About: ELKI is a framework for implementing datamining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods. Changes:Additions and Improvements from ELKI 0.6.0: ELKI is now available on Maven: https://search.maven.org/#artifactdetailsde.lmu.ifi.dbs.elkielki0.7.0jar Please clone https://github.com/elkiproject/exampleelkiproject for a minimal project example. Uncertain data types, and clustering algorithms for uncertain data. Major refactoring of distances  removal of Distance values and removed support for nondoublevalued distance functions (in particular DoubleDistance was removed). While this reduces the generality of ELKI, we could remove about 2.5% of the codebase by not having to have optimized codepaths for doubledistance anymore. Generics for distances were present in almost any distancebased algorithm, and we were also happy to reduce the use of generics this way. Support for nondoublevalued distances can trivially be added again, e.g. by adding the specialization one level higher: at the query instead of the distance level, for example. In this process, we also removed the Generics from NumberVector. The objectbased get was deprecated for a good reason long ago, and e.g. doubleValue are more efficient (even for nonDoubleVectors). Dropped some longdeprecated classes. Kmeans:
CLARA clustering. Xmeans. Hierarchical clustering:
LSDBC clustering. EM clustering was refactored and moved into its own package. The new version is much more extensible. OPTICS clustering:
Outlier detection:
Parallel computation framework, and some parallelized algorithms
LibSVM format parser. kNN classification (with index acceleration). Internal cluster evaluation:
Statistical dependence measures:
Distance functions:
Preprocessing:
Indexing improvements:
Frequent Itemset Mining:
Uncertain clustering:
Mathematics:
MiniGUI has two "secret" new options: minigui.last minigui.autorun to load the last saved configuration and run it, for convenience. Logging API has been extended, to make logging more convenient in a number of places (saving some lines for progress logging and timing).

About: This MATLAB package provides the LOMO feature extraction and the XQDA metric learning algorithms proposed in our CVPR 2015 paper. It is fast, and effective for person reidentification. For more details, please visit http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda/. Changes:Initial Announcement on mloss.org.

About: [FACTORIE](http://factorie.cs.umass.edu) is a toolkit for deployable probabilistic modeling, implemented as a software library in [Scala](http://scalalang.org). It provides its users with a succinct language for creating [factor graphs](http://en.wikipedia.org/wiki/Factor_graph), estimating parameters and performing inference. It also has implementations of many machine learning tools and a full NLP pipeline. Changes:Initial Announcement on mloss.org.

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.23772384.” The most uptodate information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm Changes:Initial Announcement on mloss.org.

About: Software to perform isoline retrieval, retrieve isolines of an atmospheric parameter from a nadirlooking satellite. Changes:Added screenshot, keywords

About: GridSoccer Simulator is a multiagent soccer simulator in a gridworld environment. The environment provides a testbed for machinelearning, and control algorithms, especially multiagent reinforcement learning. Changes:Initial Announcement on mloss.org.

About: BACKGROUND:Over the last decade several prediction methods have been developed for determining the structural and functional properties of individual protein residues using sequence and sequencederived information. Most of these methods are based on support vector machines as they provide accurate and generalizable prediction models. RESULTS:We present a general purpose protein residue annotation toolkit (svmPRAT) to allow biologists to formulate residuewise prediction problems. svmPRAT formulates the annotation problem as a classification or regression problem using support vector machines. One of the key features of svmPRAT is its ease of use in incorporating any userprovided information in the form of feature matrices. For every residue svmPRAT captures local information around the reside to create fixed length feature vectors. svmPRAT implements accurate and fast kernel functions, and also introduces a flexible windowbased encoding scheme that accurately captures signals and pattern for training eective predictive models. CONCLUSIONS:In this work we evaluate svmPRAT on several classification and regression problems including disorder prediction, residuewise contact order estimation, DNAbinding site prediction, and local structure alphabet prediction. svmPRAT has also been used for the development of stateoftheart transmembrane helix prediction method called TOPTMH, and secondary structure prediction method called YASSPP. This toolkit developed provides practitioners an efficient and easytouse tool for a wide variety of annotation problems. Availability: http://www.cs.gmu.edu/~mlbio/svmprat/ Changes:Initial Announcement on mloss.org.
