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Senate approves IEBC Amendment Bill on selection panel
By Edwin Nyarangi | 1w ago
The Senate Speaker Amason Kingi during the Senate special sitting to consider IEBC (Amendment) Bill at Parliament Buildings on January 19, 2023. [Boniface Okendo, Standard]
The process to recruit a new team of the Independent Electoral and Boundaries Commission (IEBC) is set to begin after Senate adopted the Bill on the selection passed by the National Assembly.
The approval of the IEBC (Amendment), Bill 2022 was supported by 28 senators against 7 after the Senate Justice, Legal Affairs and Human Rights Committee Chairperson Bomet Senator Hillary Sigei withdrew all proposed amendments.
In the Bill moved by Senate Majority Leader Aaron Cheruiyot (Kericho Senator) during the special senate sitting on January 19, the senators supported changes to the composition of the selection panel.
The Bill proposes to have the Parliamentary Service Commission nominate one man and one woman and the Inter-Religious Council two slots.
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The Public Service Commission, Political Parties Liaison Committee and the Law Society of Kenya will each nominate one person to the IEBC selection panel which will recruit the new commissioners.
"The Senate having adopted the proposed amendment to the IEBC (Amendment), Bill 2022 as approved by the National Assembly, it is now upon the relevant bodies to nominate individuals to serve in the selection panel for appointment to commence their duties as soon as possible," said Cheruiyot.
Senate Majority Whip Boni Khalwale said that approval of the Bill will pave way for the formation of a new IEBC and urged political players to have posterity in mind so as to have credible a commission that will oversee the next General Election while adhering to the Constitution.
Majority of the senators said that it was important for IEBC to have commissioners of integrity since in the past influential individuals have attempted to influence the membership to have an upper hand to their rivals forgetting that they may be at a disadvantage in future.
The Senate Justice, Legal Affairs and Human Rights Committee had in the withdrawn proposal sought to have Parliamentary Service Commission, Public Service Commission and Law Society of Kenya nominate one person each to the selection panel which was opposed by most senators.
The committee had proposed to have two persons of opposite gender nominated by the majority and minority parties represented in the Political Parties Liaison Committee at national level and two persons of opposite gender nominated by the Inter- Religious Council of Kenya.
The senators rejected a proposal by Nominated Senator Catherine Mumma by 26 votes against 9 votes in which she sought to have the Parliamentary Service Commission have two slots, Inter Religious Council two slots, Political Parties Liaison Committee two slots and the Law Society of Kenya one slot, denying the Public Service Commission a chance in the panel.
The Bill which has now been approved by both Houses has changed the composition of the selection panel that oversees the recruitment of IEBC commissioners, allowing the Public Service Commission and Political Parties Liaison Committee to nominate one member each.
National Assembly Majority Leader Kimani Ichungwa sponsored the Bill.
The six-year single term for Wafula Chebukati as IEBC Chairperson and Commissioners Boya Molu and Abdi Guliye ended this week while former Vice Chairperson Juliana Cherera with Commissioners Francis Wanderi and Justus Nyangaya resigned in December last year.
Commissioner Irine Masit is currently on suspension is facing a tribunal headed by Justice Aggrey Muchelule that is investigating her suitability to continue holding office.
The Independent Electoral and Boundaries Commission Act of 2011 states that the Seven-member selection panel shall consist of two men and two women nominated by the Parliamentary Service Commission (PSC) chaired by National Assembly Speaker.
IEBC Selection panel according to the Act is also supposed to have one person nominated by the Law Society of Kenya (LSK) and two persons nominated by the Inter-Religious Council of Kenya with the Ichungwah Bill seeking to make some changes to the same.
The National Assembly Majority Leader is seeking to reduce the slots allocated to the Parliamentary Service Commission from nominating four people to two with Parliamentary Service Commission and Political Parties Liaison Committee nominating one person each.
"The proposal seeks to reduce the current allocation of the Parliamentary Service Commission which nominates four out of the seven members of the panel, the proposal will allow Parliamentary Service Commission and Political Parties Liaison Committee nominate one member each," said Ichungwah last year.
The Majority Leader had clarified that the enactment of the Bill shall not occasion additional expenditure of public funds to be provided for in the estimates neither does it delegate legislative powers or contain provisions limiting fundamental rights and freedoms.
According to the Act, a person is qualified for appointment as a member of the section panel if he/she is a Kenyan, meets the requirements of leadership and integrity act, set out in Chapter Six of the Constitution and holds a degree from a university recognized in Kenya.
The respective nominating bodies shall within seven days of the declaration of a vacancy in the office of the chairperson or member of the commission, submit the names of their nominee to the PSC for transmission to the President for appointment before they embark on the exercise.
The selection panel shall elect a Chairperson and Vice Chairperson from among its membership with the Parliamentary Service Commission required to provide secretariat services and facilities required for it to perform its services.
"The selection panel shall within seven days of its appointment invite applications from qualified persons and publish the names of all applicants and their qualifications in the Gazette, two newspapers of national circulation and the website of Parliamentary Service Commission," states the act.
After conducting the interviews, the selection panel shall select two people qualified to be appointed as Chairperson and nine people qualified to be appointed as members of the commission and shall forward the names to the President for nomination of one person to be appointed as Chairperson and six people for appointment as members.
In shortlisting, nominating or appointing Chairperson and members of the commission, the selection panel, Parliament and the President shall ensure that not more than two thirds of the members are of the same gender and shall ensure regional balance.
The President shall within seven days of receiving the names approved by the National Assembly by notice in the Gazette appoint the Chairperson and the members of the commission.
IEBC Amendment Bill 2022 IEBC Senate
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Home > Tours & Destinations > Chile > Fjords of Tierra del Fuego
Fjords of Tierra del Fuego
This Chilean Vacation Travel Cruise tours Punta Arenas and includes visits to worldly attractions such as Cape Horn National Park, Punta Arenas, The Strait of Magellan, Pia Glacier, and more. This package is perfect mix of leisure time, sightseeing, and exciting activities. We have teamed up with Cruceros Australis to bring this package to you.
Check in at Arturo Prat Port in Punta Arenas on the day of your cruise departure.
- Board the M/V Stella Australis. After a welcoming toast and introduction of captain and crew, the ship departs for one of the remotest corners of planet Earth.
- During the night we cross the Strait of Magellan and enter the labyrinth of channels that define the southern extreme of Patagonian.
- The twinkling lights of Punta Arenas gradually fade into the distance as we enter the Whiteside Canal between Darwin Island and Isla Grande de Tierra del Fuego.
AINSWORTH BAY - TUCKERS ISLETS
- By dawn the ship is sailing up Admiralty Sound (Seno Almirantazgo), a spectacular offshoot of the Strait of Magellan that stretches nearly halfway across Tierra del Fuego.
- The snowcapped peaks of Karukinka Natural Park stretch along the north side of the sound, while the south shore is defined by the deep fjords and broad bays of Alberto de Agostini National Park.
- We go ashore at Ainsworth Bay, which harbors copious bird life and a colony of southern elephant seals which can sometimes be spotted from the Zodiacs.
- Two guided excursions are available:
- One is along the edge of a stream, peat bog and beaver habitat to a waterfall-and-moss-covered rock face tucked deep inside a pristine sub-polar forest.
- The other is a more strenuous hike along the crest of a glacial moraine. Both afford views of Marinelli Glacier and the Darwin Mountains.
- Leaving Ainsworth Bay behind, we sail west along the sound to the Tucker Islets.
- After lunch, we board the Zodiacs again for a close-up view of the Magellan penguins that inhabit the tiny islands.
PIA GLACIER - GLACIER ALLEY
- Overnight we sail around the western end of Tierra del Fuego via the very narrow Gabrial Channel, Magdalena Channel and Cockburn Channel.
- After rounding the remote Brecknock Peninsula, Stella Australis tacks eastward and enters the Beagle Channel again.
- By morning we are entering Pia Fjord and boarding the Zodiacs for a shore excursion to Pia Glacier.
- After disembarking we take a short hike to gain a panoramic view of the spectacular glacier, which extends from the mountaintops down to the sea or a longer much more difficult walk up a lateral moraine of the old Pia Glacier.
- Back onboard the ship, we continue east along the Beagle Channel through an area called Glacier Alley.
- Living up to its name, the passage features a number of impressive tidewater glaciers flowing down from the Darwin Mountains and Darwin Ice Sheet on the north shore. Most of them named after European countries -- Holland, Italy, Germany, Spain and France.
WULAIA BAY - CAPE HORN
- During the early morning we navigate the narrow Murray Channel between Navarino and Hoste islands and drop anchor at historic Wulaia Bay.
- Originally the site of one of the region's largest Yámana aboriginal settlements, the bay was described by Charles Darwin and sketched by Captain FitzRoy in the 1830s during their voyages on the HMS Beagle.
- This area is also renowned for its mesmerizing beauty and dramatic geography. After a visit to the Australis-sponsored museum in the old radio station -- which is especially strong on the Yámana people and European missionaries in the area -- passengers have a choice of three hikes that ascend the heavily wooden mountain behind the bay.
- On all of these you will be strolling through an enchanted Magellan forest of lengas, coigües, canelos, ferns, and other endemic fauna to reach a panoramic viewpoint overlooking the bay.
- Before leaving Wulaia Bay, drop something into the wooden mail barrel inside the museum – letters or postcards meant to be hand delivered by future travelers – an ancient mariner tradition revived by Australis.
- In the afternoon we cruise across Nassau Bay into the remote archipelago that includes Cape Horn National Park.
- Weather and sea conditions permitting, we shall go ashore on the windswept island that harbors legendary Cape Horn (Cabo de Hornos).
- The following morning we sail into Argentine waters and dock in Ushuaia, the world's southernmost city.
- Depart Cruise.
All meals and beverages
Tours as described in itinerary
International or local phone calls
Reciprocity Fee of $160 Chile and Argentina (subject to Change)
Scheduled tours may change due to weather conditions or other unforeseen limitations - an alternate comparable tour will be offered.
Alberto De Agostini National Park
Cape Horn National Park
Ainsworth Bay

redpajama_set_name: RedPajamaCommonCrawl


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\section{Introduction}
Egocentric interaction recognition \cite{fathi2012social,narayan2014action,ryoo2013first,sudhakaran2017convolutional,Yonetani_2016_CVPR} attracts increasing attention with the popularity of wearable cameras and broad applications including human machine interaction \cite{NIPS2016_6113,8205953} and group events retrieval \cite{alletto2015understanding,alletto2014ego}. Different from exocentric (third-person) videos, in egocentric videos, the camera wearers are commonly invisible and the videos are usually recorded with dynamic ego-motion (see Figure \ref{fig:challenge}). The invisibility of the camera wearer \modifier{hampers action recognition learning of the camera wearer}, and the ego-motion \modifier{hinders direct motion description of the interactor, which make egocentric interaction recognition challenging.}

\begin{figure}[t]
\centering
\subfigure[Invisibility of the camera wearer]{
\includegraphics[width=1.5in]{figs/Invisibility_1.pdf}
\includegraphics[width=1.5in]{figs/Invisibility_2.pdf}}
\subfigure[Ego-motion of the camera wearer]{
\includegraphics[width=1.5in]{figs/Ego-motion_1.pdf}
\includegraphics[width=1.5in]{figs/Ego-motion_2.pdf}}
\caption{Illustration of camera-wearer's invisibility and ego-motion. (a) compares the person (in red boxes) receiving something in exocentric (left) and egocentric (right) videos from NUSFPID dataset \cite{narayan2014action}. (b) shows adjacent frames with obvious ego-motion in an egocentric video from UTokyo PEV dataset \cite{Yonetani_2016_CVPR}.}
\label{fig:challenge}
\end{figure}

An egocentric interaction comprises the actions of the camera wearer and the interactor that influence each other with relations. So modeling the relations between the two interacting persons is important for interaction analysis. To model the relations between the the two interacting persons explicitly, we need to obtain individual action representations of the two persons \modifier{primarily}. Therefore, we formulate the egocentric interaction recognition problem into two interconnected subtasks, individual action representation learning and dual relation modeling.

In recent years, various works attempt to recognize interactions from egocentric videos. Existing methods integrated motion information using statistical properties of trajectories and optical flows \cite{narayan2014action,ryoo2013first,xia2015robot} or utilized face orientations descriptors \cite{fathi2012social} with SVM classifiers for recognition. Deep neural network was also adopted to aggregate short-term and long-term information for classification \cite{sudhakaran2017convolutional}. However, some of them \cite{ryoo2013first} aimed to recognize interactions from a static observer's view, which is impractical for most applications. Others \cite{fathi2012social,narayan2014action,sudhakaran2017convolutional} directly learned interaction as a whole through appearance and motion learning as done in common individual action analysis. They didn't learn individual action representations of the interacting persons, and thus failed to model the relations explicitly. The first-person and second-person features were introduced to represent the actions of the camera wearer and the interactor in \cite{Yonetani_2016_CVPR}. But they learned the individual action representations from multiple POV (point-of-view) videos and still lacked explicit relation modeling.

\noindent
\textbf{Overview of the framework.} In this paper, we focus on the problem of recognizing human-human interactions from single POV egocentric videos. Considering the relations in egocentric interactions, we develop a dual relation modeling framework, which integrates the two interconnected subtasks, namely individual action representation learning and dual relation modeling, for recognition as shown in Figure \ref{fig:framework}. Specifically, for dual relation modeling, we develop an interaction module, termed interactive LSTM, to model the relations between the camera wearer and the interactor explicitly based on the learned individual action representations. For individual action representations learning, we introduce an attention module and a motion module to jointly learn action features of the two interacting persons.
\modifier{We finally combine these modules into an end-to-end framework and train them with human segmentation loss, frame reconstruction loss and classification loss as supervision. Experimental results indicate the effectiveness of our method.}

\begin{figure}[t]
\centering
\includegraphics[width=3.25in]{figs/framework.pdf}
\caption{Proposed framework. Frames $\boldsymbol{I}_{i}(i=1,...,N)$ are sampled from the video as input. The Feature Extraction Module extracts basic visual features of sampled frames. The Attention Module localizes the interactor and learns appearance features. The Motion Module estimates global and local motions for motion features learning. The Interaction Module models the relations for better interaction recognition based on the learned individual features (a), (b), (c) and (d) explained in the blue box.}
\label{fig:framework}
\end{figure}

\noindent
\textbf{Our contributions. }In summary, the main contribution of this paper is three-fold. (1) An interactive LSTM module is developed to model the relations between the camera wearer and the interactor from single POV egocentric videos. (2) An interactor attention module and a global-local motion module are designed to jointly learn individual action representations of the camera wearer and the interactor from single POV egocentric video. (3) By integrating individual action representations learning and dual relation modeling into \modifier{an end-to-end} framework, our method shows its effectiveness and outperforms existing state-of-the-arts on three egocentric interaction datasets.

\section{Related Work}
\noindent
\textbf{Egocentric action recognition} aims to recognize camera wearer's actions from first-person videos. Since the ego-motion is a dominant characteristic of egocentric videos, most methods used dense flow or trajectory based statistical features \cite{abebe2016robust,4587735,kitani2011fast,mi2018recognizing,7952632} to recognize the actions of the camera wearer. In some object-manipulated actions, some works extracted hands and objects descriptors for recognition \cite{6126269,li2015delving,6248010,7780579}, and others further explored gaze information according to hand positions and motions \cite{fathi2012learning,6751511}. Recently, deep neural networks have also been applied to egocentric action recognition. Frame-based feature series analysis showed their promising results \cite{kahani2017correlation,7298691,zaki2017modeling}. CNN networks with multiple information streams were also trained on recognition task \cite{ma2016going,singh2016first}. However, these methods target on individual actions which are a bit different from human-human interactions.

\noindent
\textbf{Egocentric interaction recognition} specifically focuses on first-person human-human interactions. Ryoo \etal recognized what the persons in the videos are doing to the static observer \cite{ryoo2015robot,ryoo2013first}, but it is unrealistic in most daily life scenarios. Some works used face orientations, individual locations descriptors and hand features to recognize interactions \cite{bambach2015lending,fathi2012social}. Others used motion information based on the magnitudes or clusters of trajectories and optical flows \cite{narayan2014action,xia2015robot}. A convLSTM was utilized to aggregate features of successive frames for recognition \cite{sudhakaran2017convolutional}. These methods commonly learned interaction descriptors by direct appearance or motion learning, but didn't considered explicit relation modeling with individual action representations of the camera wearer and the interactor. Yonetani \etal learned individual action features of the two persons but also lacked explicit relations modeling \cite{Yonetani_2016_CVPR}.

Different from the existing methods above, our framework jointly learns individual actions of the camera wearer and the interactor from single POV egocentric videos and further explicitly models the relations between them by an interactive LSTM.

\section{Individual Action Representation Learning}
To model the relations, we first need individual action representations of the camera wearer and the interactor. Here, we learn interactor masks to separate the interactor from background and learn appearance features with an attention module. \modifier{In the meanwhile, a motion module is integrated to learn motion cues, so that we can jointly learn the individual appearance and motion features of the two persons, which are the basis to model relations in Section \ref{sec:dual-relation}.}

For two consecutive sampled frames ${\boldsymbol{I}_{n-1}, \boldsymbol{I}_{n}}\in\mathbb{R}^{H\times W\times 3}$, we use a feature extraction module composed of ResNet-50 \cite{7780459} to extract basic features $\boldsymbol{f}(\boldsymbol{I}_{n-1}), \boldsymbol{f}(\boldsymbol{I}_{n})\in\mathbb{R}^{H_0\times W_0\times C}$, which encode the scene or human information with multidimensional representations for further modeling on top of them. In the following, we denote $\boldsymbol{f}(\boldsymbol{I}_{n-1})$ and $\boldsymbol{f}(\boldsymbol{I}_{n})$ as $\boldsymbol{f}^{n-1}$ and $\boldsymbol{f}^{n}$ respectively for convenience.

\subsection{Attention Appearance Features Learning}
\label{subsec:attention}
Egocentric videos record the actions of the camera wearer and the interactor simultaneously. To learn individual action features of the two persons, we wish to separate the interactor from background based on the feature $\boldsymbol{f}^{n}$.

Pose-guided or CAM-guided strategy \cite{du2017rpan,du2018recurrent,8300648} is used for person attention learning. Similarly, we introduce an attention module to localize the interactor with human segmentation guidance. We employ a deconvolution structure \cite{Noh_2015_ICCV} on top of the basic feature $\boldsymbol{f}^{n}$ to generate the masks of the interactor as shown in Figure \ref{fig:attention}. Mask $\boldsymbol{M}^{(0)}\in\mathbb{R}^{H_0\times W_0}$ serves to weight the corresponding feature maps for attention features learning. Multi-scale masks $\boldsymbol{M}^{(k)}\in\mathbb{R}^{H_k\times W_k}$($k=1,2,3$) are applied to localize the interactor at different scales for finer masks generation and explicit motion estimation later in Subsection \ref{subsec:motion}.

\begin{figure}[t]
\centering
\includegraphics[width=3.0in]{figs/attention_module.pdf}
\caption{Structure of attention module. The module takes feature $\boldsymbol{f}^n$ as input and generates attention weighted features and multi-scale masks.}
\label{fig:attention}
\end{figure}

\noindent
\textbf{Mask of the Interactor.} To localize the interactor, we introduce a human segmentation loss to guide the learning of our attention module. Given a reference mask $\boldsymbol{M}^{RF}$, the human segmentation loss is a pixel-wise cross entropy loss:
\begin{equation}
\begin{split}
L_{seg}=&-\sum_{k=1}^{3}\sum_{i=1}^{H_k}\sum_{j=1}^{W_k}\frac{1}{H_k\times W_k}[\boldsymbol{M}^{RF}_{i,j}log\boldsymbol{M}^{(k)}_{i,j}+ \\ &(1-\boldsymbol{M}^{RF}_{i,j})log(1-\boldsymbol{M}^{(k)}_{i,j})],
\end{split}
\label{equ:segmentation-loss}
\end{equation}
where $k$ indexes the mask scales and the reference mask is resized to the corresponding shape for calculation. Here, the reference masks are obtained using JPPNet\cite{liang2018look}.

\noindent
\textbf{Attention Features.} An optimized attention module could localize the interactor, so the mask $\boldsymbol{M}^{(0)}$ has higher values at the positions corresponding to the interactor, which indicates concrete appearance information of the interactor.
Then the {\bf{l}}ocal {\bf{a}}ppearance feature describing the action of the interactor from its appearance can be calculated with weighted pooling as follows:
\begin{equation}
\boldsymbol{f}_{l,a}^{n}=\frac{1}{|\boldsymbol{M}^{(0)}|}{\sum_{i=1}^{H_0}\sum_{j=1}^{W_0}\boldsymbol{M}^{(0)}_{i,j}\cdot \boldsymbol{f}^{n}_{i,j,1:C}},
\label{equ:local-appearance-feat}
\end{equation}
where $|\boldsymbol{M}^{(0)}|=\sum_{i=1}^{H_0}\sum_{j=1}^{W_0}\boldsymbol{M}^{(0)}_{i,j}$. Accordingly, the {\bf{g}}lobal {\bf{a}}ppearance feature, which describes the action of the camera wearer from what is observed, is calculated using global average pooling:
\begin{equation}
\boldsymbol{f}_{g,a}^{n}=\frac{1}{H_0\times W_0}{\sum_{i=1}^{H_0}\sum_{j=1}^{W_0}\boldsymbol{f}^{n}_{i,j,1:C}}.
\label{equ:global-appearance-feat}
\end{equation}

The attention module learns local appearance features \modifier{to provide a concrete description instead of a global description of the interactor}, and thus assists the relation modeling later. Meanwhile, the interactor masks localizing the interactor plays an important role in separating global and local motion, which is employed in Subsection \ref{subsec:motion}.

\subsection{Global-local Motion Features Learning}
\label{subsec:motion}
Motion features are vital for action analysis. To learn individual action representations of the two interacting persons, we wish to describe the ego-motion (global motion) of the camera wearer and the local motion of the interactor explicitly based on the basic features $\boldsymbol{f}^{n}$, $\boldsymbol{f}^{n-1}$ and the interactor masks $\boldsymbol{M}^{(k)}$($k=0,1,2,3$).

Differentiable warping scheme \cite{jaderberg2015spatial} is used for ego-motion estimation with a frame reconstruction loss \cite{vijayanarasimhan2017sfm,zhou2017unsupervised}. Inspired by them, we design a self-supervised motion module with the differentiable warping mechanism to jointly estimate the two types of motion from egocentric videos.

\noindent
\textbf{Global-local Motion Formulation by Reconstruction.} To separate the global and local motions in egocentric videos, we reuse the interactor mask $\boldsymbol{M}^{(3)}$ generated in Subsection \ref{subsec:attention} with the same scale as the input frames to formulate the transformation between two adjacent frames. With the learnable parameters $\boldsymbol{T}$ and $\boldsymbol{D}$ denoting transformation matrix and dense motion field, we can formulate the transformation from homogeneous coordinates $\boldsymbol{X}_n$ to $\boldsymbol{X}_{n-1}$ concisely as:
\begin{equation}
\boldsymbol{\hat{X}}_{n-1}=\boldsymbol{T}(\boldsymbol{X}_n+\boldsymbol{M}^{(3)}\odot\boldsymbol{D}),
\label{equ:trasformation}
\end{equation}
where $\odot$ is element-wise multiplication, $\boldsymbol{X}_n$ and $\boldsymbol{X}_{n-1}$ are homogeneous coordinates of frame $\boldsymbol{I}_n$ and $\boldsymbol{I}_{n-1}$.

In Equation (\ref{equ:trasformation}), $\boldsymbol{M}^{(3)}\odot\boldsymbol{D}$ is the local dense motion field of the interactor, and $\boldsymbol{T}$ describes the ego-motion of the camera wearer, so Equation (\ref{equ:trasformation}) jointly formulates global and local motion explicitly by point set reconstruction.

\noindent
\textbf{Self-supervision.} To learn the parameters in Equation (\ref{equ:trasformation}), we use view synthesis objective \cite{zhou2017unsupervised} as supervision:
\begin{equation}
L_{rec}=\sum_{x}{\vert \boldsymbol{I}_n(x)-\boldsymbol{\hat{I}}_n(x)\vert},
\label{equ:reconstruction-loss}
\end{equation}
where $x$ indexes over pixel coordinates $\boldsymbol{X}_n$. And $\boldsymbol{\hat{I}}_n$ is the reconstructed frame warped from frame $\boldsymbol{I}_{n-1}$ according to the transformed point set $\boldsymbol{\hat{X}}_{n-1}$, which is employed with the bilinear sampling mechanism \cite{jaderberg2015spatial} as
\begin{equation}
\boldsymbol{\hat{I}}_n(x)=\sum_{i\in\{t,b\},j\in\{l,r\}}w^{ij}\boldsymbol{I}_{n-1}(\hat{x}^{ij}),
\label{equ:bilinear-sampling}
\end{equation}
where $\hat{x}$ indexes over projected coordinates $\boldsymbol{\hat{X}}_{n-1}$, $\hat{x}^{ij}$ is the neighboring coordinate of $\hat{x}$, $w^{ij}$ is proportional to the spatial proximity between $\hat{x}^{ij}$ and $\hat{x}$, and subject to $\sum_{i,j}w^{ij}=1$. In addition, we regularize the local dense motions with a smoothness loss for robust learning \cite{vijayanarasimhan2017sfm}.

With the reconstruction loss in Equation (\ref{equ:reconstruction-loss}), we design a motion module illustrated in Figure \ref{fig:motion} with two branches learning the parameters of global ego-motion and local motion in Equation (\ref{equ:trasformation}), from which {\bf{g}}lobal {\bf{m}}otion feature $\boldsymbol{f}_{g,m}^{n}$ and {\bf{l}}ocal {\bf{m}}otion feature $\boldsymbol{f}_{l,m}^{n}$ are extracted from the embedding layers.

\begin{figure}[t]
\centering
\includegraphics[width=3.2in]{figs/motion_module.pdf}
\caption{Structure of motion module. The module takes basic features $\boldsymbol{f}^n$, $\boldsymbol{f}^{n-1}$ and mask $\boldsymbol{M}^{(0)}$ as inputs and estimates global and local motion parameters in two branches in which global motion feature $\boldsymbol{f}_{g,m}^{n}$ and local motion feature $\boldsymbol{f}_{l,m}^{n}$ are extracted. $\ast$ in red circle is a multiplicative patch comparison \cite{dosovitskiy2015flownet} to calculate the correlations between two feature maps, which captures the relative motions between them for dense flow estimation.}
\label{fig:motion}
\end{figure}

The motion module jointly estimates explicit motions of the camera wearer and the interactor by reusing the interactor masks, from which we learn concrete individual motion features of the two interacting persons hence aid relation modeling in Section \ref{sec:dual-relation}.

\subsection{Ego-feature and Exo-feature.}
\label{subsec:ego-exo-feat}
For each frame pair $\{\boldsymbol{I}_{n-1},\boldsymbol{I}_n\}$, we obtain global appearance feature $\boldsymbol{f}_{g,a}^{n}$ and local appearance feature $\boldsymbol{f}_{l,a}^{n}$ from the attention module, and also global motion feature $\boldsymbol{f}_{g,m}^{n}$ and local motion feature $\boldsymbol{f}_{l,m}^{n}$ from the motion module. The global features describe overall scene context and ego-motion of the camera wearer, which could represent the action of the camera wearer. While the local features, obtained with the interactor masks, describe the concrete appearance and motion of the interactor, which could represent the action of the interactor. Thus we obtain the individual action representations of the two interacting persons.

For further exploring the relations between the two persons, we define the \textbf{\emph{ego-feature }} $\boldsymbol{f}_{ego}^n=[\boldsymbol{f}_{g,a}^n, \boldsymbol{f}_{g,m}^n]$ describing the camera wearer, and \textbf{\emph{exo-feature}} $\boldsymbol{f}_{exo}^n=[\boldsymbol{f}_{l,a}^n, \boldsymbol{f}_{l,m}^n]$ describing the interactor. With them we could model the relations in Section \ref{sec:dual-relation}.

\section{Dual Relation Modeling by Interactive LSTM}
\label{sec:dual-relation}
Given the action representations, a classifier may be trained for recognition as done in most previous works. However, as discussed before, a distinguishing property of egocentric human-human interactions is the relations between the camera wearer and the interactor, which deserves further exploration for better interaction representations.

We notice that only the ego-feature or exo-feature may not exactly represent an interaction. For the example shown in Figure \ref{fig:effect-interaction}, two interactions consist of similar individual actions: the camera wearer turning his head and the interactor pointing somewhere. In this case, neither the features of any action can identify an interaction sufficiently. However, some relations would clearly tell the differences of the two interactions, such as the sequential orders and the motion directions of the individual actions. To utilize the relations for recognition, we develop an interaction module to model the relations between the two persons based on the ego-feature and exo-feature defined in Subsection \ref{subsec:ego-exo-feat}.

\subsection{Symmetrical Gating and Updating}
\modifier{To model the relations such as the synchronism or complementarity between the two interacting persons, we integrate their action features using LSTM structure.}

We define \textbf{\emph{ego-state}} $\boldsymbol{F}_{ego}^{n}$ and \textbf{\emph{exo-state}} $\boldsymbol{F}_{exo}^{n}$ to denote the latent states \modifier{till the $n$\emph{-th} step to encode the evolution of the two actions, which correspond to ego-feature and exo-feature introduced in Subsection \ref{subsec:ego-exo-feat}, respectively.} We wish to mutually incorporate the action context of each interacting person at each time step to explore the relations such as the synchronism and complementarity. Thus, we utilize exo-state to filter out the irrelevant parts, enhance the relevant parts and complement the absent parts of the ego-state. Meanwhile, the exo-state is also filtered, enhanced and complemented by the ego-state. This symmetrical gating and updating mechanism is realized with two symmetrical LSTM blocks where each block works as follows:
\begin{align}
\begin{split}
[\boldsymbol{i}^n;\boldsymbol{o}^n;\boldsymbol{g}^n;\boldsymbol{a}^n]=&\sigma(\boldsymbol{W}\boldsymbol{f}^n+\boldsymbol{U}\boldsymbol{F}^{n-1}+ \\ &\boldsymbol{J}^{n-1}+\boldsymbol{b}),
\end{split}\\
\boldsymbol{J}^{n}=&\phi(\boldsymbol{V}\boldsymbol{F}^{n}_*+\boldsymbol{v}),\\
\boldsymbol{c}^n=&\boldsymbol{i}^n \boldsymbol{a}^n+\boldsymbol{g}^n \boldsymbol{c}^{n-1},\\
\boldsymbol{F}^n=&\boldsymbol{o}^n tanh(\boldsymbol{c}^n).
\label{equ:symm-lstm}
\end{align}
Here, the input gate, output gate, forget gate and update candidate are denoted as $\boldsymbol{i}^n$, $\boldsymbol{o}^n$, $\boldsymbol{g}^n$ and $\boldsymbol{a}^n$ respectively. $\sigma$ is tanh activation function for update candidate and sigmoid activation function for other gates. $\boldsymbol{F}_*$ is the latent state from the dual block, $\phi$ is ReLU activation function, and $\boldsymbol{J}^{n}$ is the modulated dual state. $\{\boldsymbol{W},\boldsymbol{U},\boldsymbol{V},\boldsymbol{b},\boldsymbol{v}\}$ are parameters of each LSTM block.

It is noted that the current ego-state integrates the historical information of the ego-actions and also the exo-actions into itself, and vice versa for exo-state. The ego-state and exo-state describe the interaction from the view of the camera weearer and the interactor respectively. In this symmetrical gating and updating manner, the symmetrical LSTM blocks model the interactive relations instead of a raw combination of two actions.

\subsection{Explicit Relation Modelling}
Besides the symmetrical LSTM blocks introduced above for implicitly encoding the dual relations into the ego-state and exo-state, we further explicitly model the dual relation. To this end, we introduce relation-feature $\boldsymbol{r}^n$ to explicitly calculate the relations with a nonlinear additive operation on the ego-state and exo-state:
\begin{equation}
\boldsymbol{r}^{n}=tanh(\boldsymbol{F}_{ego}^{n}+\boldsymbol{F}_{exo}^{n}).
\end{equation}

With the relation-feature $\boldsymbol{r}^n$ at each time step, we further model the time variant relations with another LSTM branch to integrate the historical relations into the relation-states $\boldsymbol{R}^n$, which can be formulated as follows:
\begin{align}
[\boldsymbol{i}^n;\boldsymbol{o}^n;\boldsymbol{g}^n;\boldsymbol{a}^n]&=\sigma(\boldsymbol{W}\boldsymbol{r}^n+\boldsymbol{U}\boldsymbol{R}^{n-1}+\boldsymbol{b}),\\
\boldsymbol{c}^n&=\boldsymbol{i}^n \boldsymbol{a}^n+\boldsymbol{g}^n \boldsymbol{c}^{n-1},\\
\boldsymbol{R}^n&=\boldsymbol{o}^n tanh(\boldsymbol{c}^n).
\label{equ:relation-state}
\end{align}
In the equations above, the gates and parameters are similarly denoted as those in the symmetrical LSTM blocks. In Equation (\ref{equ:relation-state}), $\boldsymbol{R}^n$ integrates historical and current relations information to explicitly represent the relations of the two actions at n-th time step during the interaction.

Combining the two components above, \ie the symmetrical LSTM blocks and the relation LSTM branch, our interaction module is illustrated in Figure \ref{fig:interaction}, which we term interactive LSTM. It captures the evolution or synchronism of the two actions and further explicitly models the relations between the two actions, which provides a better representation of the interaction.

\begin{figure}[t]
\centering
\includegraphics[width=3.2in]{figs/interaction_module.pdf}
\caption{Diagram of Interactive LSTM. The unrolled symmetrical LSTM blocks mutually gate and update each other as the green arrows depict. The unrolled relation LSTM branch is highlighted in red. All LSTM blocks contains $N$ time steps.}
\label{fig:interaction}
\end{figure}

The posterior probability of an interaction category given the final relation-state $\boldsymbol{R}^N$ can be defined as
\begin{equation}
p(\boldsymbol{y}|\boldsymbol{R}^N)=\delta(\boldsymbol{W}\boldsymbol{R}^N+\boldsymbol{b}),
\label{equ:logits}
\end{equation}
where $\boldsymbol{W}$ and $\boldsymbol{b}$ are parameters of classifier and $\delta$ is softmax function. Then a cross entropy loss function is employed to supervise parameters optimization as follow:
\begin{equation}
L_{cls}=-\sum_{k=1}^{K}y_klog[p(y_k|\boldsymbol{R}^N)],
\label{equ:classification-loss}
\end{equation}
where $K$ is the number of class.

Combining the loss functions of each module above, we train our model end-to-end with the final objective:
\begin{equation}
L_{final}=L_{cls}+\alpha L_{seg}+\beta L_{rec}+\gamma L_{smooth},
\end{equation}
where $\alpha$, $\beta$, $\gamma$ are weights of segmentation loss, frame reconstruction loss and smooth regularization, respectively.

\section{Experiments}
\label{sec:experiment}

\begin{table*}[t]
\centering
\begin{tabular}{lcccc}
\hline
Methods&PEV&NUS(first h-h)&NUS(first)&JPL\\
\hline\hline
RMF\cite{abebe2016robust}&-&-&-&86.0 \\
Ryoo and Matthies\cite{ryoo2013first}&-&-&-&89.6 \\
Narayan \etal\cite{narayan2014action}&-&74.8&77.9&96.7 \\
Yonetani \etal\cite{Yonetani_2016_CVPR} (single POV)&60.4&-&-&75.0 \\
\hline
convLSTM\cite{sudhakaran2017convolutional} (raw frames)&-&-&69.4&70.6 \\
convLSTM\cite{sudhakaran2017convolutional} (difference of frames)&-&-&70.0&90.1 \\
LRCN\cite{7558228}&45.3&65.4&70.6&78.5 \\
TRN\cite{Zhou_2018_ECCV}&49.3&66.7&74.7&84.2 \\
Two-stream\cite{NIPS2014_5353}&58.5&78.6&80.6&93.4 \\
\hline
Our method&\bf{64.2}&\bf{80.2}&\bf{81.8}&\bf{98.4} \\
\hline
Yonetani \etal\cite{Yonetani_2016_CVPR} (multiple POV)&69.2&-&-&- \\
Our method (multiple POV)&69.7&-&-&- \\
\hline
\end{tabular}
\caption{State-of-the-art comparison (\%) with existing methods. \textit{NUS(first h-h)} denotes the first-person human-human interaction subset of NUS dataset and \textit{NUS(first)} denotes the first-person subset. It is notable that only PEV dataset provides multiple POV videos so that no multiple POV result of other datasets is reported.}
\label{tab:result}
\end{table*}

\subsection{Datasets}
We evaluate our method on three egocentric human-human interaction datasets.

\noindent
\textbf{UTokyo Paired Ego-Video (PEV) Dataset} contains 1226 paired egocentric videos recording dyadic human-human interactions \cite{Yonetani_2016_CVPR}. It consists of 8 interaction categories and was recorded by 6 subjects. We split the data into train-test subsets based on the subject pairs as done in \cite{Yonetani_2016_CVPR} and the mean accuracy of the three splits is reported.

\noindent
\textbf{NUS First-person Interaction Dataset} contains 152 first-person videos and 133 third-person videos of both human-human and human-object interactions \cite{narayan2014action}. We evaluate our method on first-person human-human interaction subset to verify the effectiveness of our method. To further test our method in human-object interaction cases, we also evaluate on the first-person subset. Random train-test split scheme is adopted and the mean accuracy is reported.

\noindent
\textbf{JPL First-Person Interaction Dataset} consists of 84 videos of humans interacting with a humanoid model with a camera mounted on its head \cite{ryoo2013first}. It consists of 7 different interactions. We validate our method's effectiveness in this static observer setting and report the mean accuracy over random train-test splits.

\subsection{Implementation Details}
\noindent
\textbf{Network Details.} In the motion module, we set 5 as the maximum displacement for the multiplicative patch comparisons. In the interaction module, we reduce the size of ego-feature and exo-feature to 256 and set 256 as the hidden size of LSTM blocks. 20 equidistant frames are sampled as input as done in \cite{sudhakaran2017convolutional}.

\noindent
\textbf{Data Augmentation.} We adopt several data augmentation techniques to ease overfitting due to the absence of large amount of training data. (1) Scale jittering \cite{wang2016temporal}. We fix the size of sampled frames as 160$\times$320 and randomly crop a region, then resize it to 128$\times$256 as input. (2) Each video is horizontally flipped randomly. (3) We adjust the hue and saturation in HSV color space of each video randomly. (4) At every sampling of a video, we randomly translate the frame index to obtain various samples of the same video.

\noindent
\textbf{Training setup.} The whole network is hard to converge if we train all the parameters together. Hence, we separate the training process into two stages. At the first stage, we initialize feature extraction module with ImageNet \cite{russakovsky2015imagenet} pretrained parameters and train attention module, motion module and interaction module successively while freezing other parameters. At the second stage, the three modules are finetuned together in an end-to-end manner. \modifier{We use Adam optimizer with initial learning rate $0.0001$ to train our model using TensorFlow on Tesla M40, and decrease the learning rate when the loss saturates. To deal with overfitting, we further employ large-ratio dropout, high weight regularization and early stop strategies during training.}

\subsection{Comparison to the State-of-the-art Methods}
We compare our method with state-of-the-arts and the results are shown in Table \ref{tab:result}. The first part lists the methods using hand-crafted features. The second part presents \modifier{some deep learning based action analysis methods (reimplemented by us except convLSTM)}. The third part reports the results of our method and the fourth part compares the performance using multiple POV videos on PEV dataset.

As shown, our method outperforms existing methods. Most previous methods directly learn interaction representations without relation modeling, while
\modifier{ours explicitly models the relations between the two interacting persons.} The results show that relation modeling is useful for interaction recognition.

Among the compared deep learning methods, we obtain clear improvement over convLSTM\cite{sudhakaran2017convolutional}, LRCN\cite{7558228} and TRN\cite{Zhou_2018_ECCV}, since they mainly capture the temporal changes of appearance features, but ours further explicitly captures motions and models the relation between the two interacting persons. Two-stream network \cite{NIPS2014_5353} with the same backbone CNN as ours integrates both appearance and motion features but obtains inferior performance to ours, perhaps due to the lack of relation modeling.

On PEV dataset, Yonetani \etal\cite{Yonetani_2016_CVPR} achieves $69.2\%$ of accuracy with paired videos, certainly surpassing others using single POV video. We use our interactive LSTM to fuse the features from paired videos since there also exist some relations between the actions recorded by the paired videos. We achieve comparable result ($69.7\%$) which further proves the relation modeling ability of our interactive LSTM.

In terms of inference time, our framework takes around 0.15 seconds per video with 20 sampled frames, which is still towards real time. TRN\cite{Zhou_2018_ECCV} takes 0.04 seconds per video but it has clear lower recognition performance than ours. Although Two-stream\cite{NIPS2014_5353} obtains slightly inferior performance to ours, it takes 0.9 seconds per video since it spends much more time on extracting optical flows.

\subsection{Further Analysis}

\subsubsection{Study on Interaction Module.}
Table \ref{tab:effect-interaction} compares recognition performance about interaction. It shows that our interactive LSTM clearly improves the performance, \modifier{since it models the relations and also drives feature learning of other modules.}
On different datasets, the relation modeling obtains different performance gains. We obtain clearer improvements on PEV dataset since it contains more samples dependent on relations. While in NUS(first h-h) dataset, most samples have weaker relations between the two interacting persons.

\begin{table}[t]
\centering
\begin{tabular}{ccc}
\hline
Features&PEV&NUS(first h-h)\\
\hline\hline
Ego-features&55.2&67.9 \\
Exo-features&53.1&76.1 \\
Concat(no relation)&60.8&77.9 \\
Interaction with sym. blocks&62.7&78.1 \\
Interaction with rel. branch&63.0&79.0 \\
Interaction with both&64.2&80.2 \\
\hline
\end{tabular}
\caption{Recognition accuracy comparison (\%) about interacion. \textit{Concat(no relation)} means concatenation of ego-features and exo-features without any relation modeling. \textit{Interaction with sym. blocks} means only symmetrical LSTM blocks are used. \textit{Interaction with rel. branch} means only relation LSTM branch is used. \textit{Interaction with both} means both components are used.}
\label{tab:effect-interaction}
\end{table}

\begin{figure}[t]
\centering
\subfigure[Interaction category: none]{
\includegraphics[width=3.0in]{figs/int-ana-none.pdf}}

\subfigure[Interaction category: attention]{
\includegraphics[width=3.0in]{figs/int-ana-attention.pdf}}

\caption{Comparison of recognition results of two interaction samples. \textit{w/o relation} means concatenation of two action features without any relation modelling is used for recognition. The bar graphs on the right present the probabilities of each category.}
\label{fig:effect-interaction}
\end{figure}

As discussed in Section \ref{sec:dual-relation}, the main differences between the two interaction samples shown in Figure \ref{fig:effect-interaction} might be the sequential orders and motion directions. We further compare the recognition results on them of different methods. It is observed that both two-stream\cite{NIPS2014_5353} and
simple concatenation cannot sufficiently model the two interactions. While with explicit relation modeling, the two interactions are correctly distinguished, \modifier{which indicates that our interactive LSTM models the relations to distinguish confusing samples for better interaction recognition.}

\subsubsection{Study on Attention Module.}
We compare recognition accuracy of different appearance features in Table \ref{tab:effect-attention}. It is observed that local appearance features slightly improve the performance since it provides concrete descriptions of the interactor instead of general or overall features, which are more related to the interaction. Furthermore, relation modeling performs better than concatenation since it enhances the features through symmetrical gating or updating and relation modeling.

Figure \ref{fig:effect-attention} visualizes some learned masks. \modifier{(See more examples in supplementary material.)} As shown, the attention module learns to localize the interactor with the JPPNet reference masks as supervision. With additional classification loss, it could localize some objects around the interactor and strongly related to the interactions such as the hat in the example, which leads to around 2\% accuracy boost for local appearance features. This shows the advantage of using the designed attention module in our framework over using the JPPNet masks directly in this recognition task. In addition, with only the classification loss, our attention module fails to localize the interactor at all, indicating the necessity of reference masks for interactor localization.

The attention module is an indispensible part of our framework for individual action representation learning. It not only learns concrete appearance features, but also severs to separate the global and local motion for explicit motion features learning. Without attention module, our framework could only capture the global appearance and motion cues, and fails to model the relations between the camera wearer and the interactor, which leads to $9.0\%$ and $12.3\%$ accuracy degradation on PEV and NUS(first h-h) dataset, demonstrating the importance of attention module.

\begin{table}[t]
\centering
\begin{tabular}{ccc}
\hline
Features&PEV&NUS(first h-h)\\
\hline\hline
Two-stream\cite{NIPS2014_5353}(RGB)&40.7&63.8 \\
Global appearance&40.7&63.8 \\
Local appearance&43.2&65.1 \\
Concat(no relation)&44.2&66.8 \\
Interaction&45.9&68.2 \\
\hline
\end{tabular}
\caption{Recognition accuracy comparison (\%) using appearance features. \textit{Concat(no relation)} means simple concatenation of global and local appearance features. \textit{Interaction} means relations modeling is used with global and local appearance features.}
\label{tab:effect-attention}
\end{table}

\begin{figure}[t]
\centering
\subfigure[Frame]{
\includegraphics[width=1.5in]{figs/att-analysis-frame.pdf}}
\subfigure[JPPNet mask]{
\includegraphics[width=1.5in]{figs/att-analysis-parsing.pdf}}

\subfigure[Mask]{
\includegraphics[width=1.5in]{figs/att-analysis-mask0.pdf}}
\subfigure[Mask]{
\includegraphics[width=1.5in]{figs/att-analysis-mask1.pdf}}
\caption{Example of learned mask with different supervision. (a) is original frame; (b) is the JPPNet mask; (c) is the learned mask trained with only human segmentation loss; (d) is the learned mask trained with human segmentation loss and classification loss.}
\label{fig:effect-attention}
\end{figure}

\subsubsection{Study on Motion Module.}
We show accuracy comparisons of different motion features in Table \ref{tab:effect-motion}. It is seen that two-stream (flow) is a powerful method, but it is computational inefficient. Our method explicitly captures motions of the camera wearer and interactor and reaches comparable results with two-stream (flow), which indicates the effectiveness of our motion module. Furthermore, our method could achieve higher accuracy with relation modeling. On different datasets, global motion and local motion contribute differently to recognition, probably because global motion is important to distinguish interactions such as positive and negtive response on PEV dataset, but such interactions highly relevant to global motion are not included in NUS(first h-h) dataset.

In Figure \ref{fig:effect-motion}, we show the reconstructed frame and local dense motion field. \modifier{(See more examples in supplementary material.)} From the reconstructed frame, it is seen that the the slight head motion to the right is captured, which leaves a strip on the left highlighted in blue. The local dense motion field shows the motion of the interactor reaching out the hand towards the right. This example shows that the motion module could learn the global and local motion jointly.

\begin{table}[t]
\centering
\begin{tabular}{ccc}
\hline
Features&PEV&NUS(first h-h)\\
\hline\hline
Two-stream\cite{NIPS2014_5353}(flow)&54.0&73.2 \\
Global motion&51.9&52.3 \\
Local motion&51.0&69.6 \\
Concat(no relation)&53.2&73.4 \\
Interaction&56.6&75.0\\
\hline
\end{tabular}
\caption{Recognition accuracy comparison (\%) using motion features. \textit{Concat(no relation)} means simple concatenation of global and local motion features. \textit{Interaction} means relations modeling is used with global and local motion features.}
\label{tab:effect-motion}
\end{table}

\begin{figure}[t]
\centering
\subfigure[Frame $\boldsymbol{I}_{n-1}$]{
\includegraphics[width=1.5in]{figs/mot-analysis-frame_n-1.pdf}}
\subfigure[Frame $\boldsymbol{I}_n$]{
\includegraphics[width=1.5in]{figs/mot-analysis-frame_n.pdf}}

\subfigure[Local dense motion]{
\includegraphics[width=1.5in]{figs/mot-analysis-local_dense_motion.pdf}}
\subfigure[Global motion]{
\includegraphics[width=1.5in]{figs/mot-analysis-reconstructed_frame_n.pdf}}
\caption{Illustration of global and local motion. (a) and (b) are two consecutive sampled frames. (c) Local dense motion shows the amplitudes of the horizontal motion vectors in the interactor mask, the amplitudes to the right are proportional to the brightness of the motion field. The motion vectors outside the interacor mask is discarded. (d) Global motion shows the slight head motion to the right, which reflects on the strip highlighted in blue.}
\label{fig:effect-motion}
\end{figure}

Our motion module explicitly estimates global and local motions of the camera wearer and the interactor individually, which is important for relation modeling. Without the motion module, our method fails to capture motion information and can only use appearance features, which leads to $18.3\%$ and $12.0\%$ accuracy drop on PEV and NUS(first h-h) dataset, showing the necessity of motion modeling.

\section{Conclusion}
In this paper, we propose to learn individual action representations and model the relations of the camera wearer and the interactor for egocentric interaction recognition. We construct a dual relation modeling framework by developing a novel interactive LSTM to explicitly model the relations. In addition, an attention module and a motion module are designed to jointly model the individual actions of the two persons for helping modeling the relations. Our dual relation modeling framework shows promising results in the experiments. \modifier{In the future, we would extend our method to handle more complex scenarios such as multi-person interactions, which are not considered in this paper.}

\section*{Acknowledgement}
This work was supported partially by the National Key Research and Development Program of China (2018YFB1004903), NSFC(61522115), and Guangdong Province Science and Technology Innovation Leading Talents (2016TX03X157).

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


Melanie Coletta, ACNP is an acute care nurse practitioner in Dearborn, MI. She specializes in acute care nursing (nurse practitioner) and cardiothoracic surgery.
Check the quality of care at hospitals where Melanie Coletta treats patients.


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ChatOps is the trending buzzword now and there are some really nice going in the StackStorm community who are some phenomenal work. According to Atlassian, "ChatOps is a collaboration model that helps connect people, process, tools and automation into a transparent workflow".
I heard a podcast mentioning that ChatOps is like bringing in the work that you're already doing, in line with the context of the conversations you are already having which give goosebumps to me!
shared command line – shared context(heard from the same podcast), this could solve so many incidents where people login into a server, in some corner of the world, individually, and do something. With this everybody can execute commands, they are happening in line and everybody sees them and collaborate around things that happen, this is super amazing in incident management space.
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So this gave me an idea, rather than connecting Jenkins to my slack room, why not connect it to the Google home speaker in my office. I felt it would be awesome to potentially trigger jobs, or automate anything in Jenkins through voice commands, so that is exactly what I did last week.
I started with Dialog Flow, previously known as api.ai, is google's handy tool to build agent(apps) for Google Home, it's pretty straight forward, it has divided the flow of dialog(pun intended) between agent and user, into entities which belong to intents, so when we speak to google home, it takes the sentence identifies entities in it, for example, "build XYZ job" has two entities the way I see it, its "build" entity named @action and "xyz" entity named @var for my agent. So in intent, we gave few training phrases similar to the one exampled here, so now agent knows which intent to attach to once something similar comes along.
Having multiple intents helps because it will give us the opportunity to define customized responses for each intent, one could easily attach it in one intent, and not use the beautiful training algorithm, we have at our disposal and it would work fine, but where is the fun in that.
Then comes the fulfillment part, Google has incorporated firebase function inside dialog flow, we have the ability to edit and deploy the function from within the dialog flow, but remember you must have a billing account for this attached to the project, and you can also have local setup for the function or just use to update the function, but once done through that, you will not be able to edit or deploy function from within the dialog flow.
So in firebase function, it really depends upon you approach, I went ahead, and created a simple switch/case and filtered through the action and the job to match the one asked by user, once the api call has returned, google home would use one of the responses set in intent it matched to.


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Nicola Aceto, Ph.D., Swiss National Science Foundation Assistant Professor of Oncology, department of biomedicine, University of Basel
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Does Assassins creed unity still have a Active multiplayer community?
What do you thing will Happen After Uprising?
What happened to Paul Bellemy and the other assassins after the great purge ?
Assassin's Creed movie 2!! What's happening!?
New location for Assassin's Creed!
UBISOFT Store needs to change!!! Lets be equal between US and Europe!


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$28m SSA building sick
BARRY ALLEYNE, [email protected]
The new five-storey, unused headquarters of the Sanitation Service Authority (SSA) has been characterised as an environmental hazard even before it has been opened for business.
The structure, built at Vaucluse, St Thomas, almost four years ago under the previous Democratic Labour Party administration at a price tag of $28 million, has mould in some areas, windows that cannot be opened, floors that were carpeted, and issues with temperature spikes.
And it's become clear that workers won't be going in anytime soon.
The horror stories about the yet-to-be-occupied building were revealed in the question and answer section of the 2020 Estimates Debate in the House of Assembly yesterday by Minister of the Environment Trevor Prescod, and SSA acting general manager Janice Jones. (BA)


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Nepal Army to deploy spies to stop irregularities in recruitment
File: A Nepali Army team deployed for security
Kathmandu, June 5
Nepal Army says it planned to deploy spies to make sure there are no irregularities during its recruitment process.
Officials involved in the recruitment process will have to abide by strict policies even during the first phase (the obstacle course), sources said. According to a source at NA, a separate observation team has been set up to monitor the whole process.
"We are doing this to make sure that the recruitment process is transparent and fair. We've heard news of irregularities and want to take strict actions against those involved. Spies will be deployed from the army headquarters itself," said a source at the headquarters.
The army has also directed officials evaluating cadets to leave all personal electronic devices behind when heading to the obstacle course to conduct the preliminary exam.
According to NA spokesperson Bigyan Dev Pandey, the army has also told aspiring cadets not to give anyone money even if they promise a job in the army. "Many of them come from villages and are unaware of frauds. That is why we have been making them aware of such people during the application process," added Pandey.
The Military Court on November 29 had convicted 173 personnel for their involvement in accepting bribes in the recruitment process last year. The court sacked 112 people, among which 101 were new recruits, who managed to secure jobs by paying bribes.
Published on June 5th, Wednesday, 2019 2:49 PM
Related News Fast track: Nepal Army completes 25% construction works as half of the allocated time is over Army mobilised for development works as per constitution: Defence Minister From the Kathmandu Press: Sunday, May 26, 2019
Nepal Army Nepal Army enrollment
Thursday, July 18th | 9 mins ago


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09:02 My Hero Academia Anime Season 4's Video Previews School Festival Arc
09:00 Preorders Open for Alucard Hellsing Elite Bust
Yoshihiro Tatsumi to Attend Toronto Comic Fest in May
posted on 2009-01-23 14:02 EST by Egan Loo
Gekiga pioneer to present A Drifting Life memoir with Adrian Tomine , others
The Beguiling comic book store has announced that Yoshihiro Tatsumi will join guests from around the world at the 2009 Toronto Comic Arts Festival from May 9 to May 10. Tatsumi pioneered the avant-garde gekiga style of manga in Japan in the 1950s. Drawn & Quarterly has been publishing his works such as the Harvey Award-winning Abandon the Old in Tokyo and The Push Man and Other Stories in North America since 2006, and the publisher will debut Tatsumi 's 840-page autobiographical tome, A Drifting Life , at the festival. Adrian Tomine , A Drifting Life 's graphic designer and an award-winning graphic novel artist in his own right, will appear with Tatsumi at the festival.
The creators Ivan Brunetti, Anke Feuchtenberger, Emmanuel Guibert, Bryan Lee O'Malley, Paul Pope, and Seth round out the guest list.
Source: Publishers Weekly's The Beat
this article has been modified since it was originally posted; see change history
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• AD CONTENT •
Summertime in Québec City is heating up with these cool events
All the charm of Europe with none of the jet lag.
By Québec City Tourism on June 22, 2016
Credit: Luc Antoine Couturier
Sure, Québec City has history and charm in spades, but this Canadian locale is also a happening hotspot, with oodles of amazing events all season long. Just a quick 1.5 hour flight from New York City and a 6-hour car ride from Boston, you'll be strolling the cobblestone streets and taking in the local sights before you can say "Bonjour!"
Want to head north but not sure where to start? Here are the three can't-miss events in Québec City this summer, plus a few key phrases en français to practice during your trip!
Credit: Renaud Phillipe
Dance to the beat at the Québec City Summer Festival
Festival d'été de Québec, which takes place from July 7-17, is Canada's largest outdoor music event, drawing some 1,000 artists to the city to perform across 10 stages for 11 days. Now in its 49th year, the Summer Festival is an epic party that spills onto the streets, filling the city with amazing energy.
This year's impressive lineup will feature megastars like Sting, Selena Gomez and the Red Hot Chili Peppers, plus a slew of others from an array of genres to fill out the bill. There is something here for everyone -- from rock and pop to classical and jazz. The novel juxtaposition of a multi-stage concert set against the historic backdrop of Old Québec is not to be missed.
Key Phrase:
C'est ma chanson préférée! / This is my favorite song!
A scene from the 2015 performance of Crépuscule, which told the story of grown-up children in a floating village. Credit: Alexandre Galliez
A night at the circus with Crépuscule
After the sun goes down, the Agora in the Old Port will light up with feats of strength, agility and coordination thanks to circus troupe Flip FabriQue. Watch as acrobats stand on the heads of their fellow performers, juggling six feet in the air, and as aerialists drift and float from suspended silks as if submerged in water.
Set to live music by French Canadian electro pop group Valaire, Crépuscule - Raviver les braises tells the story of the backstreets and alleyways that come alive when the light leaves the sky. (The show's name translates to "Twilight -- Rekindling the embers.")
This outdoor circus show was a hit last summer and returns again for two full months of performances starting in July and lasting through Labor Day weekend. Even better? It's completely free.
Les acrobaties sont à couper le souffle! / The acrobatics are incredible!
Credit: Croisières AML
Light up the sky at Les Grands Feux Loto-Québec
Ready to "Ohh" and "Ahh" this summer? At 9pm every Saturday and Wednesday from August 6-24, a spectacle of fireworks will explode over the St. Lawrence River, uniting locals and tourists alike on the banks of Québec City and her neighbor Lévis across the water.
Each pyromusical presentation is themed, ranging from Opera Night to Jazz Night to Celtic Night and more. The fun doesn't end there though, as the Old Port comes alive with music, dancing and street performers.
Flock to the riverbanks surrounding Quays 21 and 22 to take in the view, or book a seat aboard a boat. To simply ride and watch, ferry tickets are $20 apiece, but for those looking for something a bit more extravagant, ride with Croisières AML(Opens in a new window). This four-hour cruise offers a five-course meal and truly unique views of the colorful explosions overhead for $125 per person.
Wow! Regardez les feux d'artifices rouges! / Wow! Look at the red fireworks!
More in Music
Substack launches 'private' newsletters
Readers have to request to gain access and subscribe.
By Meera Navlakha
Incognito mode on Chrome for Android just got a lot more useful
Stay safe from prying eyes.
Twitter changed its font, and there might be a very good reason for it
Easier to spot impersonators.
Shutterstock launches an AI image generator. Just what we needed.
Be careful: Using AI to find art is controversial.
Ivory is a great new Mastodon client with a horrible name
Want to switch from Twitter to Mastodon? This app will make it easier.
The bizarre 'To Leslie' Academy Awards controversy, explained
This is just strange.


It may now seem that "Lean" is old fashioned and outdated, especially as we are in the world of Agile and Scrum. However, and as always, any approach should be viewed in context, especially as the world is changing fast and we are truly in an evolving environment. The nature of business is changing with the digitally driven economy and AI, and leadership positions are rotating quickly with little time to embed a consistent performance-based culture.


The highlight of my work placement with Create Soho had to be going to film this video at Legoland. Originally selected to play the role of Henry, my height let me down for a change, so I was on sound and being a runner for the day. Although unfortunately not starring in the footage, my pink folder makes a sneeky appearance instead.
So here's Luke Franks, 2012 Sony Rising Star award winner, having some fun with Horrid Henry.
I was approached by BA TV students to help present this documentary, aiming to uncovering the mysteries surrounding the treatment.
It was a strange experience and I have written more about it in an upcoming feature for The Rock. However, in the meantime, enjoy the documentary.
This documentary is the final part of our television module for this year. We decided to create a piece on how stereotypes are still present in society today.
This is my next television package. It's a bit longer at 1'33" and covers the issues of Light Pollution.
Filmed in Bournemouth, I can confess I got a lot of weird looks for being that girl who was filming the street lights in Winton.
I will update this if I get a first… If I don't get a fantastic grade I may stay quiet.


On May 19, 2018, the first ever Changing Tides event was held at a seaside estate in Palos Verdes overlooking the ocean. The event was created to increase awareness of mental health in the Japanese American and Asian American communities, and to help raise money for Little Tokyo Service Center's established mental health programs. Over 150 adults and youth from the Asian American communities, including attendance from several prominent figures, attended the inaugural event. The night featured personal stories from Mariko Koda and Ty Tanioka, speeches from Dr. Michelle Furuta and Dennis Murata, art by Nancy Uyemura, a musical performance by Miko Shudo and Miharu Okamura, and catering by Katsuya. The first ever Changing Tides was extremely successful, and we are grateful to all who attended!
See footage from the event below.


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How expensive everyday life has become
FC Schalke 04: It's a pity – the fans feel a feeling they haven't known before
Schorndorf raises admission prices: how much will swimming cost? – Rems-Murr district
Landesliga Season 3: DJK Donaueschingen lose against SG Dettingen – Soccer
Watch Lost Treasure Hunters again on ZDFneo: Replay of the documentary series online and on TV
Cobra Kai – The Complete Third Season will be available from 2022. July 8 On Blu-ray in limited media books
Inka Bausė criticizes season 1
Where the fourth season takes place in Brandenburg
When will Series 5 and 6 be shown on TV for the first time?
Who wouldn't try to be happier in life if a million euros were spent on it? Three families are involved in this game in the series "Players". When does this continue?
Firat, Ines, and Jasper experience stress when an elderly gentleman makes them an interesting proposition to improve their lives. They must receive a million euros, but they must meet certain conditions.
"Players": Broadcast dates and consequences
The series will be in the first broadcast double episodes. The production, released as a mini-series, consists of a total of six episodes every wednesday be broadcast. to Therefore, the 5 and 6 series will be released on May 11th 2022 on television.
Consequence Date of broadcast first Episodin
01: "Wish List" 2022 April 27, from 8:15 p.m. Herr Herzinger finds a wish list and travels with his servant Wagner to search for the origin of the letter. He decides to play the game of luck and offers Inesa, Firat and Jasper a million euros each.
02: "Annual Subscription" 2022 April 27, from 9 p.m. The three accept the challenge, but quickly face the first challenges, such as hiding things from their partners.
03: "Knowledge is power" 2022 May 4, from 8:15 p.m. The partners understood what was being played. However, the situation is tense for other reasons as well. Jasper is stressed at work, Frat has problems with his novel, and Ines could get a job in Brussels, which is not at all pleasing to her husband Max.
04: "Good men must be divided" 2022 May 4, from 9 p.m. It's chaotic. There is a disaster at Jasper's job, Max wants to go on a big tour, Firat suddenly becomes Ines 'husband, and Natasha learns all about the man's novel.
05: "I went to the dog" 2022 May 11, from 8:15 p.m. Jasper loses his job and finds a dog, Simone finds a new job, Firat has to sell his company, and Ines can forget about her promotion. Now she can spend more time with the kids.
06: "Better success for all" 2022 May 11, from 9 p.m. Now that the business is gone, Firato's house is also in danger, but Natasha knows what to do. A dog that makes Jasper and his family very happy may have a master, and Herr Herzinger suddenly has problems with money.
The second season of the thriller with Game of Thrones star Gemma Whelan - fernsehserien.de
If you don't like the broadcast date or miss an episode, don't despair all episodes are also available online in advance can be seen in the media library. After the broadcast, you will be able to watch it there for six months without any fees or registration. Of course, the episodes can also be found as audio versions.
That's what Gamblers are all about.
Everyday life is rarely pure happiness and beyond Mr. Rossi looking for him. Elder Herr Herzinger is usually rich, but quite lonely and unhappy. After four unsuccessful marriages, he now devotes himself to watching the birds and reflecting on the meaning of life. Was his lifelong pursuit of wealth the right decision? He thinks he made the wrong choice, but now it's too late to change his life. However, he can still save other people's lives and accidentally found a letter to Santa and found a starting point. Its range of Firat, Ines and Jasper is absolutely exceptional. Each of them should get a million if they strive to live a happier life for years. They have to meet three conditions: No one can find out about it – even their spouses * have to meet with a notary every week to exchange information about their progress, and if all three do not endure a year together, no one gets it. cents. However, your success in finding happiness does not matter – the most important thing is sincere effort.
Gamblers: These are the actors
Rolle Actress
Firat Bozoklu Eco Fresh
Natasha Bozoklu Karolina Lodyga
Ines Schickling Catherine Schuettler
Fees Niemitz Sergei Moya
Jasper Lipas Manuel Ruby
Simone Eder-Lipp Lena Dörrie
Gottlieb Herzinger Branko Samarovski
Dietrich Wagner Ovid Schumacher
Mrs. Unger Bettina Mittendorfer
Antonija Herzinger Doris Schretzmayer
Mom Luise and Julia are the same
Ronis Vincentas Königas
Sammy Milo Mahir |
Hugo Bozoklu Baltazar Jacob
Yesimas Bozoklu Cosima Nath
Seda Bozoklu Purchase of Leo wine
ICH bin ICH on KiKa Live and TV: The Preschool Show Season 7 Episode 6
Could it be the second season of The Gamblers?
Since the series is still broadcast on TV, we want to …
Previous "It's just not like RB Leipzig"
Next "Then I halved"
CSI: Miami on Vox: Reruns on TV
The Neighborhood: Episode 15, Season 4 reruns online and on TV
RTL expects new series of Barbara Salesch: The sender has an emergency plan
How many people can the earth hold?
How Deco made Raphinha's transfer possible
The interactive season of Miss Germany has started, Miss Germany Studios GmbH & Co. KG", press release
What's better about Walking Dead? This zombie alternative is getting more and more insane with each season [Video] – News series
Rietberg, tomato cake with herbs, recipe from market manager Elke Sudbrock, Gütsel Online, OWL live
Mittelstand Metaversum: Singen Abendgesellschaft explains how companies stay successful
RS virus: how dangerous is this virus for children?


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Fringe S05E12 Liberty – Fringe S05E13 An Enemy of Fate
Let's watch the official trailer of the two-hour finale of the already-cult science-fiction TV series Fringe:
Don't miss the season finale because it will also concludes the series! The last two episodes, 12 and 13, of Fringe will air January 18, 2013 on Fox.
The TV series Fringe, created by J.J. Abrams, Alex Kurtzman and Roberto Orci, will go down in the books as one of the most riveting and creatively brilliant science-fiction TV series ever!
Tags: Fringe, Fringe Season 5
Fringe S05E11 The Boy Must Live
Here's the official trailer of "The Boy Must Live", the next episode of the science-fiction TV series Fringe:
Fringe 5.11 – The Boy Must Live
Fringe S05x11 The Boy Must Live
Episode summary:
"As time dramatically ticks down toward the end of this acclaimed series, Peter, Olivia and Walter stop at nothing to save the universe from Observer rule. Walter enters the infamous deprivation tank in an attempt to uncover a key piece of information about the mysterious figure, Donald. Meanwhile, Captain Windmark sets out on a revealing mission of his own as long-standing questions are answered."
So November is Donald! I can hardly recognize him with his hair!
Fringe S05E10 Anomaly XB-6783746
Let's watch the promo trailer of "Anomaly XB-6783746", the next episode of the marvelous science-fiction TV series Fringe:
Fringe 5.10 – Anomaly XB-6783746
Fringe S05x10 Anomaly XB-6783746
"As the countdown to the climactic series finale continues, the Fringe team is running out of time to save the world. As Peter, Olivia, Astrid and Walter scramble to tap into a key piece of the puzzle to defeat the Observers, they enlist the aid of Nina Sharp who conjures up a plan."
At long last that strange kid will show up again!
Fringe S05E09 Black Blotter
Here's the trailer of "Black Blotter", the next episode of the awesome science-fiction TV series Fringe:
Fringe 5.9 – Black Blotter
Fringe S05x09 Black Blotter
"Walter takes an LSD trip in an attempt to recall any of the missing pieces in the plan to defeat the Observers. Peter and Olivia trace a mysterious signal in to the woods and find the grisly aftermath of an horrific event."
So LSD is the key to take a trip down memory lane?
Fringe S05E08 The Human Kind
Check out the preview trailer of "The Human Kind", the next episode of the final season of Fringe, the science-fiction TV series created by J.J. Abrams:
Fringe 5.8 – The Human Kind
Fringe S05x08 The Human Kind
"Olivia encounters a very intuitive and oracle-like person named Simone (Jill Scott) as she looks to recover a key piece of equipment for Walter. Meanwhile, Peter finds himself in a perilous situation as he observes Windmark (Michael Kopsa)."
This new episode of Fringe will be epic! Do you think Peter will kill Windmark?
Fringe S05E07 Five-Twenty-Ten
Let's watch the trailer of "Five-Twenty-Ten", the next episode of the exciting science-fiction TV series Fringe:
Fringe 5.7 – Five-Twenty-Ten
Fringe S05x07 Five-Twenty-Ten
"As the fight for the future intensifies, a member of the Fringe team orchestrates a Fringe event of his own."
With that chip in his neck Peter got great powers, but they probably have a hidden cost, let's hope it won't be too high…
Fringe S05E04 The Bullet That Saved The World
You may enjoy below the trailer of "The Bullet That Saved The World", the next episode of Fringe Season 5:
Fringe 5.4 – The Bullet That Saved The World
Fringe S05x05 The Bullet That Saved The World
"When the Fringe team tracks a lead into a hostile and heavily guarded location, Phillip Broyles resurfaces…but can he be trusted?"
Hell yes, Olivia got an idea! The Observers will pay back for their invasion!
Fringe S05E03 The Recordist
Check out the preview trailer of "The Recordist", the next episode of the final season of Fringe, the science-fiction TV series created by J.J. Abrams:
Fringe 5.3 – The Recordist
Fringe S05x03 The Recordist
"The Fringe team meets with a group of humans who have taken refuge in the forest and seek to preserve humanity's history any way they can."
Is the history of mankind really worth saving?
:?
Fringe S05E02 In Absentia
Thanks to Fox here's the official trailer of "In Absentia", the next episode of the fifth season of the science-fiction TV series Fringe:
Fringe 5.2 – In Absentia
Fringe S05x02 In Absentia
"The fifth and final season continues when the Fringe team revisits the Harvard lab, now under Observer control, to try to find the information they need as the team fights on in their mission to save the world."
The next episode of Fringe will air on October 5, 2012.
Fringe S05E01 Transilence Thought Unifier Model-11
Here's an extended preview clip of "Transilence Thought Unifier Model-11", the first episode of the fifth season of the mind-boggling science-fiction TV series Fringe:
Fringe 5.1 – Where is Olivia?
Fringe S05x01 Transilence Thought Unifier Model-11
"The science team, preserved in amber until 2036, joins the resistance forces fighting for freedom against the technologically-advanced invasion forces from the the future. Reunited with his grown daughter after two decades, Peter and Etta set out to locate and revive Olivia to help in the battle against the facist regime."
Fringe returns this week, on Friday, September 28, 2012!
Fringe Season 5 – Peter Bishop is Wanted!
In the fifth season of Fringe, the Observers want to catch Peter Bishop (Joshua Jackson):
Fringe Season 5 – have you seen this individual?
If you have any information concerning this individual, please contact your local law enforcement agency immediately!
Fringe Final Season Trailer
Yet a new promo trailer for the fifth season of Fringe, the final season of the mind-boggling science-fiction TV series created by J. J. Abrams, Alex Kurtzman and Roberto Orci:
The heartless man from the future is torturing the poor Walter Bishop.
2 New Trailers of Fringe Season 5
We've got our hands on two new trailer of the 5th and final season of Fringe:
I'm so looking forward to the final season of Fringe!
Fringe Season 5 Trailer
You may watch below the first official trailer of Season 5 of Fringe, the marvelous science-fiction TV series created by J. J. Abrams, Alex Kurtzman and Roberto Orci:
They came without warning, they took over our world, they enslaved our citizens…
To save our future we must look to the past!
Meet humanity's last hope: a family destined to fight, a team trained to save us all.
This fall witness Fringe Division's final stand!
Rise up, strike back!
Everything they've been through has just been preparation for what they face here now!
Fringe Season 5 Poster
A first official poster for Fringe Season 5:
(Click on the poster to enlarge.)
The TV series Fringe will return for a fifth and final season on September 28, 2012. It will consist of 13 episodes.
Fringe Season 4 was supposed to be the last one but Fox finally agreed on one more season. So yep we'll get to watch Fringe Season 5! Here's the announcement promo video:
They ordered 13 new episodes for the final season. Fringe Season 5 is set to air fall 2012.


2017-12-13 - Lethal Weapon 5 2017-12-05 - Gibson on Gogglebox Ireland 2017-12-03 - Like Father, Like Son 2017-12-03 - Groundhog Day Styled Thriller 2017-11-29 - Gibson & Wahlberg 2017-11-26 - Gibson struggled to improvise 2017-11-18 - Gogglebox Ireland 2017-11-17 - A chat with the cast 2017-11-15 - Braveheart in Ireland 2017-11-12 - Weekend Box Office 2017-11-09 - Gibson hints at possible LW 5 2017-11-09 - Mel Gibson and Tina Turner 2017-11-08 - Mel Gibson coming to Dublin 2017-11-08 - Daddy's Home 2 premiere 2017-10-28 - Another LW movie 2017-10-01 - Mel Gibson Out and About 2017-09-23 - Voltage Pictures Fires Back 2017-09-22 - Gibson's Lawsuit Continues 2017-09-20 - Gibson's Lawsuit Deepens 2017-09-07 - Mel Gibson Helped Inspire 2017-08-28 - Attack Force Z Hits Blu-Ray 2017-08-26 - Hacksaw Ridge wins AWGIE 2017-08-21 - Mel on set 2017-08-21 - A day at the PNE 2017-08-19 - Mel Gibson's indie film	2017-08-08 - Milo Gibson Hurricane 2017-08-01 - Mel Gibson Sues Voltage 2017-07-25 - Tory Kittles Joins Gibson 2017-07-24 - Gibson's Former Agent reveals 2017-07-22 - What's on TV Saturday: Hacksaw Ridge 2017-07-21 - Walking Dead Actress Laurie Holden Joins Dragged Across Concrete 2017-07-20 - Mel Gibson and the Mending Kids charity 2017-07-19 - Dragged across concrete filming in Vancouver 2017-07-17 - Mel Gibson leads tributes to his Apocalypto production designer Thomas E. Sanders 2017-07-05 - Miller and Max 2017-06-18 - Mel Gibson Is Mark Wahlberg's 'Womanizer' Father 2017-06-14 - Mel Gibson - Daddy's Home 2 2017-06-09 - Mel Gibson, Danny Glover and Rene Russo 2017-06-08 - Lethal Weapon film's 30th anniversary 2017-06-08 - Richard Donner celebrated for 'Superman,' super kindness 2017-06-08 - Mel Gibson reunites with Danny Glover 2017-06-03 - Lionsgate has acquired U.S. distribution rights 2017-06-02 - Mel Gibson's son is a model 2017-05-14 - Mel Gibson strolls hand in hand with girlfriend Rosalind Ross 2017-05-12 - Daddy's Home -- and so is Mel Gibson! 2017-05-03 - Mel Gibson Visits Patriots Point 2017-04-08 - Mel Gibson's support of Holocaust survivors 2017-03-09 - Mel Gibson is a great choice for the DCEU 2017-02-19 - Mel Gibson busy with work 2017-02-03 - Dragged across concrete 2017-01-27 - Mel Gibson talks about directing 2017-01-27 - Mel Gibson welcomes his ninth child 2017-01-18 - Hacksaw Ridge celebrates real heroes 2017-01-13 - Hacksaw Ridge Crosses $150 Million 2017-01-11 - Hacksaw Ridge up for 5 BAFTA 2017-01-09 - Mel Gibson at the Golden Globes 2017-01-08 - Mel Gibson says "I wanted to strangle him!" 2017-01-07 - Mel Gibson at AFI awards and AACTA 2017-01-06 - Mel Gibson says goodbye to his beard 2017-01-05 - Hacksaw Ridge a love story 2017-01-03 - Happy Birthday Mel Gibson!


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Published: 4 June 2007
Imprint: Penguin General UK
Paris is the city of light and the city of darkness - a place of ceaseless revolution and reinvention that for two thousand years has drawn those with the highest ideals and the lowest morals to its teeming streets.
In Andrew Hussey's wonderful book we encounter the myriad citizens whose stories have shaped Paris: the nineteenth-century flaneurs aimlessly wandering Haussman's new streets; survivors and victims of ravaging plagues; the builders of Notre Dame Cathedral; those who turned the River Seine red with blood on St Bartholomew's Day; and the many others whose lives have imprinted themselves on a city that has always aroused strong emotions.
Pub date: 4 June 2007
Andrew Hussey was born in Liverpool in 1963. He has written for the Independent on Sunday, the Times Literary Supplement, the London Review of Books and other publications. He is the author of The Game of War a biography of Guy Debord.
Also by Andrew Hussey
Who Owns History?
Good Night Stories for Rebel Girls
Elena Favilli
How To Change Your Mind: The New Science of Psychedelics
Anne Frank, Anne Frank And Elie Wiesel
History of The World Map By Map
Elie Wiesel, Marion Wiesel
Empire: How Britain Made the Modern World: Popular Penguins
The Black Prince of Florence
The Zhivago Affair
Peter Finn, Petra Couvee
Simon Schama
The Water Kingdom
Philip Ball
The Special Relationship


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