Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Energy-Efficient Database Cluster Design

Energy is a growing component of the operational cost for many "big data" deployments, and hence has become increasingly important for practitioners of large-scale data analysis who require scale-out clusters or parallel DBMS appliances. Although a number of recent studies have investigated the energy efficiency of DBMSs, none of these studies have looked at the architectural design space of energy-efficient parallel DBMS clusters. There are many challenges to increasing the energy efficiency of a DBMS cluster, including dealing with the inherent scaling inefficiency of parallel data processing, and choosing the appropriate energy-efficient hardware. In this paper, we experimentally examine and analyze a number of key parameters related to these challenges for designing energy-efficient database clusters. We explore the cluster design space using empirical results and propose a model that considers the key bottlenecks to energy efficiency in a parallel DBMS. This paper represents a key first step in designing energy-efficient database clusters, which is increasingly important given the trend toward parallel database appliances.
Extraction and Spatial-Temporal Evolution of Urban Fringes: A Case Study of Changchun in Jilin Province, China

An urban fringe area, depicted as a typical ecotone, is a region where both social and environmental problems are concentrated. Identifying and evaluating the spatial–temporal characteristics of urban fringe areas is significant for future development. On the basis of the land use data extracted from remote sensing data, the Shannon diversity index (SHDI) of each unit can be calculated, and identifying the urban fringe area by the breakpoint method is reliable. By using the rapidly growing Changchun as example, this study identifies the urban fringe of Changchun in 1995, 2005, and 2015 by applying the breakpoint method. The expansion amount, change mode, direction of expansion, landscape, and influence factors are evaluated. Policy and planning are the main factors influencing the development direction of the Changchun fringe area. The urban fringe area of Changchun City is extended to the east, southeast, and north. From 1995 to 2005, the outlying expansion was the dominant type. The main change mode was the infilling type due to the reduction of available land, from 2005 to 2015. In accordance with the landscape metrics, the landscape within the urban fringe transformed from fragmentation to regularization. The development of the urban fringe also transformed from a disorderly to an orderly manner.
Difference Between Left-Sided and Right-Sided Colorectal Cancer: A Focused Review of Literature

Colorectal cancer is the third most common cancer worldwide with a high mortality rate at the advanced stages. However, colorectal cancer is not a single type of tumor; its pathogenesis depends on the anatomical location of the tumor and differs between right side and left side of the colon. Tumors in the proximal colon (right side) and distal colon (left side) exhibit different molecular characteristics and histology. In the right-sided tumors, mutations in the DNA mismatch repair pathway are commonly observed; and these tumors generally have a flat histology. In the left-sided tumors, chromosomal instability pathway-related mutations, such as KRAS, APC, PIK3CA, p53 mutations are observed and these tumors demonstrate polypoid-like morphology. Therapy responses are totally different between these tumor entities. Left-sided colorectal cancer (LCRC) patients benefit more from adjuvant chemotherapies such as 5-fluorouracil (5-FU)-based regimes, and targeted therapies such as anti- epidermal growth factor receptor (EGFR) therapy, and have a better prognosis. Right-sided colorectal cancer (RCRC) patients do not respond well to conventional chemotherapies, but demonstrate more promising results with immunotherapies because these tumors have high antigenic load. For the development of effective therapy regimes and better treatment options, it is essential to evaluate right-sided and left-sided tumors as separate entities, and design the therapy regime considering the differences between these tumors.
An Internet of Things Based Multi-Level Privacy-Preserving Access Control for Smart Living

The presence of the Internet of Things (IoT) in healthcare through the use of mobile medical applications and wearable devices allows patients to capture their healthcare data and enables healthcare professionals to be up-to-date with a patient’s status. Ambient Assisted Living (AAL), which is considered as one of the major applications of IoT, is a home environment augmented with embedded ambient sensors to help improve an individual’s quality of life. This domain faces major challenges in providing safety and security when accessing sensitive health data. This paper presents an access control framework for AAL which considers multi-level access and privacy preservation. We focus on two major points: (1) how to use the data collected from ambient sensors and biometric sensors to perform the high-level task of activity recognition; and (2) how to secure the collected private healthcare data via effective access control. We achieve multi-level access control by extending Public Key Infrastructure (PKI) for secure authentication and utilizing Attribute-Based Access Control (ABAC) for authorization. The proposed access control system regulates access to healthcare data by defining policy attributes over healthcare professional groups and data classes classifications. We provide guidelines to classify the data classes and healthcare professional groups and describe security policies to control access to the data classes.
LED Color Detection of Visual-MIMO System Using Boosting Neural Network Algorithm

LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from an LED array image, it is important to detect the color of LED on receiver side. In this paper, we propose a training algorithm, called boosting neural network (BNN) to predict the color of LED on receiver side. First, we take the image of LED array and segment the LED image by using LED detection algorithm. After segmenting the LED image, the LED image is resized in 10 by 10 dimension that means 100 pixels. Each pixel is the input to the BNN model for each RGB color channel. For studying the behavior of each color LED image in low (565 lux) and strong (2450 lux) environmental light intensity, we train our BNN model for low and strong environmental light intensity. Finally, we compare the performance of our BNN model with the regression analysis model at low and strong environmental light intensity. We obtain greater closeness accuracy for each color channel at both environmental light intensities.
The robust minimal controllability problem for switched linear continuous-time systems

In this paper, we address the robust minimal controllability problem for switched linear continuous-time systems. The problem is to determine the minimal subset of state variables to actuate such that the switching linear system is controllable, under the scenario where a set of actuators may fail along the time. Two variations of this problem are considered, depending on whether we want to design an input matrix for each mode (i.e., a different set of actuators that may fail in each mode), or if we want to design a common input matrix common across all the modes, and a set of actuators may fail. In both cases, we want to ensure that, given an initial condition, we can drive the system towards any desired state. For both problems, we characterize the sparsest input matrices which ensure that the system is controllable, whenever the autonomous dynamics' matrix of each mode is simple, and for which a left-eigenbasis is available. We reduce these problems to set multi-covering problems, showing that using a sufficient condition for controllability, the first is NP-complete. These allow us to deploy known, close-to-optimal, polynomial algorithms approximating the solutions of the problems we study.
An anatomical study of the spinous process of the seventh cervical vertebrae based on the three-dimensional computed tomography reconstruction

Palpation of the seventh cervical vertebra (C7) is important for the diagnosis and treatment of neck and chest conditions. However, the spinous process of C7 (C7-SP) displays an anatomical deviation among individuals. The present study aimed to clarify anatomic characteristics of C7-SP by using a three-dimensional (3D) computed tomographic (CT) reconstruction technique. A total of 245 subjects meeting the selection criteria were examined. After CT scanning, the images were reconstructed in 3D. All subjects were grouped according to their deviation of C7-SP: Deviating to the right (DR group), deviating to the left (DL group) and no deviation (ND group). Three distances and three angles were recorded on C7-SP. The vertical distances between the borders of the left and right transverse processes and the tip of the SP, were termed DLTS and DRTS, respectively. The length of the SP was also determined. The angle of the SP deviation was referred to as ∠α, the angle between the SP axis and the line crossing the tips of the transverse processes was referred to as ∠β and the angle between the vertebral body axis and the SP axis was referred to as ∠γ. Among the three groups, differences in ∠α and ∠β were statistically significant (P<0.05). Furthermore, the DLTS was significantly different between the DL and ND groups (P<0.05). In addition, a significant difference in the DRTS was identified between the DR and ND groups (P<0.05). 3D CT reconstruction was reliable for studying anatomic characteristics of C7-SP. Based on this, patients may be preliminarily grouped according to the deviation of their C7-SP and the measurement of the C7-SP may guide clinical diagnoses and treatments.
Extreme bioengineering to meet the nitrogen challenge

In the 1970s, investigators were already envisioning new technologies to generate crops that could utilize nitrogen (N2) from air to produce their own fixed N species, stating that “cereals that could provide their own fertilizer are beyond doubt the biggest prize of all in the gift of the new biology” (1). At this time, the N2-fixation genes ( nif ) of Klebsiella pneumoniae had just been transferred to Escherichia coli , making the naturally nonfixing bacterium capable of growing in the absence of combined N (2) and giving hope that this feature could also be carried over to more complex organisms. In PNAS, almost 50 years after the generation of the first N2-fixing E. coli , researchers from the Peking University, in collaboration with the lead author from that breakthrough study, present an ingenious approach to achieve coordinated and stoichiometric expression of nif genes in heterologous hosts (3). The method is based on regrouping genes of complex biological pathways into synthetic “giant genes,” products of which may be further processed into shorter functional proteins on demand. This technology will revolutionize our attempts to transfer the elaborate nif machinery into heterologous hosts, providing researchers with a groundbreaking advance to help generate plants capable of self-fertilization (4, 5). Successful transfer of nif genes into nonfixing organisms, such as plants, will depend on our ability to execute rational simplifications. Researchers must aim at finding the least possible complex, and most possible robust, genetic components capable of performing nitrogenase-related reactions in cellular environments different from their natural hosts. We must also identify intermediate steps of nitrogenase biosynthesis and develop methods for their analysis so that nitrogenase can be stepwise engineered—and functionally verified—before integration into a complete pathway. Once these objectives are achieved, synthetic biology, combined with increased knowledge of … [↵][1]1To whom correspondence should be addressed. Email: lm.rubio{at}upm.es. [1]: #xref-corresp-1-1
Absynth: A Comprehensive Approach for Full Front to Back Analog Design Automation

The benefits of any form of automation are well known, however unlike most other forms of automation, analog design automation (ADA) is not yet accepted by most designers as part of the work-flow. The goal of this work is to explore the needs and the targets to make ADA viable in industry and research. There are various approaches targeting different sets of designers of different experience levels. In this work, we present ABSYNTH, an ADA tool which can be used with or without expert knowledge depending on the interest and proficiency of the user. It also makes use of advanced modeling techniques like Support Vector Regression, and other neural network techniques for learning the circuit models during the initial runs and contribute during repeated runs of the circuit. Four circuits have been presented where automation of different steps have been carried out. three of these circuits have been used in a manufactured chip, that will soon be tested. A generic hybrid layout generator based on a constrained template which can be optimized while maintaining the matched structure has been presented. This improves the quality of the layout by producing results comparable or better than those predicted by simulators without parasitic extraction.
Design and Evaluation of a Hybrid D2D Discovery Mechanism in 5G Cellular Networks

Device-to-device (D2D) communications allow devices to communicate directly without using base stations for relaying, yielding several merits such as increasing the spectral efficiency and system capacity, reducing network latency and power consumption of UEs, offloading the 5G cellular networks, and extending the network coverage. However, to realize D2D communications, the first challenging issue is how to efficiently find target devices in proximity for communications. Therefore, in this paper we intend to design a hybrid D2D discovery mechanism and evaluate its performance. Simulation results demonstrate that the proposed D2D discovery mechanism achieves better performance in terms of power consumption and discovery time, compared with the direct discovery scheme that purely utilizes the contention-based transmission scheme and operates in the unlicensed band.
Antioxidant Melatonin: Potential Functions in Improving Cerebral Autoregulation After Subarachnoid Hemorrhage

Subarachnoid hemorrhage (SAH) is a subtype of stroke with high mortality and morbidity. Impaired cerebral autoregulation following SAH has been reported owing to effects on sympathetic control, endothelial function, myogenic response, and cerebral metabolism. Impaired cerebral autoregulation is associated with early brain injury, cerebral vasospasm/delayed cerebral ischemia, and SAH prognosis. However, few drugs have been reported to improve cerebral autoregulation after SAH. Melatonin is a powerful antioxidant that is effective (easily crosses the blood brain barrier) and safe (tolerated in large doses without toxicity). Theoretically, melatonin may impact the control mechanisms of cerebral autoregulation via antioxidative effects, protection of endothelial cell integrity, suppression of sympathetic nerve activity, increase in nitric oxide bioavailability, mediation of the myogenic response, and amelioration of hypoxemia. Furthermore, melatonin may have a comprehensive effect on cerebral autoregulation. This review discusses the potential effects of melatonin on cerebral autoregulation following SAH, in terms of the association between pharmacological activities and the mechanisms of cerebral autoregulation.
Data‐driven respiratory motion compensation for four‐dimensional cone‐beam computed tomography (4D‐CBCT) using groupwise deformable registration

PURPOSE To demonstrate the feasibility of using a purely data-driven, a posteriori respiratory motion modeling and reconstruction compensation method to improve 4D-CBCT image quality under clinically relevant image acquisition conditions. METHODS Evaluated workflows that utilized a combination of groupwise deformable image registration and motion-compensated image reconstruction algorithms. Groupwise registration is an approach that simultaneously registers all temporal frames of a 4D image to a common reference instead of one at a time so as to minimize the influence of any individual time point on the global smoothness or accuracy of the resulting deformation model. Four-dimensional cone-beam CT (4D-CBCT) Feldkamp-Davis-Kress (FDK) reconstructions were registered to either iteratively computed mean respiratory phase (mean-frame) or preselected respiratory phase (fixed-frame) reference images to model respiratory motion. The resulting 4D transformations were used to deform projection data during the FDK backprojection operation to create motion-compensated reconstructions. Tissue interface sharpness (TIS) was defined as the slope of a sigmoid curve fit to a mobile tissue boundary and was used to evaluate image quality in regions susceptible to motion artifacts. Image quality improvement was assessed for 19 clinical cases by evaluating mitigation of view aliasing artifacts, TIS, image noise reduction, and contrast for implanted fiducial markers. RESULTS Average (standard deviation) diaphragm TIS recovery relative to initial 4D-CBCT reconstructions was observed to be 87% (46%) using fixed-frame registration alone; 87% (47%) using fixed frame with motion-compensated reconstruction; 101% (68%) using mean-frame registration alone; and 99% (65%) using mean frame with motion-compensated reconstruction. Noise was reduced in sampled soft tissue ROIs by 58% for both fixed-frame registration and registration with motion compensation and by 57% and 58% on average for the corresponding mean-frame methods, respectively. Average improvement in local CNR was observed to be respectively 93% and 98% for fixed-frame registration and registration with motion compensation methods and 116% and 111% for the corresponding mean-frame methods. CONCLUSION Data-driven groupwise registration and motion-compensated reconstruction offer a feasible means of improving the quality of 4D-CBCT images acquired under clinical conditions. The addition of motion compensation reconstruction after groupwise registration visibly reduced the impact of view aliasing artifacts for the clinical image datasets studied.
On the Novel Approach to Parallel Coupled-Line Bandpass Filters that Have Diverse Wavelenght Impedance Scaling I/O Transformers

This paper presents a practical novel approach of realization of parallel coupled-line band-pass filters in the scope of expanding their capabilities by using various electrical-length I/O transformers. Resonators have properties of half-wavelength or full-wavelength, with steps in half of length. This approach allows the filters to be full-differently impedance-scaled. In particular, it was important to obtain arbitrary microwave filters of narrow or moderate bandwidths for TEM rods' diameters or strip width. This is the first ever in-depth study of the design with impedance-scaling levels with various I/O transformers. The method of synthesis and transformations provides accurate results and agreements in bands, and impedance level with theoretical filter functions. Several designs and one experiment confirm and verify the extended filter design. There are many filter transformation possibilities and this paper presents more thoroughly only two selected ones.
Hybrid PID control for transient performance improvement of motion systems with friction

We present a novel reset control approach to improve transient performance of a PID-controlled motion system subject to friction. In particular, a reset integrator is applied to circumvent the depletion and refilling process of a linear integrator when the system overshoots the setpoint, thereby significantly reducing settling times. Moreover, robustness for unknown static friction levels is obtained. A hybrid closed-loop system formulation is derived, and stability follows from a discontinuous Lyapunov-like function and a meagre-limsup invariance argument. The working principle of the controller is illustrated by means of a numerical example.
Early postsurgical diffusivity metrics for prognostication of long-term pain relief after Gamma Knife radiosurgery for trigeminal neuralgia.

OBJECTIVEGamma Knife radiosurgery (GKRS) is an important treatment modality for trigeminal neuralgia (TN). Current longitudinal assessment after GKRS relies primarily on clinical diagnostic measures, which are highly limited in the prediction of long-term clinical benefit. An objective, noninvasive, predictive tool would be of great utility to advance the clinical management of patients. Using diffusion tensor imaging (DTI), the authors' aim was to determine whether early (6 months post-GKRS) target diffusivity metrics can be used to prognosticate long-term pain relief in patients with TN.METHODSThirty-seven patients with TN treated with GKRS underwent 3T MRI scans at 6 months posttreatment. Diffusivity metrics of fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity were extracted bilaterally from the radiosurgical target of the affected trigeminal nerve and its contralateral, unaffected nerve. Early (6 months post-GKRS) diffusivity metrics were compared with long-term clinical outcome. Patients were identified as long-term responders if they achieved at least 75% reduction in preoperative pain for 12 months or longer following GKRS.RESULTSTrigeminal nerve diffusivity at 6 months post-GKRS was predictive of long-term clinical effectiveness, where long-term responders (n = 19) showed significantly lower fractional anisotropy at the radiosurgical target of their affected nerve compared to their contralateral, unaffected nerve and to nonresponders. Radial diffusivity and mean diffusivity, correlates of myelin alterations and inflammation, were also significantly higher in the affected nerve of long-term responders compared to their unaffected nerve. Nonresponders (n = 18) did not exhibit any characteristic diffusivity changes after GKRS.CONCLUSIONSThe authors demonstrate that early postsurgical target diffusivity metrics have a translational, clinical value and permit prediction of long-term pain relief in patients with TN treated with GKRS. Importantly, an association was found between the footprint of radiation and clinical effectiveness, where a sufficient level of microstructural change at the radiosurgical target is necessary for long-lasting pain relief. DTI can provide prognostic information that supplements clinical measures, and thus may better guide the postoperative assessment and clinical decision-making for patients with TN.
Feature Adaptive Surfel Set Simplification

In this paper we propose a novel scheme for simplifying a surfel set with the resultant surfels computed and distributed in an effort to preserve prominent geometric and textural features with a reduced number of primitives. It works by iteratively collapsing local neighborhoods around surfels until a desired data reduction ratio is reached. The local neighborhood collapses are prioritized according to a cost metric that takes into account the local complexities of both the geometric and the textural information. Experimental results demonstrate that, besides yielding outstanding model quality at a reduced model size, the proposed scheme provides the ﬂexibility for the users to specify the relative priorities in the geometric and the textural ﬁdelity.