An economic analysis of software piracy in a competitive cloud computing market: A product bundling perspective

In the cloud computing era, incumbent vendors are offering both on-premises software and cloud-based software service. Simultaneously, they are facing competition from new entries who just offer cloud-based software. However, piracy exists in on-premises software for incumbent vendors. Therefore, incumbent vendors are facing pressure from piracy and new entries at the same time. Using the framework of product bundling, this study builds a stylized analytical model to investigate the optimal product bundling strategies for software vendors in the presence of software piracy. The research found that the pure bundling strategy is the best choice for existing software vendors in the market in most cases because of the more flexible bundling price. Pure component strategy can be more profitable than pure bundling strategy when piracy costs are at the medium level

‘Clustering’ SIRPα into the Plasma Membrane Lipid Microdomains Is Required for Activated Monocytes and Macrophages to Mediate Effective Cell Surface Interactions with CD47

SIRPα, an ITIMs-containing signaling receptor, negatively regulates leukocyte responses through extracellular interactions with CD47. However, the dynamics of SIRPα-CD47 interactions on the cell surface and the governing mechanisms remain unclear. Here we report that while the purified SIRPα binds to CD47 and that SIRPα is expressed on monocytes and monocytic THP-1 or U937, these SIRPα are ineffective to mediate cell binding to immobilized CD47. However, cell binding to CD47 is significantly enhanced when monocytes transmigrating across endothelia, or being differentiated into macrophages. Cell surface labeling reveals SIRPα to be diffused on naïve monocytes but highly clustered on transmigrated monocytes and macrophages. Protein crosslink and equilibrium centrifugation confirm that SIRPα in the latter cells forms oligomerized complexes resulting in increased avidity for CD47 binding. Furthermore, formation of SIRPα complexes/clusters requires the plasma membrane ‘lipid rafts’ and the activity of Src family kinase during macrophage differentiation. These results together suggest that ‘clustering’ SIRPα into plasma membrane microdomains is essential for activated monocytes and macrophages to effectively interact with CD47 and initiate intracellular signaling

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as "delta tuning", which updates only a small subset of parameters, known as "delta modules", while keeping the backbone model's parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs' code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.Comment: Accepted to ACL 2023 Demo trac

Sparse Low-rank Adaptation of Pre-trained Language Models

Fine-tuning pre-trained large language models in a parameter-efficient manner is widely studied for its effectiveness and efficiency. The popular method of low-rank adaptation (LoRA) offers a notable approach, hypothesizing that the adaptation process is intrinsically low-dimensional. Although LoRA has demonstrated commendable performance, it is implemented with a fixed and unalterable intrinsic rank that might not always be the ideal choice. Recognizing the need for more flexible adaptation, we extend the methodology of LoRA to an innovative approach we call sparse low-rank adaptation (SoRA) that enables dynamic adjustments to the intrinsic rank during the adaptation process. We achieve this through the incorporation of a gate unit optimized with proximal gradient method in the training stage, controlling the cardinality of rank under the sparsity of the gate. In the subsequent inference stage, we eliminate the parameter blocks corresponding to the zeroed-out ranks, to reduce each SoRA module back to a concise yet rank-optimal LoRA. Our approach strengthens the representation power of LoRA by initializing it with a higher rank, while efficiently taming a temporarily increased number of parameters via updating in a sparse way. We further introduce a sparsifying scheduler for SoRA, aiming to examine the impact of the number of non-zero parameters on the model's memorization and generalization. Our experimental results demonstrate that SoRA can outperform other baselines even with 70% retained parameters and 70% training time.Comment: Accepted to EMNLP 2023 (Main Conference

Association between maternal rheumatoid arthritis and small for gestational age neonates: a systematic review and meta-analysis

BackgroundAccording to reports, maternal rheumatoid arthritis (RA) has been suggested as a possible adverse factor for developing small for gestational age (SGA) in offspring. However, some studies have also indicated a need for a more statistically significant association between the two. Understanding the relationship between maternal RA and the risk of SGA is crucial for identifying potential adverse outcomes and implementing appropriate interventions. Therefore, this study aims to elucidate the association between maternal RA and the risk of offspring developing SGA.MethodsThis study was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42022357590). A systematic literature search was conducted to identify eligible studies up to August 2022. Quality assessment was performed according to the Newcastle-Ottawa scale. The Q test and I2 test tested and estimated heterogeneity among studies. Odds ratios (ORs) with 95% CI were calculated using random or fixed effects models depending on the heterogeneity. Subgroup analyses, sensitivity analyses, and publication bias assessments were also performed.ResultsSeven studies, including 12,323,918 participants, were included in the analysis. The results showed a statistically significant association between maternal RA and SGA (OR = 1.70, 95% CI = 1.29–2.23, p < 0.001). Sensitivity analysis showed stable results. The funnel plot of the symmetric distribution and the results of Begg’s and Egger’s tests showed no publication bias.ConclusionMaternal RA is associated with an increased risk of SGA in offspring. However, more studies are still needed to explore the potential mechanisms underlying maternal RA and SGA association.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier [CRD42022357590]