclopedia. Pearson Education. Categories : Classical control theory Digital signal processing Systems theory Physical systems Dynamical systems.

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Namespaces Article Talk. Views Read Edit View history. Help Learn to edit Community portal Recent changes Upload file. Download as PDF Printable version.Python Collective matrix factorization with cold-start functionality recommender systems. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.

Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again.

Implementation of collective matrix factorization, based on Relational learning via collective matrix factorizationwith some enhancements and alternative models for cold-start recommendations as described in Cold-start recommendations in Collective Matrix Factorization. The overall idea was extended here to also be able to do cold-start recommendations for users and items that were not in the training data but which have side information available.

Although the package was developed with recommender systems in mind, it can also be used in other domains - just take any mention of users as rows in the main matrix and any mention of items as columns. For more information about the implementation here, or if you would like to cite this in your research, see "Cold-start recommendations in Collective Matrix Factorization".

For a similar package with Poisson distributions see ctpfrec. The package has been rewritten in C with Python wrappers. If you've used earlier versions of this package which relied on Tensorflow for the calculations and before that, Casadithe optimal hyperparameters will be very different now as it has changed some details of the loss function such as not dividing some terms by the number of entries.

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The new version is faster, multi-threaded, and has some new functionality, but if for some reason you still need the old one, it can be found under the git branch "tensorflow". The model consist in predicting the rating weighted confidence for implicit-feedback case that a user would give to an item by performing a low-rank factorization of an interactions matrix e. This also has the side effect of allowing recommendations for users and items for which there is side information but no ratings, although these predictions might not be as high quality.

Alternatively, can produce factorizations in wich the factor matrices are determined from the attributes directly e. Windows with unlucky setuptools : clone or download this repository and then install with setup. Requires package findblaswhich can be install with pip install findblas. As it contains C code, it requires a C compiler.

On Mac, installing this package will first require getting OpenMP modules for the default clang compiler redistributions from apple don't come with this essential component, even though clang itself does fully support itor installing gcc by default, apple systems will alias gcc to clang, which causes problems. Users and items can be reindexed internally if passing data framesso you can use strings or non-consecutive numbers as IDs when passing data to the object's methods.

Causal and Non-Causal Systems

This kind of model requires a lot more hyperparameter tuning that regular low-rank matrix factorization, and fitting a model with badly-tuned parameters might result in worse recommendations compared to discarding the side information. If your dataset is larger than the MovieLens ratings, adding product side information is unlikely to add more predictive power, but good user side information might still be valuable. For any installation problems or errors encountered with this software, please open an issue in this GitHub page with a reproducible example, the error message that you see, and description of your setup e.

Python version, NumPy version, operating system. We use optional third-party analytics cookies to understand how you use GitHub.


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