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Paper Citation Record · LEDGER

Inferring Cooperativity From Pooled Measurements

As of 20 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2607.03088.

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pith.paper-citation-record.v1
2607.03088 v1

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measured 24 of 24 reference resolution

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Outbound references

Observation 54d0f919-2863-4872-a93b-4a9f0319f3cc · outbound

This paper cites sub-gating.

Inferring Cooperativity From Pooled Measurements sub-gating

Reference 1

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Observation cc755b80-061d-49d1-b5ed-a003c9287dbc · outbound

This paper cites Moreover, the model parameters of theκ-state SDMC satisfy θj,k;s = lim δ↘0 P(Xi,t+δ=c k|Xi,t =c j,SXt =s) δ which is independent of bothi∈{1,...,L}and t≥0.

Inferring Cooperativity From Pooled Measurements Moreover, the model parameters of theκ-state SDMC satisfy θj,k;s = lim δ↘0 P(Xi,t+δ=c k|Xi,t =c j,SXt =s) δ which is independent of bothi∈{1,...,L}and t≥0

Reference 2

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Observation 4a61bc12-6349-4cf4-965c-e40c8a9586e5 · outbound

This paper cites Note that ˜z0 = x and ˜zm+n = y.

Inferring Cooperativity From Pooled Measurements Note that ˜z0 = x and ˜zm+n = y

Reference 3

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Observation 66eee352-659d-48e1-82a1-0cd0101ebdfd · outbound

This paper cites Let1 ≤i1 <···< im≤L coordinates wherex and y differ, i.e.xj̸= yj for j∈{i1,...,im}and m =∥x−y∥0.

Inferring Cooperativity From Pooled Measurements Let1 ≤i1 <···< im≤L coordinates wherex and y differ, i.e.xj̸= yj for j∈{i1,...,im}and m =∥x−y∥0

Reference 4

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Observation 32d238a9-92be-441a-a0a3-fde3ed78ea6e · outbound

This paper cites We define thediscernibilityofA κover the configuration spaceMκ L by dL(Aκ) := min (m1,...,mκ)̸=(m′ 1,...,m′ κ) ∈Mκ L ⏐⏐⏐⏐⏐ κ∑ i=1 cimi− κ∑ i=1 cim′ i ⏐⏐⏐⏐⏐.

Inferring Cooperativity From Pooled Measurements We define thediscernibilityofA κover the configuration spaceMκ L by dL(Aκ) := min (m1,...,mκ)̸=(m′ 1,...,m′ κ) ∈Mκ L ⏐⏐⏐⏐⏐ κ∑ i=1 cimi− κ∑ i=1 cim′ i ⏐⏐⏐⏐⏐

Reference 5

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Observation 954aecba-db84-4617-8e69-5727f90568cb · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 6

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Observation e731ae7f-80e0-4083-9194-124a6252cd4b · outbound

This paper cites C.3 Proof of Lemma 2.3 Proof.

Inferring Cooperativity From Pooled Measurements C.3 Proof of Lemma 2.3 Proof

Reference 7

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Observation a54d1b9f-b95c-47ce-8a4f-9bc623b17960 · outbound

This paper cites C.4 Proof of Proposition 2.4 Proof.

Inferring Cooperativity From Pooled Measurements C.4 Proof of Proposition 2.4 Proof

Reference 8

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Observation 49af5c18-ffb6-4435-9c19-27164fa19a7e · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 9

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Observation ca5dee1f-8a90-4026-abd8-76b850fbcc67 · outbound

This paper cites We extend Lemma A.6 from Vanegas et al.

Inferring Cooperativity From Pooled Measurements We extend Lemma A.6 from Vanegas et al

Reference 10

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Observation 8e1e703b-efdd-4734-9bf4-53fa52a17b5a · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 11

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Observation 8f624166-344a-4e95-a1fd-f1b874b14694 · outbound

This paper cites Conversely, if(28) holds, then, by(29), λS(0,x),λS(1,x),µS(0,x),µS(1,x) remain constant for x∈AL−1 2.

Inferring Cooperativity From Pooled Measurements Conversely, if(28) holds, then, by(29), λS(0,x),λS(1,x),µS(0,x),µS(1,x) remain constant for x∈AL−1 2

Reference 12

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Observation 4255d33d-c97c-4578-9e01-bc5b762c80ce · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 13

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Observation 1948508c-2941-4421-b1d6-377a8eebcf3b · outbound

This paper cites The above inequality is in fact strict, because the corresponding transition rate Γ((x,y),(x,y+e i)) =λs+1−λs>0, see Norris (1998, Theorem 3.2.1).

Inferring Cooperativity From Pooled Measurements The above inequality is in fact strict, because the corresponding transition rate Γ((x,y),(x,y+e i)) =λs+1−λs>0, see Norris (1998, Theorem 3.2.1)

Reference 14

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Observation 23bcdb6a-0945-4803-bfea-8be1c9b12bf4 · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

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Observation 77702ff8-1cd1-400d-8eda-492b59d49fc6 · outbound

This paper cites ii.Caseθ̸= 0.Define the events An := { |ˆθn|≥an,sign( ˆθn) = sign(θ),and|ˆθn−θ|≤an }.

Inferring Cooperativity From Pooled Measurements ii.Caseθ̸= 0.Define the events An := { |ˆθn|≥an,sign( ˆθn) = sign(θ),and|ˆθn−θ|≤an }

Reference 16

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Observation c924990d-9130-4f06-9529-961255f6c86e · outbound

This paper cites an unresolved cited work.

Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 17

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Observation 2a09f569-1c77-45d8-aa39-3a675964c297 · outbound

This paper cites E.3 Proof of Theorem 4.3 Proof.

Inferring Cooperativity From Pooled Measurements E.3 Proof of Theorem 4.3 Proof

Reference 18

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Observation c58a2ebc-9a04-4118-9476-114cc1460ada · outbound

This paper cites We next show thatF is strictly increasing on[0,∞).

Inferring Cooperativity From Pooled Measurements We next show thatF is strictly increasing on[0,∞)

Reference 19

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Observation 47c966b8-94d6-4a9b-9046-259a0ecac326 · outbound

This paper cites Therefore, each(i,j )∈H1 belongs to the first-step rejection set.

Inferring Cooperativity From Pooled Measurements Therefore, each(i,j )∈H1 belongs to the first-step rejection set

Reference 20

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Observation 5a83a31e-673d-49ae-8d02-b56487d599cf · outbound

This paper cites By Lemma E.3 and the continuous mapping theorem, max (i,j)∈H0 |ˆUn,(i,j)|D− →max (i,j)∈H0 |Z(i,j)|, Z∼N|H0| ( 0,R◦,H0 ).

Inferring Cooperativity From Pooled Measurements By Lemma E.3 and the continuous mapping theorem, max (i,j)∈H0 |ˆUn,(i,j)|D− →max (i,j)∈H0 |Z(i,j)|, Z∼N|H0| ( 0,R◦,H0 )

Reference 21

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Observation 88b2e135-d805-48f1-8195-0b65041512ab · outbound

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Inferring Cooperativity From Pooled Measurements Unresolved cited work

Reference 22

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Observation 11454b43-6758-4e27-99f9-8d62fc98fe42 · outbound

This paper cites It may instead converge to a local maximum.

Inferring Cooperativity From Pooled Measurements It may instead converge to a local maximum

Reference 23

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Observation ac66d96c-ebca-4f3e-aa12-064ebd6a2b2e · outbound

This paper cites 59 In Algorithm 2, the initial distribution is estimated freely and updated by the usual Baum–Welch update.

Inferring Cooperativity From Pooled Measurements 59 In Algorithm 2, the initial distribution is estimated freely and updated by the usual Baum–Welch update

Reference 24

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