Arab and Jewish Israelis Reconciliation as Economic factor

Status: 21.06.2021 6:04 p.m.

After the change of government in Israel, the multi-party coalition is faced with difficult tasks, above all the reconciliation between Israelis and Arabs. That would also help the country economically.

By Till Rüger, Muhamed Abofani and Mike Lingenfelser, ARD studio Tel Aviv

For the first time, Arab MPs are part of one Ruling coalition in Israel. The Islamic Ra’am party of Mansour Abbas provided the decisive votes for the so-called “Coalition of Change” of Naftali Bennett and Yair Lapid. With a wafer-thin majority of just one vote it became so Long-term Prime Minister Benjamin Netanyahu voted out of the Likud. A historic moment for an underrated minority. Arab Israelis now make up around 21 percent of Israel’s citizens – and their number is growing. Officially, they have the same rights and duties as Jewish Israelis. But many feel disadvantaged, for example when it comes to job placement or training.

Feeling of second class citizens

The villages and residential areas of the Arab Israelis also usually have poorer infrastructure. They suffer from a high level of crime, which the police have so far failed to combat. Many Israeli Arabs therefore see themselves as second-class citizens. In its Inaugural address to the Knesset , the Israeli parliament, said the new Prime Minister Bennet of the right-wing Yamina party that his new government was opening a new chapter in relations with fellow Arabs. He wanted to eliminate imbalances in education and alleviate the housing shortage of the Bedouins in the south. Everyone has the right to live in dignity. During the eleven days of rocket bombardment from the Gaza Strip, the conflict broke out even more clearly than usual. Many cities and villages with a mixed population of Arab and Jewish Israelis such as Lod, Akko and Jaffo experienced the worst unrest in decades. The deep crack became visible even where both groups had previously lived largely peacefully together.

Deep distrust with historical roots

Almost all Arab Israelis lived in what is now Israel’s territory before the establishment of Israel in 1948. In the Arab-Israeli war of 1948, they did not flee and were not expelled. Many see themselves as sisters and brothers of the Palestinians in the West Bank or Gaza Strip.

After the recent unrest, many Jewish Israelis avoided the Arab residential areas, including some areas of Jaffo, the district in the south of Tel Aviv. The pubs and restaurants in Jaffo had reported up to 70 percent fewer Jewish guests than before the rocket fire from the Gaza Strip. During the unrest, the government imposed curfews and cordoned off entire streets, but otherwise did little to counter the deep-seated mutual distrust.

Arab Israelis as an important economic factor

Private initiatives are different. The Israeli insurance company Harel, for example, posted large posters on Tel Aviv’s city motorway in Hebrew and Arabic: “The best insurance against violence is coexistence.” Many other companies followed suit with commercials and newspaper advertisements. Because the Arab Israelis are an important economic factor.

It was not until the later 1970s that a visible number of Arab Israelis began studying at an Israeli university, primarily in engineering, pharmacy and teaching. In the early 1990s, agriculture and the textile industry went downhill in Israel. Many Arab Israelis were active there.

“The best insurance against violence is coexistence” – poster of the Harel concern. Image: Till Rüger

At the same time, Russian immigrants came into the country. More than a million new homes had to be built, and so the Arab Israelis got access to a new economic sector: housing, road construction, electricity and gas management.

Locked out of the software industry

Around the year 2000 there was a small high-tech miracle in Israel: Many Israeli software companies were able to establish themselves worldwide. But this industry remained closed to the Arab Israelis because, among other things, it had to do with security issues. Many Arab computer scientists were not permanently employed.

Since then, the Arab Israelis have almost only chosen professions in which they could certainly find a job, for example in medicine or pharmacy. Many saw a solid education as the only way to improve economically. The Arab villages in Israel are now competing with each other to see who has more doctors per 1,000 inhabitants. Pharmacy and medicine are often dominated by the well-trained doctors and pharmacists from the Arab minority. In recent years, the Arab Israelis have rediscovered the high-tech industry. This is suffering from underemployment in Israel, and many start-ups are desperately looking for well-trained employees – a new opportunity for the Arab Israelis.

Unequal distribution of the workforce

If you look at the total number of employees, then the Jewish Israelis come to a good 83 percent. Conversely, this would mean that the Arab Israelis, together with the large number of Asian guest workers, make up less than 17 percent of the workforce in the country.

If you take a look at the figures from the Israeli statistics agency exactly where and how Arab Israelis are employed today, the distribution looks like this: 13 percent are unskilled, 42 percent are skilled workers in industry and construction. Two percent work in agriculture, 15 percent in the service sector, three percent in administration, five percent are engineers, eleven percent are academics and five percent run a company. The labor force participation of Jewish Israelis aged 15 and over was 65.0 percent in 2019, while that of Arab Israelis aged 15 and older was only 43.4 percent, mainly due to the significantly lower labor force participation of women.

Reconciliation as the primary goal

The reconciliation of the two population groups – also for economic reasons – is likely to become one of the most important tasks of the new government of Prime Minister Bennett and his Vice Yair Lapid of the liberal Center Party.

Only when the inner-Israeli conflict has calmed down, according to Middle East observers, the new government can look to the West Bank and Gaza again and tackle the peace process with the Palestinians, which has come to a complete standstill

The sea boundary of Ha Long – Cam Pha has a length of 18.7km, started in September 2019 and passes through the wards of Hong Ha, Ha Tu, Ha Phong (Ha Long City) and Quang Hanh (Cam Pha City). The section through Cam Pha city with a length of nearly 10km has basically completed the construction of the foundation, asphalt carpeting, lighting, etc. The contractor is expanding the roadbed to adjust 6 lanes. The section through Ha Long City has been implemented since March 2020, but the road foundation has been basically completed. Bridge 2 over Lo Phong stream (Ha Phong ward, Ha Long city) with a length of 80m has completed the main items, the contractor is preparing to construct the bridge deck. In the mountain tunnel category, the contractor is conducting 4 consecutive excavations in both directions at the same time. Up to now, both tunnels have dug 135/470m, it is expected to open the tunnel until July 15. Tunnel door through the mountain side of Ha Long city. For 4km at sea, the contractor is actively constructing the roadbed, it is expected that in August 2021, the foundation will be completed to move to the finishing stage and asphalt pavement. Do Phuong

Edit: Dimensions Microsoft Swin Transformer, which slaughtered major CV tasks, recently open sourced the code and pre-training models. Since Google proposed Transformer in June 2017, it has gradually become the mainstream model in the field of natural language processing. In the recent period, Transformer has even started its own cross-border journey and began to show its talents in the field of computer vision. A number of new Transformer-based models have emerged, such as Google’s ViT for image classification and Fudan, Oxford, Tencent SETR and other institutions. As a result, “Is Transformer a omnipotent?” has once become a hot topic in the machine learning community. Not long ago, researchers from Microsoft Asia Research proposed a hierarchical visual Transformer calculated through shifted windows, which they called Swin Transformer. Compared with the previous ViT model, Swin Transformer has made the following two improvements: First, it introduces the hierarchical construction method commonly used in CNN to build a hierarchical Transformer; second, it introduces the idea of ​​locality, which is for windows that do not overlap. Perform self-attention calculations within the area. Link to the paper: https://arxiv.org/pdf/2103.14030.pdf First look at the overall workflow of Swin Transformer. Figure 3a shows the overall architecture of Swin Transformer, and Figure 3b shows two consecutive Swin Transformer blocks. The highlight of this research is the use of moving windows to calculate the representation of hierarchical Transformers. By limiting the self-attention calculation to non-overlapping partial serial ports, it also allows cross-window connections. This hierarchical structure can be flexibly modeled on different scales and has the linear computational complexity of the image size. Figure 2 below shows the workflow of calculating self-attention using moving windows in the Swin Transformer architecture: The characteristics of the model itself enable it to achieve a very competitive performance in a series of visual tasks. Among them, an image classification accuracy rate of 86.4% was achieved on the ImageNet-1K data set, and 58.7% of the target detection box AP and 51.1% of the mask AP were achieved on the COCO test-dev data set. At present, on the COCO minival and COCO test-dev data sets, Swin-L (a variant of Swin Transformer) has achieved SOTA in both target detection and instance segmentation tasks. In addition, on the ADE20K val and ADE20K datasets, Swin-L also implements SOTA in the semantic segmentation task. Open source code and pre-trained model Not long after the Swin Transformer paper was published, Microsoft officially released the code and pre-training model on GitHub recently, covering image classification, target detection, and semantic segmentation tasks. Only two days after the launch, the project has received 1,900 stars. Project address: https://github.com/microsoft/Swin-Transformer First, the image classification task, the accuracy results of Swin-T, Swin-S, Swin-B and Swin-L variant models on ImageNet-1K and ImageNet-22K data sets are as follows: Secondly, the target detection task: The results of the variant models of Swin-T, Swin-S, Swin-B and Swin-L on the COCO target detection (2017 val) dataset are as follows: The final semantic segmentation task: The results of Swin-T, Swin-S, Swin-B and Swin-L variant models on the ADE20K semantic segmentation (val) dataset are as follows. Currently, Swin-L has achieved 53.50% of the SOTA verified mIoU score. Build new, see wisdom – 2021 Amazon Cloud Technology AI Online conference April 22, 14:00-18:00 Why do so many machine learning loads choose Amazon Cloud Technology? How to achieve large-scale machine learning and enterprise digital transformation? “Building New · Seeing Wisdom-2021 Amazon Cloud Technology AI Online Conference” is led by Alex Smola, vice president of global artificial intelligence technology and outstanding scientist of Amazon Cloud Technology, and Gu Fan, general manager of Amazon Cloud Technology Greater China Product Department, and more than 40 heavyweights Guests will give you an in-depth analysis of the innovation culture of Amazon cloud technology in the keynote speech and 6 major conferences, and reveal how AI/ML can help companies accelerate innovation. Session 1: Amazon Machine Learning Practice Revealed Session 2: Artificial Intelligence Empowers Digital Transformation of Enterprises Session 3: The Way to Realize Large-scale Machine Learning Session 4: AI services help the Internet to innovate rapidly Session 5: Open Source and Frontier Trends Sub-venue 6: Intelligent ecology of win-win cooperation Which topic are you more interested in in the 6 major conference venues?

Recently, the State Grid announced the special commitments of 27 provincial companies in 2021, and the commitments of various provincial companies also revealed key tasks and goals for 2021, which involve the development of clean energy, digitalization, and electric energy substitution.

1. State Grid Beijing Electric Power

Do a good job in ensuring power supply for important events in the 100th anniversary of the founding of the party.

2. State Grid Shanxi Electric Power

The utilization rate of new energy is maintained at more than 95%; the replacement of electricity by electric energy is more than 7 billion kwh; and the “three zeros” service for small and micro enterprises is realized.

3. State Grid Tianjin Electric Power

Expand the depth and breadth of electric energy substitution, promote new technologies and new applications, and achieve 2.8 billion kilowatt-hours of electricity replacement.

4. State Grid Shandong Electric Power

Completed inter-provincial transactions of 126 billion kilowatt-hours of electricity. Build 200 fully electrified digital power supply stations.

5. State Grid Hebei Electric Power

Achieve clean energy power generation exceeding 18 billion kilowatt-hours, and the total installed capacity of new energy will reach 18 million kilowatts.

6. State Grid Haidian Power

The city’s power supply reliability rate reached 99.991%, and the core area reached 99.999%.

7. State Grid Jibei Electric Power

The installed capacity of regional grid-connected new energy exceeds 30 million kilowatts. The proportion of clean energy installed capacity reached 63%.

8. State Grid Jiangsu Electric Power

The replacement of electricity by electric energy was 24.5 billion kilowatt-hours, and the installed capacity of clean energy exceeded 47 million kilowatts.

9. State Grid Zhejiang Electric Power

Realize the replacement of electricity by 9 billion kilowatt-hours of electricity, and the installed capacity of clean energy exceeds 350,000 kilowatts.

10. State Grid Henan Electric Power

Completion of the “Key Livelihood Practical Project”: the construction and transformation of 5,000 power distribution stations and 5,000 kilometers of 10 kV and below lines.

11. State Grid Anhui Electric Power

Newly built 6 high-speed fast charging stations, 17 dedicated stations, 68 public charging stations, and 476 charging piles in the station.

12. State Grid Jiangxi Electric Power

Promote the sharing of information on power management by government and enterprises, embed the function of power management on the Internet into the provincial government service platform, and realize users’ online “one certificate for power management”.

13. State Grid Fujian Electric Power

Consumption of clean energy in full; promote the replacement of electric energy, and realize the replacement of electricity exceeding 9 billion kwh

14. State Grid Sichuan Electric Power

The amount of hydropower transmission exceeded 137 billion kWh. Promote the development of electric vehicles, and the total charging capacity of the whole society reaches 1 billion kWh.

15. State Grid Hubei Electric Power

Completed full coverage of charging facilities in county-level urban areas, with a coverage rate of more than 10% in towns, towns and districts; built and put into operation 80 multi-station integrated data center stations.

16. State Grid Chongqing Electric Power

Low-voltage small and micro enterprises have 2 links in the whole process of power supply, zero cost of external line projects, and the whole process of power connection does not exceed 8 working days.

17. State Grid Hunan Electric Power

Complete the replacement of 7 billion kilowatt-hours of electricity with electric energy; invest no less than 1 billion yuan to help small and micro enterprises, and use telegrams to install “zero investment”.

18. State Grid Liaoning Electric Power

Consume 71 billion kWh of clean energy.

19. State Grid Jilin Electric Power

The utilization rate of new energy reached 95.6%, the wind power generation capacity was 12.7 billion kWh, and the full-caliber photovoltaic power generation capacity was 4 billion kWh.

20. State Grid Qinghai Electric Power

Improve industrial development and the construction of supporting power grids for public services after relocation to ensure that fixed-point poverty alleviation in Maduo County will not return to poverty.

21. State Grid Heilongjiang Electric Power

Expansion of clean energy consumption, new energy utilization rate not less than 97.4%. Achieve electric energy replacement of 2.3 billion kilowatt-hours of electricity.

22. State Grid Ningxia Electric Power

Achieve replacement of 2 billion kilowatt-hours of electricity; reduce the entire process of power-off time to within 20 working days; implement the “three zeros” service for small and micro enterprises.

23. State Grid Mengdong Electric Power

Clean energy electricity was 30.5 billion kWh, accounting for 81.3% of the electricity sold; it completed the delivery of 115 billion kWh of electricity.

24. State Grid Xinjiang Electric Power

Completed the remaining 287,000 households in the first phase of the “coal-to-electricity” project; replaced 4 billion kilowatt-hours of electricity.

25. State Grid Shaanxi Electric Power

Do a good job in ensuring electricity for the National Games; build 5000 charging piles to achieve full coverage of charging facilities in expressway service areas and all urban areas and counties.

26. State Grid Tibet Electric Power

Transmit 2 billion kWh of clean energy such as hydropower and photovoltaics in Tibet to 11 provinces (cities) in the central and eastern regions for consumption.

27. State Grid Gansu Electric Power

Solve 11 110kV single-line and single-transformation substations that are at risk of large-scale power outages.

The unveiling of key tasks for artificial intelligence industry innovation is an important measure taken by the Ministry of Industry and Information Technology to promote the deep integration of artificial intelligence and the real economy and accelerate the innovative development of my country’s new generation of artificial intelligence industry.

The work was launched at the end of 2018; at the end of 2019, the Ministry of Industry and Information Technology selected 203 unlisted units and potential units; in October 2020, the Ministry of Industry and Information Technology organized the evaluation and acceptance of the unlisted projects.

After on-site inspections, expert evaluations, and third-party professional organization evaluations and other multi-dimensional evaluations, the Ministry of Industry and Information Technology selected 101 winners from the 203 unlisted units.

Yuncong Technology relies on “industry resource training library”The project, after repeated tests and screening, finally stood out from 203 units, withHuawei, Tencent, China Southern Power Grid and other companies were selected as winnersList.

Regarding the goal of unveiling the list of artificial intelligence, the Ministry of Industry and Information Technology has positioned it as follows: “Select a group of innovative entities with key core technologies and strong innovation capabilities, and select leaders and vanguards in the main sub-fields of artificial intelligence. The “in command” working mechanism breaks through the bottleneck of the development of artificial intelligence industry, establishes a benchmark enterprise in the field, cultivates the main force of innovation and development, accelerates the deep integration of my country’s artificial intelligence industry and the real economy, and promotes innovative development.”

As the demand for social transformation accelerates, new technologies such as artificial intelligence have become an important force in accelerating economic development.

Yuncong Technology is committed toHuman-machine collaborationAs the core, accelerate the deep integration of the real economy and technology, and form a closed loop of industry value with the original human-machine collaborative operating system and comprehensive solutions. Setting a benchmark, taking important responsibilities and building a cornerstone, Yun has always been at the forefront of the industry:

As a national platform builder, he has undertaken many major artificial intelligence projects of the Ministry of Industry and Information Technology and the Development and Reform Commission, and undertaken dozens of major special scientific research projects at the national, provincial, and municipal levels, as well as key scientific research institutions;

As early as January 2020, the Ministry of Industry and Information Technology of the People’s Republic of China formally announced the “List of Finalists for the Key Tasks of New Generation Artificial Intelligence Industry Innovation”. Yuncong Technology’s “Intelligent Security Monitoring System Based on Multi-modal Video Information” and “Training Resource Service Platform Based on Face Recognition” were shortlisted for two major projects.

The “Industry Resource Training Library” selected this time is a high-quality artificial intelligence training resource service platform for Yuncong Technology’s key research and development products based on visual recognition technology and applied in finance, security, transportation and other industries;

Including the construction of infrastructure and cloud service platform, the construction of a high-performance artificial intelligence basic resource platform, and the application and promotion of industry users and artificial intelligence small, medium and micro enterprises.

The platform organizes and aggregates computing and communication resources in a networked manner, and can achieve the same powerful performance as a supercomputer in a short time.

The selection of the winning units this time is another major recognition of Yuncong’s leading technology and achievements. For Yuncong, this is an incentive and a driving force for continuous improvement.

Yuncong will live up to the request, continue to focus on human-machine collaboration, break through the development of the artificial intelligence industry, play its role as a “leader and pioneer”, become the main force in the innovation and development of the artificial intelligence industry and accelerate economic transformation, and accelerate my country’s artificial intelligence The industry and the real economy are deeply integrated.