Union Coliving is establishing a network of student accommodations

Union Coliving Spaces

Have you ever thought about what a nation or state really is? Maybe the first thing that comes to your mind is an established area of land. But wait there: without people, the USA or Japan would be just another patch of dirt, plants, and animals, wouldn’t they? So, people and all that they imply (minds, traditions, culture, laws, language, etc.) are also key to defining a nation. When talking about network states, they might be all that matters. In 2022, the Indian-American entrepreneur Balaji Srinivasan published the book “ ” As the second part of the title implies, , and formed by like-minded people distributed all around the world. The Network State: How to Start a New Country. he’s proposing to create a new nation from scratch, independent from others, with its own money and systems This new type of nation would start online and then would evolve into “physical nodes” funded and governed by its members. He described it like this: “A network state is a social network with a moral innovation, a sense of national consciousness, a recognized founder, a capacity for collective action, an in-person level of civility, an integrated cryptocurrency, a consensual government limited by a social smart contract, an archipelago of crowdfunded physical territories, a virtual capital, and an on-chain census that proves a large enough population, income, and real-estate footprint to attain a measure of diplomatic recognition.” As an individual, anyone could choose to join a certain network state and abide by its rules and benefits, written and shared via smart contract. For Srinivasan, this system could be better than the alternative: be governed by the left or right global powers, which will always ensure the control and surveillance of the population while competing against each other. How to Build a Network State? It may sound nice, but it requires a lot of work. As the author mentioned, a network state just doesn’t come to exist without previous steps or stages. Therefore, he describes seven points to build a network state, and they don’t even need significant resources in the beginning. One person from their computer at home could start a network state. They can begin by founding a startup society: an online community of like-minded people with ambitious aspirations. Then, organize this community into a purpose-driven network union, fostering collective action. The third phase involves building trust offline through physical meetups and concurrently developing an online economy through the creation and promotion of their own cryptocurrency. from apartments to entire towns, creating real co-living communities. The fifth step is connecting these physical nodes digitally to form a network archipelago, linking territories worldwide. Employ web3 crypto-passports for physical access and utilize mixed reality to seamlessly integrate the online and offline realms. With growing trust and accumulated funds, they can initiate crowdfunding campaigns to secure physical spaces, As the society expands, conduct a cryptographically auditable census, showcasing population, income, and real estate growth. Finally, seek diplomatic recognition from existing governments, gradually attaining increased sovereignty to evolve into a fully recognized network state. As the network state's population and economy grow to rival that of legacy states, achieving recognition from established sovereign entities, including the United Nations, becomes a foreseeable milestone. The core concept revolves around populating land from the cloud, fostering a geographically decentralized but ideologically aligned community globally. Why Build a Network State? Because the current global landscape doesn’t seem very amicable or open to free innovation. That’d be the short answer, at least. Srinivasan put it this way in the book: “We want to be able to peacefully start a new state for the same reason we want a bare plot of earth, a blank sheet of paper, an empty text buffer, a fresh startup, or a clean slate. Because we want to build something new without historical constraint.” He also talked about how the world is facing a “ ,” where three main powers and visions are competing to win more influence: The first one is described as “capitalism that enables decentralized censorship,” the second one would be fully centralized capitalism, and the last one is stateless capitalism, the decentralized and neutral sector. Tripolar Moment the American Establishment (symbolized by the paper NYT), the Communist Party of China (CCP), and the Global Internet (symbolized by BTC). In CCP-like societies, you must submit to their government's will; while in NYT-like nations you must sympathize. In the decentralized world, you must be sovereign —but that’s good only to some extent. No one wants to submit to undesirable or unjust laws, and it’s not always possible to “sympathize” with others’ ideas. On the other hand, an excess of sovereignty would mean that every person is alone with their needs, with only them to help themselves. Not only “one” network state is meant to exist, but multiple of them, destined to different groups with diverse ideas. It’s said in this way: “… rather than trying to impose preferences on everyone, what we really want are a variety of points in between these three undesirable poles: different fusions for different groups.” The concept of a network state tries to fuse the best of these three visions, in different flavors and communities. Potential Issues of Network States The concept of network states carries both opportunities and challenges. On the positive side, it enables global collaboration and democratizes participation. However, navigating legal complexities, regulatory hurdles, and the risk of widening digital inequalities poses significant challenges. To add some perspective, Bitcoin didn’t achieve “diplomatic recognition” ( ) soon after its creation, but 12 years later. And only by a couple of countries, so far. Not to mention the logistics, promotion efforts, funding, and mere years it would take to achieve something like this. legal tender status Looking deeper, unforeseen consequences could emerge. Digital nomadism offers freedom but about dependence on technology. New social norms and a potential financial revolution bring opportunities but also introduce challenges, including increased cyber threats —as we’ve often seen in DeFi apps. The network state presents a mix of promises and uncertainties, demanding careful consideration of its potential impacts on our world. raises concerns In addition to the optimistic outlook, there may be . Prioritizing financial gains over genuine connections may commodify relationships. The risk of reinforcing cult-like echo chambers, marginalizing diverse opinions, and potential ethical concerns in inhabited areas require thoughtful consideration. As we explore the concept of network states, it's crucial to remain aware of both the possibilities and the challenges they may bring to the forefront. notable risks Current Network States Initiatives Despite everything, some parties have already started their own version of this futuristic concept. aims to build cities as hubs of technological progress, emphasizing the importance of measurable economic and cultural capital. PRAX Credits, their own cryptocurrency, seeks to fund this new city in the Mediterranean. We can mention three running projects: Praxis, Afropolitan, and Epoch Island. Praxis envisions a digital nation, uniting Africans globally through a curated network. This initiative addresses issues of scarcity, weakness, and poverty experienced by Africans, proposing a Network of Abundance. The strategy involves building awareness, providing utility through the Afropolitan Super App, preparing for a transition to the physical world, and ultimately acquiring land for physical districts governed by the Afropolitan Network. Afropolitan https://www.youtube.com/watch?v=shFHD4KDYls&embedable=true Finally, sets forth a mission to create a Network State by 2030, introducing a unique country governed by Islanders and Elders. It leverages a cryptocurrency, EPOCH, allowing ownership of land and treasury, and contributing to the growing economy. Intending to acquire diplomatic recognition, Epoch Island envisions becoming the Crypto Capital of the World, providing an alternative space for crypto users seeking refuge from centralized governments. Epoch Island These ventures reflect the diverse paths toward building network states, ranging from creating cities grounded in technological progress, fostering an ethnic bond, or just establishing a new country with its own cryptocurrency. Each project navigates the complex landscape of community, governance, and economic structures in the pursuit of redefining societal models. Obyte for Network States , as a decentralized platform, While it was not explicitly designed for creating network states, its decentralized nature and capabilities could facilitate the development of innovative social structures. Obyte possesses unique features that could potentially contribute to the realization of network states or similar decentralized communities. Known for its DAG ( ) structure, Obyte provides a censorship-resistant platform. This architecture aligns with the concept of network states, where governance, decision-making, and participation are distributed among community members rather than concentrated in a central authority or a small number of central authorities — block producers in blockchains. It can foster a more inclusive and participatory model for building and governing communities. Directed Acyclic Graph Besides, the network supports smart contracts and (AAs), allowing for the creation of self-executing agreements without the need for intermediaries. It's also possible , with or without coding. Autonomous Agents to create custom tokens This feature allows communities to establish their own tokens with specific functionalities, use cases, or governance mechanisms. Economy of Network States Any country needs an economy, and the economy needs safe, properly enforced contracts. Obyte’s provide that, and Obyte is the only crypto network where they’re available. Likewise, any country needs a justice system. Obyte’s and its arbiters provide a kind of autonomous justice for commercial relationships. contracts with arbitration ArbStore Contracts with arbitration, available through the , offer secure decentralized escrow protection for two-party agreements, eliminating the need for mutual trust. Obyte wallet They can work for individuals as well as businesses, and they’re particularly well-suited for international operations —like the ones a network state would need for its members. These contracts can lock the funds until pre-set conditions are met by the parties. However, if one of the parties misbehaves, the other can call an expert arbiter to solve the dispute. They’re (human) professionals registered in the ArbStore platform with their real names and are available to solve certain types of disputes in exchange for a reasonable fee. All of these opportunities are available with a strong emphasis on privacy and security — only the contract parties and, in case of a dispute, the arbiter, can see the contract text. This focus aligns with the need for secure and private communication within network states. To explore the possibility of building an Obyte-based network state, community leaders and developers could leverage the platform's existing features and potentially customize certain aspects to suit the specific needs of their envisioned community. Whether it involves establishing a digital society, coordinating economic activities, or fostering collaborative projects, Obyte's decentralized infrastructure could serve as a foundation for innovative experiments in governance and community building. Featured Vector Image by photoroyalty / Freepik

Crypto-Powered Communities: Exploring the Era of Network States

ক্রিপ্টো-চালিত সম্প্রদায়: নেটওয়ার্ক রাজ্যের যুগ অন্বেষণ

Kryptobetriebene Communities: Erkundung der Ära der Netzwerkzustände

Comunidades impulsadas por criptomonedas: explorando la era de los estados de red

Communautés alimentées par la cryptographie : explorer l'ère des états de réseau

क्रिप्टो-संचालित समुदाय: नेटवर्क राज्यों के युग की खोज

暗号化を活用したコミュニティ: ネットワーク国家の時代を探る

암호화 기반 커뮤니티: 네트워크 상태 시대 탐구

Comunidades alimentadas por criptografia: explorando a era dos estados em rede

Крипто-сообщества: исследование эпохи сетевых состояний

Kripto Destekli Topluluklar: Ağ Durumları Çağını Keşfetmek

Cộng đồng được hỗ trợ bằng tiền điện tử: Khám phá kỷ nguyên của các trạng thái mạng

加密社区：探索网络状态时代

This article presents one of the approaches to building a computer vision-driven car damage detection solution. We will make an introduction to the popular instance and semantic segmentation architectures Mask R-CNN and U-Net and provide insights deriving from our attempt to train and test ML models for car damage detection using these architectures. Then, we will share datasets used for training and testing. After that, we will explain in depth how our computer vision solution operates and demonstrate how a car damage model works using a demo. We also provide parts of the code of the models used to build a resulting solution. Overview of Mask R-CNN and U-Net Architectures and Performance Comparison Based on Observation We considered it reasonable to try both approaches to image segmentation that are suitable for the purposes of our project. This way, we get not only desirable outcomes but also receive meaningful insights into how instance and semantic segmentation architectures work on the particular car damage detection task. Here’s a brief into the technologies utilized: : Constructed on top of the Faster R-CNN object detection model, this deep learning architecture incorporates a segmentation component. It generates region proposals using a Region Proposal Network (RPN), which suggests regions of the image that are likely to contain objects. Subsequently, it performs object detection and segmentation by predicting class labels and providing bounding boxes and masks for each proposal. Instance segmentation architecture — Mask R-CNN : U-Net is a convolutional neural network architecture designed for image segmentation tasks. It comprises two pathways: the contracting path, which extracts context and decreases the input image size, and the expansive path, which facilitates precise localization and increases the feature maps’ size. This CNN can retrieve in-depth information about segmented objects while capturing the image’s context and overall structure. Semantic segmentation architecture — U-Net The complete procedure for training, testing, and comparing the models built with these algorithms is provided in the below sections. Datasets Used in Training and Testing There is a strong correlation between the volume and quality of data and the outcomes of training AI models. After all, a widely known concept which is garbage in, garbage out (GIGO), clearly indicates that nonsense input data produces nonsense output. In ideal circumstances, we would prepare extensive datasets of damaged vehicles with varying deteriorations, from diverse angles, with different lighting, etc., and conduct data annotation ourselves. But, for this trial project, we considered it reasonable to utilize precleaned and prepacked data from these publicly available datasets: Coco car damage detection dataset. Segme image dataset. We recommend using the mentioned datasets. Yet, it may already be possible to find newer, higher-quality collections. For business-level projects, such as implementing computer vision technology in an insurance claim processing workflow, we highly recommend the following approach to data preparation: Decide on the types of damages the ML model is intended to assist with the identification of which. Choose high-quality images from the database of past car inspection cases and build an extensive collection. Perform manual image labeling assisted with the usage of ML algorithms. Discover additional information on . how to get better datasets for your computer vision tasks The Essence of our Tried and Tested Computer Vision Solution for Car Damage Detection Let’s thoroughly get through our process of choosing, training, and testing semantic and instance segmentation models. It contains insights and explanations of the unique solutions we came up with. These can come of great usefulness to anyone willing to develop their ML computer vision model for car damage detection. Choosing Models We picked a PyTorch framework, and in particular, from a segmentation_models_pytorch library, we chose a U-Net model. We tested different backbones (pre-trained models that are used for feature extraction). Still, the best tradeoff between performance and accuracy we achieved was with an efficient net-b1 (Efficient net architecture) backbone that was pre-trained on the ImageNet dataset. Semantic segmentation architecture — U-Net. import segmentation_models_pytorch as smp


CLASSES = ['damage']


model_params = {"encoder_name": 'efficientnet-b1',
               "encoder_weights": 'imagenet',
               "activation": None,
               "encoder_depth": 5,
               "decoder_channels": [256, 128, 64, 32, 16]
               }
model = smp.Unet(classes=len(CLASSES), **model_params) We used a detectron2 library and tried different models from it. Mask-RCN_R_50_FPN_3x proved itself to be the best performing on car damage detection tasks. Instance segmentation architecture — Mask R-CNN. Training Models We fine-tuned the model on a pretty small dataset consisting of 59 train images and 11 images for the eval and final tests, respectively. We increased the dataset size by using different augmentation techniques, including random flipping, scaling, rotations, adding Gaussian noise, changing brightness blurring, etc. Semantic segmentation architecture — U-Net. For the loss function, we utilized different functions such as BCEWithLogitsLoss, DiceLoss, and LovaszHingeLoss. As a result, the best scores on the test dataset were achieved with the linear combination of two losses which were DiceLoss and BCEWithLogitsLoss. Combining two loss functions is a rather unconventional approach, which, nevertheless, provided the best outcomes in the case of car damage detection task. import torch.nn as nn
import torch.nn.functional as F


class DiceLoss(nn.Module):
   def __init__(self, weight=None, size_average=True):
       super(DiceLoss, self).__init__()


   def forward(self, inputs, targets, smooth=1):
      
       #comment out if your model contains a sigmoid or equivalent activation layer
       inputs = F.sigmoid(inputs)      
       #flatten label and prediction tensors
       inputs = inputs.view(-1)
       targets = targets.view(-1)
      
       intersection = (inputs * targets).sum()                           
       dice = (2.*intersection + smooth)/(inputs.sum() + targets.sum() + smooth) 
      
       return 1 - dice criterion = DiceLoss()
criterion_2 = nn.BCEWithLogitsLoss()

# …
 
loss = criterion(output.squeeze(1), mask) + criterion_2(output.squeeze(1), mask) We trained this model using the AdamW optimizer and scheduling a learning rate via the OneCycleLR scheduler. Additionally, we adopted an early stopping algorithm to track a loss on an evaluation dataset. optimizer = torch.optim.AdamW(model.parameters(),
                              lr=params['max_lr'],
                              weight_decay=params['weight_decay'])

sched = torch.optim.lr_scheduler.OneCycleLR(optimizer,
                                            params['max_lr'],
                                            epochs=params['epoch'],
                                            steps_per_epoch=len(train_dataloader)) Again, here we fine-tuned the model on a pretty small dataset consisting of 59 train images and 11 images for the eval and final tests, respectively. For the detectron2, we used the built-in default loss with default optimizer. Instance segmentation architecture — Mask R-CNN. cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")) 
cfg.DATASETS.TRAIN = ("car_damage_dataset_train")
cfg.DATASETS.TEST = ("car_damage_dataset_val",) 
cfg.DATALOADER.NUM_WORKERS = 4 
cfg.MODEL.WEIGHTS = model_zoo.get_checkpoint_url("COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml") 
cfg.SOLVER.IMS_PER_BATCH = 4 
cfg.SOLVER.BASE_LR = 0.001 
cfg.SOLVER.WARMUP_ITERS = 700 
cfg.SOLVER.MAX_ITER = 700 
cfg.SOLVER.STEPS = (600, 800) 
cfg.SOLVER.GAMMA = 0.05 
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128 
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1 
cfg.MODEL.RETINANET.NUM_CLASSES = 1 
cfg.TEST.EVAL_PERIOD = 600 
cfg.MODEL.DEVICE = device 
trainer = CocoTrainer(cfg)
trainer.resume_or_load(resume=False) 
trainer.train() The neat part of how detectron2 makes a prediction is that it predicts multiple different instances for one damaged part. It can be seen in the below example. We tried to improve the outcomes by implementing post-processing, where we merge together all provided instances that overlap more than a certain threshold. The results of our attempt can be seen in the below example. Testing and Comparing Models ML engineers from Intelliarts AI considered the Mean Intersection-Over-Union (mIoU) metric as our primary choice for evaluating the outcomes of the U-net and Mask R-CNN model comparison. Jumping ahead, we also utilized the dice coefficiency metric, but the results were less representative compared to the ones with mIoU. So, with the U-net model, we achieved the mIoU equal to approximately 0.398. With detectron2, the mIoU values ranged from approximately 0.249 to 0.3654 depending on the threshold, which is significantly worse than the results with the U-net model. We assumed that this is because U-net considers the image as one instance while semantic segmentation tries to predict different parts of the same damage as different damages, which by the way, we tried to solve by implementing post-processing. As for the visual representation of the results, we may also compare the predictions of both models with the ground truth masks, i.e., what actually mask on the test dataset looks like, provided in the image below. We can observe that U-net tends to underestimate damages, and the detectron2, on the contractor, is more inclined to overestimate damages, masking larger segments and in a higher quantity. The End Result: Two-Model Computer Vision Solution As stated, we decided to move on with the U-net model. Not only was it justified by the better results than the U-net model showed, but also by the fact that underestimating damage is better than overestimating in our case. The reason is that we try to automate insurance claim processing by helping inspectors make assessments faster. However, we also are to minimize business risks of insurance agents that include overestimating damage and, respectively, overpaying the car owners. In the end, we built and trained two separate U-net-based models, one of which is intended for car damage detection and the other one for car part detection only. Allows for identifying the damaged area. It also provides a rough estimation of the magnitude of damage. Model 1 for car damage detection. Allows for identifying distinct car parts. It also provides the name of an affected part. Model 2 for car part detection. When a user enters an image of a damaged car, the resulting solution indicates the damage and identifies the affected car part separately. If they intersect, the solution outputs something like “left door — dent.” The results are then compared to similar cases in a prepared image database with repair cost estimates. You can observe the working principles of both models in the image below. Demo Overview We prepared a demo that shows the mechanism of how the detailed ML models work. The example is not as high-performing as the real-world model is, but it provides an appropriate insight into the implementation of the technology. You can check the below recording of the demo: Link Or you can give a car damage detection demo a try yourself here: https://huggingface.co/spaces/intelliarts/Car_parts_damage_detection Repo link https://gitlab.com/r-d-machine-learning/researches/Car-damage-detection.git Final Take This ML solution is a non-trivial combination of distinct models, one for detecting car damage and another for identifying car parts. Combined together, they allow for obtaining detailed information for car inspection. From our research, it’s evident that semantic segmentation architecture, particularly U-net, is probably the optimal algorithm for solving image processing tasks that are related to car damage detection. However, it’s crucial to understand that the technology is intended for dealing with large batches of minor vehicle assessment cases. We position it as a useful instrument that human inspectors should master. Therefore, we believe that insurance firms will find our solution useful for claims processing automation. Finally, our engineering team regards integration of the described technology in business processes as another step towards digital transformation.

Solving Car Damage Detection Task By Using a Two-Model Computer Vision Solution

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. 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Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Conclusion and References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Thomas Pethick, EPFL (LIONS) thomas.pethick@epfl.ch; (2) Wanyun Xie, EPFL (LIONS) wanyun.xie@epfl.ch; (3) Volkan Cevher, EPFL (LIONS) volkan.cevher@epfl.ch. Table of Links Abstract & Introduction Related work Setup Inexact Krasnosel’ski˘ı-Mann iterations Approximating the resolvent Last iterate under cohypomonotonicity Analysis of Lookahea Experiments Conclusion & limitations Acknowledgements & References 10 Acknowledgements This work was supported by Google. This work was supported by Hasler Foundation Program: Hasler Responsible AI (project number 21043). This work was supported by the Swiss National Science Foundation (SNSF) under grant number 200021_205011. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement n° 725594 - time-data). References Banach, S. 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Stable Nonconvex-Nonconcave Training via Linear Interpolation: Acknowledgements & References

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