wanyu/IteraTeR_full_sent huggingface dataset:
Original:  In this paper, we  propose a new OS-level abstraction, namely Deterministic Memory, to define memory regions that are specially handled by the OS and the hardware to exhibit strong real-time guarantees . We show that the deterministic memory abstraction can be introduced in the OS at the granularity of single memory pages by exploiting existing hardware support.
Revised:  In this paper, we propose a new holistic resource management approach driven by a new memory abstraction, which we call Deterministic Memory. The key characteristic of deterministic memory is that the platform - the OS and hardware support.
Edit intent: clarity
Original:  We show that the deterministic memory abstraction can be introduced in the OS at the granularity of single memory pages by exploiting existing hardware support. When deterministic memory pages are accessed, the attribute is propagated through all the levels of the memory hierarchy.
Revised:  We show that the deterministic memory abstraction can be introduced in the OS at the granularity of single memory pages by exploiting existing hardware - guarantees small and tightly bounded worst-case memory access timing. In contrast, we call the conventional memory abstraction as best-effort memory in which only highly pessimistic worst-case bounds can be achieved. We present how the two memory abstractions can be realized with small extensions to existing OS and hardware architecture. In particular, we show the potential benefits of our approach in the context of shared cache management, by presenting a deterministic memory-aware cache architecture and its manage- ment scheme. We evaluate the attribute is propagated through all the levels of the memory hierarchy.
Edit intent: meaning-changed
Original:  When deterministic memory pages are accessed, the attribute is propagated through all the levels of the memory hierarchy. Clearly, the hardware needs to be designed to ensure real-time handling of deterministic memory requests. To illustrate the potentialities of the new abstraction, we also propose a novel design for a shared cache controller that takes advantage of deterministic memory. Minimum cache space is guaranteed for deterministic memory, while unused cache space is left available to non-real-time applications. We implemented OS support for deterministic memory in the Linux kernel; and we evaluated the proposed hardware modifications in a cycle-accurate full-system simulator. We study the effectiveness of our approach on a set of synthetic and real benchmarks.
Revised:  When deterministic memory pages are accessed, the effectiveness of our approach on a set of synthetic and real benchmarks.
Edit intent: coherence
Original:  Results show that it is possible to achieve (i) temporal determinism as strong as with traditional way-based cache partitioning ; and (ii) giving 50\% of the private partition space, on average, to the non-real-time applications .
Revised:  Results show that it is possible to achieve (i) temporal determinism as strong as with traditional way-based cache partitioning for deterministic memory, (ii) giving 50\% of the private partition space, on average, to the non-real-time applications .
Edit intent: meaning-changed
Original: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion  and study estimation  of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Revised: We introduce a general class of stochastic processes driven by a multifractional Brownian motion and study estimation of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Edit intent: fluency
Original: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion  and study estimation  of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Revised: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion (mBm) and study estimation of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Edit intent: meaning-changed
Original: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion  and study estimation  of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Revised: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion and study estimation problems of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Edit intent: clarity
Original: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion  and study estimation  of their pointwise H\"older exponents (PHE) using the localized generalized quadratic variation approach.
Revised: We introduce a general class of multifractional stochastic processes driven by a multifractional Brownian motion and study estimation of their pointwise H\"older exponents (PHE) based on a new localized generalized quadratic variation approach.
Edit intent: meaning-changed
Original:  By comparing it with the other two  benchmark estimation approaches  through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Revised:  By comparing our suggested approach with the other two benchmark estimation approaches through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Edit intent: meaning-changed
Original:  By comparing it with the other two  benchmark estimation approaches  through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Revised:  By comparing it with the other two existing benchmark estimation approaches through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Edit intent: clarity
Original:  By comparing it with the other two  benchmark estimation approaches  through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Revised:  By comparing it with the other two benchmark estimation approaches (classicial LGQV and oscillation approach) through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Edit intent: meaning-changed
Original:  By comparing it with the other two  benchmark estimation approaches  through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and multifractional Brownian motion .
Revised:  By comparing it with the other two benchmark estimation approaches through a simulation study, we show that our estimator has better performance in the case where the observed process is some unknown bivariate function of time and mBm .
Edit intent: clarity
Original:  The time-varying PHE feature allows us to apply such class of multifractional processes to model stock prices under various market conditions . An empirical study on modeling cross-listed stocks provides new evidence that equity's path roughness varies via time and  price informativeness properties from global  markets.
Revised:  The time-varying PHE feature allows us to apply such class of multifractional processes to model stock prices under various market conditions , that are both time-dependent and region-dependent. As an application to finance, an empirical study on modeling cross-listed stocks provides new evidence that equity's path roughness varies via time and price informativeness properties from global markets.
Edit intent: meaning-changed
Original:  We study the  problem of guarding an orthogonal polyhedron having reflex edges in just two directions (as opposed to three  ) by placing  guards on reflex edges only.
Revised: Let an orthogonal polyhedron be the union of a finite set of boxes in \mathbb R^3 (i.e., cuboids with edges parallel to the coordinate axes), whose surface is a connected 2-manifold. We study the problem of guarding an orthogonal polyhedron having reflex edges in just two directions (as opposed to three ) by placing guards on reflex edges only.
Edit intent: meaning-changed
Original:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Revised:  We show that \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Edit intent: coherence
Original:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Revised:  We show that (r - g)/2 \left\lfloor \frac{r-g{2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Edit intent: meaning-changed
Original:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Revised:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges and g is its genus.
Edit intent: clarity
Original:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is its genus.
Revised:  We show that (r - g)/2 \left\lfloor {2} }\right\rfloor +1 reflex edge guards are sufficient, where r is the number of reflex edges in a given polyhedron and g is the polyhedron's genus.
Edit intent: clarity
Original:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Revised:  We prove that \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Edit intent: coherence
Original:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Revised:  We prove that (m - 4)/8 \left\lfloor \frac{m-4{8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Edit intent: meaning-changed
Original:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Revised:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 \rfloor edge guards (not necessarily reflex).
Edit intent: clarity
Original:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor edge guards (not necessarily reflex).
Revised:  We prove that (m - 4)/8 \left\lfloor {8} }\right\rfloor +g reflex edge guards are sufficient, whereas the previous best known bound was \lfloor 11m/72+g/6 - 1 \rfloor-1 edge guards (not necessarily reflex).
Edit intent: fluency
Original:  This problem arises implicitly in many nonequilibrium statistical physics scenarios, e.g., when designing processes to implement some desired computations , feedback-control protocols, and Maxwellian demons.
Revised:  This problem arises in various scenarios in thermodynamics of computation and nonequilibrium statistical physics scenarios, e.g., when designing processes to implement some desired computations , feedback-control protocols, and Maxwellian demons.
Edit intent: meaning-changed
Original:  This problem arises implicitly in many nonequilibrium statistical physics scenarios, e.g., when designing processes to implement some desired computations , feedback-control protocols, and Maxwellian demons. However it is known that many such conditional distributions P over a state space X cannot be implemented using  master equation dynamics over just the states in X.
Revised:  This problem arises implicitly in many nonequilibrium statistical physics scenarios, e.g., when designing processes to implement some desired computations , feedback-control protocol, or Maxwellian demon). It is known that many such conditional distributions P over a state space X cannot be implemented using master equation dynamics over just the states in X.
Edit intent: coherence
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that if there are no constraints on what master equation we can construct, then having access to one extra binary degree of freedom (doubling the number of states) is sufficient to carry out any P . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's state space.
Edit intent: meaning-changed
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that having access to one extra binary degree of freedom (doubling the total number of states) is sufficient to carry out any P . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's state space.
Edit intent: meaning-changed
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that having access to one extra binary degree of freedom (doubling the number of states) is sufficient to carry out any P with zero entropy production . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's state space.
Edit intent: meaning-changed
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that having access to one extra binary degree of freedom (doubling the number of states) is sufficient to carry out any P . Our results also imply that for certain P that can be implemented without hidden states, having additional states available permits an implementation that generates less heat. These results can be seen as uncovering and investigating a novel type of bound on the physical resources needed to perform information processing---the size of a system's state space.
Edit intent: meaning-changed
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that having access to one extra binary degree of freedom (doubling the number of states) is sufficient to carry out any P . Our results provide a novel type of cost of the physical resources needed to perform information processing---the size of a system's state space.
Edit intent: clarity
Original:  In particular, we show that  having access to one extra binary degree of freedom (doubling the  number of states) is sufficient to carry out any P  . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's  state space.
Revised:  In particular, we show that having access to one extra binary degree of freedom (doubling the number of states) is sufficient to carry out any P . Our results provide a novel type of bound on the physical resources needed to perform information processing---the size of a system's hidden state space.
Edit intent: meaning-changed
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: clarity
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F %DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: fluency
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: clarity
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L %DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: fluency
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: fluency
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A %DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: clarity
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% F imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: fluency
Original: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% S imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{  ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Revised: We present FLASH ( %DIFDELCMD < {\bf %%% F ast%DIFDELCMD < {\bf %%% L SH%DIFDELCMD < {\bf %%% A lgorithm for%DIFDELCMD < {\bf %%% Sast imilarity search accelerated with %DIFDELCMD < {\bf %%% H PC(High-Performance Computing) \textbf{ ), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation.
Edit intent: fluency