609 research outputs found
VBF vs. GGF Higgs with Full-Event Deep Learning: Towards a Decay-Agnostic Tagger
We study the benefits of jet- and event-level deep learning methods in
distinguishing vector boson fusion (VBF) from gluon-gluon fusion (GGF) Higgs
production at the LHC. We show that a variety of classifiers (CNNs,
attention-based networks) trained on the complete low-level inputs of the full
event achieve significant performance gains over shallow machine learning
methods (BDTs) trained on jet kinematics and jet shapes, and we elucidate the
reasons for these performance gains. Finally, we take initial steps towards the
possibility of a VBF vs. GGF tagger that is agnostic to the Higgs decay mode,
by demonstrating that the performance of our event-level CNN does not change
when the Higgs decay products are removed. These results highlight the
potentially powerful benefits of event-level deep learning at the LHC.Comment: 21 pages+appendices, 16 figures; added references, updated Pythia
shower scheme for VBF, and added Appendix C for version
Understanding Software-as-a-Service Performance - A Dynamic Capability Perspective
How to increase a client’s capability through outsourcing remains a problem. This papers draws on strategic management literature and the relational view to develop a theoretical model that explains the relationships between collaboration, agility, and outsourcing performance in software-as-a-service (SaaS) context. Collaboration are characterized as knowledge sharing and process alignment between a supplier and its client, agility as a supplier’s sensing agility and responding agility. This study also investigates the moderating effect of environmental turbulence on the relationships between agility and performance. The proposed hypotheses are largely supported by the empirical data from 215 firms. The results show that SaaS performance is affected by both sensing agility and responding ability, which, in turn, are impacted by collaboration between a supplier and its client. Finally, we discuss the implications of our results
Understanding Outsourcing Commitment—An Integrated Model Combining The Resoruce-Based View And Knowledge Management
The understanding on how a service provider’s (SP) process capabilities, in terms of aligning and adapting resources to deliver value to its service recipient (SR) in business process outsourcing (BPO), affect its commitment is limited. To address this, building on a strategic perspective and related theories such as the resource-based view and knowledge management, we develop a theoretical model and test it empirically. Specifically, we posit that a SP’s process capabilities, in terms of process alignment, offering flexibility, and partnering flexibility, positively affect its SR’s commitment and the above relationships is negatively moderated by the SR’s behavior control. Besides, we also examine the influence of interaction effect between antecedents of process capabilities on commitment, such as how does process alignment interact with its partnering flexibility and offering flexibility to affect commitment. Finally, we assess whether process capabilities are influenced by the SR’s absorptive capacity and the SP’s task-knowledge coordination. We test our model using survey data collected from 183 firms, supporting most proposed hypotheses. We discuss the theoretical and practical implications of how to increase the value offered to a SR by levering resources, in terms of process capabilities and knowledge management
BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot Captioning
This study aims to explore efficient tuning methods for the screenshot
captioning task. Recently, image captioning has seen significant advancements,
but research in captioning tasks for mobile screens remains relatively scarce.
Current datasets and use cases describing user behaviors within product
screenshots are notably limited. Consequently, we sought to fine-tune
pre-existing models for the screenshot captioning task. However, fine-tuning
large pre-trained models can be resource-intensive, requiring considerable
time, computational power, and storage due to the vast number of parameters in
image captioning models. To tackle this challenge, this study proposes a
combination of adapter methods, which necessitates tuning only the additional
modules on the model. These methods are originally designed for vision or
language tasks, and our intention is to apply them to address similar
challenges in screenshot captioning. By freezing the parameters of the image
caption models and training only the weights associated with the methods,
performance comparable to fine-tuning the entire model can be achieved, while
significantly reducing the number of parameters. This study represents the
first comprehensive investigation into the effectiveness of combining adapters
within the context of the screenshot captioning task. Through our experiments
and analyses, this study aims to provide valuable insights into the application
of adapters in vision-language models and contribute to the development of
efficient tuning techniques for the screenshot captioning task. Our study is
available at https://github.com/RainYuGG/BLIP-Adapte
Modeling the Combined Effects of River Stage and Groundwater Flow on Riverbank Stability Before and After Dam Removal
Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchive
Deep Learning to Improve the Sensitivity of Di-Higgs Searches in the Channel
The study of di-Higgs events, both resonant and non-resonant, plays a crucial
role in understanding the fundamental interactions of the Higgs boson. In this
work we consider di-Higgs events decaying into four -quarks and propose to
improve the experimental sensitivity by utilizing a novel machine learning
algorithm known as Symmetry Preserving Attention Network (\textsc{Spa-Net}) --
a neural network structure whose architecture is designed to incorporate the
inherent symmetries in particle reconstruction tasks. We demonstrate that the
\textsc{Spa-Net} can enhance the experimental reach over baseline methods such
as the cut-based and the Deep Neural Networks (DNN)-based analyses. At the
Large Hadron Collider, with a 14-TeV centre-of-mass energy and an integrated
luminosity of 300 fb, the \textsc{Spa-Net} allows us to establish 95\%
C.L. upper limits in resonant production cross-sections that are 10\% to 45\%
stronger than baseline methods. For non-resonant di-Higgs production,
\textsc{Spa-Net} enables us to constrain the self-coupling that is 9\% more
stringent than the baseline method
Celecoxib extends C. elegans lifespan via inhibition of insulin‐like signaling but not cyclooxygenase‐2 activity
Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86911/1/ACEL_688_sm_FigS1-S2-TableS1-S2.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/86911/2/j.1474-9726.2011.00688.x.pd
UNDERSTANDING COMPETITIVE PERFORMANCE OF SOFTWARE-AS-A-SERVICE (SAAS)—THE COMPETITIVE DYNAMICS PERSPECTIVE
Understanding the antecedents and consequences of a firm’s agility in cloud software applications is important. This papers draws on the competitive dynamics perspective to develop a model that explains the relationships between collaboration with vendors, agility, and competitive performance in software-as-a-service (SaaS) context. Collaboration reflects a firm’s ability to leverage interfirm resources, characterized as knowledge sharing and process alignment. Agility is measured by a firm’s strategy-oriented agility and service-oriented agility. This study also investigates the moderating effect of environmental turbulence. The proposed hypotheses are supported by the empirical data. The results show that competitive performance is affected by ability, which, in turn, is impacted by collaboration. Environmental turbulence positively moderates the relationship between agility and performance. Finally, we discuss the implications of our results
Inhibitory Effects of Terminalia catappa on UVB-Induced Photodamage in Fibroblast Cell Line
This study investigated whether Terminalia catappa L. hydrophilic extract (TCLW) prevents photoaging in human dermal fibroblasts after exposure to UVB radiation. TCLW exhibited DPPH free radical scavenging activity and protected erythrocytes against AAPH-induced hemolysis. In the gelatin digestion assay, the rates of collagenase inhibition by TCL methanol extract, TCLW, and its hydrolysates were greater than 100% at the concentration of 1 mg/mL. We found that serial dilutions of TCLW (10–500 μg/mL) inhibited collagenase activity in a dose-dependent manner (82.3% to 101.0%). However, TCLW did not significantly inhibit elastase activity. In addition, TCLW inhibited MMP-1 and MMP-9 protein expression at a concentration of 25 μg/mL and inhibited MMP-3 protein expression at a concentration of 50 μg/mL. TCLW also promoted the protein expression of type I procollagen. We also found that TCLW attenuated the expression of MMP-1, -3, and -9 by inhibiting the phosphorylation of ERK, JNK, and p38. These findings suggest that TCLW increases the production of type I procollagen by inhibiting the activity of MMP-1, -3 and -9, and, therefore, has potential use in anti-aging cosmetics
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