text:
\section{Introduction}
\label{intro}
Multiple Instance Learning (MIL) is a variation of classical learning methods that can be used to solve problems in which the labels are assigned to bags, i.e., a set of instances, rather than individual instances.
MIL was originally introduced to solve the problem of drug activity prediction and polymorphism ambiguity \citep{Dietterich1997}.
Then, it has been applied to several problems such as Protein-Protein Interactions (PPI) \citep{ppimil} and image regions classification in computer vision \citep{Chen:2004:ICL:1005332.1016789}.
For example, in drug activity prediction problem, each drug molecule is represented by a bag, and the alternative low-energy shapes of the molecule are represented by the instances in the bag.
In image regions classification, each image can be treated as a bag of segments that are modeled as instances, and the concept point representing the target object can be learned through MIL algorithms.

One major assumption of most existing MIL methods is that each bag contains a set of instances that are independently and identically distributed.
But, many real world applications such as bioinformatics, web mining, and text mining have to deal with sequential and temporal data.
When the tackled problem can be formulated as a MIL problem, each instance of each bag may have structural and/or temporal relation with other instances in other bags.
Considering this issue, thus, the problem we want to solve in this work is the MIL problem in sequence data that present structural dependencies between instances of different bags.
In this context, the learning data consist of a set of bags where each bag contains a set of sequences that are expressed differently for every bag.

A variety of MIL algorithms have been developed such as Diverse Density \citep{Maron1998}, Citation-kNN \citep{Wang00solvingthe}, MI-SVM \citep{andrews02} and HyDR-MI \citep{DBLP:journals/isci/ZafraPV13}.
However, most of these algorithms are not suitable for the problem of sequence data prediction since they require an attribute-value format for their processed data.
In addition, the major assumption of most existing MIL methods is that instances of different bags are independently and identically distributed.
Nevertheless, in many applications, the dependencies between across bag instances naturally exist and if incorporated in classification models, they can potentially improve the prediction performance significantly \citep{zhang2011multiple}.

In this paper, we deal with MIL problem for sequence data and we consider the case of data that present dependencies between instances of different bags.
We first provide a formalization of the problem of multiple instance learning in sequence data.
Then, we present two approaches that take into account relational structure information among across bag instances/sequences.
The first approach is the \textit{ABClass} approach which performs first a preprocessing step of the input sequences that consists in extracting motifs from the set of sequences.
These motifs will be used as attributes/features to construct a binary table where each row corresponds to a sequence.
Then a discriminative classifier is applied to the sequences of an unknown bag in order to assign its label.
The second approach is the \textit{ABSim} approach which uses a similarity measure between each sequence of an unknown bag and the corresponding sequences in the learning bags.
We applied the proposed approaches to the problem of prediction of IRR in bacteria using MIL techniques introduced and described in \citep{aridhi2015JCB}.
The problem of IRR prediction in bacteria consists in learning a classifier that classifies a bacterium to either IRRB or IRSB.
We describe an implementation of our algorithms and we present an experimental study that evaluates the performance of the proposed approaches in the case of IRR prediction in bacteria.

The remainder of this paper is organized as follows.
Section \ref{Background} defines the problem of MIL for sequence data.
In Section \ref{related}, we present an overview of some related works dealing with MIL problems.
In Section \ref{approach}, we describe the proposed MIL-based approaches for sequence data.
In Section \ref{experiments}, we describe our experimental environment and we discuss the obtained results.
Concluding points make the body of Section \ref{conclusion}.

 \section{Background}
 \label{Background}
 In this section, we present the background information related to MIL in sequence data.
 We first describe the terminology and our problem formulation.
 Then, we introduce a simple use case that serves as a running example throughout this paper.
\subsection{Problem formulation}
\label{Pbm_formulation}
A \textbf{sequence} is an ordered list of events. An event can be represented as a symbolic value, a numerical value, a vector of values or a complex data type \citep{classSurvey}. There are many types of sequences including symbolic sequences, simple time series and multivariate time series. In our work, we are interested in symbolic sequences since the protein sequences are described using symbols (amino acids).
We denote $\Sigma$ an \textit{alphabet} defined as a finite set of characters or symbols.
A \textbf{symbolic sequence} is defined as an ordered list of symbols. A sequence $s$ with length $P$ is written as: $s= s_1,s_2, \ldots , s_P$,  where $s_l \in \Sigma$ is a symbol at position $l$.
Let $DB$ be a learning database that contains a set of $n$ labeled bags $DB=\{(B_i, Y_i), i = 1, 2 \ldots, n\}$ where $Y_i=\{-1, 1\}$ is the label of the bag $B_i$.
Instances in $B_i$ are sequences and are denoted by $B_{ij}$. Formally $B_i=\{B_{ij}, j = 1, 2 \ldots, m\}$, where $m$ is the total number of instances in this bag.
We note that there is a \textbf{relation} $R$ between instances of different bags denoted across bag sequences relation.
Each instance $B_{ij}$ of a bag $B_i$ is related by $R$ to the instance $B_{hj}$ of an other bag $B_h$ in $DB$.

The problem investigated in this work is to learn a multiple instance classifier from $DB$.
Given a query bag $Q=\{Q_k, k = 1, 2 \ldots, q\}$, where $q$ is the total number of instances in $Q$, the classifier should use sequential data in this bag and in each bag of $DB$ to predict the label of $Q$.

\subsection{Running example}
\label{Running_ex}
In order to illustrate our proposed approaches, we rely on the following running example. Let $\Sigma= \{A,B, \ldots , Z\}$ be an alphabet.
Let $DB=\{(B_1, +1), (B_2, +1),$ $(B_3, -1), (B_4, -1)\}$ a learning database that contains four bags.
Positive bags consists of $B_1$ and $B_2$ and negative bags consists of $B_3$ and $B_4$.

\begin{align}
  B_1=
  \begin{cases}
   B_{11}= \textbf{AB}MS\textbf{CD} \\
   B_{12}= \textbf{EF}NO\textbf{GH}
  \end{cases}
\end{align}

\begin{align}
  B_2=
  \begin{cases}
  B_{21} = E\textbf{AB}ZQ\textbf{CD}\\
  B_{22} = CC\textbf{GH}DD\textbf{EF}
  \end{cases}
\end{align}

\begin{align}
  B_3=
  \begin{cases}
    B_{31}= \textbf{CD}X\textbf{YZ}\\
    B_{32}= \textbf{GH}WXY
      \end{cases}
\end{align}
\begin{align}
  B_4=
  \begin{cases}
    B_{41}= \textbf{AB}IJ\textbf{YZ}\\
    B_{42}= \textbf{EF}YRT\textbf{AB}
      \end{cases}
\end{align}

We note that $B_{11}$, $B_{21}$, $B_{31}$ and $B_{41}$ are related by an across bag relation $R$. The same applies to $B_{12}$, $B_{22}$, $B_{32}$ and $B_{42}$.
We need to predict the class label of an unknown bag $Q=\{ Q_1 , Q_2\}$ where:
\begin{align}
  Q=
  \begin{cases}
Q_1= \textbf{AB}WX\textbf{CD}\\
Q_2= \textbf{EF}XY\textbf{GH}N
  \end{cases}
\end{align}

\section{Related works}
\label{related}
\subsection{Sequence classification}
\label{seq_classification}
Existing sequence classification methods can be divided into three large categories \citep{classSurvey}: (1) feature based classification, (2) sequence distance based classification
and (3) model based classification.

In feature based classification, a sequence is transformed into a feature vector. This representation scheme could lead to very high-dimensional feature spaces.
The feature extraction step is very important since it would impact on the classification results.
This step should deal with many parameters such as the criteria used for selecting features (e.g. frequency and length) and the matchings type (i.e. exact or inexact with gaps). After adapting the input data format, a conventional classification method is applied.

In sequence distance based classification, a distance function should be defined to measure the similarity between a pair of sequences.
Then an existing classification method could be used such as K nearest neighbor (KNN) or SVM. The distance function determines the quality of the classification significantly \citep{classSurvey}.
In a recent work \citep{dafeSilhouettes}, authors propose algorithms to learn sequential classifiers from long and noisy discrete-event sequences.
The algorithms use a lightweight and flexible subsequence matching function and a subsequence enumeration strategy called \textit{pattern silhouettes}.

Model based classification methods define a classification model based on the probability distribution of the sequences over the different classes.
This model is then used to classify unknown sequences.
Naive Bayes is a simple model based classifier that makes the assumption that the features of the sequences are independent.
Markov Model and Hidden Markov Model (HMM) could be used to model the dependencies among sequences.

\subsection{Multiple Instance Learning (MIL) approaches}
\label{mil_approaches}
In multiple instance learning, the training set is composed of $n$ labeled bags. Each bag $i$ in the training set contains $m$ instances and has a bag label $Y_{i} \in \{{-1}, {+1}\}$.
We notice that instance $j$ of each bag has label $Y_{ij} \in \{{-1}, {+1}\}$, but this label is not known during training.
The most common assumption in this field is that a bag is labeled positive if at least one of its instances is positive, which can be expressed as follows:

\begin{equation}
\displaystyle Y_i = \max_{j} (Y_{ij}).
\end{equation}
The task of MIL is to learn a classifier from the training set that correctly predicts unseen bags.
Although MIL is quite similar to traditional supervised learning, the main difference between the two approaches can be found in the class labels provided by the data.
According to the specification given by Dietterich et al. \citep{Dietterich1997}, in a traditional setting of machine learning, an object  is represented by a feature vector (an instance), which is associated to a label.
However, in a multiple instance setting, each object may have various instances.

Recently, several MIL algorithms have been proposed including Diverse Density \cite{Maron1998}, Citation-kNN \citep{Wang00solvingthe}, MI-SVM \citep{andrews02}, and HyDR-MI \citep{DBLP:journals/isci/ZafraPV13}.
Diverse Density (DD) was proposed in \citep{Maron1998} as a general framework for solving multiple instance learning problems.
Diverse Density uses a probabilistic approach to maximize a measure of the intersection of the positive bags minus the union of the negative bags in feature space.
The key point of DD approach is to find a concept point in the feature space that are close to at least one instance from every positive bag and meanwhile far away from instances in negative bags.
The optimal concept point is defined as the one with the maximum diversity density, which is a measure of how many different positive bags have instances near the point, and how far the negative instances are away from that point.

\textit{MI-SVM} \citep{andrews02} is an adaptation of support vector machines (\textit{SVM}) to the MIL problem.
The approach adopted by \textit{MI-SVM} explicitly treats the label instance labels as unobserved hidden variables subject to constraints defined by their bag labels.
The goal is to maximize the usual instance margin jointly over the unknown instance labels and a linear or kernelized discriminant function.

In \citep{citaionKNN}, the authors present two variants of the K-nearest neighbor algorithm called Bayesian-KNN and Citation-KNN. The Bayesian method computes the posterior probabilities of the label of an unknown bag based on labels of its neighbors. Citation-kNN algorithm classifies a bag based on the labels of both the references and citers of that bag.

In \citep{DBLP:journals/isci/ZafraPV13}, \textit{HyDR-MI} (which stands for hybrid dimensionality reduction method for multiple instance learning) is proposed as a feature subset selection method for MIL algorithms.
The hybrid consists of the filter component based on an extension of the \textit{ReliefF} algorithm \citep{Zafra:2012:RER:2051381.2051674} developed to work with MIL and the wrapper component based on a genetic algorithm that optimizes the search for the best feature subset from a reduced set of features, output by the filter component.

In \citep{TrxFoldProt}, the authors present an SVM-based algorithm via Approximate Box Counting.
They reformulate GMIL-1 algorithm using a kernel for a support vector machine to reduce its time complexity from exponential to polynomial.
Computing the kernel is equivalent to counting the number of axis-parallel boxes in a discrete, bounded space that contain at least one point from each of the two multisets.

In \citep{structreddata}, an optimization algorithm that deals with multiple instance learning on structured data (MILSD) is proposed.
In MILSD there exists rich dependency/structure information between instances/bags that may be used to improve the performance of existing MIL algorithms. This additional information is represented using a graph that depicts the structure between either bags or instances.
The proposed formulation deals with two sets of constraints caused by learning on instances within individual bags and learning on structured data and has a non-convex optimization problem. To solve this problem, authors present an iterative method based on constrained concave-convex procedure (CCCP). It is an optimization method that deals with the concave convex objective function with concave convex constraints \citep{CCCP}.
However, in many real world applications, the number of the labeled bags as well as the number of links
between bags are huge. To solve the problem efficiently, the cutting plane method \citep{cuttingPlane} is used.
The authors of \citep{structreddata} have present a novel adaption of the cutting plane method that can handle the two sets of constraints simultaneously:
the goal is to find two small subsets of constraints from a larger constraint sets.
They also summarize three scenarios of the structure information in MIL:
\begin{itemize}
  \item I-MILSD: the relational structures are on the instance level (either in the same bag or across bags).
  \item  B-MILSD: the structure information is available on the bag level.
  \item BI-MILSD: the structure information is available on both instance level and bag level.
\end{itemize}
Applying the above presented algorithms on our IRR problem leads to two problems.
The first problem is that these algorithms can be used when the processed data can be simply represented by bags of instances and the labels are assigned to bags rather than individual instances. If applied on sequences, the input data should be presented in attribute-value format whereas we aim to use the input data without any adaptation of the format.
For example, in \citep{andrews02}, the empirical evaluation is done on three datasets: (1) MUSK dataset, (2) Corel dataset for image annotation and (3) TREC9 dataset for document categorisation.
The last dataset contains sequence data. Terms are used to present the text. This leads to an extremely sparse and high dimensional attribute-value representation of the processed text.
In\citep{TrxFoldProt} and \citep{structreddata}, a dataset of protein sequences was used in the empirical evaluation. The goal is to identify Trx-fold Proteins.
In each protein's primary sequence, the primary sequence motif (typically CxxC) that is known to exist in all Trx-fold proteins are found. Then, they extract a window of size 214 around each motif (20 residues upstream, 180 downstream).
Each primary protein sequence is considered as a bag, and some of its subsequences (the extracted windows) are considered as instances. These subsequences are aligned mapped to a 8-dimensional feature space: 7 numeric properties \citep{features} and an 8$^{th}$ feature that represents the residue's position. So we obtain an attribute-value format description of the dataset.
The second problem is that they don't deal with the across bag relations that may exists between instances, except the algorithms in \citep{structreddata}.
In \citep{structreddata}, the alignment score is used to identify the additional structure information between proteins: if the score between a pair of proteins exceed 25, then authors consider that there exists a link between them. Only the B-MILSD algorithm was used in experiments.

\section{MIL-based approaches for sequence data}
\label{approach}
In this section, we present the proposed approaches for MIL in sequence data.
We also present the naive approach to deal with the problem of MIL in sequence data.
\subsection{Naive approach for MIL in sequence data}
The simplest way to solve the problem of MIL for sequence data is to use standard MIL classifiers.
However, most commonly used MIL algorithms require a uniform attribute-value format description of all instances of different bags.
The naive approach for MIL in sequence data consists of a two step approach.
The first step is a preprocessing step that transforms the set of sequences to an attribute-value matrix where each row corresponds to a sequence and each column corresponds to an attribute.
The second step consists in applying an existing MIL classifier.
Figure \ref{figNaive} illustrates the naive approach for MIL in sequence data.
\begin{figure*}[t]
\centering
\includegraphics[width=14cm,height=8cm]{naiveapproach.png}
\caption{Naive approach for MIL in sequence data}
\label{figNaive}
\end{figure*}

In the case of sequence data, the most used technique to transform data to an attribute-value format is to extract motifs that serve as attributes.
We note that finding a uniform description of all instances using a set of motifs is not always an easy task.
Since our naive approach takes into account the across bag relations between instances, the preprocessing step extracts motifs from each set of related instances. The union of these extracted motifs is then used as features to construct an attribute-value matrix where each row corresponds to a sequence.
The presence or the absence of an attribute in a sequence is respectively denoted by 1 or 0.
Using this approach, we obtain an attribute-value matrix that contains a large number of motifs.
It is worthwhile to mention that only a subset of the used attributes is representative for each processed sequence.
Therefore, we may have a big sparse matrix when trying to present the whole sequence data using an attribute value format.

We apply the naive approach to our running example.
Let $AttributeList_1= \{AB, CD, YZ\}$ be the list of features extracted from the instances $B_{11}$, $B_{21}$, $B_{31}$ and $B_{41}$.
Let $AttributeList_2= \{EF, GH\}$ be the list of features extracted from the instances $B_{12}$, $B_{22}$, $B_{32}$ and $B_{42}$.
The union of $AttributeList_1$ and $AttributeList_2$ produces the list $AttributeList=\{AB, CD, YZ, EF, GH\}$.
In order to encode the learning sequence data, we generate the following  attribute-value matrix denoted $M$:

  \begin{equation}
 M=
 \setcounter{MaxMatrixCols}{20}
 \begin{matrix}
  \begin{blockarray}{cccccccccccc}
 \multicolumn{5}{c}{1^{st } instance} &|&  \multicolumn{5}{c}{2^{nd } instance} \\
\begin{block}{(ccccccccccc)c}
   1&1&0&0&0&|&0&0&0&1&1 &B_1\\
   1&1&0&0&0&|&0&0&0&1&1 &B_2\\
   0&1&1&0&0&|&0&0&0&0&1 &B_3\\
   1&0&1&0&0&|&1&0&0&1&0 &B_4\\
\end{block}
\end{blockarray}
\end{matrix}
\end{equation}

The sparsity percentage of $M$ is 60\%. We note that if we have a big learning database, $M$ could result to a huge and very sparse matrix.

\subsection{\textit{ABClass:} Across bag sequences classification}

In order to avoid the use of one large vector of features to describe sequence data,
we present \textit{ABClass}, a novel approach that takes into account the across bag relations. Each set of related instances will be presented by its own motifs vector.
This reduces the number of attributes that are not representative for the processed sequence.
Instead of using a classifier that uses a large vector to describe all the sequences data, every vector of motifs will be used to produce a prediction result.
These results will be then aggregated to have a final result.
Based on the formalization, we propose an algorithm that discriminates bags by applying a classification model to each instance of the query bag.
For each set of across bag sequences, we extract motifs and we construct a classification model.
During the execution of the \textit{ABClass} algorithm, we will use the following variables:
 \begin{itemize}
 \item A matrix $M$ to store the encoded data of the learning database.
 \item A vector $QV$ to store the encoded data of the query bag instances.
\item A vector $PV$ to store prediction results of the classification.
\end{itemize}

The algorithm works as follows (see Algorithm \ref{algo1}).
\begin{algorithm}[h]
\caption{\textsf{AcrossBagSequencesClassification}($DB$, $Q$)}
\label{algo1}
\begin{algorithmic}[1]
\REQUIRE Learning database $DB=\{(B_i, Y_i) \vert i = 1, 2, \ldots, n\}$ , Query bag $Q=\{Q_k \vert k = 1, 2, \ldots, q\}$
\ENSURE Prediction result $P$
\FORALL {$Q_k \in Q$}
\STATE $AcrossBagSeqList \leftarrow AcrossBagSeq(k, DB)$
\STATE $MotifList \leftarrow MotifExtractor(AcrossBagsList)$
\STATE $M \leftarrow EncodeData( MotifList, AcrossBagsList)$
\STATE $QV \leftarrow EncodeData( MotifList, Q_k)$
\STATE $PV_k \leftarrow ApplyModel(QV, Model)$
\ENDFOR
\STATE $P \leftarrow Aggregate(PV)$
\RETURN $P$
\end{algorithmic}
\end{algorithm}
The \textsf{acrossBagSeq } function is illustrated in Algorithm \ref{algo2}.
\begin{algorithm}[h]
\caption{\textsc{set}\ \textsf{acrossBagSeq }($k$, $DB$)}
\label{algo2}
\begin{algorithmic}[1]
\REQUIRE Sequence index $k$, Learning database $DB=\{(B_i, Y_i) \vert i = 1, 2, \ldots, n\}$
\ENSURE A set of sequences $S$
  \STATE $S \leftarrow \emptyset$
  \FORALL {$B_i \in DB$}
  \STATE $S \leftarrow  S \cup \{B_{ik}\}$
  \ENDFOR
  \RETURN $S$
\end{algorithmic}
\end{algorithm}

Informally, the main steps of the  \textit{ABClass} algorithm are:
\begin{enumerate}
\item For each instance sequence $Q_k$ in the query bag $Q$, the related instances among bags of the learning database are grouped into a list (lines 1 to 3).
\item The algorithm extracts motifs from the list of grouped instances. These motifs are used to encode instances in order to create a discriminative model (lines 4 and 5).

\item  \textit{ABClass} uses the extracted motifs to represent the instance $Q_k$ of the unknown bag into a vector $QV$,
   Then it compares it with the corresponding model. The comparison results are stored in a vector $PV$ (lines 6 and 7).

\item  \textit{ABClass} applies an aggregation method to $PV$ in order to compute the final prediction result $P$ (line 9), which consists in a positive or a negative class label.

\end{enumerate}

We notice that the proposed approach can be simply evaluated by the accuracy of its prediction result.
Another option can be used is the rate of classification models that contributes to the prediction result.

\begin{figure*}
\centering
\includegraphics[width=14cm,height=8cm]{motifapproach.png}
\caption{System overview of the \textit{ABClass} approach}
\label{figMotifs}
\end{figure*}

We apply the  \textit{ABClass} approach to our running example.
Since the query bag contains two instances $Q_1$ and $Q_2$, we will have two iterations followed by an aggregation step.
 First, the algorithm groups the set of bags that are related and extract the corresponding motifs.

 \begin{center}
  $AcrossBagsList= \{B_{11}, B_{21}, B_{31}, B_{41}\}$\\
  $MotifList = \{AB, CD, XY\}$ \\
  \end{center}

 Then, it generates the attribute-value matrix $M$ describing the data.

  \begin{equation}
 M=
 \begin{matrix}
  \begin{blockarray}{cccc}
AB & CD& YZ  \\
\begin{block}{(ccc)c}
  1 & 1 &0 & B_{11} \\
  1 & 1 & 0&B_{21} \\
    0 &1 & 1 & B_{31} \\
    1& 0 & 1 & B_{41} \\
\end{block}
\end{blockarray}
\end{matrix}
\end{equation}
The sparsity percentage of $M$ is 33\%. We note that the sparsity percentage of the produced matrix is reduced  because there is no need to use the motifs extracted from instances $\{B_{i2}, i=1,.., 4\}$ to describe instances $\{B_{i1}, i=1,.., 4\}$.
A model is then created using the encoded data ($Model= CreateModel(M)$).
Using the features list, a vector $QV$ is  generated to describe the first instance $Q_1$ of the query bag.
 \begin{equation}
QV= \begin{pmatrix}
   1\\
   1\\
    0\\
\end{pmatrix}
\end{equation}

By applying the model to the vector $QV$, we obtain the first prediction result and we store it into the vector $PV$.
 \begin{center}
$PV_1= ApplyModel(QV, Model)$\\
\end{center}

 The second iteration concerns the second instance $Q_2$ of the query bag. We do the same instructions described in the first iteration.
  \begin{center}

 $AcrossBagsList= \{B_{21}, B_{22}, B_{32}, B_{42}\}$\\

 $MotifList=\{EF, GH\}$
 \end{center}

  \begin{equation}
 M=
 \begin{matrix}
  \begin{blockarray}{ccc}
 EF & GH \\
\begin{block}{(cc)c}
  1 & 1 & B_{12} \\
  1 & 1 & B_{22} \\
    1 & 0 & B_{32} \\
    0 & 1 & B_{42} \\
\end{block}
\end{blockarray}
\end{matrix}
\end{equation}
The sparsity percentage of $M$ is reduced to 25\%.
A model is then created using the encoded data ($Model= CreateModel(M)$). Using the features list, a vector $QV$ is  generated to describe the second instance $Q_2$ of the query bag.
  \begin{equation}
QV= \begin{pmatrix}
   1\\
   1\\
\end{pmatrix}
\end{equation}
By applying the model to the vector $QV$, we obtain the second prediction result and we store it into the vector $PV$.
 \begin{center}
$PV_2 \leftarrow ApplyModel(QV, Model)$\\
\end{center}

The aggregation step is finally used to generate the final prediction result using the prediction vector $PV$.
 \begin{center}

 $P=Aggregate(PV_1, PV_2)$
\end{center}

\subsection{\textit{ABSim:} Across bag sequences similarity}
\label{milalign}

 According to the specificity of the processed data, a similarity measure can be defined and used to discriminate instances.
We propose an algorithm that focuses on discriminating bags by measuring the similarity between each instance sequence in the query bag and corresponding related sequences in the different bags of the learning database.
We note $M$ a matrix used to store similarity measurement score vectors during the execution of the algorithm. The \textit{ABSim} algorithm works as follows:

\begin{algorithm}[h]
\caption{\textsc{set}\ \textsf{AcrossBagSequencesSimilarity}($DB$, $Q$)}
\label{algo3}
\begin{algorithmic}[1]
\REQUIRE Learning database $DB=\{(B_i, Y_i) \vert i = 1, 2 \ldots, n\}$ , Query bag $Q=\{Q_k \vert k=1 ,2, \ldots, p\}
\ENSURE Prediction result $P$

\FORALL {$Q_{k} \in Q$}
\FORALL {$B_i \in DB$}
\STATE $M_{ik} \leftarrow  similarityMeasure(Q_k,B_{ik})$ \COMMENT {$B_{ik}$ is the instance number $k$ in the bag $B_i$}
\ENDFOR
\ENDFOR

\STATE $P \leftarrow Aggregate(M)$
\RETURN $P$
\end{algorithmic}
\end{algorithm}

Informally, the algorithm is described as follows:

\begin{enumerate}
\item For each instance sequence $Q_k$ in the query bag $Q$, it computes the corresponding similarity measure scores (line 1 to 4).
Similarity scores of all instances of the query bag are grouped into a matrix $M$ (line 3).
Element $M_{ik}$ corresponds to the similarity score between $Q_k$ of $Q$ and $B_{ik}$ of $B_i$.
\item An aggregation method is applied to $M$ in order to compute the final prediction result $P$ (line 6).
According to the aggregation result, a class label is associated to the query Bag.
\end{enumerate}
In our work, we define two aggregation methods: (1) Sum of Maximum Scores (SMS) and (2) Weighted Average of Maximum Scores (WAMS).
Algorithms \ref{algosms} and \ref{algowams} illustrate the SMS and WAMS aggregation methods.

\begin{algorithm}[t]
\caption{\textsf{SMS}($M$)}
\label{algosms}
\begin{algorithmic}[1]
\REQUIRE Similarity matrix $M=\{M_{ij} \vert i = 1, 2 \ldots, n$ and $j = 1, 2 \ldots, p \}$

\ENSURE A prediction result $P$
  \STATE $total_P \leftarrow 0$
  \STATE $total_N \leftarrow 0$

  \FOR{$i \in \left[1;n\right]$}
      \STATE $max_P \leftarrow 0$
  \STATE $max_N \leftarrow 0$
  \FOR{$j \in \left[1;p\right]$}
\IF{$Y_j = +1$ and $max_P \ge M_{ij}$}
      \STATE $max_P \leftarrow M_{ij}$
\ELSIF{$Y_j = -1$ and $max_N \ge M_{ij}$}
      \STATE $max_N \leftarrow M_{ij}$
\ENDIF
    \ENDFOR
    \IF{$max_P \ge max_N$}
      \STATE $total_P \leftarrow total_P+max_P$
      \ELSE
      \STATE  $total_N \leftarrow total_N+max_N$
\ENDIF
  \ENDFOR
 \IF{$total_P \ge total_N$}
      \STATE $P \leftarrow +1$
      \ELSE
      \STATE  $P \leftarrow -1$
\ENDIF
  \RETURN $P$
\end{algorithmic}
\end{algorithm}

For each sequence in the query bacterium, we scan the corresponding line of $M$, which contains the obtained scores against all the other bags of the training database.
The $SMS$ method selects the maximum score among the similarity scores against bags that belong to the positive class label (which we call $max_{P}$) and the maximum score among the similarity scores against bags that belong to the negative class label (which we call $max_{N}$).
It then compares these scores. If $max_{P}$ is greater than $max_{N}$, it adds $max_{P}$ to the total score of the positive class label (which we denote $total_{P}(M)$). Otherwise, it adds $max_{N}$ to the total score of the negative class label (which we denote $total_{N}(M)$).
When all selected sequences were processed, the $SMS$ method compares total scores of positive class label and negative class label.
If $total_{P}(M)$ is greater than $total_{N}(M)$, the prediction output is the positive class label. Otherwise, the prediction output is the negative class label.

\begin{algorithm}[t]
\caption{\textsf{WAMS}($M$,  $W$)}
\label{algowams}
\begin{algorithmic}[1]
\REQUIRE Similarity matrix $M=\{M_{ij} \vert i = 1, 2 \ldots, n$ and $ j = 1, 2 \ldots, p \}$, Weight vector $W=\{w_i \vert i = 1, 2 \ldots, p\}$

\ENSURE A prediction result $P$
  \STATE $total_P \leftarrow 0$
  \STATE $total_N \leftarrow 0$
    \STATE $nb_P \leftarrow 0$
      \STATE $nb_N \leftarrow 0$

  \FOR{$i \in \left[1;p\right]$}
      \STATE $max_P \leftarrow 0$
  \STATE $max_N \leftarrow 0$
  \FOR{$j \in \left[1;n\right]$}
\IF{$Y_j = +1$ and $max_P \ge M_{ij}$}
      \STATE $max_P \leftarrow M_{ij}$
\ELSIF{$Y_j = -1$ and $max_N \ge M_{ij}$}
      \STATE $max_N \leftarrow M_{ij}$
\ENDIF
    \ENDFOR
    \IF{$max_P \ge max_N$}
      \STATE $total_P \leftarrow total_P + (max_P \cdot w_i)$
       \STATE $nb_P \leftarrow nb_P+1$
      \ELSE
      \STATE  $total_N \leftarrow total_N + (max_N \cdot w_i)$
      \STATE $nb_N \leftarrow nb_N+1$
\ENDIF
  \ENDFOR
  \STATE $avg_{P}(M) \leftarrow total_P / nb_P$
  \STATE $avg_{N}(M) \leftarrow total_N / nb_N$
 \IF{$avg_{P}(M) \ge avg_{N}(M)$}
      \STATE $P \leftarrow +1$
      \ELSE
      \STATE  $P \leftarrow -1$
\ENDIF
  \RETURN $P$
\end{algorithmic}
\end{algorithm}

Using the $WAMS$ method, each sequence $Q_i$ has a given weight $w_i$.
For each sequence in the query bag, we scan the corresponding line of $M$, which contains the obtained scores against all other bags of the training database.
The $WAMS$ method selects the maximum score among the similarity scores against bags that belong to positive class label (which we denote $max_{P}(M)$) and the maximum score among the similarity scores against bags that belong to the negative class label (which we denote $max_{N}(M)$).
It then compares these scores.
If the $max_{P}(M)$ is greater than $max_{N}(M)$, it adds $max_{P}(M)$ multiplied by the weight of the sequence to the total score of the positive class label and it increments the number of positive bags having a max score.
Otherwise, it adds $max_{N}(M)$ multiplied by the weight of the sequence to the total score of the negative class label and it increments the number of negative bags having a max score.
When all the selected sequences were processed, we compare the average of total scores of positive class labels (which we denote $avg_{P}(M)$) and the average of total scores of negative class labels (which we denote $avg_{N}(M)$).
If $avg_{P}(M)$ is greater than $avg_{N}(M)$, the prediction output is the positive class label. Otherwise, the prediction output is the negative class label.
In the case of IRR prediction in bacteria the positive (respectively negative) class label corresponds to IRRB (respectively IRSB).

In order to apply the \textit{ABSim} approach to our running example,
we need to use a similarity measure between sequences.
Suppose that we use a very simple similarity measure that consists in the number of common symbols between two sequences. The first iteration computes the common symbols between the instance $Q_1$ of the query bag and the four instances $B_{11}$, $B_{21}$, $B_{31}$ and $B_{41}$ and stores the result in the first column of the matrix $M$.

  \begin{equation}
 M=
 \begin{matrix}
  \begin{blockarray}{ccc}
\begin{block}{(cc)c}
  4 & - & B_{1} \\
  4 & - & B_{2} \\
    3 & - & B_{3} \\
    2 & - & B_{4} \\
\end{block}
\end{blockarray}
\end{matrix}
\end{equation}

The second iteration computes the common symbols between the instance $Q_2$ of the query bag and the four instances $B_{12}$, $B_{22}$, $B_{32}$ and $B_{42}$ and stores the result in the second column of the matrix $M$.

  \begin{equation}
 M=
 \begin{matrix}
  \begin{blockarray}{ccc}
\begin{block}{(cc)c}
  4 & 5 & B_{1} \\
  4 & 4 & B_{2} \\
    3 & 4 & B_{3} \\
    2 & 3 & B_{4} \\
\end{block}
\end{blockarray}
\end{matrix}
\end{equation}

The aggregation step applies $SMS$ or $WAMS$ aggregation algorithm on the matrix $M$ in order to generate the final prediction result.

 Using the $SMS$ aggregation method, we have the following results:
  \begin{center}
  $total_{P}(M) = 9$\\
  $total_{N}(M) = 0$\\
   \end{center}
  The query bag $Q$ is finally classified as positive.

 Using the $WAMS$ aggregation method, it is needed to specify a weight value for each instance.
 We suppose that all sequences have the same weight value, then we have the following results:
   \begin{center}
$avg_{P}(M)= 4.5$ \\
$avg_{V}(M)= 0 $\\
   \end{center}
The query bag $Q$ is finally classified as positive.

\section{Experiments}
\label{experiments}
We apply the proposed approaches to the problem of phenotype prediction of bacterial Ionizing Radiation Resistance (IRR) that can be formulated as a MIL problem for sequence data \citep{aridhi2015JCB}.
Bacteria represent the bags and primary structure of basal DNA repair proteins represent the sequences.
In this context, an unknown bacterium is affiliated to either Ionizing Radiation Resistant Bacteria (IRRB) or Ionizing Radiation Sensitive Bacteria (IRSB).
For our tests, we used the dataset described in \citep{aridhi2015JCB}. This dataset consists of 28 bags (14 IRRB and 14 IRSB).
Each bacterium/bag contains 25 to 31 instances that correspond to proteins implicated in basal DNA repair in IRRB \citep{aridhi2015JCB}.
Additional and more detailed information about our datasets and our experiments in general can be found in the following link: \url{http://fc.isima.fr/~aridhi/MIL/}.

\subsection{Experimental environment}
\label{Exprim_environment}
Computations were carried out on a i7 CPU 2.49 GHz PC with 6 GB memory, operating on Linux Ubuntu.
We used WEKA \citep{hall2009weka} data mining tool in order to apply existing multiple instance classifiers when using the naive approach.
To deal with the \textit{ABSim} approach, we used a local alignment technique as a similarity measure.
In our tests, basic local alignment search tool (BLAST) \citep{citeulike:100088} was used for computing local alignments.

\subsection{Experimental protocol}
\label{Exprim_prtcol}
In our context, the Leave-One-Out (LOO) technique is considered to be the most objective test technique compared to the other techniques such as hold-out and cross validation.
This can be explained by the fact that our training set contains a small number of bags.
For each dataset (comprising $n$ bags), only one bag is kept for the test and the remaining bags are used for the training.
This action is repeated $n$ times.

In order to evaluate the naive approach and the across bag sequences classification approach, we first encode the protein sequences of each bag using a set of features/motifs generated by an existing motif extraction method.
Then, we apply an existing classifier to the encoded data.
In our tests, we used DMS \citep{DDMADDOURI} as a motif extraction method. DMS allows building motifs that can discriminate a family of proteins from other ones.
It first identifies motifs in the protein sequences.
The extracted motifs are then filtered in order to keep only the discriminative and minimal ones.
A substring is considered to be discriminative between the family $F$ and the other families if it appears in $F$ significantly more than in the other families.
DMS extracts discriminative motifs according to $\alpha$ and $\beta$ thresholds where $\alpha$ is the minimum rate of motif occurrences in the sequences of a family $F$ and $\beta$ is the maximum rate of motif occurrences in all sequences except those of the family $F$.
In the following, we present the used motif extraction settings according to the values of $\alpha$ and $\beta$:
\begin{itemize}
\item\textbf{S1} ($\alpha=1$ and $\beta=0.5$):  used to extract frequent motifs with medium discrimination.
\item\textbf{S2} ($\alpha=1$ and $\beta=1$): used to extract frequent motifs without discrimination.
\item\textbf{S3} ($\alpha=0.5$ and $\beta=1$):  used to extract motifs having medium frequencies without discrimination.
\item\textbf{S4:} ($\alpha=0$ and $\beta=1$): used to extract infrequent and non discriminative motifs.
\item\textbf{S5:} ($\alpha =1$ and $\beta=0$): used to extract frequent and strictly discriminative motifs.
\end{itemize}

\subsection{Experimental results}
\label{results}
In this section, we first provide accuracy and quality results of the proposed approaches.
Then, we present a comparison of runtime values of both naive approach, \textit{ABClass} and \textit{ABSim}.
\subsubsection{Accuracy}
In order to use standard multiple instance classifiers, we apply a preprocessing technique that consists in extracting motifs from each set of protein sequences using the DMS method.
Table \ref{tabMotifs} presents, for each value of $\alpha$ and $\beta$, the number of extracted motifs from each set of orthologous protein sequences.
\begin{table}[t]
\caption{Number of extracted motifs for each set of orthologous protein sequences.}\label{tabMotifs}
\centering
{\begin{tabular}{llllll}\hlin

\multirow{2}{*}{Protein ID} & \multicolumn{5}{c} {Motif extraction setting} \\\cline{2-6}
&S1 & S2 & S3& S4 & S5\\
\hline\noalign{\smallskip}

P1  & 397 & 459 & 856 & 2579 & 244\\
P2  & 19 & 322 & 1505 & 5550 &0\\
P3  & 10 & 223 & 997 & 4752 &0\\
P4  & 7 & 143 & 726 & 4131 &0\\
P5  & 4 & 33 & 299 & 2036 &0\\
P6  & 14 & 135 & 619 & 3756 &0\\
P7  & 9 & 142 & 663 & 4297&1 \\
P8 & 5 & 221 & 793 & 4301&0 \\
P9 & 19 & 227 & 1107 & 4599 &0\\
P10  & 31 & 289 & 1027 & 4210 &0\\
P11  & 8 & 74 & 403 & 3138 &0\\
P12  & 8 & 74 & 328 & 2236 &0\\
P13  & 4 & 26 & 268 & 1882 &0\\
P14  & 5 & 62 & 343 & 2815 &0\\
P15 & 5 & 144 & 514 & 2405 &0\\
P16 & 8 & 69 & 408 & 3031 &0\\
P17 & 10 & 130 & 600 & 3086 &0\\
P18  & 0 & 25 & 229 & 2081 &0\\
P19  & 14 & 148 & 731 & 4185&0 \\
P20  & 6 & 110 & 593 & 3589 &0\\
P21  & 25 & 286 & 1206 & 4970 &1\\
P22  & 19 & 383 & 1110 & 4295 &1\\
P23  & 11 & 205 & 843 & 4545 &0\\
P24  & 8 & 191 & 822 & 4246 &0\\
P25  & 14 & 84 & 504 & 2933 &0\\
P26  & 31 & 221 & 1077 & 3730 &2\\
P27  & 17 & 54 & 324 & 2006 &0\\
P28  & 18 & 288 & 866 & 3680&1 \\
P29 & 4 & 56 & 432 & 3114 &0\\
P30 & 11 & 174 & 501 & 2178 &0\\
P31 & 1 & 20 & 228 & 2422&0 \\ \hline\noalign{\smallskip}

\textbf{Total} &742&5018& 20922 & 106778 &250\\

\noalign{\smallskip}\hline
\end{tabular}}{

 }
\end{table}
For setting 5 ($\alpha = 1$ and $\beta = 0$), there is no frequent and strictly discriminative motifs for most proteins.
This is why we will not use these values of $\alpha$ and $\beta$ for our next experiments.
We note that the number of extracted motifs increases for high values of $\beta$ and low values of $\alpha$.
As presented in Table \ref{tabMotifs}, the number of infrequent and non discriminative motifs is very high.

In order to encode data, the union of the extracted motifs from each protein is used.
These motifs are used as attributes/features to construct a binary table where each row corresponds to sequence.
The presence or the absence of an attribute in a sequence is denoted by $1$ or $0$, respectively. This binary table is called an attribute-value matrix.
It is worthwhile to mention that the number of used motifs in the encoding step is huge.
Consequently, the attribute-value matrix representing the data becomes large and sparse since only a small subset of the used motifs is representative for each protein.
We show in Table \ref{tabArff} the sparsity of the attribute-value matrix which measure the fraction of zero elements over the total number of elements.
\begin{table*}[t]
\caption{Sparsity of the attribute-value matrix.}
\label{tabArff}
\centering
{\begin{tabular}{lll}
\hline\noalign{\smallskip}
Motif extraction setting   & Total number of motifs& Sparsity (\%)\\
\noalign{\smallskip}\hline\noalign{\smallskip}

S1 & 671&73.9\\
S2	&1490&73.8\\
S3  & 4562&85.7\\
S4 &  8077&91.1 \\
\noalign{\smallskip}\hline
\end{tabular}}{}
\end{table*}
The sparsity of our attribute-value matrix is generally proportional to the number of used motifs. For example, the sparsity of the matrix goes from 73.9\% with 671 motifs to 91.1\% with 8077 motifs.
Figure \ref{figsAcc} shows the accuracy values obtained using naive approach, \textit{ABClass} approach and \textit{ABSim} approach.
Figures \ref{figAccWeka} and \ref{figAccMot} show the impact of the set of motifs used in the preprocessing step on the results of the \textit{ABClass} and the naive approaches.
For example, using MISVM classifier, the accuracy varies from 53.5\% to 78.5\%.
\begin{figure*}[t]
\centering
\subfigure[Naive approach]{\includegraphics[height=4.4cm]{accuracyweka2.png}
\label{figAccWeka}
}
\hfil
\subfigure[ABClass approach]{\includegraphics[height=4.4cm]{accuracymot2.png}
\label{figAccMot}
}
\hfil
\subfigure[ABSim approach]{\includegraphics[height=4.4cm]{accuracyblast2.png}
\label{figAccblast}
}
\caption{Accuracy results.}
\label{figsAcc}
\end{figure*}

Although the motifs extracted using S1 motif extraction setting are discriminative, naive approach does not provide good accuracy results for this setting except for the MISMO classifier.
The reason could be that the number of discriminative motifs for some proteins is limited to at most 10 as stressed in Table \ref{tabMotifs}.
Using the naive approach, the best accuracy is always provided by MISMO classifier (92.8\%).
Accuracy of other used multiple instance classifiers depends on the used motifs.
Most of them provide good accuracy using the S3 setting (non discriminative motifs with medium frequencies).
The number of extracted motifs per protein using this setting is between 228 to 1505 which is an acceptable number of motifs used to encode a protein sequence.
Both \textit{ABClass} and \textit{ABSim} approaches provide good overall accuracy results since the least accuracy percentage is 89.2\%.
This clearly shows that our proposed approaches are efficient. Using \textit{ABSim} approach with the SMS aggregation method provides a better accuracy result compared to the \textit{WAMS} aggregation method.
The best result was reached using \textit{ABClass} approach, J48 classifier and the motif extraction settings S3 and S4.
Using these two settings, a large number of non discriminative motifs are extracted (see Table \ref{tabMotifs}).

\begin{table*}[t]
\caption{Rate of successful classification models using motif-based approach and LOO evaluation method}
\label{tabConf}
\centering
\renewcommand\tabcolsep{4pt}
{\begin{tabular}{lllllll}\hlin
\multirow{2}{*}{Bacterium ID} & \multicolumn{3}{c} {S3 motif extraction setting}&\multicolumn{3}{c} {S4 motif extraction setting} \\\cline{2-7}
&SMO & J48 & Naive Bayes &SMO & J48 & Naive Bayes\\
\hline\noalign{\smallskip}

B1 & \textbf{44} & \textbf{60} &  \textbf{36} & \textbf{60} & \textbf{64} & \textbf{68} \\
 B2 & 100 & 100 &  100 & 100 & 100 & 100 \\
 B3 & 100 & 90.3 &  100 & 100 & 90.3 & 100 \\
 B4 & 100 & 96.6 &  100 & 100 & 93.3 & 100 \\
 B5 & 100 & 90 &  100 & 100 & 90 & 100 \\
 B6 & 100 & 83.3 &  100 & 100 & 83.3 & 100 \\
 B7 & 100 & 93.5 &  100 & 100 & 93.5 & 100 \\
 B8 & 96.5 & 96.5 &  96.5 & 100 & 93.1 & 100 \\
 B9 & 100 & 84 &  100 & 100 & 84 & 100 \\
 B10 & 100 & 82.1 &  92.8 & 100 & 82.1 & 100 \\
 B11 & \textbf{17.8} & \textbf{50} &  \textbf{17.8} & \textbf{21.4} & \textbf{50} & \textbf{35.7} \\
 B12 & 100 & 92.8 &  96.4 & 100 & 92.8 & 100 \\
 B13 & 88.8 & 66.6 &  70.3 & 88.8 & 66.6 & 77.7 \\
 B14 & 90 & 73.3 &  100 & 93.3 & 70 & 96.6 \\
 B15 &\textbf{ 3.5 }& \textbf{32.1} &  \textbf{35.7 }& \textbf{0 }& \textbf{32.1} & \textbf{14.2} \\
 B16 & 100 & 96.6 &  96.6 & 100 & 96.6 & 100 \\
 B17 & 96.2 & 96.2 &  96.2 & 96.2 & 96.2 & 96.2 \\
 B18 & 100 & 100 &  100 & 100 & 100 & 100 \\
 B19 & 100 & 100 &  100 & 100 & 100 & 100 \\
 B20 & 89.6 & 62 &  96.5 & 82.7 & 62 & 86.2 \\
 B21 & 96.5 & 82.7 &  96.5 & 93.1 & 82.7 & 93.1 \\
 B22 & 100 & 100 &  96.6 & 100 & 96.6 & 100 \\
 B23 & 100 & 96.7 &  93.5 & 100 & 96.7 & 100 \\
 B24 & 100 & 100 &  93.5 & 100 & 100 & 100 \\
 B25 & 100 & 96.7 &  93.5 & 100 & 96.7 & 100 \\
 B26 & 100 & 100 &  93.5 & 100 & 100 & 100 \\
 B27 & 100 & 100 &  100 & 100 & 100 & 100 \\
 B28 & 96.6 & 100 &  96.6 & 96.6 & 93.3 & 96.6 \\

\noalign{\smallskip}\hline
\end{tabular}{}}
\end{table*}
Table \ref{tabConf} presents the rate of classification models that contribute to predict the true class of each bacterium using \textit{ABClass} approach.
We present this rate for the two motif extraction settings that already provided the best accuracy values i.e., S3 and S4.
The rate of successful classification models for B1, B11 and B15 are marked with bold text because these three bacteria generate always low rates compared to the rate of successful classification models of the other bacteria.
B1 presents variable rates that reach 68\%. Although B11 is sometimes successfully classified, its higher successful classification models rate does not exceed 50\%.
The rate of B15 does not reach 50\% which makes this bacterium always misclassified.
These results may help to understand some characteristics of the studied bacteria.
In particular, \textit{M. radiotolerans} (B11) and \textit{B. abortus} (B15) that present the lowest rates.
It means that in most cases, \textit{M. radiotolerans} is predicted as IRSB and \textit{B. abortus} is predicted as IRRB; the former is an intracellular parasite \cite{refbiohalling} and the latter is an endosymbiont of most plant species \cite{refbioFedorov}.
A probable explanation for these two failed predictions is the increased rate of sequence evolution in endosymbiotic bacteria \cite{refbiowoolfit}.
As our training set is composed mainly of members of the phylum \textit{Deinococcus}-\textit{Thermus}; expectedly, the \textit{Deinococcus} bacteria (B2-B7) present a very high rate of successful classification models.

\subsubsection{Speedup}

\begin{figure*}[t]
\centering
\subfigure[]{\includegraphics[height=3.6cm]{misvmtime.png}
\label{fig_miltime1}
}
\hfil
\subfigure[]{\includegraphics[height=3.6cm]{misotime.png}
\label{fig_miltime2}
}
\hfil\subfigure[]{\includegraphics[height=3.6cm]{citationknntime.png}
\label{fig_miltime3}
}

\hfil\subfigure[]{\includegraphics[height=3.6cm]{milrtime.png}
\label{fig_miltime4}
}
\hfil\subfigure[]{\includegraphics[height=3.6cm]{middtime.png}
\label{fig_miltime5}
}
\hfil\subfigure[]{\includegraphics[height=3.6cm]{timemotsmo.png}
\label{fig_mot1}
}

\hfil\subfigure[]{\includegraphics[height=3.6cm]{timemotj48.png}
\label{fig_mot2}
}
\hfil\subfigure[]{\includegraphics[height=3.6cm]{timemotbayes.png}
\label{fig_mot3}
}
\hfil\subfigure[]{\includegraphics[height=3.6cm]{timeblast.png}
\label{fig_timeblast}
}
\caption{Runtime results of naive approach (a, b, c, d, e), ABClass (f, g, h) and ABSim (i).}
\label{fig_time}
\end{figure*}

Figure \ref{fig_time} shows the runtime of our approaches using different settings. It is worthwhile to mention that the runtime of our approaches varies in each LOO iteration according to the number of proteins contained in the query bacterium and the length of protein sequences of the learning set.
The indicated runtime in Figure \ref{fig_time} is the average runtime of all the 28 iterations of the LOO evaluation technique.
We note that when using the naive approach, the motif extraction is done once in the preprocessing step, whereas when using the \textit{ABClass} approach it is executed in every LOO iteration.

The runtime values of the naive approach (Figures \ref{fig_miltime1}, \ref{fig_miltime2}, \ref{fig_miltime3},  \ref{fig_miltime4} and \ref{fig_miltime5}) and the \textit{ABClass} approach (Figures \ref{fig_mot1}, \ref{fig_mot2} and \ref{fig_mot3}) are mainly composed of two parts: the motif extraction runtime and the classification time.
Generally, these two parts are inversely proportional. For example, a large number of non discriminative motifs is extracted in about 9 seconds using the S4 motif extraction setting, then a larger runtime is needed in order to learn a classifier using such high number of motifs.
We notice that the motif extraction runtime increases considerably when discriminative motifs are required in the preprocessing step.
For example, it goes from 10 seconds for infrequent and non discriminative motifs to about 10 hours for the frequent and discriminative motifs.
When the number of extracted motifs is low (frequent and discriminative motifs) the motif extraction time is high but the classification time is still reasonable.
As stressed in Figure \ref{fig_timeblast}, the similarity based approach is much faster than both the naive approach and the \textit{ABClass} method with most motif extraction settings (S1, S2 and S3).
This is particularly due to the fact that the \textit{ABSim} approach does not need a preprocessing step such as a motif extraction step.

Taking into account the accuracy and the total runtime, the best result is obtained using the \textit{ABClass} approach, J48 classifier and S4 motif extraction setting.
We notice that MIDD which is an implementation of the Diverse Density algorithm \citep{Maron1998} has the longer execution time with low accuracy compared to all other classifiers.
According the above presented results, we mention that both \textit{ABClass} and \textit{ABSim} are efficient to perform MIL on sequence data that have dependencies between instances across bags. It is also important to recommend the use of \textit{ABSim} approach when a similarity measure can be easily defined and to use \textit{ABClass} approach when the data is already preprocessed.

\section{Conclusion}
\label{conclusion}
In this paper, we addressed the issue of multiple instance learning (MIL) in the case of sequence data.
We focused on data that present dependencies between instances of different bags.
We have described two novel approaches for MIL in sequence data: (1) \textit{ABClass} and (2) \textit{ABSim}.
We applied the proposed approaches to the problem of prediction of ionizing radiation resistance (IRR) in bacteria.
By running experiments, we have shown that the proposed approaches are efficient.
We are able to successfully predict IRR of most bacteria, but we do not reach a 100\% accuracy percentage using the different experimental settings with all proteins.

In the future work, we will study how the use of \textit{a priori} knowledge can improve the efficiency of our algorithm.
We specifically want to define weights for sequences by using \textit{a priori} knowledge in the learning phase.

\bibliographystyle{plain}

redpajama_set_name: RedPajamaArXiv


text:
U.S. Markets close in 2 hrs
Should You Invest in the Fidelity MSCI Consumer Discretionary Index ETF (FDIS)?
Sweta Killa
Zacks July 9, 2019
The Fidelity MSCI Consumer Discretionary Index ETF (FDIS) was launched on 10/21/2013, and is a passively managed exchange traded fund designed to offer broad exposure to the Consumer Discretionary - Broad segment of the equity market.
Passively managed ETFs are becoming increasingly popular with institutional as well as retail investors due to their low cost, transparency, flexibility and tax efficiency. They are excellent vehicles for long term investors.
Additionally, sector ETFs offer convenient ways to gain low risk and diversified exposure to a broad group of companies in particular sectors. Consumer Discretionary - Broad is one of the 16 broad Zacks sectors within the Zacks Industry classification. It is currently ranked 14, placing it in bottom 13%.
Index Details
The fund is sponsored by Fidelity. It has amassed assets over $711.72 M, making it one of the larger ETFs attempting to match the performance of the Consumer Discretionary - Broad segment of the equity market. FDIS seeks to match the performance of the MSCI USA IMI Consumer Discretionary Index before fees and expenses.
MSCI USA IMI Consumer Discretionary Index represents the performance of the consumer discretionary sector in the U.S. equity market.
When considering an ETF's total return, expense ratios are an important factor, and cheaper funds can significantly outperform their more expensive counterparts in the long term if all other factors remain equal.
Annual operating expenses for this ETF are 0.08%, making it the least expensive product in the space.
It has a 12-month trailing dividend yield of 1.16%.
Sector Exposure and Top Holdings
ETFs offer a diversified exposure and thus minimize single stock risk but it is still important to delve into a fund's holdings before investing. Most ETFs are very transparent products and many disclose their holdings on a daily basis.
This ETF has heaviest allocation in the Consumer Discretionary sector--about 99.10% of the portfolio.
Looking at individual holdings, Amazon.com Inc (AMZN) accounts for about 25.64% of total assets, followed by Home Depot Inc (HD) and Mcdonald S Corp (MCD).
The top 10 holdings account for about 55.47% of total assets under management.
Performance and Risk
Year-to-date, the Fidelity MSCI Consumer Discretionary Index ETF has added about 22.50% so far, and was up about 9.17% over the last 12 months (as of 07/09/2019). FDIS has traded between $35.51 and $46.67 in this past 52-week period.
The ETF has a beta of 1.10 and standard deviation of 14.22% for the trailing three-year period, making it a medium risk choice in the space. With about 300 holdings, it effectively diversifies company-specific risk.
Fidelity MSCI Consumer Discretionary Index ETF holds a Zacks ETF Rank of 2 (Buy), which is based on expected asset class return, expense ratio, and momentum, among other factors. Because of this, FDIS is a great option for investors seeking exposure to the Consumer Discretionary ETFs segment of the market. There are other additional ETFs in the space that investors could consider as well.
Vanguard Consumer Discretionary ETF (VCR) tracks MSCI US Investable Market Consumer Discretionary 25/50 Index and the Consumer Discretionary Select Sector SPDR Fund (XLY) tracks Consumer Discretionary Select Sector Index. Vanguard Consumer Discretionary ETF has $3.15 B in assets, Consumer Discretionary Select Sector SPDR Fund has $13.96 B. VCR has an expense ratio of 0.10% and XLY charges 0.13%.
To learn more about this product and other ETFs, screen for products that match your investment objectives and read articles on latest developments in the ETF investing universe, please visit Zacks ETF Center.
Fidelity MSCI Consumer Discretionary Index ETF (FDIS): ETF Research Reports
Amazon.com, Inc. (AMZN) : Free Stock Analysis Report
The Home Depot, Inc. (HD) : Free Stock Analysis Report
Consumer Discretionary Select Sector SPDR Fund (XLY): ETF Research Reports
Vanguard Consumer Discretionary ETF (VCR): ETF Research Reports
McDonald's Corporation (MCD) : Free Stock Analysis Report

redpajama_set_name: RedPajamaCommonCrawl


This advertisement allows us to offer you Trollface Quest: Sports for free in 15 seconds.
Break out of the gym and perform fun activities! Trollface Quest: Sports includes all sorts of athletic mini-games. Browse through each scene, and determine which items to click. Make sure your silly character doesn't get hurt!


The GET Service project consortium held its second general assembly at the PTV headquarters in Karlsruhe on 24th and 25th of April. The goal of the meeting was to develop a precise understanding of what the GET Service platform should look like in the end, based on usage scenarios and requirements. We look back on a successful meeting with many stimulating discussions.


Singapore's Prime Minister Lee Hsien Loong was seen at the Singapore Botanic Gardens with his wife and grandson having a lovely picnic.
On April 6th (Saturday), Singapore's Prime Minister Lee Hsien Loong was seen at the Singapore Botanic Gardens by some netizens.
Photos that had gone viral on Facebook showed that he was spending a day out with his wife and his grandson.
They were sitting on a picnic mat on the grass, enjoying a performance by the Singapore Chinese Orchestra (SCO). PM Lee Hsien Loong was wearing a polo t-shirt and could be seen taking pictures of his grandchild along with Mrs Lee.
PM Lee also shared on his Facebook page of his "relaxing evening" spent at the Singapore Botanical Gardens watching the SCO's Spring at the Gardens concert. He shared photos of the other audience around, six sisters on a musical outing together, and of the orchestra performing. He also took pictures of bougainvilleas near the Eco Lake, which are in full bloom.
"We've had some wet weather lately, but fortunately conditions were perfect for yesterday's performance!", said PM Lee. He also took the opportunity to "jalan-jalan" after the concert as he said the weather was good.
"It is our country's culture. No matter who you are, you are also part of citizens (sic) of Singapore," said another user. She also added that they are fortunate to have a down-to-earth prime minister.
Emeritus Senior Minister Goh Chok Tong was also among the audience last Saturday.
It is indeed lovely to see your Prime Minister in public, enjoying a concert and fresh air just like a regular person and definitely heartwarming to see him spending time with his grandchild and family.
Read also: Prime Minister Lee Hsien Loong suggests more creative varieties of playgrounds in the near future.


Restaurants are starting to see bucks in beverages. And well they should. On average, 22% of those who have been to a fast-food restaurant in the past month and participated in a Mintel survey for the upcoming Quick Service Restaurants—September 2011 report said they often consider the beverage selection at a restaurant when deciding where to dine. By age, 25-44s are most likely to ponder beverages—at nearly 30%.
I'm not sure when restaurants started to realize that improved profits weren't the sole responsibility of expensive menu R&D. But some restaurants have been quietly wowing customers with great liquid flavors, and charging a nice premium for it.
The Cheesecake Factory has long served strawberry and raspberry lemonade, which is exceptionally tasty (compared to McDonald's new strawberry lemonade) because the Cheesecake Factory uses fruit puree rather than syrup.
However, speaking of McDonald's, it credits its new interesting beverages for a 5.1% increase in global same-store sales.
Flavored teas are certainly part of the ramped-up restaurant beverage trend, which may have been started by such beverage-centric behemoths as Jamba Juice and Starbucks. Jamba Juice offers three exotic fruit tea infusions (passion fruit, pomegranate and prickly pear), while Starbucks goes every which direction with beverages.
This summer, Jamba Juice came out with a limited-time offer of frozen beverages called Fruit Refreshers that featured coconut water and one of three flavors—pineapple, strawberry lemonade and tropical mango.
Even if a restaurant's beverage bevy is limited to soft drinks, there's plenty of expansion opportunity, especially with Coca-Cola's pretty new Freestyle machine. (Theirs was my favorite booth at the National Restaurant Association expo in Chicago in May.) Just within Diet Cokes, the choices are refreshing (diet vanilla, raspberry, cherry, orange, etc.) And, imagine mixing them, as I did—diet vanilla with diet orange. It's not your average Coke.
Even with all this, I think beverages are flying under the radar when analysts examine restaurant trends. I think beverage variety will continue to increase, profits will be made and customers will like what they taste.
Who says there's nothing new under the sun? Foodservice operators better hope there is. Combine the thunderous calls for more healthful restaurant food, the emphasis on fresh, unprocessed ingredients and consumer expectations for cutting-edge foodservice fare, and chefs are ever looking to pull something magical out of their toques.
Seedless lemons. At last. What bar drink or water glass couldn't benefit from pure lemon sans-seeds? Chuck Olsen Co., Visalia, Calif., will have them available within the next few weeks through February.
Finger limes, shaped like jalapenos, will be available for only the second season in September and October from Shanley Farms out of Morro Bay, Calif.
Yellow grape tomatoes. Now restaurants can combine red and yellow versions in salads and as edible plate décor. They will soon be available from Andrew & Williamson Fresh Produce, San Diego.
The color of radicchio is so striking, but it's a bit bitter to the American palate. Now there's a new milder-flavored variety, verona, with leaves that form cups to host other ingredients. Verona radicchio is available from Royal Rose LLC, Salinas, Calif.
It's unlikely consumers will see these fresh bites in the grocery store, which makes them perfect for foodservice operators who want to offer something original to draw customers out of their comfy kitchens and into restaurant seats. -- Grow on!
Say hello to the shining stars on restaurant menus—fruits and vegetables. These darlings of the tree, vine and field have actually been around since Adam and Eve, though you may never have seen them in a restaurant, unless you carried a magnifying glass with you. Their flavor is noticeable in candy, drinks, dressings and sauces now and then, but until recently, non-fried versions have not been easily spotted when dining out.
But alas, it has become PC for chains to care about health, nutrition and obesity. Plus, they are staring pending nutrition labeling laws in the face. Shriek! What's a solution to this? Oh yes. Fruits and vegetables.
Apples in Happy Meals, anyone? I wonder how long mothers dreamed this would happen. Their dreams are coming true. But let's give some credit to Burger King, which has already had fry-shaped apples on the menu. Good for them for making the fruit more pleasant to look at by making it appear like the most popular thing on quick-serve menus.
The Cheesecake Factory recently announced its new SkinnyLicious menu with nearly 50 lower-calorie items. For example, according to the company, "The Small Plates & Appetizers and fresh-baked Flatbreads categories feature dishes all under 490 calories, while the entrée Salads and main courses are all served with 590 calories or less." The chain hasn't said it, but fruits and vegetables are the best way to lower calorie counts beyond cutting portion sizes.
BJ's Restaurants recently introduced a line of "Enlightened Entrees," each with less than 575 calories, priced $6.95-$10.95. Check out the entrees on its website. They couldn't do it without fruits and vegetables.
This summer, Wendy's boldly launched Berry Almond Chicken Salad using fresh berries. This is something big for a quick-serve restaurant, because chains easily turn to fruit-flavored syrup and make the dish sound "seasonally refreshing" in marketing messages without the benefit of the actual fresh fruit.
In the upscale-restaurant world, bars are discovering a love of mocktails, non-alcoholic drinks made with fruits and vegetables. Whodathunk?
Restaurants often start food trends. Maybe more consumers will eat fruits and vegetables out and think how crazy they are to pay the price when they could easily have that at home.


text:
Harmonic Announces Second Quarter 2012 Results
SAN JOSE, Calif. -- July 24, 2012 -- Harmonic Inc. (NASDAQ: HLIT), a global leader in video infrastructure solutions, announced today its preliminary and unaudited results for the quarter ended June 29, 2012.
Net revenue for the second quarter of 2012 was $132.6 million, compared to $127.7 million for the first quarter of 2012 and $134.0 million for the second quarter of 2011.
Total bookings in the second quarter of 2012 were approximately $139.5 million, up 6% from approximately $131.7 million for the second quarter of 2011. Total backlog and deferred revenue was $146.0 million as of June 29, 2012, compared with $122.0 million as of July 1, 2011.
The company reported a GAAP net income for the second quarter of 2012 of $17,000, or $0.00 per share, compared to a GAAP net income for the second quarter of 2011 of $0.4 million or $0.00 per share. Non-GAAP net income for the second quarter of 2012 was $7.2 million, or $0.06 per share, compared to $10.5 million, or $0.09 per share for the second quarter of 2011. See "Use of Non-GAAP Financial Measures" and "GAAP to Non-GAAP Net Income (Loss) Reconciliation" below.
Harmonic reported GAAP gross margins of 43% and GAAP operating margins of (2%) for the second quarter of 2012, compared to 46% and 1%, respectively, for the same period of 2011. Non-GAAP gross margins were 48% and non-GAAP operating margins were 7% for the second quarter of 2012, compared to 51% and 11%, respectively, for the same period of 2011.
As of June 29, 2012, the Company had cash, cash equivalents and short-term investments of $177.8 million, an increase from $168.5 million as of March 30, 2012. The company generated approximately $20.3 million of cash from operations in the second quarter of 2012 and repurchased 1.6 million shares of common stock for approximately $7.0 million, under its previously announced stock repurchase program.
"The second quarter was in-line with expectations and reflective of increased demand from US and most international geographies offset by continuing weakness in Europe," said Patrick Harshman, president and chief executive officer. "Our book-to-bill ratio is indicative of our strong competitive position and increasing system project wins that typically take several periods to fully recognize as revenue. The quarter was also characterized by greater than 25 new multiscreen wins and positive customer feedback on our in-development CCAP program. Looking ahead, our new product initiatives, our broad and growing customer base, and our continuing focus on operational excellence position Harmonic well for the future."
Harmonic anticipates net revenue in the range of $130 million to $140 million for the third quarter of 2012. GAAP gross margins and operating expenses for the third quarter of 2012 are expected to be in the range of 43% to 45% and $61 million to $62 million, respectively. Non-GAAP gross margins and operating expenses for the third quarter of 2012, which will exclude stock-based compensation and the amortization of intangibles, are anticipated to be in the range of 47.5% to 49.5% and $55 million to $56 million, respectively.
Harmonic will host a conference call to discuss its financial results at 2:00 p.m. Pacific (5:00 p.m. Eastern) on Tuesday, July 24, 2012. A listen-only broadcast of the conference call can be accessed either from the Company's website at www.harmonicinc.com or by calling +1.847.944.7317 (conference confirmation number 32828770). The replay will be available after 6:00 p.m. Pacific at the same website address or by calling +1.630.652.3042 (pass code 9176742#).
Harmonic Inc. (NASDAQ: HLIT) provides infrastructure that powers the video economy. The company enables content and service providers to efficiently create, prepare, and deliver differentiated video services for television and new media platforms. More information is available at www.harmonicinc.com.
This press release contains forward-looking statements within the meaning of Section 27A of the Securities Act of 1933 and Section 21E of the Securities Exchange Act of 1934, including statements related to our expectations: regarding our final results for the second quarter ended June 29, 2012; , regarding continuing weakness in Europe; regarding our strong competitive position and increasing system project wins; regarding positive customer feedback on our in-development CCAP program; regarding that Harmonic is well positioned for the future due to its new product initiatives, broad and growing customer base, and continuing focus on operational excellence; and regarding net revenue, GAAP gross margins, GAAP operating expenses, non-GAAP gross margins and non-GAAP operating expenses for the third quarter of 2012. Our expectations regarding these matters may not materialize, and actual results in future periods are subject to risks and uncertainties that could cause actual results to differ materially from those projected. These risks include the possibility, in no particular order, that: the trends toward more high-definition, on-demand and anytime, anywhere video will not continue to develop at its current pace or will expire; the possibility that our products will not generate sales that are commensurate with our expectations or that our cost of revenue or operating expenses may exceed our expectations; the mix of products and services sold in various geographies and the effect it has on gross margins; delays or decreases in capital spending in the cable, satellite and telco and broadcast and media industries; customer concentration and consolidation; the impact of general economic conditions, including as a result of recent turmoil in the global financial markets, particularly on our European and other international sales and operations; our ability to develop new and enhanced products and market acceptance of new or existing Harmonic products; losses of one or more key customers; risks associated with Harmonic's international operations; inventory management; the lack of timely availability of parts or raw materials necessary to produce our products; the impact of increases in the prices of raw materials and oil; the effect of competition, on both revenue and gross margins; difficulties associated with rapid technological changes in Harmonic's markets; risks associated with unpredictable sales cycles; our dependence on contract manufacturers and sole or limited source suppliers; the effect on Harmonic's business of natural disasters; and the risks that our international sales and support center will not provide the operational or tax benefits that we anticipate or that its expenses exceed our plans. The forward-looking statements contained in this press release are also subject to other risks and uncertainties, including those more fully described in Harmonic's filings with the Securities and Exchange Commission, including our Annual Report on Form 10-K for the year ended December 31, 2011 and our Current Reports on Form 8-K. The forward-looking statements in this press release are based on information available to the Company as of the date hereof, and Harmonic disclaims any obligation to update any forward-looking statements.
Editor's Note: Product and company names used herein are trademarks or registered trademarks of their respective owners.
June 29, 2012 December 31, 2011
Cash and cash equivalents $92,446 $90,983
Short-term investments 85,355 70,854
Accounts receivable 102,748 109,886
Total assets $729,737 $734,166
Accounts payable $29,857 $30,537
Income taxes payable 527 2,290
Deferred income taxes, long-term 3,850 655
Total liabilities and stockholders' equity $729,737 $734,166
June 29, 2012 July 1, 2011 June 29, 2012 July 1, 2011
Net revenue $132,634 $133,996 $260,355 $266,831
Research and development 25,641 25,662 53,470 51,811
Selling, general and administrative 32,142 32,543 64,453 66,107
Income (loss) from operations (2,395) 1,393 (11,052) 1,306
Interest and other income (expense), net (4) (225) 518 (240)
Income (loss) before income taxes (2,399) 1,168 (10,534) 1,066
Provision for (benefit from) income taxes (2,416) 778 (3,023) 160
Net income (loss) $17 $390 $(7,511) $906
Basic $0.00 $0.00 $(0.06) $0.01
Diluted $0.00 $0.00 $(0.06) $0.01
Net income (loss) $(7,511) $906
Depreciation 7,519 6,824
Stock-based compensation 9,502 11,094
Net loss on disposal of fixed assets 88 103
Deferred income taxes 1,330 76
Provision for inventories (2,261) 4,126
Allowance for doubtful accounts, returns and discounts 1,152 53
Other non-cash adjustments, net 310 322
Accounts receivable, net 5,990 (16,262)
Inventories 4,903 (7,120)
Accounts payable (684) 4,780
Deferred revenue 4,448 788
Income taxes payable (2,560) (6,925)
Accrued and other liabilities (5,605) (7,500)
Net cash provided by operating activities 28,214 9,140
Purchases of investments (57,661) (62,009)
Acquisition of property and equipment (6,708) (8,502)
Payments for repurchase of common stock (6,953) -
Net cash provided by (used in) financing activities (4,937) 13,703
Effect of exchange rate changes on cash and cash equivalents (38) 161
Net increase (decrease) in cash and cash equivalents 1,463 (26,163)
Cash and cash equivalents at end of period $92,446 $70,370
Video Processing $59,300 45% $51,525 38% $111,981 43% $115,283 43%
Edge and Access 33,592 25% 40,178 30% 70,400 27% 71,354 27%
Total $132,634 100% $133,996 100% $260,355 100% $266,831 100%
United States $61,347 46% $55,578 41% $122,201 47% $114,532 43%
Cable $64,233 48% $64,142 48% $125,987 48% $120,062 45%
Satellite and Telco 27,870 21% 28,193 21% 53,729 21% 63,345 24%
Broadcast and Media 40,531 31% 41,661 31% 80,639 31% 83,424 31%
In establishing operating budgets, managing its business performance, and setting internal measurement targets, the Company excludes a number of items required by GAAP. Management believes that these accounting charges and credits, most of which are non-cash or non-recurring in nature, are not useful in managing its operations and business. Historically, the Company has also publicly presented these supplemental non-GAAP measures in order to assist the investment community to see the Company "through the eyes of management," and thereby enhance understanding of its operating performance. The non-GAAP measures presented here are gross margin, operating expenses, net income and net income per share. The presentation of non-GAAP information is not intended to be considered in isolation or as a substitute for results prepared in accordance with GAAP, and is not necessarily comparable to non-GAAP results published by other companies. A reconciliation of the historical non-GAAP financial measures discussed in this press release to the most directly comparable historical GAAP financial measures is included with the financial statements contained in this presentation. The non-GAAP adjustments described below have historically been excluded from our GAAP financial measures. These adjustments are excess facilities and severance charges and non-cash items, such as stock-based compensation expense, amortization of intangibles, and discrete tax items and adjustments.
GAAP $57,578 $59,973 $17 $61,828 $60,435 $390
Non-GAAP $63,431 $53,886 $7,155 $68,081 $53,875 $10,486
GAAP net income per share - basic $0.00 $0.00
GAAP net income per share - diluted $0.00 $0.00
Non-GAAP net income per share - basic $0.06 $0.09
Non-GAAP net income per share - diluted $0.06 $0.09
Gross Profit Operating Expense Net Income (Loss) Gross Profit Operating Expense Net Income
GAAP $111,240 $122,292 $(7,511) $123,683 $122,377 $906
Non-GAAP $123,247 $110,020 $10,307 $135,825 $107,924 $20,746
GAAP net income (loss) per share - basic $(0.06) $0.01
GAAP net income (loss) per share - diluted $(0.06) $0.01
Chief Financial Officer Investor Relations contact for
Harmonic Inc. Harmonic Inc.


text:
Functions and powers of the General Assembly
General Assembly Secretariat
Subsidiary organs
Arrears in the payment of contributions (Article 19)
Accessibility Centre
Main Committees
First Committee (Disarmament & International Security)
Second Committee (Economic & Financial)
Third Committee (Social, Humanitarian & Cultural)
Fourth Committee (Special Political & Decolonization)
Fifth Committee (Administrative & Budgetary)
Sixth Committee (Legal)
All the documents of the 73rd session (UNBisnet)
Documents by Symbol
Meeting Records
Index to Proceedings
Conventions, Declarations and other instruments
All Sessions
Emergency special sessions
Sixth Committee (Legal) — 69th session
Report of the International Law Commission on the work of its sixty-sixth session
(Agenda item 78)
Authority: resolution 68/112
A/69/10 — Report of the International Law Commission on the work of its sixty sixth session
Background (source: A/69/100)
The International Law Commission was established by the General Assembly at its second session, in 1947, with a view to giving effect to Article 13, paragraph 1 (a), of the Charter and with the objective of promoting the progressive development of international law and its codification (resolution 174 (II)).
The statute of the Commission, annexed to resolution 174 (II), was subsequently amended (resolutions 485 (V), 984 (X), 985 (X) and 36/39). The Commission consists of 34 members elected for a term of five years. The last election was held at the sixty-sixth session of the General Assembly (decision 66/506).
At its sixty-eighth session, the General Assembly expressed its appreciation to the Commission for the work accomplished at its sixty-fifth session, recommended that the Commission continue its work on the topics in its current programme; drew the attention of Governments to the importance for the Commission of having their views on the various aspects of the topics on the agenda of the Commission, in particular on all the specific issues identified in chapter III of its report, regarding the topics "Immunity of State officials from foreign criminal jurisdiction", "Formation and evidence of customary international law", "Provisional application of treaties" and "Protection of the environment in relation to armed conflicts"; drew the attention of Governments to the importance of having their comments and observations by 1 January 2014 on the draft articles and commentaries on the topic "Expulsion of aliens", adopted on first reading by the Commission at its sixty-fourth session; noted the inclusion of the topics "Protection of the environment in relation to armed conflicts" and "Protection of the atmosphere" in the programme of work of the Commission and encouraged it to continue the examination of the topics that are in its long-term programme of work; noted the inclusion of the topic "Crimes against humanity" in the long-term programme of work of the Commission; and invited the Commission to continue to give priority to the topics "Immunity of State officials from foreign criminal jurisdiction" and "The obligation to extradite or prosecute (aut dedere aut judicare)" (resolution 68/112).
Consideration at the sixty-ninth session
The Sixth Committee considered the item at its 19th to 27th and 29th meetings, on 27, 28, 29, 31 October, and 3, 5 and 14 November 2014 (see A/C.6/69/SR.19, 20, 21, 22, 23, 24, 25, 26, 27 and 29).
The Chair of the International Law Commission at its sixty-sixth session introduced the report of the Commission on the work of that session: chapters I to V and XIV at the 19th meeting, on 27 October, chapters VI to IX at the 21st meeting, on 29 October, and chapters X to XIII at the 25th meeting, on 3 November.
Statements were made by the representative of: Costa Rica (on behalf of the Community of Latin American and Caribbean States (CELAC)), the European Union, Finland (on behalf of the Nordic countries), the Russian Federation, Austria, Switzerland, Canada, Peru, Romania, Portugal, the United Kingdom of Great Britain and Northern Ireland, Germany, Ireland, Tonga (on behalf of the Pacific Small Island Developing States), the Czech Republic, the Netherlands, China, Poland, Singapore, Slovenia, Japan, Chile, Israel, Thailand, Slovakia, Belarus, El Salvador, Croatia, South Africa, the United States of America, Algeria, Indonesia, Greece, the Islamic Republic of Iran, New Zealand, Spain, the Republic of Korea, Malaysia, Cuba, India, Belarus, the Russian Federation, Denmark (on behalf of the Nordic countries), Austria, the Federated States of Micronesia, France, Italy, Australia, Hungary, Palau, Cyprus, Mongolia, Sudan, Viet Nam, Norway (on behalf of the Nordic countries), Trinidad and Tobago, Turkey, Jamaica and Kazakhstan. A statement was also made by the observer of the International Federation of Red Cross and Red Crescent Societies.
Mr. Georg Nolte, Special Rapporteur for the topic "Subsequent agreements and subsequent practice in relation to the interpretation of treaties", Mr. Shinya Murase, Special Rapporteur for the topic "Protection of the atmosphere", and Ms. Marie Jacobsson, Special Rapporteur for the topic "Protection of the environment in relation to armed conflicts", made statements.
A summary of the discussions on this agenda item may be found in the Topical summary of the discussion held in the Sixth Committee of the General Assembly, during its sixty-ninth session, prepared by the Secretariat (forthcoming).
Action taken by the Sixth Committee
At the 29th meeting, on 14 November, the representative of Peru, on behalf of the Bureau, introduced two draft resolutions respectively entitled "Report of the International Law Commission on the work of its sixty-sixth session" (A/C.6/69/L.14) and "Expulsion of aliens" (A/C.6/69/L.15).
At the same meeting on 14 November, the Committee adopted draft resolutions A/C.6/69/L.14 and A/C.6/69/L.15 without a vote.
By the terms of draft resolution A/C.6/69/L.14, the General Assembly would, among other things, express its appreciation to the Commission for the work accomplished at its sixty-sixth session. Furthermore, the General Assembly would, inter alia, take note of the inclusion by the Commission of a new topic in its programme of work, namely the topic "Crimes against humanity", and encourage the Commission to continue the examination of the topics that are in its long-term programme of work. The General Assembly would also take note of the inclusion of the topic "Jus cogens" in the long-term programme of work of the Commission. By the terms of draft resolution A/C.6/69/L.15, the General Assembly would welcome the conclusion of the work of the International Law Commission on the expulsion of aliens and its adoption of the draft articles and a detailed commentary on the subject, express its appreciation to the International Law Commission for its continuing contribution to the codification and progressive development of international law, take note of the recommendation of the International Law Commission contained in paragraph 42 of its report on the work of its sixty-sixth session and decide that the consideration of this recommendation shall be continued at the seventy-second session of the General Assembly.
International Law Commission
Sixth Committee – Home
PaperSmart portal
Unite Share (RESTRICTED)
UN Web TV
UN Meeting Coverage & Press Releases
Previous sessions
Charter of the United Nations
General Assembly President
General Assembly Affairs
General debate
Delegates Handbook
UN Social Media


text:
Clarksville Police to get body cameras in '18 or '19
They likely won't begin wearing them until late 2018 or early 2019.
Clarksville Police to get body cameras in '18 or '19 They likely won't begin wearing them until late 2018 or early 2019. Check out this story on theleafchronicle.com: http://www.theleafchronicle.com/story/news/2017/10/05/clarksville-police-get-body-cameras-18-19/736570001/
Leaf Chronicle Published 2:05 p.m. CT Oct. 5, 2017
Clarksville Police Chief Al Ansley(Photo: Stephanie Ingersoll/The Leaf-Chronicle)Buy Photo
Clarksville Police will get body cameras by the end of 2018 or early 2019, according to city officials.
The City of Clarksville was awarded a federal grant in the amount of $337,500 by the Bureau of Justice Assistance, Office of Justice Programs, U.S. Department of Justice for a Body-Worn Camera Policy and Implementation Program.
CPD was the only agency in Tennessee to receive an award in this round and has a long history with Bureau of Justice grants totaling over $1.9 million since 2004.
The grant program requires an equal match from the city, and a budget amendment for that amount will be presented to the City Council. As part of the grant request, the Council earlier approved a resolution expressing support for the grant and committing to "sustaining the long-term program after the initial funding expires."
This grant funding will allow the Clarksville Police Department to develop policies and an implementation program to begin using body-worn cameras to improve law enforcement interactions with the public.
"We've worked hard for more than two years on this grant, and I'm thrilled to announce our application has been approved," Mayor Kim McMillan said. "The Clarksville Police Department does an excellent job with community relations, and body-worn cameras will only enhance these relationships by providing additional transparency and accountability."
Police Chief Al Ansley, who had worked to strengthen the City's application after the first request in 2015 was unsuccessful, said he was pleased the second application was approved.
"This shows the U.S. Department of Justice has faith in the Clarksville Police Department's ability to properly implement this program," Ansley said. "This is just the first step in a phased approach that will require a lot of public input, planning and training. Because this is a complex process, we expect Clarksville police officers won't begin wearing body-worn cameras until late 2018 or early 2019."
Implementation of the camera program will require engagement of law enforcement, local political leadership, and the community.
First, comprehensive policies must be established that address civil rights, domestic violence, juvenile and other privacy issues as well as data storage, retention and disclosure.
CPD also will develop methods to capture feedback from the public, create training and implementation plans and write policies for video capture, viewing, use, release, storage, and process/data audits and controls.
The public will be invited to provide input, review and comment during policy development and final review through public meetings, the City's website and social media. CPD will be scheduling public meetings regarding Clarksville's Body-Worn Camera Policy and Implementation Program.
Read or Share this story: http://www.theleafchronicle.com/story/news/2017/10/05/clarksville-police-get-body-cameras-18-19/736570001/
Man dies after crashing car into church on Madison Street
Clarksville weather: Lows will dip to teens, Manna opens warming center
Woman crashes truck through house, into parked car
Governor's Square Mall bringing back Charlotte Russe
Expert: Clarksville housing market likely to keep outperforming nation's
After 40 years on the Cumberland, a river captain passes along his knowledge
Stewart Houston Times
APSU
Clarksville Calendar
© 2020 www.theleafchronicle.com. All rights reserved.
Cars.com Clarksville