Engage Wider Audience or Facilitate Quality Answers? a Mixed-methods Analysis of Questioning Strategies for Research Sensemaking on a Community Q&A Site

Discussing research-sensemaking questions on Community Question and Answering (CQA) platforms has been an increasingly common practice for the public to participate in science communication. Nonetheless, how users strategically craft research-sensemaking questions to engage public participation and facilitate knowledge construction is a significant yet less understood problem. To fill this gap, we collected 837 science-related questions and 157,684 answers from Zhihu, and conducted a mixed-methods study to explore user-developed strategies in proposing research-sensemaking questions, and their potential effects on public engagement and knowledge construction. Through open coding, we captured a comprehensive taxonomy of question-crafting strategies, such as eyecatching narratives with counter-intuitive claims and rigorous descriptions with data use. Regression analysis indicated that these strategies correlated with user engagement and answer construction in different ways (e.g., emotional questions attracted more views and answers), yet there existed a general divergence between wide participation and quality knowledge establishment, when most questioning strategies could not ensure both. Based on log analysis, we further found that collaborative editing afforded unique values in refining research-sensemaking questions regarding accuracy, rigor, comprehensiveness and attractiveness. We propose design implications to facilitate accessible, accurate and engaging science communication on CQA platforms.Comment: 31 pages, 5 figures. Accepted for publication in Proceedings of the ACM on Human-Computer Interaction (CSCW 2024

StableMask: Refining Causal Masking in Decoder-only Transformer

The decoder-only Transformer architecture with causal masking and relative position encoding (RPE) has become the de facto choice in language modeling. Despite its exceptional performance across various tasks, we have identified two limitations: First, it requires all attention scores to be non-zero and sum up to 1, even if the current embedding has sufficient self-contained information. This compels the model to assign disproportional excessive attention to specific tokens. Second, RPE-based Transformers are not universal approximators due to their limited capacity at encoding absolute positional information, which limits their application in position-critical tasks. In this work, we propose StableMask: a parameter-free method to address both limitations by refining the causal mask. It introduces pseudo-attention values to balance attention distributions and encodes absolute positional information via a progressively decreasing mask ratio. StableMask's effectiveness is validated both theoretically and empirically, showing significant enhancements in language models with parameter sizes ranging from 71M to 1.4B across diverse datasets and encoding methods. We further show that it naturally supports (1) efficient extrapolation without special tricks such as StreamingLLM and (2) easy integration with existing attention optimization techniques.Comment: Preprin

Towards a Low-Cost Remote Memory Attestation for the Smart Grid

In the smart grid, measurement devices may be compromised by adversaries, and their operations could be disrupted by attacks. A number of schemes to efficiently and accurately detect these compromised devices remotely have been proposed. Nonetheless, most of the existing schemes detecting compromised devices depend on the incremental response time in the attestation process, which are sensitive to data transmission delay and lead to high computation and network overhead. To address the issue, in this paper, we propose a low-cost remote memory attestation scheme (LRMA), which can efficiently and accurately detect compromised smart meters considering real-time network delay and achieve low computation and network overhead. In LRMA, the impact of real-time network delay on detecting compromised nodes can be eliminated via investigating the time differences reported from relay nodes. Furthermore, the attestation frequency in LRMA is dynamically adjusted with the compromised probability of each node, and then, the total number of attestations could be reduced while low computation and network overhead can be achieved. Through a combination of extensive theoretical analysis and evaluations, our data demonstrate that our proposed scheme can achieve better detection capacity and lower computation and network overhead in comparison to existing schemes

Controlling nutritional status score is associated with renal progression, cardiovascular events, and all-cause mortality in biopsy-proved diabetic kidney disease

Background: The Controlled Nutritional Status (CONUT) score, calculated from albumin, total cholesterol, and lymphocyte count, is a useful indicator for immune-nutritional assessment and is associated with the prognosis of various diseases. However, its relationship with renal outcomes, cardiovascular disease (CVD), and all-cause mortality in patients with diabetic kidney disease is unclear.Methods: This retrospective single-center study enrolled 336 patients with biopsy-confirmed diabetic kidney disease from August 2009 to December 2018. The outcomes were progression to end-stage renal disease (ESRD), CVD events, and death. Univariate and multivariate Cox regression analyses were performed to estimate the association between confounding factors and outcomes. The Kaplan-Meier curve was used to compare the outcomes of the patients according to the median CONUT score. The area under the curve (AUC) evaluated with time-dependent receiver operating characteristics was used to test discriminative power of COUNT score.Results: During a median follow-up period of 5.1 years. The Kaplan-Meier analysis showed that patients in the high CONUT group (CONUT score > 3) had a significantly higher incidence of ESRD, CVD events, and all-cause mortality than those in the low CONUT group (CONUT score ≤ 3). The multivariate COX regression analysis indicated that, The CONUT score was an independent predictor of ESRD (hazards ration [HR] = 1.129, 95% confidence interval [CI] 1.037-1.228, p = 0.005), CVD events (HR = 1.159, 95% CI 1.057-1.271, p = 0.002), and all-cause mortality (HR = 1.299, 95% CI 1.143-1.478, p < 0.001).Conclusion: The CONUT score is an independent risk factor for ESRD, CVD events, and overall death in patients with diabetic kidney disease

Comparative Transcriptome Analysis Reveals an Efficient Mechanism for Α-Linolenic Acid Synthesis in Tree Peony Seeds

Tree peony (Paeonia section Moutan DC.) species are woody oil crops with high unsaturated fatty acid content, including α-linolenic acid (ALA/18:3; \u3e40% of the total fatty acid). Comparative transcriptome analyses were carried out to uncover the underlying mechanisms responsible for high and low ALA content in the developing seeds of P. rockii and P. lutea, respectively. Expression analysis of acyl lipid metabolism genes revealed upregulation of select genes involved in plastidial fatty acid synthesis, acyl editing, desaturation, and triacylglycerol assembly in seeds of P. rockiirelative to P. lutea. Also, in association with ALA content in seeds, transcript levels for fatty acid desaturases (SAD, FAD2, and FAD3), which encode enzymes necessary for polyunsaturated fatty acid synthesis, were higher in P. rockii compared to P. lutea. Furthermore, the overexpression of PrFAD2 and PrFAD3 in Arabidopsis increased linoleic and ALA content, respectively, and modulated the final ratio 18:2/18:3 in the seed oil. In conclusion, we identified the key steps and validated the necessary desaturases that contribute to efficient ALA synthesis in a woody oil crop. Together, these results will aid to increase essential fatty acid content in seeds of tree peonies and other crops of agronomic interest

The Protecting Effects and Mechanisms of Baicalin and Octreotide on Heart Injury in Rats with SAP

Purpose. To observe the protecting effects and mechanisms of Baicalin and Octreotide on heart injury in rats with severe acute pancreatitis (SAP). Methods. The SAP rat models were randomly divided into the model group, Baicalin-treated group, Octreotide treated group, and sham operation group. The contents of some inflammatory indexes in blood were determined. The rat mortality, pathological changes of heart, the changes of NF-κB, P-Selectin, Bax, Bcl-2, and Caspase-3 protein expression levels as well as apoptotic index were observed in all groups, respectively, at 3 hours, 6 hours, and 12 hours after operation. Results. The survival rate of model group was less than treated groups at 12 hours, difference was significant. The contents of some inflammatory indexes of the treated groups were lower than those of the model group to various degrees at different time points. The pathological myocardial changes under light microscope were milder in treated groups than in model group. The changes of NF-κB, P-Selectin, Bax, Bcl-2, and Caspase-3 protein expression levels in all groups were different. There was only a case of myocardial cell apoptosis in an Octreotide-treated group at 6 hours. Conclusion. Baicalin and Octreotide have protecting effects on heart injury of rats with SAP

Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond

Developing a universal model that can effectively harness heterogeneous resources and respond to a wide range of personalized needs has been a longstanding community aspiration. Our daily choices, especially in domains like fashion and retail, are substantially shaped by multi-modal data, such as pictures and textual descriptions. These modalities not only offer intuitive guidance but also cater to personalized user preferences. However, the predominant personalization approaches mainly focus on the ID or text-based recommendation problem, failing to comprehend the information spanning various tasks or modalities. In this paper, our goal is to establish a Unified paradigm for Multi-modal Personalization systems (UniMP), which effectively leverages multi-modal data while eliminating the complexities associated with task- and modality-specific customization. We argue that the advancements in foundational generative modeling have provided the flexibility and effectiveness necessary to achieve the objective. In light of this, we develop a generic and extensible personalization generative framework, that can handle a wide range of personalized needs including item recommendation, product search, preference prediction, explanation generation, and further user-guided image generation. Our methodology enhances the capabilities of foundational language models for personalized tasks by seamlessly ingesting interleaved cross-modal user history information, ensuring a more precise and customized experience for users. To train and evaluate the proposed multi-modal personalized tasks, we also introduce a novel and comprehensive benchmark covering a variety of user requirements. Our experiments on the real-world benchmark showcase the model's potential, outperforming competitive methods specialized for each task.Comment: ICLR 202

AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning

Clinical calculators play a vital role in healthcare by offering accurate evidence-based predictions for various purposes such as prognosis. Nevertheless, their widespread utilization is frequently hindered by usability challenges, poor dissemination, and restricted functionality. Augmenting large language models with extensive collections of clinical calculators presents an opportunity to overcome these obstacles and improve workflow efficiency, but the scalability of the manual curation process poses a significant challenge. In response, we introduce AgentMD, a novel language agent capable of curating and applying clinical calculators across various clinical contexts. Using the published literature, AgentMD has automatically curated a collection of 2,164 diverse clinical calculators with executable functions and structured documentation, collectively named RiskCalcs. Manual evaluations show that RiskCalcs tools achieve an accuracy of over 80% on three quality metrics. At inference time, AgentMD can automatically select and apply the relevant RiskCalcs tools given any patient description. On the newly established RiskQA benchmark, AgentMD significantly outperforms chain-of-thought prompting with GPT-4 (87.7% vs. 40.9% in accuracy). Additionally, we also applied AgentMD to real-world clinical notes for analyzing both population-level and risk-level patient characteristics. In summary, our study illustrates the utility of language agents augmented with clinical calculators for healthcare analytics and patient care.Comment: Work in progres