Single image reflection separation (SIRS), as a representative blind source separation task, aims to recover two layers, $\textit{i.e.}$, transmission and reflection, from one mixed observation, which is challenging due to the highly ill-posed nature. Existing deep learning based solutions typically restore the target layers individually, or with some concerns at the end of the output, barely taking into account the interaction across the two streams/branches. In order to utilize information more efficiently, this work presents a general yet simple interactive strategy, namely $\textit{your trash is my treasure}$ (YTMT), for constructing dual-stream decomposition networks. To be specific, we explicitly enforce the two streams to communicate with each other block-wisely. Inspired by the additive property between the two components, the interactive path can be easily built via transferring, instead of discarding, deactivated information by the ReLU rectifier from one stream to the other. Both ablation studies and experimental results on widely-used SIRS datasets are conducted to demonstrate the efficacy of YTMT, and reveal its superiority over other state-of-the-art alternatives.

Payment networks form the backbone of modern commerce, generating high volumes of transaction records from daily activities. Properly modeling this data can enable applications such as abnormal behavior detection and consumer-level insights for hyper-personalized experiences, ultimately improving people's lives. In this paper, we present TREASURE, TRansformer Engine As Scalable Universal transaction Representation Encoder, a multipurpose transformer-based foundation model specifically designed for transaction data. The model simultaneously captures both consumer behavior and payment network signals (such as response codes and system flags), providing comprehensive information necessary for applications like accurate recommendation systems and abnormal behavior detection. Verified with industry-grade datasets, TREASURE features three key capabilities: 1) an input module with dedicated sub-modules for static and dynamic attributes, enabling more efficient training and inference; 2) an efficient and effective training paradigm for predicting high-cardinality categorical attributes; and 3) demonstrated effectiveness as both a standalone model that increases abnormal behavior detection performance by 111% over production systems and an embedding provider that enhances recommendation models by 104%. We present key insights from extensive ablation studies, benchmarks against production models, and case studies, highlighting valuable knowledge gained from developing TREASURE.

It is therefore possible to learn the symbols by naively collecting samples from the environment, for example by random exploration.

We further propose new training methods to disentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies.

We further propose new training methods to disentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies.

A Swedish man discovered the 12th century buried treasure near his summer home. Breakthroughs, discoveries, and DIY tips sent every weekday. It only costs a few dollars to buy a tub of bait worms for fishing, but many people are fine with sourcing them straight from the ground. There's always a chance you may find more in the dirt than wriggling invertebrates. Take a recent example near Stockholm, Sweden: According to county officials last month, an unnamed fisherman scrounging for worms at his summer house discovered a corroded copper cauldron containing around 13 pounds of treasure from the Middle Ages.

Belief-Desire-Intention (BDI) is a framework for modelling agents based on their beliefs, desires, and intentions. Plans are a central component of BDI agents, and define sequences of actions that an agent must undertake to achieve a certain goal. Existing approaches to plan generation often require significant manual effort, and are mainly focused on single-agent systems. As a result, in this work, we have developed a tool that automatically generates BDI plans using Alternating-Time Temporal Logic (ATL). By using ATL, the plans generated accommodate for possible competition or cooperation between the agents in the system. We demonstrate the effectiveness of the tool by generating plans for an illustrative game that requires agent collaboration to achieve a shared goal. We show that the generated plans allow the agents to successfully attain this goal.

Breakthroughs, discoveries, and DIY tips sent every weekday. The yearslong international fight to lay claim to the suspected "world's richest shipwreck" likely won't end anytime soon, especially after a research team's most recent conclusions. Experts have confirmed that dozens of gold coins scattered across the ocean floor off the coast of Colombia belonged to the San José, an ill-fated Spanish treasure galleon that sank over 300 years ago during a battle with British warships. The findings were published on June 10 in the journal Antiquity. In June 1708, the San José and a fleet of 17 other vessels departed the capital of Colombia for Europe laden with gold, silver, and uncut gems.