Miracle-dz/UniFAR-FTUs huggingface dataset:
Heart disease prediction using machine learning

Today, heart disease is a severe hazard to one's health that may result in death or a long-term impairment because of the complicated blend of clinical and pathological evidence required to diagnose it. Despite the fact that medical diagnosis is a complex activity that plays a critical role in saving human lives, there is a dearth of appropriate tools to detect hidden linkages and patterns in electronic health data. Computer-based automated decision support systems are needed to lower the costs of clinical testing because of this complexity. " In this study, we provide a method for predicting the presence of cardiac disease based on clinical data collected from patients. In this study, the primary goal is to develop a predictive model for heart disease based on a combination of characteristics (risk factors). Different machine learning classification strategies will be deployed and evaluated based on conventional performance metrics such as accuracy in order to compare different machine learning algorithms.
High-dimensional association detection in large scale genomic data

Joint analyses of genomic datasets obtained in multiple different conditions are essential for understanding the biological mechanism that drives tissue-specificity and cell differentiation, but they still remain computationally challenging. To address this we introduce CLIMB (Composite LIkelihood eMpirical Bayes), a statistical methodology that learns patterns of condition-specificity present in genomic data. CLIMB provides a generic framework facilitating a host of analyses, such as clustering genomic features sharing similar condition-specific patterns and identifying which of these features are involved in cell fate commitment. We apply CLIMB to three sets of hematopoietic data, which examine CTCF ChIP-seq measured in 17 different cell populations, RNA-seq measured across constituent cell populations in three committed lineages, and DNase-seq in 38 cell populations. Our results show that CLIMB improves upon existing alternatives in statistical precision, while capturing interpretable and biologically relevant clusters in the data.
University of Groningen PAT Workbench: Annotation and evaluation of text and pictures in multimodal instructions

This paper presents a tool to investigate the design of multimodal instructions (MIs), i.e., instructions that contain both text and pictures. The benefit of including pictures in information presentation has been established, but the characteristics of those pictures and of their textual counterparts and the relation(s) between them have not been researched in a systematic manner. We present the PAT Workbench, a tool to store, annotate and retrieve MIs based on a validated coding scheme with currently 42 categories that describe instructions in terms of textual features, pictorial elements, and relations between text and pictures. We describe how the PAT Workbench facilitates collaborative annotation and inter-annotator agreement calculation. Future work on the tool includes expanding its functionality and usability by (i) making the MI annotation scheme dynamic for adding relevant features based on empirical evaluations of the MIs, (ii) implementing algorithms for automatic tagging of MI features, and (iii) implementing automatic MI evaluation algorithms based on results obtained via e.g. crowdsourced assessments of MIs.
An Overview of Kernelization Algorithms for Graph Modification Problems

Kernelization algorithms for graph modification problems are important ingredients in parameterized computation theory. In this paper, we survey the kernelization algorithms for four types of graph modification problems, which include vertex deletion problems, edge editing problems, edge deletion problems, and edge completion problems. For each type of problem, we outline typical examples together with recent results, analyze the main techniques, and provide some suggestions for future research in this field.
Explorer Inspecting temporal scales with non-linear signal features : A way to extract more information from brain activity ?

Citation for published version: Escudero, J 2015, 'Inspecting temporal scales with non-linear signal features: A way to extract more information from brain activity? ' General rights Copyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorer content complies with UK legislation. If you believe that the public display of this file breaches copyright please contact openaccess@ed.ac. uk providing details, and we will remove access to the work immediately and investigate your claim.
Consistent Selection of Tuning Parameters via Variable Selection Stability (Supplementary Material)

In this supplementary material, we provide Lemmas 2 and 3 and their proofs. Suppose that x 1 ,. , x n are i.i. d. from a probability distribution with mean 0 and finite covariance matrix C = (C jk). Assumption S1: Assume that x 1 has finite fourth moment, that is, E(x 1i x 1 j x 1k x 1l) is finite for any 1 ≤ i, j, k, l ≤ p. Lemma 2 Suppose that Assumption S1 is met. Assumptions 1 and 2 are satisfied by the lasso regression and the SCAD with r n = n −1/2 and s n = o(1) under the assumptions in Zhao and Yu (2006) or Fan and Li (2001), and by the adaptive lasso with r n = n −1 and s n = n −1/2 under the assumptions in Zou (2006), on the random splitting subsamples generated in Algorithm 1.
Guide to Bluetooth Security

This whitepaper provides an overview of the cryptographic procedures and algorithms for the Simple Pairing feature in the Lisbon release of the Bluetooth Core Specification. The purpose of this whitepaper is to provide information to the security community for a peer review prior to the adoption of the Lisbon release. 2006-08-03 Status Released Version Revision V10r00 Document File Name Simple Pairing_WP Document Owner Core Specification Working Group E-mail Address radio- feedback@bluetooth.org
SLIQ: A Fast Scalable Classi(cid:12)er for Data Mining

Classi(cid:12)cation is an important problem in the emerging (cid:12)eld of data mining. Although classi(cid:12)cation has been studied extensively in the past, most of the classi(cid:12)cation algorithms are designed only for memory-resident data, thus limiting their suitability for data mining large data sets. This paper discusses issues in building a scalable classi-(cid:12)er and presents the design of SLIQ 1 , a new classi(cid:12)er. SLIQ is a decision tree classi(cid:12)er that can handle both numeric and categorical attributes. It uses a novel pre-sorting technique in the tree-growth phase. This sorting procedure is integrated with a breadth-(cid:12)rst tree growing strategy to enable classi(cid:12)cation of disk-resident datasets. SLIQ also uses a new tree-pruning algorithm that is inexpensive, and results in compact and accurate trees. The combination of these techniques enables SLIQ toscale for large data sets and classify data sets irrespective of the number of classes, attributes, and examples (records), thus making it an attractive tool for data mining.
The franc shock and Swiss GDP: how long does it take to start feeling the pain?

ABSTRACT The article addresses the question on what is the typical time horizon over which a full transmission of movements in the real exchange rate takes place into real economy. We base our analysis on the mixed-frequency small-scale dynamic factor model (DFM) proposed by Siliverstovs in 2012 fitted to the Swiss data. In this article, we augment the benchmark model with the real exchange rate of the Swiss franc vis-a-vis currencies of its 24 trading partners, while keeping the rest of model specification intact. We are interested in investigating the relationship between the common latent factor, representing the Swiss business cycle, and the real exchange rate. We explore the temporal relationship between these two variables by varying the time lag with which the real exchange rate enters the factor model by recording the magnitude and statistical significance of the factor loading coefficient in the equation pertaining to the real exchange rate variable. Our main conclusion is that the fluctuations in the exchange rate start influencing real economy after 1 month and their effect is practically over after 13 months. The largest effect is recorded at the time horizon of about 6 to 9 months.
Simulation of typhoon-induced storm tides and wind waves for the northeastern coast of Taiwan using a tide-surge-wave coupled model

The storm tide is a combination of the astronomical tide and storm surge, which is the actual sea water level leading to flooding in low-lying coastal areas. A full coupled modeling system (Semi-implicit Eulerian-Lagrangian Finite-Element model coupled with Wind Wave Model II, SELFE-WWM-II) for simulating the interaction of tide, surge and waves based on an unstructured grid is applied to simulate the storm tide and wind waves for the northeastern coast of Taiwan. The coupled model was driven by the astronomical tide and consisted of main eight tidal constituents and the meteorological forcings (air pressure and wind stress) of typhoons. SELFE computes the depth-averaged current and water surface elevation passed to WWM-II, while WWM-II passes the radiation stress to SELFE by solving the wave action equation. Hindcasts of wind waves and storm tides for five typhoon events were developed to validate the coupled model. The detailed comparisons generally show good agreement between the simulations and measurements. The contributions of surge induced by wave and meteorological forcings to the storm tide were investigated for Typhoon Soudelor (2015) at three tide gauge stations. The results reveal that the surge contributed by wave radiation stress was 0.55 m at Suao Port due to the giant offshore wind wave (exceeding 16.0 m) caused by Typhoon Soudelor (2015) and the steep sea-bottom slope. The air pressure resulted in a 0.6 m surge at Hualien Port because of an inverted barometer effect. The wind stress effect was only slightly significant at Keelung Port, contributing 0.22 m to the storm tide. We conclude that wind waves should not be neglected when modeling typhoon-induced storm tides, especially in regions with steep sea-bottom slopes. In addition, accurate tidal and meteorological forces are also required for storm tide modeling.
Symbolic Automata Mixed syntactic / semantic similarity analysis of executables

Symbolic Automata Mixed syntactic/semantic similarity analysis of executables Abstract We introduce a model for mixed syntactic/semantics approximation of programs based on symbolic finite automata (SFA). The edges of SFA are labeled by predicates whose semantics specifies the de-notations that are allowed by the edge. We introduce the notion of abstract symbolic finite automaton (ASFA) where approximation is made by abstract interpretation of symbolic finite automata, acting both at syntactic (predicate) and semantic (denotation) level. We investigate in the details how the syntactic and semantics abstractions of SFA relate to each other and contribute to the determination of the recognized language. Then we introduce a family of transformations for simplifying ASFA. We apply this model to prove properties of commonly used tools for similarity analysis of binary executables. Following the structure of their control flow graphs, disassembled binary executables are represented as (concrete) SFA, where states are program points and predicates represent the (possibly infinite) I/O semantics of each basic block in a constraint form. Known tools for binary code analysis are viewed as specific choices of symbolic and semantics abstractions in our framework, making symbolic finite automata and their abstract interpretations a unifying model for comparing and reasoning about soundness and completeness of analyses of low-level code.
Leveraging Facebook ’ s Advertising Platform to Monitor Stocks of Migrants Forthcoming in Population and Development

Online advertising is the main source of revenue for social media companies. Facebook allows advertisers to target users with certain characteristics, such as age, gender, country of origin, education level, or topical interest. Before an ad is launched Facebook’s advertising platform provides an estimate of how many users match the provided criteria. This functionality, akin to a ‘digital census’ over Facebook users, has so far been untapped for demographic research. We show the feasibility of estimating stocks of migrants within and across countries and discuss the potentials and limitations of the data. The lack of much needed information about migrants, together with the rapid global expansion of social media use and the online advertising industry, o↵er new opportunities to study migration as well as monitor progress towards achieving the goals of the United Nations 2030 Agenda for Sustainable Development.
The Compact Muon Solenoid Experiment

The LCG (Worldwide LHC Computing Grid) is a grid-based hierarchical computing distributed facility, composed of more than 140 computing centers, organized in 4 tiers, by size and offer of services. Every site, although indipendent for many technical choices, has to provide services with a well-deﬁned set of interfaces. For this reason, different LCG sites need frequently to manage very similar situations, like jobs behaviour on the batch system, dataset transfers between sites, operating system and experiment software installation and conﬁguration, monitoring of services. In this context we created WHALE (WHALE Handles Administration in an LCG Environment), a software actually used at the T2 IT Rome site, an LCG Tier-2 for the CMS experiment. WHALE is a generic, site indipendent tool written in python: it allows administrator to interact in a uniform and coherent way with several subsystems using a high level syntax which hides speciﬁc commands. The architecture of WHALE is based on the plugin concept and on the possibility of connecting the output of a plugin to the input of the next one, in a pipe-like system, giving the administrator the possibility of making complex functions by combining the simpler ones. The core of WHALE just handles the plugin orchestrations, while even the basic functions (eg. the WHALE activity logging) are performed by plugins, giving the capability to tune and possibly modify every component of the system. WHALE already provides many plugins useful for a LCG site and some more for a Tier-2 of the CMS experiment, expecially in the ﬁeld of job management, dataset transfer and analysis of performance results and availability tests (eg. Nagios tests, SAM tests). Thanks to its architecture and the provided plugins WHALE makes easy to perform tasks that, even if logically simple, are technically complex or tedious
SideEffectPTM: An Unsupervised Topic Model to Mine Adverse Drug Reactions from Health Forums

Drug side effects is the fourth leading cause of death in the U.S. after heart failure, cancer and stroke. Clinical trials are not able to discover all drug side effects due to the limited patients and limited time. The post marketing drug safety mainly relies on spontaneous reports to The Food and Drug Administrations (FDA). However, the FDA’s database is hard for patients to use because of the significant mismatch between patient terminology and standard medical vocabularies. Furthermore, it may take years for the FDA to decide to withdraw any dangerous drugs. In this project, we try to discover side effects by mining the growing online health forum data where many patients voluntarily report detailed cases about suspicious side effects.
Parallel Discrete Event Simulation Based on Multi-Core Platform-A New Approach

Multi-core designs have become commonplace in the processor market, and are hence a major focus in modern computer architecture research. Thus, for both product development and research, multiple core processor simulation environments are necessary. Multi-core computer offer a new parallel computing platform with high performance-price ratio and small volume to parallel simulation. Existing parallel simulator especially PDES simulators commonly run on parallel computers or clusters with Linux or Unix OS. The prices of super computer and large scale cluster are too high to be afforded, which limits the extensive popularization of PDES. This paper discusses a brief overview of multi-core processors and existing approaches to Parallel Discrete Event Simulation based on multi-core computer platform. A novel approach to proposed to explore the Parallel Discrete Event Simulation based on multi-core computer platform that can run on desktop with windows OS directly.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

The prosperity of AI and edge computing has pushed more and more well-trained DNN models to be deployed on third-party edge devices to compose mission-critical applications. This necessitates protecting model conﬁdentiality at untrusted devices, and using a co-located accelerator (e.g., GPU) to speed up model inference locally. Recently, the community has sought to improve the security with CPU trusted execution environments (TEE). However, existing solutions either run an entire model in TEE, suffering from extremely high inference latency, or take a partition-based approach to hand-craft partial model via parameter obfuscation techniques to run on an untrusted GPU, achieving lower inference latency at the expense of both the integrity of partitioned computations outside TEE and accuracy of obfuscated parameters. We propose S OTER , the ﬁrst system that can achieve model conﬁdentiality, integrity, low inference latency and high accuracy in the partition-based approach. Our key observation is that there is often an associativity property among many inference operators in DNN models. Therefore, S OTER automatically transforms a major fraction of associative operators into parameter-morphed , thus conﬁdentiality-preserved operators to execute on untrusted GPU, and fully restores the execution results to accurate results with associativity in TEE. Based on these steps, S OTER further designs an oblivious ﬁngerprinting technique to safely detect integrity breaches of morphed operators outside TEE to ensure correct executions of inferences. Experimental results on six prevalent models in the three most popular categories show that, even with stronger model protection, S OTER
Geofluid Systems of Koryaksky-Avachinsky Volcanoes (Kamchatka, Russia)

The Koryaksky-Avachinsky volcanogenic basin, which has an area of 2530 km2, is located 25 km from Petropavlovsk-Kamchatsky City and includes five Quaternary volcanoes (two of which, Avachinsky (2750 masl) and Koryaksky (3456 masl), are active), and is located within a depression that has formed atop Cretaceous basement rocks. Magma injection zones (dikes and chamber-like shapes) are defined by plane-oriented clusters of local earthquakes that occur during volcanic activity (mostly in 2008–2011) below Koryaksky and Avachinsky volcanoes at depths ranging from −4.0 to −2.0 km and
Assessment and indicators of kinematic behavior and perceived fatigability

SUMMARY OBJECTIVE: The objective of this study was to investigate the relationship between upper limb kinetics and perceived fatigability in elderly individuals during an upper limb position sustained isometric task. METHODS: A total of 31 elderly participants, 16 men (72.94±4.49 years) and 15 women (72.27±6.05 years), performed a upper limb position sustained isometric task. Upper-limb acceleration was measured using an inertial measurement unit. Perceived fatigability was measured using the Borg CR10 scale. RESULTS: Higher mean acceleration in the x-axis throughout the activity was associated with higher final perceived fatigability scores. Moderate correlations were observed between perceived fatigability variation and mean acceleration cutoffs in all axes during the second half of the activity. In women, significant correlations were found between all perceived fatigability cutoffs and mean acceleration in the y- and x-axes. However, in men, the relationships between perceived fatigability variation and mean acceleration were more extensive and stronger. CONCLUSION: The acceleration pattern of the upper limb is linked to perceived fatigability scores and variation, with differences between sexes. Monitoring upper limb acceleration using a single inertial measurement unit can be a useful and straightforward method for identifying individuals who may be at risk of experiencing high perceived fatigability or task failure.
FireSmart®-ForestWise: Managing Wildlife and Wildfire Risk in the Wildland/Urban Interface-a Canadian Case Study

Reducing the risk of losses from wildfires that threaten homes and communities is a growing priority in Canada. To reduce risk, “FireSmart®” standards have been adopted nationwide for managing forest fuel. However, these standards largely disregard interests of wildlife and conservation of wildlife habitat – thus raising concerns among residents and other stakeholders. To be acceptable, fuel treatments in wildland/urban interface areas of Jasper National Park, Alberta, required that potential environmental impacts and the requirements of wildlife also be carefully considered. A research project conducted in conjunction with the Foothills Model Forest used literature and experimental manipulations to develop ecologically based approaches for treating fuel in ways that optimize conditions for wildlife, within constraints of current standards. The research was conducted during a 30-month prototype project on more than 250 ha of forest surrounding the community of Jasper, Alberta. The study concluded fuel treatments for the purpose of reducing wildfire risk can be compatible with wildlife habitat conservation and ecosystem restoration goals. This paper describes the interface challenges faced by park managers, explains the adaptive management approach used to develop practicable solutions, and describes resulting species-specific mitigations, guidelines, and best practices that satisfy community wildfire protection standards and ecosystem management objectives, concurrently.
Efficacy, safety, and side effects of oliceridine in acute postoperative pain, a protocol for a systematic review and meta-analysis

This will be the first meta-analysis on the efficacy, safety, and side effects of oliceridine on postoperative pain. Our aim with this work is to evaluate the clinical utility of this relatively new substance in a broad postoperative context. Oliceridine is a new so-called bias opioid that is approved for severe pain requiring an opioid. Due to its biased agonism, it is said to have fewer side effects than conventional opioids. This systematic review and meta-analysis will analyze the efficacy, safety, and side effects of oliceridine compared to placebo or morphine in acute postoperative pain for up to 72 hours. In January 2024, an extensive search in various databases will be performed without restrictions for randomized controlled trials with at least single blinding. After data extraction, data will be pooled and meta-analytic calculations performed. A random-effects model will be used. Dichotomous data will be presented as risk ratio and continuous data as standardized mean difference. Dose-dependent side effects will be evaluated with meta-regression. Heterogeneity will be assessed via the Q statistic and prediction interval in case of a sufficient number of included studies. Publication bias will be examined using funnel plot and Duval and Tweedie’s trim and fill method.