The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanced technologies into garment production, focusing on the creation of an automated machine capable of precise folding and sewing operations and eliminating the marking operation. The proposed machine incorporates key features such as a precision folding mechanism integrated into the automated sewing unit with real-time monitoring capabilities. The results demonstrate remarkable improvements: the standard labour time has been reduced by 93%, dropping from 117 seconds per piece to just 8 seconds with the automated system. Similarly, machinery time improved by 73%, and the total output rate increased by 72%. These enhancements translate into a cycle time reduction from 117 seconds per piece to an impressive 33 seconds, enabling manufacturers to meet customer demand more swiftly. By eliminating manual marking processes, the machine not only reduces labour costs but also minimizes waste through consistent pleat formation. This automation aligns with industry trends toward sustainability and efficiency, potentially reducing environmental impact by decreasing material waste and energy consumption.

Efficiency, speed, and precision are essential in modern manufacturing. AI Maglev Conveyor system, combining magnetic levitation (maglev) technology with artificial intelligence (AI), revolutionizes automated production processes. This system reduces maintenance costs and downtime by eliminating friction, enhancing operational efficiency. It transports goods swiftly with minimal energy consumption, optimizing resource use and supporting sustainability. AI integration enables real-time monitoring and adaptive control, allowing businesses to respond to production demand fluctuations and streamline supply chain operations. The AI Maglev Conveyor offers smooth, silent operation, accommodating diverse product types and sizes for flexible manufacturing without extensive reconfiguration. AI algorithms optimize routing, reduce cycle times, and improve throughput, creating an agile production line adaptable to market changes. This applied research paper introduces the Maglev Conveyor system, featuring an electromagnetic controller and multiple movers to enhance automation. It offers cost savings as an alternative to setups using six-axis robots or linear motors, with precise adjustments for robotic arm loading. Operating at high speeds minimizes treatment time for delicate components while maintaining precision. Its adaptable design accommodates various materials, facilitating integration of processing stations alongside electronic product assembly. Positioned between linear-axis and robotic systems in cost, the Maglev Conveyor is ideal for flat parts requiring minimal travel, transforming production efficiency across industries. It explores its technical advantages, flexibility, cost reductions, and overall benefits.

To understand sensory coding, we must ask not only how much information neurons encode, but also what that information is about. This requires decomposing mutual information into contributions from individual stimuli and stimulus features fundamentally ill-posed problem with infinitely many possible solutions. We address this by introducing three core axioms, additivity, positivity, and locality that any meaningful stimulus-wise decomposition should satisfy. We then derive a decomposition that meets all three criteria and remains tractable for high-dimensional stimuli. Our decomposition can be efficiently estimated using diffusion models, allowing for scaling up to complex, structured and naturalistic stimuli. Applied to a model of visual neurons, our method quantifies how specific stimuli and features contribute to encoded information. Our approach provides a scalable, interpretable tool for probing representations in both biological and artificial neural systems.

The interior of Jeff Bridges's garage, in Santa Barbara, California, has the ramshackle ease of an extravagant dorm room: a tiger-print rug, a potter's wheel, guitars, a rogue toothbrush, taped-up printouts of ideas he finds provocative or perhaps grounding ("Enlightenment is a communal experience"), and piles of books, from Richard Powers's "Bewilderment" to "Who Cares?! A black-and-white portrait of Captain Beefheart, incongruously dressed in a jacket and tie, hangs on a wall near an electric piano. When I arrived, on a recent afternoon, I did not take note of a lava lamp, but its presence didn't feel out of the question. Bridges was wearing rubber slides and a periwinkle-blue cardigan. He excitedly spread out a large furry blanket on a recliner and invited me to sit down: "Your throne, man!" he said. Earlier this month, Bridges released "Slow Magic, 1977-1978," a series of songs he recorded when he was in his late twenties, an emergent movie star, and involved in a regular Wednesday-night jam session with a coterie of musicians and oddballs from the west side of Los Angeles (the jams were organized by Steve Baim, who attended University High School with Bridges; they took place in various beach houses and, occasionally, at the Village, the recording studio where, around the same time, Fleetwood Mac was making "Tusk"). "Slow Magic" is great and also bonkers. On "Kong," Bridges recounts a story line he pitched for a potential "King Kong" sequel (in 1976, Bridges starred as the long-haired primatologist Jack Prescott in a "Kong" remake produced by Dino De Laurentiis); the track features animated narration from the actor Burgess Meredith, and its lyrics are centered on the revelation that Kong is actually a robot. "It's a sad story, but he was just a monkey machine!" Bridges wails in a tottering falsetto. On "Obnoxious," a weirdly tender song about feeling sad and having a stomachache ("I went to the bathroom / And threw up"), there are echoes of Frank Zappa and the Band. What I like most about the record is how social it feels: friends in a room, being dumb, intermittently (even inadvertently) doing something miraculous. "When recording technology kept improving, I said, 'Oh, I don't need anybody!

There is applied research for the development of the Automated Stretch Elastic Waistband Sewing Machine represents a significant advancement in garment manufacturing, addressing the industry's need for increased efficiency, precision, and adaptability. This machine integrates innovative features such as a sensor-based automatic waistband expansion system, synchronized sewing speed and rolling wheel speed, and a differential feed top-loading mechanism. These enhancements streamline the sewing process, reduce manual intervention, and ensure consistent product quality. The machine's design incorporates both 3-wheel and 2-wheel rolling systems, each optimized for different elastic band dimensions and elongation factors. The 3-wheel rolling system accommodates a larger maximum boundary, while the 2-wheel rolling system offers a tighter operational range, providing flexibility to meet diverse manufacturing requirements. The Automated Stretch Elastic Waistband Sewing Machine has a design that controls the pulling apart force so as not to break the elastic waistband. It sets a new standard for quality and innovation, empowering manufacturers to meet the demands of a competitive market with precision and ease.

This is the second time Godzilla and King Kong have made a film appearance together in recent times with 2021's Godzilla vs Kong being the first instalment. Both films were directed by Adam Wingard. Godzilla x Kong made back its budget of 135m in the first weekend when it took in 195m at cinemas, according to figures from Box Office Mojo. In total, it has sold 209m in tickets so far and has scored a very respectable 92 percent Rotten Tomatoes audience rating. The origins of Godzilla go back 70 years to the first 1954 film release in Tokyo, Japan – Gojira, directed by Ishiro Honda.

Antibodies are crucial proteins produced by the immune system in response to foreign substances or antigens. The specificity of an antibody is determined by its complementarity-determining regions (CDRs), which are located in the variable domains of the antibody chains and form the antigen-binding site. Previous studies have utilized complex techniques to generate CDRs, but they suffer from inadequate geometric modeling. Moreover, the common iterative refinement strategies lead to an inefficient inference. In this paper, we propose a \textit{simple yet effective} model that can co-design 1D sequences and 3D structures of CDRs in a one-shot manner. To achieve this, we decouple the antibody CDR design problem into two stages: (i) geometric modeling of protein complex structures and (ii) sequence-structure co-learning. We develop a novel macromolecular structure invariant embedding, typically for protein complexes, that captures both intra- and inter-component interactions among the backbone atoms, including C$\alpha$, N, C, and O atoms, to achieve comprehensive geometric modeling. Then, we introduce a simple cross-gate MLP for sequence-structure co-learning, allowing sequence and structure representations to implicitly refine each other. This enables our model to design desired sequences and structures in a one-shot manner. Extensive experiments are conducted to evaluate our results at both the sequence and structure levels, which demonstrate that our model achieves superior performance compared to the state-of-the-art antibody CDR design methods.

Tetris is one of the most popular and enduring video games of all time, with versions on just about every console, computer and gadget. Many of these iterations have endings baked into story modes and the like, but the original endless mode was considered unbeatable by humans, until now. A 13-year-old boy has become the first person to'beat' the NES version of Tetris, 34 years after it originally released back in 1989, as announced by YouTuber aGameScout. The reason we put'beat' in quotes is due to the nature of the achievement. Oklahoma teenager Willis Gibson, also known as Blue Scuti on YouTube, didn't access an authorized ending, as there isn't one.

Counterfactual Regret Minimization(CFR) has shown its success in Texas Hold'em poker. We apply this algorithm to another popular incomplete information game, Mahjong. Compared to the poker game, Mahjong is much more complex with many variants. We study two-player Mahjong by conducting game theoretical analysis and making a hierarchical abstraction to CFR based on winning policies. This framework can be generalized to other imperfect information games.

Creative sketch is a universal way of visual expression, but translating images from an abstract sketch is very challenging. Traditionally, creating a deep learning model for sketch-to-image synthesis needs to overcome the distorted input sketch without visual details, and requires to collect large-scale sketch-image datasets. We first study this task by using diffusion models. Our model matches sketches through the cross domain constraints, and uses a classifier to guide the image synthesis more accurately. Extensive experiments confirmed that our method can not only be faithful to user's input sketches, but also maintain the diversity and imagination of synthetic image results. Our model can beat GAN-based method in terms of generation quality and human evaluation, and does not rely on massive sketch-image datasets. Additionally, we present applications of our method in image editing and interpolation.