Robust, automated sleep scoring by a compact neural network with distributional shift correction.

Studying the biology of sleep requires the accurate assessment of the state of experimental subjects, and manual analysis of relevant data is a major bottleneck. Recently, deep learning applied to electroencephalogram and electromyogram data has shown great promise as a sleep scoring method, approaching the limits of inter-rater reliability. As with any machine learning algorithm, the inputs to a sleep scoring classifier are typically standardized in order to remove distributional shift caused by variability in the signal collection process. However, in scientific data, experimental manipulations introduce variability that should not be removed. For example, in sleep scoring, the fraction of time spent in each arousal state can vary between control and experimental subjects. We introduce a standardization method, mixture z-scoring, that preserves this crucial form of distributional shift. Using both a simulated experiment and mouse in vivo data, we demonstrate that a common standardization method used by state-of-the-art sleep scoring algorithms introduces systematic bias, but that mixture z-scoring does not. We present a free, open-source user interface that uses a compact neural network and mixture z-scoring to allow for rapid sleep scoring with accuracy that compares well to contemporary methods. This work provides a set of computational tools for the robust automation of sleep scoring

Block Processor: A Resource-distributed Architecture

Abstract-We present the architecture of Block Processor, task-level coprocessor, to execute vectorizable computing task migrated from main processor via command bus. The Block Processor is designed around 32 high-MVL block registers, which can be direct operands of vector instruction and be local cache of the Block Processor. The corresponding unique conflictsolving mechanism scales with the various implementations and easily supports chaining by adding extra execution states. The architecture distributes the block registers, ALUs and control logic. We implement the Block Processor which maps efficiently into the FPGA since the FPGA also distributes its inner resource. Each block register requires two FPGA Block RAM to be 2-read-1-write-port, 1024-depth and 32-bit-width. With the enhanced chaining and decoupling, it might hinder the latency of vector memory instructions and then sustain the computing abilities. With the little resource occupied, 1024-point radix-2 DIF FFT costs 11348 cycles on one Block Processor

GitFL: Adaptive Asynchronous Federated Learning using Version Control

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in AIoT (Artificial Intelligence of Things) design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of low inference accuracy and long training time. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective control of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding the performance deterioration. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88% inference accuracy improvements in various uncertain scenarios

Structural Mechanism for the Specific Assembly and Activation of the Extracellular Signal Regulated Kinase 5 (ERK5) Module

Mitogen-activated protein kinase (MAPK) activation depends on a linear binding motif found in all MAPK kinases (MKK). In addition, the PB1 (Phox and Bem1) domain of MKK5 is required for extracellular signal regulated kinase 5 (ERK5) activation. We present the crystal structure of ERK5 in complex with an MKK5 construct comprised of the PB1 domain and the linear binding motif. We show that ERK5 has distinct protein-protein interaction surfaces compared with ERK2, which is the closest ERK5 paralog. The two MAPKs have characteristically different physiological functions and their distinct protein-protein interaction surface topography enables them to bind different sets of activators and substrates. Structural and biochemical characterization revealed that the MKK5 PB1 domain cooperates with the MAPK binding linear motif to achieve substrate specific binding, and it also enables co-recruitment of the upstream activating enzyme and the downstream substrate into one signaling competent complex. Studies on present day MAPKs and MKKs hint on the way protein kinase networks may evolve. In particular, they suggest how paralogous enzymes with similar catalytic properties could acquire novel signaling roles by merely changing the way they make physical links to other proteins

HiCAST: Highly Customized Arbitrary Style Transfer with Adapter Enhanced Diffusion Models

The goal of Arbitrary Style Transfer (AST) is injecting the artistic features of a style reference into a given image/video. Existing methods usually focus on pursuing the balance between style and content, whereas ignoring the significant demand for flexible and customized stylization results and thereby limiting their practical application. To address this critical issue, a novel AST approach namely HiCAST is proposed, which is capable of explicitly customizing the stylization results according to various source of semantic clues. In the specific, our model is constructed based on Latent Diffusion Model (LDM) and elaborately designed to absorb content and style instance as conditions of LDM. It is characterized by introducing of \textit{Style Adapter}, which allows user to flexibly manipulate the output results by aligning multi-level style information and intrinsic knowledge in LDM. Lastly, we further extend our model to perform video AST. A novel learning objective is leveraged for video diffusion model training, which significantly improve cross-frame temporal consistency in the premise of maintaining stylization strength. Qualitative and quantitative comparisons as well as comprehensive user studies demonstrate that our HiCAST outperforms the existing SoTA methods in generating visually plausible stylization results

HSC-GPT: A Large Language Model for Human Settlements Construction

The field of human settlement construction encompasses a range of spatial designs and management tasks, including urban planning and landscape architecture design. These tasks involve a plethora of instructions and descriptions presented in natural language, which are essential for understanding design requirements and producing effective design solutions. Recent research has sought to integrate natural language processing (NLP) and generative artificial intelligence (AI) into human settlement construction tasks. Due to the efficient processing and analysis capabilities of AI with data, significant successes have been achieved in design within this domain. However, this task still faces several fundamental challenges. The semantic information involved includes complex spatial details, diverse data source formats, high sensitivity to regional culture, and demanding requirements for innovation and rigor in work scenarios. These factors lead to limitations when applying general generative AI in this field, further exacerbated by a lack of high-quality data for model training. To address these challenges, this paper first proposes HSC-GPT, a large-scale language model framework specifically designed for tasks in human settlement construction, considering the unique characteristics of this domain

Decoupled Land and Ocean Temperature Trends in the Early-Middle Pleistocene

Record of long-term land temperature changes remains ephemeral, discontinuous, and isolated, thus leaving the common view that Pleistocene land temperature evolution should have followed ocean temperatures unconfirmed. Here, we present a continuous land surface temperature reconstruction in the Asian monsoon region over the past 3.0 Myr based on the distribution of soil bacterial lipids from the Chinese Loess Plateau. The land temperature record indicates an unexpected warming trend over the Pleistocene, which is opposite to the cooling trend in Pleistocene ocean temperatures, resulting in increased land-sea thermal contrast. We propose that the previously unrecognized increase of land-sea thermal contrast during much of the Pleistocene is a regional climate phenomenon that provides a likely mechanism in favor of the long-term enhancement of the Pleistocene East Asian summer monsoon

Synthetic human cell fate regulation by protein-driven RNA switches

Understanding how to control cell fate is crucial in biology, medical science and engineering. In this study, we introduce a method that uses an intracellular protein as a trigger for regulating human cell fate. The ON/OFF translational switches, composed of an intracellular protein L7Ae and its binding RNA motif, regulate the expression of a desired target protein and control two distinct apoptosis pathways in target human cells. Combined use of the switches demonstrates that a specific protein can simultaneously repress and activate the translation of two different mRNAs: one protein achieves both up- and downregulation of two different proteins/pathways. A genome-encoded protein fused to L7Ae controlled apoptosis in both directions (death or survival) depending on its cellular expression. The method has potential for curing cellular defects or improving the intracellular production of useful molecules by bypassing or rewiring intrinsic signal networks

Protein Scaffolds Can Enhance the Bistability of Multisite Phosphorylation Systems

The phosphorylation of a substrate at multiple sites is a common protein modification that can give rise to important structural and electrostatic changes. Scaffold proteins can enhance protein phosphorylation by facilitating an interaction between a protein kinase enzyme and its target substrate. In this work we consider a simple mathematical model of a scaffold protein and show that under specific conditions, the presence of the scaffold can substantially raise the likelihood that the resulting system will exhibit bistable behavior. This phenomenon is especially pronounced when the enzymatic reactions have sufficiently large KM, compared to the concentration of the target substrate. We also find for a closely related model that bistable systems tend to have a specific kinetic conformation. Using deficiency theory and other methods, we provide a number of necessary conditions for bistability, such as the presence of multiple phosphorylation sites and the dependence of the scaffold binding/unbinding rates on the number of phosphorylated sites