Defendant’s vehicle was observed crossing the centre line onto the opposite side of the road and causing oncoming traffic to take evasive action

Man who was captured on video driving erratically in Longford hit with hefty fine

According to a DMRC statement, the tunnel connects Maa Anandmayee Marg to Tughlakabad Railway Colony on the Tughlakabad–Aerocity corridor.

Delhi Metro Achieves Tunnelling Breakthrough On Golden Line Corridor, Check Details

Wardley could now be in line to face the winner of the Oleksandr Usyk and Daniel Dubois unification bout next month after a 10th-round knockout of Huni who had dominated much of the bout

Fabio Wardley beats Justis Huni with stunning 10th-round knockdown to snatch victory at Portman Road in Ipswich

The 56-year-old farmer was laid to rest in Kenmare on Saturday, 11 weeks after he was last seen at a Centra shop in the Kerry town on March 20

Michael Gaine funeral: Tears flow as mourners line streets of Kenmare to bid farewell to 'proud Kerry man'

Kate Cross and Em Arlott combined to take three wickets in the first four overs as the West Indies fell to 4-3; West Indies reached 43-4 before a five-hour rain delay reduced the game to 21 overs; Nat Sciver-Brunt hit a quickfire half-century to get her side over the line

England vs West Indies: Nat Sciver-Brunt hits speedy 50 to secure ODI series whitewash in rain-affected encounter

Fashion model and car fanatic Jodie Kidd joins a stunning line-up of supercars, vintage vehicles and tractor tours

Celebrity petrolhead Jodie Kidd joins thousands for Devon car show

Police said on the front of the right hand of the woman, there was a tattoo of a heart shape followed by a heartbeat sign in the form of an ECG line, followed by words “mom” and “dad” in English.

Bodies of woman, two sons found in Pune: family member held for murder

Man United are continuing to work on a deal to sign Brentford attacker Bryan Mbeumo to strengthen their attack.

Three ways Manchester United could line-up if they seal Bryan Mbeumo transfer

The Mumbai Metro One line has played a key role in reducing travel time between Versova and Ghatkopar from 90 minutes to just 21 minutes.

Mumbai Metro One turns 11 tomorrow, serving 111 crore passengers since 2014

Drivers and motorists who fail to keep their driveways clear could cause damage to their vehicles and more issues "down the line".

UK households who keep cars on driveway face £100 fine and 'legal trouble'

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Binance to launch crypto-fiat exchange in Uganda, Korea’s new Crypto regulation; Messaging App line to release an exchange; JD working with Huobi What Crypto insiders are reading on Asia. Subscribe now 🌟 Top News Binance is supporting the Ugandan Shilling, alongside major cryptocurrencies. about to launch a crypto-fiat exchange in Uganda, http://bit.ly/2tCsO7w Korea pertaining to AML and KYC requirements for crypto exchanges. It’s investigating into three major banks and having banks alert suspicious fund movements to avoid the “kimchi premium” from happening. has revealed a new crypto regulatory framework and guidelines http://bit.ly/2N5OhOg is including bitcoin, ethereum and litecoin, at a trading fee of 0.1%. The Japanese messaging app makes a big push into financial services to compete against WhatsApp and WeChat. Line launching its own exchange supporting 30 cryptocurrencies, https://on.ft.com/2ySsKWa JD most recently was supported by the JD.com’s cloud arm is working with Huobi to build blockchain-based technology and applications. Chinese gov’t to create asset backed tokens. 💰 Coin and Exchange News China’s in a move towards European market. Huobi opens London office http://bit.ly/2tCrscU after collaborating with worldwide exchanges, and it has also disclosed the accounts and crypto assets that were stolen during the breach. Hacked Bithumbit has now reduced its stolen damage by almost half http://bit.ly/2tMv0IL the first gaming platform and the first ICO to launch on VeChainThor platform, is having a live AMA. Plair, http://bit.ly/2tCqgX0 21 nodes is too few to deter cartels and currently nodes are dominated by Bitfinex, Huobi, Chinese, Koreas. Redditor claim EOS’s http://bit.ly/2tBUU2L 💼 Business and Regulation News to develop a cross-border trade finance system that is built with distributed ledger technology. Hong Kong’s de facto central bank is planning to work Abu Dhabi http://bit.ly/2tAzW4c Blockchain can legally authenticate evidence, Chinese judge rules. http://bit.ly/2tJU96R CEO of SBI, one of Japan’s financial giants and longtime Ripple partner in Asia, talked about his belief in blockchain to get Japan’s economy back on track to regain the global position it once held. http://bit.ly/2tCJJGT for crypto-related activities, including any capital transaction that relates to distributed ledger accounts, blockchain, cryptocurrency and its derivatives, ICOs and exchange operations. China Banking Regulatory Commission researchers are advocating that domestic regulators issue licenses http://bit.ly/2ySAxDw ❗ Hey there Did I miss anything big today? Share with me the link, if you saw anything interesting. If I’m swayed, I’ll include your bit tomorrow (with attribution)! 🌹🌹🌹 Check our past 3 weekly pieces on , , and Binance Bitmain China’s Crypto winners I’m conflicted about the news that Facebook may potentially acquire Coinbase, it reinforces centralization but then also bring legitimacy to the space. Perhaps the latter is more important right now.

Today’s Crypto News on Asia — Jun 28th

How To Build a Brain With Keras and a Few Lines of Code

የ Keras እና የኮድ ጥቂት መስመሮች ጋር እንዴት አንድ መሐንዲት ለመፍጠር

Cómo construir un cerebro con Keras y unas pocas líneas de código

Comment construire un cerveau avec Keras et quelques lignes de code

Como construír un cerebro con Keras e unhas poucas liñas de código

Kerasと数行のコードで脳を構築する方法

How to Build a Brain ერთად Keras და რამდენიმე ხაზი კოდი

Keras와 코드의 몇 줄을 사용하여 두뇌를 구축하는 방법

څنګه د Keras او د کوډ د ځينو کرښه سره د دماغ جوړ کړئ

Hur man bygger en hjärna med Keras och några linjer av kod

Keras ve Birkaç Kodu Hatları ile Beyin Nasıl Oluşturulur

O‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z o‘z

如何用Keras和几行代码构建大脑

How to Train a Linear Regression Model in TensorFlow

CocoIndex now provides native support for Kuzu as a target graph data store. This integration features a high performance knowledge graph stack with real-time updates. CocoIndex What is Kuzu Kuzu is a graph database that is designed to be fast, scalable, and easy to use. We love Kuzu because it is high performant, lightweight, and open source. Kuzu CocoIndex is an ultra performant real-time data transformation framework, with dataflow programming model, CocoIndex simplifies building and maintaining knowledge graphs with continuous source updates. You can read the official CocoIndex Documentation for Property Graph Targets here. here We understand preparing data is highly use-case based and there is no one-size-fits-all solution. We take the composition approach, and instead of building everything, we provide native plugins to embrace the ecosystem and make it easier to plug in and swap any module by standardizing the interface - exactly like LEGO. If you are using CocoIndex to build your knowledge graph, you can use Kuzu as a target graph data store. How to map to Kuzu in CocoIndex The GraphDB interface in CocoIndex is standardized, if you are already using Neo4j, you just need to switch the configuration to export to Kuzu as below. CocoIndex supports exporting to Kuzu through its API server. You can bring up a Kuzu API server locally by running: KUZU_DB_DIR=$HOME/.kuzudb
KUZU_PORT=8123
docker run -d --name kuzu -p ${KUZU_PORT}:8000 -v ${KUZU_DB_DIR}:/database kuzudb/api-server:latest KUZU_DB_DIR=$HOME/.kuzudb
KUZU_PORT=8123
docker run -d --name kuzu -p ${KUZU_PORT}:8000 -v ${KUZU_DB_DIR}:/database kuzudb/api-server:latest In your CocoIndex flow, you need to add the Kuzu connection spec to your flow. kuzu_conn_spec = cocoindex.add_auth_entry(
    "KuzuConnection",
    cocoindex.storages.KuzuConnection(
        api_server_url="http://localhost:8123",
    ),
) kuzu_conn_spec = cocoindex.add_auth_entry(
    "KuzuConnection",
    cocoindex.storages.KuzuConnection(
        api_server_url="http://localhost:8123",
    ),
) What does it look like to build an indexing flow with CocoIndex + Kuzu A CocoIndex knowledge graph example that got the most love is to build knowledge graph with LLM, here is a detailed step-by-step blog. In the project, we process a list of documents,and use LLM to extract relationships between the concepts in each document. step-by-step blog We will generate two kinds of relationships from the documents: Relationships between subjects and objects. E.g., "CocoIndex supports Incremental Processing"
Mentions of entities in a document. E.g., "core/basics.mdx" mentions CocoIndex and Incremental Processing. Relationships between subjects and objects. E.g., "CocoIndex supports Incremental Processing" Mentions of entities in a document. E.g., "core/basics.mdx" mentions CocoIndex and Incremental Processing. The indexing flow looks like this for Kuzu: The code is available here. here Ingest the documents into CocoIndex


Process the documents, for each document:

Map document nodes: Use LLM to generate summary, and map the documents to Graph nodes in Kuzu.
Map relationship nodes: Use LLM to extract relationships, and export the relationships to Kuzu. Ingest the documents into CocoIndex Ingest the documents into CocoIndex Process the documents, for each document:

Map document nodes: Use LLM to generate summary, and map the documents to Graph nodes in Kuzu.
Map relationship nodes: Use LLM to extract relationships, and export the relationships to Kuzu. Process the documents, for each document: Map document nodes: Use LLM to generate summary, and map the documents to Graph nodes in Kuzu.
Map relationship nodes: Use LLM to extract relationships, and export the relationships to Kuzu. Map document nodes: Use LLM to generate summary, and map the documents to Graph nodes in Kuzu. Map relationship nodes: Use LLM to extract relationships, and export the relationships to Kuzu. Notably, it only takes ~200 lines of python to have a production ready knowledge graph; including class definitions, prompts, and configs. To highlight how the relationship extraction works, you will define a python class for structured extraction. @dataclasses.dataclass
class Relationship:
    """
    Describe a relationship between two entities.
    Subject and object should be Core CocoIndex concepts only, should be nouns. For example, `CocoIndex`, `Incremental Processing`, `ETL`,  `Data` etc.
    """

    subject: str
    predicate: str
    object: str @dataclasses.dataclass
class Relationship:
    """
    Describe a relationship between two entities.
    Subject and object should be Core CocoIndex concepts only, should be nouns. For example, `CocoIndex`, `Incremental Processing`, `ETL`,  `Data` etc.
    """

    subject: str
    predicate: str
    object: str If you have a predefined set of ontology, you can skip the entity extraction use existing entities. Call a transformation in the flow to extract the relationships from the document. with data_scope["documents"].row() as doc:
   
    # extract relationships from document
    doc["relationships"] = doc["content"].transform(
        cocoindex.functions.ExtractByLlm(
            llm_spec=cocoindex.LlmSpec(
                # Supported LLM: https://cocoindex.io/docs/ai/llm
                api_type=cocoindex.LlmApiType.OPENAI,
                model="gpt-4o",
            ),
            output_type=list[Relationship],
            instruction=(
                "Please extract relationships from CocoIndex documents. "
                "Focus on concepts and ignore examples and code. "
            ),
        )
    ) with data_scope["documents"].row() as doc:
   
    # extract relationships from document
    doc["relationships"] = doc["content"].transform(
        cocoindex.functions.ExtractByLlm(
            llm_spec=cocoindex.LlmSpec(
                # Supported LLM: https://cocoindex.io/docs/ai/llm
                api_type=cocoindex.LlmApiType.OPENAI,
                model="gpt-4o",
            ),
            output_type=list[Relationship],
            instruction=(
                "Please extract relationships from CocoIndex documents. "
                "Focus on concepts and ignore examples and code. "
            ),
        )
    ) You could use CocoInsight to verify each pair of the relationships. and then collect the relationship use entity_relationship collector. entity_relationship with doc["relationships"].row() as relationship:
    # relationship between two entities
    entity_relationship.collect(
        id=cocoindex.GeneratedField.UUID,
        subject=relationship["subject"],
        object=relationship["object"],
        predicate=relationship["predicate"],
    ) with doc["relationships"].row() as relationship:
    # relationship between two entities
    entity_relationship.collect(
        id=cocoindex.GeneratedField.UUID,
        subject=relationship["subject"],
        object=relationship["object"],
        predicate=relationship["predicate"],
    ) CocoIndex follows a dataflow programming model. Rather than defining data operations like creations, updates or deletions, developers only need to focus on transformations or formulas based on source data. The framework takes care of the data operations such as when to create, update, or delete. Once you have collected the relationships, you can directly map it to Kuzu as below. entity_relationship.export(
        "entity_relationship",
         cocoindex.storages.Kuzu(
            connection=conn_spec,
            mapping=cocoindex.storages.Relationships(
                rel_type="RELATIONSHIP",
                source=cocoindex.storages.NodeFromFields(
                    label="Entity",
                    fields=[
                        cocoindex.storages.TargetFieldMapping(
                            source="subject", target="value"
                        ),
                    ],
                ),
                target=cocoindex.storages.NodeFromFields(
                    label="Entity",
                    fields=[
                        cocoindex.storages.TargetFieldMapping(
                            source="object", target="value"
                        ),
                    ],
                ),
            ),
        ),
        primary_key_fields=["id"],
    ) entity_relationship.export(
        "entity_relationship",
         cocoindex.storages.Kuzu(
            connection=conn_spec,
            mapping=cocoindex.storages.Relationships(
                rel_type="RELATIONSHIP",
                source=cocoindex.storages.NodeFromFields(
                    label="Entity",
                    fields=[
                        cocoindex.storages.TargetFieldMapping(
                            source="subject", target="value"
                        ),
                    ],
                ),
                target=cocoindex.storages.NodeFromFields(
                    label="Entity",
                    fields=[
                        cocoindex.storages.TargetFieldMapping(
                            source="object", target="value"
                        ),
                    ],
                ),
            ),
        ),
        primary_key_fields=["id"],
    ) Amazingly, while working on this Kuzu example, I had a previous flow that I ran locally with Neo4j. It was instant to export to Kuzu. CocoIndex is based on incremental processing, and if you have already run this flow before and just switched targets, the intermediate transformation results can be reused. flow To run the Kuzu Explorer - an open source UI for Kuzu, you need to first bring down the Kuzu API server. Kuzu Explorer And then you can run the following command to start the Kuzu Explorer: KUZU_EXPLORER_PORT=8124
docker run -d --name kuzu-explorer -p ${KUZU_EXPLORER_PORT}:8000  -v ${KUZU_DB_DIR}:/database -e MODE=READ_ONLY  kuzudb/explorer:latest KUZU_EXPLORER_PORT=8124
docker run -d --name kuzu-explorer -p ${KUZU_EXPLORER_PORT}:8000  -v ${KUZU_DB_DIR}:/database -e MODE=READ_ONLY  kuzudb/explorer:latest We could then access the explorer at http://localhost:8124. We could run a Cypher query to explore the graph. http://localhost:8124 MATCH p=()-->() RETURN p MATCH p=()-->() RETURN p We are constantly improving, and more features and examples are coming soon. If this article is helpful, please drop us a star ⭐ at GitHub to help us grow. We are constantly improving, and more features and examples are coming soon. If this article is helpful, please drop us a star ⭐ at GitHub to help us grow. GitHub Thanks for reading!

This Real-Time Graph Framework Now Lets You Switch from Neo4j to Kuzu in One Line

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Author:
(1) Chameleon Team, FAIR at Meta. Author: Author: (1) Chameleon Team, FAIR at Meta. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Pre-Training 2 Pre-Training 2.1 Tokenization 2.1 Tokenization 2.2 Pre-Training Data 2.2 Pre-Training Data 2.3 Stability 2.3 Stability 2.4 Inference 2.4 Inference 3 Alignment and 3.1 Data 3 Alignment and 3.1 Data 3.2 Fine-Tuning Strategy 3.2 Fine-Tuning Strategy 4 Human Evaluations and Safety Testing, and 4.1 Prompts for Evaluation 4 Human Evaluations and Safety Testing, and 4.1 Prompts for Evaluation 4.2 Baselines and Evaluations 4.2 Baselines and Evaluations 4.3 Inter-annotator Agreement 4.3 Inter-annotator Agreement 4.4 Safety Testing 4.4 Safety Testing 4.5 Discussion 4.5 Discussion 5 Benchmark Evaluations and 5.1 Text 5 Benchmark Evaluations and 5.1 Text 5.2 Image-To-Text 5.2 Image-To-Text 6 Related Work 6 Related Work 7 Conclusion, Acknowledgements, Contributors, and References 7 Conclusion, Acknowledgements, Contributors, and References Appendix Appendix A. Samples A. Samples B. Additional Information of Human Evaluations B. Additional Information of Human Evaluations We present Chameleon, a family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence. We outline a stable training approach from inception, an alignment recipe, and an architectural parameterization tailored for the early-fusion, token-based, mixed-modal setting. The models are evaluated on a comprehensive range of tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed modal generation. Chameleon demonstrates broad and general capabilities, including state-of-the-art performance in image captioning tasks, outperforms Llama-2 in text-only tasks while being competitive with models such as Mixtral 8x7B and Gemini-Pro, and performs non-trivial image generation, all in a single model. It also matches or exceeds the performance of much larger models, including Gemini Pro and GPT-4V, according to human judgments on a new long-form mixed-modal generation evaluation, where either the prompt or outputs contain mixed sequences of both images and text. Chameleon marks a significant step forward in a unified modeling of full multimodal documents 1 Introduction Recent multimodal foundation models are very widely adopted but still model different modalities separately, often using modality specific encoders or decoders. This can limit their ability to integrate information across modalities and generate multimodal documents that can contain arbitrary sequences of images and text. In this paper, we present Chameleon, a family of mixed-modal foundation models capable of generating and reasoning with mixed sequences of arbitrarily interleaved textual and image content (Figures 2-4). This allows for full multimodal document modeling, which is a direct generalization of standard multimodal tasks such as image generation, understanding and reasoning over images, and text-only LLMs. Chameleon is instead designed to be mixed-model from inception and uses a uniform architecture trained from scratch in an end-to-end fashion on an interleaved mixture of all modalities, i.e., images, text, and code. Our unified approach uses fully token-based representations for both image and textual modalities (Figure 1). By quantizing images into discrete tokens, analogous to words in text, we can apply the same transformer architecture to sequences of both image and text tokens, without the need for separate image/text encoders (Alayrac et al., 2022; Liu et al., 2023b; Laurençon et al., 2023) or domain-specific decoders (Ramesh et al., 2022; Jin et al., 2023; Betker et al., 2023). This early-fusion approach, where all modalities are projected into a shared representational space from the start, allows for seamless reasoning and generation across modalities. However, it also presents significant technical challenges, particularly in terms of optimization stability and scaling. We address these challenges through a combination of architectural innovations and training techniques. We introduce novel modifications to the transformer architecture, such as query-key normalization and revised placement of layer norms, which we find to be crucial for stable training in the mixed-modal setting (Section 2.3). We further show how to adapt the supervised finetuning approaches used for text-only LLMs to the mixed-modal setting, enabling strong alignment at scale (Section 3). Using these techniques, we successfully train Chameleon-34B on 5x the number of tokens as Llama-2 – enabling new mixed-modal applications while still matching or even outperforming existing LLMs on unimodal benchmarks. Extensive evaluations demonstrate that Chameleon is a broadly capable model on a diverse set of tasks. On visual question answering and image captioning benchmarks, Chameleon-34B achieves state-of-the-art performance, outperforming models like Flamingo, IDEFICS and Llava-1.5 (Section 5.2). At the same time, it maintains competitive performance on text-only benchmarks, matching models like Mixtral 8x7B and Gemini-Pro on commonsense reasoning and reading comprehension tasks (Section 5.1). But perhaps most impressively, Chameleon unlocks entirely new capabilities in terms of mixed-modal reasoning and generation. As using only static, public benchmarks to evaluate model performance could be limited (Schaeffer, 2023), we also conduct a carefully designed human evaluation experiment by measuring the quality of mixed-modal long form responses to open-ended prompts. Chameleon-34B substantially outperforms strong baselines like Gemini-Pro and GPT-4V (Section 4), achieving a 60.4% preference rate against Gemini-Pro and a 51.6% preference rate against GPT-4V in pairwise comparisons. In summary, we present the following contributions: • We present Chameleon, a family of early-fusion token-based mixed-modal models capable of reasoning over and generating interleaved image-text documents, setting a new bar for open multimodal foundation models. • We introduce architectural innovations and training techniques that enable the stable and scalable training of early-fusion token-based models, addressing key challenges in mixed-modal learning. • Through extensive evaluations, we demonstrate state-of-the-art performance across a diverse set of vision-language benchmarks, while maintaining competitive performance on text-only tasks, and high quality image generation, all in the same model. • We conduct the first large-scale human evaluation on open-ended mixed-modal reasoning and generation, demonstrating the unique capabilities of Chameleon in this new setting. Chameleon represents a significant step towards realizing the vision of unified foundation models capable of flexibly reasoning over and generating multimodal content. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv available on arxiv

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