Miracle-dz/UniFAR-FTUs huggingface dataset:
Event Stream Processing in BRT Environment

Bus Rapid Transit (BRT) system is a kind of Intelligent Transportation System (ITS), which combines wireless communications, computational technologies, floating car data/floating cellular data technologies, inductive loop detection and so on. The GPS (Global Position System) data and the loop detection data are arriving at the BRT dispatch center massively at the same time so the dispatcher can know the real-time status of buses. The passenger flow data are also captured by devices deployed in the stations and then be passed to center too. How to handle those data at low latency and get some events to be used in bus dispatching is really a problem. In this paper, we propose a framework using ESP (event stream processing) technologies that solves the problem more effectively. At last we demonstrate a prototype system implementation that applies in BRT Line 1 project in Beijing.
The influence of the object-oriented language model on a supporting architecture

The MUSHROOM architecture is designed to efficiently support dynamic object-oriented languages such as Smalltalk. The authors describe the architecture, and how the object-oriented model of computation and the provision of a sophisticated computer led to various simplifications in the design and implementation of the architecture. Two particular features of the object-oriented model have been exploited in the architecture. Freedom of object identifier selection, allows for straightforward and efficient implementation in software of virtual-to-real address translation and for efficient object allocation, and the absence of address aliasing allows simple, virtually addressed caches to be used. A prototype implementation of this architecture is currently being commissioned. <<ETX>>
Critical operations in low‐level human vision

This article examines the mechanisms of the initial stages of human vision and describes the critical functions that these processes have to achieve. The basis for the report is a series of computational studies that have sought to place what is known about human vision in the contexts of real tasks. Most considerations of low‐level vision have focused on filtering and pointwise nonlinearities. Extending beyond what is already known, the present studies have identified the need for specific processes, over and above what is already known, to deal with the problems of (1) Contrast equalization: Differences in local illumination due to shadows and highlights cause variations in mean luminance and also in local contrast—the amplitude of luminance variations. This has severe effects for the filtering stage. (2) Segmentation: The image itself is a continuous and uniform data structure. It needs to be split into small primitive features from which object representations can be built. (3) Grouping: The primitive features are almost bound to be too primitive to be useful, singly, for any real visual tasks. Related primitive features that belong together need to be grouped together in a relatively tight fashion. The nature of these difficulties is examined, and potential approaches are considered and illustrated. A major feature of the work described concerns the formal properties of the data structures that will be required to hold the different types of information. Three distinct structures are considered: images, image descriptions, and visual descriptions. The latter two of these are novel. © 1996 John Wiley & Sons, Inc.
Long-term vitamin D and high-dose n-3 fatty acids’ supplementation improve markers of cardiometabolic risk in type 2 diabetic patients with CHD

Abstract This study was performed to evaluate the effects of vitamin D and n-3 fatty acids’ co-supplementation on markers of cardiometabolic risk in diabetic patients with CHD. This randomised, double-blinded, placebo-controlled trial was conducted among sixty-one vitamin D-deficient diabetic patients with CHD. At baseline, the range of serum 25-hydroxyvitamin D levels in study participants was 6·3–19·9 ng/ml. Subjects were randomly assigned into two groups either taking 50 000 IU vitamin D supplements every 2 weeks plus 2× 1000 mg/d n-3 fatty acids from flaxseed oil (n 30) or placebo (n 31) for 6 months. Vitamin D and n-3 fatty acids’ co-supplementation significantly reduced mean (P = 0·01) and maximum levels of left carotid intima–media thickness (CIMT) (P = 0·004), and mean (P = 0·02) and maximum levels of right CIMT (P = 0·003) compared with the placebo. In addition, co-supplementation led to a significant reduction in fasting plasma glucose (β −0·40 mmol/l; 95 % CI −0·77, −0·03; P = 0·03), insulin (β −1·66 μIU/ml; 95 % CI −2·43, −0·89; P < 0·001), insulin resistance (β −0·49; 95 % CI −0·72, −0·25; P < 0·001) and LDL-cholesterol (β −0·21 mmol/l; 95 % CI −0·41, −0·01; P = 0·04), and a significant increase in insulin sensitivity (β +0·008; 95 % CI 0·004, 0·01; P = 0·001) and HDL-cholesterol (β +0·09 mmol/l; 95 % CI 0·01, 0·17; P = 0·02) compared with the placebo. Additionally, high-sensitivity C-reactive protein (β −1·56 mg/l; 95 % CI −2·65, −0·48; P = 0·005) was reduced in the supplemented group compared with the placebo group. Overall, vitamin D and n-3 fatty acids’ co-supplementation had beneficial effects on markers of cardiometabolic risk.
Human Action Recognition Algorithm Based on Key Posture

A novel human action recognition algorithm based on key posture is proposed in this paper. In the method, the mesh features of each image in human action sequences are firstly calculated; then the key postures of the human mesh features are generated through k-medoids clustering algorithm; and the motion sequences are thus represented as vectors of key postures. The component of the vector is the occurrence number of the corresponding posture included in the action. For human action recognition, the observed action is firstly changed into key posture vector; then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final category. The experiments on Weizmann dataset demonstrate that our method is effective for human action recognition. The average recognition accuracy can exceed 90%.
Implantable cardioverter defibrillators

The electrical design challenges for implantable defibrillators are discussed based on the following criteria: device evolution, system architecture, therapy delivery, mid heart activity sensing circuits. The discussion includes the incidence of sudden cardiac death and the clinical requirements of device hardware. System architecture includes the three dependent entities that define the system mid the functional components of the implanted device.
Measures of Adiposity and Alzheimer's Disease-Related MRI Markers: The Northern Manhattan Study.

BACKGROUND Adiposity may increase risk for dementia and Alzheimer's disease (AD), but mechanisms are unclear. OBJECTIVE To examine associations between measures of adiposity with AD-signature region cortical thickness and hippocampal volume. METHODS We used data from the Northern Manhattan Study, a clinically stroke-free cohort of mostly Hispanic participants. Exposures of interest included body mass index (BMI), waist-hip-ratio (WHR), waist circumference (WC), and adiponectin concentration, measured at study entry. AD-signature region cortical thickness and hippocampal volume were obtained using Freesurfer. We estimated associations using multivariable linear regression, adjusting for sociodemographics and health behaviors. We re-examined estimates after adjustment for APOEɛ4 allele status or carotid intima-media thickness (cIMT), among those cognitively unimpaired, and after weighting for inverse probability of selection into the MRI sub-study. We also repeated analyses for cortical thickness in non-AD signature regions. RESULTS The sample (N = 947, 63% women, 66% Hispanic/Latino, 26% obese) had a mean (SD) age = 63 (8) years. Greater BMI and WC (both z-scored) were associated with thinner AD-signature region cortex (also z-scored) (BMI: β [95% CI] = -0.09 [-0.18, -0.01], WC: β [95% CI] = -0.11 [-0.20, -0.02]). We did not find evidence that adiposity was related to hippocampal volume. Results were consistent after adjustment for APOEɛ4 allele status or cIMT, after weighting for selection, among those cognitively unimpaired, and for non-AD signature region cortical thickness. CONCLUSION Greater BMI and WC were related to cortical thinning within and outside the AD-signature region, suggesting a global effect not specific to AD.
Reactive Oxygen Species Production from Secondary Organic Aerosols: The Importance of Singlet Oxygen.

Organic aerosols are subjected to atmospheric processes driven by sunlight, including the production of reactive oxygen species (ROS) capable of transforming their physicochemical properties. In this study, secondary organic aerosols (SOA) generated from aromatic precursors were found to sensitize singlet oxygen (1O2), an arguably underappreciated atmospheric ROS. Specifically, we quantified 1O2, OH radical and H2O2 quantum yields within photoirradiated solutions of laboratory-generated SOA from toluene, biphenyl, naphthalene, 1,8-dimethylnaphthalene. At 5 mgC L-1 of SOA extracts, the average steady-state concentration of 1O2 and of OH radicals in irradiated solutions were 3 ± 1 × 10-14 M and 3.6 ± 0.9 × 10-17 M, respectively. Furthermore, ROS quantum yields of irradiated ambient PM10 extracts were comparable to those from laboratory-generated SOA, suggesting a similarity in ROS production from both types of samples. Finally, by using our measured ROS concentrations, we predict that certain organic compounds found in aerosols, such as amino acids, organo-nitrogen compounds and phenolic compounds have shortened lifetimes by more than a factor of two when 1O2 is considered as an additional sink. Overall, our findings highlight the importance of SOA as a source of 1O2, and its potential as a competitive ROS species in photooxidation.
Targeting Seizure-Induced Neurogenesis in a Clinically Relevant Time Period Leads to Transient But Not Persistent Seizure Reduction

Mesial temporal lobe epilepsy (mTLE), the most common form of medically refractory epilepsy in adults, is usually associated with hippocampal pathophysiology. Using rodent models of mTLE, many studies including work from our laboratory have shown that new neurons born around the onset of severe acute seizures known as status epilepticus (SE) are crucial for the process of epileptogenesis and targeting seizure-induced neurogenesis either genetically or pharmacologically can impact the frequency of chronic seizures. However, these studies are limited in their clinical relevance as none of them determines the potential of blocking new neurons generated after the epileptogenic insult to alleviate the development of chronic seizures. Therefore, using a pilocarpine-induced SE model of mTLE in mice of either sex, we show that >4 weeks of continuous and concurrent ablation of seizure-induced neurogenesis after SE can reduce the formation of spontaneous recurrent seizures by 65%. We also found that blocking post-SE neurogenesis does not lead to long-term seizure reduction as the effect was observed only transiently for 10 d with >4 weeks of continuous and concurrent ablation of seizure-induced neurogenesis. Thus, these findings provide evidence that seizure-induced neurogenesis when adequately reduced in a clinically relevant time period has the potential to transiently suppress recurrent seizures, but additional mechanisms need to be targeted to permanently prevent epilepsy development. SIGNIFICANCE STATEMENT Consistent with morphological and electrophysiological studies suggesting aberrant adult-generated neurons contribute to epilepsy development, ablation of seizure-induced new neurons at the time of the initial insult reduces the frequency of recurrent seizures. In this study, we show that continuous targeting of post-insult new neurons in a therapeutically relevant time period reduces chronic seizures; however, this effect does not persist suggesting possible additional mechanisms.
The transcription factor Hey and nuclear lamins specify and maintain cell identity

The inability of differentiated cells to maintain their identity is a hallmark of age-related diseases. We found that the transcription factor Hey supervises the identity of differentiated enterocytes (ECs) in the adult Drosophila midgut. Lineage tracing established that Hey-deficient ECs are unable to maintain their unique nuclear organization and identity. To supervise cell identity, Hey determines the expression of nuclear lamins, switching from a stem-cell lamin configuration to a differentiated lamin configuration. Moreover, continued Hey expression is required to conserve large-scale nuclear organization. During aging, Hey levels decline, and EC identity and gut homeostasis are impaired, including pathological reprograming and compromised gut integrity. These phenotypes are highly similar to those observed upon acute targeting of Hey or perturbation of lamin expression in ECs in young adults. Indeed, aging phenotypes were suppressed by continued expression of Hey in ECs, suggesting that a Hey-lamin network safeguards nuclear organization and differentiated cell identity.
MRI Super Resolution Reconstruction for MRI-guided Adaptive Radiotherapy using Cascaded Deep Learning: In the Presence of Limited Training Data and Unknown Translation Model.

PURPOSE Deep learning (DL)-based super resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challenges hindering the widespread implementation of these approaches remain, however. Low-resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise that are difficult for a simple DL framework to handle. Moreover, training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high-resolution (HR) images that are often unavailable or difficult to collect. The purpose of this study is to develop a novel SR technique for MRI based on the concept of cascaded DL that allows for the reconstruction of high-quality SR images in the presence of insufficient training data, an unknown translation model, and noise. METHODS The proposed framework, based on the concept named cascaded deep learning, consists of three components: 1) a de-noising auto-encoder (DAE) trained using clinical LR noisy MRI scans that have been processed with a non-local means filter that generates denoised LR data; 2) a down-sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data for the training of a generative model; and 3) the proposed SR generative model (p-SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p-SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D breath-hold MRI axial SR reconstruction from LR axial scans (< 3 sec/vol) and in the enhancement of the spatial resolution of LR 4D-MRI acquisitions (0.5 sec/vol). RESULTS The DSN produces LR scans from HR inputs with a higher fidelity to true, LR clinical scans compared to conventional k-space down-sampling methods based on the metrics of root mean square error (RMSE) and structural similarity index (SSIM). Furthermore, HR outputs generated by the p-SRG exhibit improved scores in the peak signal-to-noise ratio (PSNR), normalized RMSE, SSIM, and in the blind/reference-less image spatial quality evaluator (BRISQUE) assessment compared to conventional approaches to MRI SR. CONCLUSIONS The robust, SR reconstruction method for MRI based on the novel cascaded deep learning framework is an end-to-end method for producing detail-preserving SR reconstructions from noisy, LR clinical MRI scans. Four-fold enhancements in spatial resolution facilitate target delineation and motion management during radiation therapy, enabling precise MRI-guided radiation therapy with 3D LR breath-hold MRI and 4D-MRI in a clinically feasible time frame. This article is protected by copyright. All rights reserved.
Human Acetyl-CoA Carboxylase 1 is an Isomerase: Carboxyl Transfer is Activated by Catalytic Effect of Isomerization.

Obesity and its related diseases such as cancer and diabetics are leading life-threatening issues in modern world. Thus, new drugs toward obesity and obesity caused diseases are highly desired. Human acetyl-CoA carboxylase 1 (hACC1) in charge of rate-limiting step of human fatty acid synthesis was recognized as an attractive target for rational drug design. The fundamental reaction mechanism and the nature of transtion state of hACC1 remain unclear. In this study, combined quantum mechanics and molecular mechanics (QM/MM) molecular dynamics (MD) and free energy simulations were performed to investigate the catalytic mechanism of hACC1 catalyzed carboxyl transfer reaction. Our computational results show a three-step mechanism for CT catalyzed reaction, including isomerization of carboxybiotin, proton transfer from acetyl-CoA to carboxybiotin, and carboxylation of acetyl-CoA enolate. Interestingly, isomerization of carboxybiotin is the rate-limiting step of the entire reaction pathway, indicating hACC1 has the catalytic effect of isomerization and thus might be an isomerase as well. The activation free energy barrier of carboxyl transfer catalyzed by hACC1 was calculated to be 16.4 kcal/mol, in excellent agreement with experimental result (16.7 kcal/mol). The obtained reaction mechanism together with the nature of transition state provide helpful knowledge not only for future investigation of other ACCs but also for rational design of hACC1 inhibitors, such as TS analogue. The catalytic effect of hACC1 isomerization is discussed.
A Study on Dynamic Quality Manage-ment Model for E-Commerce

This paper constructed a dynamic and objective model of E-commerce website quality management. The key quality factors, quality characters and sub-characters from the user’s perspective were discussed firstly, they formed the inherent causality relationship. Applying the Bayesian Network, the E-commerce website quality management model was shaped. The presented model can be utilized in two ways. The forward reasoning applied on the Bayesian Network could evaluate the E-commerce website quality. The backward reasoning could be utilized on quality diagnosis, quality improvement. Several examples were illustrated in this paper, and the advantages and disadvantages were discussed in the end.
The influence of youth soccer players’ sprint performance on the different sided games’ external load using GPS devices

ABSTRACT The aims of this study are 1) to compare sided games’ (SGs) external responses encountered by players according to pitch size and to 2) examine the relationships between sprint performance and SGs’ external physical responses. Twenty soccer players under 15 years of age (U-15) participated in this study. Each player performed a sprinting test (10 m and 30 m sprints) and played a SG on two different pitch sizes (small at 100 [SSG] and large at 200 [LSG] m2 per player). Higher external responses (p < 0.01, ES = −6.41–1.22) were found in LSG in comparison to SSG, except to distance accelerating and decelerating (p > 0.05, ES = −0.26–0.27). Players who were faster over 10 and 30 m covered higher distances cruising and sprinting (r = −0.47/-0.66; ± 0.23/± 0.30, respectively, p < 0.05), performed a greater number of sprints, achieved higher maximum velocity (Velmax) during LSG and covered a greater distance at high-intensity accelerating (r = −0.50/-0.70; ±0.21/±0.29, respectively, p < 0.05) during both SG. LSG demanded a higher external load in comparison with SSG. In addition, the improved sprint capacity could allow players to perform greater running activities and short-term actions at high-intensities during SG.
An Improved PSO Algorithm Based on Chaos and Population Core

particle swarm optimization (PSO) algorithm is often trapped in local optima and low accuracy in convergence. Following an analysis of the cause of the premature convergence, a novel particle swarm optimization algorithm based on neighborhood explored and chaos is proposed, which is called PCC-PSO. Chaos is introduced to initialized the particle's position to improve the diversity, the population core learning mechanism and global extreme mutation operator is also introduced to enhance the global search ability. Compared with other three improved algorithms, the PCC-PSO converges faster, prevents the premature convergence problem more effectively.
The morphology of the mental lexicon: Internal word structure viewed from a psycholinguistic perspective

Abstract A conceptual analysis is made of several ways in which the morphological structure of words might enter their lexical representation and/or processing. Economising on storage space seems an attractive option in light of the linguistic definition of the morpheme. However, for several word types, problems would arise in the mapping of morphs onto morphemes and of morphemic meanings onto whole-word meanings. Moreover, economy is a legal option, which might not be available to the mental lexicon. Alternatively, morphs might be put to the purpose of increasing lexical access speed, as proposed by Taft and Forster (1975) in their prefix-stripping model. It is demonstrated that such a view strips morphology from virtually all of its linguistic aspects. Furthermore, the prefix-stripping model would decrease rather than increase the access speed for several types of prefixed words in a number of languages. Linguistically interesting hypotheses are instantiated by the view that affixes are used for providi.
Fuzzy computing for data mining

The study is devoted to linguistic data mining, an endeavor that exploits the concepts, constructs, and mechanisms of fuzzy set theory. The roles of information granules, information granulation, and the techniques therein are discussed in detail. Particular attention is given to the manner in which these information granules are represented as fuzzy sets and manipulated according to the main mechanisms of fuzzy sets. We introduce unsupervised learning (clustering) where optimization is supported by the linguistic granules of context, thereby giving rise to so-called context-sensitive fuzzy clustering. The combination of neuro, evolutionary, and granular computing in the context of data mining is explored. Detailed numerical experiments using well-known datasets are also included and analyzed.
Meta-Method for Method Configuration - A Rational Unified Process Case

The world of systems engineering methods is changing as rigorous ‘off-the-shelf’ systems engineering methods become more popular. One example of such a systems engineering method is Rational Unified Process. In order to cover all phases in a software development process, and a wide range of project-types, such methods need to be of an impressive size. Thus, the need for configuring such methods in a structured way is increasing accordingly. In this thesis, method configuration is considered as a particular kind of method engineering that focuses on tailoring a standard systems engineering method. We propose a meta-method for method configuration based on two fundamental values: standard systems engineering method’s rationality and reuse. A conceptual framework is designed, introducing the concepts Configuration Package and Configuration Template. A Configuration Package is a pre-made ideal method configuration suitable for a delimited characteristic of a (type of) software artifact, or a (type of) software development project, or a combination thereof. Configuration Templates with different characteristics are built combining a selection of Configuration Packages and used as a base for a situational method. The aim of the proposed meta-method is to ease the burden of configuring the standard systems engineering method in order to reach an appropriate situational method.