Improving Image Search by Augmenting Image Queries

Matching image or video content to other content is an important requirement for content hosting platforms. A common mechanism is to construct an index of known content, e.g., that include multi-dimensional embeddings generated from the content, and match new content against the index. The precision and recall of such techniques require a high quality fingerprint, and tradeoffs between recall performance and the cost of filtering out false positives. This disclosure describes improvements to content matching techniques that generate multiple transformations of the input content, look up each transformation in the index, and limit detection of false positives or other downstream analysis to content that has at least a threshold number of matches. Performance improvements in the recall vs. cost tradeoff are obtained due to the shape of the volume in the embedding space is no longer spherical, and instead, including many smaller spheres around the different transformed versions

Finding Match Avoidance Attempts At Scale With Video Expansion

An important objective of user-generated content platforms such as audio/video hosting or streaming platforms is to ensure that content that is available via their platforms is authorized for use, e.g., is provided by the true owner or with due permission of the true owner. To ensure that unauthorized content is not made available, such platforms match uploaded videos against a repository of reference (original) videos. To avoid video content being matched, content uploaders utilize constantly evolving new content transformation strategies when uploading unauthorized content. This disclosure describes automated techniques that help speed up and scale the collection of training examples of recent techniques of content transformations designed to bypass match detection procedures. These include synthetic generation (automatically generating content examples similar to match avoiding content) and scaled up mining and filtering (which includes performing searches for other content that is similar to match avoiding content on some dimension and filtering such content using high performance matching algorithms) to detect other examples of similar match avoiding content. The corpus of data generated by the described techniques can be used to train and validate a new version of matching procedures that is robust to the recent match avoidance attempts

Teechain: a secure payment network with asynchronous blockchain access

Blockchains such as Bitcoin and Ethereum execute payment transactions securely, but their performance is limited by the need for global consensus. Payment networks overcome this limitation through off-chain transactions. Instead of writing to the blockchain for each transaction, they only settle the final payment balances with the underlying blockchain. When executing off-chain transactions in current payment networks, parties must access the blockchain within bounded time to detect misbehaving parties that deviate from the protocol. This opens a window for attacks in which a malicious party can steal funds by deliberately delaying other parties' blockchain access and prevents parties from using payment networks when disconnected from the blockchain. We present Teechain, the first layer-two payment network that executes off-chain transactions asynchronously with respect to the underlying blockchain. To prevent parties from misbehaving, Teechain uses treasuries, protected by hardware trusted execution environments (TEEs), to establish off-chain payment channels between parties. Treasuries maintain collateral funds and can exchange transactions efficiently and securely, without interacting with the underlying blockchain. To mitigate against treasury failures and to avoid having to trust all TEEs, Teechain replicates the state of treasuries using committee chains, a new variant of chain replication with threshold secret sharing. Teechain achieves at least a 33X higher transaction throughput than the state-of-the-art Lightning payment network. A 30-machine Teechain deployment can handle over 1 million Bitcoin transactions per second

piChain: When a Blockchain Meets Paxos

We present a new fault-tolerant distributed state machine to inherit the best features of its “parents in spirit”: Paxos, providing strong consistency, and a blockchain, providing simplicity and availability. Our proposal is simple as it does not include any heavy weight distributed failure handling protocols such as leader election. In addition, our proposal has a few other valuable features, e.g., it is responsive, it scales well, and it does not send any overhead messages.ISSN:1868-896

Scalable Funding of Bitcoin Micropayment Channel Networks

The Bitcoin network has scalability problems. To increase its transaction rate and speed, micropayment channel networks have been proposed; however, these require to lock funds into specific channels. Moreover, the available space in the blockchain does not allow scaling to a worldwide payment system. We propose a new layer that sits in between the blockchain and the payment channels. The new layer addresses the scalability problem by enabling trustless off-blockchain channel funding. It consists of shared accounts of groups of nodes that flexibly create one-to-one channels for the payment network. The new system allows rapid changes of the allocation of funds to channels and reduces the cost of opening new channels. Instead of one blockchain transaction per channel, each user only needs one transaction to enter a group of nodes—within the group the user can create arbitrarily many channels. For a group of 20 users with 100 intra-group channels, the cost of the blockchain transactions is reduced by 90% compared to 100 regular micropayment channels opened on the blockchain. This can be increased further to 96% if Bitcoin introduces Schnorr signatures with signature aggregationISSN:2054-570

Simple and Inexpensive Paper-Based Astrocyte Co-culture to Improve Survival of Low-Density Neuronal Networks

Bottom-up neuroscience aims to engineer well-defined networks of neurons to investigate the functions of the brain. By reducing the complexity of the brain to achievable target questions, such in vitro bioassays better control experimental variables and can serve as a versatile tool for fundamental and pharmacological research. Astrocytes are a cell type critical to neuronal function, and the addition of astrocytes to neuron cultures can improve the quality of in vitro assays. Here, we present cellulose as an astrocyte culture substrate. Astrocytes cultured on the cellulose fiber matrix thrived and formed a dense 3D network. We devised a novel co-culture platform by suspending the easy-to-handle astrocytic paper cultures above neuronal networks of low densities typically needed for bottom-up neuroscience. There was significant improvement in neuronal viability after 5 days in vitro at densities ranging from 50,000 cells/cm2 down to isolated cells at 1,000 cells/cm2. Cultures exhibited spontaneous spiking even at the very low densities, with a significantly greater spike frequency per cell compared to control mono-cultures. Applying the co-culture platform to an engineered network of neurons on a patterned substrate resulted in significantly improved viability and almost doubled the density of live cells. Lastly, the shape of the cellulose substrate can easily be customized to a wide range of culture vessels, making the platform versatile for different applications that will further enable research in bottom-up neuroscience and drug development