Table_1_Flexible and fine-grained simulation of speed in language processing.DOCX

According to the embodied cognition theory, language comprehension is achieved through mental simulation. This account is supported by a number of studies reporting action simulations during language comprehension. However, which details of sensory-motor experience are included in these simulations is still controversial. Here, three experiments were carried out to examine the simulation of speed in action language comprehension. Experiment 1 adopted a lexical decision task and a semantic similarity judgment task on isolated fast and slow action verbs. It has been shown that fast action verbs were processed significantly faster than slow action verbs when deep semantic processing is required. Experiment 2 and Experiment 3 investigated the contextual influence on the simulation of speed, showing that the processing of verbs, either depicting fast actions or neutral actions, would be slowed down when embedded in the slow action sentences. These experiments together demonstrate that the fine-gained information, speed, is an important part of action representation and can be simulated but may not in an automatic way. Moreover, the speed simulation is flexible and can be modulated by the context.</p

Table_2_Flexible and fine-grained simulation of speed in language processing.XLSX

According to the embodied cognition theory, language comprehension is achieved through mental simulation. This account is supported by a number of studies reporting action simulations during language comprehension. However, which details of sensory-motor experience are included in these simulations is still controversial. Here, three experiments were carried out to examine the simulation of speed in action language comprehension. Experiment 1 adopted a lexical decision task and a semantic similarity judgment task on isolated fast and slow action verbs. It has been shown that fast action verbs were processed significantly faster than slow action verbs when deep semantic processing is required. Experiment 2 and Experiment 3 investigated the contextual influence on the simulation of speed, showing that the processing of verbs, either depicting fast actions or neutral actions, would be slowed down when embedded in the slow action sentences. These experiments together demonstrate that the fine-gained information, speed, is an important part of action representation and can be simulated but may not in an automatic way. Moreover, the speed simulation is flexible and can be modulated by the context.</p

A four-level zero-inflated negative binomial regression model for inpatient service use^a.

A four-level zero-inflated negative binomial regression model for inpatient service usea.</p

Number (%) of outpatient and inpatient service use in 2011, 2013, 2015, and 2018.

Number (%) of outpatient and inpatient service use in 2011, 2013, 2015, and 2018.</p

STROBE statement—checklist of items that should be included in reports of observational studies.

STROBE statement—checklist of items that should be included in reports of observational studies.</p

A fair comparison of VM placement heuristics and a more effective solution

Data center optimization, mainly through virtual machine (VM) placement, has received considerable attention in the past years. A lot of heuristics have been proposed to give quick and reasonably good solutions to this problem. However it is difficult to compare them as they use different datasets, while the distribution of resources in the datasets has a big impact on the results. In this paper we propose the first benchmark for VM placement heuristics and we define a novel heuristic. Our benchmark is inspired from a real data center and explores different possible demographics of data centers, which makes it suitable when comparing the behaviour of heuristics. Our new algorithm, RBP, outperforms the state-of-the-art heuristics and provides close to optimal results quickly

SOC: satisfaction-oriented virtual machine consolidation in enterprise data centers

Server sprawl is a problem faced by data centers, which causes un- necessary waste of hardware resources, collateral costs of space, power and cooling systems, and administration. This is usually combated by virtualization based consolidation, and both industry and academia have put many e orts into solving the underlying virtual machine (VM) placement problem. However, IT managers' preferences are seldom considered when making VM placement decisions. This paper proposes a satisfaction-oriented VM consolidation mechanism (SOC) to plan VM consolidation while taking IT managers' preferences into consideration. In the mechanism, we propose: i) an XML-based description language to express managers' preferences and metrics to evalu- ate the satisfaction degree; ii) to apply matchmaking to locate entities (i.e., VMs and physical machines (PMs)) that best match each other's preferences; iii) to employ the VM placement algorithm proposed in our previous work to minimize the number of hosts required and the resource wastage on allocated hosts. SOC is compared with two baselines: placement-only and matchmaking- only. The simulation results show that most of the VM-to-PM mappings output from placement-only violate given preferences, while SOC has a satisfaction degree close to matchmaking-only, without requiring too many PMs as matchmaking-only does, but only an amount close to placement-only. In brief,SOC is e ective in minimizing the number of hosts required to support a certain set of VMs, while maximizing the satisfaction degree of both managers from the provider and requester side

Interaction between morbidity status and per-capita household expenditure quintiles for outpatient and inpatient service use^a^,^b.

Interaction between morbidity status and per-capita household expenditure quintiles for outpatient and inpatient service usea,b.</p

A four-level zero-inflated negative binomial regression model for outpatient service use^a.

A four-level zero-inflated negative binomial regression model for outpatient service usea.</p

Distribution of the number of outpatient and inpatient service use in 2011, 2013, 2015, and 2018.

Distribution of the number of outpatient and inpatient service use in 2011, 2013, 2015, and 2018.</p