text:
Owens-Illinois – przedsiębiorstwo specjalizujące się w produkcji opakowań dla produktów spożywczych. Przedsiębiorstwo utrzymuje pozycję największego producenta opakowań szklanych w obu Amerykach, Australii, Nowej Zelandii, Chinach i Europie (po nabyciu BSN Glasspack w roku 2004). Szacuje się, że co druga butelka szklana wyprodukowana na świecie jest wyrobem Owens-Illinois, podmiotów stowarzyszonych lub licencjobiorców.

Do roku 2007 przedsiębiorstwo zajmowało się również produkcją opakowań plastikowych w Ameryce Północnej, Ameryce Południowej, Azji, Australii i Europie. Wśród plastikowych opakowań produkowanych przez O-I były m.in. pojemniki, zamknięcia i opakowania na leki. W lipcu 2007 O-I sprzedał zakłady produkujące opakowania plastikowe przedsiębiorstwu Rexam PLC, producentowi opakowań z Wielkiej Brytanii.

Owens-Illinois był notowany na Dow Jones Industrial Average od 1 czerwca 1959 do 12 marca 1987 roku.

Zakłady w Polsce

Owens-Illinois pojawił się na polskim rynku wraz z prywatyzacją Huty Szkła "Jarosław" w roku 1993.

Kolejnym zakładem w Polsce jest Huta Szkła Antoninek w Poznaniu kupiona w roku 1998 od UNP Holdings BV.

1 lipca 2014 Owens-Illinois otworzył Centrum Usług Finansowych w Poznaniu w budynku Malta House.

Oznaczenie miejsca produkcji (hut) na produktach (butelki i słoiki):

E19 OI - Huta Szkła Jarosław

E18 OI- Huta Szkła Antoninek (Poznań)

Przypisy

Linki zewnętrzne
 Owens-Illinois strona koncernu

Huty szkła

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text:
Find My Advisor
About the Global Affairs Program
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703-993-9185 gloa@gmu.edu
Johanna Bockman
Globalization, neoliberalism, economic sociology, Eastern Europe, socialism and postsocialism, gentrification, Washington, DC
Professor Bockman works in globalization studies, economic sociology, urban studies, and East European Studies. Her book Markets in the Name of Socialism: The Left-Wing Origins of Neoliberalism was published by Stanford University Press. In her research and teaching, Bockman uses comparative and historical methods, moving beyond studies of nation states to explorations of transnational trends, such as neoliberalisms, socialisms, and the non-aligned movement. She has been nominated for a 2021 George Mason University Teaching Excellence Award.
Bockman is currently working on two projects, First, she is writing a book on multiple globalizations and gentrification on one block in Washington, DC. She reports on this project on her blog Sociology in My Neighborhood: DC Ward 6 and is a founding member of the Cities and Globalization Working Group. Her articles "The Aesthetics of Gentrification: Modern Art, Settler Colonialism, and Anti-Colonialism in Washington, DC" in International Journal of Urban and Regional Research and "Removing the public from public housing: Public-private redevelopment of the Ellen Wilson Dwellings in Washington, DC" published in a special issue on PPPs in the Journal of Urban Affairs are part of this project.
Second, she is working on the 1980s debt crisis from the perspectives of the second and third worlds, including non-aligned worlds. Her articles "Socialist Globalization against Capitalist Neocolonialism: The Economic Ideas behind the New International Economic Order" published in the journal Humanity (2015) and "The Struggle over Structural Adjustment: Socialist Revolution versus Capitalist Counterrevolution in Yugoslavia and the World" in History of Political Economy (2019) are part of this project.
Click here for a short video on Dr. Bockman's research and teaching.
"The Aesthetics of Gentrification: Modern Art, Settler Colonialism, and Anti-Colonialism in Washington, DC," International Journal of Urban and Regional Research (2021), http://doi.org/10.1111/1468-2427.13046.
"Removing the public from public housing: Public-private redevelopment of the Ellen Wilson Dwellings in Washington, DC," Journal of Urban Affairs 43(2)(2021): 308-328. https://doi.org/10.1080/07352166.2018.1457406.
"Democratic Socialism in Chile and Peru: Revisiting the "Chicago Boys" as the Origin of Neoliberalism," Comparative Studies in Society and History 61(3)(2019):654–679.
"The Struggle over Structural Adjustment: Socialist Revolution versus Capitalist Counterrevolution in Yugoslavia and the World," History of Political Economy 51 (annual supplement, 2019): 253-276.
Expanded Publication List
"Not the New Deal and Not the Welfare State: Karl Polanyi's Vision of Socialism." 2018. Pp. 200-208, in Karl Polanyi's Vision of a Socialist Transformation, edited by Michael Brie and Claus Thomasberger. Montreal: Black Rose Books.
"Applying Post-Socialist Studies outside Post-Soviet Space: the Many Washington, DCs," ASEEES Newsnet (March 2018).
"'Socialist Accounting' by Karl Polanyi: with preface 'Socialism and the embedded economy,'" Theory and Society 45(5)(2016): 385-427. Translated by Ariane Fischer, David Woodruff, and Johanna Bockman. Preface by Johanna Bockman. [Polanyi 1922 final publication]
"Structural Adjustment," Wiley-Blackwell Encyclopedia of Sociology (2016).
"Home Rule from Below: The Cooperative Movement in Washington, DC." Pp. 66-85 in Capital Dilemma: Growth and Inequality in Washington, DC. 2016. Routledge.
"Socialist Globalization against Capitalist Neocolonialism: The Economic Ideas behind the New International Economic Order," Humanity 6(1)(2015): 109-128.
"Neoliberalism," Contexts 12(3)(2013): 14-15.
"The Long Road to 1989: Neoclassical Economics, Alternative Socialisms, and the Advent of Neoliberalism," Radical History Review 112 (2012): 9-42.
"The Political Projects of Neoliberalism," Invited Response to Loïc Wacquant's "Three Steps to a Historical Anthropology of Actually Existing Neoliberalism," Social Anthropology 20(3) (2012): 310-317. Hungarian translation in Fordulat 2012/2.
"Scientific Community in a Divided World: Economists, Planning, and Research Priority during the Cold War," Comparative Studies in Society and History 50 (2008): 581-613. Co-authored with Michael Bernstein.
"The Origins of Neoliberalism between Soviet Socialism and Western Capitalism: 'A galaxy without borders,"' Theory and Society 36(4) (2007): 343-371.
"Eastern Europe as a Laboratory for Economic Knowledge: The Transnational Roots of Neo-Liberalism," American Journal of Sociology 108 (2002): 310-352. Co-authored with Gil Eyal.
"Structural Equation Model of Customer Satisfaction for the New York City Subway System." Transportation Research Record 1735 (2000): 133-137. Co-authored with Kenneth Stuart and Mark Mednick.
Her book Markets in the Name of Socialism: The Left-Wing Origins of Neoliberalism was published by Stanford University Press. Korean translation by Geulhangari Press 2015.
GLOA 400: Global Cities
GLOA 400/HIST 388/RUSS 354/SOCI 321: Post-Soviet Life around the World
GLOA 600: Global Competencies
GLOA 605: Interdisciplinary Research Methods
SOCI 303: Sociological Research Methods, Sociology in My Neighborhood
SOCI 320: Socialist and Capitalist Globalizations
SOCI 804: Sociology of Globalization
SOCI 860: Comparative Historical Sociology
Dissertations Supervised
Basak Durgun, Cultural Politics of Urban Green Spaces: The Production and Reorganization of Istanbul's Parks and Gardens (2019)
Joshua Tuttle, Stewards of the Kingdom: Christianity and Neoliberalism (2019)
jbockman@gmu.edu
Horizon Hall 6234
Tuesdays 1:30-2:30pm in my office and 4:20-5:20pm outside HH 1009 (or other times by appointment)
History and Art History
Sociology in My Neighborhood
Coop DC
GLOA 101-002:
Intro to Global Affairs
03:00 PM to 04:15 PM MW
SOCI 320-001:
Global DC
Spring 2023 Classes
Post-Soviet Life
03:00 PM to 04:15 PM TR
RUSS 354-001:
Hist/Comprtive Sociology
07:20 PM to 10:00 PM T
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Email: gloa@gmu.edu

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text:
Albanian Eagle Football
How football helped me show Sweden another side of Albanians
Posted on November 24, 2014 by kosovaremezini
Sweden is a rather small country in Scandinavia where more than 9 million people reside. When you mention Sweden to non Swedes there are several of them who believe that there are polar bears walking around the Swedish cities on a daily basis but the chances are higher of you bumping in to an Albanian football player playing in the Swedish League than a polar bear.
Out of those 9 million people there are approximately 40 000 Albanians living here and you sometimes forget that you are in Sweden just because there is always one Albanian around the corner. We all know that Albanians in general have a bad reputation, especially in Sweden. On numerous occasions people have called me a Swede just because I am not the trouble maker type which often made me raise my voice and remind them that being Albanian does not mean you automatically come from a criminal family. Do not get me wrong, I've been living in this country for more than 20 years and I love Sweden with all my heart but my roots are Red and Black. Anyway, I eventually was fed up with this and had always wanted to help give Albanians a better image but never quite knew how to do so. However, in 2010 a wonderful idea came to my mind.
It began on June 2009, as an 18-year-old girl I first started watching football, or soccer as the Americans say, after seeing the U21 Euro Cup that was hosted by Sweden. Sweden was represented by Emir Bajrami and Labinot Harbuzi amongst others and I was shocked when I first heard the commentators mention their names. "Since when do Albanians know how to play football?" I thought to myself. The first League I followed was the Swedish League which is called Allsvenskan in Swedish. I was surprised by the fact that so many Albanians played in the highest league and not to mention their National Teams as well. When I mentioned them to my fellow school mates and friends there were a few of them that had heard of our Albanian talents.
Being new to the football world meant that I had to stay up late several nights in order to learn everything about this wonderful game. Not to mention all those hours I spent at the library reading every single book they had just because I was so fascinated by this sport. I memorized the entire career paths of several Albanian football players and gladly bombarded people with facts whenever I heard someone say all Albanians are bad."Well yeah, did you know that Besart Berisha scored the fastest hattrick ever (6 minutes) in Australia's A-League?" was one of my favorite phrases. Eventually it came to my conclusion that there were so many more people out there that still had a negative image of Albanians so about a year later a blog named Albankollen was born.
Albankoll en means keeping track of the Albanians in Swedish and I started writing the few things I knew. As my knowledge grew, so did Albankollen. It grew bigger in Sweden and I was all of the sudden followed by thousands around the country. My work was even caught by Kosovo's head coach, Albert Bunjaki himself! The former Örebro SK and Kalmar FF assistant coach was impressed by me and called me to help him with the team when Kosovo were allowed to play their first official friendly game.
Kosovo welcomed Haiti to Kosovo as a historical first contenders and the home team announced the former Swedish NT assistant coach Tord Grip as Kosovo's assistant coach. This caught media's attention even more. However he was not the only familiar face amongst the Swedish newspapers.
Kosovo's NT were led by the rock solid captain Anel Rashkaj, a former Halmstads BK player but he currently plays in Norweigan Sandnes Ulf. Alongside him Ardian Gashi was lined up as well together with Loret Sadiku and the duo both played for Helsigborgs IF at that time. Another Swedish player was striker Shpetim Hasani, Örebro SK at that time. The game against Haiti ended in a draw (0-0) and several of the Scandinavian teams welcomed back their players during the spring of 2014.
Throughout out these years I've introduced several players and young talents to the Swedish crowd. Apart from the one's I have mentioned earlier we have also had players in Sweden such as the current Malmö FF player Agon Mehmeti and his former team mate Dardan Rexhepi. Etrit Berisha, first goalkeeper of Albania's National Team, managed to make his way to the Swedish All start team last season but had left for Kalmar FF for Lazio shortly before the season ending. In Kalmar FF he was accompanied by Besnik Rustemaj, Liridon Leci and Alban Dragusha. Another Albanian NT player Valdet Rama played in Örebro SK a few years ago together with Ilir Berisha, Kushtrim Lushtaku and Shpetim Hasani. 22 year old Dardan Mustafa in Gefle scored his debut goal in the Swedish League this year, but he was not the only one to do so. Even Elfsborg's youngster Arber Zeneli, born in 1995, accomplished the same this season.
However these are only a hand full of Albanians in Sweden alone and I am certain that I managed to achieve my goal of showing Sweden a better image of Albanians during these past years. One time I even overheard a group of young guys talking on the bus. "Hey man, did you know we had so many talents around the world. I know, I've heard about them on Albankollen. Best page ever." . Those words made me walk out with a huge smile on my face and my heart was filled with joy. That is why I decided to put down Albankollen last month and begin a new era. Now it is time to show the rest of the world what we are capable of. Welcome to Albanian Eagle Football!
This entry was posted in Uncategorized by kosovaremezini. Bookmark the permalink.


The Legal Status of Jerusalem: Is Trump Right?
The Hague Initiative for International Cooperation (thinc.) is an initiative dedicated to the promotion of the Rule of Law in international relations to advance peace and security, friendly relations among nations, and the peaceful resolution of conflicts. The focus of our initiative is the Israel-Palestine conflict.
Our goal is to help politicians and policy-makers to make informed decisions on the fair and non-discriminatory application of international law to the Israel-Palestine conflict, and, in doing so, to promote the peaceful resolution of disputes based on UN Charter principles such as the sovereign equality of states, friendly relations between nations, and the Rule of Law.
thinc. is an independent research foundation.The statutory seat of thinc. is The Hague – City of Peace and Justice.
thinc. has the ANBI status.


text:
Man arrested in extraordinary airport car chase
FILE - In this Dec.17, 2011 file photo, a passenger waits for his flight during a strike at Lyon-Saint Exupery airport, central France. Authorities say police have arrested a man who rammed his car through two glass doors at Lyon's international airport and sped onto the runway. (AP Photo/Laurent Cipriani, File)
By JOHN LEICESTER, AP
Pursued by police vehicles, a man smashed a stolen car Monday into security barriers at two airports in Lyon, southeast France, and then crashed through two glass doors of a terminal and sped onto the runway, before being arrested.
The dramatic car chase was captured on amateur video. It showed the car hitting a bump, going airborne and landing in a cloud of dust at Lyon's international Saint-Exupery airport. The driver jumped out while the vehicle was still rolling and sprinted off on foot, pursued by a police van.
Other police vehicles and officers on foot then tried to cut off his escape until one of them managed to catch up with the suspect and wrestle him to the ground, the video showed.
The man's motives were unknown and nothing was being ruled out, regional government press officer Christelle Monteagudo said.
Another official with direct knowledge of the investigation said there was no immediate evidence pointing to a terrorist motive. The official spoke on condition of anonymity because they weren't authorized to publicly discuss the probe.
The man, aged 31 and believed to be French, was previously known to police only for traffic violations, in 2007 and 2009, the official said. The vehicle he used as a battering ram was stolen, the official added.
The man was first spotted driving the wrong way on the A43 highway that links Lyon to the Alps. Highway police and a police helicopter laid chase, the regional government said in a statement. Police fired shots during the pursuit, the official said.
The man then rammed into security barriers at Lyon's Bron airport used for business flights, before driving to the nearby, larger Saint-Exupery airport, where flights were quickly suspended.
There, the man rammed through more security barriers. Still chased by police, he then crashed through two glass doors at the airport's Terminal 1 to drive onto the runways.
All air traffic at the airport was halted. There were no casualties.
The Lyon prosecutors' office is investigating possible charges of attempted murder against the suspect.
Travel - U.S. Daily News: Man arrested in extraordinary airport car chase
https://2.bp.blogspot.com/-XkFx506fVQI/W5ijpb-EcUI/AAAAAAAA96E/DCl_WUH_2MY3gsxAt_NIMep964V5biuDwCLcBGAs/s1600/6.jpg
https://2.bp.blogspot.com/-XkFx506fVQI/W5ijpb-EcUI/AAAAAAAA96E/DCl_WUH_2MY3gsxAt_NIMep964V5biuDwCLcBGAs/s72-c/6.jpg
https://travel.dailynews.us.com/2018/09/man-arrested-in-extraordinary-airport.html


text:
Woman guard on goods train molested at Kalyan station
Thane | Published: September 21, 2018 10:20:04 PM
Two unidentified men allegedly molested a woman security guard of the Central Railway on a goods train at Kalyan station in Thane district of Maharashtra, police said Friday.
Woman guard on goods train molested at Kalyan station (Representative image)
Two unidentified men allegedly molested a woman security guard of the Central Railway on a goods train at Kalyan station in Thane district of Maharashtra, police said Friday. A case under IPC section 354 (molestation) has been registered and police were searching for the culprits, an official said. The complainant was posted as a security guard on a goods train. When the train was in the railway yard at Kalyan late Thursday evening, two men entered its brake-van and molested her, the official said. Kalyan Railway Police are conducting further probe.


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text:
\section{Introduction}
\label{sec:intro}
Data shortage is often an issue when analyzing physiology based time-series signals with deep learning models. Unlike images and text data used in computer vision (CV) and natural language processing (NLP) tasks, which are abundant on the web, such signals are collected as sensor measurements resulting from physical or biological process. Especially when such processes involve human subjects, data collection, annotation, and interpretation is a costly endeavour. Furthermore, differences in the various collection configurations make it harder for data collected in different settings to be merged together to form larger datasets. Deep learning models require large amounts of data to train successfully. Training deep learning models with a high number of trainable parameters on small datasets results in over-fitting and low generalization capabilities. As a compromise researchers are forced to train shallower deep learning models that are not capable of capturing the full complexity of the problem at hand. This is a common situation encountered in medical and health-related machine learning research.

Generative Adversarial Networks (GANs), first introduced in 2014~\cite{goodfellow2014generative}, have been gaining traction in the deep learning research field. They have successfully generated and manipulated data in CV and NLP domains, such as high-quality image generation~\cite{ledig2017photo}, style transfer~\cite{bousmalis2017unsupervised}, text-to-image synthesis~\cite{zhang2017stackgan}, etc. There has also been a movement towards using GANs for time series and sequential data generation, and forecasting. The review paper~\cite{brophy2021generative} gives a thorough summary of GAN implementations on time series data.

A GAN is a generative model consisting of a generator and discriminator, typically two neural network (NN) models. The generator takes as input random vectors of specified dimensions and generates output vectors of the same dimension that are similar to the real training data. The discriminator is a binary classifier used to distinguish the real data and generated data. The generator and discriminator are updated by back-propagation alternately, playing a zero-sum game against each other and until they reach an equilibrium.

The transformer architecture, which relies on multiple self-attention layers~\cite{vaswani2017attention}, has recently become a prevalent deep learning model architecture. It has been shown to surpass many other popular neural network architectures, such as CNN over images and RNN over sequential data~\cite{dosovitskiy2020image,devlin2018bert}, and it has even displayed properties of a universal computation engine~\cite{lu2021pretrained}. Some works have already tried to utilize the transformer model in GAN model architecture design with the goal to either improve the quality of synthetic data or to create a more efficient training process~\cite{jiang2021transgan,diao2021tilgan} for image and text generation tasks. In work~\cite{jiang2021transgan}, the author, for the first time, built a pure transformer-based GAN model and verified its performance on multiple image synthesis tasks.

Previous efforts for creating a time-series GAN have mainly relied on Recurrent Neural Network (RNN)-based architectures~\cite{esteban2017real,yoon2019time,ni2020conditional}. Since the transformer was first invented to handle very long sequential data and does not suffer from a vanishing gradient problem, theoretically, a transformer GAN model should perform better than other RNN-based models on time-series data. In this work, we follow a process similar to the one Jiang et.al.~\cite{jiang2021transgan} followed for image generation, adapted for time-series data.

Since time-series data are not easily interpretable by humans, we use PCA~\cite{wold1987principal} and t-SNE~\cite{van2008visualizing} to map the multi-dimensional output sequence vectors into two dimensions to visually observe the similarity in the distribution of the synthetic data and real data instances. For a more quantitative comparison, we also measure several well-known signal properties and compare the similarity of the transformer-generated as well as RNN-generated sequences with real sequences of the same class.

Our contributions can be summarized as follows:
\begin{itemize}
    \item We create a pure transformer-based GAN model to generate synthetic time-series data.
    \item We propose several heuristics to more effectively train a transformer-based GAN model on time-series data.

    \item We qualitatively and quantitatively compare the quality of the generated sequences against real ones and against sequences generated by other state-of-the-art time-series GAN algorithms.

\end{itemize}

The rest of the paper is organized as follows. Section~\ref{sec:background} discusses the background and most popular applications of GANs and transformer models. In section~\ref{sec:methodology}, we provide the details of our TTS-GAN model architecture and how we process time-series data to feed this model. In section~\ref{sec:experiments}, we visually and quantitatively verify the fidelity of the synthetic data.
Section~\ref{sec:conclude} summarizes our work and concludes this paper.

\section{Background}
\label{sec:background}

\subsection{Generative Adversarial Networks (GANs)}
GANs consist of two models, a generator and a discriminator. These two models are typically implemented by neural networks, but they can be implemented with any form of differentiable system that maps data from one space to the other. The generator tries to capture the distribution of true examples for new data example generation. The discriminator is usually a binary classifier, discriminating generated examples from the true examples as accurately as possible. The optimization of GANs is a minimax optimization problem, in which the goal is to reach Nash equilibrium~\cite{ratliff2013characterization} of the generator and discriminator. Then, the generator can be thought to have captured the real distribution of true examples.

GANs have had many applications in different areas, but mostly in CV and NLP. For example, it can generate examples for image datasets~\cite{goodfellow2020generative},  front view faces~\cite{huang2017beyond}, text-to-image translation~\cite{zhang2017stackgan}, etc. While these successes have drawn much attention, GAN applications have diversified across disciplines such as time-series data generation. The work~\cite{brophy2021generative} gives a thorough summary of the GAN implementations in this field. The applicability of GANs to this type of data can solve many issues that current dataset holders face. For example, GANs can augment smaller datasets by generating new, previously unseen data. GANs can replace the artifacts with information representative of clean data. And it can also be used to denoise signals. GANs can also ensure an extra layer of data protection by generating deferentially private datasets containing no risk of linkage from source to generated datasets.

\subsection{Transformer}
The transformer is the state-of-the-art neural network architecture. Unlike recurrent neural networks, which consume a sequence token by token, in a transformer network, the entire sequence is fed into layers of transformer modules. The representation of a token at a layer is then computed by attending to the latent representations of all the other tokens in the preceding layer. Many works in the NLP field have proved its performance~\cite{vaswani2017attention,devlin2018bert}.

Given its strong representation capabilities, researchers have also applied transformers to computer vision tasks. In a variety of visual benchmarks, transformer models perform similar to or better than other types of networks, such as convolutional and recurrent networks. The work in~\cite{dosovitskiy2020image} builds a model named ViT, which applies a pure transformer directly to sequences of image patches. The work in~\cite{jiang2021transgan} builds a pure transformer GAN model to generate synthetic images, where the discriminator designing idea is from the ViT model. The multi-dimension time-series data we are dealing with has similarities from both texts and images, meaning a sequence contains both temporal and spatial information. Each timestep in a sequence is like a pixel on one image. The whole sequence contains an event or multiple events happening, which is similar to a sentence in NLP tasks.

In this work, we adapt the ideas used in~\cite{dosovitskiy2020image} and~\cite{jiang2021transgan} for images, and view a time-series sequence as a $C \times H \times W $ tuple, where \emph{C} is the number of channels of the time-series data, \emph{H} corresponds to the height of the image, but for time-series that value is set to 1, and\emph{W} corresponds to the width of the image, which for times-series is the number of timesteps in the sequence.
We divide the tuple into multiple patches on the \emph{W} axis and provide positional encoding to each patch. To our best knowledge, it is the first work to implement such an idea to process time-series data and apply it to a transformer GAN model.

\section{Methodology}
\label{sec:methodology}

\subsection{Transformer Time-Series GAN Model Architecture}

The TTS-GAN model architecture is shown in Fig.~\ref{fig:GAN}. It contains two main components, a generator, and a discriminator. Both of them are built based on the transformer encoder architecture~\cite{vaswani2017attention}. An encoder is a composition of two compound blocks. A multi-head self-attention module constructs the first block and the second block is a feed-forward MLP with GELU activation function. The normalization layer is applied before both of the two blocks and the dropout layer is added after each block. Both blocks employ residual connections.

The generator first takes in a 1D vector with N uniformly distributed random numbers values within the range (0,1), i.e. $N_i \sim U(0,1)$ . N represents the latent dimension of the synthetic signals, which is a hyperparameter that can be tuned. The vector is then mapped to a sequence with the same length of the real signals and M embedding dimensions. M is also a hyperparameter that can be changed and not necessarily equal to real signal dimensions. Next, the sequence is divided into multiple patches, and a positional encoding value is added to each patch. Those patches are then input to the transformer encoder blocks. Then the encoder blocks outputs are passed through a Conv2D layer to reduce the synthetic data dimensions. The Conv2D layer is set to have a kernel size $(1, 1)$, which won't change the width and height of the synthetic data. The filter size is set to the same dimension size as the real data sequences. Therefore, a synthetic data sequence after the generator transformer encoder layers with a data shape $(hidden dimensions, 1, timesteps)$ will be mapped to $(real data dimensions, 1, timesteps)$. In this way, a random noise vector is transformed into a sequence with the same shape as the real signals.

The discriminator architecture is similar to the ViT model~\cite{dosovitskiy2020image}, which is a binary classifier to distinguish whether the input sequence is a real signal or synthetic one. In the ViT model, an image is divided evenly into multiple patches with the same width and height. However, in TTS-GAN, we view any input sequences like an image with a height of 1. The timesteps of the inputs are image widths. Therefore, to add positional encoding on time series inputs, we only need to divide the width evenly into multiple pieces and keep the height of each piece unchanged. This process is explained in detail in section~\ref{sec:processingdata}.

\begin{figure}[t]
\centering
\includegraphics[width=9cm, height=9.5cm]{TST-GAN.pdf}
\caption{TTS-GAN model architecture}
\label{fig:GAN}
\end{figure}

\subsection{Processing Time-Series Data like an image}
\label{sec:processingdata}
We view a time-series data sequence like an image with a height equal to 1. The number of timesteps is the width of an image, $W$. A time-series sequence can have a single channel or multiple channels, and those can be viewed as the number of channels (RGB) of an image, $C$. So the input sequences can be represented with the matrix of size $(Batch Size, C, 1, W)$. Then we choose a patch size $N$ to divide a sequence into $W / N$ patches. We then add a soft positional encoding value by the end of each patch, the positional value is learned during model training. Therefore the inputs to the discriminator encoder blocks will have the data shape $(Batch Size, C, 1, (W/N) + 1)$. This process is shown in Fig.~\ref{fig:PositionalEncoding}.

\begin{figure}[t]
\centering
\includegraphics[scale=0.7]{PositionalEncoding.pdf}
\caption{Processing time-series data}
\label{fig:PositionalEncoding}
\end{figure}

\subsection{Updating Generator and Discriminator Parameters}
\label{sec:loss}
The transformer blocks in the generator and discriminator both use the Mean Squared Error loss to update the parameters. We can use $z$ to denote input vectors to the generator. Use $G(z)$ to represent the synthetic data generated by the generator. We use the prefix $real$ to represent the real input signals. $D(x)$ is the classification output of the discriminator. $x$ can be the real signals or synthetic signals. $real\_label$ is set to 1 and $fake\_label$ is set to 0. To stabilize the GAN model training, some heuristics can be used when setting label values. For example, we can use soft labels that $real\_label$ is a float number close to 1 and $fake\_label$ is a float number close to 0. Sometimes, we can also flip the values of the $real\_label$ and the $fake\_label$. The usefulness of these strategies has been so fat been tested only on a case-by-case basics.
The discriminator loss can be represented as:
\begin{gather*}
d\_real\_loss = MSELoss(D(real), real\_label) \\
d\_fake\_loss = MSELoss(D(G(z)), fake\_label) \\
d\_loss = d\_real\_loss + d\_fake\_loss
\end{gather*}
The discriminator loss is the sum of real data loss and fake data (synthetic data) losses.
The generator loss can be represented as:
\begin{equation*}
g\_loss = MSELoss(D(G(z)), real\_label)
\end{equation*}

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

\subsection{Datasets}
We evaluate the TTS-GAN model on three datasets. Simulated sinusoidal waves,  UniMiB human activity recognition (HAR) dataset~\cite{app7101101} and the PTB Diagnostic ECG Database~\cite{bousseljot1995nutzung,goldberger2000physiobank}. A few raw data samples for each dataset are shown in Fig.~\ref{fig:realSignals}.

The \textbf{sinusoidal waves} are simulated with random frequencies $A$ and phases $B$ values between [0, 0.1]. The sequence length is 24 and the number of dimensions is 5. For each dimension $i \in \{1, ..., 5\}$, the sequence can be represented with the formula $x_i(t) = sin(At + B)$, where $A \in (0, 0.1)$ and $B \in (0, 0.1)$. A total number of 10000 simulated sinusoidal waves are used to train the GAN model.

For the \textbf{UniMiB datase}~\cite{app7101101}, we select 2 categories (Jumping and Running) samples from 24 subjects' recordings to train GAN models. The two classes have 600 and 1572 samples respectively. Every sample has 150 timesteps and three accelerator values at each timestep. All of the recordings are channel-wisely normalized to a mean of 0 and a variance of 1.

The \textbf{PTB Diagnostic ECG dataset}~\cite{bousseljot1995nutzung,goldberger2000physiobank} contains human heartbeat signals in two categories, normal and abnormal with 4046 and 10506 samples respectively. Each sequence represents a heart beat sampled at 125Hz. The original length of each sequence is 188, padded with zeros at the end to create fixed-length sequences. We only use the timesteps 5 to 55 of each sample, which is the part of the sequence containing the most useful information of the heatbeat.

\begin{figure*}[t]
\centering
\begin{subfigure}{0.495\textwidth}
\centering
\includegraphics[width=\columnwidth]{images/Simulated_Sinusoidal_Waves.pdf} \\
\includegraphics[width=\columnwidth]{images/RealJumping.pdf} \\
\includegraphics[width=\columnwidth]{images/RealRunning.pdf} \\
\includegraphics[width=\columnwidth]{images/RealPtbdbNormal.pdf} \\
\includegraphics[width=\columnwidth]{images/RealPtbdbAbnormal.pdf}

\caption{Real signals}
\label{fig:realSignals}
\end{subfigure}
\hfill
\begin{subfigure}{0.495\textwidth}
\centering
\includegraphics[width=\columnwidth]{images/Synthetic_Sinusoidal_Waves.pdf} \\
\includegraphics[width=\columnwidth]{images/SyntheticJumping.pdf} \\
\includegraphics[width=\columnwidth]{images/SyntheticRunning.pdf} \\
\includegraphics[width=\columnwidth]{images/SyntheticPtbdbNormal.pdf} \\
\includegraphics[width=\columnwidth]{images/SyntheticPtbdbAbnormal.pdf}

\caption{TTS-GAN Synthetic signals}
\label{fig:syntheticSignals}
\end{subfigure}

\caption{A visual comparison of real data and their corresponding synthetic data generated by TTS-GAN.}
\vspace{-5mm}
\end{figure*}

\subsection{Evaluation}

We evaluate TTS-GAN using qualitative visualizations and quantitative metrics, and compare it with Time-GAN~\cite{yoon2019time}, which is the best current alternative.

\noindent \textbf{Raw data visualization:}
Fig.~\ref{fig:syntheticSignals} shows samples of synthetic data generated by TTS-GAN. Comparing them to the real data in Fig.~\ref{fig:realSignals}, we can observe that the synthetic data present visually similar signal patterns to the real data.

\noindent \textbf{Visualizations with PCA and t-SNE:}
To further illustrate the similarity between the real data and synthetic data, we plot visualization example graphs of data point distributions mapped to two dimensions using PCA and t-SNE in Figure~\ref{fig:smallvisual}. In these plots, red dots denote original data, and blue dots denote synthetic data generated by TTS-GAN. Again, we notice a similar distribution pattern between real and synthetic data.

\begin{figure*}

    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/Jumping-pca.pdf}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/Running-pca.pdf}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/ptbdbnormal-pca.pdf}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/ptbdbAbnormal-pca.pdf}
    \end{subfigure}

    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/Jumping-tsne.pdf}
        \caption{Jumping}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/Running-tsne.pdf}
        \caption{Running}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/ptbdbnormal-tsne.pdf}
        \caption{Normal ECG}
    \end{subfigure}
    \begin{subfigure}{0.245\textwidth}
        \centering
        \includegraphics[width=\columnwidth]{images/ptbdbAbnormal-tsne.pdf}
        \caption{Abnormal ECG}
    \end{subfigure}

    \caption{The PCA and t-SNE test for real and synthetic data generated by TTS-GAN.}

    \label{fig:smallvisual}

\end{figure*}

\noindent \textbf{Similarity scores:}
To quantitatively compare the similarity of the real and generated sequences, we defined two similarity scores, average cosine similarity ($avg\_cos\_sim$) and average Jensen-Shannon distance ($avg\_jen\_dis$). The detailed definition of these similarity metrics is given in Appendix~\ref{sec:similarityScores}. We first extract 7 well-known signal features from each signal channel $C$, to form a $7 \times C$ dimensional feature vector for each sequence. The $avg\_cos\_sim$ measures the average cosine similarity among all real signals and synthetic signals of the same class. Values closer to 1 indicate high similarity between two feature vectors. The Jensen–Shannon distance is a method of measuring the similarity between two probability-like distributions. We consider each extracted feature to be a normally distributed array of values and compute the Jensen-Shannon distance for corresponding features between real and synthetic feature vectors. The $avg\_jen\_dis$ is the average of all feature vector distances. A value closer to zero means a pair of signals have a small distance from each other and thus share similar distributions. As it can be observed from the experimental results shown in Table~\ref{tbl:similarityscores}, synthetic samples show a high average cosine similarity and low Jensen–Shannon distance for different signal classes. In addition, TTS-GAN wins against Time-GAN in 7 out of 10 cases.

\begin{table}[]
\centering
\begin{tabular}{c|c|ccccc}
\hline
Model Name & Similarity Score & Sinusoidal      & Jumping         & Running         & Normal          & Abnormal        \\ \hline
TTS-GAN    & avg\_cos\_sim    & \textbf{0.9936} & \textbf{0.9982} & 0.9988          & 0.9855          & \textbf{0.9768} \\
           & avg\_jen\_dis    & \textbf{0.0980} & \textbf{0.0870} & 0.0497          & \textbf{0.1861} & \textbf{0.2911} \\
Time-GAN   & avg\_cos\_sim    & 0.9935          & 0.9980          & \textbf{0.9989} & \textbf{0.9878} & 0.9719          \\
           & avg\_jen\_dis    & 0.1226          & 0.0924          & \textbf{0.0470} & 0.1883          & 0.3354          \\ \hline
\end{tabular}
    \caption{The similarity scores between real data and synthetic data of 5 different datasets. $avg\_cos\_sim$, the bigger the better. $avg\_jen\_dis$, the smaller the better. Bold texts identify better results.}
    \label{tbl:similarityscores}

\vspace{-5mm}
\end{table}

\section{Conclusions}
\label{sec:conclude}

In this work, we build a transformer-based GAN model (TTS-GAN) that is able to generate multi-dimensional time-series data of various lengths. A visual comparison of the raw signal patterns as well as data point distributions mapped in two dimensions show the similarity of the original data and the synthetic data. Two similarity scores are also used to quantitatively further verify the fidelity of the synthetic data. Overall, the experimental results demonstrate the viability of TTS-GAN as a generator of realistic time-series, when trained on real samples.

\bibliographystyle{ieeetr}

redpajama_set_name: RedPajamaArXiv


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text:
The Long Shadow of Rwanda on (Central) Africa
April 13, 2014 – Alemayehu G. Mariam — Comments ↓
Déjà vu 1994 Rwanda in 2014 Central African Republic
Last week, the people of Rwanda began a solemn week of official mourning to commemorate the 20th anniversary of the Rwanda Genocide. On April 6, 1994, Hutu extremist leaders in government, their political supporters and organized militiamen coordinated a systematic killing spree, which lasted over 100 days and consumed the lives of more than 800,000 mostly ethnic Tutsis and moderate Hutus. In 1994, Rwanda had a population of 5.5 million, of which 14 percent were Tutsi. Today Rwanda has a population of 11.5 million, of which the Tutsi population is less than 10 percent.
Last week, the people of the Central African Republic (CAR) continued to face their own "Rwanda-esque" nightmare of unspeakable horror. United Nations Secretary-General Ban Ki-moon was so disheartened by the ongoing "ethno-religious cleansing" in CAR that he declared, "The international community failed the people of Rwanda 20 years ago… And we are at risk of not doing enough for the people of the CAR today . . . Some say this is a forgotten crisis. I am here to help make sure the world does not forget… And we are at risk of not doing enough for the people of the CAR today… Atrocity crimes are being committed in this country. Ethno-religious cleansing is a reality. Most members of the Muslim minority have fled. We cannot just continue to say 'never again'. This, we have said so many times. We must act concertedly and now to avoid continued atrocities on a massive scale…"
Twenty years ago when Rwandan President Juvénal Habyarimana, a Hutu, died after his plane was shot down, Hutu extremists who opposed a 1993 ceasefire agreement for power sharing Hutus and Tutsis for the creation of a power-sharing government launched their "final war" to "exterminate the [Tutsi] cockroaches.' The "akazu" (top Hutu political leaders and elites) began hatching a concerted plan to exterminate Tutsis at least a year before the onset of the genocide. They set up their own radio station (Radio Télévision Libre des Mille Collines) and began a virulent and systematic campaign of demonization of Tutsis and moderate Hutus. At the onset of the genocide in April 1994, they used their radio station to embolden and encourage the killers. They read out the names of people to be killed and directed murderous militias (Interahamwe and Impuzamugambi) to different locations to commit horrific crimes. They repeatedly proclaimed on radio, "In truth, all Tutsis will perish. They will vanish from this country … They are disappearing little by little thanks to the weapons hitting them, but also because they are being killed like rats." In 1994, there were about 120,000 people living in Nyamata, the largest city in Rwanda located 30 miles from the capital Kigali. In less than a month and a half, only 50,000 were left. Five out of every six Tutsis had been killed.
Today, CAR has exploded into sectarian and communal strife and civil war. In 2013, Seleka militia (an alliance of rebel militia factions that overthrew the CAR government in March 2013) launched "a murderous rampage that started in the north-east and spread out across the country, seizing the capital Bangui and ousting then-President François Bozizé. Over the following 10 months, Seleka forces killed countless civilians, burned numerous villages, and looted thousands of homes." Although an estimated 90 per cent of CAR's population is Christian including the ousted president Bozizé, most of the Seleka forces and their leader Michel Djotodia are Muslims. According to Amnesty International, Seleka "abuses spurred the emergence of the loosely organized "anti-balaka" militia ("machete proof" in Sango), made up of Christians and animists. In the last four months of 2013, anti-balaka fighters carried out horrific attacks on Muslim communities, particularly in CAR's northwest." The violence has continued to intensify and currently international peacekeepers have "failed to prevent the ethnic cleansing of Muslim civilians in the western part of the Central African Republic." In the town of Yaloke, less than 150 miles from the capital Bangui, there were an estimated 30,000 Muslims with 8 mosques a year ago . According to Human Rights Watch observers, today there are fewer than 500 Muslims and one mosque left.
For the past year, the international human rights organizations have been ringing the alarm bells over the impending "ethno-religious cleansing"-cum-genocide in the CAR. In October 2013, Amnesty International issued a report warning of the "human rights crisis in CAR is spiraling out of control." As the violence and carnage increased, a token force of several thousand international peacekeepers (2,000 French troops and some 6,000 African Union forces) was sent to CAR. Last week, the U.N. authorized the deployment of 12,000 peacekeepers to CAR but that force will not arrive until September 2014. Human Rights Watch issued a dire warning indicating that the extreme level of violence in CAR is "forcing entire communities to leave the country. At this rate, if the targeted violence continues, there will be no Muslims left in much of the Central African Republic."
The parallels between 1994 Rwanda and 2014 CAR are chilling. In 1994 Rwanda, the Organization of African Unity and the international community including the U.S. and the U.N. turned a blind eye to the spiraling genocide. In 2014 CAR, the international community is giving lip service and performing window dressing with token military presence as tens of thousands of innocent civilians are being massacred and hundreds of thousands displaced in "ethno-religious cleansing". For a full year, it was manifest to the African Union and the international community that a backlash in CAR was foreseeable and preventable. Amnesty International noted, "In power for nearly 10 months, the Seleka were responsible for massacres, extrajudicial executions, rape, torture, and looting, as well as massive burning and destruction of Christian villages. As the Seleka withdrew, the international forces allowed the anti-balaka militias to take control of town after town. The resulting violence and forcible expulsion of Muslim communities were predictable."
Doomed by history?
Has Africa learned its lessons from 1994 Rwanda? Does the world care about Africa? Is Africa condemned to live in the long shadow of Rwanda? Are we witnessing Rwanda 1994 in CAR 2014? What is the difference between "ethno-religious cleaning" and genocide (the same difference between tomAto and tomayto)? Is CAR Rwanda redux? Was Rwanda the future of CAR, and CAR the future of Africa?
There is not a single country in Africa that is immune from the Ebola virus of communalism and sectarianism. Like Ebola, the initial symptoms of communalism and sectarianism appear to be not out of the ordinary. Those who claim to struggle against ethnic and religious oppression often proclaim righteousness. When they become the victors, they commit equally or more atrocious crimes that make a travesty of their cause and causes the deaths and suffering of millions. Like the Ebola virus, communalism and sectarianism are a deadly combination in the body politics of Africa. A vaccine must be found soon if Africa is to be spared the scourge of sectarianism and communalism.
The specter of genocide and crimes against humanity in Africa
In his "Communist Manifesto", Karl Marx announced to the world that "A spectre is haunting Europe — the specter of communism." In a metaphorical equivalent, "A specter is haunting Africa – the specter of genocide and crimes against humanity." Marx declared, "The history of all hitherto existing society is the history of class struggles." Is the future of Africa going to be a struggle against genocide and crimes against humanity?
Article 5 (a) of the Rome Statute grants prosecutorial jurisdiction to the International Criminal Court over the crimes of genocide and crimes against humanity. Article 6 defines "genocide" to include specific "acts committed with intent to destroy, in whole or in part, a national, ethnical, racial or religious group." These acts include, among others, "killing or causing serious bodily or mental harm to members of the group, deliberately inflicting on the group conditions of life calculated to bring about its physical destruction in whole or in part.
Article 7 defines crimes against humanity which includes "murder, enslavement, deportation or forcible transfer of population, imprisonment or other severe deprivation of physical liberty in violation of fundamental rules of international law, torture, rape, and persecution against any identifiable group or collectivity on political, racial, national, ethnic, cultural, religious, gender.
Crimes against humanity and mini-genocides are commonplace occurrences in Africa today. With the exception of the international human rights organizations, few Africans (including leaders and members of the African intelligentsia) and fewer Western media and diplomatic communities are prepared to talk about the genocide and crimes against humanity taking place in Africa. In April 1994, when the Clinton Administration pretended to be ignorant of the unspeakable massacres in Rwanda, Susan Rice, President Obama's current national Security Advisor, casually inquired of her colleagues, "If we use the word 'genocide' and are seen as doing nothing, what will be the effect on the November [congressional] election?"
Beginning in 2003, the Sudanese government exploited tribal/ethnic differences in the Darfur region pitting nomadic Arab herders against pastoralist African groups. The Sudanese government armed ethnic Arab militia groups, known as the "Janjaweed," (not unlike Rwanda's Interahamwe) to attack the ethnic African groups. As the government rained bombs from the sky, the Janjaweed burned villages, poisoned wells, raped and massacred over one-half million people and displaced millions more. In 2009, the International Criminal Court issued an arrest warrant for Sudanese President Omar al-Bashir and other officials for directing a campaign of mass killing, rape and pillage against civilians in Darfur. Bashir sneered at the ICC indictment, "Tell them all, the ICC prosecutor, the members of the court and everyone who supports this court that they are under my shoes." Uhuru Kenyatta, along with other co-defendants including his deputy president William Ruto, is currently facing trial in the ICC for his alleged role in masterminding the post-election violence in Kenya in late 2007 and early 2008. Over 1,100 people are believed to have died in that violence and 600 thousand displaced. Kenyatta is charged in a five-count indictment under the Rome Statute for crimes against humanity including murder, deportation or forcible transfer, rape, persecution and other inhumane acts. However, Uhuru Kenyatta will never see the inside of the ICC courtroom in The Hague. Truth be told, it is not only Kenyatta and his partners in crimes against humanity who have escaped justice. Many other "genocidaires" and criminals against humanity in Africa have thumbed their noses on the ICC and other tribunals and gotten away. In southern Ethiopia, the indigenous people along the Omo River valley are facing extinction as a result of displacement and villagization caused by the construction of the so-called Gibe III dam. In August 2012, the world-renowned conservationist and paleoanthropologist Dr. Richard Leakey predicted that the Gibe III "dam will produce a broad range of negative effects, some of which would be catastrophic to both the environment and the indigenous communities living downstream." Is the regime in Ethiopia culpable for genocide and crimes against humanity for constructing a dam that certainly damns the indigenous people of the Omo River Basin?
In western Ethiopia, the people of Gambella have lost their ancestral lands and homes to Indian multinationals. The regime in Ethiopia "leased" to an Indian corporation known as Karuturi "2,500 sq km of virgin, fertile land an area the size of Dorset, England" in Ethiopia for £150 a week ($USD245). To make way for Karuturi, tens of thousands of Ethiopians in Gambella were forced to move and become part of the regime's villagization program. A UNICEF field study concluded: " The deracination [uprooting from ancestral lands] of indigenous people that is evident in rural areas of Gambella is extreme. It is very likely that Anuak (and possibly other indigenous minorities) culture will completely disappear in the not-so-distant future." Is the regime in Ethiopia responsible for genocide and crimes against humanity in its use of villagization policy in Gambella?
For nearly a quarter of a century, the regime in Ethiopia has been repackaging an atavistic style of tribal politics in a fancy wrapper called "ethnic federalism." The regime has managed to segregate the Ethiopian people by ethno-tribal classifications and corralled them like cattle into grotesque regional political units called "kilils" (literally means "reservation"; semantically, the word also suggests the notion of an exclusion zone, an enclave). The ideology of "kililism" shares many of the attributes of Apartheid's "Bantustanism" ("black African tribal homelands"). Both ideologies aim to concentrate members of designated ethnic groups into "homelands" by creating ethno-linguistically homogenous territories which could ultimately morph into "autonomous" nation states. "Kililistization" is "villagization" on steroids; it reduces Ethiopia to a bunch of glorified villages. "Kilils" are basically a kinder-and-gentler form of Apartheid-style Bantustans. Is "kililism" the seedbed of genocide and crimes against humanity? Is the regime in Ethiopia responsible for genocide and crimes against humanity for its "kililization" policy? For over a decade, the regime in Ethiopia, despite claims of "ethnic federalism", has mounted an indiscriminate counterinsurgency campaign in the Ogaden region of Ethiopia to suppress the Ogadenis basic demands for autonomy. The regime has adopted policies aimed at starving the Ogadeni population and economically blockade Ogadeni towns and villages. The regime has indiscriminately strafed and bombed Ogadeni civilians and cut off humanitarian aid to the region. Massacres, torture, rape and disappearances have been used as weapons of war by the regime against Ogadenis. Human Rights Watch issued a 130 page report entitled, "Collective Punishment: War Crimes and Crimes against Humanity in the Ogaden area of Ethiopia's Somali Region" documenting the regime's crimes. Is the regime in Ethiopia responsible for genocide, crimes against humanity and war crimes in the Ogaden?
In 2005, the regime in Ethiopia orchestrated the massacre of hundreds of unarmed protesters in the aftermath of parliamentary elections in May of that year. Police and security officials under the personal and direct command and control of the late Meles Zenawi and his top officials coordinated the massacres. An official Inquiry Commission appointed by Meles documented the extrajudicial killing of at least 193 unarmed protesters, wounding of 763 others and arbitrary imprisonment of nearly 30,000 persons. The Commission's evidence further showed that nearly all of the 193 unarmed protesters were killed by the regime's sharpshooters. The Commission further documented that on November 3, 2005, during an alleged disturbance at the infamous Kality prison near Addis Ababa, guards sprayed more than 1,500 bullets into inmate cells in 15 minutes, killing 17 and severely wounding 53. Was Meles personally responsible for crimes against humanity? Are those officials currently in the regime or others who participated but are no longer in the regime responsible for crimes against humanity in the 2005 massacres?
In May 2012, Meles hectored his rubberstamp parliament to justify his forced expulsion (or as some have described it "ethnic cleansing") of Amharas from southern Ethiopia and zapped his critics for their irresponsibility in reporting and publicizing it. Meles said the "Amharas" or as he described them the "sefaris from North Gojam" (how humiliating to be called a "safari" or "squatter" in one's own country!) had to be removed from their homesteads in the south purely out of environmental conservation concerns. There was not a scintilla of evidence that the removal was justified by environmental concerns. Was Meles personally responsible for crimes against humanity for forcibly removing thousands of "Amharas" from the southern part of their country?
Last April, the regime in Ethiopia authorized the forced removal of "Amharas" from the Benishangul-Gumuz region (one of the nine "kililistans" in Ethiopia) in act widely described as "ethnic cleansing". Prof. Yacob Hailemariam, a prominent Ethiopian opposition leader and a former senior Prosecutor for the International Criminal Tribunal for Rwanda commented that the expulsion of members of the Amhara ethnic group from Benishangul-Gumuz was a de facto ethnic cleansing. "The forceful deportation of people because they speak a certain language could destabilize a region, and if reported with tangible evidence, the UN Security Council could order the International Criminal Court to begin to examine the crimes."
Large swaths of Ethiopia today are afflicted by famine and starvation which the regime has kept secret for a long time. In February of this year, the regime bureaucratically announced that 2.7 million of the 91 million people in Ethiopia are starving and in need of humanitarian aid. The total food requirement (which in the past the regime has low-balled) is estimated at 388,635 metric tons. The regions most affected by famine include Gambella (16.7%) , Somali (13.8%), Tigray (11.3%) and Afar (10%). The regime has budgeted only $51.6 million dollars, which represents only 12.8 percent of what is required. As the alarm over the impending famine was being broadcast, Hailemariam Desalegn, the ceremonial prime minster, was crowing about a "double digit economic growth". Thousands of Ethiopians are dying from starvation every month unseen and unregistered because the regime maintains a complete chokehold on information coming from famine afflicted areas. Is the regime in Ethiopia responsible for genocide for the famine deaths by benign neglect and depravity for failing to properly budget when it had had full advance knowledge of the impending food catastrophe?
Since the regime in Ethiopia took power in May 1991, it has been engaged in inflammatory ethnic talk demonizing "Amharas" and others. As recently as a few months ago, the regime was coordinating and orchestrating a full-court press demonization and vilification campaign against Atse Menelik II, the Nineteenth Century Ethiopian emperor whose centennial is being celebrated this year (Ethiopian calendar). The regime was using Menelik as the straw man to methodically organize a campaign to incite hatred and ill-will between members of ethnic groups. The incitement campaign was conducted largely through regime lackey-proxies, stooges and puppets. Through its minions, the regime has used the most loathsome words, inflammatory rhetoric and repugnant imagery to describe Menelik's alleged brutality. One hundred years after his death, the regime has tried to resurrect Menelik as the devil incarnate. What is the purpose of such propaganda? Is inflammatory rhetoric calculated to stoke the fires of ethnic strife a crime against humanity?
The late Meles Zenawi was a man of much intelligence (if his foreign cheerleaders including Susan Rice and Clare Short are to be believed) and little wisdom and common sense. He liked to play with the specter of genocide as much as little children like to invoke the mythical bogeyman to scare each other. In 2010, Meles not only defiled and desecrated the memory of the Rwandan Genocide victims but also tried to take political advantage by resurrecting through inflammatory rhetoric the ghastly ghosts of Rwanda comparing the Voice of America Amharic Service to Radio Mille Collines of Rwanda. Meles said, "We have been convinced for many years that in many respects, the VOA Amharic Service has copied the worst practices of radio stations such as Radio Mille Collines of Rwanda in its wanton disregard of minimum ethics of journalism and engaging in destabilizing propaganda." Zenawi used the bogus "genocide" excuse to spend millions of dollars to jam broadcasts of VOA Amharic into Ethiopia, precious resources that could have been used to aid famine victims and provide health care and education.
Such were the outrageous words that dripped from Meles' mealy-mouth.When Meles said "the VOA Amharic Service has copied the worst practices of radio stations such as Radio Mille Collines", what was his real message, the message between the lines? Was he asserting that the Amharic service has called for a "final war" and the "extermination" of some Ethiopians like "cockroaches", "vermins" and "rats"? Who are the "genocidaires" in the country being mobilized by the VOA transmissions? Was he saying that the Amharic service is directing and coordinating murderous militias and groups for genocidal activities to make sure that some Ethiopians "will perish and vanish from the country." Meles was intentionally using the Rwanda Genocide for political purposes; in effect he was trying to convince Ethiopians and his foreign bankrollers that without him and his regime Ethiopia will be another Rwanda. Any regime that seeks to cling to power by invoking the specter of genocide must eternally be opposed!
Every African man and woman must struggle to invent a thousand and one reasons to hope
Genocide and crimes against humanity in any African country must be regarded as genocide and crimes against humanity in every African country.The Rwanda genocide is an African genocide. It is not a crime inflicted against Tutsis and moderate Hutus. It is a dastardly crime committed against all Africans and humanity. It is a crime that has left deep and indelible scars on the soul of every living African. Africans must remember, not despair. To paraphrase Elie Wiesel, the Holocaust Survivor and Nobel Peace Laureate, "All Africans must remember the killers. Each African must remember the victims, even as they individually struggle to invent a thousand and one reasons to hope. Because we remember, we despair. Because we remember, we have the duty to reject despair. Hope is possible beyond despair."
The crucible for genocide and crimes against humanity is the human heart
The genocide in Rwanda did not begin on April 6, 1994 when machete-wielding thugs began roaming the streets. The seeds of that genocide were planted decades before in the hearts of misguided and spiteful Rwandans regardless of ethnicity. Hate and bigotry have neither race nor religion. Genocide slowly germinates in the minds of men and women who nurse hatred and anger and silently sizzle in frustration. Like mushrooms that grow in the darkness of the cave, the seeds of the genocide mushroom in the darkness of the heart. Africans must not despair; they must remember the message of Dr. Martin Luther King, Jr. "Darkness cannot drive out darkness; only light can do that. Hate cannot drive out hate; only love can do that." Only the sunlight of truth can illuminate the dark heart.
Silence and indifference are the oxygen of genocide and crimes against humanity
Genocide and crimes against humanity are quintessentially crimes of silence and crimes of indifference. The great powers were silent witnesses to the genocide in Rwanda. They knew exactly what was going on beginning on April 6, 1994. They chose to remain silent. The African Union, the U.N and the European Union chose to remain silent, but they knew. The slaughter continued for over 100 days and cost the lives of 800 thousand people because those who could have stopped it did not care. Africans must not wait for others to save them. They must save themselves from themselves. That is why all Africans must be of a single mind in condemning those who promote ethnic hatred and religious intolerance; they must actively resist those who demonize individuals and groups on the basis of race, ethnicity, religion, language, gender or any other pernicious classification.
Truth and justice before reconciliation
Jean Bosco Mutangana, a Rwandan prosecutor in charge of that country's international crimes unit recently said, "In our lifetime we shall continue to pursue them, and those who come after us will continue to pursue them. You cannot have reconciliation without real, true justice being done." That is a message for all Africans. Justice and truth/reconciliation are two faces of the same coin. Whichever way the coin is tossed, it shows only one face of human rights. Nelson Mandela said, "Reconciliation means working together to correct the legacy of past injustice." Past injustices are what cast long dark shadows on the future and fate of Africa.
The responsibility to educate
Commemorating the victims of genocide in Rwanda and Darfur and condemning "ethno-religious cleansing" in CAR is not enough. Africa's young people must be educated about genocide and crimes against humanity. Such information must be part of the curriculum in every African school, college and university. Ignorance is the fertilizer of genocide and crimes against humanity. Ban Ki-moon told the people of Rwanda, "We must not be left to utter the words 'never again', again and again". These are empty words that come too late to aid the innocent men, women and children of CAR who have already lost their lives and those desperately fleeing to save their lives.
Instead of criticizing and antagonizing the international human rights organizations, African leaders should invite and challenge them to help create human rights education for young Africans who could in turn use their training and knowledge to empower their peer groups and communities with knowledge, skills and attitudes that promote universally recognized human rights principles. Human rights education is the sine qua non and a prerequisite for the resolution of conflict in Africa. Those who do not learn from the genocide and crimes against humanity of the past are doomed to repeat them.
Africa's short men and their long shadows
Scholars say the problem of governance in Africa is "Big Man" rule where leaders privatize the state and use state resources to service their clients in the general population who in turn help them cling to power. I believe the problem of African governance is the opposite. An old saying teaches, "You know it is near sunset when short men cast long shadows." There are too many short men in high offices in Africa pretending to be Big Men and great leaders. The short men of Africa are in fact leaders of wolf packs who prey on the people. They are small men of little intelligence and boundless malevolence. They are short men with little vision and infinite power obsession. They are small men with little compassion; they brim with hate and aggression. These short men in Africa are not only bad leaders but also bad men, bad human beings. They cast long shadows and have made Africa a "dark continent". "Old sins cast long shadows." The sins of the small men cast long dark shadows of war, genocide and crimes against humanity in Africa. It is the duty of every African to stand tall over Africa's short men and obliterate their long dark shadows with the bright sunlight of love, truth, tolerance, harmony, justice and reconciliation.
I remember Rwanda 1984. I remember the Central African Republic 2014. Hope is possible beyond despair.
Professor Alemayehu G. Mariam teaches political science at California State University, San Bernardino and is a practicing defense lawyer.
Previous commentaries by the author are available at:
http://open.salon.com/blog/almariam/
www.huffingtonpost.com/alemayehu-g-mariam/
Amharic translations of recent commentaries by the author may be found at:
http://www.ecadforum.com/Amharic/archives/category/al-mariam-amharic http://ethioforum.org/?cat=24


text:
La Serra d'Orpinell és una serra situada als municipis de Carme, la Llacuna i Orpí (Anoia), amb una elevació màxima de 736 metres.

Referències

Carme
Serres de la Llacuna
Geografia d'Orpí


text:
https://www.greenwichtime.com/sports/article/LaVine-powers-Bulls-past-Wizards-115-106-14979243.php
LaVine powers Bulls past Wizards 115-106
Jay Cohen, Ap Sports Writer
CHICAGO (AP) — Zach LaVine wants to play in the All-Star Game next month in Chicago.
He is making quite the case. Especially lately.
LaVine had 30 points, seven rebounds and seven assists, and the banged-up Bulls beat Bradley Beal and the Washington Wizards 115-106 on Wednesday night.
"I've been blessed to coach a lot of All-Stars, and I think he should be in the game," Bulls coach Jim Boylen said.
Chicago placed seven players in double figures in its second win in its last nine games. Thaddeus Young had 18 points and eight boards, and Tomas Satoransky scored 18 points against his former team.
The Bulls outscored the Wizards 60-46 in the second half.
"They were getting in transition, some switches, made some tough shots," Wizards coach Scott Brooks said. "We have to be better."
LaVine went 6 for 11 from 3-point range in his career-best fifth straight game with at least 25 points. It's the longest such streak for the Bulls since Derrick Rose accomplished the feat in six consecutive games in 2010.
LaVine's All-Star argument is weighed down by Chicago's 15-27 record, but he is averaging a career-best 24.6 points in his sixth NBA season.
Chicago Bulls guard Zach LaVine, left, looks to pass the ball as Washington Wizards guard Gary Payton II defneds during the first half of an NBA basketball game in Chicago, Wednesday, Jan. 15, 2020.
Photo: Nam Y. Huh, AP
"It would mean a lot. It's one of my goals," LaVine said of the All-Star Game. "I think it would be great for the city, having one of (its) guys there. I don't feel like I need the verification of it. I think I know where I am as a player and how I've been playing."
Beal scored 23 points on 8-for-13 shooting after he was listed as questionable because of right shoulder soreness. The high-scoring star also has been hampered lately by soreness in his lower right leg.
Jordan McRae had 16 points for Washington, which has dropped three of four. Isaiah Thomas and Davis Bertans scored 12 points apiece.
"We just have to figure out how to get it done, honestly," Beal said. "I really don't know, you just have to get it done."
The Bulls grabbed control with a 10-0 run in the fourth quarter. LaVine made a 3-pointer to make it 106-95 with 4:56 left.
Chicago held on down the stretch despite playing short-handed after Daniel Gafford and Chandler Hutchison left with injuries.
Gafford dislocated his right thumb on a steal in the first quarter. The 6-foot-11 Gafford called timeout after picking off a pass by Ian Mahinmi, and then jogged off the court in the direction of the Bulls' locker room.
X-rays were negative, and the team said he is expected to miss two to four weeks.
"I thought it was just like a slight jam of my thumb and I looked at my thumb and it was way more than a jam," a chuckling Gafford said.
Hutchison re-injured his right shoulder when he was fouled on a dunk in the fourth, leading to a hard fall. The 23-year-old Hutchison, who had 11 points on 5-for-7 shooting, just returned last week from a right shoulder sprain.
"Chandler is going to be re-evaluated tomorrow," Boylen said. "Same shoulder that's been bothering him, so we'll see where that goes."
QUITE UNUSUAL
The teams played the second half with red streamers hanging from the overhead scoreboard, remnants of a Disney-themed halftime show. One of the streamers fell down during a timeout with 8:53 left in the third quarter, and two workers scurried out to pick it up before play resumed.
Wizards: Rookie F Rui Hachimura traveled with the team to Chicago, but remains out with a groin injury. He hasn't played since Dec. 16. "He's doing well," Brooks said. "Shot around a little bit, very, very light shooting yesterday. ... Hopefully we can get him on the court more each day." ... C Thomas Bryant played about 12 minutes in his second game back after missing 20 games with a foot injury. The 6-foot-10 Bryant is on a minutes restriction. "He's missed a lot of time," Brooks said. "Throwing him back into the fire in the NBA competitiveness. We'll take our time still."
Bulls: F Otto Porter Jr. missed his 33rd consecutive game with a broken left foot. Boylen said he had no update on his status. "He's very frustrated," Boylen said. "I just try to keep him up and be positive and you know we'll take you back when you come back." Boylen said there has been no talk of shutting Porter down for the season.
Wizards: Visit Toronto on Friday night. They lost 122-118 to the Raptors on Dec. 20.
Bulls: Visit the Philadelphia 76ers on Friday night for their first meeting of the season.
Jay Cohen can be reached at https://twitter.com/jcohenap
More AP NBA: https://apnews.com/NBA and https://twitter.com/AP_Sports


text:
Arrests at protest over New York Times' 'unacceptable' climate coverage
By The Guardian
A climate change protest orchestrated by the Extinction Rebellion activist group briefly blocked Eighth Avenue in New York on Saturday afternoon, between the Port Authority transit hub and the home of the New York Times.
The New York police department (NYPD) said 70 people were arrested as they called for more effective media coverage of the dangers of climate change, in a dramatic demonstration that saw people stage a die-in in front of the newspaper building and disrupt traffic in midtown Manhattan.
Two demonstrators scaled the front of the Port Authority building, which houses a major bus terminal and subway interchange, to hang a protest banner. Protesters also climbed on to the canopy outside the Times headquarters and unfurled a banner that encouraged the use of the phrase "climate emergency" instead of "climate change".
One protester, Donna Nicolino, told the Guardian she was ready to be arrested, because "we want the New York Times as well as all the other media to treat climate change as the crisis it is".
She joined a line of people with arms linked on West 40th Street and Eighth Avenue, blocking the road. The group remained until they were arrested by New York City police officers.
"The lack of coverage of the climate crisis is completely unacceptable," said Becca Trabin, a member of Extinction Rebellion's press and fundraising teams. "It's a public safety crisis on a global scale."
Trabin said that though it might not be convenient to report on, read about and discuss climate change every day, it should nonetheless be a media priority.
"No one wants to hear about the climate crisis every day but if we don't read about it every day we endanger ourselves," she said.
A spokeswoman for the New York Times, Danielle Rhoades Ha, said that in 2018 the newspaper published 795 articles about the climate, including investigative stories and dispatches from across the globe about the impact of climate change.
"There is no national news organization that devotes more time, staff or resources to producing deeply reported coverage to help readers understand climate change than The New York Times," Rhoades Ha said.
"We fully support this group's right to express their point of view, even when we disagree with it as it relates to our coverage."
For nearly an hour, the block outside the Times building was crowded with demonstrators, onlookers and NYPD officers. A police recording repeated a warning that anyone who did not clear the street would be arrested. A helicopter flew overhead.
"Seas are rising and so are we," chanted one group of demonstrators on the sidewalk, where surprised tourists, workers and bystanders asked for more information.
The block remained closed after the demonstration ended as police officers processed the arrest of several demonstrators, including two who had rappelled down the face of Port Authority to hang a sign.
Passersby stopped police to ask if a movie was being shot or if there had been a terrorist attack.
More than half the US experiencing dangerous heat wave
In 1995, a heat wave in Chicago killed more than 700 people; emergency rooms are on alert, bracing for an influx of heat-related emergencies.
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NAMM 2019 VIDEO: Is Mooer's GE300 its crowning achievement?
NAMM 2019: Mooer has steadily been making a name for itself outside of its trademark mini pedals, and the GE300 looks set to be the Chinese company's crowning achievement.
Touted as the company's new flagship model, the GE300 packs 108 digital amp models, as well as 164 effects, 43 optional factory cab sims and a fully featured IR loader.
We stopped by Mooer's NAMM stand for a first-hand view.