In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues.

LSTM is decomposed into vertical and horizontal LSTMs to enhance performance.

In keeping with ViP, the first stage of Sequencers involves patch embedding with a 7x7 kernel. The second stage of Sequencers performs patch embedding with a 2x2 kernel, but the following two stages have no downsampling.

LSTM is decomposed into vertical and horizontal LSTMs to enhance performance.

In recent computer vision research, the advent of the Vision Transformer (ViT) has rapidly revolutionized various architectural design efforts: ViT achieved state-of-the-art image classification performance using self-attention found in natural language processing, and MLP-Mixer achieved competitive performance using simple multi-layer perceptrons. In contrast, several studies have also suggested that carefully redesigned convolutional neural networks (CNNs) can achieve advanced performance comparable to ViT without resorting to these new ideas. Against this background, there is growing interest in what inductive bias is suitable for computer vision. Here we propose Sequencer, a novel and competitive architecture alternative to ViT that provides a new perspective on these issues. We also propose a two-dimensional version of Sequencer module, where an LSTM is decomposed into vertical and horizontal LSTMs to enhance performance.

Debris disks, which consist of dust, planetesimals, planets, and gas, offer a unique window into the mineralogical composition of their parent bodies, especially during the critical phase of terrestrial planet formation spanning 10 to a few hundred million years. Observations from the $\textit{Spitzer}$ Space Telescope have unveiled thousands of debris disks, yet systematic studies remain scarce, let alone those with unsupervised clustering techniques. This study introduces $\texttt{CLUES}$ (CLustering UnsupErvised with Sequencer), a novel, non-parametric, fully-interpretable machine-learning spectral analysis tool designed to analyze and classify the spectral data of debris disks. $\texttt{CLUES}$ combines multiple unsupervised clustering methods with multi-scale distance measures to discern new groupings and trends, offering insights into compositional diversity and geophysical processes within these disks. Our analysis allows us to explore a vast parameter space in debris disk mineralogy and also offers broader applications in fields such as protoplanetary disks and solar system objects. This paper details the methodology, implementation, and initial results of $\texttt{CLUES}$, setting the stage for more detailed follow-up studies focusing on debris disk mineralogy and demographics.

Abstract--This paper presents a novel controller for the bipedal robot Bolt. Our approach leverages a whole-body model predictive controller in conjunction with a footstep sequencer to achieve robust locomotion. Simulation results demonstrate effective velocity tracking as well as push and slippage recovery abilities. In addition to that, we provide a theoretical sensitivity analysis of the footstep sequencing problem to enhance the understanding of the results. A. Context Bipedal robotics, with its origins tracing back to the end of the last century, has witnessed a significant surge in recent years.

The new method, published in Nature last week, is more efficient, storing 350 bits at a time by encoding strands in parallel. Peking University's Long Qian and team got the idea for such templates from the way cells share the same basic set of genes but behave differently in response to chemical changes in DNA strands. "Every cell in our bodies has the same genome sequence, but genetic programming comes from modifications to DNA. If life can do this, we can do this," she says. Once the bricks are locked into their assigned spots on the strand, researchers select which bricks to methylate, with the presence or absence of the modification standing in for binary values of 0 or 1.

FoundationDB is an open-source transactional key-value store created more than 10 years ago. It is one of the first systems to combine the flexibility and scalability of NoSQL architectures with the power of ACID transactions. FoundationDB adopts an unbundled architecture that decouples an in-memory transaction management system, a distributed storage system, and a built-in distributed configuration system. Each sub-system can be independently provisioned and configured to achieve scalability, high availability, and fault tolerance. FoundationDB includes a deterministic simulation framework, used to test every new feature under a myriad of possible faults. This rigorous testing makes FoundationDB extremely stable and allows developers to introduce and release new features in a rapid cadence. FoundationDB offers a minimal and carefully chosen feature set, which has enabled a range of disparate systems to be built as layers on top. FoundationDB is the underpinning of cloud infrastructure at Apple, Snowflake, and other companies, due to its consistency, robustness, and availability for storing user data, system metadata and configuration, and other critical information. Many cloud services rely on scalable, distributed storage backends for persisting application state. Such storage systems must be fault tolerant and highly available, and at the same time provide sufficiently strong semantics and flexible data models to enable rapid application development. Such services must scale to billions of users, petabytes or exabytes of stored data, and millions of requests per second. More than a decade ago, NoSQL storage systems emerged offering ease of application development, making it simple to scale and operate storage systems, offering fault-tolerance and supporting a wide range of data models (instead of the traditional rigid relational model). In order to scale, these systems sacrificed transactional semantics, and instead provided eventual consistency, forcing application developers to reason about interleavings of updates from concurrent operations. FoundationDB (FDB)3 was created in 2009 and gets its name from the focus on providing what we saw as the foundational set of building blocks required to build higher-level distributed systems.