Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptation to European Spanish and psychometric properties of the Patient Activation Measure 13 in patients with chronic diseases

Background . The 13-item Patient Activation Measure (PAM-13) is an instrument that assesses people’s knowledge, skills and confidence for self-management of their health and health care. Scores on the PAM-13 have been shown to predict adherence to health behaviours, health-related outcomes and health care costs. Objectives . To develop a European Spanish adaptation of the original PAM-13 and to examine its psychometric properties in a sample of chronic patients. Methods . The PAM-13 was forward-backward translated and then completed by chronic patients attending a primary health care centre. Data were analysed with a Rasch model. We assessed the functioning of the rating scale, its reliability, the item goodness-of-fit, differential item functioning (DIF), local dependence, unidimensionality and correlation analysis. Results . A total of 208 patients (80%) completed the questionnaire. Data showed a fit to the Rasch model. More than 50% of patients endorsed all the items. Item rank for the Spanish sample was similar to the original, with few differences. We found significant differences ( P < 0.05) in PAM-13 measures according to adherence to prescribed medicines and positive correlations with self-efficacy and physical quality of life. Conclusions . The European Spanish PAM-13 is a reliable and valid instrument for assessing activation in patients with chronic disease in Spain. We suggest adding new items to the instrument so as to examine patients with higher activation levels in the future. Further studies are needed to evaluate the usefulness of this new Spanish PAM-13 in different settings and populations, as well as to examine the relationship between PAM-13 scores and other health-related outcomes.
How Can We Provide Better Simulation-Based Policy Support?

This paper collects the positions of four experts who participated in a panel on simulation-based policy support. The experts are well known for their contributions in this domain, predominantly for their use of agent-based approaches. The first section addresses the increasing requirements to integrate social modeling to support the evaluation of the socio-economic impact of policies, including questions of equity. Artificial societies are presented as enablers. The second section observes that the purpose of a simulation model is very much linked to its usefulness for supporting policy decisions. This implies requirements for learning agents and better representation of time and space. The third section focuses on the need to give agents more active behavior, to let them drive the action. While digital twin technology promises to help, the current state of the art seems insufficient. The section closes by looking at trust in simulation models, which will be needed for policy support.
Managing Risks for AI

Overview: Over the years, many privacy and security attacks, such as model inversion attacks and membership inference attacks, have been targeting Artificial Intelligence (AI) models [1]. In addition, many AI-caused bias/fairness issues have been identified. These practical challenges suggest an urgent need of a framework that can assess a spectrum of AI risks throughout the AI development pipeline. We propose an AI risk-assessment framework that concerns three broad issues: a. ) feasible threat modeling; b. ) data pre-processing (e.g., removing the sensitive information from data before it is used by the AI model) and model post-processing (e.g, providing a wrapper for a given classifier to reduce model inversion attacks); and c. ) AI model parameter selection (e.g. for differential private AI models, or robustness parameters for adversarial training) to get good utility and adequate protection against realistic risks. Threat modeling-based risk assessment is in common use in several security domains. For example, to measure door lock security, the American National Standards Institute defines a series of tests that simulate real life attacks against door locks. These attacks range from the application of force (e.g., kicking down the door) to a battery of lock picking tests. The lock is assigned a grade based on its success against these types of likely attacks. The above threat modeling-based approach implies that we can first pre-process the data using a technique Q to get a sanitized copy Q(D) where certain biases and/or privacy sensitive information could be removed. For example, we can replace the sensitive information such as a credit card number with a realistic random card number before an AI model is built so that a text generation algorithm will not accidentally memorize them. Once the sanitized data is created, a private, robust and transparent learning algorithm L with appropriate parameters p is chosen to learn a model M . Later on this model is post-processed using a post-processing technique P to make sure that the concerned risks are addressed. For example, for a deep learning model M , a few layers could be added to M using publicly available data (hence no privacy risk) to reduce the effectiveness of specific attacks and improve fairness. This approach leads to an optimization problem (See Equation 1) where we find an optimal combination of p, P, Q such that we maximize the model utility U (e.g., accuracy) while making sure that risk (e.g., sensitive attribute prediction accuracy) due to a threat i, denoted as RTi, is less than the desired risk limit γi.
The Hubble Higher z Supernova Search: Supernovae to z ≈ 1.6 and Constraints on Type Ia Progenitor Models

We present results from the Hubble Higher z Supernova Search, the first space-based open field survey for supernovae (SNe). In cooperation with the Great Observatories Origins Deep Survey, we have used the Hubble Space Telescope with the Advanced Camera for Surveys to cover ~300 arcmin2 in the area of the Chandra Deep Field South and the Hubble Deep Field North on five separate search epochs (separated by ~45 day intervals) to a limiting magnitude of F850LP ≈ 26. These deep observations have allowed us to discover 42 SNe in the redshift range 0.2 < z < 1.6. As these data span a large range in redshift, they are ideal for testing the validity of Type Ia supernova progenitor models with the distribution of expected "delay times," from progenitor star formation to Type Ia SN explosion, and the SN rates these models predict. Through a Bayesian maximum likelihood test, we determine which delay-time models best reproduce the redshift distribution of SNe Ia discovered in this survey. We find that models that require a large fraction of "prompt" (less than 2 Gyr) SNe Ia poorly reproduce the observed redshift distribution and are rejected at greater than 95% confidence. We find that Gaussian models best fit the observed data for mean delay times in the range of 2-4 Gyr.
Saddle point formulation for a cartoon-texture decomposition

We consider the image-decomposition into a cartoon and texture part proposed by Yves Meyer. We formulate the corresponding optimization problem as a saddle point problem and show the existence of a saddle point. This also leads to a dual problem from which we can ﬁnd conditions on the regularization parameter when a trivial decomposition occurs. The saddle point formulation allows us to state the Euler-Lagrange equations for optimality, which take the form of a nonlinear indeﬁnite system. We propose to choose the dual parameter as regularization parameter. Finally, we discuss some numerical algorithms for solving the optimality conditions and investigate their convergence numerically.
Adaptive Discretizations for Vision

Variational problems in vision are solved numerically on the pixel lattice because it provides the simplest computational grid to discretize the input images, even though a uniform grid is seldom well-matched to the complexity of the solution. To adapt the complexity of the discretization to the solution, it is necessary to adopt ﬁnite-element techniques that match the resolution of piecewise polynomial bases to the resolving power of the variational model, but such techniques have been neglected in vision. To address this issue, we investigate the advantages and drawbacks of ﬁnite-element discretizations for variational models in vision, their multiresolution properties, and the optimization algorithms to resolve them. Our 2 and 3D experiments in image segmentation, optical ﬂow, stereo, and depth fusion reveal the conditions where ﬁnite-element can outperform ﬁnite-diﬀerence discretizations by achieving signiﬁcant computational savings with a minimal loss of accuracy.
Hybrid Inverse Problems for Nonlinear Elasticity

We consider the problem of nonlinear elasticity in two and three dimensions. Under the hypothesis the ﬂuid is incompressible, we recover the displaced ﬁeld and the Lame parameter µ from power density measurements in two dimensions. A stability estimate is shown to hold for small displacement ﬁelds, under some natural hypotheses on the direction of the displacement, with the background pressure ﬁxed. We also prove in dimensions two and three a stability result for the (nonlinear) Saint Venant model in the case of displaced solution measurements. The techniques introduced show the diﬃculties of using hybrid imaging techniques for non-linear inverse problems.
Creating Walk-though Images from Traffic Video Sequence

—In this paper, we present a novel scheme which can let users experience the feel of navigating into traffic scenes. Constructing 3D scene models from video for this process is difficult, due to the complex traffic conditions such as moving background and changing foreground objects. We develop the Tour Into the Traffic Video scheme (TITV) including mainly 4 stages: foreground traffic elements segmentation, background scene inpainting, scene models construction and walkthrough images rendering. For the first stage, we improved the Fast Two Cycle (FTC) level set method based on narrow band superpixels to automatically extract foreground traffic elements in the video sequence. A competition term based on maximum a posteriori is also integrated into the FTC level set framework. After the segmentation stage, the optical flows in occlusion foreground regions are inpainted by BP neutral networks and Gaussian regression process to keep their continuity. Gaussian Mixture Model is applied to inpaint the background since the pixels are corresponded by inpainted optical flows. Control points on road edges are specified to construct our TITV scene models. The 3D scene structure is constructed for each frame based on the control points. Foreground models such as vehicles and traffic signs are projected onto the background scene models. Users can change their viewpoints according to their own interpretations. Panoramic mode and non-photorealistic mode can also be switched. A database is constructed to represent the connection relationships of traffic elements for each frame. The touring trajectory is shown on the maps since we have GPS coordinates. Comprehensive user studies and comparisons well demonstrate the effectiveness of the proposed framework for giving user touring experience.
How The Urban Microclimate And Outdoor Thermal Comfort Can Affect Intra-City Mobility Patterns: Evidence From New York City

Employing urban planning and design to promote active travel modes, such as walking and biking, are important for decarbonizing urban mobility. This paper proposes a modeling framework that investigates the interdependencies between the built environment and travel behaviors through the lens of urban microclimates. We combine travel data, built environment data, and Universal Thermal Climate Index (UTCI) calculations for New York City and train a predictive model for intra-city mobility patterns. Then, impacts of UTCI features are studied through a sensitivity analysis and a spatial heterogeneity analysis. Results show that impacts of UTCI features can account for up to 4% change in the choice of active travel mode in dense urban areas. Also, impacts of microclimate vary across different travel contexts regarding season, time of day, activity, built environment, and traveler type. Our methodology and findings can inform future decision-making of microclimate-oriented spatial planning and design interventions.
Identi cation of a Metabolism-Related Risk Signature to Predict the Outcome and Immune In ltration of Esophageal Squamous Cell Carcinoma

Yu Liu Chinese Academy of Medical Sciences and Peking Union Medical College Liyu Wang Chinese Academy of Medical Sciences and Peking Union Medical College Hengchang Liu Chinese Academy of Medical Sciences and Peking Union Medical College He Tian Chinese Academy of Medical Sciences and Peking Union Medical College Tao Fan Chinese Academy of Medical Sciences and Peking Union Medical College Liyan Xue Chinese Academy of Medical Sciences and Peking Union Medical College Bo Zheng Chinese Academy of Medical Sciences and Peking Union Medical College Fengwei Tan Chinese Academy of Medical Sciences and Peking Union Medical College Qi Xue Chinese Academy of Medical Sciences and Peking Union Medical College Shugeng Gao Chinese Academy of Medical Sciences and Peking Union Medical College chunxiang li (  lichunxiang@cicams.ac.cn ) Chinese Academy of Medical Sciences and Peking Union Medical College Jie He Chinese Academy of Medical Sciences and Peking Union Medical College
in The Relationship between Integral Assessment of Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease and Clinical and Functional Status in the Acute Period of Ischemic Stroke

The Relationship between Integral Assessment of Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease and Clinical and Functional Status the Acute Period of Ischemic Stroke Cerebral small vessel disease (CSVD) is the most common neurological pathological process and contributes to the process of aging and to the development of dementia and stroke. At the same time, the role of CSVD as a factor influencing the course of acute ischemic stroke (IS) has been little studied. There is no generally accepted magnetic resonance imaging (MRI) scale for the integrated assessment of CSVD markers. Objective: to carry out an integrated assessment of the MRI manifestations of CSVD in acute ischemic stroke and to analyze a correlation of both individual markers and the final indicator with the clinical and functional status of patients. Patients and methods. 100 patients with acute IS were examined. All patients underwent standard clinical, laboratory and instrumental exam-inations, as well as brain MRI estimating the number of lacunae, visible perivascular spaces (PVSs) and leukoaraiosis. The number of cerebral microbleeds (CMBs) was additionally calculated in 57 patients. Integral scale scores were calculated by gradation and summation of four MRI markers of CSVD. Results. The patients with acute IS showed the high representativeness of individual markers for CSVD. The values of MRI markers for CSVD correlated with age, education level, and cardiovascular parameters in patients. An integrated CSVD severity assessment scale was developed. The overall manifestations of CSVD, which were assessed using this scale, were associated with the severity of a stenotic process in the brachy-cephalic arteries, with BP levels at admission, ejection fraction, hyperglycemia, and atherogenic index of blood lipids. The high CSVD score was also correlated with low mobility and more severe disability in patients being discharged from hospital. The high severity of CSVD was associated with lower neurological deficit regression during inpatient treatment. Subgroup analysis showed the greatest negative impact of CSVD on the severity of stroke in female patients, young and middle-aged ones, diabetics, as well as in patients with noncardioembolic stroke, a small-sized focus, and intima-media thickening. overall manifestations of CSVD calculated using the original scale based on the analysis of the degree of lacunae, PVSs, leukoaraiosis, and CMBs are associated with premorbid cardiovascular parameters in a patient and are important indicators for the neurological, cognitive, and functional outcomes of acute IS.
2 . 1 The Bak-Karch ansatz

We analyze in the spirit of hep-th/0110039 the possible existence of supersymmetric Dp-D̄p brane systems in flat ten dimensional Minkowski space. For p = 3, 4 we show that besides the solutions related by T-duality to the D2-D̄2 systems found by Bak and Karch there exist other ansatz whose compatibility is shown from general arguments and that preserve also eight supercharges, in particular a D4-D̄4 system with D2-branes dissolved on it and Taub-NUT charge. We carry out the explicit construction in Weyl basis of the corresponding Killing spinors and conjecture the existence of new solutions for higher dimensional branes with some compact directions analogous to the supertube recently found.
Institutional Knowledge at Singapore Management University What makes professors credible: The effect of demographic characteristics and ideological beliefs

Five studies are conducted to examine how ideology and perceptions regarding gender, race, caste, and affiliation status affect how individuals judge researchers’ credibility. Support is found for predictions that individuals judge researcher credibility according to their egalitarian or elitist ideologies and according to status cues including race, gender, caste, and university affiliation. Egalitarians evaluate low-status researchers as more credible than high-status researchers. Elitists show the opposite pattern. Credibility judgments affect whether individuals will interpret subsequent ambiguous events in accor-dance with the researcher’s findings. Effects of diffuse status cues and ideological beliefs may be mitigated when specific status cues are presented to override stereotypes.
Human Action Recognition Based On Multiview

: This paper presents the different approaches and datasets for recognition of human actions under view changes. Visual analysis of human action is currently one of the most active research topics. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc. Human action analysis concerns the detection, tracking and recognition of people, and more generally, the understanding of human behaviors, from image sequences involving humans. We consider the task of labeling videos containing human motion with action classes. The interest in the topic is motivated by the promise of many applications, both offline and online. In this paper, we specifically addressed multi-view front and top independent video analysis, with human action recognition for training and detection of different actions.
IINTERLEUKIN-6 LEVELS IN CASES OF ANGINA PECTORIS

: Chest pain is well reported in emergency settings and angina pectoris reveals sever underlying cardiovascular disease. The search for various inflammatory markers including interleukin 6 (IL-6) to label it is under investigation. Objective: To determine the frequency of raised Interleukin-6 levels in patients with angina pectoris. Methods; This cross sectional study was conducted during July to December 2018 at Benazir Bhutto Hospital, Rawalpindi; where 100 cases of angina pectoris with normal ECG and cardiac enzymes were included and their detailed sociodemographic and clinical history was taken. Three ml of venous blood was collected in a sterilized disposable syringe and assessed for IL-6 level. The values more than 5 ng/ml were considered as raised. Results: In this study there were total 100 cases out of which 66 (66%) were males and 34 (34%) females. The mean age was 54.19±6.72 years. There were 70 (70%) cases that had age more than 50 years. There were 18 (18%) cases with DM, 28 (28%) had HTN and 42 (42%) had smoking history positive. Raised IL-6 was seen in 72 (72%) of cases. Raised IL-6 were almost equal in both genders (p= 0.89) while it affected more with age group of more than 50 years comprising 80% of its respective group with p= 0.07. IL-6 levels were significantly higher in cases with DM and HTN with p values of 0.04 and 0.02 respectively while this difference was not significant in smokers and non smokers. Conclusion; Angina pectoris is a first step towards to ischemic cardiac diseases. Raised IL-6 levels are almost seen in every 3 out of 4 (72%) cases. These levels are significantly raised in cases with DM and HTN.
02 12 38 6 v 1 3 0 D ec 2 00 2 DESY 02-155 Measuring the FSR – inclusive π + π − cross section ⋆

Final state radiation (FSR) in pion–pair production cannot be calculated reliably because of the composite structure of the pions. However, FSR corrections have to be taken into account for a precise evaluation of the hadronic contribution to g − 2 of the muon. The role of FSR in both energy scan and radiative return experiments is discussed. It is shown how FSR influences the pion form factor extraction from experimental data and, as a consequence, the evaluation of a μ . In fact the O(α) FSR corrections should be included to reach the precision we are aiming at. We argue that for an extraction of the desired FSR–inclusive cross section σ (γ) had a photon–inclusive scan measurement of the “e e → ππ + photons” cross section is needed. For exclusive scan and radiative return measurements in contrast we have to rely on ad hoc FSR models if we want to obtain either σ (γ) had or the FSR–exclusive cross section σ (0) had. We thus advocate to consider seriously precise photon–inclusive energy scan measurements at present and future low energy ee–facilities. Then together with radiative return measurements from DAΦNE and BABAR and forthcoming scan measurements at VEPP-2000 we have a good chance to substantially improve the evaluation of a μ in the future.
KEGG Analysis of De-noised Mutual Information based Microarray Data using Empirical Mode Decomposition

— The expression of thousands of genes can be monitored in parallel with the help of the Microarray Technique. Clustering methods have become a key step in microarray data analysis because it can identify groups of genes or samples displaying a similar expression profile. The clustering approach used here is the Fuzzy C means method in which one gene or one piece of data can belong to more than one cluster. Clustering co-expressed genes usually requires the definition of ‘distance’ or ‘similarity’ between measured datasets, the most common choices being Pearson correlation or Euclidean distance. Here we use mutual information i.e. nonlinear distance measurement in cluster analysis and visualization of large-scale gene expression data. Noise can be present in the gene expression data. The noise in data would affect the clustering results. Noise can be removed from gene expression data by a simple denoising scheme called Emperical Mode Decomposition(EMD. FCM method is combined with EMD for clustering microarray data in order to reduce the effect of noise. The results shows the clustering structures of de-noised data are more reasonable and genes have tighter association with their clusters. After obtaining the results i.e. with EMD and without EMD, the validation has been done based based on KEGG pathways. The aim is to determine whether the clusters produced by the different dissimilarity measures are enriched with KEGG pathways.