text:
Switzerland Vote on Married Couples Tax Law Invalidated
The Federal Tribunal in Switzerland has invalidated a 2016 vote on a proposed constitutional amendment that would have treated married couples and unmarried cohabiting couples equally for tax purposes. The proposal was defeated, but the Tribunal found that the government had provided materially incorrect information that may have undermined the vote. The Tribunal ordered a revote.
The proposed amendment was intended to address the so-called "marriage penalty" under Swiss law. Married couples pay tax on their combined income, while cohabiting couples who are unmarried each pay tax on their own income, which frequently results in a lower tax total tax for the cohabiting couples. The problem has long been recognized, but incompletely dealt with. In 1984, the Federal Tribunal held that the tax on married couples was unconstitutional if it exceeded 10% of the amount the couple would have paid if they paid tax separately. Although many of the Swiss Cantons changed their laws in response, the problem persisted.
The constitutional amendment stated that: "Marriage consists of long-term cohabitation under the framework of law between a man and a woman. For tax purposes, marriage is considered an economic union. It should not be penalized in comparison with other living arrangements, particularly as regards tax and social insurance."
However, the initiative proved problematic because of its characterization of marriage as "between a man and a woman," which opponents argued would preclude the possibility of same-sex marriage. In addition, the government provided statistics before the vote that it said showed only 80,000 of Switzerland's 1.5 million married couples were adversely affected by the current law, while 370,000 came out ahead.
The initiative failed by a vote of 50.8% to 49.2%.
Subsequently, however, information came to light that the statistics provided by the government came from 2001 data that had never been updated and included only married couples without children. New data indicated that some 454,000 married couples were adversely affected by the current law. According to the Tribunal's 10 April decision, "the incompleteness and the lack of transparency of the information provided…violated the freedom of the citizens to vote."
It is not clear when a new vote might be scheduled. Nor is it clear that any new vote will be held. The Swiss Parliament has the power to revise the tax law as it applies to couples without a constitutional amendment.
How This Impacts Mobility
A revision of the tax regime as applied to couples would save tax costs for employers of married expats living in Switzerland.


text:
Quinta Brunson on Using Comedy to Address America's Education Problems in Abbott Elementary
By Katie Reilly
February 4, 2022 10:56 AM EST
As a child, Quinta Brunson loved being able to gather around the TV with her family for the shows they all enjoyed, from 227 to Martin to The Fresh Prince of Bel-Air. Now, the writer and executive producer of Abbott Elementary is hoping the new sitcom will become that kind of show for families today.
Brunson stars in the mockumentary-style show as Janine Teagues, an idealistic second-grade teacher eager to solve the many problems plaguing her underfunded Philadelphia public school, from outdated textbooks to broken hallway lights. It premiered at a time when the pandemic has made teachers' jobs more challenging than ever and has drawn more attention to the important role of public schools within a community.
Brunson, a comedian who previously produced and starred in videos for BuzzFeed and co-starred on HBO's A Black Lady Sketch Show, says she has heard "an overwhelming response" from real teachers who see themselves in the show's characters. She named the show after her middle-school teacher, Ms. Abbott, and also drew inspiration from her mother, who taught kindergarten in Philadelphia public schools for about 40 years before retiring.
Brunson talked to TIME about transitioning to network TV and finding a way to address big issues in education while making sure her show remains a comedy.
Why did you want to set this workplace sitcom in a school environment?
I'm a big fan of workplace sitcoms, and I got inspired from spending some time with my mom before she retired. For about 40 years, she had taught and I'd been with her so much. I was in her kindergarten class when I was little. I went to the school she taught at. I rode to school with her; I rode home with her. That environment felt very natural to me, and I felt that I could tell some really strong stories there.
How did your mom shape your understanding of what it's like to be a teacher today?
The hours my mom put in were crazy, and the impact she had on children's lives alone meant it was a huge job. And they do it for not only one student, but 20 to 30 students at a time. People don't get how difficult that is.
What has your mom thought of the show?
She loves it. She called me about this past episode. There's a scene where the teacher Barbara Howard, who's kind of inspired by my mom, has no idea who a student is who is praising her. He's a grown man because she's been teaching for years. That was ripped right from my mother's life. She was like, "I'm gonna start needing a royalty check."
Your middle-school teacher Ms. Abbott inspired the name of the show. Have you spoken with her since it premiered?
I ended up calling her. She was overjoyed, just so happy to hear from me. I'm like, "You know, you inspired the name?" She burst out in tears. It turns out she's retiring this year, after so many years of phenomenal teaching, so she said this was a wonderful retirement gift.
What have you heard from real public school teachers in response to the show?
I have heard an overwhelming response from teachers. I've had teachers who have messaged me online, friends who are teachers who are really enjoying it. A friend of mine actually messaged me and said, "Look, I hope this show makes people become teachers, because a lot of us are leaving." Ultimately, they're hoping it creates some type of change.
The show touches on big problems in education—underfunded public schools, high teacher turnover. How did you find a balance between addressing tough issues, but keeping the show funny?
We didn't try to make anything funny that couldn't be made funny. There are some things that can be background jokes. In an episode about new technology, we very quickly talked about the school-to-prison pipeline. It's a huge issue, but that probably can't be a full-episode concept because that would change the nature of our show. We want to make sure it stays a comedy. There are always ways to insert these larger issues into the picture to get people thinking and talking about what's going on in schools.
Some of the plotlines on the show are tied to pandemic-related challenges, whether that's crowdfunding for school supplies or adjusting to a virtual learning technology. Did you consider whether the pandemic should be part of the show?
When we developed it, in 2018, COVID wasn't even a factor. Also, personally, I was watching TV shows try to handle COVID, and some shows did it really well. Some shows really felt flat on their faces because it's just hard, between the masks of it all. I didn't want to even try to do that, at least not on this first season. I thought we had a show without it, and I thought it was a stronger play to show what teachers are dealing with outside of COVID.
Your character, Janine, has a great dynamic with Sheryl Lee Ralph's character, Barbara, a veteran teacher who is kind of a mentor. Is there someone in your career who has been that person for you?
If I had to say there was one who's most like Barbara in my life, it'd be Larry Wilmore. I feel like Larry Wilmore is my unofficial mentor. I worked on a show with him, and the dynamic was very like Janine and Barbara: "Quinta, do the job. Do the writing. Write the good work." Very honest with me and very stern. I don't know if Larry thinks I'm his mentee, but he is my mentor.
What has it been like expanding into network comedy after your work on BuzzFeed and A Black Lady Sketch Show?
BuzzFeed is a platform about reaching everyone, and I enjoyed that. It's part of why I joined network TV, because you can reach everyone with it. When I was a kid, network television was the way everyone in my house got to watch together. I loved being able to watch TV with my family, so I wanted to do that for people. I wanted to create a sitcom that had a strong point of view, from the millennial me, but could also span generations.
Write to Katie Reilly at Katie.Reilly@time.com.
8 Science Fiction and Fantasy Books That Deserve a Screen Adaptation

redpajama_set_name: RedPajamaCommonCrawl


Why do we need the Holy Spirit if Jesus forgave our sin?
 21 Then Eleazar the priest said to the soldiers who had gone into battle, "This is what is required by the law that the Lord gave Moses: 22 Gold, silver, bronze, iron, tin, lead 23 and anything else that can withstand fire must be put through the fire, and then it will be clean. But it must also be purified with the water of cleansing. And whatever cannot withstand fire must be put through that water.
Jesus came that we might be forgiven our sins, but also came that we could overcome sin. He sent the Holy Spirit to give us the power to overcome sin and that it would no longer master us, but we would master it. You see, Satan's power over us in this world is sin and if we continue in sin he can control us. Jesus sent the fire of the Holy Spirit to cleanse us of the control of sin in this life. Satan has no control over a sinless life. Even Balaam told the king of Moab that the only way to make Israel fall is if you can get them to sin against their God and He will hand them over to you. Fire was used in the Old Testament to cleanse. God used fire to consume the sinners in Israel often times to get the sin out of the camp. Israel could not stand before AI because of one sin and they were but a small city. Sin is the power of Satan and righteousness is the power of God. How can we be witnesses for God? Get the sin out and Satan has no power. We can overcome the world.


Kinfonet Quote of the Day: As long as there is a guiding principle in our lives to which we are trying to be true, there must be duality in action, and therefore a problem.
A problem exists as long as there is reaction - either a reaction to external standards, or a reaction to an inner standard, as when you say, ''I must be true to this idea,'' or, ''I must be true to this belief.'' Most educated, thoughtful people have discarded external standards, but they have developed inner standards. We discard an external standard because we have created an inner standard to which we are trying to be true, a standard which is continually guiding us and shaping us, a standard which creates duality in our action. As long as there are standards to which we are trying to be true, there will be problems, and hence the continual search for the solution of these problems.
These inner standards exist as long as we do not meet the experiences and incidents of life wholly. As long as there is a guiding principle in our lives to which we are trying to be true, there must be duality in action, and therefore a problem. That duality will exist as long as there is conflict, and conflict exists wherever there is the limitation of self-consciousness, the 'I'. Though we have discarded external standards and have found for ourselves an inner principle, an inner law, to which we are trying to be true, there is still distinction in action, and hence an incompleteness in understanding. It is only when we understand, when we no longer search for understanding, then there is an effortless existence.
So when I say, do not seek a solution, do not search for an end, I do not mean that you must turn to the opposite and become stagnant. My point is: Why do you seek a solution? Why are you incapable of meeting life openly, nakedly, simply, fully? Because, you are continually trying to be consistent. Therefore there is the exertion of will to conquer the immediate obstacle; there is conflict, and you do not try to find out the cause of the conflict. To me this continual search for truth, for understanding, for the solution of various problems, is not progress; this going from one problem to another is not evolution. Only when the mind and heart meet every idea, every incident, every experience, every expression of life, fully - only then can there be a continual becoming which is not stagnation. But the search for a solution, which we mistakenly call progress, is merely stagnation.


text:
Council to introduce Armed Forces discounts at local leisure centres
Written by Dean Churm on February 26, 2019
Armed Forces personnel and veterans will get concessionary discounts to leisure centres and activities operated by Telford & Wrekin Council.
The new discounts will come into effect in April and include swimming for £1, at least 40% off other non-instructed activities such as gym and public ice-skating sessions, and 15% off instructed activities.
Councillor John Minor, Cabinet Member for Leisure, Green Spaces and Parks, said: "Our Armed Forces families here in the borough are a huge part of our town. We are delighted to be able to extend our existing leisure concession rates to both serving and veteran personnel to help them stay fit and healthy. We also look forward to marking Armed Forces Day later in the summer which is a great celebratory occasion to say thank you to the servicemen and women who are committed to protecting our country, and also to their families who support them throughout their service."
The Council says the Armed Forces discount is part of a series of new one-off investments which will total £3.6m and has been made possible following changes to the council's finances including a one-off VAT refund.
Armed Forces personnel and veterans will be asked to show ID when using the discounted services and they are not available on group activities or block bookings.
Telford and Wrekin Council will introduce Armed Forces Discounts from April 2019
Tagged as Armed Forces Nova FM News Telford and Wrekin Council


text:
Deniz Yücel (; born 10 September 1973) is a German-Turkish journalist and publisher. He has been a contributor to several German publications, most notably Die Tageszeitung and Die Welt.

Espionage accusations and imprisonment
The Turkish government repeatedly accused Yücel of espionage on behalf of Germany's federal intelligence agency, the Bundesnachrichtendienst, and in support of the alleged terrorist organizations FETÖ and PKK. The government further claimed that Yücel aided these two groups in inciting violence in Turkey.  In a speech in 2017, Turkish President Recep Tayyip Erdogan declared that Yücel "is a spy, not a journalist".

On 14 February 2017, Yücel was formally indicted by a Turkish court and imprisoned for espionage.  His incarceration was widely criticized by journalists, politicians, and the German public. Sigmar Gabriel, the German Minister of Foreign Affairs, immediately summoned the Turkish ambassador to the Foreign Office to protest Yücel's sentence. On 16 February 2018, just over a year after his imprisonment began, Yücel was released. On 28 June 2019, the Constitutional Court of Turkey ruled that the detention of Yücel had been unlawful.

Yücel received strong public and political support in Germany throughout his imprisonment such as the one of Cem Özdemir or the one of the popular Twitter hashtag #FreeDeniz campaign. The German newspaper Die Welt, for which Yücel is a correspondent, offered to forward letters of support to the journalist. It also published Yücel's prison address in Turkey and suggested that letters written in Turkish could be sent directly to the journalist's prison cell. The Prison letter reading commission at times only let him read the letters of his mother-in-law, but not those of his wife.

On 25 January 2022, the European Court of Human Rights (ECHR) ruled that Turkey had violated Deniz Yücel's human rights. The court ruled that Yücel's pre-trial detention violated his rights in three cases, including his right to liberty and security, right to compensation for unlawful detention, as well as freedom of expression.

Works
 Taksim ist überall. Die Gezi-Bewegung und die Zukunft der Türkei. 2014, .
 Und morgen die ganze Türkei. Der lange Aufstieg des Recep Tayyip Erdoğan, in: Kursbuch 188, 27 November 2016 (excerpts here).
 Wir sind ja nicht zum Spaß hier. Edition Nautilus, Hamburg 2018,
 Agentterrorist. Eine Geschichte über Freiheit und Freundschaft, Demokratie und Nichtsodemokratie. Kiepenheuer & Witsch, Cologne 2019,

See also
 Censorship in Turkey
 List of arrested journalists in Turkey
 Germany–Turkey relations
 Turkey's media purge after the failed July 2016 coup d'état

References

External links

Turkish journalists
German journalists
German male journalists
German newspaper journalists
Journalists imprisoned in Turkey
1973 births
Prisoners and detainees of Turkey
Living people
Articles containing video clips
Die Welt people
Die Tageszeitung people
21st-century German journalists

redpajama_set_name: RedPajamaWikipedia


Our Sweet Love Story: A Masquerade Ball on Friday the 13th!
A Masquerade Ball on Friday the 13th!
Yes, we did! If law school has done nothing else for us, it has definitely given us the opportunity to experience some interesting things. On Friday (the 13th), the annual Barrister's Ball was held. It was truly a night of intrigue as the theme was a masquerade ball, and it was a black tie formal affair. I love getting all dressed up with the husband for a grown-up night out. The kids loved spending the evening with Grandma, of course. I mean, how many people really get to experience formal events after their high school prom? Yes, I know we are so lucky for these opportunities! The ball was held at a very unique and amazing location this year. In Charlotte, the center of the city is the iconic structure of the Bank of America building (the tallest building in Charlotte and one of the tallest in the southeast at 60 plus floors) and symbolizes the city's status as the second largest financial district in the country and home to the second largest megabank in the country. With the financial crisis that hit in recent years, you can imagine that our beloved city was hit pretty hard, but it will continue to thrive! In the beautiful Bank of America Center is a large open hall called Founders Hall with marble floors and surrounded by two levels of amazing shops and eateries. It really just symbolizes all that Charlotte has to offer, and we could not imagine a more elegant and fitting place for the annual ball of Charlotte's first and only law school. I am so proud of this city and my school sometimes!
Me and my handsome prince are ready for the ball!
Still in love after almost 9 years of marriage and two kids!
A group of us met for dinner before the ball.
Oh my, he has my heart!
Me and my pal, Brooke, before the ball.
Me and some of my wonderful schoolmates, Chuck Lifford and his wife, Jolee, Kathy, and Brooke.
Another group shot with Jolee's sister and significant other.
Alexis and Jessica are quite an adventurous pair.
Kathy's handsome son escorted her to the ball.
Jolee and her beau, Kevin, were stylin'. And Brooke had to get in the picture too.
Mr. and Mrs. Lifford. Chuck is one of the most accomplished members of our class. He has proudly served on the Gastonia police force for many years and was recently promoted to chief.
Me and my handsome husband enjoyed our night on the town.
Me with some more fun associates, Jessica, Kelly, and Bethany.
Happy 1st Birthday, Baby Brody!
Video: Brody enjoys his cake!
The Truth About NC's Amendment One!


text:
\section{INTRODUCTION}

Predicting future agent states is a crucial task for robot planning and control in general \cite{chai2019multipath}. In this paper, we address this problem for self-driving vehicles to be able to predict behaviors of surrounding vehicles, which is vital for safe and efficient driving. For example, it is important when deciding whether to yield to a coming vehicle or to merge into traffic.

To safely drive vehicles and navigate through other traffic, human drivers need to anticipate the intentions of other road users. Experienced human drivers are able to confidently infer future behaviors of the surrounding vehicles \cite{zyner2019naturalistic}. This is critical when making decisions like crossing an unsignalized intersection.

In order to build fully autonomous vehicles, they should be able to predict the future states of its environment and respond appropriately. Predicting the behavior of human drivers is necessary for such autonomous platforms to share the road with humans \cite{gupta2018social}. More importantly, being able to predict human driver behaviors will lead to \textit{human-like} responding strategies, which is crucial for human road users to predict the autonomous vehicle behavior. While human drivers can do this prediction seamlessly, it is still an open problem for self-driving cars.

Vehicle motion prediction is challenging due to the inherent uncertainty about latent variables such as the motivations of road users. Additionally, driver behavior tends to be multi-modal: a driver could make one of many decisions under the same traffic circumstances \cite{deo2018convolutional}.

Although this problem has been extensively studied on highways or on highly structured intersections, fewer works addressed un-signalized intersections like roundabouts \cite{zyner2018recurrent}. Roundabouts are popular in urban areas as they do not require expensive traffic lights, improve safety and have higher throughput than a four-way stop.  Being able to safely navigate these highly dynamic scenarios is critical for autonomous vehicles to function properly.

We focus on addressing the problem of trajectory prediction of the human-driven vehicles surrounding an autonomous vehicle in a roundabout environment. In line with the existing work \cite{alahi2016social, deo2018convolutional}, this is achieved by iteratively predicting the trajectory of each surrounding agent around the autonomous one. We call the subject surrounding vehicle to be predicted as \quotes{\textit{ego}} and the vehicles around the ego vehicle as \quotes{\textit{neighbors}}. One concrete task to this end is: given the observed motion trajectories of natural human drivers (position and heading of the past e.g. \(2 \,s\)) of an ego vehicle and all its neighbor vehicles on a roundabout, to predict the future trajectory of this ego vehicle. We seek a predictive model that outputs a multi-modal distribution over the predicted trajectory.

We propose a model that employs a fixed set of anchor trajectories that cover the space of possible outcomes at the roundabout environment. The anchor trajectories are associated with a corresponding set of maneuvers that model the (short-term) intention of human drivers. These maneuvers (intentions) represent semantic concepts like \quotes{slow down} and \quotes{advance to a specific zone of the roundabout}.

Our model can hierarchically factor the uncertainty inherent in the prediction process. First, the maneuver prediction captures the uncertainty about the driver's intention and is encoded as a distribution over a set of discrete intentions. We model a discrete set of anchor trajectories corresponding to the maneuver set covering the space of modelled human intentions. Second, given the intended maneuvers and the corresponding anchor trajectory, the predicted trajectory models the uncertainty performing such maneuvers by estimating a maneuver-specific residual from the given anchor trajectory.

Thus, we introduce an encoder-decoder based model for vehicle motion prediction that can evaluate the likelihood of each maneuver and regress a maneuver-specific future trajectory segment as a residual from the corresponding anchor trajectory, providing a multi-modal distribution over the predicted future trajectories based on the maneuver-specific anchors.

The contributions of our work are three fold: (1) incorporating anchor trajectories into recurrent models to predict vehicle behaviors, (2) parameterization of the whole maneuver space in a roundabout by maneuver-specific anchor trajectories, and (3) extending a pooling strategy to account for vehicle poses that are predicted as a multi-variate Gaussian distribution.

\section{Related Work}
\textbf{Recurrent networks for trajectory prediction.}
Recurrent Neural Networks (RNNs) represent a rich class of dynamic models which extend feedforward networks for sequence generation \cite{graves2013generating} in diverse domains like speech recognition \cite{chorowski2014end}, machine translation \cite{chung2015recurrent} and image captioning \cite{vinyals2015show}. Motion prediction is considered as a sequence generation task. Hence, a number of RNN-based approaches have been proposed for trajectory prediction \cite{alahi2016social, gupta2018social, deo2018convolutional, zyner2019naturalistic, altche2017lstm, park2018sequence, xue2018ss}  of pedestrians and vehicles. In their seminal work Social LSTM (Long-Short Term Memory), Alahi et al. \cite{alahi2016social} extended RNNs to human trajectory prediction using a social pooling layer that models nearby pedestrians. Gupta et al. proposed a Generative Adversarial Networks \cite{goodfellow2014generative} (GAN): a RNN Encoder-Decoder generator and a RNN based encoder discriminator, to predict socially-acceptable multimodal pedestrian trajectories \cite{gupta2018social}. Social LSTM approach was further improved in \cite{deo2018convolutional} by using convolutional social pooling applied to vehicle motion prediction on highways. LSTM network is also used to predict the location of vehicles in an occupancy grid \cite{kim2017probabilistic} at different future intervals. Convolutional networks and LSTM were combined to predict multi-modal trajectories for an agent on a bird's eye view image \cite{bhattacharyya2018accurate}. The majority of the vehicle prediction literature addressed the problem on highways and structured intersections, whereas we focus on the vehicle trajectory prediction on roundabouts.

\textbf{Multimodal predictive distribution.}
Deo et al. proposed a model \cite{deo2018convolutional} that outputs a multi-modal predictive distribution over future trajectories based on maneuver classes. Three lateral and two longitudinal maneuver classes have been considered. MultiPath model \cite{chai2019multipath} can predict a discrete distribution over a set of future state-sequence anchors and output multi-modal future distributions. The GAN based encoder-decoder architecture in \cite{gupta2018social} encourages diverse multimodal predictions of pedestrian trajectories with the introduced variety loss. A multi-modal probabilistic prediction approach was presented in \cite{hu2019multi} based on a Conditional
Variational Autoencoder and is capable of jointly predicting sequential motions of each pair of interacting vehicles. Zyner et al. \cite{zyner2019naturalistic} presented a model based on RNN with a mixture density network output layer, for predicting driver intent at urban single-lane roundabouts through multi-modal trajectory prediction with uncertainty. Neither maneuver recognition nor anchor-based regression were considered in \cite{hu2019multi} or \cite{zyner2019naturalistic} where the roundabout problem was addressed.

\textbf{Anchor trajectories.}
The concept of predefined anchors has been effectively applied in machine learning and computer vision to handle multi-modal problems \cite{bishop2006pattern, erhan2014scalable, yang2012articulated}. These approaches predict the likelihood of anchors as well as continuous refinements of state conditioned on these anchors. For the sake of vehicle trajectory prediction, MultiPath model \cite{chai2019multipath} employs a fixed set of trajectory anchors that are found in the training data via unsupervised learning. At inference time, the model predicts a discrete distribution over the anchors and, for each anchor, regresses offsets from anchor waypoints along with uncertainties. Considering the set of anchors as the driver intents, these were not associated with semantic concepts like  \quotes{slow down}  or \quotes{lane change}. Phan-Minh et. al introduced CoverNet \cite{phan2020covernet} for multimodal, probabilistic trajectory prediction for urban driving.  The trajectory prediction problem was framed as a classification over a diverse set of trajectories \cite{boulton2020motion}. The trajectory set was structured to cover the state space, and eliminated physically impossible trajectories.

Our work contrasts with the previous approaches in the following aspects: (1) we design the anchor trajectories to correspond to the human driver maneuvers, (2) our model is based on recurrent (not convolutional like \cite{chai2019multipath}, \cite{phan2020covernet} and  \cite{boulton2020motion}) networks to encode the trajectory sequence history and decode its future prediction, and (3) it can capture the inter dependencies between all the participating vehicles (not being restricted by a specific grid size like \cite{alahi2016social} and \cite{deo2018convolutional}).

\section{RounD Dataset}

The RounD dataset \cite{rounDdataset} is a new dataset of naturalistic road user trajectories recorded at German roundabouts. Traffic was recorded using a drone at three different locations, and the trajectory for each road user and its type was extracted. Using state-of-the-art computer vision algorithms, the positional error is typically less than 10 centimetres. The dataset provides \(X\) and \(Y\) position, velocity and acceleration, heading, lateral and longitudinal velocity and accelerations of the tracked objects in a local coordinate system, recorded at 25 Hz. In addition, the RounD dataset contains the dimensions (length and width) and class (e.g. car, truck, pedestrian) of the tracked objects.

\begin{figure
\vspace{2mm}
\centering
\includegraphics[scale=0.245]{figures/rounD_lanes.jpg}
\caption{The roundabout zones after segmentation.}
\label{fig:lanes}
\vspace{-5mm}
\end{figure}

\subsection{Dataset Processing}

The roundabout environment was segmented -following \cite{Paschalidis2021estimation}- into zones that include: the entrance lanes, the main roundabout areas, the conflict zones and the exit lanes (Fig. \ref{fig:lanes}). To focus on the vehicle trajectory prediction at the roundabout scenario, we refined the dataset to exclude trajectories\st{:} of pedestrian, bicycle and motorcycle classes, and in none-relevant lanes (IDs 5,8,11 and 200 in Fig. \ref{fig:lanes}). The vehicle behavior long before it enters the roundabout or long after it leaves the roundabout is out of the scope of the paper. Therefore, we additionally removed the trajectory segments that are farther away from the roundabout (entries/exits) than the vehicle length.

\section{Problem Formulation}

Inline with the previous work \cite{deo2018convolutional, chai2019multipath}, We formulate the problem of vehicle trajectory prediction by estimating the probability distribution of the possible future trajectories of an ego vehicle conditioned on its track history and on the track histories of the vehicles around it, at each discrete time step \(t\). Thus, given observations \(X\) of past trajectories of the ego and its neighbor vehicles in a scene, our model seeks to provide a distribution over future trajectories of the ego vehicle \(P(Y|X)\).

\subsection{Inputs and Outputs}
The inputs to our model are track histories:
\begin{align}
\begin{split}
X = [x^{(t-t_h)}, ..., x^{(t-1)}, x^{(t)}]
\end{split}
\end{align}
where \(t_h\) is a fixed (history) time horizon, and at any time instant \(t\),
\begin{align}
\begin{split}
x^{(t)} = [x_0^{(t)}, y_0^{(t)}, \theta_0^{(t)}, x_1^{(t)}, y_1^{(t)}, \theta_1^{(t)}, ..., x_n^{(t)}, y_n^{(t)}, \theta_n^{(t)}]
\end{split}
\end{align}
represents the pose of the ego vehicle (subscript \(0\)), and the surrounding vehicles (subscripts \(1\) to \(n\)). The pose is given by the position (\(x\) and \(y\) coordinates) and orientation \(\theta\). We assume that the sensors/computers on-board the autonomous vehicle can measure/compute the pose of the ego and neighbor vehicles. The output of the model is a probability distribution:
\begin{align}
\begin{split}
Y = [y^{(t+1)}, ..., y^{(t+t_f)}]
\end{split}
\end{align}
where \(t_f\) is a fixed (future) time horizon, and:
\begin{align}
\begin{split}
y^{(t)} = [x_0^{(t)}, y_0^{(t)}, \theta_0^{(t)}]
\end{split}
\end{align}
represents the future pose of the ego vehicle being predicted at any time instant $t$.

\subsection{Multi-modal Probabilistic Trajectory Prediction}

The multi-modal distribution over future trajectories can be hierarchically factorized \cite{chai2019multipath}. First, estimate the intent uncertainty over the set of maneuver classes, and get the corresponding anchor trajectory. For example, this may be the uncertainty about advancing to a specific zone of the roundabout with an intended speed profile (e.g. slowing down). Second, given the intended maneuvers and their corresponding anchor, predict the vehicle trajectory as a residual (offset) from the given anchor trajectory.

Maneuvers are chosen to semantically represent how human drivers may plan their decisions. Drivers may first decide a target zone, then choose some acceleration to reach this target according to the traffic situation. Accordingly, intentions of drivers are modelled using two discrete sets of maneuver types: (1) location-wise maneuvers \(M_l=\{m_{l_p}\}_{p=1}^P\), and (2) acceleration-wise maneuvers \(M_a=\{m_{a_q}\}_{q=1}^Q\). We model the uncertainty over each discrete set of maneuvers using a softmax distribution. For example, the distribution over \(M_l\) is given by:
\begin{align}
\begin{split}
P(m_{l_p}|X)=\frac{exp(f_{l_p}(X))}{\sum_i exp(f_{l_i}(X))}
\end{split}
\end{align}
where \(f_{l_p}(X)\) is the output of a deep neural network whose input is the encoding of the history trajectories.

Correspondingly, we model a discrete set of \(K\) maneuver-specific anchor trajectories \(A=\{a_k\}_{k=1}^K\). Each anchor trajectory is modelled as a sequence of future poses of the ego vehicle:
\begin{align}
\begin{split}
a_k = [y_k^{(t+1)}, ..., y_k^{(t+t_f)}]
\end{split}
\label{eq:anch}
\end{align}
Anchor trajectories are defined to cover the joint maneuver space, i.e. \(K = PQ\). The distribution over the anchor trajectories can be computed from the probabilities of the maneuver classes as follows:
\begin{align}
\begin{split}
P(a_k|X) = P(m_{l_p}|X) P(m_{a_q}|X)
\end{split}
\label{eq:anch_probability}
\end{align}

Given the intended maneuvers and the corresponding anchor trajectory, the uncertainty over the future trajectory is modelled as a Gaussian distribution:
\begin{align}
\begin{split}
P_{\Theta_k}(Y|a_k, X) = N(Y|a_k+\mu_k(X), \Sigma_k(X))
\end{split}
\end{align}
where in the Gaussian distribution mean \(a_k+\mu_k(X)\), the term \(\mu_k(X)\) represents an offset from the given anchor trajectory \(a_k\).  This allows the model to refine the static maneuver-specific anchor trajectories to the current context \cite{chai2019multipath}, with variations coming from the history trajectories of the vehicles in the scene. This distribution is parameterized by \cite{deo2018convolutional}:
\begin{align}
\begin{split}
\Theta_k = [\Theta_k^{(t+1)}, ..., \Theta_k^{(t+t_f)}]
\end{split}
\end{align}
which are the parameters of a multi-variate Gaussian distribution at each time step in the future. At any time \(t\), this is given by the Gaussian parameters: \( \Theta_k^t = [\mu_k^{(t)}, \Sigma_k^{(t)}]\), corresponding to the mean and variance of the future vehicle pose as an offset from the anchor trajectory pose at the same time step.

The multi-modal output conditional distribution over future trajectories can now be expanded in terms of the anchors as:
\begin{align}
\begin{split}
P(Y|X) = \sum_k P(a_k|X) P_{\Theta_k}(Y|a_k, X)
\end{split}
\end{align}
which yields to a Gaussian Mixture Model distribution (GMM) \cite{chai2019multipath}. The mixture weights are defined by the probabilities of the anchors.

\section{Maneuver-based Anchor Trajectories}
\subsection{Maneuver Classes}
\label{sec:mnvrs}
We consider two types of maneuvers: location-wise \(M_l\) and acceleration-wise \(M_a\) maneuvers. The first type defines the intended future location on the roundabout (\(P\) = 8 classes), whereas the second type defines the intended speed-profile (\(Q\) = 3 classes) when advancing to occupy this space.

The circular infrastructure of the roundabout is segmented into 16 zones (101 to 108 and 111 to 118 as shown in Fig. \ref{fig:lanes}). We group these zones into eight sections which define the location-wise classes. For example, the trajectories shown in Fig. \ref{fig:intentions} are for vehicles belonging to the same class, as their future trajectories will occupy the section grouping the two zones 106 and 116. This includes vehicles: advancing from the previous section (the one grouping zones 105 and 115), entering the roundabout (from zones 1 and 2) and changing zones within the same section (between zones 106 and 116). Although this definition of location classes depends on the given roundabout segmentation, our model can cope with any given segmentation as long as it provides a reasonable number of well-distinguished classes.

Three acceleration-wise maneuvers are considered: slowing down, constant speed and speeding up. The RounD dataset provides the acceleration of each vehicle. We annotated trajectory segments with an acceleration less/greater than \(-a\)/\(+a\) with the slowing/speeding class labels respectively while marking trajectory segments under the constant-speed class otherwise.

According to the intended entry and exit lanes, a vehicle trajectory passes through a sequence of zones of the roundabout. This is accompanied by a sequence of acceleration intents (e.g. slowing down before the entrance or speeding up once accepting a gap). Accordingly, the pose of the vehicle at each time instant is annotated with the labels of the location and acceleration classes.

\begin{figure
\vspace{2mm}
\centering
\includegraphics[width=0.4\textwidth]{figures/locClass_1.png}
\caption{An example of location-wise maneuver classes. The shown trajectories belong to same class vehicles, since their future will occupy the section grouping lanes 106 and 116.}
\label{fig:intentions}
\vspace{-5mm}
\end{figure}

\subsection{Anchor Trajectories}
Anchor trajectories are defined over the set of the joint location-acceleration maneuver space. This results in a total of \(K\) = 24 anchor trajectories corresponding to the grid of maneuvers, as for each of the eight spatial classes in Fig. \ref{fig:intentions}, we have three anchor trajectories correspond to this zone of the roundabout (one anchor for each acceleration class).

Given a vehicle trajectory, the pose at each time \(t\) is annotated by two maneuver labels as given in Sec \ref{sec:mnvrs}. The future trajectory segment from \(t+1\) to \(t+t_f\) is extracted and annotated by the same maneuver labels. The vehicle poses of this segment are referenced to the pose at time \(t\). Repeating this process for all trajectories and for each time step, we end up with \(K\) trajectory sets, each of them includes a number of trajectory segments annotated by the same maneuver labels.

Anchor trajectories are obtained by finding the best representative trajectory of each set. This is done in our work by finding the mean trajectory of a subset of  empirically chosen candidates from each set. The resulting set of anchor trajectories employed in this work are shown in Fig. \ref{fig:anchors}. Each anchor trajectory is modelled as a sequence of future poses of the ego vehicle (as given by (\ref{eq:anch})) that starts with a zero pose and has a fixed number of \(t_f\) discrete time steps.

\begin{figure
\vspace{2mm}
\centering
\includegraphics[width=0.26\textwidth]{figures/anchor_traj_full_2.png}
\caption{The set of anchor trajectories. Each of the 8 location classes includes 3 anchors (corresponding to the acceleration classes) that are plotted with the same color. }
\label{fig:anchors}
\vspace{-5mm}
\end{figure}

\section{Model}

The proposed model is shown in Fig. \ref{fig:model} consisting of an LSTM encoder, a pooling module, a maneuver recognition module and an LSTM decoder.

\begin{figure*
\vspace{2mm}
\centering
\includegraphics[width=0.8\textwidth]{figures/roundabout_model_2.png}
\caption{Proposed model having the following modules: (1) encoder: learns the vehicle dynamics, (2) pooling: learns the interaction between vehicles, (3) maneuver: estimates maneuver class probabilities, and (4) decoder: outputs a multi-modal distribution of the ego vehicle future conditioned on the maneuver-based anchor trajectories.}
\label{fig:model}
\vspace{-4mm}
\end{figure*}

\subsection{Encoder}
We encode the state of motion of each vehicle using an LSTM encoder. At any time \(t\), a sequence of \(t_h\) time steps of the track history is passed through the encoder. The LSTM states for each vehicle are updated frame by frame over the \(t_h\) past frames \cite{deo2018convolutional}. This is applied to all vehicles: the one whose future is being predicted (ego) and its surrounding vehicles (neighbors). The final LSTM (hidden) state of each vehicle is assumed to encode its motion dynamics, and the LSTM weights are shared across the sequences of all vehicles \cite{alahi2016social}.

\subsection{Pooling Module}
\label{sec:pooling}
The interaction of vehicles in a given scene is captured by the pooling module. This is achieved by pooling the LSTM states of all the neighbor vehicles around the ego vehicle. The output is a pooling vector summarizing the context information. Social Pooling \cite{alahi2016social} and its convolutional extension \cite{deo2018convolutional} addressed this problem by proposing a grid based pooling scheme. However, this solution fails to capture global context, and may not suit the roundabout environment. Hence, we extend the pooling mechanism in \cite{gupta2018social} to model vehicle poses.

First, at any time \(t\) the neighbor vehicles around the ego vehicle are identified. The relative pose between each neighbor and the ego vehicle is then computed, relative to the ego pose at \(t\). Second, the relative poses are concatenated with each vehicle's LSTM hidden state, and processed independently by a Multi-Layer Perceptron (MLP). Finally, The MLP output is Max-pooled to compute the pooling vector of the ego vehicle. This method can capture the inter-dependencies among all vehicles, without being restricted to a specific grid size.

\subsection{Maneuver Recognition}

The maneuver recognition module consists of two softmax layers to recognize the location and acceleration classes. The input to this module is the LSTM state of the ego vehicle augmented by the pooling vector. Each softmax layer outputs the probability of each maneuver class. Probabilities output by the two layers are multiplied to calculate the probability for choosing each anchor trajectory (\(P(a_k|X)\) in (\ref{eq:anch_probability})). The maneuver encoding block in Fig. \ref{fig:model} encodes the output from each softmax layer into a one-hot vector. Both vectors are concatenated with the trajectory encoding and the resulting tensor is passed to the decoder module.

\subsection{Decoder}
An LSTM decoder is used to generate the conditional distribution over future trajectories of the ego vehicle. At any time \(t\), the decoder generates future trajectories over the next \(t_f\) steps in the form of multi-modal distribution. For each anchor trajectory, the decoder estimates an anchor-specific mean and variance residual of the vehicle pose.

\section{Experiments and Results}

\subsection{Implementation Details}
We use the RounD dataset (consisting of 22 subsets recorded at three different locations) in our experiments. We use the data from the 20 subsets that were fully recorded at the same roundabout (Fig. \ref{fig:lanes}). The whole data are split into training (\(71\%\)), validation (\(10\%\)) and testing (\(19\%\)) sets. Each of the three sets is randomly sampled from all the employed subsets of RounD. Anchor trajectories are computed offline using the training set.

Vehicle trajectories are split into \(6 \,s\)-segments, where \(t_h=2 \,s\) track history and \(t_f=4 \,s\) prediction horizon are used. These \(6 \,s\)-segments are sampled at the dataset sampling rate of 25 Hz, and then downsampled by a factor of 4 before going to the LSTMs, to reduce the model complexity.

We implemented our proposed model based on the basic features of \cite{deo2018convolutional} using the same negative log likelihood loss. We extended these features to account for: (1) anchor-based prediction, (2) the pooling mechanism given in Sec. \ref{sec:pooling}, and (3) the multi-variate Gaussian distribution. The LSTM encoder and decoder have 32- and 64-dimensional states respectively. The fully connected layer used to embed the vehicle dynamics encoding has a size of 16. The MLP used in the pooling module has a size of 256 and is followed by batch-normalization and leaky-ReLU layers. The model is implemented using PyTorch \cite{paszke2017automatic} and trained end-to-end.

\subsection{Evaluation metric}

All results are reported in terms of the root of the mean squared error (RMSE) of the predicted trajectories with respect to the ground truth future trajectories, over the prediction horizon. Since the LSTM models generate multi-variate Gaussian distributions, the means of the Gaussian components are used for RMSE calculation.

\subsection{Baselines}
We characterised the performance of the following models in increasing complexity:
\begin{itemize}
  \item \textbf{2D}: vehicle trajectory is represented by the position (\(x\) and \(y\)), similar to \cite{alahi2016social, gupta2018social, deo2018convolutional}. Maneuvers and anchors are not included.

  \item \textbf{3D}: vehicle trajectory is represented by the pose (position and heading \(\theta\)). Maneuvers and anchors are not included.

  \item \textbf{3D-M}: adds the maneuver recognition module to the previous 3D model.

  \item \textbf{3D-A-P}: adds the anchors estimation to the previous model. This is our full proposed model. At the evaluation time, the model outputs the maximum \textit{a posteriori} probability (\emph{MAP}) trajectory estimate using the anchor trajectory having the maximum probability.

  \item \textbf{3D-A-W}: a variation of our full proposed model. At evaluation time, the model outputs a \emph{weighted} sum of the MAP trajectory estimates from all anchors. The weights are defined by anchor probabilities \(P(a_k|X)\).

\end{itemize}

\subsection{Results}
RMSE results of the baseline models are reported in Table \ref{table:results}. Although the results of the 2D model are better than its counterpart 3D model at the short horizon (up to 1s), its prediction accuracy degrades with increasing prediction horizon. This shows how the heading of a vehicle is a valuable signal, especially when crossing a roundabout. Using the maneuver-recognition module in the 3D-M model improves the results further by reducing the RMSE average by nearly 24\(\%\).

The full proposed model has been evaluated in two ways: (1) using the 3D-A-P model that predicts a single MAP trajectory estimate using the most-likely anchor trajectory, and (2) through the 3D-A-W model that outputs a GMM distribution, with mixture of weights defined by the probabilities of the anchors. Table \ref{table:results} shows that the latter improves the prediction accuracy over all other baselines. More importantly, comparing the results shows the effectiveness of generating a multi-modal distribution against a single best-estimate.

\vspace{4mm}
\begin{center}
\begin{table
\caption{Performance comparison of the proposed model in increasing complexity using the RMSE of the predicted trajectories (m) at different prediction horizons (s).}
\centering
\begin{tabular}{c c c c c c}
\hline\hline
Prediction\\ Horizon (s) & 2D & 3D & 3D-M & 3D-A-P & 3D-A-W\\
\hline \hline
1 & 0.53  & 0.55  & 0.42 & 0.39 & \textbf{0.38}\\
2 & 1.62  & 1.32 & 0.87 & 0.84  & \textbf{0.80}\\
3 & 3.10 & 2.50 & 1.95 & 1.87 & \textbf{1.76}\\
4 & 4.92 & 4.02 & 3.31 & 3.27 & \textbf{3.08}\\
\hline\hline
\end{tabular}
\label{table:results}
\end{table}
\vspace{-7mm}
\end{center}

\vspace{-3mm}
\section{CONCLUSIONS}
We demonstrated how existing encoder-decoder based models can be advantageously combined with anchor trajectories to predict vehicle behaviors. We also argue for the benefit of using anchor trajectories that are based on human drivers' intentions. The effectiveness of the proposed model employing multi-modal estimation and anchor-based regression, has been proved through experiments on the public RounD dataset. One limitation of the proposed approach is its pure dependence on vehicle track histories. Contextual information like road semantics and visual cues can be used to improve the prediction accuracy. Incorporating such information into the current model will be addressed in our future work.

\addtolength{\textheight}{-12cm}

\section*{ACKNOWLEDGMENT}
The work described in this paper is supported by VeriCAV project, which is funded by the Centre for Connected and Autonomous Vehicles, via Innovate UK (Grant number 104527). This paper is published with kind permission from the VeriCAV consortium: Horiba MIRA, Aimsun, University of Leeds, and Connected Places Catapult. This work was undertaken on ARC4, part of the High Performance Computing facilities at the University of Leeds, UK.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
HomePoliticsPresidential aspirant Dr Samura Kamara accused of tribalism – but is President Bio not tribalistic?
Presidential aspirant Dr Samura Kamara accused of tribalism – but is President Bio not tribalistic?
July 27, 2022 Abdul Rashid Thomas Politics 13
Sierra Leone Telegraph: 27 July 2022:
Many would argue that discrimination has existed since the creation of Adam and Eve, be it tribal, religious, gender, or any other social construct. But the reality is that some societies are more fixated on these social constructs than others, especially in poorer countries where decisions about the distribution of wealth, and the prioritisation and allocation of scarce resources hardly pass the good governance test.
It is very often in this context that politicians and leaders can show their true colours. Take Sierra Leone for example – one of the poorest countries in the world, where both the Head of State – Julius Maada Bio and the former Presidential candidate of the main opposition APC party – Dr Samura Kamara, are being accused of tribalism.
Dr Samura Kamara is accused of stoking up tribal conflict in the country, when he spoke last week in Makeni, Bombali district, in the northern province about tribalism. This is what he is alleged to have said, when describing the SLPP government's appalling record of creating a fairer and just society:
"If you are a Kamara, you won't get work. If you are Bangura, you won't get work. If you are from Makeni, you won't get work. If you are not from Kenema, Bo, Kailahun, Moyamba, Pujehun, you won't get work. If you don't say 'buwa bisseh', you won't get work".
Since uttering that statement, Dr Samura has said he is not a tribalist but a realist, pointing out to the fact that his wife belongs to the Mende tribe which he is accusing the government of prioritising over other tribes.
But is Dr Samura right in accusing President Bio and his government of tribalism when it comes to the allocation of resources and public sector appointments?
Critics of the government and opposition politicians say that over eighty percent of the top jobs in the country's public sector, including ministerial and civil service positions have gone to people of Mende origin.
President Bio himself is not denying the fact that he is making appointments based on who and what he knows. He said clearly in a radio interview last year that he only appoints those he knows into his government.
So, what are the facts?
The following is a list of appointments President Bio has made to his government which the opposition and critics say reflects the President's tribalistic nature and orientation:
Green hydrogen: – A viable option for transforming Africa's energy sector
Sierra Leone's Inspector General of police – Ambrose Sovula is finally sacked
Dauda Yillah says:
Of course, the people on the list of tribal appointments are all Sierra Leoneans. But the point is that Sierra Leone is an ethnically and regionally composite polity. What the list therefore most staggeringly shows is an unapologetically wholesale South-Easternization and indeed Mendenization of national governance. This approach to governance undermines national unity and cohesion, threatening as it were the very existence of our nation.
Bio and his Paopa regime have the benefit of hindsight and should have made it a point to learn from the mistakes of previous administrations. It is clear that they have taken the easiest of options by repeating on a scale never seen before those same mistakes.
Unless and until we voters give power to people that are prepared to put a stop to this insane drive to fragment, fracture and fissure our country into bits and pieces that are impossible to put back together, we are and will remain a nation of losers.
What an absolute nonsense! Even if the Mende's are the dominant amongst the selected few are they not Sierra Leoneans? The country has become a pie to both the Temne and Mende tribalist yawning for a cut from taxpayers.
Someone like Samura Kamara after 40yrs of service to governance and within a good old age cannot retire in solitude. Why? because him and others like him believed that governance comes with a lot of armouries to bask in.
Playing tribal card is no different from a fraudster looking to swindle his way to power.
Abraham amadu Jalloh. says:
Mr David Samura you've just prove a point that Tribalism is deeply entrenched in our society.This merry go round of tribal political appointments is not only a massive road block to country's national aspiration for developments it goes to the heart of the matter and realistically answer all the pressing questions we have for country's leaders .Why is our country rich in both natural and human resources but yet is classed as one of the poorest nations on planet earth?.More like politicians are taking it in turns to fleece our country's natural and human resources for there own benefit. Corrupution and political polarisation is just the fuel that is used to keep the status quo.Unless there is a massive demographic change that tilt the population growth to smaller tribes chances are we will continue to elect public officials that have no interest for the common good of the nation rather the common good of their tribesmen and women.
David Samura says:
This is the list of President Koroma's First Cabinet when it was in coalition with the PMDC
Ernest Bai Koroma- Limba/Loko/Themne – President
Sahr Sam-Sumana-Kono – Vice-President
Zainab Hawa Bangura-Themne – Minister of Foreign Affairs
David Carew- Krio – Minister of Finance and Development
Paolo Conteh-Limba – Minister of Defence
Abdul Serry-Kemal-Themne – Minister of Justice and Attorney-General
Alimamy Koroma-Themne – Minister of Trade and Industry
Haja Afsatu Kabba-Themne – Minister of Energy and Power
Alhaji I.B. Kargbo-Limba – Minister of Information and Communications
Dr. Soccoh Kabia- Themne from the PMDC – Minister of Health
John Saad- of Lebanese/Themne origin-from the PMDC – Minister of Housing and Infrastructural Development
Dauda Sulaiman Kamara- Limba – Minister of Internal Affairs, Local Government and Rural Development
Alhaji Abubakarr Jalloh- Fullah – Minister of Mineral Resources
Dr. Sam Sesay-Limba – Minister of Agriculture
Benjamin O.N. Davies- Krio – Minister of Lands, Country Planning and the Environment
Dr. Moses Moisa-Kapu-Mende from the PMDC – Minister of Marine Resources
Musu Kandeh-Kuranko – Minister of Social Welfare, Gender and Children's Affairs
Hindolo Trye- Mende – Minister of Tourism and Cultural Affairs
Minkailu Mansaray-Themne – Minister of Employment and Social Security
Dr. Minkailu Bah-Fullah – Minister of Education, Youths and Sports
Kemoh Sesay – Themne – Minister of Transport and Aviation
Alpha Kanu-Themne – Minister of Presidential and Public Affairs
Squire J. Koroma says:
Thanks so much Mr. Samura for the Work. I think this Forum will appreciate it very much and please check If you could lay hands on some more Information on this Topic.
To me it seems as If this Carbinet is equally tribalistic, infact more than that of Bio's. Maybe others will prove me wrong.
Bob Massally says:
Have you done your homework properly Mr Samura? The percentage of temnes in EBK'S first cabinet is 10 out of 23 which is 43.5 per cent – more than the about 1/3 proportion of temnes in the population. Those considered half-temnes are excluded from my analysis. EBK appointed a galf-lebanese in tgat cabinet and made an improvement in his second term by bringing down that proportion in line with the country's demographics. He even appointed Victor Foh, a mende from the SLPP's heartland, as vice president.
The percentage of mendes in Bio's cabinet is 19 out of 30 which is 63.4 per cent – far more than and almost doubling the 1/3 proportion of mendes in the population. Those considered half-mendes are again excluded from my analysis. Will Bio accept that his present appointments are also an abberration and make an improvement in his second term, if it ever comes to pass? He ought to bring down that 63.4 percent to 33.3 percent to truely reflect the country's demographics including second or third generation immigrants such as the Lebanese. He should know that even a Bo School prefect list is even more diverse and inclusive than the present cabinet list. The lists of deputy ministers and the head of parastatals are even worse.
Any list of appointments in the cabinet and civil service that does not reflect our rich and diverse heritage and does not contribute to any drive for national cohesion and unity should be condemned in no uncertain terms. All other cabinets including J S Momoh's Ekutay and Albert Margai had no one tribe grabbing more than 33.3 percent of the posts. None was one-tribe-geng.
Mohamed Kamara says:
Just to point out that the author of the list is asserting that everyone who speaks Mende is a Mende. If Doctor Samura Kamara's name was on that list he would have been listed as a Mende because he speaks Mende fluently having been schooled in Bo and married into a Mende family. Calling the minister of justice Mr. Mohamed Lamin Tarawalie for example a Mende is akin to call Samura Kamara a Mende. Further, the author might not know that names like Sahr (Saa) Tamba, Aiah belongs to people of either Kono or Kissy and tribe. So calling K. E. S Boyah a Mende might be another flaw. Mrs. Naibela Tunis has been converted from a krio because he is Married to Mr. Tunis a Vie from Zimmi Makpe. The same goes for Mrs Memunatu Pratt.
One last one, there are many Kamara's and Koroma's who are pure Mende. I am one of them.
I sometimes pity the plight of DRUNKARD Samura Kamara based on the fact that he had 100% assurance from the former lifetime leader of the APC party that he will be the next president in 2018. Currently, based on the fact that there is mounting evidence of corruption allegations against him, and the possibility of democratic process within the APC party in choosing the next flag bearer rather than handpicked, his chances for that position is getting slimmer every day. Alcohol has become his only solace and best friend.
This DRUNKARD who is a baptized Catholic Christian has continuously visited mosques without holding ablution and even climbed the pulpit (maimba) that is only meant for the Imam. He sometimes fall asleep during some sermons from the Imam due to his alcoholism.
Now he is trying to distract people from his ongoing corruption allegations trial and his frustration about the possibility of Sam Sumana as the next flag bearer of APC party, by shocking the nation with this tribal ranting. Let's continue to hope and pray for the new Chairman Alfred Peter Conteh (Mr. APC), who has sacrificed his time ,money and life for the transformation of the dictatorial APC party to a new democratic APC party.
Fortunately, with the emergence of the Social Media, tribalism will have no place or success in our political campaign as compared to 1967 and 2007.
Finally, "the winner takes all" democratic election and the constitution gave the president the power to hire and fire his cabinet appointees at will. To minimize firing his appointees, President Bio needed to choose people that believes in his Manifesto and Ideology to deliver his promises to the voters. Thanks to the Almighty that within 4 years, these Sierra Leoneans have transformed our country in the Fight against Corruption, Free Quality Education, Infrastructures ( Roads,Bridges, New Airport) and 24 hours electricity in Freetown, Bo and Kenema, which will soon be extended to all district headquarters), Sports – African Nations Cup and the total renovation of our stadium, Health ( fight against coronavirus , reduction in infant mortality and malaria ) , New currency, Transportation ( School Buses and new ferries), to name but few.
May the Almighty continue to bless President "Talk and Do" Bio and the nation.
That list is apparently dominated by former students of the Bo Government Secondary School – fondly called The Bo School. Bo school is, however, definitely more diverse than the list above. Where is Bo School Boys from other tribes? There are thousands out there if the president wants to appoint people he only knows. He certainly knows a few non-mende Bo School boys.
Could someone post a list of appointments under Ernest Bai Koroma and Ahmad Tehan Kabba and perhaps J S Momoh so that the lists could be juxtopsed to make any differences more glaring? I am not sure even Momoh's Ekutay was that much of a "one-tribe geng".
One thing i can definitely say, and without pre-empting whoever wants to take the challenge above, is that:
The most diverse and inclusive appointments in Government to date are more or less in the order:
Siaka Stevens
Valentine Strasser
Juxon Smith
Sir Milton Margai
Ahmed Tejan Kabbah
Ernest Bai Koroma
Joseph Saidu Momoh
Sir Albert Margai
Juluis Maada Bio
I leave you with late Juxon-Smith lamenting about Sierra Leonean politicians with their penchant for tribal politics.
https:///video/VLVA9MC0NX90AL9BJ3WXCY243JRW0-SIERRA-LEONE-COLONEL-JUXON-SMITH-TELLS-NEWSMEN-IN-FREETOWN-THAT/query/tribal
Fatorma Wilfred Gabba says:
Let's compare the ministerial appointments of the present SLPP government to that of previous APC
appointees to see the difference.
Two of the major curses that have hampered our country's development has been the twin evil of tribalism and corruption.Every Sierra Leonean knows this to be the truth and it goes to the heart of the problems facing our country .Bio didn't invent tribalism and the way it is weaved in our country's political ,social and cultural and economic DNA since it's founding back in the sixties . Bio inherited it and have enhanced it and used it effectively for his own benefit. He could have done more to promote coexistence but that is not part of his DNA .Bio for all his bolster thrive on division and chaos, that what's give him the kick .Sierra leone's very existence was founded on tribal and regional divisions.
Former President Saika Stevens was one of the original members of the SLPP party that was created to fight for our country's independence. But due to disagreements with fellow members he and others broke away from the SLPP party and formed their own independent party which later became the All People's Congress.So the seeds of North /South divide which have ultimately grown and became huge dividing trees that dot our political landscape that we are still wrestling with today .Unless we work hand in gloves and get rid of these trees that offer shade to one half of the country's population ,while the other half of the population is left to their own devices under the hot burning economic,social and cultural sun , there is very little we can say about future developments prospect for our country. No one knows this better than Bio himself.He more than any other had the opportunity to confront the twin problems of Tribalism and corruption , but under his watch we have seen how tribalism , nipotism, and political violence and vendetta politics has become the main focal point of his desire to creates a one party and one tribal state .The league table of political and public appointments will attest to that. To be fair to him he did say he only appoint peoples he knows .Majority of his school friends are now in position of power .Since he grew up in the South that is reflected on his public officials that occupy the offices of state
Maybe is about time we start to introduce students exchange between our learning institution in Sierra Leone so that School children from Bo or Kenema can visit Kabala Secondary school and St Francis or Benvolent In Makeni so they can see the other parts of the country and interact with their fellow pupils.Bio's ethnic cleavage and elite political polarisation has made the upcoming Presidential election a referendum on his legacy .The brutal truth his record has fueled more ethnic division than any other President before him. Bio will go down as the worst President in living memory .Bottom line , our country's fascination with tribal politics , turbocharged charged with hierarchical politics ,regional divisions , corruption on industrial scales seen by the general population like political theater and entertainment rather than a means to address real issues that affect families up and down the country , is unpatriotic and hypocritical by the very elected public officials that claimed to be the representatives of the people. A government of the people by the people and for the people remain as elusive as ever. On the present state we found ourselves that concept remains a distance pipe dream .
What a list of tribal appointments! That is Paopa-styled promotion of national unity and cohesion for you. Absolutely staggering.
Peter Dauda says:
Maada Bio is just carrying on from where Ernest Bai Koroma left. In this particular instance it is right to say that government is continuity.


The Business Model Database (tbmdb.com) - A blog about business models: What is an Open Business Model?
The term Open Business Model was coined by Professor Henry Chesbrough, author of Open Business Models: How to Thrive in the New Innovation Landscape. Chesbrough, who coined the term Open Innovation a couple of years earlier, almost rewrote the same book replacing "Innovation" with "Business Models" thus roughly defining Open Business Models as Business Models using external sources for innovation or external vehicles for commercialization of non-core innovations.
What does Open mean? In relation to what?
What makes the concept of Open Business Models tricky is not only that the term Business Model is ill defined, but the term Open has gained many different meanings in relation to businesses and their business models. Concepts such as Open Source, Open Data, Open Cloud, Open Standards, Open Business, Open in relation to democratization, Open in relation to public accessibility, Open in relation to the lack of lock-ins, Open in relation to transparency in businesses and government, and so on.
I see most of the concepts above as different value propositions towards users, partners, developers or other value recipients such as society. In relation to business models I separate between Open (or Collaborative) Business Models and Transparent Business Models and will write a separate blog post about the latter one in the near future.
I agree with Chesbrough that Open in relation to Business Models should relate to the boundaries of an organization and its transactions with external actors. However, I would not limit it to transactions of innovations from research, but include all transactions between actors in the value network. Also, I would include transactions with own customers and users (or actors at the end of Chesbrough's innovation funnel) to include customers as a source of ideas, as co-developers, testers or distributors. Transactions can be with or without restrictions, with or without the exchange of money, with outside researchers, developers, competitors, distributors, users, customers or other stakeholders.
Thus according to my definition all business models are to some extent open, and there is a scale from fully integrated companies only having transactions with its customers, to companies extensively using external assets and capabilities to create and capture value. Basically I see Open Business Models as complementing internal ownership of assets, capabilities and activities, with access to needed resources and processes from outside actors, to create or capture value.
What is then new with Open Business Models?
Businesses have for a long time used outside resources, expertise and information for research, development and commercialization. However, with increasing competition, reduction of technology and product life-cycles, increasing technology and product complexity and high level of uncertainty, increasingly companies realize the potential of outside resources for value creation and value capturing. A frequently used example is Procter & Gamble, sourcing 50% or its ideas and innovations from outside, and capturing value through own products, technology and IP licensing, joint ventures with competitors and so on. Another interesting fact is what can be seen in the picture below: companies increasingly relying on external sources of technology.
Loss of control over assets, capabilities or position in value network.
What is your Collaborative Advantage?
So why should other companies collaborate with you and not with your competitors? Why shouldn't you pay for access to certain external assets or capabilities as everyone else? Why should anyone provide you with exclusivity to technology or markets?
When realizing that value creation or capturing can be made more profitable using external actors, one quickly understands that the design of value propositions and incentives are important pieces of the puzzle. What are your value propositions towards partners? Is it access to unique assets or capabilities? Is it access to certain market segments through established relationships or brands? Strong distribution channels? Access to capital or infrastructure? etc.
In a business environment characterized by increasing competition, reduction of technology and product life-cycles, increasing technology and product complexity and high level of uncertainty, companies realize they must identify and decide on where the core of their competitive advantage lies, and identify external actors to complement their own assets and capabilities, to stay competitive. This is not limited to new ideas or innovations as in the concept of Open Innovation, but applicable to all parts of the business model.
I will follow up this post with tips on how to approach and apply Open Business Models.
Hi, Its a very nice posting, i like it. you have given us a very good information about business.
It will help a lots of people & i hope that u will help in future also. Nice blog Keep it up!!!!!!!!!!!


text:
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{
    QApplication a(argc, argv);
    project w;
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    return a.exec();
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redpajama_set_name: RedPajamaGithub


text:
Automation/AI
Culture and engagement
Leadership/succession planning
Recruitment and staffing
Relocation/travel
Wellness/mental health
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Government's 'Working with Millennials' webinar faces criticism
'Public service managers need to prioritize treating all employees the same, regardless of age'
By Jim Wilson
The Senate of Canada is offering a course to help staff members better understand millennials in the workplace – and some groups aren't happy about it.
With the "Working with Millennials" training course, the institution is hoping to get older workers to face their stereotypes about millennials who are stereotyped as "entitled praise-seekers who are easily distracted by technology," reports CBC.
The senate hopes this will help stop "negative stereotyping in multi-generational teams."
Senate human resources pitched the course as a way for employees and managers to deal with "an evolution of work environments" as workers returned to the office, says the CBC report.
Sixty-eight per cent of 2,000 workers aged 18 to 38 say that opportunities to progress within an organization must be "plentiful and diverse" when considering an employer, according to a previous report.
Wrong message?
But the course – offered as recently as last month – sends the wrong message to young staff, says Joel Thiessen, a professor of sociology at Ambrose University in Calgary, in the CBC report.
It also perpetuates the "ageist" myth that millennials are uniquely difficult to work with compared to earlier generations, he says.
"There's a perception out there that millennials are lazy or entitled or don't want to work hard. That's not accurate at all."
Meanwhile, Jennifer Chan, an employment lawyer at JTC Litigation in Toronto, finds it "a bit odd" that the senate HR singled out millennials since they have been in the workplace now for quite some time, according to the CBC.
"They're the sandwich generation, in between the upcoming gen Z and gen X. It's not clear to me why millennials are the focus," she says.
The senate does not offer course about working with gen Z, gen X or baby boomers, who the course refers to as "risk-averse, inflexible or lacking in enthusiasm," reports CBC.
While 84 per cent of U.S. employers feel they are offering opportunities, work arrangements and training and tools needed for employees of all ages to be successful, only 65 per cent of workers agree, according to a previous report.
Workers of different generations have differing ways of working, and this must be recognized by their employer, one expert previously told Canadian HR Reporter.
Nearly half of HR leaders considering 4-day work week: Report
Will biophilic design entice workers back to the office?
How do remote workers spend their 'commuting' time?
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New rule for business, IT consultants takes effect in Ontario
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text:
# Apache Johnzon

Apache Johnzon is a project providing an implementation of JsonProcessing (aka jsr-353) and a set of useful extension
for this specification like an Object mapper, some JAX-RS providers and a WebSocket module provides a basic integration with Java WebSocket API (JSR 356).

## Status

Apache Johnzon is a Top Level Project at the Apache Software Foundation (ASF).
It fully implements the JSON-P_1.0 specification (JSR-353).
Johnzon also targets the upcoming JSON-P_1.1 and JSON-B_1.0 specifications.

The current version is Apache Johnzon 1.0.0.

## Get started

Johnzon comes with four main modules.

### Core (stable)

<![CDATA[
<dependency>
  <groupId>org.apache.johnzon</groupId>
  <artifactId>johnzon-core</artifactId>
  <version>${johnzon.version}</version>
</dependency>
]]>

This is the implementation of the specification. You'll surely want to add the API as dependency too:

<![CDATA[
<dependency>
  <groupId>org.apache.geronimo.specs</groupId>
  <artifactId>geronimo-json_1.0_spec</artifactId>
  <version>${jsonspecversion}</version>
  <scope>provided</scope>
</dependency>
]]>

### Mapper (stable)

<![CDATA[
<dependency>
  <groupId>org.apache.johnzon</groupId>
  <artifactId>johnzon-mapper</artifactId>
  <version>${johnzon.version}</version>
</dependency>
]]>

The mapper module allows you to use the implementation you want of Json Processing specification to map
Json to Object and the opposite.

<![CDATA[
final MySuperObject object = createObject();

final Mapper mapper = new MapperBuilder().build();
mapper.writeObject(object, outputStream);

final MySuperObject otherObject = mapper.readObject(inputStream, MySuperObject.class);
]]>

The mapper uses a direct java to json representation.

For instance this java bean:

<![CDATA[
public class MyModel {
  private int id;
  private String name;

  // getters/setters
}
]]>

will be mapped to:

<![CDATA[
{
  "id": 1234,
  "name": "Johnzon doc"
}
]]>

Note that Johnzon supports several customization either directly on the MapperBuilder of through annotations.

#### @JohnzonIgnore

@JohnzonIgnore is used to ignore a field. You can optionally say you ignore the field until some version
if the mapper has a version:

<![CDATA[
public class MyModel {
  @JohnzonIgnore
  private String name;

  // getters/setters
}
]]>

Or to support name for version 3, 4, ... but ignore it for 1 and 2:

<![CDATA[
public class MyModel {
  @JohnzonIgnore(minVersion = 3)
  private String name;

  // getters/setters
}
]]>

#### @JohnzonConverter

Converters are used for advanced mapping between java and json.

There are several converter types:

1. Converter: map java to json and the opposite based on the string representation
2. Adapter: a converter not limited to String
3. ObjectConverter.Reader: to converter from json to java at low level
4. ObjectConverter.Writer: to converter from java to json at low level
4. ObjectConverter.Codec: a Reader and Writer

The most common is to customize date format but they all take. For that simple case we often use a Converter:

<![CDATA[
public class LocalDateConverter implements Converter<LocalDate> {
    @Override
    public String toString(final LocalDate instance) {
        return instance.toString();
    }

    @Override
    public LocalDate fromString(final String text) {
        return LocalDate.parse(text);
    }
}
]]>

If you need a more advanced use case and modify the structure of the json (wrapping the value for instance)
you will likely need Reader/Writer or a Codec.

Then once your converter developed you can either register globally on the MapperBuilder or simply decorate
the field you want to convert with @JohnzonConverter:

<![CDATA[
public class MyModel {
  @JohnzonConverter(LocalDateConverter.class)
  private LocalDate date;

  // getters/setters
}
]]>

#### @JohnzonProperty

Sometimes the json name is not java friendly (_foo or foo-bar or even 200 for instance). For that cases
@JohnzonProperty allows to customize the name used:

<![CDATA[
public class MyModel {
  @JohnzonProperty("__date")
  private LocalDate date;

  // getters/setters
}
]]>

#### @JohnzonAny

If you don't fully know you model but want to handle all keys you can use @JohnzonAny to capture/serialize them all:

<![CDATA[
public class AnyMe {
    @JohnzonAny // ignore normal serialization of this field
    private String name; // known
    private Map<String, Object> any = new TreeMap<String, Object>(); // unknown

    public String getName() {
        return name;
    }

    public void setName(final String name) {
        this.name = name;
    }

    @Any
    public Map<String, Object> getAny() {
        return any;
    }

    @Any
    public void handle(final String key, final Object val) {
        any.put(key, val);
    }
}
]]>

#### AccessMode

On MapperBuilder you have several AccessMode available by default but you can also create your own one.

The default available names are:

* field: to use fields model and ignore getters/setters
* method: use getters/setters (means if you have a getter but no setter you will serialize the property but not read it)
* strict-method (default based on Pojo convention): same as method but getters for collections are not used to write
* both: field and method accessors are merged together

You can use these names with setAccessModeName().

### JAX-RS (stable)

<![CDATA[
<dependency>
  <groupId>org.apache.johnzon</groupId>
  <artifactId>johnzon-jaxrs</artifactId>
  <version>${johnzon.version}</version>
</dependency>
]]>

JAX-RS module provides two providers (and underlying MessageBodyReaders and MessageBodyWriters):

* org.apache.johnzon.jaxrs.[Wildcard]JohnzonProvider: use Johnzon Mapper to map Object to Json and the opposite
* org.apache.johnzon.jaxrs.[Wildcard]ConfigurableJohnzonProvider: same as JohnzonProvider but with setters to ease the configuration of the provider in most servers/containers
* org.apache.johnzon.jaxrs.[Wildcard]JsrProvider: allows you to use JsrArray, JsrObject (more generally JsonStructure)

Note: Wildcard providers are basically the same as other provider but instead of application/json they support */json, */*+json, */x-json, */javascript, */x-javascript. This
split makes it easier to mix json and other MediaType in the same resource (like text/plain, xml etc since JAX-RS API always matches as true wildcard type in some version whatever the subtype is).

Tip: ConfigurableJohnzonProvider maps most of MapperBuilder configuration letting you configure it through any IoC including not programming language based formats.

### TomEE Configuration

TomEE uses by default Johnzon as JAX-RS provider for versions 7.x. If you want however to customize it you need to follow this procedure:

1. Create a WEB-INF/openejb-jar.xml:

<![CDATA[
<?xml version="1.0" encoding="UTF-8"?>
<openejb-jar>
  <pojo-deployment class-name="jaxrs-application">
    <properties>
      # optional but requires to skip scanned providers if set to true
      cxf.jaxrs.skip-provider-scanning = true
      # list of providers we want
      cxf.jaxrs.providers = johnzon,org.apache.openejb.server.cxf.rs.EJBAccessExceptionMapper
    </properties>
  </pojo-deployment>
</openejb-jar>
]]>

2. Create a WEB-INF/resources.xml to define johnzon service which will be use to instantiate the provider

<![CDATA[
<?xml version="1.0" encoding="UTF-8"?>
<resources>
  <Service id="johnzon" class-name="org.apache.johnzon.jaxrs.ConfigurableJohnzonProvider">
    # 1M
    maxSize = 1048576
    bufferSize = 1048576

    # ordered attributes
    attributeOrder = $order

    # Additional types to ignore
    ignores = org.apache.cxf.jaxrs.ext.multipart.MultipartBody
  </Service>

  <Service id="order" class-name="com.company.MyAttributeSorter" />

</resources>
]]>

Note: as you can see you mainly just need to define a service with the id johnzon (same as in openejb-jar.xml)
and you can reference other instances using $id for services and @id for resources.

### JSON-B (not yet fully compliant)

Johnzon provides a module johnzon-jsonb and jsonb-api implementing JSON-B standard based on Johnzon Mapper.

It fully reuses the JSON-B as API.

### Websocket (beta)

<![CDATA[
<dependency>
  <groupId>org.apache.johnzon</groupId>
  <artifactId>johnzon-websocket</artifactId>
  <version>${johnzon.version}</version>
</dependency>
]]>

WebSocket module provides a basic integration with Java WebSocket API (JSR 356).

Integration is at codec level (encoder/decoder). There are two families of codecs:

* The ones based on JSON-P (JsonObject, JsonArray, JsonStructure)
* The ones based on Johnzon Mapper

#### JSON-P integration

Encoders:

*  `org.apache.johnzon.websocket.jsr.JsrObjectEncoder`
*  `org.apache.johnzon.websocket.jsr.JsrArrayEncoder`
*  `org.apache.johnzon.websocket.jsr.JsrStructureEncoder`

Decoders:

*  `org.apache.johnzon.websocket.jsr.JsrObjectDecoder`
*  `org.apache.johnzon.websocket.jsr.JsrArrayDecoder`
*  `org.apache.johnzon.websocket.jsr.JsrStructureDecoder`

#### Mapper integration

Encoder:

*  `org.apache.johnzon.websocket.mapper.JohnzonTextEncoder`

Decoder:

*  `org.apache.johnzon.websocket.mapper.JohnzonTextDecoder`

#### Sample

##### JSON-P Samples

On server and client side configuration is easy: just provide the `encoders` and `decoders` parameters to `@[Server|Client]Endpoint`
(or `EndpointConfig` if you use programmatic API)):

    @ClientEndpoint(encoders = JsrObjectEncoder.class, decoders = JsrObjectDecoder.class)
    public class JsrClientEndpointImpl {
        @OnMessage
        public void on(final JsonObject message) {
            // ...
        }
    }

    @ServerEndpoint(value = "/my-server", encoders = JsrObjectEncoder.class, decoders = JsrObjectDecoder.class)
    public class JsrClientEndpointImpl {
        @OnMessage
        public void on(final JsonObject message) {
            // ...
        }
    }

##### WebSocket Samples

Server configuration is as simple as providing `encoders` and `decoders` parameters to `@ServerEndpoint`:

    @ServerEndpoint(value = "/server", encoders = JohnzonTextEncoder.class, decoders = JohnzonTextDecoder.class)
    public class ServerEndpointImpl {
        @OnMessage
        public void on(final Session session, final Message message) {
            // ...
        }
    }

Client configuration is almost the same excepted in this case it is not possible for Johnzon
to guess the type you expect so you'll need to provide it. In next sample it is done just extending `JohnzonTextDecoder`
in `MessageDecoder`.

    @ClientEndpoint(encoders = JohnzonTextEncoder.class, decoders = ClientEndpointImpl.MessageDecoder.class)
    public class ClientEndpointImpl {
        @OnMessage
        public void on(final Message message) {
            // ...
        }

        public static class MessageDecoder extends JohnzonTextDecoder {
            public MessageDecoder() {
                super(Message.class);
            }
        }
    }

## Thanks

We would like to thank ej-technologies for their [Java profiler JProfiler](http://www.ej-technologies.com/products/jprofiler/overview.html) which helped us a lot optimizing memory footprint and speed. ![JProfiler](http://www.ej-technologies.com/images/banners/jprofiler_small.png)


Thanks for the advice. Will see if i can hook up a good streaming device.
I think i figured what it stands for. Its the collision control. Has a little button on the steering wheel to indicate radar.
Yes i agree. I have mine in shimmering red and love it. You mention ACC - can i ask what that is ?
Sorry I meant earlier models. As I don't see this feature.
I presume this feature is not available on the later models.
Hi, mines a 2007 model. What's the S-Flow option? thanks.
I saw this DIY job on youtube. Looks like a lot of work.
Thanks for all the reply, it's made me more at ease now. Not looking to spend more money in a garage at this time of the year. Well I learnt something new today as well. White grease. 😊 Thanks to everyone for the help and input.
Yes I just had a service at the garage recently, changed the engine oil. It is oily, but do you know why would it turn white. Thanks.


Upon my return I was a bit too busy to work in my garden, and a bit scared too. We were away for 7 months and 2 days, a long time for a veggie garden! But it wasn't bad at all, there were a few weeds, of course, but mostly the big type, easy to remove in one go (or two). Before leaving NZ I covered the garden with a very thick layer of bark mulch, spread all around the vegetables that were already growing there (and were eaten by my house sitters). The mulch kept the weeds at bay in a fantastic way! And Nature is amazing: a few plants self seeded, I found some tomatillo plants, some with flowers and tiny fruit, a few tomato seedlings, borage and strawberry. The rhubarb looked dead, but after a few days it sprouted again (maybe it was waiting for me?), and there was plenty of green, red and yellow silver beet, plus a few leeks. Mint took over half of the garden, and while pulling it up I discover quite a few new potatoes. I did a big cleaning and this is what I had at the end of it.
Not bad! But then I turned the soil, put compost down and planted some seeds and seedlings... and felt that in a matter of week the weeds (the small annoying types) would appear on the fresh turned soil, so I gave my seedling something to read.
The zucchini got the Italian and Spanish papers, the tomatoes and capsicum the real estate pages, and the celery and new leeks the sport section. Beans, corn, cucumber etc, strips of current affairs. I don't know if it will work, but it looks... well, different! Pots and stones keep the papers down for now, and when I have time I hope to find some more mulch.
And now to my first harvest: The leeks differ in size, I grow them because they do better than onions and garlic in the rainy bush, and they last a long time in the soil. I have quite a few.
Then my first lot of potatoes, they are all different types. To tell the true I never 'plant' potatoes. Sometimes I buy them, and if they have a root I cut the piece off and bury it in the garden. So I have new potatoes all year round.
Ah, I also had a last rotten celery plant that only had a crispy top (the rest had to go in the compost). I used that too and made a soup. Self-sufficiency except for adding a little organic vegetable stock.
In the end I didn't have enough potatoes to get a very thick soup, and after blending it I added a bit of Vietnamese mint (of that, I have plenty!) to add flavour and compensate for poor texture and strange colour! It wasn't my best soup, but it was OK.
Silver beet... too much! I washed it and washed (at least 7 times, my mum told me) and recycled the water for the garden. I do the same with the potatoes, too much good soil in that water!
In the last four weeks I put the silver beet in lasagne, use it as a side vegetable, done quiches and frittata, and (like in the photo below) rolled it inside filo pastry (just a few sheets, no need for oil, just brush the top and edges with water, a trick I discovered so that it doesn't burn). In the filling I put cooked chopped-up silverbeet, tofu, salt, Moroccan seasoning and smoked paprika. And next to it my second harvest of potatoes, previously boiled and peeled (it took a long time, had to do it with fingernails). The potatoes too had salt, Moroccan seasoning, and smoked paprika, plus olive oil. Maybe I should have done them differently, but I really like them like this!
I added mayo and capers and, instead of spring onion, a chopped baby leek. Good to discover that raw chopped baby leeks taste better than spring onions!
Hope you have fun gardening!! Great post!
Oh quante cose appetitose! E soprattutto genuine!
Se predno l'aereo, posso soggiornare da te fino all'estate?!Che meraviglie Ale!!!!
@ Mery. Welcome back. I do try to grow different allum, but nothing works like leeks, I am really happy about them!
What a pretty garden you have, Alessandra. Everything tastes so much better when picked from your yard, doesn't it?
So you used fresh baby leek...and it tasted great...that's great to know! Your garden is awesome Alessandra. It survived really well for you being away for so long. I love the photos, and the way you described the special newspapers each seed received.
Watch that mint though!! AHgrrrsive.
tuttavia volevo dirti che io chiamo "ravioli cinesi" (definizione qui molto usata)ogni "saccottino", qualsiasi sia la cottura.
Al risto cinese infatti, accanto a ciascuna denominazione in lingua relativa ai ravioli, c'è la traduzione in italiano : ravioli cinesi o ravioli arrosto.
How lovely to be back in your garden, I stick to herbs, citrus and the odd rhubarb plant since I can't keep upnwithnthe bugs to grow veg!