Ad-hoc Approaches Research Articles

The Paris Agreement objectives will likely halt future declines of emperor penguins.

The Paris Agreement is a multinational initiative to combat climate change by keeping a global temperature increase in this century to 2°C above preindustrial levels while pursuing efforts to limit the increase to 1.5°C. Until recently, ensembles of coupled climate simulations producing temporal dynamics of climate en route to stable global mean temperature at 1.5 and 2°C above preindustrial levels were not available. Hence, the few studies that have assessed the ecological impact of the Paris Agreement used ad-hoc approaches. The development of new specific mitigation climate simulations now provides an unprecedented opportunity to inform ecological impact assessments. Here we project the dynamics of all known emperor penguin (Aptenodytes forsteri) colonies under new climate change scenarios meeting the Paris Agreement objectives using a climate-dependent-metapopulation model. Our model includes various dispersal behaviors so that penguins could modulate climate effects through movement and habitat selection. Under business-as-usual greenhouse gas emissions, we show that 80% of the colonies are projected to be quasiextinct by 2100, thus the total abundance of emperor penguins is projected to decline by at least 81% relative to its initial size, regardless of dispersal abilities. In contrast, if the Paris Agreement objectives are met, viable emperor penguin refuges will exist in Antarctica, and only 19% and 31% colonies are projected to be quasiextinct by 2100 under the Paris 1.5 and 2 climate scenarios respectively. As a result, the global population is projected to decline by at least by 31% under Paris 1.5 and 44% under Paris 2. However, population growth rates stabilize in 2060 such that the global population will be only declining at 0.07% under Paris 1.5 and 0.34% under Paris 2, thereby halting the global population decline. Hence, global climate policy has a larger capacity to safeguard the future of emperor penguins than their intrinsic dispersal abilities.

Retrieving Relevant Passages Using N-grams for Open-Domain Question Answering

Question Answering is most likely one of the toughest tasks in the field of Natural Language Processing. It aims at directly returning accurate and short answers to questions asked by users in human language over a huge collection of documents or database. Recently, the continuously exponential rise of digital information has imposed the need for more direct access to relevant answers. Thus, question answering has been the subject of a widespread attention and has been extensively explored over the last few years. Retrieving passages remains a crucial but also a challenging task in question answering. Although there has been an abundance of work on this task, this latter still implies non-trivial endeavor. In this paper, we propose an ad-hoc passage retrieval approach for Question Answering using n-grams. This approach relies on a new measure of similarity between a passage and a question for the extraction and ranking of the different passages based on n-gram overlapping. More concretely, our measure is based on the dependency degree of n-gram words of the question in the passage. We validate our approach by the development of the “SysPex” system that automatically returns the most relevant passages to a given question.

Multi-Player Multi-Armed Bandits for Stable Allocation in Heterogeneous Ad-Hoc Networks

Next generation networks are expected to be ultra-dense and aim to explore spectrum sharing paradigm that allows users to communicate in licensed, shared as well as unlicensed spectrum. Such ultra-dense networks will incur significant signaling load at base stations leading to a negative effect on spectrum and energy efficiency. To minimize signaling overhead, an ad-hoc approach is being considered for users communicating in the unlicensed and shared spectrums. For such users, decisions need to be completely decentralized as: 1) No communication between users and signaling from the base station is possible which necessitates independent channel selection at each user. A collision occurs when multiple users transmit simultaneously on the same channel, 2) Channel qualities may be heterogeneous, i.e., they are not same across all users, and moreover, are unknown, and 3) The network could be dynamic where users can enter or leave anytime. We develop a multi-armed bandit based distributed algorithm for static networks and extend it for the dynamic networks. The algorithms aim to achieve stable orthogonal allocation (SOC) in finite time and meet the above three constraints with two novel characteristics: 1) Low complexity narrowband radio compared to wideband radio in existing works, and 2) Epoch-less approach for dynamic networks. We establish convergence of our algorithms to SOC and validate via extensive simulation experiments.

Relativistic celestial mechanics at sub-microarcsecond accuracy level

Einstein stated two dictums, so that more experimental facts can replace the previously adopted hypotheses and General Relativity (GR) can evolve to “grand aim” or perfection. In the absence of appropriate experimental facts during pre-CEREPAC (Century-long Experience of Relativity-related Experiments on Physics, Astronomy and Celestial-mechanics) era, Einstein found no “escape” from the consequence of non-Euclidean geometry, while keeping all frames permissible based on contemporary knowledge. Bergmann also stated in 1968 that the “principle of general covariance” has brought about serious complication in GR. During CEREPAC, relativists and mathematical-astronomers invariably identified the appropriate “nature’s preferred-frame,” which was later found essential for operation of conservation laws. Based on CEREPAC, replacing the experimentally unverifiable hypotheses with experimentally proven principles, and improving upon the GR-astronomers model (developed by JPL, USA, as an evolved-version of GR-conventional model) in two successive stages, GR was remodeled to what became evident as Evolved GR (EGR), after it enabled the elimination of all earlier-adopted ad-hoc methods or approaches, and of the problems, paradoxes and anomalies, associated with the applications of GR, during CEREPAC, and after it unraveled the “General-relativistic nature of speed-of-light (c)” which links the variable c r with F r, the local Gravitational Red-Shift Factor (as stated by Einstein between 1911‐1921). As a consequence of the space-age developments in numerical simulation and in the availability of precision observational data, it got proven that nature itself operates the conservation laws of energy, and of linear and angular momentums (both magnitudes and directions), with respect to the appropriate “nature’s preferred frame”; this provided sufficient reason for giving up the “relativity of all frames,” bringing back Euclidean geometry in EGR. Euclidean space in EGR enabled development of a Prototype of future Ephemerides, leading to five orders-of-magnitude improvement in accuracy of computation of precession of celestial orbits, using three independent methods; this methodology of Prototype Ephemeris and “three-methods-match” can also be applied while remaining exclusively within the precincts of GR, by using one alternative mode of running the EGR program for planetary and Lunar orbits, by opting for GRTOPT=Y; this mode utilizes exclusively the GR equations instead of EGR equations; in fact, this mode is the GR-astronomers (modified) model that was really an intermediate stage of evolution (as mentioned above) between the GR-astronomers model and the EGR model. This model incorporates: (1) All good (and, experimentally proven) features from the three generations of GR models (Einstein’s original, Bergmann’s and Misner-Thorne-Wheeler or MTW), and (2) The “Nature-adopted” real orbital model (as proven from comparison of the computed precession values at Micro-arcsecond {μas} level using the “three-methods-match”) for its Methodology for Conservation of Linear and Orbital Angular Momentums, in a polar coordinate system (r, , Φ). This model computes: (1) Precession of celestial orbits at nearly the same accuracy as that done using the EGR model, and (2) About three digit more accurate (than reported by Folkner in 2014, from fitting lunar laser ranging data with an updated lunar gravity field from the GRAIL mission, etc.) orbits of inner planets and the Moon.

Unsteady interface boundary conditions for near-wall turbulence modeling

Numerical modeling near-wall turbulent flows is one of major challenges in the fluid dynamics. It inevitably requires significant computational resources. To overcome these difficulties, there are two principal ways based on simplification of the mathematical model and development of ad-hoc computational approaches. The near-wall non-overlapping domain decomposition approach with the use of Robin interface boundary conditions proved to be very efficient. As has been shown, to apply this approach to essentially unsteady flows, the interface boundary conditions must be nonlocal in time and should be modified to include a memory term. In the current paper, it is proven that the memory term must be caused by the unsteadiness of both the solution at the interface boundary and driving force. The properties of the derived unsteady interface boundary conditions are studied in detail in the application to oscillatory and pulsating laminar flows in a channel and pipe. In particular, we study the effect of the memory term on the reproduction of the unsteady effects. The convergence to the exact solution is theoretically proven and numerically demonstrated. A practical calculation of the memory term is based on a Fourier expansion. It is also proven that the convergence is quadratic with respect to the inverse number of Fourier terms. A key question whether the near-wall domain decomposition can damage an external instability plays an important role to identify the perspectives of the technique to be extended to RANS/ LES (or DNS) decompositions.

Review of methodologies and tasks in swarm robotics towards standardization

Swarm Robotics (SR) is an extension of the study of Multi-Robot Systems that exploits concepts of communication, coordination and collaboration among a large number of robots. The massive parallelization yielded by the robots working together can make a task faster than in the case of the usage of a single complex robot. One of the main aspects in robotic swarms is that the control is decentralized by definition and distributed among the robots of the swarm, improving the system robustness and fault-tolerance. Furthermore, this characteristic often allows the emergence of collective behaviors from the robot's interaction with each other and with the environment through their embodied sensors and actuators. In most cases, the number of inputs from sensor readings turns analytical solutions hard or even impossible. Thus, many ad-hoc approaches are contributed to deal with the situation at hand. The main goal of this review is to find out, through the study of existing research works of the field, the reason behind the lack of exploitation of swarm robotic systems in real-world applications. For this purpose, we first review the different possibilities of study in SR: physical and simulated robotic platforms, development methodologies and the variety of basic tasks and collective behaviors. We then briefly describe some fields related do SR that have a big impact on the development of SR. After that, based on existing taxonomies found in literature, we categorize existing research works regarding SR in two large main groups: those that deal with SR design and those that deal with tasks as required in SR. The review of both existing robots and techniques in the literature show a diversity of approaches to discuss SR issues. Nonetheless, it is easily noticeable from these works that there is a clamant absence of solid real-world applications of SR. An analysis of the interests and bottlenecks of this field indicates that the number of research works is smaller than those in other related areas. This suggests that, even though with many research studies, the field of SR is not yet mature enough, mainly due the absence of a universal methodology and generic robots that can be used in any, or at least in many, applications. Thus, we emphasize, discuss and analyze the urgent need for standardization of many aspects in SR, including hardware and software, as to allow a possible flourishing of SR applicability to real-world applications. This standardization could accelerate a great deal the field of SR, thus facilitating the development of SR solutions for applications that impact our daily life.

Conceptual modeling for improved understanding of the Rio Grande/Río Bravo socio-environmental system

Social processes are essential components of human-environment systems and their dynamics. However, modeling a tightly coupled socio-environmental system over a large area and across wide social and environmental diversity presents several challenges, given the complexity of the interactions and their spatial heterogeneity. The transboundary Rio Grande/Río Bravo (RGB) Basin is an excellent case study to address these challenges. Water scarcity and over-allocation of water are present in a highly engineered system with extensive damming and a complex structure of agreements and compacts that govern the distribution of hydrological resources among users. Since no basin-wide approaches to modeling the RGB as a socio-environmental system exist, we attempt to close this gap. Building on data collected through extensive ethnographic fieldwork, we used a structured, collaborative, and integrative approach for documenting existing knowledge on and modeling of the RGB socio-environmental system. We assess different models for conceptualizing human behavior applied in the RGB, identify a need to redefine the (spatial) boundaries of the system and produce inductively generated knowledge about the interlinkages of social processes with environmental system components in the form of a semi-quantitative conceptual model. Our research demonstrates an alternative to ad-hoc approaches to defining “the social” in socio-environmental models and is a first step towards the development of a basin-wide computer simulation model of the RGB socio-environmental system.

Getting it right first time: The importance of a structured tissue sampling protocol for diagnosing fracture-related infections

IntroductionFracture-related infection (FRI) is an important complication following surgical fracture management. Key to successful treatment is an accurate diagnosis. To this end, microbiological identification remains the gold standard. Although a structured approach towards sampling specimens for microbiology seems logical, there is no consensus on a culture protocol for FRI. The aim of this study is to evaluate the effect of a structured microbiology sampling protocol for fracture-related infections compared to ad-hoc culture sampling. MethodsWe conducted a pre-/post-implementation cohort study that compared the effects of implementation of a structured FRI sampling protocol. The protocol included strict criteria for sampling and interpretation of tissue cultures for microbiology. All intraoperative samples from suspected or confirmed FRI were compared for culture results. Adherence to the protocol was described for the post-implementation cohort. ResultsIn total 101 patients were included, 49 pre-implementation and 52 post-implementation. From these patients 175 intraoperative culture sets were obtained, 96 and 79 pre- and post-implementation respectively. Cultures from the pre-implementation cohort showed significantly more antibiotic use during culture sampling (P = 0.002). The post-implementation cohort showed a tendency more positive culture sets (69% vs. 63%), with a significant difference in open wounds (86% vs. 67%, P = 0.034). In all post-implementation culture sets causative pathogens were cultured more than once per set, in contrast to pre-implementation. Despite stricter tissue sampling and culture interpretation criteria, the number of polymicrobial infections was similar in both cohorts, approximately 29% of all culture sets and 44% of all positive culture sets. Significantly more polymicrobial cultures were found in early infections in the post-implementation cohort (P = 0.048). This indicates a better yield in the new protocol. ConclusionA standardised protocol for intraoperative sampling for bacterial identification in FRI is superior than an ad-hoc approach. It has a positive effect on both surgeon and microbiologist by increasing awareness about the problem at hand. This resulted in more microbiologically confirmed infections and more certainty when identifying causative pathogens.

Superposition coded OFDM transmissions in a downlink cooperative relay network based on statistical channel state information

Non-orthogonal multiple access has recently emerged as a promising multiple access technique for future wireless technology. In this study, the authors investigate superposition coding in a downlink cooperative cellular system based on the orthogonal frequency division multiplexing (OFDM) modulation in the presence of frequency selective multipath fading channels, a setup that has not been studied before. Specifically, the base station communicates with two paired mobile users simultaneously via the OFDM modulation with the help of a half-duplex relay under either the decode-and-forward (DF) or amplify-and-forward (AF) scheme, where the power allocation between two mobile users depends on the statistical channel state information (CSI) rather than perfect CSI. They derive the ergodic rate regions under Rayleigh fading channels and present an ad-hoc approach to determine the power splitting parameters when the target data rates are given. Simulation results based on the outage probabilities show that superposition coding consistently outperforms frequency division, however, the relative advantage of the DF and AF schemes in a superposition-coded OFDM relay system depends on the system geometry.

Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data.

There exists significant interest in developing statistical and computational tools for inferring ‘who infected whom’ in an infectious disease outbreak from densely sampled case data, with most recent studies focusing on the analysis of whole genome sequence data. However, genomic data can be poorly informative of transmission events if mutations accumulate too slowly to resolve individual transmission pairs or if there exist multiple pathogens lineages within-host, and there has been little focus on incorporating other types of outbreak data. We present here a methodology that uses contact data for the inference of transmission trees in a statistically rigorous manner, alongside genomic data and temporal data. Contact data is frequently collected in outbreaks of pathogens spread by close contact, including Ebola virus (EBOV), severe acute respiratory syndrome coronavirus (SARS-CoV) and Mycobacterium tuberculosis (TB), and routinely used to reconstruct transmission chains. As an improvement over previous, ad-hoc approaches, we developed a probabilistic model that relates a set of contact data to an underlying transmission tree and integrated this in the outbreaker2 inference framework. By analyzing simulated outbreaks under various contact tracing scenarios, we demonstrate that contact data significantly improves our ability to reconstruct transmission trees, even under realistic limitations on the coverage of the contact tracing effort and the amount of non-infectious mixing between cases. Indeed, contact data is equally or more informative than fully sampled whole genome sequence data in certain scenarios. We then use our method to analyze the early stages of the 2003 SARS outbreak in Singapore and describe the range of transmission scenarios consistent with contact data and genetic sequence in a probabilistic manner for the first time. This simple yet flexible model can easily be incorporated into existing tools for outbreak reconstruction and should permit a better integration of genomic and epidemiological data for inferring transmission chains.

Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm.

Application of Optimization Principle in Landmark University Project Selection under Multi-Period Capital Rationing Using Linear and Integer Programming

The current structure of Landmark University (LU) was induced by raising a generation of solution providers through a qualitative and life-applicable training system that focuses on values and creative knowledge by making it more responsive and relevant to the modern-day demands of demonstration, industrialization and development. The challenge facing Landmark University is the question of which of its numerous projects they should invest to give maximum output with minimum input. In this paper, we maximize the Net Present Value (NPV) and maintain the net discount cash overflow of each project per period as contained and extracted as the secondary data of cash inflows of the Landmark University (LU) monthly financial statement and annual reports from 2012 to 2017 of which the documents have been regrouped as small and large scale projects as many enterprises make more use of the trial-and-error method and as such firms have been finding it difficult in allocating scarce resources in a manner that will ensure profit maximization and/or cost minimization with a simple and accurate decision making by the company through an optimization principle in selecting LU project under multi-period capital rationing using linear programming (LP) and integer programming (IP). The annual net cash flow which is the difference between the cash inflows and cash outflows during each period for the project was estimated and recorded. The discount factors were estimated at cost of capital of 10% for each cash flow per period with the corresponding NPV at 10% which revealed that the optimal decision achieves maximum returns of $110 × 102 and this assisted the project manager to select a large number of the variable projects that can maximize the profit which is far better than relying on an ad-hoc judgmental approach to project investment that could have cost 160 × 102 for the same project. Sensitivity analysis on the project parameters are also carried out to test the extent to which project selection is sensitive to changes in the parameters of the system revealed that a little reduction and or addition of reduced cost by certain amount or percentages to its corresponding coefficient in the objective function effect no changes in the shadow prices with solution values for variables (x1), (x4), (x5) and the optimal objective function.

A Cyber-Enabled Mission-Critical System for Post-Flood Response: Exploiting TV White Space as Network Backhaul Links

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm.

Optimizing the cost of DBaaS object placement in hybrid storage systems

In a Cloud context, Solid State Drive (SSD) became a must-have technology. This technology is too expensive to replace Hard Disk Drive (HDD), both are used in Hybrid Storage Systems (HSS). When it comes to storing data, placement strategies are employed to find the best storage class to use (SSD or HDD). While for many applications, those strategies need to be I/O performance driven, in a Cloud context, they must be cost driven: minimize the cost of data placement while satisfying Service Level Objectives. This paper presents two Cost based Object Placement Strategies (COPS) for DBaaS objects in HSS: a genetic algorithm based approach (G-COPS) and an ad-hoc heuristic approach (H-COPS) based on incremental optimizations. Both algorithms were tested for small and large instance problems. While G-COPS proved to be closer to the optimal solution in case of small instance problems, H-COPS showed a better scalability as it approached the exact solution even for large instance problems (by 10% on average). Both performed better than state-of-the-art solutions as they reduced the overall cost by more than 40%. In addition, H-COPS showed small execution times which makes it a good candidate for a runtime use. Moreover, H-COPS drastically limits the over-provisioning of resources.

Google data in bridge equation models for German GDP

Interest in the use of “big data” when it comes to forecasting macroeconomic time series such as private consumption or unemployment has increased; however, applications to the forecasting of GDP remain rather rare. This paper incorporates Google search data into a bridge equation model, a version of which usually belongs to the suite of forecasting models at central banks. We show how such big data information can be integrated, with an emphasis on the appeal of the underlying model in this respect. As the decision as to which Google search terms should be added to which equation is crucial —- both for the forecasting performance itself and for the economic consistency of the implied relationships —- we compare different (ad-hoc, factor and shrinkage) approaches in terms of their pseudo real time out-of-sample forecast performances for GDP, various GDP components and monthly activity indicators. We find that sizeable gains can indeed be obtained by using Google search data, where the best-performing Google variable selection approach varies according to the target variable. Thus, assigning the selection methods flexibly to the targets leads to the most robust outcomes overall in all layers of the system.

On the feasibility of SISO control-theoretic DVFS for power capping in CMPs

Power capping are increasingly being deployed in modern processors to meet performance/power requirements of new workloads. These computing systems are limited in their power dissipation, demanding a dependable power management scheme to guarantee the system’s efficiency and dependability. Although several ad-hoc and heuristic power management approaches can be found in the literature, their main shortcoming is the lack of formal guarantees to ensure dependability of the processors. Control-theoretic approaches promise flexibility and robustness for DVFS strategies. However, the creation of a responsive yet stable controller requires the often neglected tasks of proper system identification and performance analysis for target applications. This paper presents dependability evaluation of Single-Input Single-Output (SISO) controllers for power capping on single-core as well as multi-core processor architectures. We evaluate responsiveness of different class of applications to computer system control inputs (i.e., DVFS). We illustrate the feasibility of SISO controllers for power capping using the Sniper simulator running PARSEC, SPLASH2 and a set of custom microbenchmarks. Based on our observations, we provide guidelines for developing stable and robust SISO controllers for power capping, show the scenarios where simple classic SISO controllers might not be effective, and identify early symptoms that may result in instability for power capping controllers.

Flatness and structural analysis as a constructive framework for private communication

This paper deals with the use of control theoretical concepts, essentially flatness, along with structural analysis, in the context of private communication. A new and systematic methodology to design a cryptographic architecture belonging to the special class of ciphers called Self Synchronizing Stream Ciphers (SSSC) is proposed. Till now, the constructions of SSSC were based on automata with finite input memory involving shifts or triangular functions (T-functions) as state transition functions. Besides, only ad-hoc approaches were available in the literature. The contribution of this paper is to propose a general framework to design SSSC involving dynamical systems taking the form of switched linear systems over finite fields with arbitrary state transition functions. An example of cipher with complete specifications is given. Its implementation on a small scale breeding of an ICS-SCADA equipment is described.

Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values

The statistical analysis of positron emission tomography (PET) standardized uptake value (SUV) measurements is challenging due to the skewed nature of SUV distributions. This limits utilization of powerful parametric statistical models for analyzing SUV measurements. An ad-hoc approach, which is frequently used in practice, is to blindly use a log transformation, which may or may not result in normal SUV distributions. This study sought to identify optimal transformations leading to normally distributed PET SUVs extracted from tumors and assess the effects of therapy on the optimal transformations. Methods. The optimal transformation for producing normal distributions of tumor SUVs was identified by iterating the Box–Cox transformation parameter (λ) and selecting the parameter that maximized the Shapiro–Wilk P-value. Optimal transformations were identified for tumor SUVmax distributions at both pre and post treatment. This study included 57 patients that underwent 18F-fluorodeoxyglucose (18F-FDG) PET scans (publically available dataset). In addition, to test the generality of our transformation methodology, we included analysis of 27 patients that underwent 18F-Fluorothymidine (18F-FLT) PET scans at our institution. Results. After applying the optimal Box–Cox transformations, neither the pre nor the post treatment 18F-FDG SUV distributions deviated significantly from normality (P > 0.10). Similar results were found for 18F-FLT PET SUV distributions (P > 0.10). For both 18F-FDG and 18F-FLT SUV distributions, the skewness and kurtosis increased from pre to post treatment, leading to a decrease in the optimal Box–Cox transformation parameter from pre to post treatment. There were types of distributions encountered for both 18F-FDG and 18F-FLT where a log transformation was not optimal for providing normal SUV distributions. Conclusion. Optimization of the Box–Cox transformation, offers a solution for identifying normal SUV transformations for when the log transformation is insufficient. The log transformation is not always the appropriate transformation for producing normally distributed PET SUVs.

A deep learning-based method for detecting non-certified work on construction sites

The construction industry is a high hazard industry. Accidents frequently occur, and part of them are closely relate to workers who are not certified to carry out specific work. Although workers without a trade certificate are restricted entry to construction sites, few ad-hoc approaches have been commonly employed to check if a worker is carrying out the work for which they are certificated. This paper proposes a novel framework to check whether a site worker is working within the constraints of their certification. Our framework comprises key video clips extraction, trade recognition and worker competency evaluation. Trade recognition is a new proposed method through analyzing the dynamic spatiotemporal relevance between workers and non-worker objects. We also improved the identification results by analyzing, comparing, and matching multiple face images of each worker obtained from videos. The experimental results demonstrate the reliability and accuracy of our deep learning-based method to detect workers who are carrying out work for which they are not certified to facilitate safety inspection and supervision.

Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models.

We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on ad-hoc approaches to the high-dimensional estimation. To overcome these limitations, we propose a Markov-switching dynamic factor model, which allows the dynamic connectivity states in functional magnetic resonance imaging (fMRI) data to be driven by lower-dimensional latent factors. We specify a regime-switching vector autoregressive (SVAR) factor process to quantity the time-varying directed connectivity. The model enables a reliable, data-adaptive estimation of change-points of connectivity regimes and the massive dependencies associated with each regime. We develop a three-step estimation procedure: 1) extracting the factors using principal component analysis, 2) identifying connectivity regimes in a low-dimensional subspace based on the factor-based SVAR model, and 3) constructing high-dimensional state connectivity metrics based on the subspace estimates. Simulation results show that our estimator outperforms -means clustering of time-windowed coefficients, providing more accurate estimate of time-evolving connectivity. It achieves percentage of reduction in mean squared error by 60% when the network dimension is comparable to the sample size. When applied to the resting-state fMRI data, our method successfully identifies modular organization in the resting-statenetworksin consistencywith other studies. It further reveals changes in brain states with variations across subjects and distinct large-scale directed connectivity patterns across states.