Facebook

About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Computer science & Artificial neural network. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.

From primed concepts to action: A meta-analysis of the behavioral effects of incidentally presented words.

Abstract: A meta-analysis assessed the behavioral impact of and psychological processes associated with presenting words connected to an action or a goal representation. The average and distribution of 352 effect sizes (analyzed using fixed-effects and random-effects models) was obtained from 133 studies (84 reports) in which word primes were incidentally presented to participants, with a nonopposite control group, before measuring a behavioral dependent variable. Findings revealed a small behavioral priming effect (dFE = 0.332, dRE = 0.352), which was robust across methodological procedures and only minimally biased by the publication of positive (vs. negative) results. Theory testing analyses indicated that more valued behavior or goal concepts (e.g., associated with important outcomes or values) were associated with stronger priming effects than were less valued behaviors. Furthermore, there was some evidence of persistence of goal effects over time. These results support the notion that goal activation contributes over and above perception-behavior in explaining priming effects. In summary, theorizing about the role of value and satisfaction in goal activation pointed to stronger effects of a behavior or goal concept on overt action. There was no evidence that expectancy (ease of achieving the goal) moderated priming effects. (PsycINFO Database Record

Automatically generating nodes and edges in an integrated social graph

Abstract: In one embodiment, a method includes maintaining access to a data store of information corresponding to nodes and edges; receiving a user-generated character string comprising one or more characters of text entered by a user in an input form as they are entered by the user; searching the stored information for matches between the user-generated character string and existing nodes; determining whether or not a match between the user-generated character string and an existing node exists; and when it is determined that at least one match exists, generating an edge between the node corresponding to the user and the node for which the best match is determined; and when it is determined that no match between the user-generated character string and an existing node exists, generating a new node based on the user-generated character string, and generating an edge between the node corresponding to the user and the new node.

A MultiPath Network for Object Detection

Abstract: The recent COCO object detection dataset presents several new challenges for object detection. In particular, it contains objects at a broad range of scales, less prototypical images, and requires more precise localization. To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization. The result of these modifications is that information can flow along multiple paths in our network, including through features from multiple network layers and from multiple object views. We refer to our modified classifier as a `MultiPath' network. We couple our MultiPath network with DeepMask object proposals, which are well suited for localization and small objects, and adapt our pipeline to predict segmentation masks in addition to bounding boxes. The combined system improves results over the baseline Fast R-CNN detector with Selective Search by 66 overall and by 4x on small objects. It placed second in both the COCO 2015 detection and segmentation challenges.

Asking and Answering Questions to Evaluate the Factual Consistency of Summaries

Abstract: Practical applications of abstractive summarization models are limited by frequent factual inconsistencies with respect to their input. Existing automatic evaluation metrics for summarization are largely insensitive to such errors. We propose an automatic evaluation protocol called QAGS (pronounced "kags") that is designed to identify factual inconsistencies in a generated summary. QAGS is based on the intuition that if we ask questions about a summary and its source, we will receive similar answers if the summary is factually consistent with the source. To evaluate QAGS, we collect human judgments of factual consistency on model-generated summaries for the CNN/DailyMail (Hermann et al., 2015) and XSUM (Narayan et al., 2018) summarization datasets. QAGS has substantially higher correlations with these judgments than other automatic evaluation metrics. Also, QAGS offers a natural form of interpretability: The answers and questions generated while computing QAGS indicate which tokens of a summary are inconsistent and why. We believe QAGS is a promising tool in automatically generating usable and factually consistent text.

Generalizing DTW to the multi-dimensional case requires an adaptive approach

Abstract: In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW's efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we should trust at the time of classification. Our method allows us to ensure that classification results are at least as accurate as the better of the two rival methods, and, in many cases, our method is significantly more accurate. We demonstrate our ideas with the most extensive set of multi-dimensional time series classification experiments ever attempted.