Lowering blood pressure after acute intracerebral haemorrhage: protocol for a systematic review and meta-analysis using individual patient data from randomised controlled trials participating in the Blood Pressure in Acute Stroke Collaboration (BASC)

INTRODUCTION: Conflicting results from multiple randomised trials indicate that the methods and effects of blood pressure (BP) reduction after acute intracerebral haemorrhage (ICH) are complex. The Blood pressure in Acute Stroke Collaboration is an international collaboration, which aims to determine the optimal management of BP after acute stroke including ICH. METHODS AND ANALYSIS: A systematic review will be undertaken according to the Preferred Reporting Items for Systematic review and Meta-Analysis of Individual Participant Data (IPD) guideline. A search of Cochrane Central Register of Controlled Trials, EMBASE and MEDLINE from inception will be conducted to identify randomised controlled trials of BP management in adults with acute spontaneous (non-traumatic) ICH enrolled within the first 7 days of symptom onset. Authors of studies that meet the inclusion criteria will be invited to share their IPD. The primary outcome will be functional outcome according to the modified Rankin Scale. Safety outcomes will be early neurological deterioration, symptomatic hypotension and serious adverse events. Secondary outcomes will include death and neuroradiological and haemodynamic variables. Meta-analyses of pooled IPD using the intention-to-treat dataset of included trials, including subgroup analyses to assess modification of the effects of BP lowering by time to treatment, treatment strategy and patient's demographic, clinical and prestroke neuroradiological characteristics. ETHICS AND DISSEMINATION: No new patient data will be collected nor is there any deviation from the original purposes of each study where ethical approvals were granted; therefore, further ethical approval is not required. Results will be reported in international peer-reviewed journals. PROSPERO REGISTRATION NUMBER: CRD42019141136

Blood Pressure Variability and Outcome in Acute Ischemic and Hemorrhagic Stroke: A Post-Hoc Analysis of the HeadPoST Study

The Head Positioning in Acute Stroke Trial (HeadPoST) is a pragmatic, international, cluster crossover randomized trial of 11,093 patients with acute stroke assigned to a lying-flat (0o) or sitting-up (head elevated ≥30o) position. This post-hoc analysis aimed to determine the association between BPV and outcomes for patients from a wide range of international clinical settings and how the association was modified by randomized head position. BPV was defined according to standard criteria with the key parameter considered the coefficient of variation (CV) of systolic BP (SBP) over 24 hours. Outcome was ordinal 90-day modified Rankin Scale (mRS) score. The association was analyzed by ordinal, logistic regression, hierarchical, mixed models with fixed intervention (lying-flat vs. sitting-up), and fixed period, random cluster, and random cluster-period, effects. 9,156 (8,324 AIS and 817 ICH; mean age 68.1 years; 39.2% women) were included in the analysis. CV of SBP had a significant linear association with unfavorable shift of mRS at 90 days (adjusted odds ratio [OR] 1.06, 95% confidence interval [CI] 1.02-1.11; P=0.01). There was no heterogeneity of the association by randomized head positioning. In addition, CV of diastolic BP (DBP) (1.08, 1.03-1.12; P=0.001) over 24 hours post stroke, was significantly associated with 3-month poor outcome. The association was more apparent in sitting-up position (1.12, 1.06-1.19) compared with lying-flat position (1.03, 0.98-1.09) (P interaction = 0.005). BPV was associated with adverse stroke outcome, the magnitude of the association was greater with sitting-up head positioning in terms of DBP variability

GPT-NPC: Enhancing NPC Human-Likeness and Autonomy in Video Games

Non-player Characters (NPCs) in video games have been ubiquitous for many years. However, the interactions players have with these NPCs have typically been limited to pre-defined dialogue options, limiting the player’s immersion within the game world. Recent advances in AI and Natural Language Processing (NLP) have opened up new possibilities for creating NPCs that are capable of engaging in more naturalistic conversational interactions with players. We explore the use of GPT-powered NPCs in video games in the context of virtual reality and develop NPCs that possess long-term memory, autonomous behaviour, and believable interactions. We pro pose a novel GPT-NPC framework that utilises GPT models, in a modularised approach, to generate coherent and engaging dialogue for NPCs, while also incorporating thought and emotion modelling to create more immersive characters. To assess the effectiveness of our framework, human participants are involved, to rate the human likeness of NPCs. We demonstrate a mixed level of general believability, showing a rating score of 60%. From this, we further discuss ways to better improve and utilise the framework. This paper aims to contribute to the growing field of AI-powered game development and create more engaging gaming experiences for players

Effects of road pricing on freight carrier behaviors analyzed with experimental economics

This paper uses experimental economics to examine the route choice behavior of freight carriers in peak hours and off-hours. The study finds that carriers tend to choose toll roads in peak hours to ensure on-time delivery, regardless of the type of cargo. In contrast, carriers are reluctant to choose toll roads in off-hours. For a delivery of 50 mi, the receiver's final cost is lowered by approximately 25% if the receiver switches from peak hour delivery to off-hour delivery. The paper sheds light on the route choice behaviors of freight carriers under the constraint of delivering the cargo within a given time window. Findings of this study reinforce the empirical observation of freight carrier behaviors. The study also offers a data set that can be used for further econometric analysis of the freight sector

Online scheduling of coflows by attention-empowered scalable deep reinforcement learning

With the abstraction of parallel data transmission flows being a coflow, data transmissions in large-scale computing jobs can be modeled by a coflow directed acyclic graph (coflow DAG) in which nodes are coflows and edges represent dependencies between coflows. Efficient scheduling of coflows on network links is crucial for reducing the overall communication and job completion time. The known best coflow scheduling method deploying deep reinforcement learning (DRL), DeepWeave (Sun et al., 2020), suffers from poor scalability due to the requirement of O(dn)-size policy network for processing n coflows of d dimensions which is difficult to train. This paper extends the directed acyclic graph neural network (DAGNN) to Pipelined-DAGNN that embeds the features of different stages of input coflow DAGs in pipeline to effectively speed up the feature extraction process. To effectively process the feature vectors of coflow DAGs of arbitrary size and shape without compromising scheduling accuracy (quality), we propose a novel self-attention empowered DRL coflow scheduling model to generate coflow scheduling policies, which enables the scale of policy network depends only on features (dimensions) rather than coflows, without the need of packing all individual embedding vectors from Pipelined-DAGNN into a long flat vector. Our model reduces the size of the policy network in DRL from previously O(dn) to O(d), achieving a high scalability independent of the number of coflows. Simulation results on Facebook trace show that our model reduces the average weighted job completion time by up to 33.88%, apart from being more scalable and robust, compared with the state-of-the-art methods

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Prefac

Stock prices and the location of trade : evidence from China-backed ADRs

This study examines whether the trading location affects equity returns of China-backed American Depository Receipts (ADRs) traded in the US. If international financial markets are integrated, stock prices should be affected only by their fundamentals; otherwise, stock prices may also be affected by their trading locations/investor sentiment. We find that China ADRs’ returns are affected more by the US market fluctuations than by Chinese market returns. We interpret the results as suggesting that country-specific investor sentiment affects stock prices

Searching for a word in Chinese text: Insights from eye movement behaviour

Locating relevant information in text is an important aspect of the reading process, however relatively few studies have examined this, especially for logographic languages such as Chinese. The present study examines eye movement behaviour during search for a target word in Chinese sentences, compared with reading the sentences for comprehension. Although there were clear effects of word frequency during reading for comprehension, the study shows no evidence for an influence of the word frequency of non-target words on eye movement behaviour during target word search. The results are in line with previous research undertaken in English (Rayner, K., & Fischer, M. H. (1996). Mindless reading revisited: Eye movements during reading and scanning are different. Perception & Psychophysics, 58, 734–747.), such that during search for a target word, eye movement behaviour for non-target words is largely driven by superficial processing of those words. The study also highlights the prevalence of word skipping, indicating that words are often sampled only in visually degraded parafoveal vision during target word search in Chinese

Scheduling Coflows in Hybrid Optical-Circuit and Electrical-Packet Switches With Performance Guarantee

Scheduling of coflows, each a collection of parallel flows sharing the same objective, is an important task of data transmission that arises in the networks supporting data-intensive applications such as data center networks (DCNs). The hybrid switch design combining the optical circuit switch (OCS) and electrical packet switch (EPS) for transmitting high-volume and low-volume traffic separately has received considerable research attention. To support this design, efficient scheduling of coflows on hybrid network links is crucial for reducing the overall communication time. However, because it needs to consider both reconfiguration delay of circuit switching in the OCS and bandwidth limitation of packet switching in the EPS, coflow scheduling on hybrid network links is more challenging than on monotonic network links of either OCS or EPS. The existing coflow scheduling algorithms in hybrid switches are all heuristic and provide no performance guarantees. In this work, we first propose an approximation algorithm with a worst-case performance guarantee of $2\tau$ , where $\tau\le N$ is the maximum number of non-zero elements of each row and column of coflow’s demand matrix, for single coflow scheduling in an $N\times N$ hybrid switch to minimize the coflow completion time (CCT). We then extend the algorithm for scheduling multiple coflows to minimize the total weighted CCT with a provable performance guarantee of $\mu\tau_{\max}$ , where $\mu=4M\cdot\frac{w_{\max}}{w_{\min}}$ , $\tau_{\max}=\max_{1\le m\le M}\tau_{m}\leq N$ , $w_{\max}$ and $w_{\min}$ are respectively the maximum and minimum weights of the $M$ coflows. Extensive simulations using Facebook data traces show that our algorithms outperform the state-of-the-art coflow scheduling schemes. Specifically, our algorithms transmit a single coflow up to 1.08 $\times$ faster than Solstice (hybrid switch) and 1.42 $\times$ faster than Reco-Sin (pure OCS), and multiple coflows up to 1.11 $\times$ faster than Solstice and 1.17 $\times$ faster than Reco-Mul $+$ (pure OCS).</p