The comER Gene Plays an Important Role in Biofilm Formation and Sporulation in both Bacillus subtilis and Bacillus cereus

Bacteria adopt alternative cell fates during development. In Bacillus subtilis, the transition from planktonic growth to biofilm formation and sporulation is controlled by a complex regulatory circuit, in which the most important event is activation of Spo0A, a transcription factor and a master regulator for genes involved in both biofilm formation and sporulation. In B. cereus, the regulatory pathway controlling biofilm formation and cell differentiation is much less clear. In this study, we show that a novel gene, comER, plays a significant role in biofilm formation as well as sporulation in both B. subtilis and B. cereus. Mutations in the comER gene result in defects in biofilm formation and a delay in spore formation in the two Bacillus species. Our evidence supports the idea that comER may be part of the regulatory circuit that controls Spo0A activation. comER likely acts upstream of sda, a gene encoding a small checkpoint protein for both sporulation and biofilm formation, by blocking the phosphor-relay and thereby Spo0A activation. In summary, our studies outlined a conserved, positive role for comER, a gene whose function was previously uncharacterized, in the regulation of biofilm formation and sporulation in the two Bacillus species

A wearable non-contact optical system based on muscle tracking for ultra-long-term and indirect eyetracking

Eye signals have become popular worldwide due to their potential in multiple fields. Such data can be regarded as indications of diseases such as epilepsy, a sign of fatigue, expression of potential interests during shopping and so on. In this paper, we demonstrate a novel optical eye-tracking system, NIRSense, which is able to track eye activities based on muscle movements, without any touching with the cornea. The NIRSense sensors, six pairs of near-infrared (NIR) light-emitting diodes (LEDs) and photodiodes are emitting 940 nm lights on skins below eyes in a contactless distance, where lights are able to penetrate and be reflected back to photodiodes. These signals are then transmitted into electric currents and processed to build relationships between signals and pupil locations. It is proved that the NIRSense system can track eye activities not only accurately (1.05 degrees) and precisely (0.6 degrees), but also in a safe, long-term used way. Moreover, the hardware design of the NIRSense system allows the assembling easily on diverse near-eye frames, involving Virtual reality (VR) goggles, Augmented reality (AR) headsets and normal glasses frames, via attaching sensors near the bottoms of lenses. In this way, this system has enriched the possibilities of exploring human reactions to virtual or real environments. Functions of accurate and precise eye trace prediction, pupil tracking with eyelids closure, comparison of performances with a commercial eye tracker Tobii Pro Glasses 3 and clinical application on the concussion assessment are presented in this paper

DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization

In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping. DISA, for the first time, eliminates the dependence of the convergent step-size range on the Euclidean norm of the linear mapping, while inheriting the advantages of the classic Primal-Dual Proximal Splitting Algorithm (PD-PSA): simple structure and easy implementation. This indicates that DISA can be executed without prior knowledge of the norm, and tiny step-sizes can be avoided when the norm is large. Additionally, we prove sublinear and linear convergence rates of DISA under general convexity and metric subregularity, respectively. Moreover, we provide a variant of DISA with approximate proximal mapping and prove its global convergence and sublinear convergence rate. Numerical experiments corroborate our theoretical analyses and demonstrate a significant acceleration of DISA compared to existing PD-PSAs

MG-Skip: Random Multi-Gossip Skipping Method for Nonsmooth Distributed Optimization

Distributed optimization methods with probabilistic local updates have recently gained attention for their provable ability to communication acceleration. Nevertheless, this capability is effective only when the loss function is smooth and the network is sufficiently well-connected. In this paper, we propose the first linear convergent method MG-Skip with probabilistic local updates for nonsmooth distributed optimization. Without any extra condition for the network connectivity, MG-Skip allows for the multiple-round gossip communication to be skipped in most iterations, while its iteration complexity is $\mathcal{O}\left(\kappa \log \frac{1}{\epsilon}\right)$ and communication complexity is only $\mathcal{O}\left(\sqrt{\frac{\kappa}{(1-\rho)}} \log \frac{1}{\epsilon}\right)$, where $\kappa$ is the condition number of the loss function and $\rho$ reflects the connectivity of the network topology. To the best of our knowledge, MG-Skip achieves the best communication complexity when the loss function has the smooth (strongly convex)+nonsmooth (convex) composite form

SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection

This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structures of self-augmented compound cross-domain samples to enhance the model's generalization ability. Different from DGOD trained on multiple source domains, Single-DGOD is far more challenging to generalize well to multiple target domains with only one single source domain. Existing methods mostly adopt a similar treatment from DGOD to learn domain-invariant features by decoupling or compressing the semantic space. However, there may have two potential limitations: 1) pseudo attribute-label correlation, due to extremely scarce single-domain data; and 2) the semantic structural information is usually ignored, i.e., we found the affinities of instance-level semantic relations in samples are crucial to model generalization. In this paper, we introduce Semantic Reasoning with Compound Domains (SRCD) for Single-DGOD. Specifically, our SRCD contains two main components, namely, the texture-based self-augmentation (TBSA) module, and the local-global semantic reasoning (LGSR) module. TBSA aims to eliminate the effects of irrelevant attributes associated with labels, such as light, shadow, color, etc., at the image level by a light-yet-efficient self-augmentation. Moreover, LGSR is used to further model the semantic relationships on instance features to uncover and maintain the intrinsic semantic structures. Extensive experiments on multiple benchmarks demonstrate the effectiveness of the proposed SRCD.Comment: 10 pages, 5 figure

The Application of Driver Models in the Safety Assessment of Autonomous Vehicles: A Survey

Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model realistic driver behavior. With the development of AVs, driver models attract much attention again due to their potential contributions to AV certification. The simulation-based testing method is considered an effective measure to accelerate AV testing due to its safe and efficient characteristics. Nonetheless, realistic driver models are prerequisites for valid simulation results. Additionally, an AV is assumed to be at least as safe as a careful and competent driver. Therefore, driver models are inevitable for AV safety assessment. However, no comparison or discussion of driver models is available regarding their utility to AVs in the last five years despite their necessities in the release of AVs. This motivates us to present a comprehensive survey of driver models in the paper and compare their applicability. Requirements for driver models in terms of their application to AV safety assessment are discussed. A summary of driver models for simulation-based testing and AV certification is provided. Evaluation metrics are defined to compare their strength and weakness. Finally, an architecture for a careful and competent driver model is proposed. Challenges and future work are elaborated. This study gives related researchers especially regulators an overview and helps them to define appropriate driver models for AVs

Revisiting Decentralized ProxSkip: Achieving Linear Speedup

The ProxSkip algorithm for decentralized and federated learning is gaining increasing attention due to its proven benefits in accelerating communication complexity while maintaining robustness against data heterogeneity. However, existing analyses of ProxSkip are limited to the strongly convex setting and do not achieve linear speedup, where convergence performance increases linearly with respect to the number of nodes. So far, questions remain open about how ProxSkip behaves in the non-convex setting and whether linear speedup is achievable. In this paper, we revisit decentralized ProxSkip and address both questions. We demonstrate that the leading communication complexity of ProxSkip is $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon^2}\right)$ for non-convex and convex settings, and $\mathcal{O}\left(\frac{p\sigma^2}{n\epsilon}\right)$ for the strongly convex setting, where $n$ represents the number of nodes, $p$ denotes the probability of communication, $\sigma^2$ signifies the level of stochastic noise, and $\epsilon$ denotes the desired accuracy level. This result illustrates that ProxSkip achieves linear speedup and can asymptotically reduce communication overhead proportional to the probability of communication. Additionally, for the strongly convex setting, we further prove that ProxSkip can achieve linear speedup with network-independent stepsizes