Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface wave focal mechanisms for eastern North American earthquakes with tectonic implications

Using P wave first motions together with amplitude and phase data from long-period Love and Rayleigh waves, the focal mechanisms of 22 earthquakes occurring in eastern North America between 1962 and 1976 are determined. The techniques used yield good results for events with body wave magnitudes as small as 4.0. Eighteen of the events have depths between 5 and 16 km; 15 have significant components of strike slip motion; and for focal mechanisms having nearly horizontal pressure axes, 70% of the pressure axes trend northeast or east. Though the earthquake sample may be biased toward the central United States, it is significant that no focal depths greater than 25 km have been encountered. A confidence factor is assigned to each focal mechanism for use in future tectonic syntheses.
Association of RPS26 gene polymorphism with different types of diabetes in Chinese individuals

Different types of diabetes show distinct genetic characteristics, but the specific genetic susceptibility factors remain unclear. Our study aimed to explore the associations between the ribosomal protein S26 (RPS26) gene rs1131017 polymorphisms and susceptibility to type 1 diabetes mellitus, latent autoimmune diabetes in adults (LADA) and type 2 diabetes mellitus in the Chinese Han population, and their correlations with clinical features.
Fully automatic summarization of radiology reports using natural language processing with language models.

Natural language processing using language models has yielded promising results in various fields. The use of language models may help improve the workflow of radiologists. This retrospective study aimed to construct and evaluate language models for the automatic summarization of radiology reports. Two datasets of radiology reports were used: MIMIC-CXR and the Japan Medical Image Database (JMID). MIMIC-CXR is an open dataset comprising chest radiograph reports. JMID is a large dataset of CT and MRI reports comprising reports from 10 academic medical centers in Japan. A total of 128,032 and 1,101,271 reports from the MIMIC-CXR and JMID, respectively, were included in this study. Four Text-to-Text Transfer Transformer (T5) models were constructed. Recall-Oriented Understudy for Gisting Evaluation (ROUGE), a quantitative metric, was used to evaluate the quality of text summarized from 19,205 and 58,043 test sets from MIMIC-CXR and JMID, respectively. The Wilcoxon signed-rank test was utilized to evaluate the differences among the ROUGE values of the four T5 models. In addition, subsets of automatically summarized text in the test sets were manually evaluated by two radiologists. Based on the Wilcoxon signed-rank test, the best T5 models were selected for the automatic summarization. The quantitative metrics of the best T5 models were as follows: ROUGE-1 = 57.75, ROUGE-2 = 49.96, and ROUGE-L = 54.07 in MIMIC-CXR; ROUGE-1 = 50.00, ROUGE-2 = 39.66, and ROUGE-L = 47.87 in JMID. The radiologists' evaluations revealed that 86% (86/100) and 85% (85/100) of the texts automatically summarized from MIMIC-CXR and JMID, respectively, were clinically useful. The T5 models constructed in this study were capable of automatic summarization of radiology reports. The radiologists' evaluations revealed that most of the automatically summarized texts were clinically valuable.
Preference and Perception of Indian Caregivers Towards Formal Long-Term Supportive Services - A Mixed-Methods Study

Purpose of the Study: In India, the rate of population ageing and greater burden of chronic disease-related care and smaller families question the sustainability of traditional family-based, in-home long-term supports and services (LTSS). However, little is still known about Indians' perception of formal LTSS. Design and Methods: A mixed-methods design of family caregivers for older adults from Jodhpur, Rajasthan (n=30 in-depth interviews; n=100 quantitative survey). Inductive qualitative data analysis identified emergent themes about perceptions of either informal or formal LTSS. Caregivers self-reported which common LTSS they needed in a quantitative survey. Multivariable Poisson and logistic regression models were used to estimate the average total number of LTSS and probabilities of self-reporting wanting individual LTSS, respectively. Results: The main theme was a negative perception of formal LTSS, especially the idea of paid helpers. A second theme served as the rationale for the first theme: caregivers reported a ''duty'' to provide care to one's family that ''others'' and those ''doing it for money'' could not meet. Caregivers reported on average 2.8 LTSS needs, of 10 options. Formal LTSS, like home-health care assistance with instrumental activities of daily living, were least frequently reported; caregiver education and self-care activities were the most reported. Implications: Despite providing intensive amounts of informal LTSS and care for their family members, Indian caregivers consistently reported disinterest in using formal LTSS alternatives in both qualitative and quantitative data. Caregivers reported a stronger desire for services that support their ability to carry out their caregiving roles. Keywords: caregiving, India, long-term care, long term supports and services, qualitative research; quantitative research
Neutrophil responses to RSV infection show differences between infant and adult neutrophils

Introduction Respiratory syncytial virus (RSV) causes a severe respiratory condition, bronchiolitis, in infants but not in adults. Bronchiolitis is characterised by neutrophilic infiltration in the airways, but whether neutrophils enhance recovery from infection or contribute to its pathology remains unknown. Methods We used a novel in-vitro model to compare term umbilical cord blood (infant) (n=17 donors) and adult neutrophils (n=15 donors) during migration across RSV-infected differentiated human nasal airway epithelial cells (AECs) in a basolateral to apical direction. Results Greater numbers of infant neutrophils (mean (95% CI)) (336 684 (242 352 to 431 015)) migrated across RSV-infected AECs to the apical compartment (equivalent to the airway lumen) compared with adult neutrophils (56 586 (24 954 to 88 218)) (p<0.0001). Having reached the apical compartment of infected AECs, much greater numbers of infant neutrophils (140 787 (103 117 to 178 456)) became apoptotic compared with adult (5853 (444 to 11 261)) (p=0.002). Infant neutrophils displayed much greater expression of CD11b, CD64, neutrophil elastase (NE) and myeloperoxidase (MPO) than adult neutrophils at baseline and at all points of migration. However, as adult neutrophils migrated, expression of CD11b, CD64, NE and MPO became greater than at baseline. Discussion The high proportion of infant neutrophils migrating across RSV-infected AECs correlates with the neutrophilic infiltrate seen in infants with severe RSV bronchiolitis, with large numbers undergoing apoptosis, which may represent a protective mechanism during infection. Compared with adult neutrophils, infant neutrophils already have high expression of surface markers before contact with AECs or migration, with less capacity to increase further in response to RSV infection or migration.
Lifestyle factors predict gout outcomes: Results from the NOR-Gout longitudinal 2-year treat-to-target study

Objective Gout is associated with lifestyle, body mass index (BMI) and comorbidities, including dyslipidaemia. We studied how in actively treated patients, anthropometric measures and lipid levels changed over 2 years and whether they predicted gout outcomes. Methods Patients with a recent gout flare and elevated serum urate (sUA) received gout education and treat-to-target urate-lowering therapy over 1 year. Anthropometric measures with BMI, waist circumference (WC) and waist–height ratio (WHR) as well as lipid levels were measured yearly over 2 years. We examined whether baseline anthropometric measures and lipid levels were related to flares and to achieving the sUA target. Results At baseline, patients (n=211) were with mean age of 56.4 years and 95% were male. Over 2 years, anthropometric measures were largely unchanged while cholesterol and low-density lipoprotein cholesterol (LDL-C) were reduced at year 1. Anthropometric measures were associated with presence of tophi. Higher baseline WC (OR: 0.96 per cm, 95% CI: 0.93 to 0.99) decreased and high level of high-density lipoprotein cholesterol (OR: 5.1 per mmol/L, 95% CI: 1.2 to 22.1) increased the chance of sUA target achievement at year 2. High LDL-C (OR: 1.8 per mmol/L, 95% CI: 1.2 to 2.6) predicted the chance of having a gout flare during year 2. Conclusion In actively treated patients with gout, anthropometric measures were largely unchanged over 2 years and lipid levels were reduced. High WC and lipid levels predicted unfavourable gout outcomes after 2 years.
FS3: Few-Shot and Self-Supervised Framework for Efficient Intrusion Detection in Internet of Things Networks

Securing the Internet of Things is critical for its successful deployment in various industries. While Machine Learning techniques have shown promise for intrusion detection in the Internet of Things, existing methods require large amounts of labeled training data; moreover, they encounter challenges with the presence of extreme class imbalance, i.e., some classes are underrepresented in the datasets used. Supervised methods rely on extensive labeled data, which can be costly and time-consuming to obtain. Class imbalance in datasets further exacerbates the challenge by skewing the model’s learning process toward the majority classes, leading to poor detection of attacks belonging to minority classes. This issue is particularly pronounced in the Internet of Things environments due to diverse devices and the varying frequency of intrusions targeting them. To overcome these challenges, we present a Few-Shot and Self-Supervised framework, called , for detecting intrusions in IoT networks. works in three phases. The first phase employs self-supervised learning to learn latent patterns and robust representations from unlabeled data. The second phase introduces Few-shot learning with contrastive training. Few-shot learning enables the model to learn from a few labeled examples, thereby eliminating the dependency on a large amount of labeled data. Contrastive Training addresses the class imbalance issue by improving the discriminative power of the model. The third phase introduces a novel K-Nearest neighbor algorithm that sub-samples the majority class instances to further reduce imbalance and improve overall performance. Experimental results based on three publicly available benchmark datasets demonstrate the efficacy of in addressing the challenges posed by the limited availability of labeled data as well as class imbalance in datasets. Our proposed framework , utilizing only of labeled data, outperforms fully supervised state-of-the-art models by up to and with respect to the metrics precision and F1 score, respectively.
Secure MLaaS with Temper: Trusted and Efficient Model Partitioning and Enclave Reuse

Machine Learning as a Service (MLaaS) is becoming a highly available and cost-efficient way to embrace machine learning techniques in various domains. But it suffers from data privacy risks as user data must be uploaded to untrusted clouds. We propose a trusted and efficient MLaaS system, Temper, based on secure hardware enclaves such as Intel SGX. Temper significantly improves the performance without sacrificing the data security guarantees or the model inference accuracy. With the two key techniques of enclave reuse and model partitioning, it reduces the enclave initialization and model loading costs, and alleviates the secure paging overheads due to the limited hardware-protected memory capacity in SGX. We also provide rigorous security guarantees for enclave sharing and batched processing, by ensuring stateless, non-interference, and data-oblivious processing and data transfers across model partitions. Temper achieves on average 2.2 × and 1.8 × improvements over the state-of-the-art designs for latency and throughput, respectively, and within 2.1 × slowdown of untrusted native execution. Its distributed paradigm provides a more scalable way for future MLaaS with large models.
Secure Softmax/Sigmoid for Machine-learning Computation

Softmax and sigmoid, composing exponential functions (ex) and division (1/x), are activation functions often required in training. Secure computation on non-linear, unbounded 1/x and ex is already challenging, let alone their composition. Prior works aim to compute softmax by its exact formula via iteration (CrypTen, NeurIPS ’21) or with ASM approximation (Falcon, PoPETS ’21). They fall short in efficiency and/or accuracy. For sigmoid, existing solutions such as ABY2.0 (Usenix Security ’21) compute it via piecewise functions, incurring logarithmic communication rounds. We study a rarely-explored approach to secure computation using ordinary differential equations and Fourier series for numerical approximation of rational/trigonometric polynomials over composition rings. Our results include 1) the first constant-round protocol for softmax and 2) the first 1-round error-bounded protocol for approximating sigmoid. They reduce communication by and , respectively, shortening the private training process of state-of-the-art frameworks or platforms, namely, CryptGPU (S&P ’21), Piranha (Usenix Security ’22), and quantized training from MP-SPDZ (ICML ’22), while maintaining competitive accuracy.
Differentially Private Resource Allocation

Recent studies have shown that systems with limited resources like Metadata-private Messenger (MPM) suffer from side-channel attacks under resource allocation (RA). In the case of MPM, which is designed to keep the identities and activities of both callers and callees private from network adversaries, an attacker can compromise a victim’s friends and keep calling the victim to infer whether the victim is busy, which breaks the privacy guarantee of MPM. In this work, we systematically study how to protect the privacy of RA against the aforementioned attacks with differential privacy (DP). Though DP has been tested by Angel et al. (IEEE S&P 2020) in protecting RA, which lets the allocator add dummy requests following a biased Laplace distribution to hide the existence of the victim and then assign resources randomly, we identify that this approach does not leverage the uncertainty from the attacker’s view, thus leading to a loose bound of DP. As a result, more than 40% of the resources are wasted to satisfy DP. To make the DP solutions more practical, we precisely model the RA process from the attacker’s view and present a thorough study of the noisy allocation mechanisms by considering different distributions, scales, and biases of noise. We identify four new mechanisms and prove that they all follow ϵ -DP (Angel et al. follow (ϵ, δ)-DP). Through theoretical and empirical analysis, we found these approaches can outperform Angel et al. by a large margin in privacy-utility tradeoff.
SePanner: Analyzing Semantics of Controller Variables in Industrial Control Systems based on Network Traffic

Programmable logic controllers (PLCs), the essential components of critical infrastructure, play a crucial role in various industrial manufacturing processes. Recent attack events show that attackers have a strong interest in tampering with the controller variables, such as the device status and internal program logic. A typical attack strategy is that the attackers just send malicious network traffic of industrial control protocols (ICPs) to change the controller variables of PLCs. To defend against this attack, a lot of countermeasures have been proposed to detect anomalies in network traffic based on the semantic analysis. However, the proprietary nature of ICPs poses a challenge to extracting the required semantics for evaluating the controller variables. In this paper, we propose a novel framework named SePanner to extract the semantics of controller variables from proprietary ICPs based on network traffic. Specifically, SePanner conducts the multi-state comparison to locate the semantic fields directly, then removes the interfering fields by the single-state comparison and filtering criteria. Our experiments demonstrate that SePanner can precisely extract the semantics of controller variables from proprietary ICPs, providing protection for PLCs while remaining compatible with various proprietary binary protocols.
ANDetect: A Third-party Ad Network Libraries Detection Framework for Android Applications

Third-party advertising libraries, which furnish mobile applications with ads, offer a revenue stream for Android application developers. However, the loaded ads potentially expose application users to privacy infringements and security threats. For instance, tracking scripts embedded in third-party ads monitor user behavior and can entice users into downloading malicious files. Therefore, the detection of advertising libraries in mobile applications is crucial for mobile security protection and serves as the foundation for preventing third-party ads from compromising user privacy. In this paper, we propose ANDetect, a tool specifically designed for identifying advertising libraries in Android applications. Utilizing static analysis of resource characteristics, ANDetect efficiently uncovers advertising libraries embedded in Android applications, thereby addressing the limitation of traditional third-party library detection methods that struggle with encrypted applications. ANDetect leverages a manual collection of 833 unique versions of third-party advertising libraries, combined with profiling and machine learning techniques. This approach utilizes distinctive semantic features in advertising and non-advertising libraries to identify advertising libraries outside of the established ad network database. We conducted an experiment using ANDetect on over 140,000 applications downloaded from Google Play and APPCHINA. Upon manual verification, it was revealed that ANDetect had detected a total of 16 noval advertising libraries, previously unregistered in the database. This underlines ANDetect’s potency in enhancing mobile application security by identifying potentially intrusive advertising libraries.
Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones.

Spatiotemporal yield heterogeneity presents a significant challenge to agricultural sustainability efforts and can strain the economic viability of farming operations. Increasing soil organic matter (SOM) has been associated with increased crop productivity, as well as the mitigation of yield variability across time and space. Observations at the regional scale have indicated decreases in yield variability with increasing SOM, however the mechanisms by which this variability is reduced remain poorly understood, especially at the farm scale. To better understand the relationship between SOM and yield 15 heterogeneity, we examined its distribution between particulate organic matter (POM) and mineral associated organic matter (MAOM), at the sub-field scale within 9 farms located in the central United States. We expected that the highest SOM concentrations would be found in stable, high-yielding zones, and that the SOM pool in these areas would have a higher proportion of POM relative to other areas in the field. However, in contrast to our predictions, we found that unstable yield areas had significantly higher SOM than stable yield areas, and that there was no significant difference in the relative 20 contribution of POM to total SOM across different yield stability zones. Our results further indicate that MAOM abundance was primarily explained by interactions between crop productivity and edaphic properties such as texture, which varied amongst stability zones. However, we were unable to link POM abundance to soil properties or cropping system characteristics. Instead, we posit that POM dynamics in these systems may be controlled by differences in decomposition patterns between stable and unstable yield zones. Our results show that, at the subfield scale, increasing SOM may not directly confer increased 25 yield stability. Instead, in fields with high spatiotemporal yield heterogeneity, SOM stocks may be determined by interactive effects of topography, weather, and soil characteristics on crop productivity and SOM decomposition. These findings suggest that POM has the potential to be a useful indicator of yield stability, with higher POM stocks in unstable zones, and highlights the need to consider these factors during soil sampling campaigns, especially when attempting to quantify farm-scale soil C stocks.
Analysis of multicomponent walkaway vertical seismic profile data

Summary A multicomponent walkaway VSP data was processed for PP and PS imaging as well to study the AVO response. To date, a PP wave corridor stack and VSP-CDP mapping have been completed and are correlated to synthetic seismograms. Overall, we saw a good correlation between VSP and synthetic data, and observed changes inside the reservoir, interpreted to be due to production. A common shot stack reflectivity gather was produced for AVO analysis. At the top and bottom of the target reservoir, the AVO responses of VSP PP wave data and synthetic gathers show similar trends. The results give us promise for inverting walkaway VSP data for reservoir properties.
Elaborating domain descriptions (NMR 2006)

In this work we address the problem of elaborating domain descriptions (alias action theories), in particular those that are expressed in dynamic logic. We de(cid:2)ne a general method based on contraction of formulas in a version of propositional dynamic logic with an incorporated solution to the frame problem. We present the semantics of our theory change and de(cid:2)ne syntactical operators for contracting a domain description. We establish soundness and completeness of the operators w.r. t. the semantics for descriptions that satisfy a principle of modularity that we have proposed elsewhere. We also investigate an example of changing non-modular domain descriptions.
Index to FAA Office of Aerospace Medicine Reports: 1961 Through 2006

Abstract : An index to Federal Aviation Administration Office of Aerospace Medicine Reports (1964-2006) and Civil Aeromedical Institute Reports (1961-1963) is presented for those engaged in aviation medicine and related activities. The index lists all FAA aerospace medicine technical reports published from 1961 through 2006: chronologically, alphabetically by author, and alphabetically by subject. A foreword describes the index's sections and explains how to obtain copies of published Office of Aerospace Medicine technical reports. An historical vignette describes some aspects of early toxicological research accomplishments at the Institute.