Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence, risk factors, and mortality of COPD in young people in the USA: results from a population-based retrospective cohort

Background Chronic obstructive pulmonary disease (COPD) has been considered a disease of the elderly, but it could also occur in young people aged 20–50 years. However, the characteristics and prognosis of COPD in such young people remain unclear. Methods Our retrospective cohort study was based on the National Health and Nutrition Examination Survey (NHANES). Participants who 20–50 years old at baseline and completed the pulmonary function test were enrolled in our study cohort. These participants were followed up to 31 December 2019. The sample weight and Taylor Linearization Procedures were adapted to make representative estimations of prevalence and baseline characteristics. The weighted logistic regression model was used to assess the risk factors. The propensity score method and Cox proportional hazard models were applied to calculate the risk of mortality. Results The weighted prevalence of COPD in young people in the USA was 1.64% and it increased with age, with a higher prevalence in males than females (2.59% vs 0.72%, p<0.001). The proportion of Global Initiative for COPD 1–2 was 96.7%. Males (OR=4.56, 95% CI: 2.74 to 7.61), non-Hispanic black (OR=2.77; 95% CI: 1.14 to 6.75), non-Hispanic white (OR=4.93; 95% CI: 2.16 to 11.28) and smoking (current smoking, OR=2.36; 95% CI: 1.40 to 3.98; ever smoking, OR=1.92; 95% CI: 1.05 to 3.51; passive smoking, OR=2.12; 95% CI: 1.41 to 3.20) were shown to be independent risk factors for COPD in young people. Compared with those matched by sex, age and race, the young people with COPD had a higher risk of all-cause death (HR=3.314, p<0.001). Conclusion COPD in young people has a low prevalence in the USA and its independent risk factors included male, race (non-Hispanic black and non-Hispanic white) and smoking. Young COPD has a higher risk of all-cause mortality than the matched non-COPD.
Effectiveness and clinical application of multidisciplinary combined exercise and nutrition intervention for sarcopenic older adults with metabolic syndrome: study protocol for a multicentre randomised controlled trial

Introduction Among chronic diseases affecting older adults, metabolic syndrome (MetS) is known to be closely related to sarcopenia. Insulin resistance may play a key role in the increased frequency of sarcopenia associated with metabolic disorders. To date, an exercise–nutrition combined intervention has been the treatment of choice for sarcopenia. However, trials of combined interventions for individuals with sarcopenia and MetS are still lacking. This study aims to develop and conduct a standardised intervention, named the Multidisciplinary combined Exercise and Nutrition inTervention fOR Sarcopenia (MENTORS), for sarcopenic older patients with MetS. Methods and analysis This multicentre, randomised controlled trial includes 168 community-dwelling older adults with sarcopenia and MetS. The 12-week MENTORS comprises an exercise intervention consisting of an introductory phase (3 weeks; twice-weekly visits), an expanded phase (3 weeks; twice-weekly visits) and a maintenance phase (6 weeks; once-weekly visits); and a nutrition intervention tailored to the nutritional status of individual subjects. Outcomes will be measured at 0-week, 12-week and 24-week postintervention. The data will be analysed using the intention-to-treat and per-protocol principle. Ethics and dissemination Before screening, all participants will be provided with oral and written information. Ethical approval has already been obtained from all participating hospitals. The study results will be disseminated in peer-reviewed publications and conference presentations. Trial registration number NCT04948736
Denial of service attacks – an updated perspective

ABSTRACT Network security is a specialized field consisting of the provisions and policies to prevent and monitor unauthorized access, misuse, modification or denial of a computer network and network-accessible resources as well as ensuring their availability through proper procedures. Many security mechanisms tools are being developed and deployed to defend against network attacks and to make the network computing resources available to the legitimate users. In spite of all these efforts, the organizations around the world continue to suffer security attacks specially called denial of service (DoS) attacks. DoS attacks constitute one of the major threats and among the hard security problems in today's Internet. These attacks can easily consume computing and communication resources of the victim or disrupt clog availability of resources to the intended users within a short period of time. The problem is a serious concern in today's network security field. Several defence mechanisms have been proposed to tackle the problem of DoS attacks. This paper highlights a structural way to understand DoS attacks with respect to different layers of the Open System Interconnection (OSI) reference model proposed by International Organization of Standardization. Moreover, various attack vectors, attack tools, and trends in detection and mitigation mechanisms are delineated. The goal of the paper is to communicate an updated perspective of DoS attacks and their detection and mitigation mechanisms for a better understanding of these attacks; consequently more efficient and effective mechanisms to combat these attacks may be developed.
Age Stereotypes and Perceived Control of Health Predict COVID-19 Health- and Economy-Focused Beliefs

ABSTRACT In March 2020, many U.S. states issued stay-at-home orders to reduce COVID-19 infections and deaths. In early messaging from governments and public health officials, older adults were often portrayed as vulnerable and frail, perpetuating negative age stereotypes. Thus, views of older adults were intertwined in discussions of whether to prioritize the economy versus public health. Among a community sample, greater endorsement of positive age stereotypes and a Democrat identity significantly predicted COVID-19 health-focused beliefs (reducing infections), whereas, younger age, greater perceived health control, and a Republican identity significantly predicted COVID-19 economy-focused beliefs (reopening economy). Implications and future directions are discussed.
Patient-specific computational models predict prognosis in B cell lymphoma by quantifying pro-proliferative and anti-apoptotic signatures from genetic sequencing data

Genetic heterogeneity and co-occurring driver mutations impact clinical outcomes in blood cancers. Grouping tumours into clusters based on genetic alterations is prognostically informative. However, predicting the emergent effect of co-occurring mutations that impact multiple complex and interacting signalling networks remains challenging. Here, we used mathematical models to predict the impact of co-occurring mutations on cellular signalling and cell fates in diffuse large B cell lymphoma (DLBCL) and multiple myeloma (MM). Simulations predicted adverse impact on clinical prognosis when combinations of mutations induced both pro-proliferative and anti-apoptotic signalling. So, we established a pipeline to integrate patient-specific mutational profiles into personalised lymphoma models. Using this approach, we identified a subgroup (19%) of patients characterised by simultaneous upregulation of anti-apoptotic and pro-proliferative (AAPP) signalling. AAPP patients have dismal prognosis and can be identified within all current genomic and cell-of-origin classifications. Combining personalised molecular simulations with mutational clustering enabled stratification of patients into clinically informative prognostic categories: good (80% progression-free survival at 120 months), intermediate (median progression-free survival of 93 months), and poor (AAPP, median progression-free survival of 26 months). This study shows that personalised computational models enable identification of novel high-risk patient subgroups, providing a valuable tool for future risk-stratified clinical trials.
Spatial reorganization of Escherichia coli chromosome contextualizes triclosan stress-related genetic, epigenetic and transcriptome changes

Changes in the spatial organization of bacterial chromosomes under stress conditions and its biological implications remain poorly understood. We mapped the structural landscape of wild-type and Δdcm E. coli chromosomes under triclosan stress using Hi-C to identify triclosan-induced chromosomal interaction domains (CIDs). Two CIDs were common to the wild-type and Δdcm E. coli, including a CID with a common boundary at fabI gene, which encodes the triclosan target. All mutations and structural variants under triclosan stress were observed within or in close proximity to triclosan-induced CIDs. Absence of Dcm methylation impacts both short- and long-range interactions in triclosan stress. Single-base resolution methylome maps reveal hypermethylation of adenines (in wild-type and Δdcm) and cytosines (in wild-type) in the two common triclosan-induced CIDs. Furthermore, global gene expression profiling identified enrichment of highly expressed genes within the two common CIDs. Our findings suggest that stress-induced CIDs in E. coli are hotspots for genetic variations and are associated with enhanced transcriptional activity and hypermethylation of Dam/Dcm motifs.
Adaptive Contrastive Knowledge Distillation for BERT Compression

In this paper, we propose a new knowledge distillation approach called adaptive contrastive knowledge distillation (ACKD) for BERT compression. Different from existing knowledge distillation methods for BERT that implicitly learn discriminative student features by mimicking the teacher features, we first introduce a novel contrastive distillation loss (CDL) based on hidden state features in BERT as the explicit supervision to learn discriminative student features. We further observe sentences with similar features may have completely different meanings, which makes them hard to distinguish. Existing methods do not pay sufficient attention to these hard samples with less discriminative features. Therefore, we propose a new strategy called sample adaptive reweighting (SAR) to adaptively pay more attention to these hard samples and strengthen their discrimination abilities. We incorporate our SAR strategy into our CDL and form the adaptive contrastive distillation loss, based on which we construct our ACKD framework. Comprehensive experiments on multiple natural language processing tasks demonstrate the effectiveness of our ACKD framework.
APRON: Authenticated and Progressive System Image Renovation

The integrity and availability of an operating system are important to securely use a computing device. Conventional schemes focus on how to prevent adversaries from corrupting the operating system or how to detect such corruption. However, how to recover the device from such corruption securely and efficiently is overlooked, resulting in lengthy system downtime with integrity violation and unavailability. In this paper, we propose A PRON , a novel scheme to reno-vate a corrupt or outdated operating system image securely and progressively. A PRON concurrently and selectively re-pairs any invalid blocks on demand during and after the sys-tem boot, effectively minimizing the system downtime needed for a recovery. A PRON verifies whether requested blocks are valid in the kernel using a signed Merkle hash tree computed over the valid, up-to-date system image. If they are invalid, it fetches corresponding blocks from a reliable source, verifies them, and replaces the requested blocks with the fetched ones. Once the system boots up, A PRON runs a background thread to eventually renovate any other non-requested invalid blocks. Our evaluation shows that A PRON has short down-time: it outperforms conventional recovery mechanisms by up to 28 × . It runs real-world applications with an average runtime overhead of 9% during the renovation and with negligible overhead (0.01%) once the renovation is completed.
Incomplete Utterance Rewriting by A Two-Phase Locate-and-Fill Regime

Rewriting incomplete and ambiguous utterances can improve dialogue models’ understanding of the context and help them generate better results. However, the existing end-to-end models will have the problem of too large search space, resulting in poor quality of rewriting results. We propose a 2-phase rewriting framework which ﬁrst predicts the empty slots in the utterance that need to be completed, and then generate the part to be ﬁlled into each positions. Our framework is simple to implement, fast to run, and achieves the state-of-the-art results on several public rewriting datasets.
Exploring the Impact of Vision Features in News Image Captioning

The task of news image captioning aims to generate a detailed caption which describes the specific information of an image in a news article. However, we find that recent state-of-art models can achieve competitive performance even without vision features. To resolve the impact of vision features in the news image captioning task, we conduct extensive experiments with mainstream models based on encoder-decoder framework. From our exploration, we find 1) vision features do contribute to the generation of news image captions; 2) vision features can assist models to better generate entities of cap-tions when the entity information is sufficient in the input textual context of the given article; 3) Regions of specific objects in images contribute to the generation of related entities in captions. 1
Enhancing Hierarchical Text Classification through Knowledge Graph Integration

Hierarchical Text Classification (HTC) is an essential and challenging subtask of multi-label text classification with a taxonomic hierarchy. Recent advances in deep learning and pre-trained language models have led to significant breakthroughs in the HTC problem. However, despite their effectiveness, these methods are often restricted by a lack of domain knowledge, which leads them to make mistakes in a variety of situations. Generally, when manually classifying a specific document to the taxonomic hierarchy, experts make inference based on their prior knowledge and experience. For machines to achieve this capability, we propose a novel Knowledge-enabled Hierarchical Text Classification model (K-HTC), which incorporates knowledge graphs into HTC. Specifically, K-HTC innovatively integrates knowledge into both the text representation and hierarchical label learning process, addressing the knowledge limitations of traditional methods. Additionally, a novel knowledge-aware contrastive learning strategy is proposed to further exploit the information inherent in the data. Extensive experiments on two publicly available HTC datasets show the efficacy of our proposed method, and indicate the necessity of incorporating knowledge graphs in HTC tasks.
Ship your Critical Section, Not Your Data: Enabling Transparent Delegation with TCLOCKS

Today’s high-performance applications heavily rely on various synchronization mechanisms, such as locks. While locks ensure mutual exclusion of shared data, their design impacts application scalability. Locks, as used in practice, move the lock-guarded shared data to the core holding it, which leads to shared data transfer among cores. This design adds unavoidable critical path latency leading to performance scalability issues. Meanwhile, some locks avoid this shared data movement by localizing the access to shared data on one core, and shipping the critical section to that specific core. However, such locks require modifying applications to explicitly package the critical section, which makes it virtually infeasible for complicated applications with large code bases, such as the Linux kernel. We propose transparent delegation, in which a waiter automatically encodes its critical section information on its stack and notifies the combiner (lock holder). The combiner executes the shipped critical section on the waiter’s behalf using a lightweight context switch. Using transparent delegation, we design a family of locking protocols, called TCLocks, that requires zero modification to applications’ logic. The evaluation shows that TCLocks provide up to 5.2× performance improvement compared with recent locking algorithms.
Light-Dedup: A Light-weight Inline Deduplication Framework for Non-Volatile Memory File Systems

Emerging NVM is promising to become the next-generation storage media. However, its high cost hinders its development. Recent deduplication researches in NVM file systems demonstrate that NVM’s cost can be reduced by eliminating redundant data blocks, but their design lacks complete insights into NVM’s I/O mechanisms. We propose Light-Dedup, a light-weight inline deduplication framework for NVM file systems that performs fast block-level deduplication while taking NVM’s I/O mechanisms into consideration. Specifically, Light-Dedup proposes Light-Redundant-Block-Identifier (LRBI), which combines non-cryptographic hash with a speculative-prefetch-based byte-by-byte content-comparison approach. LRBI leverages the memory interface of NVM to enable asynchronous reads by speculatively prefetching in-NVM data blocks into the CPU/NVM buffers. Thus, NVM’s read latency seen by content-comparison is markedly reduced due to buffer hits. Moreover, Light-Dedup adopts an in-NVM Light-Meta-Table (LMT) to store deduplication metadata and collaborate with LRBI. LMT is organized in the region granularity, which significantly reduces metadata I/O amplification and improves deduplication performance. Experimental results suggest Light-Dedup achieves 1.01–
Task-adaptive Label Dependency Transfer for Few-shot Named Entity Recognition

Named Entity Recognition (NER), as a crucial subtask in natural language processing (NLP), suffers from limited labeled samples (a.k.a. few-shot). Meta-learning methods are widely used for few-shot NER, but these existing methods overlook the importance of label dependency for NER, resulting in suboptimal performance. However, applying meta-learning methods to label dependency learning faces a special challenge, that is, due to the discrepancy of label sets in different domains, the label dependencies can not be transferred across domains. In this paper, we propose the T ask-adaptive L abel D ependency T ransfer (TLDT) method to make label dependency transferable and effectively adapt to new tasks by a few samples. TLDT improves the existing optimization-based meta-learning methods by learning general initialization and individual parameter up-date rule for label dependency. Extensive experiments show that TLDT achieves significant improvement over the state-of-the-art methods.
Retrieval-augmented Video Encoding for Instructional Captioning

Instructional videos make learning knowledge more efficient, by providing a detailed multimodal context of each procedure in instruction. A unique challenge posed by instructional videos is key-object degeneracy , where any single modality fails to sufficiently capture the key objects referred to in the procedure. For machine systems, such degeneracy can disturb the performance of a downstream task such as dense video captioning, leading to the generation of incorrect captions omitting key objects. To repair degeneracy, we propose a retrieval-based framework to augment the model representations in the presence of such key-object degeneracy. We validate the effectiveness and generalizability of our proposed framework over baselines using modalities with key-object degeneracy.
Comosum: An Extensible, Reconfigurable, and Fault-Tolerant IoT Platform for Digital Agriculture

This work is an experience with a deployed networked system for digital agriculture (or DA). DA is the use of data-driven techniques toward a sustainable increase in farm productivity and efficiency. DA systems are expected to be overlaid on existing rural infrastructures, which are known to be less robust than urban infrastructures. While existing DA approaches partially address several infrastructure issues, challenges re-lated to data aggregation, data analytics, and fault tolerance remain open. In this work, we present the design of Comosum, an extensible, reconfigurable, and fault-tolerant architecture of hardware , software , and distributed cloud abstractions to sense, analyze, and actuate on different farm types. We also present FarmBIOS, an implementation of the Comosum architecture. We analyze FarmBIOS by leveraging various applications, deployment experiences, and network differences between urban and rural farms. This includes, for instance, an edge analytics application achieving 86% accuracy in vine-yard disease detection. An eighteen-month deployment of FarmBIOS highlights Comosum’s tolerance to intermittent network outages that lasted for several days during many periods of the deployment. We offer practical insights on how FarmBIOS adapts to new DA vendors, reconfigurability challenges in the cloud, persistent failures that are unique to the DA context, and the system’s current limitations.
Coherent or Not? Stressing a Neural Language Model for Discourse Coherence in Multiple Languages

In this study, we investigate the capability of a Neural Language Model (NLM) to distinguish between coherent and incoherent text, where the latter has been artificially created to gradually undermine local coherence within text. While previous research on coherence assessment using NLMs has primarily focused on English, we extend our investigation to multiple languages. We employ a consistent evaluation framework to compare the performance of monolingual and multilingual models in both in-domain and out-domain settings. Additionally, we explore the model’s performance in a cross-language scenario.
A Hierarchical Explanation Generation Method Based on Feature Interaction Detection

The opaqueness of deep NLP models has motivated efforts to explain how deep models predict. Recently, work has introduced hierarchical attribution explanations, which calculate attribution scores for compositional text hierarchically to capture compositional semantics. Existing work on hierarchical attributions tends to limit the text groups to a continuous text span, which we call the connecting rule. While easy for humans to read, limiting the attribution unit to a continuous span might lose important long-distance feature interactions for reflecting model predictions. In this work, we introduce a novel strategy for capturing feature interactions and employ it to build hierarchical explanations without the connecting rule. The proposed method can convert ubiquitous non-hierarchical explanations (e.g., LIME) into their corresponding hierarchical versions. Experimental results show the effectiveness of our approach in building high-quality hierarchical explanations.
QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition

Multimodal emotion recognition for video has gained considerable attention in recent years, in which three modalities (i.e., textual, visual and acoustic) are involved. Due to the diverse levels of informational content related to emotion, three modalities typically possess varying degrees of contribution to emotion recognition. More seriously, there might be inconsistencies between the emotion of individual modality and the video. The challenges mentioned above are caused by the inherent uncertainty of emotion. Inspired by the recent advances of quantum theory in modeling uncertainty, we make an initial attempt to design a quantum-inspired adaptive-priority-learning model (QAP) to address the challenges. Specifically, the quantum state is introduced to model modal features, which allows each modality to retain all emotional tendencies until the final classification. Additionally, we design Q-attention to orderly integrate three modalities, and then QAP learns modal priority adaptively so that modalities can provide different amounts of information based on priority. Experimental results on the IEMOCAP and MOSEI datasets show that QAP establishes new state-of-the-art results.