Miracle-dz/UniFAR-FTUs huggingface dataset:
The complex iron line in NGC 7469 observed by BeppoSAX

In this letter we present analysis of BeppoSAX data from a long look at the Seyfert 1 galaxy NGC 7469. The presence of a soft excess below 0.8 keV is confirmed by our analysis and no warm absorber component is required tofit the spectrum. A complex iron emission line and the Compton reflection hump are clearly detected. The profile of the line is too broad to associate this feature with distant matter. In addition, the observed soft excess and the energy of the iron line E F e = 6.8 keV strongly support a scenario in which the hard X-rays are reprocessed by a photoionized accretion disc. This hypothesis was tested by fitting the BeppoSAX spectrum with the ionized disc reflection model of Ross & Fabian. A second narrow line component, in addition to that produced in the disc, is also required to fit the observed iron line profile. A high energy cut-off around 150 keV is clearly detected in the spectrum.
Optimal Location for Shunt Connected FACTS Devices in a Series Compensated Long Transmission Line

Shunt FACTS devices are used for controlling transmission voltage, power flow, reducing reactive losses, and damping of power system oscillations for high power transfer levels. In this paper the optimal location of a shunt FACT device is investigated for an actual line model of a transmission line having series compensation at the center. Effect of change in degree of series compensation on the optimal location of the shunt FACTS device to get the highest possible benefit is studied. It is found that the optimal location of the shunt FACTS device varies with the change in the level of series compensation to get the maximum benefit in terms of power transfer capability and stability of the system.
Combining constraint Propagation and meta-heuristics for searching a Maximum Weight Hamiltonian Chain

This paper presents the approach that we developed to solve the ROADEF 2003 challenge problem. This work is part of a research program whose aim is to study the benefits and the computer-aided generation of hybrid solutions that mix constraint programming and meta-heuristics, such as large neighborhood search (LNS). This paper focuses on three contributions that were obtained during this project: an improved method for propagating Hamiltonian chain constraints, a fresh look at limited discrepancy search and the introduction of randomization and de-randomization within our combination algebra. This algebra is made of terms that represent optimization algorithms, following the approach of SALSA[1], which can be generated or tuned automatically using a learning meta-strategy [2]. In this paper, the hybrid combination that is investigated mixes constraint propagation, a special form of limited discrepancy search and large neighborhood search.
A Theory of Linear Objects

Recently, linearity has been proposed as a mechanism for memory management, alias control, and typestate tracking. While linear type systems have been extensively studied in functional programming, their use in object-oriented systems has been limited to useful but ad-hoc annotation systems that track unique pointers. In this paper, we study object-oriented linearity at level of foundational object calculi. Our system tracks not only linear objects (to which there may be only one pointer), but linear methods as well (which may be called at most once). Tracking linear objects allows us to ensure type safety for imperative, type-changing update to methods and imperative, type-changing dynamic inheritance. Because some aliasing is important in practical systems, our system supports linear and non-linear objects and methods and a novel region system that permits borrowed aliases to linear objects. To enforce safety, we allow type-changing modifications only on unborrowed linear objects, but permit such changes again when these borrowed references are no longer accessible. Note: an earlier version of this paper appeared in the proceedings of FOOL ’06. The current paper includes a simpler and cleaner formalism (with only methods, no lambdas) and significantly extends the previous system with borrowing via regions.
Performance evaluation of FlexRay/CAN networks interconnected by a gateway

The coexistence of both CAN and FlexRay networks in contemporary and future automobiles necessitates the use of FlexRay/CAN gateways that support the timely data exchange among the different networks. In this paper, we report on the implementation of such FlexRay/CAN gateway. Moreover, for the first time, we investigate appropriate network and gateway configurations that are beneficial for the overall network performance in the sense of small delays of gateway messages.
Critical tissue residue approach linking accumulated metals in aquatic insects to population and community-level effects.

Whole body Zn concentrations in individuals (n = 825) from three aquatic insect taxa (mayflies Rhithrogena spp. and Drunella spp. and the caddisfly Arctopsyche grandis) were used to predict effects on populations and communities (n = 149 samples). Both mayflies accumulated significantly more Zn than the caddisfly. The presence/absence of Drunella spp. most reliably distinguished sites with low and high Zn concentrations; however, population densities of mayflies were more sensitive to increases in accumulated Zn. Critical tissue residues (634 μg/g Zn for Drunella spp. and 267 μg/g Zn for Rhithrogena spp.) caused a 20% reduction in maximum (90th quantile) mayfly densities. These critical tissue residues were associated with exposure to 7.0 and 3.9 μg/L dissolved Zn for Drunella spp. and Rhithrogena spp. , respectively. A threshold in a measure of taxonomic completeness (observed/expected) was observed at 5.4 μg/L dissolved Zn. Dissolved Zn concentrations associated with critical tissue residues in mayflies were also associated with adverse effects in the aquatic community as a whole. These effects on populations and communities occurred at Zn concentrations below the U.S. EPA hardness-adjusted continuous chronic criterion.
Detection performance with improved principal component analysis for MIMO radar

Motivated by the development of MIMO communication, Multiple-Input Multiple-Output (MIMO) radar has drawn considerable attention. Currently, most articles about MIMO Radar analysis focus on angle diversity, the article investigates principal component analysis used in MIMO Radar detection, further studies the performance of transformation of logarithm as its improvement. The simulation results indicates that the method used in this paper can approach to the original target simulation results, meanwhile this method reduces computation complexity and saves computation time.
Normative values for selected linear indices of the intracranial fluid spaces based on CT images of the head in children

Summary Background: Currently, a few imaging methods are used in CNS diagnostics: computed tomography – CT, magnetic resonance imaging – MRI, and ultrasonography – USG. The ventricular system changes its dimensions with child’s development. Linear indices commonly used in the diagnostics of hydrocephalus do not consider developmental changes of the intracranial fluid spaces. The aim of our work was to identify reference values for selected linear indices in specific age groups. Material/Methods: The material included 507 CT examinations of the head in children of different age and both sexes. There were 381 CT examinations considered as normal and they were used to establish the reference values. They were compared with 126 CTs from the observational zone (3–10 percentile and 90–97 percentile). The children were divided into 7 following age groups: 0–12 months, >12–36 months, >3–6 years, >6–9 years, >9–12 years, >12–15 years, >15–18 years. For every group, the 10th, 25th, 50th, 75th and 90th percentile was calculated. The range between the 10th and the 90th percentile was described as a norm. Results: Reference values for particular indices: Huckman Number from 3.3 to 5.0 cm with correlation coefficient according to age equal to 0.34; Evans’ Index from 0.218 to 0.312 with correlation coefficient of −0.12; Bifrontal Index from 0.265 to 0.380 with correlation coefficient of 0.18; Bicaudate / Frontal Index from 0.212 to 0.524 with correlation coefficient of −0,33; Bicaudate Index from 0.059 to 0.152 with correlation coefficient of −0.26; Bicaudate / Temporal Index from 0.051 to 0.138 with correlation coefficient of 0.32; Schiersmann’s Index from 3.545 to 6.038 with correlation coefficient of 0.42. Conclusions: The intracerebral CSF spaces increased in a non-uniform manner with age. All indices established on the basis of linear parameters were relatively higher in younger children than in the older ones. In proportion to the cranial size, the intracranial fluid spaces decreased according to the age.
On-line adaptive learning of CDHMM parameters based on multiple-stream prior evolution and posterior pooling

Based on the concept of multiple-stream prior evolution and posterior pooling, we propose a new incremental adaptive Bayesian learning framework for e cient on-line adaptation of the continuous density hidden Markov model (CDHMM) parameters. As a rst step, we apply the a ne transformations to the mean vectors of CDHMMs to control the evolution of their prior distribution. This new stream of prior distribution can be combined with another stream of prior distribution evolved without any constraints applied. In a series of comparative experiments on the task of continuous Mandarin speech recognition, we show that the new adaptation algorithm achieves a similar fast-adaptation performance as that of incremental MLLR (maximum likelihood linear regression) in the case of small amount of adaptation data, while maintains the good asymptotic convergence property as that of our previously proposed quasi-Bayes adaptation algorithms.
OptiThin - Implementation of Precision Horticulture by Tree-Specific Mechanical Thinning

Apples show biennial fluctuations in yields (alternate bearing). The alternate bearing cycle can be broken by reducing excessive flowers using either chemical or mechanical means. Currently, automatic thinning systems are treating the trees uniformly despite the fact that neighbouring trees can have a very different numbers of flowers resulting in different thinning requirements. In order to solve this problem the joint project OptiThin has been started. The aim of OptiThin was to develop a system that adapts thinning intensity individually to each tree by reducing the flowers efficiently and eco-friendly. In the result, OptiThin comprises a set of new technologies including: a) a stereo camera with software for real-time determination of flower density per tree; b) a shock absorbing camera platform; c) a mobile geographic information system; d) a decision support tool to calculated optimum thinning intensity based on current flower density and ancillary data (e.g. yield, soil); e) a mechanical thinning unit which is controlled in real-time. It was shown that adaptive management of excessive flowers could improve yield of marketable fruits and that environmental friendly mechanical thinning – without chemicals – is feasible.
T-Cell Therapy: Options for Infectious Diseases

The emergence of drug-resistant tuberculosis is challenging tuberculosis control worldwide. In the absence of an effective vaccine to prevent primary infection with Mycobacterium tuberculosis and tuberculosis disease, host-directed therapies may offer therapeutic options, particularly for patients with multidrug-resistant and extensively drug-resistant tuberculosis where prognosis is often limited. CD8+ and CD4+ T cells mediate antigen-specific adaptive cellular immune responses. Their use in precision immunotherapy in clinical conditions, especially in treating cancer as well as for prevention of life-threatening viral infections in allogeneic transplant recipients, demonstrated safety and clinical efficacy. We review key achievements in T-cell therapy, including the use of recombinant immune recognition molecules (eg, T-cell receptors and CD19 chimeric antigen receptors), and discuss its potential in the clinical management of patients with drug-resistant and refractory tuberculosis failing conventional therapy.
Visual Perception and Memory Impairments in Children at Risk of Nonverbal Learning Disabilities

Visuospatial working memory (VSWM) and visual perception were examined in two groups aged 11–13, one with children displaying symptoms of nonverbal learning disability (NLD) (n = 18) and the other a control group without learning disabilities (n = 18). The two groups were matched for general verbal abilities, age, gender, and socioeconomic level. The children were presented with VSWM tests involving visual and spatial-simultaneous processes, and also with a classical visual illusion, a classical ambiguous figure, as well as visual perception tests specifically devised for the present study. Results revealed that performance of children at risk of NLD was worse than controls in some VSWM and in visual perception tests without memory involvement; these latter required comparisons of visual stimuli and locations in space with distractors. Moreover, the two groups differed in perceiving the classical ambiguous figure. Findings are discussed in the light of both theoretical and clinical implications.
DeepSea: self-adaptive data partitioning and replication in scalable distributed data systems

While originally proposed to provide fault-tolerance and scalability for data analysis queries on unstructured data over massive clusters, MapReduce systems today are being used for analysis of rich combinations of unstructured, semi-structured and structured data. To achieve performance on these new workloads, MapReduce systems (and the distributed file systems on which they are built) can no longer rely on static data placement strategies. In this thesis, we propose new physical data independence and adaptive data tuning solutions that can greatly improve the performance of analysis queries in systems where workloads are not static and where workloads may include complex queries with overlapping or related computations (subqueries). While profiting from the work on physical data independence in relational systems, we propose novel strategies that recognize the central role of data partitioning (and co-partitioning) in shared-nothing distributed file systems.
Serum Deprivation via MAP Kinase Pathway Endothelin-1 Inhibits Apoptosis of Vascular Smooth Muscle Cells Induced by Nitric Oxide

Endothelin (ET)-1, an endothelium-derived vasoconstrictor and mitogen, acts as an antiapoptotic factor against serum deprivation–induced apoptosis of endothelial cells and fibroblasts but enhances apoptosis of some cancer cells. In the present study, we examined whether nitric oxide (NO) and ET-1 modulate apoptosis of rat vascular smooth muscle cells (VSMCs) via the mitogen-activated protein (MAP) kinase pathway. Both serum deprivation and NO donors (FK409 and SNAP) caused apoptosis of VSMCs, as demonstrated by TdT-mediated dUTP-biotin nick end-labeling, appearance of fragmented DNA, and induction of caspase-3 activity. ET-1 dose-dependently antagonized apoptosis induced by serum deprivation and NO donors. A selective ET A receptor antagonist (BQ123) and a nonselective ET A/B receptor antagonist (TAK044), but not a selective ET B receptor antagonist (BQ788), inhibited the antiapoptotic effect of ET-1, indicating that the antiapoptotic effect of ET-1 is mediated via the ET A receptor. ET-1 activated MAP kinase, whose effect was inhibited by FK409. Transfection with an unphosphorylated wild-type MAP kinase kinase-1 (MAPKK-1) or its constitutively activated mutant protected VSMCs against apoptosis induced by serum deprivation and NO donors. Inhibition of MAP kinase activity with PD98059, a specific inhibitor of MAPKK-1, or by transfection of a dominant-negative MAPKK-1 mutant antagonized the antiapoptotic effect of ET-1, suggesting the involvement of MAP kinase in the antiapoptotic effect. The potent inhibitory effect of ET-1 on apoptosis of VSMCs induced by serum deprivation and NO suggests that the counterbalance between the 2 endothelium-derived factors contributes to the process of vascular remodeling by determining VSMC survival and death, respectively, via a common MAP kinase pathway. (Arterioscler Thromb Vasc Biol . 2000;20:989-997.)
How Does Biological Belief in Race Relate to Our Feelings towards In-Group and Out-Groups?: A Cognitive Dissonance Framework.

This study considered the effect of belief in race as a biological construct (RACEBIO) and intergroup anxiety (IGA) on in-group racial salience (IGRS) and out-group discomfort (OGD). Participants included 66 racially and ethnically diverse high school boarding students. As hypothesized, RACEBIO was positively related to both IGRS and OGD. In addition, the relationship between RACEBIO and IGRS was significantly stronger among participants with high IGA. The findings support a cognitive dissonance model suggesting that people with high IGA might rationalize their in-group racial salience by strengthening their belief that racial groups are biologically distinct and socially incompatible. Implications for integrating cognitive and affective experiences in educational and clinical interventions are discussed.
The language of digital constitutionalism and the role of national parliaments

Attempts to establish constitutional provisions for the Internet have been promoted since the late 1990s, mainly by the global civil society and intergovernmental organisations. More recently, a new wave of digital constitutionalism has emerged from the nation-state level, and particularly from national parliaments. In order to better understand this process, the article seeks to investigate, from both a theoretical and an empirical perspective, whether and to what extent parliamentary initiatives exhibit specific political features compared to constitutional attempts emerging from other kinds of sources. Further, the study aims to assess if drafting initiatives overlap or, rather, respond in different ways to different constitutional concerns.
k-NN Based Neuro-fuzzy System for Time Series Prediction

Neuro-fuzzy systems have been proposed for different applications for many years. In this paper, a k-NN based neuro-fuzzy predictor is developed for time series prediction. We use a neuro-fuzzy system to generate prediction results. A set of fuzzy rules can be generated by a self-constructing clustering method. These rules can be refined by a hybrid learning algorithm. In stead of using all training data to training a model, we utilize the k-NN method to dynamically select k instances for each prediction. Experimental results show that our approach can provide more accurate predictions than other methods.
Video Steganalysis Exploiting Motion Vector Reversion-Based Features

Unlike traditional image or video steganography in spatial/transform domain, motion vector (MV)-based methods target the internal dynamics of video compression and embed messages while performing motion estimation. However, we have noticed that some existing methods adopt nonoptimal selection rules and modify MVs in somewhat arbitrary manners which violate the encoding principles a lot. Aiming at these weaknesses, we design a calibration-based approach and propose MV reversion-based features for steganalysis. Experimental results demonstrate that the proposed features are very sensitive to the tendency of MV reversion during calibration and can be used to effectively detect some typical MV-based steganography even with low embedding rates.
Decision-Driven Models with Probabilistic Soft Logic

We introduce the concept of a decision-driven model, a probabilistic model that reasons directly over the uncertain information of interest to a decision maker. We motivate the use of these models from the perspective of personalized medicine. Decision-driven models have a number of benefits that are of particular value in this domain, such as being easily interpretable and naturally quantifying confidences in both evidence and predictions. We show how decision-driven models can easily be constructed using probabilistic soft logic, a recently introduced framework for statistical relational learning and inference which allows the specification of medical domain knowledge in concise first-order-logic rules with assigned confidence values.
Scheduler for data-intensive workflows in public clouds

Data-intensive workflows can require the use of intermediary data storage in the order of terabytes. Thus, planning the execution of such workflows in the cloud considering only processing demand, regardless its data storage needs, leads to performance decrease and potential increase in costs. In this paper, we present an integer linear program scheduler that considers disk storage scheduling besides the task scheduling based on processor time. The proposed scheduler aims to achieve the lowest economic cost while serving a deadline set by the user. The results show that the scheduler can find good schedules in situations where the disk size of rented virtual machines is a limiting factor.
Planning with Tests, Branches, and Non-Deterministic Actions as Satisfiability

We present an effective SAT encoding of planning with partial knowledge, tests, branches, and non-deterministic actions. As in recent work on compiling conformant and contingent planning into STRIPS, our encoding is based on representing knowledge states. Unlike previous approaches, however, fluents are conditioned on threads of execution, rather than on alternative choices of the initial state. Tests and other non-deterministic actions activate threads of execution, which are later deactivated by join operations. The experiments we present in this paper compare our SAT approach with state of the art heuristic search planners on contingent and conformant planning problems. We then present results on solving conditional planning problems that cannot even be represented by competing approaches.
Comparison of VM Live Migration Process Impacts on QoS of Web Server Hosted in Cloud

Virtual Machine (VM) Live Migration improves system manageability through VMs rearrangement in their physical hosts. System managers can redistribute VMs to tackle different challenges as: VMs consolidation for energy consumption reduction, load balancing and software rejuvenation. VM live migration consists in successive transfers of memory pages from one host to another. Thus, system capacity planning may consider VM live migration overhead. This paper presents impacts of VM live migration operations on system performance of different RAM-sized VMs (512MB, 1GB and 2GB). These experiments aims to observe how RAM size changes can affect responsiveness and reliability of a Web Server hosted on a single VM. The experiment has external client which sends web requests in a constant rate through Autobench (httperf) benchmark tool. This benchmark tool provides results of Web Server errors and response time during VM live migration process. Experiments results also consider network throughput during VM live migration. The results presents that network overhead due to VM live migration causes major system performance degradation in VMs with more amount of RAM. Therefore, this paper results may be useful for managers to consider VM live migration impacts in capacity planning.
A 98nW wake-up radio for wireless body area networks

A 0. 13μm CMOS low power wake-up radio is presented. The wake-up radio operates with -41dBm sensitivity at 915MHz using OOK modulation with a data rate of 100kbps while consuming 98nW active power, 11pW sleep power, and has an energy efficiency of 0.98pJ/bit. The wake-up radio occupies 0.03mm2 and uses two off-chip components (an inductor and a capacitor). All biasing and calibration for process variation and mismatch is included on-chip. The entire radio operates from a single 1.2V supply.