Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of repeated passive source travel time tomography to reveal weak velocity changes related to the 2011 Tohoku‐Oki Mw 9.0 earthquake

Temporal changes of seismic velocities may provide important information on the processes that occur inside the Earth. However, using body wave data with passive sources faces the problem of an uneven distribution of rays, which may cause artifacts with stronger amplitudes than the actual velocity changes in the Earth. We propose an algorithm for the selection of similar data sets in different time periods that minimize the artifacts related to variable data distributions. In this study, we used the data of the Japan Meteorological Agency for several years before and after the Mw 9.0 Tohoku‐Oki event that occurred on 11 March 2011. We performed careful testing using different synthetic models, showing that the selected data subsets allow detecting weak velocity changes with amplitudes above 0.2%. The analysis of the experimental data revealed important features associated with the stress and deformation distributions after the megathrust event. In the upper crust, we found a large zone along the coast with significant P velocity increase likely caused by compression of crustal rocks. This zone was cut by several elongated anomalies with local velocity decrease coinciding with the limits of the maximum slip area. These anomalies possibly mark the areas of major ruptures and deformations after the Tohoku‐Oki earthquake. In the coupling zone at a depth of 40 km, we observe a velocity decrease in the area of the Mw 7.7 aftershock representing strong fracturing in the focal zone. Beneath the volcanic arc, we observe significant (up to 0.5%) decrease of P velocity but less prominent S velocity changes.
A three-stage classifier for remote sensing of mountain environments

The use of spatial satellite image information and digital elevation models in remote sensing classification is described for a mountainous region in southwest Yukon. A three-stage classification method was devised that incorporates a quadtree-based segmentation operator, a Gaussian minimum-distance-to-means test, and a final test involving ancillary geomorphometric data and a spectral curve measure. The overall increase in accuracy is significant compared to maximum-likelihood techniques, and the resulting map products are consistent with few unclassified areas. The three-stage classifier can produce an output map in significantly less time than that required for the perpixel maximum-likelihood classifier, and used a minimum of field or training data which are often difficult and expensive to acquire in complex terrain.
OrthoHand Extend: Design, Modeling and Evaluation of a Simple Wrist-Hand Stretching Orthosis for Neurologically Impaired Patients

—To better understand the upper-limb needs and challenges survivors of neurological events face, and the issues associated with existing technology, customer discovery conversations were conducted with 153 people in the ecosystem (60 patients, 30 caregivers, and 63 medical providers). Patients with upper-limb effects fell into two populations: spastic (stiff, clenched hands) and flaccid (limp hands). Focusing on the first category, a set of design constraints was developed based on the information collected from the customer discovery. With these in mind, the OrthoHand Extend was designed and prototyped as a powered wrist-hand stretching orthosis (exoskeleton) to aid in recovery. The orthosis was tested on two patients, one survivor of stroke and one of traumatic brain injury. Finally, a mathematical modeling technique was developed to characterize joint stiffness based on experimental testing. The prototype met most of the target design constraints and was able to consistently open both patients’ hands. Donning and doffing times averaged 76 and 12.5 s, respectively, for each subject unassisted. These are approximately twice as fast than the next closest times shown in literature. This device has the potential as effective therapy devices accessible to patients in the home, and lays the foundation for clinical trials and further device development. The study was approved by the Vanderbilt University Institutional Review Board, study number 221203.
Edinburgh Research Explorer A Framework for the Game-theoretic Analysis of Censorship Resistance

: We present a game-theoretic analysis of optimal solutions for interactions between censors and censorship resistance systems (CRSs) by focusing on the data channel used by the CRS to smuggle clients’ data past the censors. This analysis leverages the inherent errors (false positives and negatives) made by the censor when trying to classify trafﬁc as either non-circumvention trafﬁc or as CRS trafﬁc, as well as the underlying rate of CRS trafﬁc. We identify Nash equilibrium solutions for several simple censorship scenarios and then extend those ﬁndings to more complex scenarios where we ﬁnd that the deployment of a censorship apparatus does not qualitatively change the equilibrium solutions, but rather only affects the amount of trafﬁc a CRS can support before being blocked. By leveraging these ﬁndings, we describe a general framework for exploring and identifying optimal strategies for the censor-ship circumventor, in order to maximize the amount of CRS trafﬁc not blocked by the censor. We use this framework to analyze several scenarios with multiple data-channel protocols used as cover for the CRS. We show that it is possible to gain insights through this framework even without perfect knowledge of the censor’s (secret) values for the parameters in their utility function.
Real-Time Fashion-guided Clothing Semantic Parsing : a Lightweight Multi-Scale Inception Neural Network and Benchmark Yuhang

Currently two barriers exist that sabotage clothing semantic parsing research: existing methods are time-consuming and the lack of large publicly available dataset that enables parsing at multiple scales. To mitigate these two dilemmas, we hereby embrace deep learning method and design a lightweight multi-scale inception neural network which is at both inside and outside multi-scale inception during training. Moreover, atrous convolution block is involved to enlarge the field of view while bringing neither extra computation cost nor parameters. Then the pre-trained model is further pruned and compressed by fine-tuning on a lightweight version of the same network used earlier, in which the inactive feature response and connections below a pre-defined threshold are directly removed. Besides, we construct so far the largest fashion guided clothing semantic parsing dataset (FCP) which contains a total of 5,000 clothing images and each image associates with both pixel-level, object-level and image-level annotations. All clothing in the dataset are recommended by fashion experts or trendsetters and contains as many as 65 common clothing items, accessories. We organize the dataset as Wordnet tree structure so that it enables fashionably parsing hierarchically. Finally, we conduct extensive experiments on three currently available datasets. Both quantitative and qualitative results demonstrate the priority and feasibility of our method, comparing with several other deep learning based methods. Our method achieves 35 FPS in a single Nvidia Titian X GPU with only minimal accuracy loss.
Introducing new features to a critical software system

In response to changing requirements and other environmental inﬂuences, software systems are increasingly developed incrementally. Successful implementation of new features in existing software is often difﬁcult, whilst many software systems simply ‘break’ when features are introduced. Size and complexity of modern software, poor software de-sign, and lack of appropriate tools are some of the factors that often confound the issue. In this paper, we report on a successful industrial experience of evolving a feature-rich program analysis tool for dependable software systems. The experience highlights the need for a development framework to maintain rich traceability between development artifacts, and to satisfy certain minimal necessary conditions of artifacts during and after the implementation of a new feature.
Recent advances in colonoscopy [version 1; peer review: 2 approved]

Colonoscopy is an important and frequently performed procedure. It is effective in the prevention of colorectal cancer and is an important test in the investigation of many gastrointestinal symptoms. This review focuses on developments over the last 5 years that have led to changes in aspects of colonoscopy, including patient preparation, technical factors, therapeutic procedures, safety, and quality.
A History of Violence : The ‘ Culture of Honor ’ as a Determinant of Homicide in the US South

This paper investigates the origins of lethal violence in the Southern United States. A debated hypothesis argues that the high prevalence of homicides in the US South stems from the fact that it was settled by herders, chief among them the Scots-Irish. Herding societies develop cultures of honor because of their precariousness: violence is a necessary condition to preserve a reputation for toughness and deter animal theft. Using historical census data and relating contemporaneous violence to early Scots-Irish settlers and livestock counts, this paper confirms that high numbers of Scots-Irish immigrants to the US South are associated with higher homicide rates today, especially among whites and between acquaintances, and all the more so in counties more heavily engaged in herding in the early 19 century. Evidence suggests that the relationship is causal: the results are robust to a wide array of socio-economic controls and to controlling for slavery, they do not hold for other countries of origin of settlers or other offenses and are robust to instrumentation of Scots-Irish settlements by the distance to major crossroads on the settlers’ road to the South. Regarding cultural transmission, results suggest that horizontal transmission between early Scots-Irish settlers and their –whitepeers and successors prevailed. The paper highlights the complementarities between culture, economic factors and the institutional environment in explaining the prevalence – and persistenceof violence.
X4SR: Post-Hoc Explanations for Session-based Recommendations

Session-based recommendation (SR) approaches have extensively employed deep neural networks (DNN) to provide high-quality recommendations based on a user’s current interactions and item features. However, these approaches are black-box models, providing recommendations that are not understandable to the users and system designers. To further trust and transparency in the recommendation system and extrapolate insights into customer behavior, it is essential to provide explanations for why an item is recommended to a certain user. In this paper, we propose a novel post-hoc explainability method that provides explanations for a benchmark recommendation system NISER[1] at two levels; (i) Local explanations, where the method provides explanations for why an item is recommended in the current session, and (ii) Global explanations, where the method provides explanation for why an item is recommended in general across all sessions. Our method utilizes the learned item and session embeddings from the recommendation model in order to determine the most influential items for a recommendation. In contrast to using proxy models like LIME[2] or SHAP[3], utilizing the same model embeddings that were used for recommendations ensures that the explanations generated are of high fidelity and reflect the models’ true behavior. Through quantitative evaluation on two publicly available datasets, we demonstrate that our approach is able to generate quality explanations in terms of salient items for a recommendation. To the best of our knowledge, our method is the first to provide a quantitative evaluation in terms of commonly used metrics for recommendation systems. We also demonstrate the value of providing verbalized explanations for various examples using LLMs 1 to improve readability of explanations.
Cardiac, skeletal, and smooth muscle mitochondrial respiration: Cardiac, skeletal, and smooth muscle mitochondrial respiration: are all mitochondria created equal? are all mitochondria created equal?

Unlike cardiac and skeletal muscle, little is known about vascular smooth muscle mitochondrial respiration. Therefore, the present study examined mitochondrial respiratory rates in smooth muscle of healthy human feed arteries and compared with that of healthy cardiac and skeletal muscles. Cardiac, skeletal, and smooth muscles were harvested from a total of 22 subjects (53 ± 6 yr), and mitochondrial respiration was assessed in permeabilized fibers. Complex I + II, state 3 respiration, an index of oxidative phosphorylation capacity, fell progressively from cardiac to skeletal to smooth muscles (54 ± 1, 39 ± 4, and 15 ± 1 pmol·s -1 ·mg -1 , P < 0.05, respectively). Citrate synthase (CS) activity, an index of mitochondrial density, also fell progressively from cardiac to skeletal to smooth muscles (222 ± 13, 115 ± 2, and 48 ± 2 µmol·g -1 ·min -1 , P < 0.05, respectively). Thus, when respiration rates were normalized by CS (respiration per mitochondrial content
The Role of Online Disinhibition Towards Cyberbullying on Second-Account Users on Instagram

This study aims to determine the role of online disinhibition towards cyberbullying on second-account users on Instagram. The participants of this study were 150 second-account users on Instagram. The sampling technique used was purposive sampling. Online disinhibition is measured using a scale compiled by researchers based on the aspects proposed by Stuart & Scott (2020). Cyberbullying is measured using the cyberbullying scale, which is also compiled by researchers based on aspects proposed by Langos (2012). Data analysis was performed using simple regression techniques. Data analysis resulted in the value of R square = 0.520 F = 160.136 and p = 0.000 (p <0.005). Data analysis resulted in a value of p = 0.000 (p <0.005), which indicated that there was a role for online disinhibition in cyberbullying with an R square = 0.520, which means that it gave a role of 52%.
Automated Optimization of Residual Reduction Algorithm Parameters in Opensim

The residual reduction algorithm (RRA) in OpenSim is designed to improve dynamic consistency of kinematics and ground reaction forces in movement simulations of musculoskeletal models. RRA requires the user to select numerous tracking weights for the joint kinematics to reduce residual errors. Selection is often performed manually, which can be time-consuming and is unlikely to yield optimal tracking weights. A multi-heuristic optimization algorithm was used to expedite tracking weight decision making to reduce residual errors. This method produced more rigorous results than manual iterations and although the total computation time was not significantly reduced, this method does not require the user to monitor the algorithm’s progress to find a solution, thereby reducing manual tuning. Supporting documentation and code to implement this optimization is freely provided to assist the community with developing movement simulations.
Snapping Scapula Syndrome Resulting from a Subscapular Osteochondroma: Case Report and Literature Review

Snapping scapula syndrome is a rare pathology first described by Boinet in 1867. Its pathogenesis is caused by an incongruency in the thoracic scapular joint, associated with multiple causes including bursitis, exostosis, bone mass, fibrotic tissue or muscular abnormalities, defective consolidation of fractures or anatomical rib or scapular fractures. The purpose of this article is to present a clinical case of an adolescent with incapacitating clinical pain in the right scapular region, as well as a deformity at this level that had evolved for more than 4 years, and which, when diagnostic tests were performed, revealed a subscapular bone mass suggestive of a single large osteochondroma measuring more than 2.5 cm x 4 cm x 4 cm. A review and update of the literature on the diagnosis and current treatment of this pathology is made.
Data-Driven Time Dependent Reliability Prediction Approach for Overhead Contact Lines Subjected to Imperfect Maintenance, Common Cause Failure and Lightning Strike

To ensure the high-reliability operation of overhead contact lines (OCLs), this paper proposes a data-driven time dependent reliability prediction approach, incorporating the maintenance factor, common cause failure (CCF), and lightning strike into reliability modeling and prediction for multi-component OCLs. Firstly, a new lightning-related failure probability calculation method is developed, including probabilistic lightning modeling, lightning identification, tracking and association analysis (LITAA), lightning movement trajectory prediction, and electric geometric model-based failure rate calculation. Moreover, a Bayesian network-based reliability model is embedded to integrate historical maintenance information, predictive lightning-related failure rate and fault tree model into reliability assessment of OCLs, which effectively captures the dependencies between failure events and depicts the impact of CCF on OCL components. In this context, the reliability of OCL components and OCLs can be predicted, with update of lightning strike data. Finally, the actual maintenance and lightning-related failure records are implemented to validate the effectiveness of the proposed approach. The result analysis demonstrates that our approach can not only illustrate how the reliability can be predicted using the predictive lightning-related failure rate, simultaneously accounting for imperfect maintenance and CCF, but also enhance risk prevention against lightning strike and faulty OCL components.
Rebound Thrombocytosis after Induction Chemotherapy is a Strong Biomarker for Favorable Outcome in AML Patients

Abstract Whereas the molecular events underlying acute myeloid leukemia (AML) are increasingly identified, dynamics of hematologic recovery following induction chemotherapy remain mysterious. Platelet recovery may vary between incomplete and excess recovery among patients achieving remission. We analyzed platelet recovery after the first induction cycle in 291 consecutive AML patients. We defined excess platelet rebound (EPR) as platelet increase above 500 G/L. We observed EPR in 120 (41.2%) patients. EPR+ patients had lower platelets at diagnosis, higher marrow infiltration, more frequently NPM1 mutations, and were associated with ELN favorable risk. Absence of EPR correlated with complex karyotypes, ELN intermediate-I and adverse risk, and therapy-related AML. Overall survival was better in EPR+ patients than EPR- (median 125 vs 41 months; p = 0.04), as was disease-free survival. By multivariate analysis, EPR+ was an independent parameter associated with favorable survival. Plasma thrombopoietin (TPO) levels at diagnosis indicated EPR+ (p < 0.0001), while GATA-1, GATA-2, and MPL mRNA expression did not differ between EPR+ and EPR- patients. Finally, transcription factors blocking early megakaryopoiesis were upregulated in EPR- patients, while NFE2 involved in late megakaryocyte differentiation was increased in EPR+ patients. Our work identifies mechanisms involved in platelet recovery after induction chemotherapy.
Computational Intelligence for Emerging Systems and Applications

Computational intelligence represents a collection or set of computational techniques in soft computing, machine learning, and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena (see, e.g. [1]). They provide us with the opportunity to use our knowledge and raw data to approach extremely complex problems (see, e.g., [2]). This multidisciplinary research field is in continuous expansion in the artificial intelligence research community (see, e.g., [3]).
Participation in a multicomponent lifestyle intervention for people with obesity improves glycated hemoglobin (HbA1c)

Introduction Obesity is associated with compromised glucose metabolism. Hence, it is of interest to investigate if the lifestyle interventions used in the LIBRA-cohort, which aimed at not only weight loss, but also patient well-being, could also help obese patients improve glucose metabolism by evidence of reduced HbA1c. The aim of the study was to retrospectively investigate if patients who were referred to a lifestyle intervention for obesity, were able to alter HbA1c. Research design and methods Patients with a BMI≥30 undergoing a 6-month lifestyle intervention, who also completed physical and mental health surveys and whose baseline and 6-month blood samples were available, were included in the analysis. For changes in HbA1c and body weight a clinically relevant change of 5≥mmom/mol and 5%≥, respectively, was chosen. Participants were divided into groups according to their baseline HbA1c level: “Diabetes”: HbA1c of ≥6.5% (≥48 mmol/mol), “Prediabetes”: HbA1c of 5.7% to 6.4% (39-47.99 mmol/mol) or “Normal” HbA1c <5.7% (<39 mmol/mol). Results 180 patients met the stated inclusion criteria and these patients were divided into groups (median age (25th;75th quartile): Diabetes: n=47, age 54 (43;60), 51% women, Prediabetes: n=68, age 60 (50;66), 71% women and Normal: n=65, median age 61 (50;66), 85% women. Significant reductions were found in all three groups and specifically in the diabetes group HbA1c was reduced (mean [95%CI]) -5[-8;-2] mmol/mol from baseline to the end of the intervention. Furthermore, 35% of patients with prediabetes normalized their HbA1c (<39) and 30% patients with diabetes reduced their HbA1c <48. All groups had clinically relevant (≥5%) reductions in body weight (p<0.01). There was an association between body weight reduction and HbA1c reduction in the diabetes group (p<0.01). All groups reported improvements in physical health (p<0.01). Conclusion In this retrospective cohort study, all patients achieved clinically relevant weight loss after participation in the lifestyle intervention and obese patients with diabetes achieved clinically relevant reductions in HbA1c after 6-months. More than 1/3 of patients with prediabetes normalized their HbA1c.
Archaeological survey in the Kaokoveld, Namibia

Cette etude ethnologique porte sur le climat aride en Afrique notamment en Namibie et sur l'adaptation de l'homme. Pour palier a cela l'homme a adopte le pastoralisme. L'A etudie donc ici l'origine et la signification culturelle et economique de ce mode de vie.
What affects the direct economic burden of non-communicable diseases on middle-aged and older adult people in Shaanxi Province?

Non-communicable diseases (NCDs) are the leading cause of death worldwide. NCDs affect the health status and the quality of life. In addition, continuous NCDs treatment expenses place a heavy economic burden on families and cause huge economic losses to the society. The prevention and treatment of NCDs and reduction of their economic burden are key public health issues. Considering middle-aged and older adult people as the focus, their basic socio-demographic characteristics and health behavior status of this group, and a pooled cross-sections regression model was then used to analyze the main factors affecting the direct economic burden. The results showed that from 2013 to 2018, the prevalence of NCDs among the middle-aged and older adult people in Shaanxi province as well as the direct economic burden of NCDs increased. The effect factors primarily included sex, age, employment status, income level, type of medical insurance, urban or rural residency, level of the health care-providing institutions, visiting times of 2-week, and length of hospital stay. Several measures can be taken to control the onset of NCDs and reduce their direct economic burden.