| 1. |
- Jones, Benedict C, et al.
(författare)
-
To which world regions does the valence-dominance model of social perception apply?
- 2021
-
Ingår i: Nature Human Behaviour. - : Springer Science and Business Media LLC. - 2397-3374. ; 5:1, s. 159-169
-
Tidskriftsartikel (refereegranskat)abstract
- Over the past 10 years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgements of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions, we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods and correlate and rotate the dimension reduction solution. PROTOCOL REGISTRATION: The stage 1 protocol for this Registered Report was accepted in principle on 5 November 2018. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.7611443.v1 .
|
|
| 2. |
- Grundy, Myriam M.L., et al.
(författare)
-
INFOGEST inter-laboratory recommendations for assaying gastric and pancreatic lipases activities prior to in vitro digestion studies
- 2021
-
Ingår i: Journal of Functional Foods. - : Elsevier BV. - 1756-4646. ; 82
-
Tidskriftsartikel (refereegranskat)abstract
- In vitro digestion studies often use animal digestive enzyme extracts as substitutes of human gastric and pancreatic secretions. Pancreatin from porcine origin is thus commonly used to provide relevant pancreatic enzymes such as proteases, amylase and lipase. Rabbit gastric extracts (RGE) have been recently introduced to provide gastric lipase in addition to pepsin. Before preparing simulated gastric and pancreatic extracts with targeted enzyme activities as described in in vitro digestion protocols, it is important to determine the activities of enzyme preparations using validated methods. The purpose of this inter-laboratory study within the INFOGEST network was to test the repeatability and reproducibility of lipase assays using the pH-stat technique for measuring the activities of gastric and pancreatic lipases from various sources. Twenty-one laboratories having different pH-stat devices received the same protocol with identical batches of RGE and two pancreatin sources. Lipase assays were performed using tributyrin as a substrate and three different amounts (50, 100 and 200 µg) of each enzyme preparation. The repeatability results within individual laboratories were satisfactory with coefficients of variation (CVs) ranging from 4 to 8% regardless of the enzyme amount tested. However, the inter-laboratory variability was high (CV > 15%) compared to existing standards for bioanalytical assays. We identified and weighted the contributions to inter-laboratory variability of several parameters associated with the various pH-stat equipment used in this study (e.g. reaction vessel volume and shape, stirring mode and rate, burette volume for the automated delivery of sodium hydroxide). Based on this, we established recommendations for improving the reproducibility of lipase assays using the pH-stat technique. Defining accurate and complete recommendations on how to correctly quantify activity levels of enzyme preparations is a gateway to promising comparison of in vitro data obtained from different laboratories following the same in vitro digestion protocol.
|
|
| 3. |
- Hoseiny Farahabady, M. Reza, et al.
(författare)
-
Data-Intensive Workload Consolidation in Serverless (Lambda/FaaS) Platforms
- 2021
-
Ingår i: 2021 IEEE 20TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665495509 ; , s. 1-8
-
Konferensbidrag (refereegranskat)abstract
- A significant amount of research studies in the past years has been devoted on developing efficient mechanisms to control the level of degradation among consolidate workloads in a shared platform. Workload consolidation is a promising feature that is employed by most service providers to reduce the total operating costs in traditional computing systems [1]-[3]. Serverless paradigm - also known as Function as a Service, FaaS, and Lambda - recently emerged as a new virtualization run-time model that disentangles the traditional state of applications' users from the burden of provisioning physical computing resources, leaving the difficulty of providing the adequate resource capacity on the service provider's side. This paper focuses on a number of challenges associated with workload consolidation when a serverless platform is expected to execute several data-intensive functional units. Each functional unit is considered to be the atomic component that reacts to a stream of input data. A serverless application in the proposed model is composed of a series of functional units. Through a systematic approach, we highlight the main challenges for devising an efficient workload consolidation process in a data-intensive serverless platform. To this end, we first study the performance interference among multiple workloads to obtain the capacity of last level cache (LLC). We show how such contention among workloads can lead to a significant throughput degradation on a single physical server. We expand our investigation into a general case with the aim to prevent the total throughput never falling below a predefined utilization level. Based on the empirical results, we develop a consolidation model and then design a computationally efficient controller to optimize the throughput degradation among a platform consists fs multiple machines. The performance evaluation is conducted using modern workloads inspired by data management services, and data analytic benchmark tools in our in-house four node platform showing the efficiency of the proposed solution to mitigate the QoS violation rate for high priority applications by 90% while can enhance the normalized throughput usage of disk devices by 39%.
|
|
| 4. |
- HoseinyFarahabady, M. Reza, et al.
(författare)
-
QSpark : Distributed Execution of Batch & Streaming Analytics in Spark Platform
- 2021
-
Ingår i: 2021 IEEE 20TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA). - : IEEE. - 9781665495509
-
Konferensbidrag (refereegranskat)abstract
- A significant portion of research work in the past decade has been devoted on developing resource allocation and task scheduling solutions for large-scale data processing platforms. Such algorithms are designed to facilitate deployment of data analytic applications across either conventional cluster computing systems or modern virtualized data-centers. The main reason for such a huge research effort stems from the fact that even a slight improvement in the performance of such platforms can bring a considerable monetary savings for vendors, especially for modern data processing engines that are designed solely to perform high throughput or/and low-latency computations over massive-scale batch or streaming data. A challenging question to be yet answered in such a context is to design an effective resource allocation solution that can prevent low resource utilization while meeting the enforced performance level (such as 99-th latency percentile) in circumstances where contention among applications to obtain the capacity of shared resources is a non negligible performance-limiting parameter. This paper proposes a resource controller system, called QSpark, to cope with the problem of (i) low performance (i.e., resource utilization in the batch mode and p-99 response time in the streaming mode), and (ii) the shared resource interference among collocated applications in a multi-tenancy modern Spark platform. The proposed solution leverages a set of controlling mechanisms for dynamic partitioning of the allocation of computing resources, in a way that it can fulfill the QoS requirements of latency-critical data processing applications, while enhancing the throughput for all working nodes without reaching their saturation points. Through extensive experiments in our in-house Spark cluster, we compared the achieved performance of proposed solution against the default Spark resource allocation policy for a variety of Machine Learning (ML), Artificial Intelligence (AI), and Deep Learning (DL) applications. Experimental results show the effectiveness of the proposed solution by reducing the p-99 latency of high priority applications by 32% during the burst traffic periods (for both batch and stream modes), while it can enhance the QoS satisfaction level by 65% for applications with the highest priority (compared with the results of default Spark resource allocation strategy).
|
|
