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Key:JTJMJGYZQZDUJJ-UHFFFAOYSA-N YSee also:N YPhencyclidine ( PCP), also known as angel dust among other names, is a drug used for its mind-altering effects. PCP may cause, distorted perceptions of sounds,. As a, it is typically, but may be taken,. It may also be mixed with or.may include, and an increased risk of.
May occur despite stopping usage. Chemically, PCP is a member of the, and, it is a. PCP works primarily as an.PCP is most commonly used in the United States. While usage peaked there in the 1970s, between 2005 and 2011 an increase in visits to emergency departments as a result of the drug occurred. As of 2017 in the United States about 1% of people in grade twelve reported using PCP in the prior year while 2.9% of those over the age of 25 reported using it at some point in their life.PCP was initially made in 1926 and brought to market as an medication in the 1950s. Its use in humans was disallowed in the United States in 1965 due to the high rates of side effects while its use in other animals was disallowed in 1978. Moreover, was discovered and was better tolerated as an anesthetic.
PCP is classified as a in the United States. A number of derivatives of PCP have been sold for recreational and non-medical use. See also:Behavioral effects can vary by dosage. Low doses produce a numbness in the extremities and intoxication, characterized by staggering, unsteady gait, slurred speech, bloodshot eyes, and loss of balance.
Moderate doses (5–10 mg intranasal, or 0.01–0.02 mg/kg intramuscular or intravenous) will produce and anesthesia. High doses may lead to. The drug is often illegally produced under poorly controlled conditions; this means that users may be unaware of the actual dose they are taking.Psychological effects include severe changes in,. Psychosis, agitation and dysphoria, hallucinations, blurred vision, and are also reported, as well as occasional aggressive behavior.: 48–49 Like many other drugs, PCP has been known to alter mood states in an unpredictable fashion, causing some individuals to become detached, and others to become animated. PCP may induce feelings of strength, power, and invulnerability as well as a numbing effect on the mind.Studies by the in the 1970s show that media reports of PCP-induced violence are greatly exaggerated and that incidents of violence are unusual and often limited to individuals with reputations for aggression regardless of drug use.: 48 Although uncommon, events of PCP-intoxicated individuals acting in an unpredictable fashion, possibly driven by their delusions or hallucinations, have been publicized. One example is the case of, a former with a history of violent crime, who was convicted of murdering and cannibalizing his roommate while under the influence of PCP. Other commonly cited types of incidents include inflicting property damage and self-mutilation of various types, such as pulling one's own teeth.: 48 These effects were not noted in its medicinal use in the 1950s and 1960s however, and reports of physical violence on PCP have often been shown to be unfounded.Recreational doses of the drug also occasionally appear to that resembles a episode.
Users generally report feeling detached from reality.Symptoms are summarized by the device RED DANES: rage, (redness of skin), dilated pupils, delusions, (oscillation of the eyeball when moving laterally), excitation, and skin dryness. Addiction PCP is self-administered and induces expression in the of the, and accordingly, excessive PCP use is known to cause. PCP's and effects are at least partly mediated by blocking the in the glutamatergic inputs to D1-type medium spiny neurons in the nucleus accumbens. PCP has been shown to produce and in animal studies. Methods of administration PCP comes in both powder and liquid forms (PCP base is dissolved most often in ), but typically it is sprayed onto leafy material such as, or leaves, then smoked.
PCP can be ingested through smoking. 'Fry' or 'sherm' are street terms for marijuana or tobacco cigarettes that are dipped in PCP and then dried. PCP hydrochloride can be insufflated (snorted), depending upon the purity. The is quite hydrophobic and may be absorbed through skin and mucus membranes (often inadvertently).Management of intoxication Management of PCP intoxication mostly consists of supportive care – controlling breathing, circulation, and body temperature – and, in the early stages, treating psychiatric symptoms., such as, are the to control agitation and seizures (when present).
Such as and have been used to control psychotic symptoms, but may produce many undesirable side effects – such as – and their use is therefore no longer preferred; phenothiazines are particularly risky, as they may lower the, worsen, and boost the effects of PCP. If an antipsychotic is given, haloperidol has been recommended.(with or, more safely, ) may increase clearance of PCP from the body, and was somewhat controversially recommended in the past as a measure. However, it is now known that only around 10% of a dose of PCP is removed by the kidneys, which would make increased urinary clearance of little consequence; furthermore, urinary is dangerous, as it may induce and worsen (muscle breakdown), which is not an unusual manifestation of PCP toxicity. Pharmacology Pharmacodynamics Phencyclidine SiteK i (nM)ActionSpeciesRef44–59AntagonistHuman10,000NDHuman10,000NDHuman10,000NDHuman10,000NDHuman10,000AgonistGuinea pig136AgonistRat10,000NDHuman2.7–4.3144 AgonistRat/humanHuman10,000NDHuman≥5,000Agonist?Rat2,234InhibitorHuman10,000InhibitorHuman10,000InhibitorHuman154NDHuman1,424 InhibitorRat16,628 (IC 50)InhibitorRat347 (IC 50)InhibitorRat1,547 (IC 50)InhibitorRatValues are K i (nM). The smaller the value, the more strongly the drug binds to the site.PCP is well known for its primary action on the, an, in rats and in rat brain homogenate. As such, PCP is an.
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The role of NMDAR antagonism in the effect of PCP, and related dissociative agents was first published in the early 1980s by and colleagues. Other NMDA receptor antagonists include ketamine, and (MK-801).Research also indicates that PCP inhibits (nAChRs) among other mechanisms. Analogues of PCP exhibit varying potency at nACh receptors and NMDA receptors. Findings demonstrate that presynaptic nAChRs and NMDA receptor interactions influence postsynaptic maturation of glutamatergic synapses and consequently impact synaptic development and plasticity in the brain. These effects can lead to inhibition of excitatory glutamate activity in certain brain regions such as the and thus potentially leading to memory loss as one of the effects of prolonged use. Acute effects on the manifest as changes in blood pressure, breathing rate, pulse rate, and loss of muscular coordination during intoxication.PCP, like ketamine, also acts as a potent in rat brain homogenate and has affinity for the human cloned D 2 High receptor.
This activity may be associated with some of the other more psychotic features of PCP intoxication, which is evidenced by the successful use of D 2 receptor antagonists (such as ) in the treatment of PCP psychosis.In addition to its well explored interactions with NMDA receptors, PCP has also been shown to, and thereby leads to increased extracellular levels of dopamine and hence increased. However, PCP has little for the human, including the (DAT). Instead, its may be mediated by interactions with on the monoamine transporters. PCP is notably a high-affinity of the (K i = 154 nM), a not-well-characterized site associated with monoamine reuptake inhibition.Studies on rats indicate that PCP interacts indirectly with ( and ) to produce analgesia.A binding study assessed PCP at 56 sites including and and found that PCP had K i values of 10,000 nM at all sites except the (MK-801) site of the NMDA receptor (K i = 59 nM), the (K i = 136 nM), and the (K i = 2,234 nM). The study notably found K i values of 10,000 nM for the, the, the, and the.
These results suggest that PCP is a highly selective ligand of the NMDAR and σ 2 receptor. However, PCP may also interact with allosteric sites on the monoamine transporters to produce inhibition of monoamine reuptake. Mechanism of action Phencyclidine is an NMDA receptor antagonist that blocks the activity of the NMDA receptor to cause anaesthesia and analgesia without causing cardiorespiratory depression.
NMDA is an excitatory receptor in the brain, when activated normally the receptor acts as an ion channel and there is an influx of positive ions through the channel to cause nerve cell depolarisation. Phencyclidine enters the ion channel and binds, reversibly and non-competitively, inside the channel pore to block the entry of positive ions to the cell therefore inhibiting cell depolarisation.
Neurotoxicity Some studies found that, like other NMDA receptor antagonists, PCP can cause a kind of called in rats. Studies conducted on rats showed that high doses of the NMDA receptor antagonist caused reversible to form in certain regions of the rats' brains. All studies of Olney's lesions have only been performed on non-human animals and may not apply to humans. One unpublished study by Frank Sharp reportedly showed no damage by the NDMA antagonist, ketamine, a similar drug, far beyond recreational doses, but due to the study never having been published, its validity is controversial.PCP has also been shown to cause schizophrenia-like changes in N-acetylaspartate and N-acetylaspartylglutamate levels in the rat brain, which are detectable both in living rats and upon necropsy examination of brain tissue. It also induces symptoms in humans that mimic schizophrenia. PCP not only produced symptoms similar to schizophrenia, it also yielded changes in the thalamocortical pathway (increased delta decreased alpha) and in the hippocampus (increase theta bursts) that were similar to those in schizophrenia.
PCP induced augmentation of dopamine release may link the NMDA and DA hypothesis of schizophrenia. Pharmacokinetics PCP is metabolized into,. 90% of phencycledine is metabolised by oxidative hydroxylation in the liver on first pass. Metabolites are glucroniated and excreted in the urine. 9% of the drug is excreted in its unchanged form.When smoked, some of the compound is broken down by heat into (PC).
Possible analogues of PCPFewer than 30 different of PCP were reported as being used on the street during the 1970s and 1980s, mainly in the United States. Only of a few of these compounds were widely used including (PCPy), (PCE), and (TCP). Less common analogs include, and.The generalized structural motif required for PCP-like activity is derived from structure-activity relationship studies of PCP derivatives. All of these derivatives are likely to share some of their psychoactive effects with PCP itself, although a range of potencies and varying mixtures of anesthetic, dissociative, and stimulant effects are known, depending on the particular drug and its substituents.
In some countries such as the United States, Australia, and New Zealand, all of these compounds would be considered controlled substance analogs of PCP under the and are hence illegal drugs if sold for human consumption. Use PCP began to emerge as a in major cities in the United States in 1960s.
In 1978, magazine and of called PCP the country's 'number one' drug problem. Although recreational use of the drug had always been relatively low, it began declining significantly in the 1980s. In surveys, the number of students admitting to trying PCP at least once fell from 13% in 1979 to less than 3% in 1990.: 46–49 History It is commonly mistakenly reported that PCP was first synthesized in 1926. This early synthesis, in fact, refers to the PCP PCC. PCP was actually discovered by Victor Maddox, a chemist at in Michigan, while investigating synthetic analgesic agents.
Although unexpected, PCP was identified as potentially interesting, and as such, was submitted for pharmacological testing. The promising results of these pharmacological investigations led to the rapid development of PCP. It was approved for use as an investigational drug under the brand names Sernyl and Sernylan in the 1950s as an anesthetic, but because of its long and adverse, such as hallucinations, and, it was removed from the market in 1965 and limited to veterinary use. Regulation PCP is a substance in the United States and its is 7471. Its manufacturing quota for 2014 was 19 grams.It is a Schedule I drug by the Controlled Drugs and Substances act in Canada, a List I drug of the in the, and a substance in the United Kingdom. References.
Agonists (abridged; see for a full list):. PAMs:.
Antagonists:. Unknown/unsorted:.
PCP File SummaryThere are five file types associated with the PCP File Extension, with the most widely-observed being the Windows Installer Patch Creation Properties File format. Tip: If you know of another program that can open your PCP file, you can try opening it by selecting the application from the programs listed. Wrong Version of Microsoft Windows SDK is InstalledIn some cases, you might have a newer (or older) version of a Windows Installer Patch Creation Properties File file that is unsupported by your installed application version. If you do not have the proper version Microsoft Windows SDK (or any of the other programs listed above), you may need to try downloading a different version of it, or one of the other software applications listed above.
This problem is most common when you have an older version of the software application, and your file was created by a newer version that it cannot recognize. Other Causes of PCP File Opening ProblemsAlthough you might already have Microsoft Windows SDK or another PCP-associated software installed on your computer, you can still encounter problems opening Windows Installer Patch Creation Properties File files. If you are still having problems opening PCP files, there may be other issues that are preventing you from opening these files. Occasionally you might experience a flawed software installation, which may be due to a problem encountered during the install process.
This can prevent your operating system from associating your PCP file with the right software application, affecting what is known as 'file extension associations'.Sometimes, simply reinstalling Microsoft Windows SDK will solve your problem, properly associating your PCP with Microsoft Windows SDK. Other times, poor software programming on behalf of the software developer can cause problems with file associations, and you may need to contact the developer for further assistance. If all other steps fail, and you are still experiencing problems opening PCP files, it might be due to a lack of available system resources. Some versions of PCP files can require substantial resources (eg. Memory/RAM, processing power) to be properly opened by your computer. This is quite common if your computer hardware is older, and you are using a much newer operating system.This issue can occur when your computer is having a hard time keeping up because the operating system (and other services running in the background) might be consuming too many resources for your PCP file to open.
Try closing all applications on your PC before attempting to open your Windows Installer Patch Creation Properties File. Freeing up all of the available resources on your computer provides the best environment for attempting to open your PCP file.
If you've tried all of the steps above, and your PCP file still won't open, you might be due for a hardware upgrade. In most cases, even if you have older hardware, processing power is still more than adequate for most user's applications (unless you do a lot of CPU resource-intensive work such as 3D rendering, financial / scientific modeling, or intensive multimedia work).
Therefore, it's likely that your computer is lacking the necessary amount of memory (more commonly referred to as 'RAM', or random access memory) to complete the file opening task.Try upgrading your memory to see if that helps you open your PCP file. These days, memory upgrades are quite affordable and very easy for even the casual computer user to install in their PC. As a bonus, you'll probably see a nice performance bump in other tasks carried out on your computer.
An interactive-PCP (say, for the membership x ∈ L) is a proof that can be verified by reading only one of its bits, with the help of a very short interactive-proof. We show that for membership in some languages L, there are interactive-PCPs that are significantly shorter than the known (non-interactive) PCPs for these languages. Our main result is that the satisfiability of a constant depth Boolean formula Φ(z1,., zk) of size n (over the gates ∧,∨, ⊕,¬) can be proved by an interactive-PCP of size poly(k), followed by a short interactive proof of communication complexity polylog(n).
That is, we obtain interactivePCPs of size polynomial in the size of the witness. This compares to the known (non-interactive) PCPs that are of size polynomial in the size of the instance. By reductions, this result extends to many other central NP languages (e.g., SAT, k-clique, Vertex-Cover, etc.). More generally, we show that the satisfiability of Vn i=1Φi(z1,., zk) = 0, where each Φi(z1,., zk) is an arithmetic formula of size n (say, over GF2) that computes a polynomial of degree d, can be proved by an interactive-PCP of size poly(k, d), followed by a short interactive proof of communication complexity poly(d, log n). We give many cryptographic applications and motivations for our results. In particular, we show the following: 1.
The satisfiability of a constant depth formula Φ(z1,., zk) of size n (as above) has an interactive zero-knowledge proof of communication complexity poly(k) (rather than poly(n))1. As before, this result extends to many other central NP languages.
This zero-knowledge proof has some additional desired properties that will be elaborated on in the body of the paper. Alice can commit to a Boolean formula Λ of size m, by a message of size poly(m), and later on prove to Bob any N statements of the form Λ(x1) = z1,.,Λ(xN ) = zN by a zero-knowledge proof of communication complexity poly(m, log N). Moreover, if Λ is a constant depth Boolean formula then the zero-knowledge proof has communication complexity poly(log m, log N).
We further motivate this application in the body of the paper.
Additional InformationDefinitionWe evaluated 4 techniques for our experiment (PCP, SCP-rotated, SCP-common, SCP-staircase), see figure below.Below are the take away lessons from this study. The first take away lesson is both SCP-rotated and SCP-staircase perform badly for the value retrieval task, so they are not recommended, see figure below.The second set of major take away lessons are:. PCP and SCP-common performed better and were preferred by participants. However, these two techniques seem suited for different scenarios: PCP is better at low dimensionality and low density, and SCP-common is better when these are higher.
The performance of PCP is dependent on dimensionality, while the performance of SCP-common seems roughly independent of dimensionality. Increasing density affects the performance of PCP more than it affects SCP-common.Figure 10 shows the recommendation of techniques for value retrieval under varied dimensionality and densities. Cells with “PCP” or “SCP-common” means PCP or SCP-common has significant better performance. Cells with the “ ” symbol means PCP and SCP-common have comparable performance. And, cells with question mark is the condition which we have not covered in this study.It can be seen that, PCP is recommended for cells in top left corner, which represent multivariate data with lower dimensionalities and densities. The cells in the bottom right corner represent multivariate data with high dimensionality and densities, and SCP-common is preferred. Cells on the diagonal line can use either of the two approaches, which offers users a choice depending on other considerations.Figure 10: The recommendation of techniques based on experiment results.
The dotted red rectangle highlights the conditions in experiment 1; the solid green rectangle highlights the conditions in experiment 2. ABSTRACTOne of the fundamental tasks for analytic activity is retrieving (i.e., reading) the value of a particular quantity in an information visualization. However, few previous studies have compared user performance in such value retrieval tasks for different visualizations. We present an experimental comparison of user performance (time and error distance) across four multivariate data visualizations. Three variants of scatterplot (SCP) visualizations, namely SCPs with common vertical axes (SCP-common), SCPs with a staircase layout (SCP-staircase), and SCPs with rotated axes between neighboring cells (SCP-rotated), and a baseline parallel coordinate plots (PCP) were compared.
Results show that the baseline PCP is better than SCP-rotated and SCP-staircase under all conditions, while the difference between SCP-common and PCP depends on the dimensionality and density of the dataset. PCP shows advantages over SCP-common when the dimensionality and density of the dataset are low, but SCPcommon eventually outperforms PCP as data dimensionality and density increase.
The results suggest guidelines for the use of SCPs and PCPs that can benefit future researchers and practitioners. INTRODUCTIONMultivariate data is a commonly encountered type of data (e.g., in relational databases), consisting of a list of points or tuples, each corresponding to a row in a table, whose columns are the attributes or variables of the data.
Two widely used visualization techniques for multivariate data are parallel coordinate plots (PCP) and scatterplots (SCP) Weg90,KD09,TGS04,SS05,AR11. PCPs display each tuple as a polygonal line intersecting parallel axes, each representing one of the variables, thus providing a continuous view of the multidimensional values of the data tuples Ins85. SCPs, on the other hand, show only 2 variables per plot, but can be combined to visualize multivariate data with more than 2 dimensions, such as in a scatterplot matrix Har75.Despite the call for rigorous evaluation of experimental visualization techniques over a decade ago WB97, to date, much still remains unknown about the respective advantages of PCPs and SCPs for different user analytic tasks.
To our knowledge, there are only two empirical comparisons of these techniques. One LMvW10 asked users to estimate correlation coefficients using PCPs and SCPs, and another HvW10 asked users to count clusters; both studies found SCPs to be superior. Given these results, it seems unclear what advantage, if any, PCPs provide. However, the tasks in the two previous studies are just two of many possible tasks. Several other tasks with visualizations have been identified Shn96,AES05 and have yet to be tested.We extend previous efforts by comparing SCPs and PCPs for the task of value retrieval, a fundamental task that is the first in the taxonomy of analytic tasks by Amar et al.’s AES05 and said to be a building block of other tasks such as finding extrema or sorting AES05.
As an initial exploration, our study focuses on differences due to the basic visual designs of SCPs and PCPs in their static form. We believe it is important to understand trade-offs due to their basic visual designs before investigating the effects of visual or interactive enhancements. Therefore, brushing, linking as well as additional visual enhancements such as gridlines are not included in this investigation. Furthermore, value retrieval by visual scan is commonly performed in practice since it is an integral component of many higher-level tasks in which explicit clicks would be inappropriate. We conducted two controlled experiments involving four visualization techniques: three SCP variants (SCP-common, SCP-rotated, and SCP-staircase) and the baseline PCP, on datasets of varied dimensionalities and densities. It was found that SCP-rotated and SCP-staircase are not suitable for value retrieval. PCP and SCP-common yield better performance and are preferred by participants, but each is suited for different scenarios: PCP is better at low dimensionality and low density, while SCP-common is better in the opposite case.
Increasing dimensionality seems to only affect performance with PCP, not SCP-common. Increasing density, while affecting both visualizations, has a stronger effect on PCP than SCP-common.
Such differences are likely due to the different value retrieval strategies adopted by users and the different visual encodings of data tuples in the two visualization techniques (points versus lines). These results may be used by researchers and practitioners to better understand the differences between PCPs and SCPs, and to promote their appropriate use in the future. RELATED WORKTwo aspects of previous research are related to our study: variants and hybrids involving scatterplots and parallel coordinate plots, and their comparisons.A single SCP depicts two variables, and is thus insufficient for multivariate data.
The scatterplot matrix (SPLOM) Har75 shows every possible pairing of variables with multiple SCPs. Other variants with multiple SCPs have been proposed QCX ∗07, VMCJ10 that show a subset of the SCPs in a SPLOM, arranged with various layouts. QCX ∗07 showed a row of SCP cells, where consecutive SCPs have an axis in common that is rotated (a technique we call SCP-rotated).
These SCPs correspond to cells that are adjacent to the diagonal in a SPLOM. VMCJ10 consider rows of SCPs taken directly from a SPLOM, in which all the SCPs of the row have the same vertical axis (a technique we call SCP-common), privileging the variable along the shared, vertical axis. VMCJ10 also presented a novel “staircase” arrangement (we call SCPstaircase), where adjacent SCPs share a common axis.Parallel coordinates Ins85 lend themselves naturally to multivariate data due to their inherently multidimensional design. Research into PCP variants has examined the use of curves instead of polylines The00, variations in colors and transparency, and animation for line disambiguation, as surveyed by Holten and van Wijk HvW10. QCX ∗07 have extended PCPs with S-shaped axes to indicate wind direction. Artero et al. AdOL04 proposed an interactive PCP variant.Hybrid visualizations that combine SCPs and PCPs have included embedding SCP cells between PCP axes HvW10, scattering points along curves between PCP axes YGX ∗09, the parallel scatterplot matrix VMCJ10, and highly flexible custom visualizations integrating SCPs and PCPs CvW11.In contrast to the many variants and hybrids of SCPs and PCPs, and evaluation within PCP variants HLKW12, comparisons between these two families of visualizations have been rare.
LMvW10 found SCPs to be significantly superior to PCPs for judging correlation coefficients. Holten and van Wijk HvW10 compared cluster identification performance over several PCP variants, and found that the PCP variant with embedded SCPs significantly outperformed other variants, implying that SCPs hold an advantage over PCPs. Our work extends these previous studies by comparing performance in value retrieval with PCPs and three variants of SCPs. EXPERIMENT DESIGNWe conducted two controlled experiments to compare SCP and PCP visualizations. The next few subsections first describe aspects common to both experiments.
TaskWe define a “value retrieval” task AES05 in the context of multivariate data: given the numerical value of one attribute of a data tuple, find the numerical value of another attribute of the same data tuple. Value retrieval is a common, fundamental user analytic task. For example, if a user wants to find the average mileage for a car with 230 horsepower in a multivariate visualization, s/he may first locate the horsepower axis and find a data tuple corresponding to 230 horsepower, and then trace the tuple to the mileage axis and read its value off that axis. In general, it is possible for some axes to correspond to categorical (such as car brands) or ordinal (such as degree of satisfaction) variables, however our study focuses on the most general case: quantitative variables. Independent VariablesOur experiments involved three independent variables: visualization technique, data dimensionality, and data density.3.2.1.
Visualization TechniquePCPs have a single straightforward layout (Figure 1:(a)). SCPs, in contrast, afford many different layouts. The aforementioned full SPLOM shows all pairings of variables, so its space utilization is quadratic with the number of variables.
PCPs, however, have space requirements linear with the number of variables. A fair comparison requires all techniques occupy the same space. Therefore, we evaluated three SCP variants with linear space requirements.
Figure 1: The four evaluated techniques (a). Baseline PCP; (b). SCP-common; (c). SCP-rotated; (d). SCP-staircase. SCP-common: a row of SCPs taken from a standardSPLOM. Figure 2: The stimuli used in the experiment.
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1) Basic experimental information: trial number, time spent on the current trial, and task description; 2) the red × indicating the value for the tuple of interest; 3) the highlighted target axis. StimuliFigure 2 illustrates an example of stimuli used in the experiment.
The top of the screen displays information about the current trial: number, time spent, and task description (e.g. For an N dimensional dataset, it shows “with the highlighted X1 value, what’s the corresponding XN value?”). Just below this is the main experimental area in which the data and the visualization techniques are displayed.Cell size: To fully utilize the screen estate while allowing the participants to simultaneously view the maximum number of dimensions without scrolling, each plot cell has a fixed length of 70mm, which translates to 300 pixels in our display configuration. This allows a maximum of 8 dimensions to be comfortably displayed (e.g. 300×7 = 2100 pixels for the 7 scatterplots + 50 × 6 = 300 pixels for the 6 visible gaps of 50 pixels each between adjacent scatterplots + spaces before and after the first and last scatterplot).Tuple size and color: The data tuples in SCP are visualized using points of 4-pixel radius; for PCP, each data tuple is represented using a line of 1 pixel in width, both rendered with anti-aliasing.
Based on our observation, these are the minimum data tuple sizes for participants to comfortably recognize under the current screen resolution. All data tuples are displayed in blue. All axes, numeric labels, and tick marks on the axes are in black. The value for the target data tuple is highlighted in red on the corresponding axis.Stimuli generation: Data tuples are generated randomly with uniform distribution along each dimension according to the density requirement. The numeric values of all data tuples are integers between 0 and 50. This range is fixed for all axes across all conditions and techniques so that it can serve as a constant. To avoid possible ambiguity of multiple data tuples having the same value as the highlighted tuple (in which case the users are unable to determine the tuple to trace from), when choosing the target tuple, we purposely avoid those with neighbors that are closer than 8 pixels or equivalently 1.9mm on all dimensions.
ProcedurePrior to the experiment, each participant was introduced to the visualization techniques and the value retrieval task. They were also instructed to finish the trials as quickly and accurately as possible while not using any visual aid (mouse cursor, finger, ruler, pen tip, etc.) other than their eyes. They were also informed that there is no ambiguity in the highlighted value.A training session familiarized the participants with the techniques. They were instructed to continue practicing until they were fully comfortable with the value retrieval tasks with each technique before starting the main experiment.For each trial in the main experiment, upon determination of the numeric value on the target axis, the participants were expected to hit the space bar, after which the timer is stopped and the visual stimuli is masked. The participants are then required to take their time to key in the numeric value in the provided input box. The visual stimuli were masked to prevent participants’ visual residue from affecting their responses, which should not change after hitting the space bar.Considering the switch between different techniques may result in relative longer response time to readapt, a pop-up window is shown whenever there is a change in techniques between the trials to remind the participants and to facilitate mental adjustment between different techniques. Upon finishing all the trials in the official session, the participants were invited to a brief interview to collect their subjective opinions.
Their responses were audio recorded with their consent. Result Analysis MethodBoth experiments used the within-subject design involving three independent variables: technique, density, and dimensionality. Data were analyzed using factorial RepeatedMeasures ANOVA, with significance level of α =.05. Mauchly’s test was used to verify the assumption of sphericity.
Pairwise comparisons for the main effects of different variables were corrected using Bonferroni adjustments. EXPERIMENT 1This first experiment is to provide an overall understanding of the performance differences among the four techniques and to identify the winning techniques.Participants: 12 participants, 5 females and 7 males aged 20 to 25 years, from the university community, volunteered for the experiment.
All participants had seen and used 2D SCP before, but none had experience with either PCPs or one of the SCP variants on multivariate data.Experiment setup: Techniques were counterbalanced using balanced Latin Square. Participants were randomly assigned to four groups of three participants each.For each technique, participants perform 3 trials in each of the three different data densities: 10, 20, and 30 tuples.Within each technique and dimension combination, participants perform the trials in three different dimensions (2D, 4D, 6D).
Presentation order of the dimensions and densities is both from easy to hard, (i.e., 2D, 4D, 6D for dimensions, and 10-tuple, 20-tuple, 30-tuple for densities) to allow participants to ease gradually to more difficult conditions. Note that since the main purpose of this experiment is to obtain an overall picture for the performance differences among the four techniques, we only counterbalanced the main factor, technique, in this first experiment.After training, each participant performed the entire experiment in one sitting, including breaks, and post questionnaires in approximately 1 hour. In summary, the design was as follows (excluding trainings): 12 participants × 4 visualization techniques (PCP, SCP-common, SCP-rotated, SCPstandard) × 3 levels of data dimension (2D, 4D, 6D) × 3 levels of data density (10 tuples, 20 tuples, 30 tuples) × 3 repetitions of trails = 1296 trials in total. ResultsFor experiment 1, we focus on revealing the overall performance for the four techniques.
With regards to the main effect of the techniques, Mauchly’s test verified the assumption of sphericity has been met in both error distance ( p =.119) and completion time ( p =.057) analysis. Error DistanceFigure 3:(a) shows the average error distance of each technique. Repeated-measures ANOVA tests suggest that there is a significant main effect of the technique ( F ( 3, 33 ) = 22.34, p.05) and “ ” to indicate the technique on the left of the operator is significantly better than the technique on the right side ( p SCP-rotated (5.04) SCP-staircase (7.31). Completion TimeFigure 3:(b) shows the average completion time with standard errors for the four techniques. Similar with the error distance, it shows that both PCP and SCP-common techniques are better than the SCP-rotate and SCP-staircase ( p. Figure 3: The average error distance (left) and completion time (right) with standard error bars among four techniquesRepeated-measures ANOVA tests suggest that the four techniques have significant difference in the completion time of tracing tuples across dimensions ( F (3,33 ) = 27.83, p SCP-rotated (18.58s), SCP-staircase (17.93s). Experiment 1 SummaryComparing error distance and completion time among the four techniques, PCP and SCP-common are clearly the two better techniques.
Both SCP-rotated and SCP-staircase are not suitable for value retrieval tasks, taking significantly longer time and are more error-prone.Furthermore, the subjective feedback of both SCP-rotated and SCP-staircase is consistent with the quantitative results: 6 out of 12 participants ranked the SCP-staircase as the least preferred technique while the other half ranked SCP-rotated as the least preferred one. The reported reason for disliking SCP-staircase is the difficulty in tracing tuples across non-horizontal lines. The 45-degree tilted cells require the participant to “tilt the head to see (through imagined projection) the correct value”. This is not only “more tiring”, but also “more difficult to judge whether two points are on the same level”.
To many participants, such combined difficulties are so discouraging that they “gave up after a while”.While fatigue and perceptual difficulties caused by tilting are the main reasons for participants to dislike SCP-staircase, the difficulty in using SCP-rotated was reported to have a different reason. In SCP-rotated, to trace a tuple from one cell to another, it requires the following set of actions: find the target data tuple based on the value marked on the first axis, read the value of that tuple on the second axis, remember that value and locate that value on the same axis in the adjacent cell, and find the tuple in the adjacent cell with that value. As reported by one participant, “you have to always find and remember the value on the axis to move to the next cell (plot). This is too much work when the number of dimensions increases”.
Figure 4: The average completion time among four techniques under varied data dimensions and densities.While no significant overall performance differences are found between PCP and SCP-common, a further breakdown of the results (Figure 4) struck us with several interesting phenomena.It is observed that in the 2D case, the performance difference between PCP and SCP-common is small. As the number of dimensions increases to 4, PCP seems to have advantages over SCP-common in all three densities. As the number of dimension increases to 6D, we found that PCP seems to have an advantage over SCP-common in the 10tuple density case, but becomes inferior to SCP-common in the 30-tuple density case.While PCP seems to have comparable overall performance with SCP-common, fine-grained investigation revealed that there are differences under different conditions. PCP seems to have advantages over SCP-common when density and dimension are low, but this advantage diminishes as dimension and density increase, indicating the strategy and cost for retrieving values for the two techniques are likely to be different. EXPERIMENT 2In experiment 1, we identified PCP and SCP-common as the two winning techniques for value retrieval.
In experiment 2, we attempt to further investigate the influence of dimensionality and density on these two techniques. While not counterbalancing dimensionality and densities were less of a concern in experiment 1, proper counterbalancing is needed for both factors in this experiment as they become the focus of the study.
Furthermore, in experiment 1, we learned that both techniques have similar performance in the 2D condition, but as the dimensionality and density increase, greater performance differences seem to emerge. This motivated us to use both higher dimensionality and density conditions in the second experiment.Participants: 18 participants, 7 females and 11 males, aged between 20 to 30 years, from the university community, volunteered for the experiment. None had participated in experiment 1. All participants had seen and used 2D SCP before, but none had experience with either PCPs or one of the SCP variants with multivariate data.Experiment setup: Similar to experiment 1, a withinsubject design was used.
However, instead of only counterbalancing the technique, all three factors (technique, dimensionality, and density) are counterbalanced. The technique, with only two levels (PCP and SCP-common), is fully counterbalanced. The dimensionality and density both have three levels (4D, 6D, 8D for dimensionality and 20-tuple, 30-tuple, 40-tuple for density), were counterbalanced using Latin Square.Combining the 2 techniques with 3 different order sequences in dimensions and with 3 different order sequences in density leads to 18 arrangements of the three factors (2 × 3 × 3 = 18). Participants were randomly assigned to one of the 18 experiment arrangements. For each of the technique, dimensionality, and density combination, participants were asked to perform 5 randomly generated trials.The flow of the experiment procedure is exactly the same as experiment 1. Each experiment session took approximately 1 hour. The design of experiment 2 can be summarized as follows (excluding trainings):18 participants × 2 techniques (PCP, SCP-common) × 3 dimensions (4D, 6D, 8D) × 3 densities (20 tuples, 30 tuples, 40 tuples) × 5 trials for each technique, dimension, density combination = 1620 trials in total.
ResultsFor experiment 2, we counterbalanced all three independent variables (e.g. Technique, density, and dimensionality). Mauchly’s tests verified that the assumption of sphericity have been met for the main effects and interaction effects of these variables we mentioned as follows ( p.05). The observed power for all significant effects were above.80. Error DistanceOverall, the repeated-measures ANOVA tests revealed no significant differences between techniques ( p =.436).
However, there were significant main effects in both dimensionality ( F (2,34 )= 6.124, p 6D (2.85), 8D (2.48) and 20 tuples (1.18) 30 tuples (2.59), 40 tuples (2.99), respectively. These results are less surprising as the error distance is likely to increase as the dimensionality and density increase (as the task becomes more difficult). Figure 5: The interaction effect for technique × density (left) and technique × dimension (right) in terms of error distance.However, we found a number of significant interaction effects. There were a significant Technique × Density interaction ( F ( 2, 34 ) = 7.05, p 6D (18.37s), 8D (19.30s) for dimensionality and 20 tuples (13.50s) 30 tuples (17.50s) 40 tuples (20.29s) for density. Just like the observations we made with error distance, the significant effects found in dimensionality and density are expected as the completion time is likely to increase as the dimensionality and density increase.However, the significant effect found in technique is somewhat surprising as it differs from what we got from experiment 1. In experiment 1, we found that the completion time is comparable ( p.05) between the two techniques with PCP (8.99s) being slightly quicker than that of SCP (12.02s), but experiment 2 tells an almost opposite story, as PCP-common is significantly slower than SCP-common.
To understand the reason behind this phenomenon, we need to further analyze the interaction effects below.Similar to the results found with error distance, we found two significant interaction effects. There were a significant Technique × Density interaction ( F ( 2, 34 ) = 8.74, p.
Figure 7: The average completion time for SCP-common and PCP under varied dimensions and densities.We found that the increase of dimensionality has almost no effect on SCP-common, but causes the significant performance degradation to that of PCP. On the other hand, the Technique x Density interaction showed that both PCP and SCP-common are affected by increased density. However, the increase in density seems to cause more damage to PCP than that of SCP-common (i.e., at the density of 20 tuples, PCP has almost equal performance with SCP-common, but when the density is increased to 30 or 40 tuples, PCP is much slower than SCP-common, and the performance gap between the techniques increases with number of dimension).This effect is further elaborated in Figure 7, in which the effects of all three factors on completion time are simultaneously displayed. Overall, it shows that PCP has advantages over SCP-common when dimension and density are low.Under each particular (density, dimensionality) condition, we applied Pairwise T-test to compare these two techniques. The results show the advantage of PCP over SCP-common in two low dimension and density cases (4D, 20 tuples; 4D, 30 tuples) (both p.05).
Finally, further increase in either dimension or density will make PCP inferior to SCP-common, as demonstrated by pairwise T-test on the rest of the 5 conditions. (8D, 20 tuples; 6D, 30 tuples; 8D, 30 tuples; 6D, 40 tuples; 8D, 40 tuples) (all p.