Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical big data and deep learning: Applications, challenges, and future outlooks

The explosion of digital healthcare data has led to a surge of data-driven medical research based on machine learning. In recent years, as a powerful technique for big data, deep learning has gained a central position in machine learning circles for its great advantages in feature representation and pattern recognition. This article presents a comprehensive overview of studies that employ deep learning methods to deal with clinical data. Firstly, based on the analysis of the characteristics of clinical data, various types of clinical data (e.g., medical images, clinical notes, lab results, vital signs and demographic informatics) are discussed and details provided of some public clinical datasets. Secondly, a brief review of common deep learning models and their characteristics is conducted. Then, considering the wide range of clinical research and the diversity of data types, several deep learning applications for clinical data are illustrated: auxiliary diagnosis, prognosis, early warning, and other tasks. Although there are challenges involved in applying deep learning techniques to clinical data, it is still worthwhile to look forward to a promising future for deep learning applications in clinical big data in the direction of precision medicine.
How people describe their place: Approaches to interpreting and formalizing place descriptions

This cumulative thesis deals with human place descriptions and their interpretation. In particular, it poses four different research questions: What are dominant types of place descriptions? What different types of hierarchical structures do they use? What is the role of spatial relationships in defining the actual location? Can violations in form of flat structures or gaps in levels be related to the applied classification scheme?
A Game-Theoretic Framework for Safety and Security

The purpose of this paper is to describe the Framework described in [BG16] in a game theoretic way. The idea behind this is that for modelling security (i. e. the assumption of an intelligent attacker) the language of game theory seems to be a very good choice. Game theory can deal with the problem where the actions of each subject are interdependent, e.g. an attacker will change his strategy whenever a new security feature will prevent his old strategy from succeeding or a new attack seems to be more promising. Moreover, game theory has been thoroughly studied and hence changing the description language of the model, gives access to many results. Additionally, we view the Beyerer and Geisler Framework as part of security economics.
Integrating Planning and Scheduling : A Constraint-based Approach

Automated decision making is one of the important problems of Artificial Intelligence (AI). Planning and scheduling are two sub-fields of AI that research automated decision making. The main focus of planning is on general representations of actions, causal reasoning among actions and domain-independent solving strategies. Scheduling generally optimizes problems with complex temporal and resource constraints that have simpler causal relations between actions. However, there are problems that have both planning characteristics (causal constraints) and scheduling characteristics (temporal and resource constraints), and have strong interactions between these constraints. An integrated approach is needed to solve this class of problems efficiently. The main contribution of this thesis is an integrated constraint-based planning and scheduling approach that can model and solve problems that have both planning and scheduling characteristics. In our representation problems are described using a multi-valued state variable planning language with explicit representation of different types of resources, and a new action model where each action is represented by a set of transitions. This action-transition model makes the representation of actions with delayed effects, effects with different durations, and the representation of complex temporal and resource constraints like time-windows, deadline goals, sequence-dependent setup times, etc simpler. Constraint-based techniques have been successfully applied to solve scheduling problems. Therefore, to solve a combined planning/scheduling problem we compile it into a CSP. This compilation is bounded by the number of action occurrences. The constraint model is based on the notion of “support” for each type of transition. The constraint model can be viewed as a system of CSPs, one for each state variable and resource, that are synchronized by a simple temporal network for action start times. Central to our constraint model is the explicit representation and maintenance of the precedence constraints between transitions on the same state variable or resource. We propose a branching scheme for solving the CSP based on establishing supports for transitions, which imply precedence constraints. Furthermore, we propose new propagation and inference techniques that infer precedence relations from temporal and mutex constraints, and infer tighter temporal bounds from the precedence constraints. The distinguishing feature of these inference and propagation techniques is that they not only consider the transitions and actions that are included in the plan but can also consider actions and transitions that are not yet included in or excluded from the plan.
bsamGP: An R Package for Bayesian Spectral Analysis Models Using Gaussian Process Priors

The Bayesian spectral analysis model (BSAM) is a powerful tool to deal with semiparametric methods in regression and density estimation based on the spectral representation of Gaussian process priors. The bsamGP package for R provides a comprehensive set of programs for the implementation of fully Bayesian semiparametric methods based on BSAM. Currently, bsamGP includes semiparametric additive models for regression, generalized models and density estimation. In particular, bsamGP deals with constrained regression models with monotone, convex/concave, S-shaped and U-shaped functions by modeling derivatives of regression functions as squared Gaussian processes. bsamGP also contains Bayesian model selection procedures for testing the adequacy of a parametric model relative to a non-specific semiparametric alternative and the existence of the shape restriction. To maximize computational efficiency, we carry out posterior sampling algorithms of all models using compiled Fortran code. The package is illustrated through Bayesian semiparametric analyses of synthetic data and benchmark data.
Enhanced Multimedia Exchanges over the Internet

Although the Internet was not originally designed for exchanging multimedia streams, consumers heavily depend on it for audiovisual data delivery. The intermittent nature of multimedia tra c, the unguaranteed underlying communication infrastructure, and dynamic user behavior collectively result in the degradation of Quality-of-Service (QoS) and Quality-ofExperience (QoE) perceived by end-users. Consequently, the volume of signalling messages is inevitably increased to compensate for the degradation of the desired service qualities. Improved multimedia services could leverage adaptive streaming as well as blockchain-based solutions to enhance media-rich experiences over the Internet at the cost of increased signalling volume. Many recent studies in the literature provide signalling reduction and blockchainbased methods for authenticated media access over the Internet while utilizing resources quasie ciently. To further increase the e ciency of multimedia communications, novel signalling overhead and content access latency reduction solutions are investigated in this dissertation including: (1) the first two research topics utilize steganography to reduce signalling bandwidth utilization while increasing the capacity of the multimedia network; and (2) the third research topic utilizes multimedia content access request management schemes to guarantee throughput values for servicing users, end-devices, and the network. Signalling of multimedia streaming is generated at every layer of the communication protocol stack; At the highest layer, segment requests are generated, and at the lower layers, byte tracking messages are exchanged. Through leveraging steganography, essential signalling information is encoded within multimedia payloads to reduce the amount of resources consumed by non-payload data. The first steganographic solution hides signalling messages within multimedia payloads, thereby freeing intermediate node bu↵ers from queuing non-payload packets. Consequently, source nodes are capable of delivering control information to receiving nodes at no additional network overhead. A utility function is designed to minimize the volume of overhead exchanged while minimizing visual artifacts. Therefore, the proposed scheme is designed to leverage the fidelity of the multimedia stream to reduce the largest amount of
Parameter inference with deep jointly informed neural networks

A common challenge in modeling inertial confinement fusion (ICF) experiments with computer simulations is that many of the simulation inputs are unknown and cannot be directly measured. Often, parameters that are measured in the experiment are used to infer the unknown inputs by solving the inverse problem: finding the set of simulation inputs that result in outputs consistent with the experimental observations. In ICF, this process is often referred to as a “post‐shot analysis. ” Post‐shot analyses are challenging as the inverse problem is often highly degenerate, the input parameter space is vast, and simulations are computationally expensive. In this work, deep neural network models equipped with model uncertainty estimates are used to train inverse models, which map directly from output to input space, to find the distribution of post‐shot simulations that are consistent with experimental observations. The inverse model approach is compared to Markov chain Monte Carlo (MCMC) sampling of the forward model, which maps from input to output space, for parameter inference tasks of varying complexity. The inverse models perform best when searching vast parameter spaces for post‐shot simulations that are consistent with a large number of observables, where MCMC sampling can be prohibitively expensive. We demonstrate how augmenting inverse models with autoencoders enable the inclusion of several dozen observables in the inverse mapping, reducing the degeneracy of the model and improving the accuracy of the post‐shot analysis.
Recent Advance in Biomedical Omics Data Analysis

Sequencing technology is dominant in the study of complex human genetic diseases. Identified risk genes and other biomarkers will make genetic counselling, risk assessment and avoidance, and disease diagnosis and treatment possible. Benefit from the discovered biomarkers, many diseases could be accurate diagnosed and prevented, as well as accurate treated. Nevertheless, the molecular mechanisms of many neuropsychiatric disorders are still unclear and there are no effective treatments for these neuropsychiatric disorders. The rapid development of whole genome sequencing technology, large amounts of genetic data are generated concerning larger cohorts with diverse neuropsychiatric disorders [1], making it possible to detect various genetic variants, i.e. SNVs, InDels, and SVs. Genetic research of these disorders are focused on genome-wide association analysis (GWAS), pedigree analysis, twin family studies, trio family studies, etc., Over the last decades, GWAS have identified several hundred loci for neuropsychiatric disorders successfully [2,3]. Whole exome/ genome sequencing have greatly facilitated the identification of risk de novo variants [4]. Besides, with the increasing amount of patient cohorts, more and more risk mutations have been discovered [5].
Visualization of Uncertain Boundaries of Undersea Features

There have been several studies that detect, measure, analyze, and visualize the undersea features by using technologies in multiple disciplines including geography and oceanography. However, definitions of the undersea features often vary among the existing leading literature. Due to this reason the geographical boundary for a certain undersea feature is sometimes not identical among the definitions. In this study, we explore semantic uncertainty in the definitions of some undersea features and apply approaches from fuzzyset theory and geographic information science on empirical bathymetric data to visualize the uncertain boundaries of the undersea features. Results from this study demonstrate that the representation based on the fuzzy-set approach can be useful for dealing with the semantic uncertainty of the undersea features.
Truthful Mechanism Design for Wireless Powered Network With Channel Gain Reporting

Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs.
Game-Theoretic Based Power Allocation for a Full Duplex D2D Network

This work addresses the power allocation optimization problem of a full-duplex (FD) D2D underlaying cellular network. In particular, we aim at providing a distributed power allocation (PA) algorithm for this type of network. Towards this end, first, we formulate the PA problem as a non-cooperative game in which each user decides how much power to transmit over its allocated channel to maximize its link's energy-efficiency (EE). Next, we show that this game admits a unique Nash equilibrium (NE) point which can be obtained through an iterative process. After that, we show that this iterative algorithm can be implemented in a fully distributed manner. Finally, we compare our proposed distributed algorithm with the conventional centralized algorithms and simulation results verify the importance of the proposed distributed algorithm.
A Hybrid Systems Approach to Tracking Control for a Fully Actuated Walking Bipedal Robot

Author(s): Short, Brendan Edward | Advisor(s): Sanfelice, Ricardo G | Abstract: We model a three-link fully actuated biped as a hybrid system, and propose a prediction-based control algorithm for global tracking of reference trajectories, with nominal robustness to perturbations, in order to track a periodic gait in finite time. Our hybrid model consists of continuous dynamics that describe the bipedal motion in between the impacts where both feet are in contact with the ground, which we consider to be a discrete event. The proposed control strategy consists of a reference system that generates the desired periodic gait, a virtual system that generates a suitable reference trajectory using prediction, and a tracking control law that steers the biped to the virtual trajectory. The proposed algorithms achieves, in finite time, tracking in two steps. We present mathematical properties that define how the hybrid predictive controller is capable of achieving convergence to the reference within the first two steps. Additionally, we validate our results through numerical simulations.
Design of inverted-F antenna for long-term evolution-based wireless handhelddevices

: In this article, a compact triple-band inverted-F antenna for long-term evolution (LTE)-based wireless handheld devices is presented. The proposed antenna comprises three arms, namely the radiating arm, shorting arm, and feeding arm. The structure of the designed antenna looks like an inverted F with folded L-shaped strip antenna. It has three resonant frequencies of 795, 2050, and 3405 MHz with a reflection coefficient of –11.7 dB, –15.1 dB, and –30.5 dB, respectively. The size of the suggested antenna is 10 (cid:2) 35 (cid:2) 0.8 mm 3 and it is built on the FR-4 substrate which is more compact and easy to integrate with the handheld devices such as laptops, tablets, and smart phones. The performances of the proposed antenna are analyzed with the help of computer simulation using Ansys High Frequency Structure Simulation Software Simulator (HFSS). The measured –10 dB impedance bandwidth of the suggested antenna covers 780–820, 2020–2070, and 3230–3490 MHz, which satisfies the LTE 700, LTE 2100, and LTE 3300. The proposed antenna has the acceptable voltage standing wave ratio (VSWR (cid:20) 2), bandwidth, gain, and radiation characteristics across the operating bands and thus proves its suitability for wireless handheld devices.
Smart Beta: The Good, the Bad, and the Muddy

The authors describe the background of factor investing in its smart beta form and discuss why factor investing has become a popular investing style. They also discuss a number of reasons for skepticism regarding ex ante expected factor returns. TOPICS: Portfolio management/multi-asset allocation, factors, risk premia, style investing Key Findings • Factor investing is typically discussed using the language and machinery of efficient-markets models, yet investors are primarily expecting anomalous excess returns. • There is reason to be skeptical of claims that factor investing is delivering gross investment returns similar to that of traditional active managers but with lower fees. • For factors with risk-based explanations, even in the presence of significant factor premiums, the market portfolio is still likely to be the optimal portfolio for most investors.
An LEK-Based Design Algorithm for MDS Linear Network Error Correction Codes on Cyclic Multicast Networks

A linear network (LN) code can be described by either global encoding kernels (GEKs) or local encoding kernels (LEKs). In the literature, the multicast property of an LN code is described using GEKs, so the design algorithms for multicast LN codes employ GEKs to check this property. In this paper, a criterion is developed so that LEKs rather than GEKs can be used to check the multicast maximum distance separable (MDS) property of linear network error correction (LNEC) codes on erroneous networks. Further, it is used to develop a design algorithm for multicast MDS LNEC codes on erroneous cyclic networks. This algorithm is more efficient than the algorithm that uses GEKs when the number of edges is high and the interconnection of these edges is low.
Organización automática de documentos mediante técnicas de análisis de redes

La organizacion automatica de documentos permite conocer la estructura tematica de grandes colecciones documentales. En este trabajo se plantea modelar la coleccion de documentos mediante una red o grafo de nodo y enlaces y aplicar tecnicas de Analisis de Redes Sociales. Se describe un experimento llevado a cabo con una coleccion de noticias de prensa, analizando la estructura tematica resultante tras aplicar tecnicas de deteccion de comunidades de nodos en dos niveles de agrupamiento. Los resultados parecen bastante prometedores, planteando como trabajo futuro la implementacion y comparacion de diferentes algoritmos de deteccion de comunidades.
Steps for Software Project Management and Its Applications

In this paper we empirically explore knowledge sharing in a group of project managers. We apply a framework of sensemaking that focuses on how people participate in creating shared meanings. The framework is applied in the analysis of the case where project managers create shared knowledge in a handbook for software project management. The framework provides a rich apprehension of how meaning is created. [3]It explains the importance of impacts and negotiation of the project managers’ expectations and experience. The findings add to existing thesis of knowledge sharing in software development. We give in particular with an in [1]depth explanation of the complex process where personal knowledge gradually turns into shared knowledge and some of it in codified form becomes part of the software project management handbook. Keywords: Knowledge sharing, software development, project management, sense making.
Game-Based Multimedia for Horizontal Numeracy Learning

Recently, game-based multimedia development has been increasing rapidly. Game-based multimedia is not only used for entertaining but also for teaching and learning process. Meanwhile, for the majority of elementary school students, numeracy learning is still considered as a difficult subject to be learned and has not attracted students’ interest because it still uses vertical methods. The objective of this research is to examine the implementation of game-based multimedia in learning horizontal numeracy. In this game-based multimedia, horizontal numeracy material is presented in the form of a game. Horizontal numeracy material begins with addition, subtraction, multiplication, and division. Game-based multimedia is implemented to 20 students in grade 3 elementary schools in Bandung, Indonesia. Subsequently, the way students learn the materials and their learning outcomes are explored after using this multimedia. The results show that the students can learn horizontal numeracy excitingly; moreover, they can easily understand the concept of horizontal numeracy quickly. It is indicated by by the increasing of average post-test value.