Verb Benders Mac OS

Like its Mac OS counterpart, FinSpy for Linux is also obfuscated using LLVM-Obfuscator. The udev2 installer then checks that the system is not a virtual machine, extracts files from itself and stores them in a hidden folder in the user’s home, such as at /.cache/.cfg or /.local/.apps. We would like to show you a description here but the site won’t allow us. Sidebar width: (Mac OS X 10.4) an integer value that indicates the width of the Finder window’s sidebar in pixels. To close the sidebar in Mac OS X 10.4 (Tiger) without hiding the toolbar or the status bar, assign this property a value of 0. In Leopard, setting the sidebar width property to.

  1. Mac Os Download
  2. Mac Os Catalina
  3. Verb Benders Mac Os Download

Recent bugfixes

Version 2.1 (6 Aug 2004)
- includes new MontyNLGenerator component generates sentences and summaries

Version 2.0.1
- fixes API bug in version 2.0 which prevents java api from being callable

What is MontyLingua?[top]

MontyLingua is a free*, commonsense-enriched, end-to-end natural language understander for English. Feed raw English text into MontyLingua, and the output will be a semantic interpretation of that text. Perfect for information retrieval and extraction, request processing, and question answering. From English sentences, it extracts subject/verb/object tuples, extracts adjectives, noun phrases and verb phrases, and extracts people's names, places, events, dates and times, and other semantic information. MontyLingua makes traditionally difficult language processing tasks trivial!

Version 2.0 is substantially FASTER, MORE ACCURATE, and MORE RELIABLE than version 1.3.1. It has now been tested across Windows, many flavors of UNIX, and Mac OS X, and several flavors of Java, and is in use by several university research projects and under several commercial settings.

MontyLingua differs from other natural language processing tools because:

  • it is complete end-to-end.. input raw_text; output semantic interpretation
  • not many dated tools and implementations sewn together; it is one well-integrated implementation
  • it does not require 'training' and other fidgetting, and will work right out-of-the-box
  • it is enriched with 'common sense' knowledge about the everyday world, allowing it to escape many stupid interpretive mistakes. e.g.:
    • '(NX the/DT mosquito/NN bit/NN NX) (NX the/DT boy/NN NX)' corrected>
    • '(NX the/DT mosquito/NN NX) (VX bit/VBD VX) (NX the/DT boy/NN NX)'
  • it is lightweight and portable across platforms, written in portable Python and also available as a compiled Java library
  • it is easy to customize by allowing for a user lexicon

MontyLingua performs the following tasks over text:

  1. MontyTokenizer - Tokenizes raw English text (sensitive to abbreviations), and resolve contractions, e.g. 'you're' > 'you are'
  2. MontyTagger - Part-of-speech tagging based on Brill94, enriched with common sense.
  3. MontyChunker - Lightning fast regular expression chunker
  4. MontyExtractor - Extracts phrases and subject/verb/object triplets from sentences
  5. MontyLemmatiser - Strips inflectional morphology, i.e. changes verbs to infinitive form and nouns to singular form
  6. MontyNLGenerator - Uses MontyLingua's concise predicate-arg representation to generate naturalistic English sentences and text summaries

* free for non-commercial use. please see MontyLingua Version 2.0 License

Terms of Use [top]

Author: Hugo Liu <hugo@media.mit.edu>
Project Page: <http://web.media.mit.edu/~hugo/montylingua/>

Terms of Use

Copyright (c) 2002-2004 by Hugo Liu, MIT Media Lab
All rights reserved.
Non-commercial use is free, as provided in the MontyLingua version 2.0 License. By downloading and using MontyLingua, you agree to abide by the additional copyright and licensing information in 'license.txt', included in this distribution.
If you use this software in your research, please acknowledge MontyLingua and its author, and link to back to the project page http://web.media.mit.edu/~hugo/montylingua.

Please cite montylingua in academic publications as:

Liu, Hugo (2004). MontyLingua: An end-to-end natural
language processor with common sense. Available
at: web.media.mit.edu/~hugo/montylingua.

Documentation [top]

python documentation and api (html) [.html]
java documentation and api [.html]

MontyLingua license [.txt]
by downloading and using MontyLingua you must agree to these terms

Version 2.1 (6 Aug 2004)
- includes new MontyNLGenerator component generates sentences and summaries

Version 2.0.1
- fixes API bug in version 2.0 which prevents java api from being callable

New in version 2.0 (29 Jul 2004)

  • 2.5X speed enhancement for whole system, 2X speed enhancement for tagger component
  • rule-based chunker replaced with much faster and more accurate regular expression chunker
  • common sense added to MontyTagger component improves word-level tagger accuracy to 97%
  • updated and expanded lexicon for English
  • added a user-customizable lexicon CUSTOMLEXICON.MDF
  • improvements to MontyLemmatiser incorporating exception cases
  • html documentation added
  • speed optimizations to all code
  • improvements made to semantic extraction
  • expanded Java API

Download MontyLingua[top]

Please read the following information to proceed to the download of Version 2.1 for Java and Python.

If you have read and agree to the terms of use, click below to continue to the download
(your IP address will also be recorded):

(Download is a 12 MB zip file)

READ THIS if you are running ML on Mac OS X, or Unix

  • The distribution ZIP includes datafiles designed for windows. If you are running MontyLingua on Unix or Mac OS X, and the phrase 'I love you' is tagged incorrectly, then the datafiles need to be rebuilt. This is simple:
  1. delete all files of the form, FASTLEXICON_n.MDF, where n is a number.
  2. re-run the MontyLingua program, either from Python, or Java, and the correct datafiles will be rebuilt. If running Java and you run out of memory during the rebuild process, use the -MX or -Xmx option in Java to increase the memory size. You will only need to rebuild these datafiles once.

Research and Industry Applications which use MontyLingua [top]
These are some of the research and industry projects which use MontyLingua and MontyTagger. To submit your project, email a web url and short description to the author.

William W. Cohen (2004) Minorthird: Methods for Identifying Names and Ontological Relations in Text using Heuristics for Inducing Regularities from Data, http://minorthird.sourceforge.net (website)

Jacob Eisenstein and Randall Davis. Visual and Linguistic Information in Gesture Classification. Accepted to International Conference on Multimodal Interfaces (ICMI'04)(paper)

L. Xie, L. Kennedy, S.-F. Chang, A. Divakaran, H. Sun, C.-Y. Lin (2004). 'Discovering Meaningful Multimedia Patterns with Audio-visual Concepts and Associated Text.' IEEE International Conference on Image Processing (ICIP 2004), Singapore, October 2004. (paper)

Ashwani Kumar, Sharad C. Sundararajan, Henry Lieberman (2004). Common Sense Investing: Bridging the Gap Between Expert and Novice. Conference on Human Factors in Computing Systems (CHI 04), Vienna, Austria.

Hugo Liu and Push Singh (2004) ConceptNet: A Practical Commonsense Reasoning Toolkit. BT Technology Journal, upcoming. Kluwer Academic Publishers. (website)
Google for MontyLingua and MontyTagger to see who else has been using this software.

Software Downloads

Which version of NAMD should I download?

The versions of NAMD below are distinguished first by OS, followed by the type of network interface, and whether or not CUDA is supported. If you are installing NAMD on a standalone workstation, we recommend downloading Linux-x86_64-multicore for Linux or Win64 for Windows. If your workstation has a CUDA-capable GPU, you should try downloading Linux-x86_64-multicore-CUDA or Win64-CUDA. If you wish to run multi-copy algorithms, such as replica-exchange MD, you should try the 'netlrts' builds, such as Linux-x86_64-netlrts or Linux-x86_64-netlrts-smp-CUDA.

If you have a modern NVIDIA GPU (Pascal or newer), you might also be interested to try the new version 3.0 alpha, which includes the option of running standard MD simulations entirely on a single GPU for much faster performance. See the support web page for more information.

Download NAMD:

NAMD is a parallel, object-oriented molecular dynamics code designed for high-performance simulation of large biomolecular systems. Simulation preparation and analysis is integrated into the visualization package VMD.Visit the NAMD website for complete information and documentation.

Selecting an archive below will lead to a user registration and login page. Your download will continue after you have registered or logged in.

Version Nightly Build (2021-05-02) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)

Version 3.0 GPU-Resident Single-Node-Per-Replicate ALPHA Release (2020-11-16) Platforms:

  • Linux-x86_64-multicore-CUDA-SingleNode (NVIDIA CUDA acceleration (single-node))
  • Linux-x86_64-netlrts-smp-CUDA-SingleNode (NVIDIA CUDA acceleration, multi-copy algorithms, single process per copy)

Version 2.15 ALPHA Release (2020-11-03) Platforms:

  • Linux-x86_64-multicore-AMDHIP (AMD HIP/ROCm acceleration)
  • Linux-x86_64-multicore-AVX512 (x86_64 AVX-512)

Version 2.14 (2020-08-05) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-netlrts (Multi-copy algorithms, single host or ethernet)
  • Linux-x86_64-netlrts-smp-CUDA (Multi-copy algorithms, single process per copy)
  • Linux-x86_64-verbs (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp-CUDA (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-KNL-multicore (Intel Xeon Phi KNL processor single node)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86_64-CUDA (NVIDIA CUDA acceleration)
  • MacOSX-x86_64-netlrts (Multi-copy algorithms)
  • Win64 (Windows 7, 8, 10, etc.)
  • Win64-CUDA (NVIDIA CUDA acceleration)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)
  • Win64-MPI-smp-CUDA (HPC Server with CUDA)

Version 2.13 (2018-11-09) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-smp-CUDA (NVIDIA CUDA with InfiniBand)
  • Linux-x86_64-netlrts (Multi-copy algorithms, single host or ethernet)
  • Linux-x86_64-netlrts-smp-CUDA (Multi-copy algorithms, single process per copy)
  • Linux-x86_64-verbs (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp-CUDA (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-KNL-multicore (Intel Xeon Phi KNL processor single node)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86_64-CUDA (NVIDIA CUDA acceleration)
  • MacOSX-x86_64-netlrts (Multi-copy algorithms)
  • Win64 (Windows 7, 8, 10, etc.)
  • Win64-CUDA (NVIDIA CUDA acceleration)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)
  • Win64-MPI-smp-CUDA (HPC Server with CUDA)

Version 2.12 (2016-12-22) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-smp-CUDA (NVIDIA CUDA with InfiniBand)
  • Linux-x86_64-netlrts (Multi-copy algorithms, single host or ethernet)
  • Linux-x86_64-verbs (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp-CUDA (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-KNL-multicore (Intel Xeon Phi KNL processor single node)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86_64-CUDA (NVIDIA CUDA acceleration)
  • MacOSX-x86_64-netlrts (Multi-copy algorithms)
  • Win32 (Windows XP, etc.)
  • Win64 (Windows 7, 8, 10, etc.)
  • Win64-CUDA (NVIDIA CUDA acceleration)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)
  • Win64-MPI-smp-CUDA (HPC Server with CUDA)

Version 2.11 (2015-12-22) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-smp-CUDA (NVIDIA CUDA with InfiniBand)
  • Linux-x86_64-multicore-MIC (Intel Xeon Phi coprocessor acceleration)
  • Linux-x86_64-netlrts (Multi-copy algorithms, single host or ethernet)
  • Linux-x86_64-verbs (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • Linux-x86_64-verbs-smp-CUDA (InfiniBand, no MPI needed, supports multi-copy algorithms)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86_64-CUDA (NVIDIA CUDA acceleration)
  • MacOSX-x86_64-netlrts (Multi-copy algorithms)
  • Win32 (Windows XP, etc.)
  • Win64 (Windows 7, 8, 10, etc.)
  • Win64-CUDA (NVIDIA CUDA acceleration)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)
  • Win64-MPI-smp-CUDA (HPC Server with CUDA)
Verb Benders Mac OS

Version 2.10 (2014-12-11) Platforms:

  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-smp-CUDA (NVIDIA CUDA with InfiniBand)
  • Linux-x86_64-multicore-MIC (Intel Xeon Phi coprocessor acceleration)
  • Linux-x86_64-netlrts (Multi-copy algorithms, single host or ethernet)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86_64-netlrts (Multi-copy algorithms)
  • Win64 (Windows 7, 8, 10, etc.)
  • Win64-CUDA (NVIDIA CUDA acceleration)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)

Version 2.9 (2012-04-30) Platforms:

  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-x86-TCP (TCP may be better on gigabit)
  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-multicore-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-smp-CUDA (NVIDIA CUDA with InfiniBand)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-x86 (Mac OS X for Intel processors, fails on 10.7 'Lion')
  • Win32 (Windows XP, etc.)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)

Version 2.8 (2011-05-31) Platforms:

  • AIX-POWER-lapi (IBM POWER clusters)
  • AIX-POWER-multicore (IBM POWER single node)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-x86-TCP (TCP may be better on gigabit)
  • Linux-x86_64-multicore (64-bit Intel/AMD single node)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-ibverbs-smp (InfiniBand plus shared memory, no MPI needed)
  • Linux-x86_64-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-CUDA (NVIDIA CUDA with InfiniBand)
  • MacOSX-x86_64 (Mac OS X for 64-bit Intel processors)
  • MacOSX-PPC (Mac OS X for PowerPC)
  • MacOSX-x86 (Mac OS X for Intel processors, fails on 10.7 'Lion')
  • Win32 (Windows XP, etc.)
  • Win64-MPI (Windows HPC Server, multi-copy algorithms)

Mac Os Download

Version 2.7 (2010-10-15) Platforms:

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-x86-TCP (TCP may be better on gigabit)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-x86_64-ibverbs (InfiniBand via OpenFabrics OFED, not for Omni-Path, no MPI needed)
  • Linux-x86_64-CUDA (NVIDIA CUDA acceleration)
  • Linux-x86_64-ibverbs-CUDA (NVIDIA CUDA with InfiniBand)
  • Linux-Itanium-Altix (original SGI Altix, not Altix UV)
  • MacOSX-PPC (Mac OS X for PowerPC)
  • MacOSX-x86 (Mac OS X for Intel processors, fails on 10.7 'Lion')
  • Win32 (Windows XP, etc.)

Version 2.6 (2006-08-31) Platforms:

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • BlueGeneL (bypasses MPI for better scaling)
  • BlueGeneP-MPI (Blue Gene/P)
  • Linux-amd64-Clustermatic5-TCP (Clustermatic 5)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-x86-TCP (TCP may be better on gigabit)
  • Linux-x86_64 (64-bit Intel/AMD with ethernet)
  • Linux-x86_64-TCP (TCP may be better on gigabit)
  • Linux-i686-Clustermatic4 (Clustermatic 4 or 5)
  • Linux-i686-Clustermatic4-TCP (TCP may be better on gigabit)
  • Linux-i686-Scyld29 (Scyld Beowulf 29)
  • Linux-i686-Scyld29-TCP (TCP may be better on gigabit)
  • Linux-Itanium (Itanium)
  • Linux-Itanium-Altix (original SGI Altix, not Altix UV)
  • MacOSX-PPC-xlC (Mac OS X for PowerPC, needs IBM libraries)
  • MacOSX-x86 (Mac OS X for Intel processors, fails on 10.7 'Lion')
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)
  • Tru64-Alpha-Elan (AlphaServer SC with Quadrics)
  • Tru64-Alpha-MPI (HP MPI, no Quadrics or Elan)
  • Win32 (Windows XP, etc.)

Version 2.5 (2003-09-29) Platforms:

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-x86-TCP (TCP may be better on gigabit)
  • Linux-i686-Clustermatic (Clustermatic 3)
  • Linux-i686-Clustermatic-TCP (TCP may be better on gigabit)
  • Linux-i686-Scyld (Scyld Beowulf)
  • Linux-i686-Scyld-TCP (TCP may be better on gigabit)
  • MacOSX-PPC (Mac OS X for PowerPC)
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)
  • Solaris-Sparc-MPI (Sun HPC ClusterTools)
  • Tru64-Alpha-Elan (AlphaServer SC with Quadrics)
  • Tru64-Alpha-MPI (HP MPI, no Quadrics or Elan)
  • Win32 (Windows XP, etc.)

Version 2.4 (2002-03-11) Platforms:

Mac Os Catalina

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-i686-Scyld (Scyld Beowulf)
  • MacOSX-PPC (Mac OS X for PowerPC)
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)
  • Solaris-Sparc-MPI (Sun HPC ClusterTools)
  • Tru64-Alpha-Elan (AlphaServer SC with Quadrics)
  • Win32 (Windows XP, etc.)

Version 2.3 (2001-08-02) Platforms:

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Linux-i686-Scyld (Scyld Beowulf)
  • MacOSX-PPC (Mac OS X for PowerPC)
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)
  • Solaris-Sparc-MPI (Sun HPC ClusterTools)
  • Tru64-Alpha-Elan (AlphaServer SC with Quadrics)
  • Win32 (Windows XP, etc.)

Version 2.2 (2000-09-29) Platforms:

  • AIX-POWER (IBM POWER)
  • AIX-POWER-MPI (IBM POWER clusters)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)
  • Win32 (Windows XP, etc.)

Version 2.1 (1999-11-11) Platforms:

Verb Benders Mac Os Download

  • AIX-POWER (IBM POWER)
  • Linux-x86 (32-bit Intel/AMD with ethernet)
  • Origin2000 (any SGI, shared-memory only)
  • Origin2000-MPI (SGI MPI)