Renormalization-group exponents for superconducting phases in two-leg ladders

In previous studies, we proposed a scaling ansatz for electron-electron interactions under renormalization group transformation. With the inclusion of phonon-mediated interactions, we show that the scaling ansatz, characterized by the divergent logarithmic length $l_d$ and a set of renormalization-group exponents, also works rather well. The superconducting phases in a doped two-leg ladder are studied and classified by these renormalization-group exponents as demonstration. Finally, non-trivial constraints among the exponents are derived and explained.Comment: 4 pages, 3 figures; minor revisions with references adde

An Optimal Energy Efficient Design of Artificial Noise for Preventing Power Leakage based Side-Channel Attacks

Side-channel attacks (SCAs), which infer secret information (for example secret keys) by exploiting information that leaks from the implementation (such as power consumption), have been shown to be a non-negligible threat to modern cryptographic implementations and devices in recent years. Hence, how to prevent side-channel attacks on cryptographic devices has become an important problem. One of the widely used countermeasures to against power SCAs is the injection of random noise sequences into the raw leakage traces. However, the indiscriminate injection of random noise can lead to significant increases in energy consumption in device, and ways must be found to reduce the amount of energy in noise generation while keeping the side-channel invisible. In this paper, we propose an optimal energy-efficient design for artificial noise generation to prevent side-channel attacks. This approach exploits the sparsity among the leakage traces. We model the side-channel as a communication channel, which allows us to use channel capacity to measure the mutual information between the secret and the leakage traces. For a given energy budget in the noise generation, we obtain the optimal design of the artificial noise injection by solving the side-channel's channel capacity minimization problem. The experimental results also validate the effectiveness of our proposed scheme

Reinforcement Mechanism Design for E-Commerce

We study the problem of allocating impressions to sellers in e-commerce websites, such as Amazon, eBay or Taobao, aiming to maximize the total revenue generated by the platform. We employ a general framework of reinforcement mechanism design, which uses deep reinforcement learning to design efficient algorithms, taking the strategic behaviour of the sellers into account. Specifically, we model the impression allocation problem as a Markov decision process, where the states encode the history of impressions, prices, transactions and generated revenue and the actions are the possible impression allocations in each round. To tackle the problem of continuity and high-dimensionality of states and actions, we adopt the ideas of the DDPG algorithm to design an actor-critic policy gradient algorithm which takes advantage of the problem domain in order to achieve convergence and stability. We evaluate our proposed algorithm, coined IA(GRU), by comparing it against DDPG, as well as several natural heuristics, under different rationality models for the sellers - we assume that sellers follow well-known no-regret type strategies which may vary in their degree of sophistication. We find that IA(GRU) outperforms all algorithms in terms of the total revenue

The Multifaceted Impact of Matching Policy on Crowdfunding Platforms: Evidence from DonorsChoose

Donation-based crowdfunding platforms use matching policies where leadership donors match contributions at certain rates. While matching policy have been applied in many crowdfunding platforms, a lot remains unknown about their effectiveness and how they can be optimized to incentivize charitable donations. Leveraging data from donors choose, this study explores the policy in boosting charitable donations. Our findings demonstrate that, at the platform level, matching policy have a positive impact on the overall donation performance of the platform, but also compromise the fairness of donations. At individual level, we find that donors who have made donations on the platform before are less influenced by matching policy, and it has higher utility for less experienced donors. This work provides one of the first systematic analyses that connect micro-level data patterns with macro-level donor behaviors to disentangle the matching policy

Enhancing Transformers without Self-supervised Learning: A Loss Landscape Perspective in Sequential Recommendation

Transformer and its variants are a powerful class of architectures for sequential recommendation, owing to their ability of capturing a user's dynamic interests from their past interactions. Despite their success, Transformer-based models often require the optimization of a large number of parameters, making them difficult to train from sparse data in sequential recommendation. To address the problem of data sparsity, previous studies have utilized self-supervised learning to enhance Transformers, such as pre-training embeddings from item attributes or contrastive data augmentations. However, these approaches encounter several training issues, including initialization sensitivity, manual data augmentations, and large batch-size memory bottlenecks. In this work, we investigate Transformers from the perspective of loss geometry, aiming to enhance the models' data efficiency and generalization in sequential recommendation. We observe that Transformers (e.g., SASRec) can converge to extremely sharp local minima if not adequately regularized. Inspired by the recent Sharpness-Aware Minimization (SAM), we propose SAMRec, which significantly improves the accuracy and robustness of sequential recommendation. SAMRec performs comparably to state-of-the-art self-supervised Transformers, such as S$^3$Rec and CL4SRec, without the need for pre-training or strong data augmentations

Glucagon-like peptide-1 receptor agonists as a disease-modifying therapy for knee osteoarthritis mediated by weight loss:Findings from the Shanghai Osteoarthritis Cohort

Objective: Obesity is a risk factor for knee osteoarthritis (KOA) development and progression. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are indicated for type 2 diabetes mellitus (T2DM) and obesity. However, whether KOA patients can benefit from GLP-1RA therapies has not been sufficiently investigated, especially in the long term. Methods: The Shanghai Osteoarthritis Cohort study is a prospective, observational, multicentre study of &gt;40 000 adults with clinically diagnosed osteoarthritis aged &gt;45 years in Shanghai. We identified all KOA participants with comorbid T2DM enrolled from 1 January 2011 to 1 January 2017. Primary outcome was incidence of knee surgery after enrolment. Secondary outcomes included pain-relieving medication use, number of intra-articular therapies, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and medial femorotibial joint cartilage thickness. To evaluate the effects of GLP-1RA, we performed before-and-after comparison and comparison with participants who had no GLP-1RA exposure. Results: For an intergroup comparison (non-GLP-1RA vs GLP-1RA), more weight loss (adjusted mean difference in weight change from baseline-7.29 kg (95% CI-8.07 to-6.50 kg), p&lt;0.001) and lower incidence of knee surgery (93/1574 (5.9%) vs 4/233 (1.7%), adjusted p=0.014) were observed in the GLP-1RA group. Statistically significant differences in mean change from baseline for the WOMAC total and pain subscale scores were observed (adjusted mean difference in WOMAC total score-1.46 (95% CI-2.84 to-0.08), p=0.038; adjusted mean difference in WOMAC pain subscore-3.37 (95% CI-5.79 to-0.94), p=0.007). Cartilage-loss velocity of the medial femorotibial joint was significantly lower in the GLP-1RA group postadjustment for baseline characteristics (adjusted mean difference-0.02 mm (95% CI-0.03 to-0.002 mm), p=0.004). For the before-and-after comparison within the GLP-1RA group, we observed a significant decrease of symptom-relieving medication consumption and cartilage loss velocity of medial femorotibial joint (after-treatment vs before-treatment:-0.03±0.05 vs-0.05±0.07 mm/year, p&lt;0.001). The association between GLP-1RA exposure and decreased incidence of knee surgery was mediated by weight reduction (mediation proportion: 32.1%), instead of glycaemic control (too small to calculate). Conclusion: With sufficient treatment duration, GLP-1RA therapies might be disease-modifying for KOA patients with comorbid T2DM, possibly mediated by weight loss. Further investigation is needed to elucidate effects of GLP-1RA on disease process, joint structure and patient-reported outcomes of osteoarthritis.</p

How Fragile is Relation Extraction under Entity Replacements?

Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE models should be able to correctly identify the relations reflected by the textual context. However, existing work has found that the RE models memorize the entity name patterns to make RE predictions while ignoring the textual context. This motivates us to raise the question: ``are RE models robust to the entity replacements?'' In this work, we operate the random and type-constrained entity replacements over the RE instances in TACRED and evaluate the state-of-the-art RE models under the entity replacements. We observe the 30\% - 50\% F1 score drops on the state-of-the-art RE models under entity replacements. These results suggest that we need more efforts to develop effective RE models robust to entity replacements. We release the source code at https://github.com/wangywUST/RobustRE

How do different Industry 4.0 technologies support certain Circular Economy practices?

Purpose: Uncovering the relationship between Industry 4.0 (I4.0) technologies and circular economy (CE) practices is critical not only for implementing CE but also for leveraging I4.0 to achieve sustainable development goals. However, the potential connection between them – especially how different I4.0 technologies may influence various CE practices – remains inadequately researched. The purpose of this study was to quantitatively explore the impacts of various I4.0 technologies on CE practices. Design/methodology/approach: A mixed method consisting of a systematic literature review, content analysis, and social network analysis was adopted. First, 266 articles were selected and mined for contents of I4.0 technologies and CE practices; 27 I4.0 technologies and 21 CE practices were identified. Second, 62 articles were found that prove the positive influence of I4.0 technologies on CE practices, and 124 relationships wereidentified. Third, based on evidence supporting the link between I4.0 technologies and CE practices, a two-mode network and two one-mode networks were constructed, and their network density and degree centrality indicators were analyzed. Findings: I4.0 technologies have a low application scope and degree for promoting CE. The adoption of a single I4.0 technology has limited effect on CE practices, and wider benefits can be realized through integrating I4.0 technologies. The Internet of Things (IoT), additive manufacturing, big data and analytics, and artificial intelligence (AI) are among the top technologies promoting CE implementation and reduction and recycling were identified as the main mechanism. The integration of these technologies is the most popular and effective. Twelve CE practices were identified to be the most widely implemented and supported by I4.0 technologies. Research limitations/implications: First, only journal articles, reviews, and online publications written in English were selected, excluding articles published in other languages. Therefore, the results obtained only represent a specific group of scholars, which may be fragmented to a certain extent. Second, because the extraction of the impact of I4.0 on CE mainly relies on a manual literature review, this paper only provides the statistics of the number of publications involving relationships, while lacking the weight measurement of relationships. Originality/value: A comprehensive, quantitative, and visual analysis method was employed to unveil the current implementation levels of I4.0 technologies and CE practices. Further, it was explored how different I4.0 technologies can affect various CE aspects, how different I4.0 technologies are integrated to promote CE realization, and how various CE practices are implemented simultaneously by I4.0 technologies

One independent or many independent? The relationship among self-construal, number of brand endorsers, and brand attitudes

IntroductionIt was common for brands to use different numbers of endorsers in marketing practice. Nevertheless, research on brand endorsers’ quantity has not yielded a uniform consensus. The previous research about brand endorsers mainly focuses on the appeal of endorsement, brand category, and endorser characteristics, paying less attention to the impact of cultural factors, particularly self-construal. This study delves into selecting brand endorsers across diverse cultural regions for the same brand.MethodsDrawing on the principles of self-consistency theory and self-construal theory, our research, conducted through three distinct experiments, reveals that consumers tend to hold more favorable opinions about brands endorsed by a single individual. Furthermore, self-consistency emerges as a crucial mediating factor in this phenomenon. Additionally, self-construal is an essential factor among consumers from various cultural backgrounds.ResultsConsumers with an independent self-construal exhibit more favorable brand perceptions when it comes to single-endorser brands compared to their counterparts with an interdependent self-construal. Conversely, individuals with an interdependent self-construal demonstrate a more positive disposition towards brands with multiple endorsers than those with an independent self-construal.DiscussionThis research not only enriches and extends our theoretical understanding of the impact of the number of brand endorsers on consumer brand attitudes but also provides valuable practical insights for optimizing the selection of brand endorsers for companies