Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Learning for Classification of Alzheimer Disease based on Cortical Thickness and Hippocampal Shape Analysis

Objectives Mobile healthcare applications are becoming a growing trend. Also, the prevalence of dementia in modern society is showing a steady growing trend. Among degenerative brain diseases that cause dementia, Alzheimer disease (AD) is the most common. The purpose of this study was to identify AD patients using magnetic resonance imaging in the mobile environment. Methods We propose an incremental classification for mobile healthcare systems. Our classification method is based on incremental learning for AD diagnosis and AD prediction using the cortical thickness data and hippocampus shape. We constructed a classifier based on principal component analysis and linear discriminant analysis. We performed initial learning and mobile subject classification. Initial learning is the group learning part in our server. Our smartphone agent implements the mobile classification and shows various results. Results With use of cortical thickness data analysis alone, the discrimination accuracy was 87.33% (sensitivity 96.49% and specificity 64.33%). When cortical thickness data and hippocampal shape were analyzed together, the achieved accuracy was 87.52% (sensitivity 96.79% and specificity 63.24%). Conclusions In this paper, we presented a classification method based on online learning for AD diagnosis by employing both cortical thickness data and hippocampal shape analysis data. Our method was implemented on smartphone devices and discriminated AD patients for normal group.
Engineering tourism system of systems for guided emergence and desired capability From a perspective of intelligence approach

Tourism is an industry features multiple types of associated socio-economic sectors. Though complexity in the industry is recognized, research methodology has generally been reductionist, and has not understood tourism as a monolithic meta-system composed of independent and contributive component systems. Therefore, interpretations of tourism development are highly focused and sector-specific, thus limiting understanding of the complex inter-component relationships, the emergent behavior in system evolution and the impact of different component on the overall industrial capacity. This paper explores system of systems (SoS) science as an alternative paradigm to analyze the industry, and suggests that intelligence approaches is required to realize the engineering of the system of systems' operation towards desired outcomes. The industrial analysis and policy making will benefit from a system of systems engineering (SoSE) program. Therefore, this paper discusses a framework of an intelligence-approach based SoSE process focusing on guided emergence and overall capacity promotion of the tourism SoS. Intelligence approach and pattern recognition techniques are of great potential for system of systems research, particularly in identifying SoS architecture, characteristics, and the complex relationships between different component systems or stakeholders with different interests.
Structural Biology and Crystallization Communications High-quality Crystals of Human Haematopoietic Prostaglandin D Synthase with Novel Inhibitors

Human haematopoietic prostaglandin D synthase (H-PGDS; EC 5.3.99.2) produces prostaglandin D 2 , an allergic and inflammatory mediator, in mast cells and Th2 cells. H-PGDS has been crystallized with novel inhibitors with half-maximal inhibitory concentrations (IC 50) in the low nanomolar range by the counter-diffusion method onboard the Russian Service Module on the International Space Station. The X-ray diffraction of a microgravity-grown crystal of H-PGDS complexed with an inhibitor with an IC 50 value of 50 nM extended to 1.1 A ˚ resolution at 100 K using SPring-8 synchrotron radiation, which is one of the highest resolutions obtained to date for this protein.
A low-complexity sphere detection technique for orthogonal frequency division multiplexing systems in selective fading channels

This paper presents a low-complexity detection scheme, which consists of a lattice sphere decoder (LSD), for Orthogonal Frequency Division Multiplexing (OFDM) systems. The inter-carrier interference (ICI) resulting from selective fading channel destroys the orthogonality of the OFDM signal and poses a significant limitation on wireless OFDM systems. The aim of this paper is to propose an efficient detection technique in order to provide reliable communications using OFDM in selective fading channels. The proposed detection technique combines the new LSD with two regularization methods. Theoretical analysis and simulation results show that the proposed detection scheme substantially improves the symbol error rate (SER) performance of OFDM systems. Moreover, its complexity is 22 times lower than that reported in a previous work, and 51 times lower than the complexity of the original sphere decoding (SD) technique.
Heuristics and pedigrees for drawing directed graphs

We describe here a collection of heuristics for producing "nice"- drawings of directed graphs, and a simple dual-mode software tool for testing and evaluating them. In playing mode, the heuristics are applied in random sequence over a set of drawings, in the manner of an asynchronous team (A-team). As new drawings are added to the set, others are deleted based on a multi-valued aesthetic evaluation function. By inspecting the "pedigree" of the best solutions found in playing mode, the user can obtain insights into the best order in which the heuristics should be applied. Then the user can test these insights in the working mode, where the heuristics are applied in a fixed sequence.Some of the heuristics that we describe here are similar to the steps of Sugiyama's D-ABDUCTOR graph-drawing package; and indeed we can obtain results similar to those of D-ABDUCTOR, by applying our heuristics in the proper sequence.
Classification of remote sensed data using linear kernel based support vector machines

Study of remote sensed imagery has gained practical significance in various domains such as environmental monitoring, fire risk mapping, change detections and land use. Classification is a data mining methodology which is used to assign class labels to data instances and build a model so as to be able to predict class labels for unlabelled data. In this paper algorithms based on parametric distribution model like k nearest neighbor classifier and linear kernel based support vector machines classifier are used for classifying remote sensed data. A generic algorithm is discussed to implement the said classification. We finally analyze the performance of these algorithms based on various parameters.
Slicing Machine Code

Machine-code slicing is an important primitive for building binary analysis and rewriting tools, such as taint trackers, fault localizers, and partial evaluators. However, it is not easy to create a machinecode slicer that exhibits a high level of precision. Moreover, the problem of creating such a tool is compounded by the fact that a small amount of local imprecision can be amplified via cascade effects. Most instructions in instruction sets such as Intel’s IA-32 and ARM are multi-assignments: they have several inputs and several outputs (registers, flags, and memory locations). This aspect of the instruction set introduces a granularity issue during slicing: there are often instructions at which we would like the slice to include only a subset of the instruction’s multiple assignments, whereas the slice is forced to include the entire instruction. Consequently, the slice computed by state-of-the-art tools is very imprecise, often including essentially the entire program. We present an algorithm to slice machine code more accurately. To counter the granularity issue, our algorithm attempts to include in the slice only the subset of assignments in an instruction’s semantics that is relevant to the slicing criterion. Our experiments on IA-32 binaries of FreeBSD utilities show that, in comparison to slices computed by a state-ofthe-art tool, our algorithm reduces the number of instructions in backward slices by 36%, and in forward slices by 82%.
Multiple Instance Regression

This paper introduces multiple instance regression, a variant of multiple regression in which each data point may be described by more than one vector of values for the independent variables. The goals of this work are to (1) understand the computational complexity of the multiple instance regression task and (2) develop an efficient algorithm that is superior to ordinary multiple regression when applied to multiple instance data sets.
On the Regularization of Zeno Hybrid Automata

Fundamental properties of hybrid automata, such as existence and uniqueness of executions , are studied. Particular attention is devoted to Zeno hybrid automata, which are hybrid automata that take innnitely many discrete transitions in nite time. It is shown that regularization techniques can be used to extend the Zeno executions of these automata to times beyond the Zeno time. Diierent types of regularization may, however, lead to diierent extensions. A water tank control problem and a bouncing ball system are used to illustrate the results.
Estimation of urbanization effect on climatic warming over the recent 30 years in Guangzhou, South China

The urbanization effect on climate warming over the recent three decades (1978~2007) in Guangzhou, south China, where has experienced rapid urbanization process since China's reform and opening-up initiated in the late 1978, was estimated by comparing surface air temperature (SAT) observations in the urban meteorological station with those in its two surrounding rural stations. The climatic warming trend in Guangzhou city is 0.44°C/decade for the urban station and an average of 0.32°C/decade for the rural stations, resulting in an urban warming rate of 0.12°C/decade and a 27.3% contribution proportion to the climatic trend. In addition, there are obvious seasonal changes in the urban heat island intensity and its contribution to urban warming in the climatic change. The largest seasonal urban warming rate due to UHI is in summer and spring with 0.14°C/decade, the second in autumn with 0.11°C/decade, and the smallest in winter being 0.09°C/decade. The largest contribution of urban warming to SAT trend in season is in summer (51.9%), the second in autumn (27.5%), the third in spring (25.9%), and the least in winter (16.4%). Results indicate that urbanization effect could produce different seasonal pattern in UHI intensity variation, enhance the climatic warming rate, and result in overestimating the climatic warming trend to some considerable extent at the local scale. This study can help further improve the understanding of urban climatic change and global warming.
SIGNIFICANCE OF BIOCHEMICAL PARAMETERS IN DIAGNOSIS, TREATMENT, AND PREVENTION OF DEPRESSIVE-DISORDERS

There is a suggestive evidence for a relationship between central 5-HT and the occurrence of certain types of depressions. This evidence is derived from three sources: postmortem studies; measurement of CSF 5-HIAA; accumulation of CSF 5-HIAA after transport blockade by probenecid. Disturbances of central 5-HT metabolism are not typical for any depression but for certain types of vital (endogenous) depression. This implies that the group of vital depression, though tending towards homogeneity in terms of symptomatology, is heterogenous in biochemical terms and comprises patients with and without disorders in central 5-HT metabolism. It is plausible that disorders of the 5-HT metabolism play a role in the pathogenesis of depression, instead of resulting from them. This statement is based on the following findings: (i) 5-HTP can abolish or alleviate the depressive syndrome or some of its elements. (ii) This 5-HTP effect can be potentiated by clomipramine (Anafranil), a relative selective inhibitor of 5-HT reuptake. (iii) There exists a negative correlation between 5-HT turnover in the CNS and the therapeutic effect of clomipramine. The alleged distrurbances in central 5-HT are more likely to be predisposing than of direct causative significance. This assumption is based on two observations: (i) In more that 50% of cases, the 5-HT turnover remains low after clinical recovery, the patient being drug-free. (ii) There is suggestive evidence that abolition of the 5-HT deficit (by means of 5-HTP) exerts a prophylactic effect in uni-and bipolar depression.
Defeating Memory Corruption Attacks using Object Level Dynamic Taint Analysis

Computer security is severely threatened by memory corruption vulnerabilities, such as buffer overflows and format string bugs. Information flow tracking or taint analysis has proved to be one of the most effective techniques in defending against a wide range of such attacks. It works by tracking the source of each byte of data that is manipulated by the program, and detects attacks that overwrites pointers with untrusted data. However, current taint analysis based approaches are not very practical as they either require source code or non-trivial hardware extensions or incur prohibitive run time overheads. More importantly, they do not defend against non-control data attacks, making them susceptible to privilege escalation. In this paper, we propose Object Level Dynamic Taint Analysis, a very low-overhead information flow tracking technique. We propagate taint on contiguous chunks of memory designated as objects, rather than individual words of memory. Our experiments with several exploits show that Object Level Dynamic Taint Analysis effectively detects various types of memory corruption attacks, including noncontrol data attacks. Also, as it incurs modest performance overhead, it is practical for use in production environments.
Efficacité de la thérapie assistée par l’animal sur les symptômes psychologiques et comportementaux de la démence

Les symptomes psychologiques et comportementaux de la demence (SPCD) sont frequents et peuvent concerner jusqu’a 90 % des patients atteints de la maladie d’Alzheimer. Considerant l’efficacite limitee et l’ampleur des effets secondaires observes avec les traitements psychotropes en France, la majorite des directives existantes soulignent l’importance de la recherche clinique sur la maladie d’Alzheimer et une amelioration de l’evaluation des approches non pharmacologiques (ANP). En 2016, Le recours a la mediation animale, comme prise en soins, en etablissements d’hebergement pour personnes âgees dependantes (EHPAD) est de plus en plus frequent. Nous avions montre les bienfaits de cette ANP sur l'apathie dans la maladie d'Alzheimer et avons souhaite en demontrer son efficacite. Notre etude evalue et mesure, principalement a l’aide de l’Inventaire Neuropsychiatrique version Equipe soignante (NPI-ES), comment la presence du chien dans la psychotherapie des malades Alzheimer est associee a des niveaux de SPCD chez des femmes et des hommes, âges en moyenne de 85 ans qui vivent en institution. Nous nous sommes concentres sur le bien-etre et la construction d’emotions positives de la personne âgee demente, en particulier sur la revalorisation de l’estime de soi, la stimulation, la remobilisation et le maintien des capacites cognitives preservees, comme base therapeutique possible dans l’association de la presence du chien avec la diminution significative des SPCD.
Leukotriene antagonists in nasal polyposis: A meta-analysis and systematic review

Background Leukotriene antagonists (LTAs) provide a potential strategy for the management of chronic rhinosinusitis with nasal polyposis (CRSwNP), which is often refractory to medical and surgical treatment. The purpose of this study is to determine the impact of LTA treatment alone and in conjunction with intranasal corticosteroids (INCSs) on nasal symptoms, objective clinical outcomes, and immune parameters in CRSwNP. Methods A systematic review was performed including studies that assessed the effectiveness of LTAs on clinical outcome measures of CRSwNP. Exclusion criteria were trials assessing LTAs in CRS without nasal polyps or asthma symptoms only. Quantitative analysis was performed using a random effects model. Results Twelve studies fulfilled eligibility: five randomized control trials and seven case series. LTAs showed significant improvements in CRSwNP symptoms over placebo; however, these randomized trials were unable to be combined via meta-analysis. The two studies used in meta-analysis showed a standardized mean difference of pooled overall symptom scores of 0.02 (95% confidence interval, −0.39–0.44) between LTA and INCS study arms, indicating no difference between the treatment modalities. Improvement was described by all studies in symptoms, clinical outcomes, and/or immune parameters after LTA treatment, with greater improvements in a subset of symptoms beyond that observed with INCSs. Concomitant asthma, aspirin-exacerbated respiratory disease, and atopy did not significantly or consistently affect these results. Conclusion LTAs are an effective tool for treating CRSwNP, with limited benefit as an adjunctive therapy. Additional study is required to determine the most beneficial strategy and patient population for their use.
Zeroth-Order Resonators With Variable Reactance Loads at Both Ends

Effects of the reflection condition at both ends of a zeroth order-resonator are investigated. The proposed resonator is composed of a composite right/left-handed transmission line (CRLHTL) and two reactive elements as reflectors at both ends. The load reactance at one end is chosen as the complex conjugate of the other. The balanced case without bandgaps between right- and left-handed passbands, as well as the unbalanced cases, are discussed for CRLHTLs employed in the resonators. The superposition of stored energies of series resonance in the series branch and parallel resonance in the shunt branch in the ladder network can be continuously and arbitrarily controlled by changing only the load reactance at the terminals. For unbalanced cases, the resonance occurs within the bandgap. Coexistence of the two different resonances is verified by directly observing the near-field distribution of the resonator in the numerical simulation and the measurement.
FARM DECISION INFORMATION COSTS AND RETURNS REVISITED

With the explosion of computer technology there has been a major effort put into farm management decision aid research and development. Too much of this work has ignored the basic costs and returns of the information provided. The farmer will determine which systems are useful to their decision making as shown by the software and systems used. A review of farmers' views on the economics of farm computers, particularly in New Zealand, is provided. Reference is also made to studies in other countries. As information provision is closely related to computer technology, farm computer adoption rates are reviewed. This is an important test of the value of information. Farmers' conclusions on the data and information to collect must be a very personal decision that will be different for each case.
1 An Introduction to Event-Related Potentials and Their Neural Origins

This chapter introduces the event-related potential (ERP) technique. The first section describes the goals of this book and discusses the perspective from which I’ve written it. The second section provides a brief history of the ERP technique. The third section describes two simple ERP experiments as examples that introduce some of the basic concepts of ERP experimentation. The fourth section describes the advantages and disadvantages of the ERP technique in relation to other techniques. The fifth section describes the neural and biophysical origins of ERPs and the associated event-related magnetic fields. The final section contains a brief description of the most commonly observed ERP components in cognitive neuroscience experiments.
Rational-Harmonic Balancing Approach to Nonlinear Phenomena Governed by Pendulum-Like Differential Equations

This paper presents a new approach for solving accurate approximate analytical solutions for nonlinear phenomena governed by pendulum-like differential equations. The new approach couples Taylor series expansion with rational harmonic balancing. An approximate rational solution depending on a small parameter is considered. After substituting the approximate solution into the governing differential equation, this equation is expanded in Taylor series of the parameter prior to harmonic balancing. The approach gives a cubic equation, which must be solved in order to obtain the value of the small parameter. A method for transforming this cubic equation into a linear equation is presented and discussed. Using this approach, accurate approximate analytical expressions for period and periodic solutions are obtained. We also compared the Fourier series expansions of the analytical approximate solution and the exact one. This allowed us to compare the coefficients for the different harmonic terms in these solutions. These analytical approximations may be of interest for those researchers working in nonlinear physical phenomena governed by pendulum-like differential equations in fields such as classical mechanics, vibrations, acoustics, electromagnetism, electronics, superconductivity, optics, gravitation, and others
Cross-Layer Design and Optimization forWireless Sensor Networks

Cross-layer design and optimization is a new technique which can be used to design and improve the performance in both wireless and wireline networks. The central idea of cross-layer design is to optimize the control and exchange of information over two or more layers to achieve significant performance improvements by exploiting the interactions between various protocol layers. In this paper, we propose a cross-layer design and optimization framework and the concept of using an optimization agent to provide the exchange and control of information between the various protocol layers to improve the performance in wireless sensor networks. The approach is to investigate the effects of the wireless channel and the physical layer performance of a small scale wireless sensor network (WSN) to develop insights that can be used in the design and development of the optimization agent in the proposed cross-layer framework. Performance measurements were conducted to study the effects of interference and transmission range for a group of networked wireless sensors
Learning to Improve Software Processes: Making Sense of Practice

Software Process Improvement (SPI) programs are frequently considered to be planned in nature. However, there is recent evidence to suggest that SPI can be understood as a form of learning. Drawing on the organizational learning literature, this paper proposes an active learning perspective of improvements in processes. This view recognizes the various actors in the project to be reflective in their actions, making sense of the current context and thus designing their use of the process to best suit their needs at the time. The changes in the processes emerge through ongoing adjustments, experimentation and improvisation as developers and managers seek to improve their product development.