wanyu/IteraTeR_v2 huggingface dataset:
Original:  We describe three parameterizations of MinLOB and prove that two of them are NP-complete for every value of the parameter, but the third one is fixed-parameter tractable (FPT).
Revised:  We describe three parameterizations of MinLOB . We prove that two of them are NP-complete for every value of the parameter, but the third one is fixed-parameter tractable (FPT).
Edit intent: coherence
Original:  The coauthorship network was created from the bibliographic record of an overt interdisciplinary research group focused on  sensor networks and wireless communication.
Revised:  The coauthorship network was created from the bibliographic record of an overt interdisciplinary research group focused on the study of sensor networks and wireless communication.
Edit intent: clarity
Original:  A generalization of this result indicates that, contrary to the common conception of a multi-institutional interdisciplinaryresearch group, collaboration is primarily driven by scholar expertise and physical proximity .
Revised:  A generalization of this result suggests that, even in interdisciplinary, multi-institutional research groups, coauthorship is primarily driven by scholar expertise and physical proximity .
Edit intent: clarity
Original:  A generalization of this result indicates that, contrary to the common conception of a multi-institutional interdisciplinaryresearch group, collaboration is primarily driven by scholar expertise and physical proximity .
Revised:  A generalization of this result indicates that, contrary to the common conception of a multi-institutional interdisciplinaryresearch group, collaboration is primarily driven by departmental and institutional affiliation .
Edit intent: clarity
Original: Consider a point set %DIFDELCMD < \D %%%  with a measure function \mu :%DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%%  be the set of subsets of %DIFDELCMD < \D %%%  induced by containment in a shape from some geometric family (e.g. axis-parallel rectangles, half planes, balls, k-oriented polygons).
Revised: Consider a point set %DIFDELCMD < \D %%% with a measure function %DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%% be the set of subsets of %DIFDELCMD < \D %%% induced by containment in a shape from some geometric family (e.g. axis-parallel rectangles, half planes, balls, k-oriented polygons).
Edit intent: clarity
Original: Consider a point set %DIFDELCMD < \D %%%  with a measure function \mu :%DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%%  be the set of subsets of %DIFDELCMD < \D %%%  induced by containment in a shape from some geometric family (e.g. axis-parallel rectangles, half planes, balls, k-oriented polygons).
Revised: Consider a point set %DIFDELCMD < \D %%% with a measure function \mu :%DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%% A be the set of subsets of %DIFDELCMD < \D %%% induced by containment in a shape from some geometric family (e.g. axis-parallel rectangles, half planes, balls, k-oriented polygons).
Edit intent: clarity
Original: Consider a point set %DIFDELCMD < \D %%%  with a measure function \mu :%DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%%  be the set of subsets of %DIFDELCMD < \D %%%  induced by containment in a shape from some geometric family (e.g. axis-parallel rectangles, half planes, balls, k-oriented polygons).
Revised: Consider a point set %DIFDELCMD < \D %%% with a measure function \mu :%DIFDELCMD < \D \to %%% | R. Let %DIFDELCMD < \A %DIFDELCMD < %%% be the set of subsets of %DIFDELCMD < \D %%% induced by containment in a shape from some geometric family (e.g. axis-aligned rectangles, half planes, balls, k-oriented polygons).
Edit intent: clarity
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% %DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: coherence
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | w( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: fluency
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: fluency
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) ) / w(P ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: fluency
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) ) / (\mu(P) ) - %DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: fluency
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / %DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: fluency
Original:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%%  ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A  | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% . We extend the range spaces for which%DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Revised:  We say a range space ( %DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an %DIFDELCMD < \eps%%% -approximation P if \max_{R \in \A | (\mu( R \cap P) ) / (\mu(P) ) - (\mu(R \cap%DIFDELCMD < \D%%% )) / (\mu(%DIFDELCMD < \D%%% ) ) | <= %DIFDELCMD < \eps%%% %DIFDELCMD < \eps%%% -approximations of size O(1/%DIFDELCMD < \eps %DIFDELCMD < %%% polylog (1/%DIFDELCMD < \eps%%% )) exist, and we give constructions and algorithms .
Edit intent: clarity
Original:  In particular we show that a range space (%DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an%DIFDELCMD < \eps%%% -
Revised:  In particular we show that a range space (%DIFDELCMD < \D%%% %DIFDELCMD < \A%%% ) has an%DIFDELCMD < \eps%%% -
Edit intent: fluency
Original:  In particular we show that a range space (%DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% ) has an%DIFDELCMD < \eps%%% -
Revised:  In particular we show that a range space (%DIFDELCMD < \D%%% ,%DIFDELCMD < \A%%% %DIFDELCMD < \eps%%% -
Edit intent: coherence
Original: approximation of size O(k /%DIFDELCMD < \eps %%% polylog(1/%DIFDELCMD < \eps%%% )) and can be constructed in time O(kn poly(1/%DIFDELCMD < \eps%%% )) when%DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Revised: approximation of size O(k /%DIFDELCMD < \eps %%% %DIFDELCMD < \eps%%% )) and can be constructed in time O(kn poly(1/%DIFDELCMD < \eps%%% )) when%DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Edit intent: clarity
Original: approximation of size O(k /%DIFDELCMD < \eps %%% polylog(1/%DIFDELCMD < \eps%%% )) and can be constructed in time O(kn poly(1/%DIFDELCMD < \eps%%% )) when%DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Revised: approximation of size O(k /%DIFDELCMD < \eps %%% polylog(1/%DIFDELCMD < \eps%%% %DIFDELCMD < \eps%%% )) when%DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Edit intent: coherence
Original: approximation of size O(k /%DIFDELCMD < \eps %%% polylog(1/%DIFDELCMD < \eps%%% )) and can be constructed in time O(kn poly(1/%DIFDELCMD < \eps%%% )) when%DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Revised: approximation of size O(k /%DIFDELCMD < \eps %%% polylog(1/%DIFDELCMD < \eps%%% )) and can be constructed in time O(kn poly(1/%DIFDELCMD < \eps%%% %DIFDELCMD < \A %%% is defined by k-oriented polytopes (polytopes where each face has one of k predefined normal directions) and%DIFDELCMD < \D %%% is a discrete point set or a point set that can be decomposed into or approximated by n constant size polytopes.
Edit intent: coherence
Original:  For instance, we show how to construct an%DIFDELCMD < \eps%%% -approximation of size O(1/ %DIFDELCMD < \eps %%% log ^4( 1/ %DIFDELCMD < \eps%%% ) polyloglog (1/ %DIFDELCMD < \eps%%% )) for a terrain in |R^3, with N vertices, for the family of axis-parallel rectangles, in time O(N/(%DIFDELCMD < \eps%%% ^4) polylog ( 1/ %DIFDELCMD < \eps%%% )).
Revised:  For instance, we show how to construct an%DIFDELCMD < \eps%%% -approximation of size O(1/ %DIFDELCMD < \eps %%% log ^4( 1/ %DIFDELCMD < \eps%%% ) polyloglog (1/ %DIFDELCMD < \eps%%% )) for a terrain in |R^3, with N vertices, for the family of axis-parallel rectangles, in time O(N/(%DIFDELCMD < \eps%%% eps polylog 1/ %DIFDELCMD < \eps%%% )).
Edit intent: clarity
Original:  The road coloring problem is the problem of synchronizing coloring of a directed finite strongly connected graph with constant outdegree of all its vertices if the greatest common divisor of  lengths of all its cycles is one.
Revised:  The road coloring problem is the problem of synchronizing coloring of a directed finite strongly connected graph with constant outdegree of all its vertices if the greatest common divisor of the lengths of all its cycles is one.
Edit intent: fluency
Original:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented  .
Revised:  The problem posed in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented .
Edit intent: clarity
Original:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented  .
Revised:  The problem was posed by Adler, Goodwyn and Weiss in 1970 had evoked a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented .
Edit intent: coherence
Original:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented  .
Revised:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in majority of studied cases for the road coloring of the considered graph is presented .
Edit intent: clarity
Original:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented  .
Revised:  The problem was posed by Adler, Goodwyn and Weiss in 1970 and had evoked many years a noticeable interest among the specialists in the theory of graphs, deterministic automataand symbolic dynamics . A polynomial time algorithm of O(n^3) complexity in the most worst case and quadratic in most cases for the road coloring of the considered graph is presented below .
Edit intent: clarity
Original:  The work is based on my recent positive solution of the road coloring problem. 
Revised:  The work is based on recent positive solution of the road coloring problem. 
Edit intent: clarity
Original: Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated.
Revised: Transient and equilibrium synchronizations in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated.
Edit intent: clarity
Original: Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated.
Revised: Transient synchronization in complex neuronal networks as a consequence of dynamics induced by having sources placed at specific neurons is investigated.
Edit intent: clarity
Original: Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons is investigated.
Revised: Transient synchronization in complex neuronal networks as a consequence of activation-conserved dynamics induced by having sources placed at specific neurons are investigated.
Edit intent: fluency
Original:  The synchronization of the activation of the network is then quantified along time in terms of its normalized instantaneous entropy.
Revised:  The synchronization of the activation of the network is then quantified in terms of its normalized instantaneous entropy.
Edit intent: clarity
Original:  The synchronization of the activation of the network is then quantified along time in terms of its normalized instantaneous entropy.
Revised:  The synchronization of the activation of the network is then quantified along time in terms of its normalized entropy.
Edit intent: clarity
Original:  The  potential of such concepts and measurements is explored with respect to 6 theoretical models, as well as for the neuronal network of C. elegans.
Revised:  The potential of such concepts and measurements is explored with respect to several theoretical models, as well as for the neuronal network of C. elegans.
Edit intent: clarity
Original:  A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend heavily on the topological features of the networks  .
Revised:  A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend on the topological features of the networks .
Edit intent: clarity
Original:  A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend heavily on the topological features of the networks  .
Revised:  A series of interesting results are obtained and discussed, including the fact that all models led to a transient period of synchronization, whose specific features depend heavily on the topological structures of the networks .
Edit intent: clarity
Original:  A more rigorous result, in the form of a bound, is found by mapping to particle coalescence dynamics on a network.
Edit intent: coherence
Original:  By interpreting our method as a rank-one approximation of the residue matrix, we also extend it to the nonnegative tensor factorization  .
Revised:  By interpreting this method as a rank-one approximation of the residue matrix, we also extend it to the nonnegative tensor factorization .
Edit intent: clarity
Original: In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developed fast block coordinate method  .
Revised: In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method .
Edit intent: fluency
Original:  By interpreting this method as a rank-one approximation of the residue matrix, we   also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.
Revised:  By interpreting this method as a rank-one approximation of the residue matrix, we prove that it also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.
Edit intent: coherence
Original:  By interpreting this method as a rank-one approximation of the residue matrix, we   also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.
Revised:  By interpreting this method as a rank-one approximation of the residue matrix, we and also extend it to the nonnegative tensor factorization and introduce some variants of the method by imposing some additional controllable constraints such as: sparsity, discreteness and smoothness.
Edit intent: fluency
Original:  In some cases, this decimation procedure can be carried out analytically and is consistent with other analytical approaches, like in the problem of the random walk in a double-well potential.
Revised:  In some cases, this decimation procedure can be analytically carried out and is consistent with other analytical approaches, like in the problem of the random walk in a double-well potential.
Edit intent: fluency
Original:  As a corollary, we show that, unless P=NP, there  no output polynomial-time algorithm to generate all the vertices of a 0/1-polyhedron.
Revised:  As a corollary, we show that, unless P=NP, there is no output polynomial-time algorithm to generate all the vertices of a 0/1-polyhedron.
Edit intent: fluency
Original:  This strengthens the NP-hardness result of Khachiyan et al. (2006) for non 0/ 1 polyhedra , and comes in contrast with the polynomiality of vertex enumeration for 0/1-polytopes  [Bussieck and L\"ubbecke (1998)].
Revised:  This strengthens the NP-hardness result of Khachiyan et al. (2006) for non 0/ 1-polyhedra , and comes in contrast with the polynomiality of vertex enumeration for 0/1-polytopes [Bussieck and L\"ubbecke (1998)].
Edit intent: fluency