How cluster, firm, and regional business environment influence different types of innovative activities in European Union

As widely accepted, innovations are of great importance for regional and national economic growth and competitiveness. Innovation Union is one of flagship targets of European Union Horizon 2020 initiative. However, to understand innovation is still challenging, give its complicated nature; moreover, among factors within policy influence, which variable could help facilitate innovation is also inconclusive. This paper will carry out Regional Competitive Framework to understand how cluster, firm behavior, and business environment impact on innovations performance in a both static and dynamic way, and further provide policy implications for promoting innovations. In this paper, Innovation would be perceived as innovative activities from firms’ subjective views, measured from Community Innovation Survey (CIS). Consequently, six aspects of innovation activities would be discussed, with EPO patents as objective innovation measurement for reference

Cholesterol-directed nanoparticle assemblies based on single amino acid peptide mutations activate cellular uptake and decrease tumor volume.

Peptide drugs have been difficult to translate into effective therapies due to their low in vivo stability. Here, we report a strategy to develop peptide-based therapeutic nanoparticles by screening a peptide library differing by single-site amino acid mutations of lysine-modified cholesterol. Certain cholesterol-modified peptides are found to promote and stabilize peptide α-helix formation, resulting in selectively cell-permeable peptides. One cholesterol-modified peptide self-assembles into stable nanoparticles with considerable α-helix propensity stabilized by intermolecular van der Waals interactions between inter-peptide cholesterol molecules, and shows 68.3% stability after incubation with serum for 16 h. The nanoparticles in turn interact with cell membrane cholesterols that are disproportionately present in cancer cell membranes, inducing lipid raft-mediated endocytosis and cancer cell death. Our results introduce a strategy to identify peptide nanoparticles that can effectively reduce tumor volumes when administered to in in vivo mice models. Our results also provide a simple platform for developing peptide-based anticancer drugs

Measuring incompatibility and clustering quantum observables with a quantum switch

The existence of incompatible observables is a cornerstone of quantum mechanics and a valuable resource in quantum technologies. Here we introduce a measure of incompatibility, called the mutual eigenspace disturbance (MED), which quantifies the amount of disturbance induced by the measurement of a sharp observable on the eigenspaces of another. The MED provides a metric on the space of von Neumann measurements, and can be efficiently estimated by letting the measurement processes act in an indefinite order, using a setup known as the quantum switch, which also allows one to quantify the noncommutativity of arbitrary quantum processes. Thanks to these features, the MED can be used in quantum machine learning tasks. We demonstrate this application by providing an unsupervised algorithm that clusters unknown von Neumann measurements. Our algorithm is robust to noise can be used to identify groups of observers that share approximately the same measurement context.Comment: 14 pages, 2 figure

VidPlat: A Tool for Fast Crowdsourcing of Quality-of-Experience Measurements

For video or web services, it is crucial to measure user-perceived quality of experience (QoE) at scale under various video quality or page loading delays. However, fast QoE measurements remain challenging as they must elicit subjective assessment from human users. Previous work either (1) automates QoE measurements by letting crowdsourcing raters watch and rate QoE test videos or (2) dynamically prunes redundant QoE tests based on previously collected QoE measurements. Unfortunately, it is hard to combine both ideas because traditional crowdsourcing requires QoE test videos to be pre-determined before a crowdsourcing campaign begins. Thus, if researchers want to dynamically prune redundant test videos based on other test videos' QoE, they are forced to launch multiple crowdsourcing campaigns, causing extra overheads to re-calibrate or train raters every time. This paper presents VidPlat, the first open-source tool for fast and automated QoE measurements, by allowing dynamic pruning of QoE test videos within a single crowdsourcing task. VidPlat creates an indirect shim layer between researchers and the crowdsourcing platforms. It allows researchers to define a logic that dynamically determines which new test videos need more QoE ratings based on the latest QoE measurements, and it then redirects crowdsourcing raters to watch QoE test videos dynamically selected by this logic. Other than having fewer crowdsourcing campaigns, VidPlat also reduces the total number of QoE ratings by dynamically deciding when enough ratings are gathered for each test video. It is an open-source platform that future researchers can reuse and customize. We have used VidPlat in three projects (web loading, on-demand video, and online gaming). We show that VidPlat can reduce crowdsourcing cost by 31.8% - 46.0% and latency by 50.9% - 68.8%

Where to Go Next for Recommender Systems? ID- vs. Modality-based Recommender Models Revisited

Recommendation models that utilize unique identities (IDs) to represent distinct users and items have been state-of-the-art (SOTA) and dominated the recommender systems (RS) literature for over a decade. Meanwhile, the pre-trained modality encoders, such as BERT and ViT, have become increasingly powerful in modeling the raw modality features of an item, such as text and images. Given this, a natural question arises: can a purely modality-based recommendation model (MoRec) outperforms or matches a pure ID-based model (IDRec) by replacing the itemID embedding with a SOTA modality encoder? In fact, this question was answered ten years ago when IDRec beats MoRec by a strong margin in both recommendation accuracy and efficiency. We aim to revisit this `old' question and systematically study MoRec from several aspects. Specifically, we study several sub-questions: (i) which recommendation paradigm, MoRec or IDRec, performs better in practical scenarios, especially in the general setting and warm item scenarios where IDRec has a strong advantage? does this hold for items with different modality features? (ii) can the latest technical advances from other communities (i.e., natural language processing and computer vision) translate into accuracy improvement for MoRec? (iii) how to effectively utilize item modality representation, can we use it directly or do we have to adjust it with new data? (iv) are there some key challenges for MoRec to be solved in practical applications? To answer them, we conduct rigorous experiments for item recommendations with two popular modalities, i.e., text and vision. We provide the first empirical evidence that MoRec is already comparable to its IDRec counterpart with an expensive end-to-end training method, even for warm item recommendation. Our results potentially imply that the dominance of IDRec in the RS field may be greatly challenged in the future