Step Optimal Implementations of Large Single-Writer Registers

We present two wait-free algorithms for simulating an l-bit single-writer register from k-bit single-writer registers, for any k >= 1. Our first algorithm has big-theta(l/k) step complexity for both Read and Write and uses big-theta (4^(l-k)) registers. An interesting feature of the algorithm is that Read operations do not write to shared variables. Our second algorithm has big-theta (l/k + (log n)/k) step complexity for both Read and Write, where n is the number of readers, but uses only big-theta (nl/k + n(log n)/k) registers. Combining both algorithms gives an implementation with big-theta (l/k) step complexity using big-theta (nl/k) space for any 1 <= k < l. We also prove that any implementation with big-O (l/k) step complexity for Read requires big-omega (l/k) step complexity for Write. Since reading l-bits requires at least ceiling(l/k) reads of k-bit registers, our lower bound shows that our implementation is step optimal

Language and Sketching: An LLM-driven Interactive Multimodal Multitask Robot Navigation Framework

The socially-aware navigation system has evolved to adeptly avoid various obstacles while performing multiple tasks, such as point-to-point navigation, human-following, and -guiding. However, a prominent gap persists: in Human-Robot Interaction (HRI), the procedure of communicating commands to robots demands intricate mathematical formulations. Furthermore, the transition between tasks does not quite possess the intuitive control and user-centric interactivity that one would desire. In this work, we propose an LLM-driven interactive multimodal multitask robot navigation framework, termed LIM2N, to solve the above new challenge in the navigation field. We achieve this by first introducing a multimodal interaction framework where language and hand-drawn inputs can serve as navigation constraints and control objectives. Next, a reinforcement learning agent is built to handle multiple tasks with the received information. Crucially, LIM2N creates smooth cooperation among the reasoning of multimodal input, multitask planning, and adaptation and processing of the intelligent sensing modules in the complicated system. Extensive experiments are conducted in both simulation and the real world demonstrating that LIM2N has superior user needs understanding, alongside an enhanced interactive experience

Safe distance prediction for braking control of bridge cranes considering anti-swing

Cranes are widely deployed for lifting and moving heavy objects in dynamic environments with human coexistence. Suddenly appeared workers, vehicles, and robots can affect the safety of the cranes. To avoid possible collisions, the cranes must have prediction ability to know how dangerous the situation is. In this paper, we address the safety issues of bridge cranes based on its online physical states and control model. Due to the swing of the payload, the safe braking distance cannot be a constant value. Therefore, we here propose a model prediction control (MPC)-based anti-swing method for non-zero initial states, where a new reference trajectory and a new cost function for optimization are proposed, such that the proposed MPC method can control the crane to follow the proposed reference trajectory and achieve a stable stop state with anti-swing. Furthermore, an offline learning mechanism is introduced to learn a statistical model between the velocity of the crane and the safe braking distance achieved by using the proposed MPC braking control method. In this way, we can predict how far the crane would require to safely stop without swing based on its current velocity, which is the safe distance prediction to evaluate the dangerous level of the dynamic obstacle. Experiments using both a simulated crane and a real crane demonstrate that the proposed safe braking distance prediction method is effective for safe braking control of the bridge cranes

In silico Genetic Network Models for &#xb;Pre-clinical Drug Prioritization

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call &#x2018;synergistic outcome determination&#x2019; (SOD), a concept similar to &#x2018;Synthetic Lethality&#x2019;. SOD is defined as the synergy of a gene pair with respect to cancer patients&#x27; outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies.&#xd;&#xa

Spatiotemporal patterns and spatial risk factors for visceral leishmaniasis from 2007 to 2017 in Western and Central China: a modelling analysis

Visceral leishmaniasis (VL) is a neglected disease caused by trypanosomatid protozoa in the genus Leishmania, which is transmitted by phlebotomine sandflies. Although this vector-borne disease has been eliminated in several regions of China during the last century, the reported human VL cases have rebounded in Western and Central China in recent decades. However, understanding of the spatial epidemiology of the disease remains vague, as the spatial risk factors driving the spatial heterogeneity of VL. In this study, we analyzed the spatiotemporal patterns of annual human VL cases in Western and Central China from 2007 to 2017. Based on the related spatial maps, the boosted regression tree (BRT) model was adopted to explore the relationships between VL and spatial correlates as well as predicting both the existing and potential infection risk zones of VL in Western and Central China. The mined links reveal that elevation, minimum temperature, relative humidity, and annual accumulated precipitation make great contributions to the spatial heterogeneity of VL. The maps show that Xinjiang Uygur Autonomous Region, Gansu, western Inner Mongolia Autonomous Region, and Sichuan are predicted to fall in the highest infection risk zones of VL. Approximately 61.60 million resident populations lived in the high-risk regions of VL in Western and Central China. Our results provide a better understanding of how spatial risk factors driving VL spread as well as identifying the potential endemic risk region of VL, thereby enhancing the biosurveillance capacity of public health authorities

The neural correlates of value hierarchies: a prospective typology based on personal value profiles of emerging adults

IntroductionValue hierarchies, as motivational goals anchored in the self-schema, may be correlated with spontaneous activity in the resting brain, especially those involving self-relevance. This study aims to investigate the neural correlates of value hierarchies from the perspective of typology.MethodsA total of 610 Chinese college students (30.31% women), aged 18 to 23, completed the personal values questionnaire and underwent resting-state functional magnetic resonance imaging.ResultsThe latent profile analysis revealed three personal value profiles: traditional social orientation, modernized orientation, and undifferentiated orientation. Neuroimaging results revealed that individuals with modernized orientation prioritized openness to change value, and this personal-focus is related to the higher low-frequency amplitude of the posterior insula; individuals with traditional social orientation prioritized self-transcendence and conservation values, and this social-focus is related to the stronger functional connectivity of the middle insula with the inferior temporal gyrus, temporal gyrus, posterior occipital cortex, and basal ganglia, as well as weaker functional connections within the right middle insula.DiscussionTaken together, these findings potentially indicate the intra-generational differentiation of contemporary Chinese emerging adults’ value hierarchies. At the neural level, these are correlated with brain activities involved in processing self- and other-relevance

Micropathogen community analysis in Hyalomma rufipes via high-throughput sequencing of small RNAs

Ticks are important vectors in the transmission of a broad range of micropathogens to vertebrates, including humans. Because of the role of ticks in disease transmission, identifying and characterizing the micropathogen profiles of tick populations have become increasingly important. The objective of this study was to survey the micropathogens of Hyalomma rufipes ticks. Illumina HiSeq2000 technology was utilized to perform deep sequencing of small RNAs (sRNAs) extracted from field-collected H. rufipes ticks in Gansu Province, China. The resultant sRNA library data revealed that the surveyed tick populations produced reads that were homologous to St. Croix River Virus (SCRV) sequences. We also observed many reads that were homologous to microbial and/or pathogenic isolates, including bacteria, protozoa, and fungi. As part of this analysis, a phylogenetic tree was constructed to display the relationships among the homologous sequences that were identified. The study offered a unique opportunity to gain insight into the micropathogens of H. rufipes ticks. The effective control of arthropod vectors in the future will require knowledge of the micropathogen composition of vectors harboring infectious agents. Understanding the ecological factors that regulate vector propagation in association with the prevalence and persistence of micropathogen lineages is also imperative. These interactions may affect the evolution of micropathogen lineages, especially if the micropathogens rely on the vector or host for dispersal. The sRNA deep-sequencing approach used in this analysis provides an intuitive method to survey micropathogen prevalence in ticks and other vector species

The global state of research in stem cells therapy for spinal cord injury (2003–2022): a visualized analysis

ObjectiveOur study aimed to visualize the global status and frontiers in stem cell therapy for spinal cord injury by using bibliometric methodology.MethodsPublication citation information related to stem cell therapy for spinal cord injury (SCI) studies between 2003 and 2022 was retrieved from the Web of Science Core Collection database. For the visualized study, VOS viewer software and Graph Pad Prism 9.5 were used to perform bibliometric analysis of included data and publication number statistics in stem cell therapy for the SCI domain.ResultsA total of 6,686 publications were retrieved. The USA and China made the highest contributions to global research with the highest number of citations and link strength. The journal Experimental Neurology ranks as the top journal, combining the publication amount and bibliometrics results. The University of Toronto, based in Canada, was the first-ranking institution. The directions of the current study could be divided into five clusters. The research of Transplantation and Regenerative Medicine and Neurosciences Mechanism Research may be the emerging frontiers in this domain.ConclusionIn summary, stem cell therapy for spinal cord injuries is poised for more valuable advances