Miracle-dz/UniFAR-FTUs huggingface dataset:
R-U policy frontiers for health data de-identification

OBJECTIVE The Health Insurance Portability and Accountability Act Privacy Rule enables healthcare organizations to share de-identified data via two routes. They can either 1) show re-identification risk is small (e.g., via a formal model, such as k-anonymity) with respect to an anticipated recipient or 2) apply a rule-based policy (i.e., Safe Harbor) that enumerates attributes to be altered (e.g., dates to years). The latter is often invoked because it is interpretable, but it fails to tailor protections to the capabilities of the recipient. The paper shows rule-based policies can be mapped to a utility (U) and re-identification risk (R) space, which can be searched for a collection, or frontier, of policies that systematically trade off between these goals. METHODS We extend an algorithm to efficiently compose an R-U frontier using a lattice of policy options. Risk is proportional to the number of patients to which a record corresponds, while utility is proportional to similarity of the original and de-identified distribution. We allow our method to search 20 000 rule-based policies (out of 2(700)) and compare the resulting frontier with k-anonymous solutions and Safe Harbor using the demographics of 10 U.S. states. RESULTS The results demonstrate the rule-based frontier 1) consists, on average, of 5000 policies, 2% of which enable better utility with less risk than Safe Harbor and 2) the policies cover a broader spectrum of utility and risk than k-anonymity frontiers. CONCLUSIONS R-U frontiers of de-identification policies can be discovered efficiently, allowing healthcare organizations to tailor protections to anticipated needs and trustworthiness of recipients.
Comprehensive noise analysis in PPG read-out chains

This paper presents an analytical comparison of two commonly used read-out structures for pulse oximeters, in a wearable application context. The objective is to analyze and highlight the trade-off between the SNR and the power consumption. The proposed analysis clarifies, by the means of analytical equations, the impact of the input photocurrent, the photodiode parasitic capacitance, the feedback impedance and the system bandwidth on the SNR of the two proposed front-end topologies. The paper shows that using a charge-amplifier in the front-end requires 8 times less LED power than a transresistance amplifier for achieving the same SNR.
Characterization of Guinea Pig C-Type Virus 1

Summary A guinea pig C-type virus was successfully isolated from primary and passaged cells derived from leukemic and normal strain 2, and strain 2-Hartley hybrid guinea pigs. Activation of the virus required BUDR treatment. Morphologically, both in infected cells and in negatively stained preparations, the guinea pig C-type virus resembles other members of the C-type virus group. Purified guinea pig C-type virus labeled with 3H-uridine had an equilibrium density of 1.17 ± 0.01 g/ml in sucrose.
ExpoSE: practical symbolic execution of standalone JavaScript

JavaScript has evolved into a versatile ecosystem for not just the web, but also a wide range of server-side and client-side applications. With this increased scope, the potential impact of bugs increases. We introduce ExpoSE, a dynamic symbolic execution engine for Node.js applications. ExpoSE automatically generates test cases to find bugs and cover as many paths in the target program as possible. We discuss the specific challenges for symbolic execution arising from the widespread use of regular expressions in such applications. In particular, we make explicit the issues of capture groups, backreferences, and greediness in JavaScript's flavor of regular expressions, and our models improve over previous work that only partially addressed these. We evaluate ExpoSE on three popular JavaScript libraries that make heavy use of regular expressions, and we report a previously unknown bug in the Minimist library.
Human-model hybrid Korean air quality forecasting system

ABSTRACT The Korean national air quality forecasting system, consisting of the Weather Research and Forecasting, the Sparse Matrix Operator Kernel Emissions, and the Community Modeling and Analysis (CMAQ), commenced from August 31, 2013 with target pollutants of particulate matters (PM) and ozone. Factors contributing to PM forecasting accuracy include CMAQ inputs of meteorological field and emissions, forecasters’ capacity, and inherent CMAQ limit. Four numerical experiments were conducted including two global meteorological inputs from the Global Forecast System (GFS) and the Unified Model (UM), two emissions from the Model Intercomparison Study Asia (MICS-Asia) and the Intercontinental Chemical Transport Experiment (INTEX-B) for the Northeast Asia with Clear Air Policy Support System (CAPSS) for South Korea, and data assimilation of the Monitoring Atmospheric Composition and Climate (MACC). Significant PM underpredictions by using both emissions were found for PM mass and major components (sulfate and organic carbon). CMAQ predicts PM2.5 much better than PM10 (NMB of PM2.5: -20~-25%, PM10: -43~-47%). Forecasters’ error usually occurred at the next day of high PM event. Once CMAQ fails to predict high PM event the day before, forecasters are likely to dismiss the model predictions on the next day which turns out to be true. The best combination of CMAQ inputs is the set of UM global meteorological field, MICS-Asia and CAPSS 2010 emissions with the NMB of -12.3%, the RMSE of 16. 6μ/m3 and the R2 of 0.68. By using MACC data as an initial and boundary condition, the performance skill of CMAQ would be improved, especially in the case of undefined coarse emission. A variety of methods such as ensemble and data assimilation are considered to improve further the accuracy of air quality forecasting, especially for high PM events to be comparable to for all cases. Implications: The growing utilization of the air quality forecast induced the public strongly to demand that the accuracy of the national forecasting be improved. In this study, we investigated the problems in the current forecasting as well as various alternatives to solve the problems. Such efforts to improve the accuracy of the forecast are expected to contribute to the protection of public health by increasing the availability of the forecast system.
E 4 — Machine Learning

Machine learning’s focus on ill-defined problems and highly flexible methods makes it ideally suited for KDD applications. Among the ideas machine learning contributes to KDD are the importance of empirical validation, the impossibility of learning without a priori assumptions, and the utility of limited-search or limited-representation methods. Machine learning provides methods for incorporating knowledge into the learning process, changing and combining representations, combatting the curse of dimensionality, and learning comprehensible models. KDD challenges for machine learning include scaling up its algorithms to large databases, using cost information in learning, automating data pre-processing, and enabling rapid development of applications. KDD opens up new directions for machine learning research, and brings new urgency to others. These directions include interfacing with the human user and the database system, learning from non-attribute-vector data, learning partial models, and learning continuously from an open-ended stream of data. E4.1 Use of machine learning methods for KDD Machine learning is characterized by a focus on complex representations, ill-defined problems, and search-based methods. Representations studied include most of those described in Section B2, but particularly decision trees, sets of propositional or first-order rules, sets of instances, clusters, concept hierarchies and probabilistic networks. Ill-defined problems studied include generalizing from a set of tuples in the absence of a known model structure [C5.1], clustering [C5.5], combining logic theories of a domain with learning (de Raedt, 1996), and learning from delayed feedback in very large decision spaces (Sutton & Barto, 1998). Search methods [B8] used for learning include greedy search, gradient descent, expectation maximization, genetic algorithms, and some forms of lookahead and pruned breadth-first search. Other types of search frequently found in artificial intelligence, like best-first search and simulated annealing, tend to see less use in machine learning, for reasons discussed below. The flexibility of most machine learning methods makes them well suited to applications where little is known a priori about the domain, and/or relevant knowledge is hard to elicit. This flexibility also means they are often able to successfully learn from data that was not gathered by a purposely designed experimental procedure, but rather obtained by some process whose end goal was not necessarily knowledge discovery. The flip side of this is that theoretical analysis of machine learning methods is often difficult, and strong guarantees regarding the correctness of results are
Musical Structure Retrieval by Aligning Self-Similarity Matrices

We propose a new retrieval system based on musical structure using symbolic structural queries. The aim is to compare musical form in audio files without extracting explicitly the underlying audio structure. From a given or arbitrary segmentation, an audio file is segmented. Irrespective of the audio feature choice, we then compute a selfsimilarity matrix whose coefficients correspond to the estimation of the similarity between entire parts, obtained b y local alignment. Finally, we compute a binary matrix from the symbolic structural query and compare it to the audio segmented matrix, which provides a structural similarity score. We perform experiments using large databases of audio files, and prove robustness to possible imprecisions in the structural query.
Home-Based Palliative Care Study

Abstract Purpose: To examine differences in site of death and costs of services by primary diagnosis for patients receiving home-based palliative care as compared to usual care at the end of life verity of illness, service use, and site of death. Design and Methods: A nonequivalent group design was employed with 298 terminally ill patients diagnosed with cancer, CHF, or COPD enrolled. The treatment group received an interdisciplinary home-based palliative care program and the comparison group received usual Kaiser Permanente services. Data collected included patient demographics, se- Results: Among all diseases, patients enrolled in palliative care were more likely to die at home. Enrollment in palliative care was significant associated with cost reductions for patients with cancer, COPD, and CHF. No significant difference was found between diagnostic groups in terms of magnitude of cost savings. Implications: Provision of interdisciplinary home-based palliative care at end of life can effectively increase the likelihood of dying at home for patients with CHF, COPD, and cancer while realizing significant COSt savings.
Care or Control?: Defining Learners' Needs for Lifelong Learning

Concerns about non-participation in lifelong learning may indicate an emerging moral authoritarianism arising from pessimism about the future. Low expectations of potential for social progress, human agency and learners' motivation to take part in formal learning, exacerbate moves towards a ‘minimalist pedagogy’ regulated by government agencies and encourages the idea that lifelong learning should be compulsory for adults ‘at risk’.
Interconnecting bilayer networks

A typical complex system should be described by a supernetwork or a network of networks, in which the networks are coupled to some other networks. As the first step to understanding the complex systems on such more systematic level, scientists studied interdependent multilayer networks. In this letter, we introduce a kind of interdependent multilayer networks, i.e., interconnecting networks, for which the component networks are coupled each other by sharing some common nodes. Based on the empirical investigations, we revealed a common feature of such interconnecting networks, namely, the networks with smaller averaged topological differences of the interconnecting nodes tend to share more nodes. A very simple node-sharing mechanism is proposed to analytically explain the observed feature of the interconnecting networks.
Teachers’ perceptions of school culture as a barrier to shared decision‐making (SDM) in Egypt’s secondary schools

This article explores cultural factors impeding members of the school community from engaging in shared decision‐making (SDM) processes. It reports on findings from a larger qualitative research study of SDM in Egypt’s secondary schools. The purpose of the study was to identify barriers to SDM, using data collected from nine general secondary schools in Damietta County. Identifying these barriers is crucial as Egypt moves towards decentralisation with SDM being a key component in this move. Whilst SDM has been extensively researched in Western countries, little is known about it in the Egyptian context. The findings indicate that the highly centralised nature of the educational system was perceived to be a major barrier. However, school culture also seemed to play a significant role in hindering both teachers and head teachers from engaging in SDM processes. There seems to be a mismatch between the collegial, collaborative culture embedded in SDM as an imported policy and the more traditional cultural norms prevalent in Egyptian schools.
A heuristic approach for overlay content-caching network design in 5G wireless networks

With a wide variety of mobile applications and social networks, there is a growing demand for anytime, anywhere access to high quality video and other multimedia content. Fifth generation wireless networks are aiming to keep up with user demands by incorporating new spectrum bands at high frequencies, which results in shrinking of cell sizes and eventually leads to small cells. Dense deployment of small cells and base stations open up new opportunities for faster and energy-efficient access to content. Content caching at small cell base stations, for the purpose of reducing delay, has been studied in several previous works. In our previous work, we used Integer Linear Programming (ILP) models to show that content caching can also reduce energy consumption of User Equipment (UE). In particular, selecting a subset of small cell base stations to cache the content, reduces uplink energy budget, cuts down caching energy budget and simplifies management of distributed caches. Yet, ILP based methods are powerful for obtaining benchmark results but they are computationally intensive for large size problems. In this paper, we propose a particle swarm optimization (PSO) based scheme to deploy an overlay small cell content-caching network with the aim of reducing UE energy consumption. We show that PSO is computationally efficient while it slightly increases energy consumption with respect to the ILP-based methods.
Toward Human-Carnivore Coexistence: Understanding Tolerance for Tigers in Bangladesh

Fostering local community tolerance for endangered carnivores, such as tigers (Panthera tigris), is a core component of many conservation strategies. Identification of antecedents of tolerance will facilitate the development of effective tolerance-building conservation action and secure local community support for, and involvement in, conservation initiatives. We use a stated preference approach for measuring tolerance, based on the ‘Wildlife Stakeholder Acceptance Capacity’ concept, to explore villagers’ tolerance levels for tigers in the Bangladesh Sundarbans, an area where, at the time of the research, human-tiger conflict was severe. We apply structural equation modeling to test an a priori defined theoretical model of tolerance and identify the experiential and psychological basis of tolerance in this community. Our results indicate that beliefs about tigers and about the perceived current tiger population trend are predictors of tolerance for tigers. Positive beliefs about tigers and a belief that the tiger population is not currently increasing are both associated with greater stated tolerance for the species. Contrary to commonly-held notions, negative experiences with tigers do not directly affect tolerance levels; instead, their effect is mediated by villagers’ beliefs about tigers and risk perceptions concerning human-tiger conflict incidents. These findings highlight a need to explore and understand the socio-psychological factors that encourage tolerance towards endangered species. Our research also demonstrates the applicability of this approach to tolerance research to a wide range of socio-economic and cultural contexts and reveals its capacity to enhance carnivore conservation efforts worldwide.
ST485: AN RS-485 BASED INTERFACE WITH LOWER DATA BIT ERRORS

ST485 is a RS-485 based interface designed for multipoint differential transmission on a single twisted pair cable. It allows half duplex bi-directional transmission, long cable length and high data rate. Typical applications include LANs, industrial (PLC devices), automotive and computer interfaces. The system evolution in the data communication field leads to the development of faster devices with lower data bit errors and ST485 meets all these requirements.
Increased prevalence and severity of radiographic hand osteoarthritis in patients with HIV-1 infection associated with metabolic syndrome: data from the cross-sectional METAFIB-OA study

Objective To determine radiographic hand osteoarthritis (HOA) prevalence in patients with HIV-1 infection in comparison with the general population and to address whether metabolic syndrome (MetS) may increase the risk of HOA during HIV-1 infection. Patients Patients with HIV-1 infection and MetS (International Diabetes Federation, IDF criteria) aged 45–65 years were matched by age and gender to HIV-1-infected subjects without MetS and underwent hand radiographs. Framingham OA cohort was used as general population cohort. Methods Radiographic HOA was defined as Kellgren–Lawrence (KL) score ≥2 on more than one joint. Radiographic severity was assessed by global KL score and number of OA joints. HOA prevalence was compared with that found in the Framingham study, stratified by age and sex. Logistic and linear regression models were used to determine the risk factors of HOA in patients with HIV-1 infection. Results 301 patients (88% male, mean age 53.4±5.0 years) were included, 152 with MetS and 149 without it. Overall, HOA prevalence was 55.5% and was higher for those with MetS than those without it (64.5% vs 46.3%, p=0.002). When considering men within each age group, HOA frequency was greater in patients with HIV-1 infection than the general population (all ages: 55.8% vs 38.7%; p<0.0001), due to the subgroup with MetS (64.9%; p<0.0001), as well as the subgroup without MetS, although not significant (46.6%; p=0.09). Risk of HOA was increased with MetS (OR 2.23, 95% 95% CI 1.26% to 3.96%) and age (OR 1.18, 95% CI 1.12 to 1.25). HOA severity was greater for patients with MetS than those without. HOA was not associated with previous or current exposure to protease inhibitors or HIV infection-related markers. Conclusions HOA frequency is greater in patients with HIV-1 infection, especially those with MetS, than the general population. Trial registration number NCT02353767.
Factors That Affect Students’ Capacity to Fulfill the Role of Online Learner

Because most undergraduate students are digital natives, it is widely believed that they will succeed in online courses. But factors other than technology also affect students’ ability to fulfill the role of online learner. Self-reported data from a sample of more than 200 undergraduates across multiple online courses indicate that students generally view themselves as having attributes that equip them for online learning. Additionally, course-level factors affect students’ online learning experiences. Specifically, students in qualitative (vs. quantitative) courses and in introductory (vs. advanced) classes reported more positive perceptions of their online learning and various aspects of their coursework.
2$\,\times\,$ 2 Microstrip Patch Antenna Array Fed by Substrate Integrated Waveguide for Radar Applications

A 2 × 2 microstrip patch antenna array fed by a substrate integrated waveguide (SIW) feeding network for 24-GHz radar applications is proposed in this letter. The microstrip patches are aperture-coupled with the feeding network consisting of a perpendicular coax-to-SIW transition and two Y-junction power dividers. The antenna design is performed in the full-wave electromagnetic solver ANSYS HFSS and verified by measurement. At the operation frequency 24 GHz, a fabricated prototype has a gain of 11.1 dBi and a low sidelobe level below -22 dB in the E-plane and -33 dB in the H-plane.
Nowcasting the Finnish economy with a large Bayesian vector autoregressive model

Timely and accurate assessment of current macroeconomic activity is crucial for policymakers and other economic agents. Nowcasting aims to forecast the current economic situation ahead of official data releases. We develop and apply a large Bayesian vector autoregressive (BVAR) model to nowcast quarterly GDP growth rate of the Finnish economy. We study the BVAR model’s out-of-sample performance at different forecasting horizons, and compare to various bridge models and a dynamic factor model.
ll 111 U I 1 Simple Protocol Processing for High-Bandwidth Low-Latency Networking

Protocol and operating system overheads have become the limiting factor for communications performance on fast networks such as ATM. A large component of these overheads stems from the protocol redundancy that arises when layering higher level protocols on top of lower level ones. ATM, for example, requires speci c mechanisms for connection management, ow control, congestion avoidance, and segmentation and reassembly. With these mechanisms in place, it is relatively simple and inexpensive to provide for reliable sequenced delivery at the network interface level, making similar functionality in higher-level protocols such as TCP/IP redundant. In this paper we present a protocol architecture speci cally tailored for communication over high-bandwidth low-latency local or metropolitan ATM networks. Our architecture yields high performance by eliminating protocol redundancy and by exploiting common-case communication behavior. With this approach, we can combine the functionality typically found in four separate layers of the ISO model | data link through session | in a single pass over the data, delivering high throughput and low latency. Our protocol architecture requires minimal hardware support from the network interface and switch fabric, yet e ciently provides services such as segmentation and reassembly, ow control, congestion avoidance, and error recovery. We have implemented our protocol architecture on a switch-based ATM network consisting of DECstation 5000/200 workstations running the Mach 3.0 operating system. Our implementation achieves latencies and bandwidths close to the physical limitations imposed by the hardware, yet o ers applications a high-level reliable transport interface.