A polynomial time algorithm for solving a quality control station configuration problem

AbstractWe study unreliable serial production lines with known failure probabilities for each operation. Such a production line consists of a series of stations; existing machines and optional quality control stations (QCS). Our aim is to simultaneously decide where and if to install the QCSs along the line and to determine the production rate, so as to maximize the steady state expected net profit per time unit from the system.We use dynamic programming to solve the cost minimization auxiliary problem where the aim is to minimize the time unit production cost for a given production rate. Using the above developed O(N2) dynamic programming algorithm as a subroutine, where N stands for the number of machines in the line, we present an O(N4) algorithm to solve the Profit Maximization QCS Configuration Problem

Transcranial Alternating Current Stimulation Increases Risk-Taking Behavior in the Balloon Analog Risk Task

The process of evaluating risks and benefits involves a complex neural network that includes the dorsolateral prefrontal cortex (DLPFC). It has been proposed that in conflict and reward situations, theta-band (4–8 Hz) oscillatory activity in the frontal cortex may reflect an electrophysiological mechanism for coordinating neural networks monitoring behavior, as well as facilitating task-specific adaptive changes. The goal of the present study was to investigate the hypothesis that theta-band oscillatory balance between right and left frontal and prefrontal regions, with a predominance role to the right hemisphere (RH), is crucial for regulatory control during decision-making under risk. In order to explore this hypothesis, we used transcranial alternating current stimulation, a novel technique that provides the opportunity to explore the functional role of neuronal oscillatory activities and to establish a causal link between specific oscillations and functional lateralization in risky decision-making situations. For this aim, healthy participants were randomly allocated to one of three stimulation groups (LH stimulation/RH stimulation/Sham stimulation), with active AC stimulation delivered in a frequency-dependent manner (at 6.5 Hz; 1 mA peak-to-peak). During the AC stimulation, participants performed the Balloon Analog Risk Task. This experiment revealed that participants receiving LH stimulation displayed riskier decision-making style compared to sham and RH stimulation groups. However, there was no difference in decision-making behaviors between sham and RH stimulation groups. The current study extends the notion that DLPFC activity is critical for adaptive decision-making in the context of risk-taking and emphasis the role of theta-band oscillatory activity during risky decision-making situations

TokenFlow: Consistent Diffusion Features for Consistent Video Editing

The generative AI revolution has recently expanded to videos. Nevertheless, current state-of-the-art video models are still lagging behind image models in terms of visual quality and user control over the generated content. In this work, we present a framework that harnesses the power of a text-to-image diffusion model for the task of text-driven video editing. Specifically, given a source video and a target text-prompt, our method generates a high-quality video that adheres to the target text, while preserving the spatial layout and motion of the input video. Our method is based on a key observation that consistency in the edited video can be obtained by enforcing consistency in the diffusion feature space. We achieve this by explicitly propagating diffusion features based on inter-frame correspondences, readily available in the model. Thus, our framework does not require any training or fine-tuning, and can work in conjunction with any off-the-shelf text-to-image editing method. We demonstrate state-of-the-art editing results on a variety of real-world videos. Webpage: https://diffusion-tokenflow.github.io

From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI

Reconstructing observed images from fMRI brain recordings is challenging. Unfortunately, acquiring sufficient "labeled" pairs of {Image, fMRI} (i.e., images with their corresponding fMRI responses) to span the huge space of natural images is prohibitive for many reasons. We present a novel approach which, in addition to the scarce labeled data (training pairs), allows to train fMRI-to-image reconstruction networks also on "unlabeled" data (i.e., images without fMRI recording, and fMRI recording without images). The proposed model utilizes both an Encoder network (image-to-fMRI) and a Decoder network (fMRI-to-image). Concatenating these two networks back-to-back (Encoder-Decoder & Decoder-Encoder) allows augmenting the training with both types of unlabeled data. Importantly, it allows training on the unlabeled test-fMRI data. This self-supervision adapts the reconstruction network to the new input test-data, despite its deviation from the statistics of the scarce training data.Comment: *First two authors contributed equally. NeurIPS 201

Spatiotemporal characterization of endothelial cell motility and physical forces during exposure to Borrelia burgdorferi

Cell motility and biomechanics are critical in various (patho)physiological processes, including the regulation of vascular barrier integrity, which can be subverted by bacterial pathogens. Here, we present a protocol on how to expose endothelial cells (ECs) to vector-borne Borrelia burgdorferi (Bb) and characterize EC kinematics and dynamics during exposure to live or heat-inactivated Bb through traction force and monolayer stress microscopy. Modifications to this protocol may be necessary for studying how different cell types interact with Bb or other microorganisms