Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems

Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success in sequential decision-making problems. This research presents a Reinforcement Learning approach for scheduling problems. In particular, this study delivers an OpenAI gym environment with search-space reduction for Job Shop Scheduling Problems and provides a heuristic-guided Q-Learning solution with state-of-the-art performance for Multi-agent Flexible Job Shop Problems

Tuning-Free Visual Customization via View Iterative Self-Attention Control

Fine-Tuning Diffusion Models enable a wide range of personalized generation and editing applications on diverse visual modalities. While Low-Rank Adaptation (LoRA) accelerates the fine-tuning process, it still requires multiple reference images and time-consuming training, which constrains its scalability for large-scale and real-time applications. In this paper, we propose \textit{View Iterative Self-Attention Control (VisCtrl)} to tackle this challenge. Specifically, VisCtrl is a training-free method that injects the appearance and structure of a user-specified subject into another subject in the target image, unlike previous approaches that require fine-tuning the model. Initially, we obtain the initial noise for both the reference and target images through DDIM inversion. Then, during the denoising phase, features from the reference image are injected into the target image via the self-attention mechanism. Notably, by iteratively performing this feature injection process, we ensure that the reference image features are gradually integrated into the target image. This approach results in consistent and harmonious editing with only one reference image in a few denoising steps. Moreover, benefiting from our plug-and-play architecture design and the proposed Feature Gradual Sampling strategy for multi-view editing, our method can be easily extended to edit in complex visual domains. Extensive experiments show the efficacy of VisCtrl across a spectrum of tasks, including personalized editing of images, videos, and 3D scenes.Comment: Under revie

A compact butterfly-style silicon photonic-electronic neural chip for hardware-efficient deep learning

The optical neural network (ONN) is a promising hardware platform for next-generation neurocomputing due to its high parallelism, low latency, and low energy consumption. Previous ONN architectures are mainly designed for general matrix multiplication (GEMM), leading to unnecessarily large area cost and high control complexity. Here, we move beyond classical GEMM-based ONNs and propose an optical subspace neural network (OSNN) architecture, which trades the universality of weight representation for lower optical component usage, area cost, and energy consumption. We devise a butterfly-style photonic-electronic neural chip to implement our OSNN with up to 7x fewer trainable optical components compared to GEMM-based ONNs. Additionally, a hardware-aware training framework is provided to minimize the required device programming precision, lessen the chip area, and boost the noise robustness. We experimentally demonstrate the utility of our neural chip in practical image recognition tasks, showing that a measured accuracy of 94.16% can be achieved in hand-written digit recognition tasks with 3-bit weight programming precision.Comment: 17 pages,5 figure

ADEPT: Automatic Differentiable DEsign of Photonic Tensor Cores

Photonic tensor cores (PTCs) are essential building blocks for optical artificial intelligence (AI) accelerators based on programmable photonic integrated circuits. PTCs can achieve ultra-fast and efficient tensor operations for neural network (NN) acceleration. Current PTC designs are either manually constructed or based on matrix decomposition theory, which lacks the adaptability to meet various hardware constraints and device specifications. To our best knowledge, automatic PTC design methodology is still unexplored. It will be promising to move beyond the manual design paradigm and "nurture" photonic neurocomputing with AI and design automation. Therefore, in this work, for the first time, we propose a fully differentiable framework, dubbed ADEPT, that can efficiently search PTC designs adaptive to various circuit footprint constraints and foundry PDKs. Extensive experiments show superior flexibility and effectiveness of the proposed ADEPT framework to explore a large PTC design space. On various NN models and benchmarks, our searched PTC topology outperforms prior manually-designed structures with competitive matrix representability, 2-30x higher footprint compactness, and better noise robustness, demonstrating a new paradigm in photonic neural chip design. The code of ADEPT is available at https://github.com/JeremieMelo/ADEPT using the https://github.com/JeremieMelo/pytorch-onn (TorchONN) library.Comment: Accepted to ACM/IEEE Design Automation Conference (DAC), 202

DOTA: A Dynamically-Operated Photonic Tensor Core for Energy-Efficient Transformer Accelerator

The wide adoption and significant computing resource consumption of attention-based Transformers, e.g., Vision Transformer and large language models, have driven the demands for efficient hardware accelerators. While electronic accelerators have been commonly used, there is a growing interest in exploring photonics as an alternative technology due to its high energy efficiency and ultra-fast processing speed. Optical neural networks (ONNs) have demonstrated promising results for convolutional neural network (CNN) workloads that only require weight-static linear operations. However, they fail to efficiently support Transformer architectures with attention operations due to the lack of ability to process dynamic full-range tensor multiplication. In this work, we propose a customized high-performance and energy-efficient photonic Transformer accelerator, DOTA. To overcome the fundamental limitation of existing ONNs, we introduce a novel photonic tensor core, consisting of a crossbar array of interference-based optical vector dot-product engines, that supports highly-parallel, dynamic, and full-range matrix-matrix multiplication. Our comprehensive evaluation demonstrates that DOTA achieves a >4x energy and a >10x latency reduction compared to prior photonic accelerators, and delivers over 20x energy reduction and 2 to 3 orders of magnitude lower latency compared to the electronic Transformer accelerator. Our work highlights the immense potential of photonic computing for efficient hardware accelerators, particularly for advanced machine learning workloads.Comment: The short version is accepted by Next-Gen AI System Workshop at MLSys 202

Reliability and performance of the IRRAflow® system for intracranial lavage and evacuation of hematomas—A technical note

Background Intraventricular hemorrhage (IVH) is a severe condition with poor outcomes and high mortality. IRRAflow® (IRRAS AB) is a new technology introduced to accelerate IVH clearance by minimally invasive wash-out. The IRRAflow® system performs active and controlled intracranial irrigation and aspiration with physiological saline, while simultaneously monitoring and maintaining a stable intracranial pressure (ICP). We addressed important aspects of the device implementation and intracranial lavage. Method To allow versatile investigation of multiple device parameters, we designed an ex vivo lab setup. We evaluated 1) compatibility between the IRRAflow® catheter and the Silverline f10 bolt (Spiegelberg), 2) the physiological and hydrodynamic effects of varying the IRRAflow® settings, 3) the accuracy of the IRRAflow® injection volumes, and 4) the reliability of the internal ICP monitor of the IRRAflow®. Results The IRRAflow® catheter was not compatible with Silverline bolt fixation, which was associated with leakage and obstruction. Design space exploration of IRRAflow® settings revealed that appropriate settings included irrigation rate 20 ml/h with a drainage bag height at 0 cm, irrigation rate 90 ml/h with a drainage bag height at 19 cm and irrigation rate 180 ml/h with a drainage bag height at 29 cm. We found the injection volume performed by the IRRAflow® to be stable and reliable, while the internal ICP monitor was compromised in several ways. We observed a significant mean drift difference of 3.16 mmHg (variance 0.4, p = 0.05) over a 24-hour test period with a mean 24-hour drift of 3.66 mmHg (variance 0.28) in the pressures measured by the IRRAflow® compared to 0.5 mmHg (variance 1.12) in the Raumedic measured pressures. Conclusion Bolting of the IRRAflow® catheter using the Medtronic Silverline® bolt is not recommendable. Increased irrigation rates are recommendable followed by a decrease in drainage bag level. ICP measurement using the IRRAflow® device was unreliable and should be accompanied by a control ICP monitor device in clinical settings.</p

Outcomes and complications of external ventricular drainage in primary and secondary intraventricular hemorrhage:a descriptive observational study

OBJECTIVE: Intraventricular hemorrhage (IVH) is a serious condition with high mortality rates and poor functional outcome in survivors. Treatment includes external ventricular drains (EVDs), which are associated with several complications. This study reports the clinical outcome and complication rate in patients with primary IVH (pIVH) and secondary IVH treated with EVDs.METHODS: The authors conducted a retrospective observational study using the Danish National Patient Registry. Patients treated with EVDs for pIVH or secondary IVH between September 2012 and August 2022 at Aarhus University Hospital were included. Demographic data, clinical treatment, and outcomes were extracted and analyzed.RESULTS: A total of 436 patients with 615 EVDs were included. Of these, 4.1% had pIVH, 60.6% had IVH secondary to subarachnoid hemorrhage, and 35.3% had IVH secondary to intracerebral hemorrhage. During EVD treatment, 38.8% of patients experienced complications, including complete occlusion (17.2%), partial occlusion (16.1%), ventriculitis (7.1%), and other complications (9.6%). Of patients surviving the initial 30 days, 34.2% received a ventriculoperitoneal shunt, and 29.9% remained shunt dependent 6 months after inclusion. Mortality rates were 28.9% at 30 days and 33.7% at 90 days. A total of 31.0% of patients had good functional outcomes at 90 days.CONCLUSIONS: This study provides a comprehensive historical reference of complications, mortality rate, and functional outcome of EVD-treated patients with pIVH and secondary IVH. These findings provide a baseline for evaluating novel catheter-based interventions in IVH management.</p

Trans-lesion synthesis and mismatch repair pathway crosstalk defines chemoresistance and hypermutation mechanisms in glioblastoma

Almost all Glioblastoma (GBM) are either intrinsically resistant to the chemotherapeutical drug temozolomide (TMZ) or acquire therapy-induced mutations that cause chemoresistance and recurrence. The genome maintenance mechanisms responsible for GBM chemoresistance and hypermutation are unknown. We show that the E3 ubiquitin ligase RAD18 (a proximal regulator of TLS) is activated in a Mismatch repair (MMR)-dependent manner in TMZ-treated GBM cells, promoting post-replicative gap-filling and survival. An unbiased CRISPR screen provides an aerial map of RAD18-interacting DNA damage response (DDR) pathways deployed by GBM to tolerate TMZ genotoxicity. Analysis of mutation signatures from TMZ-treated GBM reveals a role for RAD18 in error-free bypass of O6mG (the most toxic TMZ-induced lesion), and error-prone bypass of other TMZ-induced lesions. Our analyses of recurrent GBM patient samples establishes a correlation between low RAD18 expression and hypermutation. Taken together we define molecular underpinnings for the hallmark tumorigenic phenotypes of TMZ-treated GBM

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data