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Put $a=\sqrt{n\log d/2}$ and $b=\sqrt{n\log (1/\delta )/2}$. Use the scan statistic $C$ and the statistical hypothesis testing ruleThe union bound and the Hoeffding inequality give $P_0(\psi =1)\le d\exp [-2(a+b{)}^2/n]\le \delta$, since $(a+b{)}^2\ge a^2+b^2$. Conditional on index $j$, accepting the null hypothesis implies $c_j\le n\epsilon +a+b$. The assumed separation gives $n\pi (1-\epsilon )>a+2b$, so this threshold lies more than $b$ below the alternative expected value $n{p}_1$. The lower-tail Hoeffding inequality therefore gives $Q_j(\psi =0)\le \delta$. Averaging the conditional error over the mixture model gives $P_1(\psi =0)\le \delta$ as well.

Put $\ell =\log (d/\delta )$ and $r=\sqrt{\ell /n}\le 1$. Use the quadratic scan statistic $\Lambda$ and reject when $\Lambda >1+4r$. Under the null hypothesis, each $nu(T{)}^{\top }\hat{\Sigma }u(T)$ has the chi-squared distribution with $n$ degrees of freedom. The chi-squared concentration inequality gives $P_0^{\otimes n}(\Lambda >1+2r+2r^2)\le d{e}^{-\ell }=\delta$, by the union bound. Since $2r+2r^2\le 4r$, the proposed Type I error is at most $\delta$.

For the true set $S$, $nu(S{)}^{\top }\hat{\Sigma }u(S)/(1+\theta )$ has the same chi-squared distribution. We need a lower-tail bound that remains positive even when $\ell /n$ is close to one. Set $a=e^{-4r}$. The chi-squared Chernoff lower-tail bound gives $P(V\le na)\le \exp [-n(a-1-\log a)/2]\le e^{-n{r}^2}=e^{-\ell }$: indeed $e^{-4r}-1+4r\ge 2r^2$ for $0\le r\le 1$. To verify this last inequality, its derivative is $4(1-r-e^{-4r})$; the expression in parentheses is strictly concave, starts at zero, and changes sign once, so the minimum occurs at an endpoint, where the inequality holds.

The function $g(r)=(1+4r)e^{4r}-1$ is convex with $g(0)=0$, so $g(r)\le rg(1)<300r$ on this interval. Consequently $\theta >300r$ implies $(1+\theta )e^{-4r}>1+4r$. Failure to reject then requires $V\le na$, giving Type II error at most $e^{-\ell }\le \delta$, uniformly in $S$. An explicit, deliberately conservative answer isThe sharp constant is unnecessary; the detection scale is $\sqrt{\log (d/\delta )/n}$.

With the usual variance-proxy convention for a sub-Gaussian random vector, every column inner product is a sub-Gaussian random variable with proxy at most ${\tau }^2$. The two-sided Chernoff bound and union bound implywith probability at least $1-\delta$. No independence between these inner products is required.

The printed coefficient-one bound is false under this convention. Take $k=m=1$, $A=[1]$, and $g=\pm \tau$ with equal probability. This Rademacher random variable has the stipulated variance proxy, but for $\delta =0.99$ the printed threshold is less than $\tau$, whereas $|g|=\tau$ surely. Even a general sub-Gaussian assumption does not imply Gaussian concentration inequality for arbitrary Lipschitz functions. Under the stronger convention $\mathbb{E}{e}^{⟨a,g⟩}\le e^{{\tau }^2\Vert a{\Vert }_2^2/4}$, the same union bound proves the printed constants. That convention, however, differs from the binomial variance proxy used in 1(b).

Use the normalizationfor the Lasso. A centered sub-Gaussian random variable with parameter $\sigma$ satisfies $\mathbb{E}{e}^{tW}\le e^{{\sigma }^2{t}^2/2}$ for all $t\in \mathbb{R}$; for a noncentered variable apply this definition to $W-\mathbb{E}W$. With fixed $X$, independence of the errors givesThe exponential Markov bound, optimized over $t$ separately for the two signs, gives $\mathrm{Pr}(|X_j^T\epsilon |/n>u)\le 2e^{-n{u}^2/(2{\sigma }^2)}$. Take $u=\lambda /2=\sigma A\sqrt{\log p/n}$ and apply the union bound over columns:The specified positive tuning parameter requires $p\ge 2$; at $p=1$ its formula is zero. For small $A$ the probability lower bound can be negative and hence uninformative. Although centering makes the centered errors dependent, $X^{T}1=0$ means $X^T(\epsilon -\bar{\epsilon }1)=X^T\epsilon$, so the preceding proof uses the original independent errors, not independence after centering.

The Karush-Kuhn-Tucker conditions, using the subdifferential of the L1 norm, areConsequently, for nonempty $B\subseteq \hat{S}$, on $\Omega$,This establishes the required active-set inequality without any rank condition on $X$.

For $s>0$, a sufficient Compatibility condition for the Lasso isIndeed, for $\delta =\hat{\beta }-{\beta }^0$, comparison of the two Lasso objective values and the score bound on $\Omega$ give the Basic inequality for the LassoIt follows that $\delta$ satisfies the Lasso cone condition and $\Vert {\delta }_S{\Vert }_1\le \sqrt{s{P}_E}/\varphi$. Thus $P_E\le 3\lambda \sqrt{s{P}_E}/\varphi$, including the trivial $P_E=0$ case. This proves the slightly stronger $P_E\le 9{\lambda }^{2}s/{\varphi }^2$, and in particular the requested $16{\lambda }^{2}s/{\varphi }^2$. If $s=0$, the same basic inequality forces $\hat{\beta }=0$ on $\Omega$; no nonempty-support compatibility constant is needed.

For the support-size argument, use the stated prediction bound with constant $16$. Set $b=|B|$. By the Cauchy-Schwarz inequality and the definition of the sparse maximum eigenvalue,Combining with the lower bound and squaring gives $\boxed{b\le 64{\kappa }_b^{2}s/{\varphi }^2}$ for every nonempty $B\subseteq \hat{S}$. If $m_{\ast }<\infty$ and $\hat{s}\ge m_{\ast }$, choose $B$ of size $m_{\ast }$; this contradicts the strict inequality defining $m_{\ast }$. If $m_{\ast }=\infty$, $\hat{s}\le p<\infty$ proves the assertion directly. Hence $\boxed{\hat{s}<m_{\ast }}$ in both cases.

When $1<m_{\ast }\le p$, minimality and monotonicity of the sparse maximum eigenvalues giveWhen $m_{\ast }=1$, the first result gives $\hat{s}=0$, so the same final bound holds. Thus the finite-index conclusion is $\boxed{\hat{s}\le 64{\kappa }_{m_{\ast }}^{2}s/{\varphi }^2(m_{\ast }<\infty )}$.

There is a genuine domain omission in the printed last assertion: the definition of ${\kappa }_m$ only makes sense for $1\le m\le p$, so ${\kappa }_{m_{\ast }}$ is undefined when $m_{\ast }=\infty$. This case can occur: take centered orthogonal columns with $X^{T}X/n=I_p$, $n\ge p+1$, $s=p$, and ${\varphi }^2=1$. Then ${\kappa }_m^2=1$ for every available $m$ and none exceeds $64p$, so $m_{\ast }=\infty$. A universally defined replacement, from $B=\hat{S}$ and monotonicity, isAlternatively, explicitly extend the definition by ${\kappa }_m={\kappa }_p$ for $m>p$, including infinity. Under that added convention the printed final expression is meaningful and follows from this replacement bound. Neither convention nor finiteness should be silently assumed.

We use planar duality for rectangle crossings, Harris-FKG inequality and the permitted exponential decay of subcritical percolation. A critical-point detail matters: uniqueness stated only for $p>p_c$ cannot by itself be applied at $p=p_c$. We prove the Burton-Keane theorem by the boundary counting proof of percolation uniqueness, establishing at most one infinite percolation cluster at every parameter and avoiding that gap.

For $0<p<1$, the number $N$ of infinite percolation clusters is almost surely constant by translation ergodicity of Bernoulli percolation. It cannot be a finite constant greater than one. A sufficiently large box meets two different infinite percolation clusters with positive probability; opening its finitely many interior edges joins them without creating a new infinite percolation cluster, decreasing $N$. Finite modification of Bernoulli percolation gives positive probability to this modification, contradicting constancy.

Nor can $N=\infty$. A finite box then meets three different infinite percolation clusters with positive probability. Select one infinite exterior branch from each. In the box and a finite collar retain just an embedded three-armed tree joining those branches and close all other incident edges there. Its branching graph vertex separates three infinite components when removed, so is a trifurcation vertex in percolation. All selections concern finitely many possible entrances and edge patterns; finite-energy property of Bernoulli percolation gives positive probability for at least one such pattern. Translation invariance then gives a common positive trifurcation probability $q$.

Here is the counting contradiction in detail. For a finite box $W$, contract each open component outside $W$ that touches $W$ to a boundary terminal. In each cluster, the resulting incidence graph is finite and connected. Every trifurcation in $W$ separates at least three sets of terminals, since each infinite branch must leave $W$. A minimal subtree joining all terminals must therefore contain that graph vertex with degree of a vertex at least three. Its leaves are terminals. The tree identity ${\sum }_v(\mathrm{deg}(v)-2)=-2$ bounds the number of those branching graph vertices by the terminal count. Different terminals are represented by different exterior graph neighbours of $W$. Thus the trifurcation boundary-counting lemma givesFor $W=[-r,r{]}^2\cap {\mathbb{Z}}^2$, the two sizes are $(2r+1{)}^2$ and $8r+4$. Letting $r\to \infty$ contradicts $q>0$. Therefore $N\in \{0,1\}$ almost surely, with the same conclusion for the translated planar dual graph.

Now prove absence of percolation at $p=1/2$ by Zhang's argument. Suppose instead that $\theta (1/2)>0$. Almost surely an infinite primal cluster exists, and self-duality gives an infinite dual cluster as well. An infinite connected subgraph of this locally finite lattice contains a graph ray, by the König infinity lemma. Expanding square boxes meet the infinite percolation clusters with probability tending to one.

For $B_r=[-r,r{]}^2\cap {\mathbb{Z}}^2$, let $A_N,A_E,A_S,A_W$ mean that an open graph ray takes an outward edge through the indicated side and subsequently uses graph vertices outside $B_r$. The union occurs whenever the box meets an infinite percolation cluster: take the last exit of an infinite graph ray from the finite box. Conversely an exterior arm supplies an infinite percolation cluster meeting its boundary. Quarter-turn symmetry makes the four probabilities equal. Their complements are decreasing events, so the square-root trick for positively associated events givesFor the planar dual graph use the box with boundary coordinates $\pm (r+1/2)$ and define its exterior side-arm events in the same way. This box also has quarter-turn symmetry and meets the dual infinite percolation cluster with probability tending to one. Thus each dual side has an infinite exterior arm with probability tending to one. Primal outward edges cross this dual-box contour on the corresponding sides; their remaining graph vertices stay outside it. A union bound makes the simultaneous event of primal north/south arms and dual east/west arms have positive probability for a sufficiently large box.

The four arms alternate around the contour. Open all edges with both endpoints in $B_r$, leaving all outward and exterior edges intact. This joins the two primal entrance points and preserves all four exterior arms. Finite-energy property of Bernoulli percolation keeps the event's probability positive. The primal north and south arms are now connected through the box. The two dual arms must lie in different infinite dual clusters. To see the separation, a hypothetical finite dual graph path joining the east and west arms cannot enter the dual-box interior: its boundary-crossing and interior edges cross primal edges that were just opened. Erase its loops and cut it at successive hits of the contour. A resulting exterior dual crosscut, together with the corresponding contour arc, encloses one of the two intervening primal entrance points. The infinite primal graph ray from that point would have to cross a dual-open edge, which is impossible. This is the planar separation used in the alternating arms argument at the self-dual percolation parameter. It contradicts uniqueness of the infinite dual cluster. HenceFinally suppose $p_c(2)>1/2$. At $p=1/2$ the permitted exponential decay of subcritical percolation would supply $C,c>0$ with ${\mathbb{P}}_{1/2}(v\leftrightarrow \partial B_n(v))\le C{e}^{-cn}$. Use the rectangle with graph vertices $\{0,\dots ,n\}\times \{0,\dots ,n-1\}$, and let $H_n$ be its left-to-right open crossing. Planar duality for rectangle crossings identifies its complement with a dual top-to-bottom crossing of a rectangle of width $n-1$ and height $n$. Rotation and translation give the same crossing law; side edges at the entrance and exit boundaries are irrelevant. Thus the exact self-dual rectangle crossing probability isBut a crossing has some starting graph vertex on its $n$-vertex left side connected to distance $n$. The union bound and exponential decay of subcritical percolation imply ${\mathbb{P}}_{1/2}(H_n)\le nC{e}^{-cn}\to 0$, a contradiction. ThereforeNo Russo-Seymour-Welsh theorem or continuity assertion for $\theta$ is being used, and critical uniqueness was proved rather than inferred from the supercritical hypothesis.

Use natural logarithms. A $K_t$ graph minor is represented by $t$ disjoint nonempty branch sets of a graph minor, each inducing a connected graph, with an edge between every pair. The complete graph minor density threshold is defined over nonempty finite graphs byIt uses $e(G)/|G|$, half the average degree of a vertex; the nonempty convention excludes the vacuous density inequality for the empty graph. The PDF has the intended definition with a universal implication over $G$; the TeX transcription corrupts that definition.

Probabilistic lower bound. Put $N=\lfloor t\sqrt{\log t}/4\rfloor$ and take the binomial random graph $G(N,1/2)$. Its edge count has mean $N(N-1)/4$. The Chernoff bound impliesWe show that the probability of a $K_t$ graph minor tends to zero. Ignore connectedness, which only enlarges the set of possible models, and assign each of the $N$ vertices one of $t$ branch labels or an unused label. There are at most $(t+1{)}^N$ assignments.

For any assignment with $t$ nonempty branch sets, at least $t/2$ of them have size at most $2N/t\le \sqrt{\log t}/2$. Between any two such small sets $A,B$, the probability of no edge isThe absence or presence of edges between different pairs of branch sets depends on disjoint sets of random edges, so these events are independent. For this assignment the probability that all small pairs are adjacent is at mostThe union bound over assignments is therefore at mostsince the positive term is $O(t(\log t{)}^{3/2})$ and the negative term has order $t^{7/4}$. There is consequently a graph with no $K_t$ graph minor and with $e(G)/N\ge N/8$. Any constant having the universal forcing property must exceed this ratio. Since $N/8\ge t\sqrt{\log t}/64$ for large $t$,

The auxiliary density lemma contains a typo. Both the original PDF and the TeX print $e(G)\le 11k|G|$ in the hint. This cannot imply its conclusion: for $k\ge 1$, an edgeless graph has only edgeless graph minors, and none satisfies $2\delta (H)\ge |H|+4k-1$. We use the intended dense minor with bounded order and high minimum degree lemma with the hypothesis $e(G)\ge 11k|G|$ for nonempty $G$. The upper-bound argument below depends on that corrected supplied lemma; the literal printed hint is false.

Probabilistic upper bound and the constant seven. Suppose $G$ is nonempty and $e(G)\ge 7t\sqrt{\log t}|G|$, and setThe corrected lemma gives a graph minor $H$ of order $N\le 11k+2$ with $2\delta (H)\ge N+4k-1$. Since $\delta (H)\le N-1$, we also have $N\ge 4k+1$. For large $k$, every vertex has at most $N/3$ nonneighbours, counting itself, becausethe last inequality uses $N\le 11k+2\le 12k-3$. Moreover any two distinct vertices have at leastcommon neighbours.

Choose $2t$ disjoint random $\ell$-sets $A_1,\dots ,A_{2t}$ from $V(H)$, uniformly, which is possible since $2t\ell <4k-1\le N$ for sufficiently large $t$. For a set $A$, let $U(A)$ be the set of vertices having no neighbour in $A$. For each vertex, the probability that all members of a uniformly sampled $\ell$-set are its nonneighbours is at most $3^{-\ell }$, even when sampling without replacement. By linearity of expected value,Call $A$ good if $|U(A)|\le N{3}^{-19\ell /20}$. The Markov inequality shows that a random set is bad with probability at most $3^{-\ell /20}=o(1)$. Thus the expected number of bad sets among our $2t$ samples is $o(t)$.

Conditional on a fixed good $A_i$, the marginal distribution of $A_j$ is uniform among $\ell$-sets outside $A_i$. For there to be no edge between them, all its members must lie in $U(A_i)$. HenceHere $(N/(N-\ell ){)}^{\ell }=\exp (O({\ell }^2/N))=1+o(1)$. Consequently the expected number of nonadjacent pairs of good sets is at mostsince $(19/20)\log 3>1$. Applying the Markov inequality to both counts shows that there exists a choice with fewer than $t$ bad sets and at most $t$ nonadjacent pairs of good sets. Select $t$ of the good sets; they still have at most $t$ missing adjacencies.

Make each selected set connected by fixing one of its vertices as a root and, for each of its other members, adding one unused common neighbour of that member and the root. This uses at most $t(\ell -1)$ additional vertices. Then for each pair with no original edge, choose one vertex in each set and add an unused common neighbour to one of the sets. The added vertex is joined to that set and to the other set, so it preserves connectedness and repairs their adjacency. At most $t$ such repairs are required.

Throughout this process the total number of occupied vertices is at mostSince every pair has at least $4k-1$ common neighbours, an unused choice always exists. The final sets are disjoint connected branch sets of a graph minor with every pair adjacent. They represent $K_t$ in $H$, and hence in $G$, by transitivity of graph minors. This random branch-set construction of a complete graph minor proves

The sharp linear threshold for a four-vertex complete minor. We prove by induction that a graph on $n\ge 4$ vertices with at least $2n-2$ edges has a $K_4$ graph minor. The case $n=4$ is $K_4$ itself. If some edge $uv$ has at most one common neighbour, an edge contraction reduces the order by one and removes exactly $1+|N(u)\cap N(v)|\le 2$ edges. The contracted graph has at least $2(n-1)-2$ edges, so induction applies.

Otherwise every edge is in at least two triangles in a graph. Choose a vertex $v$ incident with an edge. Each vertex $u\in N(v)$ has at least two neighbours of a vertex in $N(v)$, since these are the common neighbours of $u,v$. Thus $G[N(v)]$ has minimum degree of a graph at least two. A longest path in this finite graph has an endpoint adjacent to an earlier nonconsecutive path vertex, and hence contains a cycle in a graph. Together with $v$, this cycle in a graph gives a wheel graph as a subgraph. Divide its rim into three consecutive nonempty connected arcs; these arcs and $\{v\}$ form four pairwise adjacent branch sets of a graph minor. Therefore

With $2n-3$ edges the answer is no. Take the join of graphs of $K_2$ and an independent set of $n-2$ vertices. It has $1+2(n-2)=2n-3$ edges. Among four disjoint putative branch sets of a graph minor, at most two can contain the two vertices of $K_2$. Any connected branch set avoiding them is a singleton in the independent set. At least two such singleton sets would therefore have no edge between them. This excludes a $K_4$ graph minor for every $n\ge 4$ and proves sharpness.

Estimate the signal by one-dimensional total variation denoising, which penalizes adjacent differences rather than the constant level:This is a fused Lasso objective. Its L1 norm penalty makes many adjacent differences exactly zero, producing a piecewise-constant fit. For change-point detection, estimate the locations by $\hat{S}=\{i:{\hat{\mu }}_i\ne {\hat{\mu }}_{i+1}\}$. The tuning parameter controls the strength of the penalty; it can be chosen, for example, using a validation criterion appropriate to the noise model.

For a general family of elementary nulls $H_1,\dots ,H_m$, the closed testing procedure forms every nonempty intersection $H_B={\bigcap }_{j\in B}{H}_j$ and gives it a local test of level $\alpha$. Reject $H_j$ precisely when all intersections with $j\in B$ are locally rejected. This gives strong control of the familywise error rate: let $T_0$ be the indices of the true elementary nulls. If $T_0$ is empty there is no false rejection. Otherwise, rejecting any $H_j$ with $j\in T_0$ requires rejection of the true intersection $H_{T_0}$. Its local test has rejection probability at most $\alpha$, henceThis proof of closed-testing control of the familywise error rate needs no independence of the tests.

For the interval procedure, a true tested interval is contained in one maximal constant block $B\in \mathcal{T}$. If $I\subseteq B$, the intervals over which $p_B$ takes its maximum are a subset of those used for $p_I$, so $p_I\ge p_B$. Therefore rejection of a true $I$ forces rejection of its containing block. Conversely, a rejected block is itself a rejected true null.

There is a small indexing issue in the PDF: its testing family excludes singleton intervals, while $\mathcal{T}$ may contain singleton blocks. Put ${\mathcal{T}}_{\ast }=\{B\in \mathcal{T}:|B|\ge 2\}$. Every true tested interval lies in one of these blocks, so the exact statement, using only hypotheses defined in the paper, isSingleton blocks contain no eligible tested interval and can be ignored. Equivalently, one may define their nulls as never rejected.

For each $B\in {\mathcal{T}}_{\ast }$, the term $J=B$ occurs in the maximum defining $p_B$, giving $p_B\ge n{q}_B/|B|$. A valid p-value is a super-uniform random variable under its null, so for $0\le \alpha \le 1$,The union bound and disjointness of the constant blocks now giveThis establishes weighted interval testing for a piecewise-constant mean even when the interval p-values are dependent. Values $p_I>1$ do not affect rejection at levels at most one.

For a diagonal entry, put $Z_j={\sum }_{i=1}^d(A_{ij}^2-1)$. Independence and the given moment-generating function inequality for the chi-squared distribution giveThe Chernoff bound with $a=t/4$ yieldsApply the same argument to $-Z_j$ and sum the two tails:

To obtain the off-diagonal bound, set $U=(V+W)/\sqrt{2}$ and $D=(V-W)/\sqrt{2}$. They are jointly normal with unit variances and covariance zero, so independence of uncorrelated jointly normal variables makes them independent standard normals. Since $VW=(U^2-D^2)/2$, the moment-generating function of the product of two independent standard normal random variables isFor $j\ne k$, $(A^{T}A{)}_{jk}$ is a sum of $d$ independent such products. The given inequality implies $\mathbb{E}{e}^{a(A^{T}A{)}_{jk}}\le e^{d{a}^2}$ for $|a|\le 1/2$. Taking $a=t/2$, and repeating for the negative tail, gives

Now take $t=c\sqrt{\log p/d}\in (0,1)$ and $E=A^{T}A/d-I_p$. There are $p$ diagonal entries but only $p(p-1)/2$ distinct off-diagonal entries, since $E$ is symmetric. By the union bound, the event $\Vert E{\Vert }_{\max }\le t$ has probability at leastThis is the required entrywise concentration of a Gaussian Gram matrix.

On that single event, any vector $z$ supported on at most $s$ coordinates satisfieswhere the last step is the Cauchy-Schwarz inequality on the support. If $u,v$ each have at most $s/2$ nonzero coordinates, $z=u-v$ has at most $s$. The same entrywise event applies simultaneously to all of them, so no union over vectors is needed. The sparse norm preservation from entrywise Gram control givesThe printed ratio is undefined at $u=v$. Its equivalent quadratic statement, $|\Vert A(u-v){\Vert }_2^2/d-\Vert u-v{\Vert }_2^2|\le st\Vert u-v{\Vert }_2^2$, includes that case trivially. The nontrivial concentration argument assumes $p\ge 2,c>0$; at $p=1$ the stated probability lower bound is nonpositive and hence vacuous.

We use the uniqueness of the infinite percolation cluster on ${\mathbb{Z}}^d$: for $p>p_c$, there is almost surely exactly one infinite percolation cluster, denoted $I$. Fix $m$ and let $N>m$. Two observations account for the possible distinct endpoints of the face connections.

First, the probability that some finite percolation cluster meeting $B(m)$ reaches $\partial B(N)$ tends to zero as $N\to \infty$. There are only finitely many vertices in $B(m)$, and each of their finite percolation clusters has finite radius; apply the union bound and continuity from above of a measure.

Second, all vertices of $I\cap B(m)$ are joined to one another inside $B(N)$ with probability tending to one. On each configuration, this is a finite set of vertices of one connected percolation cluster. Choose a finite open path in a graph from one such vertex to each of the others; the union of these finitely many paths is contained in some finite box. The assertion is vacuous if the set is empty.

By part (b.ii), each of the two face-connection events within $B(N)$ fails with probability at most ${\delta }_m^{1/(2d)}$. Outside the two exceptional events just described, their endpoints in $B(m)$ belong to $I$ and can be joined inside $B(N)$. Concatenating the left-face path, that joining path, and the right-face path produces a crossing of $B(N)$. ConsequentlyNow let $m\to \infty$. Since ${\delta }_m\to 0$,The order of limits matters: $m$ is fixed while the finitely many relevant percolation clusters are connected inside increasingly large boxes.

Declare an edge of the planar dual graph open exactly when its crossed primal edge is closed. This dual bond percolation has parameter $q=1-p<1/2=p_c$, using the Harris-Kesten theorem. We use the standard exponential tail of subcritical cluster size: for some $K,a>0$, uniformly in the dual vertex $v$,This is decay of the number of vertices in the percolation cluster, not merely its radius.

On $\{|C|=n\}$, the geometric fact allowed in the paper supplies a simple dual cycle in a graph surrounding $C$, with length $\ell \ge \alpha \sqrt{n}$. Every crossed edge is in the edge boundary of $C$ and is therefore closed, so the dual cycle is open. Its $\ell$ distinct vertices lie in one dual percolation cluster of size at least $\ell$.

A dual cycle in a graph of length $\ell$ surrounding the origin has all its vertices within sup-norm distance $\ell +1$ of the origin: its coordinate spans are at most $\ell$, and the origin lies between each pair of extreme coordinates. There are at most $K_0(\ell +1{)}^2$ possible dual vertices there. The union bound over lengths and possible vertices yieldsfor some $K_1,b>0$, since a polynomial factor can be absorbed into a slower exponential decay.

To remove the constant prefactor for all $n\ge 1$, first choose $n_0$ so that the bound is at most $e^{-(b/2)\sqrt{n}}$ for $n\ge n_0$. For the remaining finitely many $n$, use ${\mathbb{P}}_p(|C|=n)\le 1-\theta (p)<1$ and chooseFor $1\le n<n_0$, the chosen $c_2$ ensures $e^{-c_2\sqrt{n}}\ge 1-\theta (p)$.

For $\theta >0$, the Exponential martingale for Brownian motion and the Doob maximal inequality for a nonnegative submartingale giveTaking $\theta =a/s$ gives $e^{-a^2/(2s)}$. Apply the same argument to $-B$ and use the union bound:

Put $\Delta =\Theta -\Sigma$ and let $\beta$ lie in the compatibility cone. ThenSubtracting this from the defining lower bound for ${\beta }^T\Sigma \beta$ and taking the infimum gives the stability of a compatibility constant under entrywise perturbation:

A centered random variable $W$ is a sub-Gaussian random variable with parameter $\sigma$ whenThe Chernoff bound gives $\mathbb{P}(W>t)\le e^{-t^2/(2{\sigma }^2)}$.

For fixed $j,k$, the variablesare independent, centered, and lie in $[-2,2]$, hence are sub-Gaussian with parameter $2$. Their mean is sub-Gaussian with parameter $2/\sqrt{n}$, soA union bound over at most $p^2$ pairs with $t=4\sqrt{2\log (p)/n}$ yields

The minimum-eigenvalue bound and Cauchy-Schwarz inequality implyso ${\varphi }_{\Sigma }^2(S)\ge c_{\min }$. A sufficient uniform condition isIndeed, on the concentration event the entrywise error is at most $c_{\min }/(32s)\le {\varphi }_{\Sigma }^2(S)/(32|S|)$ for every $0<|S|\le s$. The perturbation result then gives ${\varphi }_{\hat{\Sigma }}^2(S)\ge c_{\min }/2$ simultaneously, with probability at least $1-2p^{-2}$.

By the union bound,Hence some $z\in \partial B(n)$ has connection probability at least the left side divided by $|\partial B(n)|$. Some coordinate of $z$ equals $n$ or $-n$. A coordinate permutation and reflection of ${\mathbb{Z}}^d$ sends that face to the face $x_1=n$ and preserves the bond percolation law. Its image $x$ therefore satisfies

Use the monotone grand coupling in every coordinate: at each step use the same attempted move for chains started from the bottom and top states, censoring moves that leave the interval. The two copies coalesce after the lower copy has accumulated enough upward drift and boundary censoring has removed their initial separation. Away from the boundary, one step has mean displacementStandard exponential concentration for sums of bounded independent increments shows that for every fixed $C>6$ the one-coordinate coupling time $\tau$ satisfiesAll other initial states lie between the extremal copies. Coupling the $d$ coordinates independently and using the union bound givesbecause $\log d=o(n)$. The coupling inequality for total variation therefore gives

For a fixed sample $z_{1:n}$, its Empirical Rademacher complexity isIts sample expected value is ${\mathcal{R}}_n(\mathcal{F})$.

Because every $f\in \mathcal{F}$ takes values in $\{0,1\}$, replacing one sample point changes $\hat{\mathcal{R}}$ by at most $1/n$. PutThe lower-tail form of the Bounded differences inequality givesoutside an event of probability at most $\delta /3$.

For the excess-loss supremum $G$ from part (b), replacing one observation changes $G$ by at most $2/n$. A second application of the same inequality givesoutside another event of probability at most $\delta /3$. On the intersection of these two events, symmetrization and the union bound yieldThis event has probability at least $1-2\delta /3\ge 1-\delta$, and $4s=2\sqrt{2\log (3/\delta )/n}$. Thus

LetOn $A$, one has $T\le n$, $S_T\ge \mu T+\epsilon n$, and convexity of $\psi$ gives $\psi (\theta )-\mu \theta \ge 0$. Hence for $\theta >0$,The optional stopping theorem applies because $T$ is bounded, so $\mathbb{E}{Z}_T=1$. Therefore

Optimize over $\theta >0$ for the upper deviation and apply the same argument with $\theta <0$ to the lower deviation. Since the supremum of the linearly interpolated centered walk is attained at grid points, the Legendre transform of a cumulant-generating functionand the union bound give

Because $\Vert X_j{\Vert }_2=\sqrt{n}$, each coordinate of $X^T\epsilon /n$ is $N(0,{\sigma }^2/n)$. The Gaussian tail bound and a union bound giveOn the complementary score event, the standard Basic inequality for the Lasso, cone argument, and compatibility oracle inequality give, for $S=\mathrm{supp}({\beta }^0)$,Using part b and ${\lambda }^2=A^2{\sigma }^2\log p/n$ proveswith the required probability.