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Nondecreasing functions $\mathbb{N}\to \mathbb{N}$ form an uncountable set: every subset $A$ is encoded by the strictly increasing function $n+{\sum }_{k=1}^n{1}_A(k)$. In contrast, nonincreasing functions form a countable set because each is determined by a finite initial segment and its eventual constant value.

// Target: classical-mechanics.bigb

The ceiling function gives the least integer large enough to meet a real lower bound. For a real number $x$,Thus $\lceil 2.7\rceil =3$, $\lceil -2.7\rceil =-2$, and $\lceil 2\rceil =2$. The operation rounds upward in the total order on the real numbers, which can move a negative input towards zero. Its difference from the input obeys $0\le \lceil x\rceil -x<1$. Python's math.ceil implements this least-integer definition. ^[ref].

The ceiling count for fixed-size batches is a direct application. Suppose each box holds at most $k$ items, with positive integer $k$, and there are $N\ge 0$ items. The smallest number of boxes with enough capacity is $\lceil N/k\rceil$: a candidate count $b$ must satisfy $bk\ge N$. For $N=101$, $k=20$, six boxes are needed, leaving $120-101=19$ unused places. Exactly 100 items need only five boxes, and zero items need zero boxes. Adding one to the floor function result unconditionally would therefore overcount at exact multiples of $k$.

For nonnegative integer $N$ and positive integer $k$, Euclidean division gives the exact identityWrite $N=qk+r$, $0\le r<k$. The count is $q$ when the remainder is zero and $q+1$ otherwise, and the right-hand side encodes these two cases. This argument specifies the domain of the identity; arbitrary negative denominators require their own sign conventions.

The ceiling function is a nondecreasing function, constant on each of the half-open intervals $(m-1,m]$, and has a discontinuity of a function at every integer. It is tied to the floor function by $\lceil x\rceil =-\lfloor -x\rfloor$. Adding an integer $j$ shifts its output by the same amount: $\lceil x+j\rceil =\lceil x\rceil +j$. At an integer the floor and ceiling agree; at every other input they differ by one. These statements refer to exact real numbers; an input already perturbed by floating-point arithmetic can land on the other side of a threshold.

The two Chebyshev functions measure the total natural logarithm weight of prime numbers or of prime powers greater than one below a real cutoff $x\ge 0$:Here $p$ ranges over prime numbers, $n$ over positive integers, and $k$ over positive integers. The Chebyshev theta function $\vartheta$, also written $\theta$, weights each prime number once. The second Chebyshev function $\psi$ weights every positive power of each prime number separately. Both vanish for $0\le x<2$; the inclusive cutoff makes them right-continuous functions and nondecreasing functions, constant between their jumps. ^[ref]

At $x=10$, the prime numbers are $2,3,5,7$, while the extra prime powers greater than one are $4,8,9$. Exactly,At each prime number $p$, both Chebyshev functions jump by $\log p$. At a prime power $p^k$ with $k\ge 2$, only the second Chebyshev function jumps, by $\log p$. At $6$ and $10$ neither jumps. Notice that the gap is already positive at $5$ and $7$: a shared jump does not remove the earlier contribution from $4$.

Grouping the prime powers by their exponent gives, for $x\ge 2$,The floor function cutoff is exact: $p\ge 2$ forces $2^k\le x$ whenever $p^k\le x$. For each exponent, $p^k\le x$ is equivalent to $p\le x^{1/k}$. An upper cutoff smaller than the lower one means an empty sum. ^[ref]

The Brownian first-passage subordinator has a countable dense set of jump levels almost surely, and is continuous at every other level. Its paths are finite nondecreasing functions, so on a bounded level interval there can be only finitely many jumps larger than any fixed positive size; a countable union proves countability. For density, suppose its path were continuous throughout an open level interval. It is strictly increasing, since the original continuous Brownian path takes different values at different passage times. Its continuous inverse on that interval would parametrize a positive-length time interval on which the Brownian path increases monotonically. This contradicts nowhere monotonicity of Brownian motion. Thus every level interval contains a jump. In particular dense discontinuities do not mean that the path is nowhere continuous: it is continuous at every level outside a countable random set.

Suppose performances on fixed tasks $A$ and $B$ are $f_A(r)$ and $f_B(r)$, where both functions are nondecreasing in the same scalar resource $r$. If patient $P$ performs strictly better than $Q$ on $A$, then $r_P>r_Q$: the reverse ordering would contradict monotonicity. If $Q$ performs strictly better on $B$, the same argument gives $r_Q>r_P$, a contradiction. Thus a genuine crossover rejects this single-resource model.

The conclusion depends on the fixed mappings and task comparability. It does not prove isolated anatomical stores, and it need not apply when patients differ in strategy, sensory access or other task parameters.

The cumulative distribution function of a real random variable $X$ isIt records the probability accumulated up to a threshold. It is a nondecreasing function and is right-continuous; its limits are zero at $-\infty$ and one at $+\infty$. These properties follow from ordered threshold events, continuity from above of a measure, and continuity from below of a measure. A cumulative distribution function determines the entire probability distribution on the real line. ^[ref].

For $a<b$,where $F_X(x^{-})={\lim }_{t\uparrow x}{F}_X(t)$ is its left-hand limit. The second formula measures a jump. Thus $\mathbb{P}(a\le X\le b)=F_X(b)-F_X(a^{-})$; changing an endpoint is harmless only when its point probability is zero.

For the finite example under probability distribution, $F_X(x)$ is $0$ below zero, $0.2$ on $[0,1)$, $0.7$ on $[1,3)$, and $1$ on $[3,\infty )$. In particular $F_X(1)=0.7$, but $F_X(1^{-})=0.2$, so the jump gives $\mathbb{P}(X=1)=0.5$. For the density example, $F_Y(y)=y^2$ on $[0,1]$, and its increase from $1/4$ to $3/4$ is $9/16-1/16=1/2$.

When an absolutely continuous probability distribution has probability density function $f$, its cumulative distribution function is $F(x)={\int }_{-\infty }^{x}f(t)dt$ and $F^{\prime }=f$ almost everywhere. A continuous cumulative distribution function by itself does not guarantee such a density: the Cantor distribution is an important exception.

Let $I$ be a real interval, and let $f:I\to \mathbb{R}$ be differentiable in its interior and continuous at any endpoints belonging to $I$. If the derivative satisfies $f^{\prime }(x)\ge 0$ throughout the interior, then $f$ is a nondecreasing function on $I$. For $x<y$ in $I$, the mean value theorem supplies $c\in (x,y)$ withChanging $\ge 0$ to $\le 0$, $>0$, or $<0$ gives a nonincreasing function, strictly increasing function, or strictly decreasing function, respectively. The endpoint continuity assumption lets the mean value theorem apply even when $x$ or $y$ is an endpoint. ^[ref].

Conversely, if a nondecreasing function on a real interval is differentiable at an interior point $x$, then $f^{\prime }(x)\ge 0$: the difference quotient $(f(x+h)-f(x))/h$ is nonnegative for either sign of $h$. A differentiable nonincreasing function has derivative $f^{\prime }(x)\le 0$. Strict monotonicity still only forces these weak signs: the strictly increasing function $x^3$ has derivative zero at zero. The sign of the derivative helps locate intervals of monotonicity when sketching a graph. ^[ref].

The real interval hypothesis matters because a connected subset of the real line contains every point between its inputs. On a disconnected domain of a function, positive derivatives need not give a nondecreasing function. For example, takeHere $f^{\prime }(x)=1$ everywhere in $D$, but $-3/2<3/2$ while $f(-3/2)=-3/2>-17/2=f(3/2)$. Each component has a strictly increasing function, yet their outputs are ordered the wrong way across the gap. This function is neither a nondecreasing function nor a nonincreasing function on all of $D$; the mean value theorem cannot cross the missing interval.

Every real function on $[a,b]$ has bounded variation if and only if it is a difference of two nondecreasing functions. For a normalized version, let $v(t)=V_a^t(f)$ and $v(a)=0$. Its accumulated positive and negative variations areBoth are nondecreasing functions, $P(a)=N(a)=0$, andThis is the Jordan decomposition of a function of bounded variation.^[ref] Equivalently one may write $f=f_{+}-f_{-}$ with $f_{+}=(v+f)/2$ and $f_{-}=(v-f)/2$; those functions need not vanish at $a$.

For the piecewise linear function in partition example of total variation, the successive values of $P$ are $0,2,2,6,6$, while those of $N$ are $0,0,3,3,5$. At $t=4$, the total rise is $6$ and the total fall is $5$: their difference gives the net change $1$, and their sum gives the total variation $11$. A declining segment increases $N$ while $P$ stays constant; an ascending segment does the opposite.

For a short proof outline, total variation is additive over adjacent intervals, because any two partitions of an interval have a common partition refinement. Thus $v(t)-v(s)=V_s^t(f)\ge |f(t)-f(s)|$ for $s<t$. Substituting this inequality into the formulas for $P$ and $N$ shows their increments are nonnegative. Conversely, if $f=g-h$ for two nondecreasing functions, the triangle inequality bounds each partition sum by $g(b)-g(a)+h(b)-h(a)$. This proves finite total variation without restricting the number of changes of direction.

A real-valued function $f:D\to \mathbb{R}$, with domain of a function $D\subseteq \mathbb{R}$ in the usual order of the real numbers, is a monotonic function if it is either a nondecreasing function throughout $D$ or a nonincreasing function throughout $D$. The same direction must work for every pair $x<y$ in $D$. Being a strictly increasing function on one part and a strictly decreasing function on another does not make the whole function monotone: for example, $x^2$ on the real line changes direction at zero. A constant function is both a nondecreasing function and a nonincreasing function.

The word "increasing" is ambiguous across textbooks: some allow equal outputs and some require unequal outputs. Use nondecreasing function for the weak inequality and strictly increasing function for the strict inequality; use nonincreasing function and strictly decreasing function in the other direction. Neither continuity nor differentiability is part of the definition of a monotonic function.

Strict monotonicity gives injectivity, so an equation $f(x)=b$ has at most one solution and an inverse function exists on the image of a function. Weak monotonicity permits repeated outputs and jumps. In probability theory, a cumulative distribution function is a nondecreasing function; its quantile function uses a generalized inverse to handle those flat parts and jumps. Monotonicity alone does not guarantee continuity, even for a strictly increasing function.

Suppose $-u^{\prime \prime }+u+q(u)=f$ and $-v^{\prime \prime }+v+q(v)=g$ on a bounded interval, the solutions have the same Dirichlet boundary conditions, and $q$ is a nondecreasing function. Multiplying the difference equation by $w=u-v$ and using integration by parts givesThe nonlinear integral is nonnegative. Young's inequality for products gives the displayed energy estimate. It proves uniqueness and continuous dependence on forcing. The same argument holds for nodal values when integration by parts is replaced by discrete summation by parts.

A real-valued function $f:D\to \mathbb{R}$ is a nondecreasing function if every pair $x<y$ in its domain of a function satisfies $f(x)\le f(y)$. Flat intervals are allowed, unlike for a strictly increasing function. For example,is a continuous nondecreasing function, but it repeats its output on each flat interval.

A nondecreasing function can also be discontinuous. The step function $F(x)=0$ for $x<0$ and $F(x)=1$ for $x\ge 0$ has a jump discontinuity at zero without ever decreasing. It is the cumulative distribution function of a random variable that equals zero with probability one; quantile functions handle such jumps through a generalized inverse. Nondecreasing allocation functions permit critical-value payments in single-parameter mechanisms.

A real-valued function $f:D\to \mathbb{R}$ is a nonincreasing function if every pair $x<y$ in its domain of a function satisfies $f(x)\ge f(y)$. Equal outputs and flat intervals are allowed. For example, $f(x)=-\min (1,\max (0,x))$ is a nonincreasing function, with a falling segment between two flat portions. Requiring $f(x)>f(y)$ instead gives a strictly decreasing function. Negating a nondecreasing function gives a nonincreasing function.

A nonincreasing sequence of natural numbers has finitely many strict drops, so it is eventually constant. This differs from a strictly decreasing sequence, which cannot continue indefinitely within the natural numbers.

// Target: foundations-of-mathematics.bigb

For a continuous unbounded nondecreasing function $f$ on the nonnegative half-line and $a>f(0)$, the non-strict inverse $h$ is left-continuous in its level argument. It equals the left limit of the strict generalized inverse of a nondecreasing function $G(a)=\inf \{t:f(t)>a\}$. Indeed $G(b)\le h(a)$ for $b<a$, while for every $t<h(a)$, $f(t)<a$ and choosing $b\in (f(t),a)$ gives $G(b)\ge t$. Similarly $h(a+)=G(a)$. A flat part of $f$ at level $a$ gives a discrepancy between the two inverses: $h(a)$ selects the beginning of the flat part and $G(a)$ its end. For a Brownian running maximum, these are the non-strict and strict passage times.

The crossed profiles of medial temporal lobe amnesia and severe verbal short-term memory impairment argue against a single undifferentiated human memory store and against obligatory transfer through one verbal short-term buffer. They do not establish that all short- and long-term memory processes occupy two isolated anatomical boxes. The tasks, material and type of learning must be specified before converting a clinical dissociation into a theory.

Henry Molaison, originally reported as H.M., developed profound difficulty forming new lasting declarative memories after bilateral medial temporal lobe surgery. The classic report by William Beecher Scoville and Brenda Milner connected substantial new-learning impairment with the operation. Yet immediate retention within a limited span could remain much better than new long-term explicit learning. The contrast shows that retaining a small amount for immediate use and establishing enduring new declarative memories can be disrupted differently.^[ref]

The interpretation is more specific than “all long-term memory was lost.” Earlier knowledge could remain accessible, and different learning systems were affected differently. Suzanne Corkin tested motor-skill acquisition in Henry Molaison using rotary pursuit, bimanual tracking and tapping. Performance improved with practice, and retention could be tested after several days on rotary pursuit. The finding shows lasting learning despite severe amnesia; it does not imply normal performance on every motor task, because absolute performance could still be below that of comparison participants.^[ref]

The complementary profile is exemplified by Patient K. F.. Elizabeth Warrington and Tim Shallice reported a very restricted span for auditory verbal short-term memory, and subsequent experiments examined free recall, interference, short-delay performance and recognition. Despite the short-term difficulty, long-term verbal learning was relatively good on the tests used. This matters because the impairment was investigated through several procedures rather than inferred from one failed digit span response.^[ref][ref]

Together, these profiles are stronger evidence than one patient who fails a difficult delayed test but passes an easier immediate one. A simple general reduction in ability can explain many single dissociations through differing task difficulty. With fixed tasks, crossed performance rejects a single monotone resource: both performances cannot be fixed nondecreasing functions of the same scalar. Nevertheless, clinical reports do not automatically provide such a perfectly matched experiment: different materials, floor and ceiling effects and measurement reliability can distort apparent crossings. Quantitative comparisons and adequate control data strengthen the inference.

The findings also challenge a strong serial two-store model of memory. If the particular verbal short-term store damaged in K.F. were the obligatory gateway for all new long-term encoding, severe impairment there should prevent the relatively preserved long-term learning. Parallel access or alternative routes become plausible. This conclusion does not show that no temporary representation is ever required for learning: visual, semantic or residual verbal processing might support encoding. The result targets a specified mandatory route, not the logical possibility of retaining information transiently by other means.

Later distinctions between working memory, episodic memory, semantic memory and procedural memory explain why delay alone is an inadequate taxonomy. Immediate span depends on perceptual coding, rehearsal and response requirements. New episodic learning depends on operations supporting binding and enduring retention. Motor skills can improve through systems not sufficient for explicit recollection. Such functional distinctions can coexist with interaction: prior knowledge helps immediate retention, and attention and temporary maintenance affect what is learned.

Anatomy similarly requires refinement. Henry Molaison's operation was not confined to one microscopic hippocampal computation; multiple medial temporal lobe structures were affected. Conversely, a selective verbal span deficit after a different injury does not locate all short-term storage in one cortical patch. More circumscribed brain lesion evidence and converging task manipulations are needed to separate structures, connections and computations. The duration of a test is not an anatomical label.

Evidence that medial temporal lobe regions can contribute to online processing of complex objects further qualifies a strict short-delay/long-delay border. Morgan D. Barense and colleagues found that human medial temporal lobe damage affected tasks involving complex object representations even when demands could not simply be described as conventional long-term learning. This supports examining representational requirements, such as feature conjunctions, rather than assuming every immediate judgment bypasses the medial temporal lobe.^[ref]

The lasting contribution of these cases is methodological as well as theoretical. Selective deficits motivate decomposing human memory into processes; complementary preservation constrains claims that one process is necessary for another. The resulting theory should predict transfer, interference, cueing and learning across matched tasks, rather than merely attach a store name to each time interval. Double dissociations establish important functional separability under the tested conditions, while the broader evidence favors interacting human memory systems and multiple routes to retention.

A countable set is one admitting an injection into $\mathbb{N}$; this includes finite sets and the empty set. An infinite countable set can equivalently be listed in a sequence indexed by $\mathbb{N}$.

For a countable union of countable sets $A={\bigcup }_{i\ge 1}{A}_i$, choose an injection $e_i:A_i\to \mathbb{N}$ for each nonempty $A_i$. Assign $x\in A$ the pair $(i,e_i(x))$, where $i$ is the least index with $x\in A_i$. This is an injection into ${\mathbb{N}}^2$. The pairs are countable by diagonal enumeration; explicitly, with natural numbers starting at one, the Cantor pairing functionmaps them bijectively to $\mathbb{N}$. Composing the two maps proves that the union is countable. Empty members contribute no elements.

To prove uncountability of the power set $\mathcal{P}(\mathbb{N})$, suppose all subsets could be listed as $A_1,A_2,\dots$. DefineFor every $n$, $D$ differs from $A_n$ on whether it contains $n$. Thus $D$ is missing from the supposedly exhaustive list. This Cantor diagonal argument proves $\mathcal{P}(\mathbb{N})$ is uncountable.

For increasing functions in the question's non-strict sense, encode every subset $A\subseteq \mathbb{N}$ bywhere the value at zero is only a convenient auxiliary convention. ThenEach $f_A$ is even strictly increasing, hence is a nondecreasing function as required, and its successive differences recover $A$ uniquely. This is an injection from the uncountable power set into the requested collection. Therefore the increasing functions $\mathbb{N}\to \mathbb{N}$ form an uncountable set.

For a decreasing function in the question's sense, $f$ is a nonincreasing function with positive integer values. Each strict decrease lowers its value by at least one, so there can be at most $f(1)-1$ strict decreases. There is consequently a final drop, after which the function is constant. Every such function is determined by a finite initial list whose final value repeats forever. The set of all these lists is contained ina countable union of countable sets, hence countable by the result just proved. Thus the decreasing functions $\mathbb{N}\to \mathbb{N}$ form a countable set. These contrasting cardinalities of monotone natural-number functions come from the absence of an upper bound for increasing values and the finite number of drops available to decreasing natural-number values. If the natural numbers include zero, the same proof uses the bound $f(1)$ instead.

Use the usual independent private values model, quasilinear utility, and voluntary participation with zero outside utility. These assumptions matter: without individual rationality, arbitrary type-independent entry charges make revenue unbounded, and correlated types cannot in general be described by their marginal priors alone.

The revelation principle lets us optimize over direct revelation mechanisms satisfying Bayesian incentive compatibility. Write $x(v)\in [0,1]$ for the common project allocation, $X_i(t)={\mathbb{E}}_{V_{-i}}x(t,V_{-i})$ for player $i$'s interim allocation, and $P_i(t)$ for its interim payment. The interim payment identity givesSince interim individual rationality requires $U_i({\underline{v}}_i)\ge 0$, the virtual-surplus revenue identity bounds expected revenue byThe best feasible common allocation at each valuation profile therefore provides the project when total virtual surplus is nonnegative:Because each regular prior has a nondecreasing virtual valuation, this allocation is a nondecreasing function of each player's report. Hold $v_{-i}$ fixed and charge the critical-value paymentThis is the winning threshold when it lies in the support, the lowest allowed value if every type wins, and zero if the player loses. A truthful winner never pays more than its value; a losing type cannot profit by crossing the threshold. Thus the mechanism has dominant-strategy incentive compatibility and ex post individual rationality, with zero utility at every lowest type. It attains the revenue bound, proving optimality even among mechanisms requiring only Bayesian incentive compatibility. At a zero-virtual-surplus tie, choose any fixed rule that preserves monotonicity.

For independent uniform distributions on $[0,1]$, the virtual valuations are ${\varphi }_i(v_i)=2v_i-1$. The revenue-optimal public-project auction becomesWhen it is provided, player $i$ paysotherwise every payment is zero. If the displayed threshold exceeds one, player $i$ cannot induce provision within its allowed support; if it is negative, provision is independent of its own report and its payment is zero. For $n=1$, this specializes to a reserve value and payment of $1/2$.

Take any $a<u<v<b$. By differentiability implies continuity, $f$ is continuous on $[u,v]$, and it is differentiable on $(u,v)$. The mean value theorem gives $c\in (u,v)$ for whichTherefore $\boxed{f(u)\le f(v)\text{ whenever }u<v}$: $f$ is a nondecreasing function, which is the non-strict meaning of increasing here.

Write $d\mu =f(x)dx$, with $f\ge 0$. Applying polar coordinates and Fubini's theorem gives, for bounded nonnegative measurable $h$,Also $\tilde{\mu }(\{0\})=\mu (\{0\})=0$ by absolute continuity of measures. Extending $g$ by zero on the negative half-line proves

Both radial pushforward measures have compact support of a measure, so Brenier's theorem on the line supplies a unique optimal transport plan and a transport map $\tau$, unique $\tilde{\mu }$-almost everywhere, withIn one dimension, the gradient of a convex function is a nondecreasing function. Nonnegativity holds $\tilde{\mu }$-almost everywhere because ${\tau }_{\#}\tilde{\mu }=\tilde{\nu }$ is carried by $[0,\infty )$. To choose a nonnegative, nondecreasing representative everywhere, let $F,G$ be the radial cumulative distribution functions and use the monotone rearrangementAt $F(r)=0$ set $\tau (r)=0$, and at $F(r)=1$ use the largest point of the support of a measure $\tilde{\nu }$. These choices preserve monotonicity and boundedness. Since $F$ is continuous, $F(R)$ has the uniform distribution on $(0,1)$ when $R$ has law $\tilde{\mu }$, and the quantile function $G^{-1}$ sends this uniform distribution to $\tilde{\nu }$. Thus this representative has the required pushforward measure, even if the target has atoms.

Choose the bounded, nonnegative nondecreasing function $\tau$ from part (d), and defineThe function $h$ is convex and nondecreasing. For $0\le a\le 1$, the triangle inequality, monotonicity, and convexity giveHence $U$ is a convex function. By the gradient of a radial function formula and the fundamental theorem of calculus,for $x\ne 0$ whenever $h^{\prime }(|x|)=\tau (|x|)$. A monotone function has at most countably many discontinuities, so part (d) makes the exceptional radii $\tilde{\mu }$-null. The origin is also $\mu$-null. Define $\mathcal{T}(0)=0$; thus $\mathcal{T}=\nabla U$ $\mu$-almost everywhere.

For every $Q$ in the orthogonal group, $\mathcal{T}(Qx)=Q\mathcal{T}(x)$, including at zero. Consequently ${\mathcal{T}}_{\#}\mu$ is invariant under the orthogonal group. Moreover $|\mathcal{T}(x)|=\tau (|x|)$ away from the null origin, and composition of pushforward measures givesPart (c) therefore gives ${\mathcal{T}}_{\#}\mu =\nu$. The Knott–Smith optimality criterion makes a transport map given by a convex function's gradient optimal for the quadratic transport cost. Uniqueness from Brenier's theorem yieldsSince directions agree, this also identifies the optimal value:

A Homogeneous Poisson process models arrivals at a constant rate $\lambda >0$. Its counting process $N_t=N(t)$ counts arrivals in $(0,t]$, with $N_0=0$. Its paths are right-continuous nondecreasing functions, take nonnegative integer values, and have unit jumps almost surely. The defining Poisson distribution of each increment isIt has stationary increments: the probability distribution above depends on the duration $t$, not the starting time $s$. It also has independent increments: counts in any finite collection of disjoint time intervals are independent random variables. Together these conditions specify the joint count laws, not merely the Poisson distribution at a single time.^[ref]

The rate has units of arrivals per unit time; $\lambda t$ is dimensionless. The Poisson distribution givesFor a toy rate $\lambda =2$ per minute and a duration of $1.5$ minutes, the expected value and variance of the count are both $3$. The probability of no arrival is $e^{-3}\approx 0.049787$, and the probability of exactly three is $3^3{e}^{-3}/3!=\frac{9}{2}{e}^{-3}\approx 0.224042$. In two consecutive one-minute intervals, the probability of exactly two arrivals in the first and exactly one in the second is $(2e^{-2})(2e^{-2})=4e^{-4}\approx 0.073263$, using independent increments. These are exact model calculations with rounded decimals, not measured frequencies.

The diagram's nine hand-chosen arrivals illustrate a path; the line $\lambda t$ is an expected value, not a prescribed trajectory. Constant rate does not imply evenly spaced arrivals. Poisson interarrival times connect the count description to waiting, while Poisson thinning explains how independently routing arrivals produces separate streams. This makes the Poisson process useful as a simple arrival model for a queue, provided constant rate and independent increments are plausible. For a deterministic changing rate, use an Inhomogeneous Poisson process; bunching or history-dependent arrivals need additional assumptions. Conditional on $N_t=n$, the arrival times are the order statistics of $n$ independent uniform distributions on $[0,t]$, as developed in Poisson process conditional arrival times.