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Humans have used sketching to depict our visual world since prehistoric times. Even today, sketching is possibly the only rendering technique readily available to all humans. This paper is the first large scale exploration of human sketches. We analyze the distribution of non-expert sketches of everyday objects such as 'teapot' or 'car'. We ask humans to sketch objects of a given category and gather 20,000 unique sketches evenly distributed over 250 object categories. With this dataset we perform a perceptual study and find that humans can correctly identify the object category of a sketch 73% of the time. We compare human performance against computational recognition methods. We develop a bag-of-features sketch representation and use multi-class support vector machines, trained on our sketch dataset, to classify sketches. The resulting recognition method is able to identify unknown sketches with 56% accuracy (chance is 0.4%). Based on the computational model, we demonstrate an interactive sketch recognition system. We release the complete crowd-sourced dataset of sketches to the community.
Human classifications on the full dataset. In the left column of each category page, we show the sketches that have been correctly classified. In the middle column we show the sketches that actually belong to the category but have not been recognized. In the last column we show the false positives, i.e. those sketches that humans incorrectly predicted to belong to the category.
Computational classification results on the test dataset using the best-performing SVM model as described in the paper. In the first column of each category page, we show 5 samples of the training dataset. In the second column, we show sketches that have been correctly classified. In the third column we show the sketches that actually belong to the category but have not been recognized. In the last column we show the false positives, i.e. those sketches that have been incorrectly predicted to belong to that category.
For each category, we apply dimensionality reduction on the sketch feature space described in the paper (down to two dimensions). We plot the results as a 2D layout of the sketches that nicely illustrates the variety of sketching styles within each category.
For each category, we compute a representative, iconic sketch. We first cluster the category using mean shift. Next, for each cluster, we compute the average descriptor and identify the nearest neighbor of this average descriptor as the cluster representative.
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API Platform Core relies on the concept of operations. Operations can be applied to a resource exposed by the API. From an implementation point of view, an operation is a link between a resource, a route and its related controller.
API Platform automatically registers typical operations and describes them in the exposed documentation (Hydra and Swagger). It also creates and registers routes corresponding to these operations in the Symfony routing system (if it is available).
The behavior of built-in operations is briefly presented in the Getting started guide.
The list of enabled operations can be configured on a per resource basis. Creating custom operations on specific routes is also possible.
There are two types of operations: collection operations and item operations.
Collection operations act on a collection of resources. By default two routes are implemented:
POST
and
GET
. Item operations act on an individual resource. 3 default routes are defined
GET
,
PUT
and
DELETE
(
PATCH
is also supported when
using the JSON API format
, as required by the specification).
When the
ApiPlatform\Core\Annotation\ApiResource
annotation is applied to an entity class, the following built-in CRUD operations are automatically enabled:
Collection operations
Item operations
If no operation are specified, all default CRUD operations are automatically registered. It is also possible - and recommended for large projects - to define operations explicitly.
Keep in mind that
collectionOperations
and
itemOperations
behave independently. For instance, if you don't explicitly configure operations for
collectionOperations
,
GET
and
POST
operations will be automatically registered, even if you explicitly configure
itemOperations
. The reverse is also true.
Operations can be configured using annotations, XML or YAML. In the following examples, we enable only the built-in operation for the
GET
method for both
collectionOperations
and
itemOperations
to create a readonly endpoint.
itemOperations
and
collectionOperations
are arrays containing a list of operation. Each operation is defined by a key corresponding to the name of the operation that can be anything you want and an array of properties as value. If an empty list of operations is provided, all operations are disabled.
If the operation's name match a supported HTTP methods (
GET
,
POST
,
PUT
or
DELETE
), the corresponding
method
property will be automatically added.
The previous example can also be written with an explicit method definition:
A small series of studies has investigated the impact of experimenter gender on physical performance, and, again, significant results were observed. In one study, the effect of experimenter gender was investigated for participants performing a 50-yard dash, a shuttle run, and sit-ups. The study demonstrated that, for sit-ups, male experimenters elicited better scores for both genders of participants (
). On the other hand, both the 50-yard dash and the shuttle run participants performed significantly better when paired with an opposite-gender experimenter, regardless of their own gender. However, other studies have demonstrated a lack of effect with regard to physical performance. One study, for example, investigating the impact of experimenter gender on performance on grip strength and hand steadiness tests found no interaction for either task (
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). Thus, much like intelligence and learning, physical performance appears to generally be enhanced by opposite-gender experimenters, although there are some inconsistencies and null results.
Where measurable physical performance is altered, one should of course expect biological systems underlying this to be modified as well. In particular, experiments reveal that—perhaps unsurprisingly—sex steroids such as testosterone are affected by experimenter gender, which, in turn, causes differences in physical performance. For instance, one study revealed that young male skateboarders take increased physical risks in the presence of an attractive female (
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). This increased risk taking leads to not only more successes but also more crash landings in front of a female observer. Mediational analyses reveal that this effect is influenced in part by elevated testosterone levels in men who performed in front of the attractive female. In addition, performance on a reversal-learning task predicted physical risk taking, and reversal-learning performance was also disrupted by the presence of the attractive female, and the female’s presence moderated the observed relationship between risk taking and reversal learning. These data of course fit closely with earlier data suggesting an impact of experimenter gender on learning. Combined, these results suggest that men use physical risk taking as a sexual display strategy and that this may be moderated by elevated testosterone levels in the presence of a woman (be she an experimenter or otherwise).
Further evidence reveals not only that testosterone is selectively elevated in the presence of a female experimenter but also that it appears that this is quantifiable in perspiration. More specifically, men excrete higher levels of the sex steroids 17β-estradiol and testosterone when performing rigorous exercise in the presence of a female experimenter ( ). In turn, these hormones are absorbed by the experimenter, surely having additional effects on the experimenter and his or her instructions and behavior. Combined, these papers reveal a critically important link: Experimenter gender affects hormonal substrates. The question of how far-reaching this is remains unanswered, but sex steroids could represent the tip of the iceberg. The implications for clinical therapeutics should be clear: There could be, for example, a huge biasing effect produced in estimates of the efficacy of testosterone boosting medications, if the tests are administered by females.
Starting in the 1990s, a growing body of literature on pain sensitivity revealed that experimenter gender was biasing results. Initial findings suggested that male participants demonstrate a significantly higher pain threshold (reporting significantly less pain) when tested by female experimenters (
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). The same study found a trend toward women actually reporting higher pain when tested by a male experimenter, but this did not reach significance. Several years later, studies investigated the phenomenon of male participants demonstrating lower pain sensitivity when tested by females, and the early result has generally been supported (
,
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). A recent meta-analysis helps make sense of these findings. Alabas analyzed 13 studies that looked at gender role and pain thresholds. The consensus finding was that participants who viewed themselves as more masculine and less sensitive to pain demonstrated higher pain thresholds and tolerance (
). Another study investigated whether these findings of reduced pain sensitivity for men with female experimenters were mirrored by alterations in autonomic pain response (as measured by heart rate variability and skin conductance levels). The study found that lower pain reports in male participants with female experimenters were not mediated by changes in autonomic parameters and the effect was thus likely more the result of psychosocial factors (
). For example, it could be that men in general tolerate higher levels of pain with a female experimenter as a function of their attempt to display higher degrees of masculinity.
With the preceding sections, the cascade of mental and physical reactions to experimenter gender should reveal a system-wide effect on general functioning. That said, it should be unsurprising that behavior is also affected. Again, the extent of the effect is still understood only for a few dimensions of interpersonal interaction, but the results thus far provide fertile ground for future hypothesis testing. They also, unfortunately, create the same pervasive concern regarding study replicability for behavior-based research and interventions.
"Every decision is emotional." By utteringthis simple sentence on the main stage of AA-ISP's Inside Sales Leadership Summit, Tom Snyder, founder of VorsightBP, got a roomful of attendees to nodtheir heads in agreement.
Making a B2B purchase is undoubtedly a serious decision, and if you asked buyers how they came to a conclusion, they'd likely say "logic." Butas psychologists know, we can fool ourselves into thinking we're more rational than we actually are.
"In his book, Descartes Error , Antonio Damasio, professor of neuroscience at the University of Southern California, argues that emotion is a necessary ingredient to almost all decisions," Peter Noel Murray wrote in a Psychology Today article . "When we are confronted with a decision, emotions from previous, related experiences affix values to the options we are considering. These emotions create preferences which lead to our decision."
Consider this piece of evidence alongside the following explanation of cognitive dissonance, from
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:
"Cognitive dissonance theory holds that when people hold contradictory beliefs ('I’m a rational, autonomous person' yet 'I just did something pointless'), they experience an unpleasant state, cognitive dissonance, which they mitigate by bringing one of the beliefs into consistency with the other."
With this in mind, it's not hard to imagine a prospect buying solely on emotion and then justifying it as a "rational" decision later on.
Emotions, then, should be of prime importance to salespeople. If your pitch doesn't hit an emotional high note, your chances making a sale diminish considerably.
But which emotion is the right one to appeal to? Sales expert Geoffrey James explains that all buying decisions come down to a mixture of the following six feelings :
Greed. Altruism Pride. Shame.Choosing which to focus on depends on your buyer's personality (what will resonate most with them?) and your offering (what most closely reflects your value proposition?)
Once you've determined which emotional lever to pull, use the following tips to create a tear-jerking, heart-stopping, chest-puffing presentation your prospect willnever forget.
Just because someone is motivated by greed doesn't mean they're a bad person. Think about it -- we're all motivated by money, promotions, and rewards. Does that make us awful, villainous scoundrels?Nah -- it just makes us human.