Abstract: An online system receives content items from a third party content provider. For each content item, the online system inputs an image into a neural network and extracts a feature vector from a hidden layer of the neural network. The online system compresses each feature vector by assigning a label to each feature value representing whether the feature value was above a threshold value. The online system identifies a set of content items that the user has interacted with and determines a user feature vector by aggregating feature vectors of the set of content items. For a new set of content items, the online system compares the compressed feature vectors of the content item with the user feature vector. The online system selects one or more of the new content items based on the comparison and sends the selected content items to the user.

Abstract: An online system receives content items from a third party content provider. For each content item, the online system inputs an image into a neural network and extracts a feature vector from a hidden layer of the neural network. The online system compresses each feature vector by assigning a label to each feature value representing whether the feature value was above a threshold value. The online system identifies a set of content items that the user has interacted with and determines a user feature vector by aggregating feature vectors of the set of content items. For a new set of content items, the online system compares the compressed feature vectors of the content item with the user feature vector. The online system selects one or more of the new content items based on the comparison and sends the selected content items to the user.

Abstract: Systems and user interfaces enable integration of data items from disparate sources to generate optimized packages of data items. For example, the systems described herein can obtain data items from various sources, score the data items, and present, via an interactive user interface, options for packaging the data items based on the scores. The systems may include artificial intelligence algorithms for selecting optimal combinations of data items for packaging. Further, the interactive user interfaces may enable a user to efficiently add data items to, and remove data items from, the data packages. The system may interactively re-calculate and update scores associated with the package of data items as the user interacts with the data package via the user interface. The systems and user interfaces may thus, according to various embodiments, enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

Abstract: Systems and user interfaces enable integration of data items from disparate sources to generate optimized packages of data items. For example, the systems described herein can obtain data items from various sources, score the data items, and present, via an interactive user interface, options for packaging the data items based on the scores. The systems may include artificial intelligence algorithms for selecting optimal combinations of data items for packaging. Further, the interactive user interfaces may enable a user to efficiently add data items to, and remove data items from, the data packages. The system may interactively re-calculate and update scores associated with the package of data items as the user interacts with the data package via the user interface. The systems and user interfaces may thus, according to various embodiments, enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

Abstract: Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Techniques for automatically scheduling builds of derived datasets in a distributed database system that supports pipelined data transformations are described herein.

Abstract: Methods, systems, and computer programs are presented for evaluating the accuracy of predictive systems and quantifiable measures of incremental value. One method provides a scientific solution to test and evaluate predictive systems in a transparent, rigorous, and verifiable way to allow decision-makers to better decide whether to adopt a new predictive system. In one example, objects to be evaluated are assigned to a control group or an experiment group. The testing provides an equal or better distribution of scores in the control group for the scores obtained with the first predictor, but the method aims at maximizing the scores of objects obtained with the second predictor in the experiment group. Since the first scores are evenly distributed in both groups, any result improvements may be attributed to the better accuracy of the second predictor when the results of the experiment group are better than the results of the control group.

Abstract: To select the content to be presented to the user, a first latent vector is determined for a content item based on a first object associated with the content item. A second latent vector is determined for the content item based on a second object associated with the content item. A content item vector is then determined based on the first and second latent vectors. Furthermore, a user vector is determined based on interactions of the user with the first set of content objects and the second set of content objects. A score indicative of the likelihood of the user interacting with the content item is determined based on the content item vector and the user vector.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: Systems and user interfaces enable integration of data items from disparate sources to generate optimized packages of data items. For example, the systems described herein can obtain data items from various sources, score the data items, and present, via an interactive user interface, options for packaging the data items based on the scores. The systems may include artificial intelligence algorithms for selecting optimal combinations of data items for packaging. Further, the interactive user interfaces may enable a user to efficiently add data items to, and remove data items from, the data packages. The system may interactively re-calculate and update scores associated with the package of data items as the user interacts with the data package via the user interface. The systems and user interfaces may thus, according to various embodiments, enable the user to optimize the packages of data items based on multiple factors quickly and efficiently.

Abstract: Computer implemented systems and methods are disclosed for automatically clustering and canonically identifying related data in various data structures. Data structures may include a plurality of records, wherein each record is associated with a respective entity. In accordance with some embodiments, the systems and methods further comprise identifying clusters of records associated with a respective entity by grouping the records into pairs, analyzing the respective pairs to determine a probability that both members of the pair relate to a common entity, and identifying a cluster of overlapping pairs to generate a collection of records relating to a common entity. Clusters may further be analyzed to determine canonical names or other properties for the respective entities by analyzing record fields and identifying similarities.

Abstract: A method for determining pectin content in a plant sample includes: 1) adding an acidic alcohol solution to the plant sample, heating in a water bath and filtration; 2) soaking residue from step 1 with an acidic solution, heating in a water bath, a second filtration, bringing to volume after cooling and obtaining filtrate; 3) adding an acetic acid/sodium acetate buffer solution to residue of step 2, adding a pectinase solution, heating under vibration in a water bath, and a third filtration to obtain a filtrate; 4) adding an acetic acid/sodium acetate buffer solution and a pectinase solution to filtrate from step 2, heating the mixture under vibration in a water bath to obtain an enzymatic hydrolysate, adding filtrate obtained in step 3 to the enzymatic hydrolysate, bringing to volume, and obtaining a test solution; 5) drawing the test solution into a continuous flow analyzer to perform analysis.

Abstract: Disclosed is a method for determining pectin content in a plant sample, comprising the following steps: 1) adding an acidic alcohol solution to the plant sample, then heating the resulting mixture to reflux in a water bath, followed by a first filtration; 2) soaking the filtered residue obtained from the first filtration with an acidic solution, then heating the resulting mixture to reflux in a water bath, followed by a second filtration, then bringing to volume after cooling, obtaining filtrate for later use; 3) adding an acetic acid/sodium acetate buffer solution to treat the filtered residue obtained from the second filtration, then adding a pectinase solution and heating the resulting mixture under vibration in a water bath, followed by a third filtration to obtain a filtrate for later use; 4) sequentially adding an acetic acid/sodium acetate buffer solution and a pectinase solution to the filtrate obtained in step 2) and heating the resulting mixture under vibration in a water bath to obtain an enzymatic

Abstract: A photovoltaic device and method of making a photovoltaic device are disclosed. The method includes laminating an organic layer onto an inorganic semiconductor layer. A first electrical contact is electrically coupled to the organic layer and a second electrical contact is coupled to the inorganic semiconductor layer. The inorganic semiconductor layer may include a second organic layer. At least one of the organic layer and the second organic layer may form a heterojunction with the inorganic semiconductor layer. The organic layer may further comprise a metal layer. At least one of the organic layer, the inorganic semiconductor layer and the metal layer may be patterned.

Abstract: An implantable direct cardiac compression (DCC) device (101) has a body (102) comprising a flexible frontal cardiac compression wall (103) and a rear wall (104) together defining a pressurisable chamber (106). The cardiac compression wall (103) is adapted to be affixed to the wall of a ventricle (21) of a heart (20) and to compress the ventricle (21) upon pressurisation of the chamber (106). The device (101) is provided with two flexible flaps, one extending from each of two of opposing lateral sides of the body (102), adapted to be affixed to the ventricle wall (23). An implantable DCC system comprises left and right DCC devices (101, 201) for affixing to the left and right ventricles (21, 22). Straps (216) are provided on the right DCC device (206) for wrapping around the heart (2) and left DCC device (101) to secure the DCC devices (101, 201) to the left and right ventricles (21, 22) respectively.

Abstract: A heart actuator device for use in heart assist apparatus, which device includes a paddle-like main body. The main body has a heart compressing wall, which in use is adapted to be affixed to at least a region of heart, and a distal wall, which in use is adapted to be distal that region of the heart. The heart compressing wall is movable in a direction relatively away from the distal wall, so as, in use to compress at least that region of the heart thereby assisting movement of the heart wall.

Abstract: A method and system measure the instantaneous volume of blood contained within a chamber of a heart, irrespective of its shape, whereby stroke volume and cardiac output volume can be continuously monitored and feedback to a non-blood contacting cardiac assist device. In a preferred form the device uses the distances between the sensors which are implanted in a biomaterial that integrates with a heart surface to determine changes in heart volume. Sonomicrometry crystal measurements are disclosed as a preferred mode of obtaining distance readings. A computer readable medium carries instructions to convert data from dimension sensors into sensor positions within a predetermined coordinate system. Ventricular volume is based on the sensor positions.