Zero - Searching
In June 2019, the clickstream analysis service Jumpshot (which has since been shut down following controversy over data use and privacy) published its latest analysis of desktop and mobile zero click searches on Google.com in the US. The results can be summarized as follows:
Zero - Searching
What this means is that more than half of all searches now generate no traffic for third-party websites outside Google services. In the US in particular, the number of zero click searches was found to have risen steadily over the past few years:
The SEO community is somewhat divided over how to respond to this increasing trend toward zero click searches. Websites are losing organic traffic to Google & Co., whose growing number of SERP features are helping them earn on traffic, either through ads or through direct conversions in the form of hotel and flight bookings, for instance.
Methodology caveats. This latest statistic and the 50% figure from 2019 did not come from the same data provider and included different searching methods. The new data, from SimilarWeb, includes a pool of 5.1 trillion worldwide Google searches and combines both mobile and desktop devices, including iOS devices. The data Fishkin used in 2019, from now-defunct clickstream data provider Jumpshot, included only one billion web browser searches from domestic desktop and Android devices.
Searching for a target object in a cluttered scene constitutes a fundamental challenge in daily vision. Visual search must be selective enough to discriminate the target from distractors, invariant to changes in the appearance of the target, efficient to avoid exhaustive exploration of the image, and must generalize to locate novel target objects with zero-shot training. Previous work on visual search has focused on searching for perfect matches of a target after extensive category-specific training. Here, we show for the first time that humans can efficiently and invariantly search for natural objects in complex scenes. To gain insight into the mechanisms that guide visual search, we propose a biologically inspired computational model that can locate targets without exhaustive sampling and which can generalize to novel objects. The model provides an approximation to the mechanisms integrating bottom-up and top-down signals during search in natural scenes.
Visual search constitutes a ubiquitous challenge in natural vision, including daily tasks such as looking for the car keys at home. Localizing a target object in a complex scene is also important for many applications including navigation and clinical image analysis. Visual search must fulfill four key properties: (1) selectivity (to distinguish the target from distractors in a cluttered scene), (2) invariance (to localize the target despite changes in its appearance or even in cases when the target appearance is only partially defined), (3) efficiency (to localize the target as fast as possible, without exhaustive sampling), and (4) zero-shot training (to generalize to finding novel targets despite minimal or zero prior exposure to them).
In contrast with the development of such bottom-up recognition models, less attention has been devoted to the problem of invariance in visual search. A large body of behavioral9,10,11,12 and neurophysiological13,14,15,16 visual search experiments has focused on situations that involve identical target search. In those experiments, the exact appearance of the target object is perfectly well defined in each trial (e.g., searching for a tilted red bar, or searching for an identical match to a photograph of car keys). Some investigators have examined the ability to search for faces rotated with respect to a canonical viewpoint17, but there was no ambiguity in the target appearance, therefore circumventing the critical challenge in invariant visual search. In hybrid search studies, the observer looks for two or more objects, but the appearance of those objects is fixed18. Several studies have evaluated reaction times during visual search for generic categories as a function of the number of distractors19,20.
As emphasized in the previous paragraph, there are multiple directions to improve our quantitative understanding of how humans actively explore a natural image during visual search. The current model provides a reasonable initial sketch that captures how humans can selectively localize a target object amongst distractors, the efficiency of visual search behavior, the critical ability to search for an object in an invariant manner, and zero-shot generalization to novel objects including the famous Waldo. Waldo cannot hide anymore.
Generalization allows responses acquired in one situation to be transferred to similar situations. For temporal stimuli, a discontinuity has been found between zero and non-zero durations: responses in trials with no (or 0-s) stimuli and in trials with very short stimuli differ more than what would be expected by generalization. This discontinuity may happen because 0-s durations do not belong to the same continuum as non-zero durations. Alternatively, the discontinuity may be due to generalization decrement effects: a 0-s stimulus differs from a short stimulus not only in duration, but also in its presence, thus leading to greater differences in performance. Aiming to reduce differences between trials with and without a stimulus, we used two procedures to test whether a potential reduction in generalization decrement would bring performance following zero and non-zero durations closer. In both procedures, there was a reduction in the discontinuity between 0-s and short durations, supporting the hypothesis that 0-s durations are integrated in the temporal subjective continuum.
Either interval or both lower and upper must bespecified: the upper endpoint must be strictly larger than the lowerendpoint.The function values at the endpoints must be of opposite signs (orzero), for extendInt="no", the default. Otherwise, ifextendInt="yes", the interval is extended on both sides, insearch of a sign change, i.e., until the search interval [l,u]satisfies f(l) \cdot f(u) \le 0.
uniroot() uses Fortran subroutine zeroin (from Netlib)based on algorithms given in the reference below. They assume acontinuous function (which then is known to have at least one root inthe interval).
A list with at least five components: root and f.rootgive the location of the root and the value of the function evaluatedat that point. iter and estim.prec give the number ofiterations used and an approximate estimated precision forroot. (If the root occurs at one of the endpoints, theestimated precision is NA.)init.it contains the number of initial extendIntiterations if there were any and is NA otherwise.In the case of such extendInt iterations, iter contains thesum of these and the zeroin iterations.
The fact that researchers have continually hiked back the likely date of the earliest infection means there still may not be enough evidence to identify "patient zero," but the new Chinese government data reported by the Post sharpens what we know.
"We don't know who the very first patient zero was, presumably in Wuhan, and that leaves a lot of unanswered questions about how the outbreak started and how it initially spread," Sarah Borwein, a doctor at Hong Kong's Central Health Medical Practice, told the Post last month.
Significant progress has been achieved in automating the design of various components in deep networks. However, the automatic design of loss functions for generic tasks with various evaluation metrics remains under-investigated. Previous works on handcrafting loss functions heavily rely on human expertise, which limits their extendibility. Meanwhile, existing efforts on searching loss functions mainly focus on specific tasks and particular metrics, with task-specific heuristics. Whether such works can be extended to generic tasks is not verified and questionable. In this paper, we propose AutoLoss-Zero, the first general framework for searching loss functions from scratch for generic tasks. Specifically, we design an elementary search space composed only of primitive mathematical operators to accommodate the heterogeneous tasks and evaluation metrics. A variant of the evolutionary algorithm is employed to discover loss functions in the elementary search space. A loss-rejection protocol and a gradient-equivalence-check strategy are developed so as to improve the search efficiency, which are applicable to generic tasks. Extensive experiments on various computer vision tasks demonstrate that our searched loss functions are on par with or superior to existing loss functions, which generalize well to different datasets and networks. Code shall be released.
Based on this data, we can see 2X more searches for this keyword during the period than what Ahrefs or Semrush estimated. This seems to back up the idea that zero-volume keywords probably get some searches and are worth targeting.
Sparktoro followed up their previous article with another study: "In 2020, Two-Thirds of Google Searches Ended Without a Click" discusses the continued rise of zero-click searches. According to their study, 64.82% of Google searches resulted in zero-clicks. The remaining resulted in either organic clicks or paid clicks.
Despite the inherent achievement, appearing as a featured snippet upends traditional SEO and the potential loss of site visits is not always viewed as an advantage. The following steps for an updated SEO strategy can help overcome perceived drawbacks, while taking full advantage of the potential of zero-click searches.
After identifying the keywords that have the zero-click feature in SERP, the next step is to identify current web pages that are already ranking for this keyword. Once you have identified the pages, the next step is to optimize them and add to or edit the content of the page.
Zero-click searches may not always result in a visit to your website, but they boost your visibility because you give quick, easy-to-digest answers to user questions. Targeting featured snippets and other types of zero-click content is essential for success. 041b061a72