Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Window Aggregation with General Stream Slicing

Window aggregation is a core operation in data stream processing. Existing aggregation techniques focus on reducing latency, eliminating redundant computations, and minimizing memory usage. However, each technique operates under different assumptions with respect to workload characteristics such as properties of aggregation functions (e.g., invertible, associative), window types (e.g., sliding, sessions), windowing measures (e.g., time- or count-based), and stream (dis)order. Violating the assumptions of a technique can deem it unusable or drastically reduce its performance. In this paper, we present the first general stream slicing technique for window aggregation. General stream slicing automatically adapts to workload characteristics to improve performance without sacrificing its general applicability. As a prerequisite, we identify workload characteristics which affect the performance and applicability of aggregation techniques. Our experiments show that general stream slicing outperforms alternative concepts by up to one order of magnitude.
Patients’ experiences of living with a stoma in rural areas in Northern Sweden

ABSTRACT Introduction: Stoma complications are common and interfere with many aspects of everyday life. Stoma problems are usually managed by a specialised stoma nurse, a service not present in the rural areas of South Lapland in Sweden. The aim of this study was to describe how stoma patients in rural areas experience living with a stoma. Methods: A qualitative descriptive study with semi-structured interviews were conducted with 17 stoma patients living in rural municipalities and who received a part of their care at the local cottage hospital. Qualitative content analysis was employed. Results: Initially, the stoma was experienced as very depressing. Participants had difficulties in properly managing the dressing. Over time they learned how to properly care for their stoma, making their life easier. Both satisfaction and dissatisfaction with the healthcare were experienced. Those who were dissatisfied expressed a lack of competence in dealing with stoma-related problems. Conclusions: Living with a stoma in a rural area in northern Sweden is experienced as a learning process and acceptance of the stoma’s existence is important. This study emphasises the need for increased knowledge of stoma-related problems in rural primary healthcare in order to help patients cope with everyday life.
SURGICAL TECHNIQUE

Purpose: To report a technique for scleral buckling surgery under the operating microscope with wide-ﬁ eld viewing. Methods: The identi ﬁ cation and reliable marking of retinal breaks is one of the key steps for successful surgery in scleral buckling. For suf ﬁ cient visualization of retinal breaks, some kind of indentation and illumination is necessary. In this technique, one instrument (Visible Periphery illuminated scleral indentor; Oertli Instrumente AG, Switzerland) combines scleral depression, illumination, and color marking (using a surgical marking pen) of the corresponding sclera above the retinal break. Results: The illuminated scleral depressor enables the surgeon to safely and precisely mark the retinal break while stabilizing and rotating the eye with the other hand. This technique simpli ﬁ es scleral buckling surgery by improving the handling and visualization of marking retinal breaks at the corresponding scleral. Neither intraoperative indirect ophthalmoscopy nor sclerotomies for chandelier light systems or any intraocular instrument are necessary. Conclusion: The illuminated scleral depressor facilitates scleral buckling surgery in a convenient way for easy and precise scleral marking of the retinal breaks even in challenging situations and positions of retinal breaks under the operating microscope with wide-ﬁ eld viewing. Indirect ophthalmology or other illumination sources are no longer needed.
An Optimization Method for an Integrated Energy System Scheduling Process Based on NSGA-II Improved by Tent Mapping Chaotic Algorithms

The integrated energy system is a vital part of distributed energy industries. In addition to this, the optimal economic dispatch model, which takes into account the complementary coordination of multienergy, is an important research topic. Considering the constraints of power balance, energy supply equipment, and energy storage equipment, a basic model of optimal economic dispatch of an integrated energy system is established. On this basis, a multiobjective function solving algorithm of NSGA-II, based on tent map chaos optimization, is proposed. The proposed model and algorithm are applied. The simulation results show that the optimal economic scheduling model of the integrated energy system established in this paper can provide a more economic system operation scheme and reduce the operation cost and risks associated with an integrated energy system. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II) multiobjective function solving algorithm, based on tent map chaos optimization, has better performance and efficiency.
Contributors’ enrollment in collaborative online communities: the case of OpenStreetMap

Abstract The number of people registering in an online community depends on two main factors: interest in, and awareness of, the project. Registering to a project does not, however, imply contributing to it, as lacking the knowledge and skills can be a barrier to participation. In order to identify the nature of events that might have facilitated or hindered enrollments in the OpenStreetMap (OSM) project over time, we analyzed the correlations between the number of new participants and the events that dotted its history. Four different metrics were defined to characterize participants’ behaviors: the daily number of registrations, the daily number of participants that made a first contribution, the delays between contributors’ registration and their first edits, and a daily contribution ratio built from the number of new contributors and the number of new registered members. Time series analyses were used to identify trends, and outstanding variations of the number of participants. An inventory of events that took place along the OSM project’s history was created and appreciable variations of the metrics have been linked to events that seemed to be meaningful. Although a correlation does not imply causality, many of the explanations these correlations suggest are supported by the results of other studies, either directly or indirectly. For instance, when considering the time participants spend as “lurker”, as well as on the nature of the contribution of early participants. In other cases, they suggest new explanations for the origin of the spam accounts that affect registration statistics, or the decline in the proportion of registered members who actually become contributors.
Design of a microwave tomography system for medical imaging applications

Microwave tomography (MT) is an emerging imaging modality which aims to recover the dielectric profile of a domain by solving an inverse problem. This is a challenging problem that requires sophisticated algorithms as well as hardware design. This thesis presents a simple and low cost design of a MT system that can operate in the 1-3 GHz frequency range. The hardware prototype of the system is developed from concept to physical realization and is validated against numerical and experimental studies using an in-house inversion algorithm. As with most experimental MT systems presented in the literature, this thesis focuses on cylindrical setups that can be imaged efficiently with a two-dimensional (2-D) inversion algorithm. Using an antenna that can operate efficiently in the desired MT frequency spectrum is essential for any MT system. To this end, the thesis studies several antenna designs and evaluates their performance by calculating return loss and transmission levels in the desired frequency range. Based on this analysis, we select a custom-made printed monopole antenna with very small size which can operate efficiently across the selected frequency range when immersed in various materials that are used as coupling liquids in microwave tomographic systems. The selection of coupling medium is quite vital in the design of a microwave imaging system, which is subject to various signals that obscure the response from the object to be imaged. In particular, multipath signal propagation and surface waves along with other degrading factors such as noise, coupling etc., pose significant challenges to data integrity. We address this issue by studying the performance of the selected antenna (stand-alone and as an array) in various coupling liquids, such as water mixed with glycerine or corn syrup. The aim of this study is to ensure that the sensitivity of our MT system is sufficient to detect weak target responses in a practical measurement with a standard vector network analyzer (VNA), while at the same time effects such as antenna coupling and multi-path propagation are minimised. The thesis also presents a comparison of two possible MT setups: non-immersed configuration, where the array of antennas operates in free space but very close to the imaging
Annual and herbaceous perennial native Australian plant species are symptomless hosts of Phytophthora cinnamomiin the Eucalyptus marginata (jarrah) forest of Western Australia

Resistant annual and herbaceous perennial plant species were identified as key hosts which allow Phytophthora cinnamomi to persist on severely impacted black gravel sites within the Eucalyptus marginata (jarrah) forest of southwest Western Australia. Of the annual and herbaceous perennial plant species present on black gravel sites, 15 out of 19 species were found to be hosts of P. cinnamomi, and 10 of these were symptomless hosts. In particular, the native annual Trachymene pilosa and the two native herbaceous perennials Stylidium diuroides and Chamaescilla corymbosa were commonly found to be hosts of the pathogen. Species from 12 new genera including three from new families (Crassulaceae, Droseraceae and Primulaceae) are reported for the first time to be hosts of P. cinnamomi. The species from which P. cinnamomi was recovered were the native species: Chamaescilla corymbosa, Crassula closiana, Drosera erythrorhiza, Hydrocotyle callicarpa, Levenhookia pusilla, Paracaleana nigrita, Podotheca angustifolia, Pterochaeta paniculata, Rytidosperma caespitosum, Siloxerus multiflorus, Stylidium diuroides and Trachymene pilosa, and the introduced annual weeds Hypochaeris glabra, Lysimachia arvensis and Pentameris airoides.
No Delay: Latency-Driven, Application Performance-Aware, Cluster Scheduling

Given the network latency variability observed in data centers, applications' performance is also determined by their placement within the data centre. We present NoMora, a cluster scheduling architecture whose core is represented by a latency-driven, application performance-aware, cluster scheduling policy. The policy places the tasks of an application taking into account the expected performance based on the measured network latency between pairs of hosts in the data center. Furthermore, if a tenant's application experiences increased network latency, and thus lower application performance, their application may be migrated to a better placement. Preliminary results show that our policy improves the overall average application performance by up to 13.4% and by up to 42% if preemption is enabled, and improves the task placement latency by a factor of 1.79x and the median algorithm runtime by 1.16x compared to a random policy on the Google cluster workload. This demonstrates that application performance can be improved by exploiting the relationship between network latency and application performance, and the current network conditions in a data center, while preserving the demands of low-latency cluster scheduling.
Development of combined-supramolecular microextraction with ultra-performance liquid chromatography-tandem mass spectrometry procedures for ultra-trace analysis of carbaryl in water, fruits and vegetables

ABSTRACT For the fast and selective preconcentration-separation of carbaryl from environmental samples, a simple and cheap supramolecular solvent microextraction method (Ss-ME) has been applied with ultra-performance liquid chromatography-tandem mass spectrometry. Various analytical parameters that have a noticeable effect on the extraction of carbaryl by supramolecular liquid phase microextraction were optimised by selecting the peak area as the response. The parameters including pH of solution, type and volume of supramolecular formula, matrix effect and the sample volume were optimised. The quantitative recoveries were obtained between pH 2.0 and 4.0 of sample solution phase by using a supramolecular phase consist of heptanol and tetrahydrofuran. The limit of detection and limit of quantification were 0.03 mg L−1 and 0.09 mg L−1 respectively. The relative standard deviation was 7.11 and the preconcentration factor was 15. The microextraction procedure for trace carbaryl was used for various samples such as fruits, vegetables and water samples with the recovery% range between 90% and 102%.
Variation in the Height of Rectal Cancers According to the Diagnostic Modalities

Purpose Although the height of a rectal tumor above the anal verge (tumor height) partly determines the treatment strategy, no practical standard exists for reporting this. We aimed to demonstrate the differences in tumor height according to the diagnostic modality used for its measurement. Methods We identified 100 patients with rectal cancers located within 15 cm of the anal verge who had recorded tumor heights measured by using magnetic resonance imaging (MRI), colonoscopy, and digital rectal examination (DRE). Tumor height measured by using MRI was compared with those measured by using DRE and colonoscopy to assess reporting inconsistencies. Factors associated with differences in tumor height among the modalities were also evaluated. Results The mean tumor heights were 77.8 ± 3.3, 52.9 ± 2.3, and 68.9 ± 3.1 mm when measured by using MRI, DRE, and colonoscopy, respectively (P < 0.001). Agreement among the 3 modalities in terms of tumor sublocation within the rectum was found in only 39% of the patients. In the univariate and the multivariate analyses, clinical stage showed a possible association with concordance among modalities, but age, sex, and luminal location of the tumor were not associated with differences among modalities. Conclusion The heights of rectal cancer differed according to the diagnostic modality. Tumor height has implications for rectal cancer’s surgical planning and for interpreting comparative studies. Hence, a consensus is needed for measuring and reporting tumor height.
Real‐time removal of impulse noise from MR images for radiosurgery applications

In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real‐time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.
Avoiding non-Manhattan obstacles based on projection of spatial corners in indoor environment

Monocular vision-based navigation is a considerable ability for a home mobile robot. However, due to diverse disturbances, helping robots avoid obstacles, especially non-Manhattan obstacles, remains a big challenge. In indoor environments, there are many spatial right-corners that are projected into two dimensional projections with special geometric configurations. These projections, which consist of three lines, might enable us to estimate their position and orientation in 3D scenes. In this paper, we present a method for home robots to avoid non-Manhattan obstacles in indoor environments from a monocular camera. The approach first detects non-Manhattan obstacles. Through analyzing geometric features and constraints, it is possible to estimate posture differences between orientation of the robot and non-Manhattan obstacles. Finally according to the convergence of posture differences, the robot can adjust its orientation to keep pace with the pose of detected non-Manhattan obstacles, making it possible avoid these obstacles by itself. Based on geometric inferences, the proposed approach requires no prior training or any knowledge of the camera ʼ s internal parameters, making it practical for robots navigation. Furthermore, the method is robust to errors in calibration and image noise. We compared the errors from corners of estimated non-Manhattan obstacles against the ground truth. Furthermore, we evaluate the validity of convergence of differences between the robot orientation and the posture of non-Manhattan obstacles. The experimental results showed that our method is capable of avoiding non-Manhattan obstacles, meeting the requirements for indoor robot navigation.
PYTHIA: a Framework for the Automated Analysis of Web Hosting Environments

A common approach when setting up a website is to utilize third party Web hosting and content delivery networks. Without taking this trend into account, any measurement study inspecting the deployment and operation of websites can be heavily skewed. Unfortunately, the research community lacks generalizable tools that can be used to identify how and where a given website is hosted. Instead, a number of ad hoc techniques have emerged, e.g., using Autonomous System databases, domain prefixes for CNAME records. In this work we propose Pythia , a novel lightweight approach for identifying Web content hosted on third-party infrastructures, including both traditional Web hosts and content delivery networks. Our framework identifies the organization to which a given Web page belongs, and it detects which Web servers are self-hosted and which ones leverage third-party services to provide contents. To test our framework we run it on 40,000 URLs and evaluate its accuracy, both by comparing the results with similar services and with a manually validated groundtruth. Our tool achieves an accuracy of 90% and detects that under 11% of popular domains are self-hosted. We publicly release our tool to allow other researchers to reproduce our findings, and to apply it to their own studies.
Catecholamine Modulation of Evidence Accumulation during Perceptual Decision Formation: A Randomized Trial

Recent behavioral modeling and pupillometry studies suggest that neuromodulatory arousal systems play a role in regulating decision formation but neurophysiological support for these observations is lacking. We employed a randomized, double-blinded, placebo-controlled, crossover design to probe the impact of pharmacological enhancement of catecholamine levels on perceptual decision-making. Catecholamine levels were manipulated using the clinically relevant drugs methylphenidate and atomoxetine, and their effects were compared with those of citalopram and placebo. Participants performed a classic EEG oddball paradigm that elicits the P3b, a centro-parietal potential that has been shown to trace evidence accumulation, under each of the four drug conditions. We found that methylphenidate and atomoxetine administration shortened RTs to the oddball targets. The neural basis of this behavioral effect was an earlier P3b peak latency, driven specifically by an increase in its buildup rate without any change in its time of onset or peak amplitude. This study provides neurophysiological evidence for the catecholaminergic enhancement of a discrete aspect of human decision-making, that is, evidence accumulation. Our results also support theoretical accounts suggesting that catecholamines may enhance cognition via increases in neural gain.
Deep Text-to-Speech System with Seq2Seq Model

Recent trends in neural network based text-to-speech/speech synthesis pipelines have employed recurrent Seq2seq architectures that can synthesize realistic sounding speech directly from text characters. These systems however have complex architectures and takes a substantial amount of time to train. We introduce several modifications to these Seq2seq architectures that allow for faster training time, and also allows us to reduce the complexity of the model architecture at the same time. We show that our proposed model can achieve attention alignment much faster than previous architectures and that good audio quality can be achieved with a model that's much smaller in size. Sample audio available at this https URL.
Delay and Power Tradeoff With Consideration of Caching Capabilities in Dense Wireless Networks

Enabling caching capabilities in dense small-cell networks (DSCNs) has a direct impact on file delivery delay and power consumption. Most of the existing works studied these two performance metrics separately in cache-enabled DSCNs. However, file delivery delay and power consumption are coupled with each other and cannot be minimized simultaneously. In this paper, we investigate the optimal tradeoff between these two performance metrics. First, we formulate the joint file delivery delay and power consumption optimization (JDPO) problem where power control, user association, and file placement are jointly considered. Then, we convert it into a form that can be handled by the generalized Benders decomposition (GBD). With GBD, we decompose the converted JDPO problem into two smaller problems, i.e., the primal problem related to power control and the master problem related to user association and file placement. An iterative algorithm is proposed and proved to be an $\epsilon $ -optimal, in which the primal problem and the master problem are solved iteratively to approach the optimal solution. To further reduce the complexity of the master problem, an accelerated algorithm based on semi-definite relaxation is proposed. Finally, the simulation results demonstrate that the proposed algorithm can approach the optimal tradeoff between file delivery delay and power consumption.
SALMNet: A Structure-Aware Lane Marking Detection Network

Lane marking detection is a fundamental task, which serves as an important prerequisite for automatic driving or driver-assistance systems. However, the complex and uncontrollable driving road environment as well as the discontinuous lane marking appearance make this task challenging. In this work, a novel deep neural network architecture is presented to detect lane markings in a complex environment by analyzing their structure information. There are two contributions to the network design. Firstly, a semantic-guided channel attention (SGCA) module is developed to select the low-level features of a deep convolutional neural network by taking the high-level features as the guidance. Secondly, a pyramid deformable convolution (PDC) module is formulated to enlarge the receptive fields and to capture the complex structures of lane markings by applying deformable convolutions on multiple feature maps with different scales. Hence, our network can better reduce false detection and enhance lane marking structures simultaneously. The experimental results on three benchmark datasets for lane marking detection show that our method outperforms other methods on all the benchmark datasets.