Miracle-dz/UniFAR-FTUs huggingface dataset:
Shared risk link Group (SRLG)-diverse path provisioning under hybrid service level agreements in wavelength-routed optical mesh networks

The static provisioning problem in wavelength-routed optical networks has been studied for many years. However, service providers are still facing the challenges arising from the special requirements for provisioning services at the optical layer. In this paper, we incorporate some realistic constraints into the static provisioning problem, and formulate it under different network resource availability conditions. We consider three classes of shared risk link group (SRLG)-diverse path protection schemes: dedicated, shared, and unprotected. We associate with each connection request a lightpath length constraint and a revenue value. When the network resources are not sufficient to accommodate all the connection requests, the static provisioning problem is formulated as a revenue maximization problem, whose objective is maximizing the total revenue value. When the network has sufficient resources, the problem becomes a capacity minimization problem with the objective of minimizing the number of used wavelength-links. We provide integer linear programming (ILP) formulations for these problems. Because solving these ILP problems is extremely time consuming, we propose a tabu search heuristic to solve these problems within a reasonable amount of time. We also develop a rerouting optimization heuristic, which is based on previous work. Experimental results are presented to compare the solutions obtained by the tabu search heuristic and the rerouting optimization heuristic. For both problems, the tabu search heuristic outperforms the rerouting optimization heuristic.
Scarce face recognition via two-layer collaborative representation

The recent significant progress in face recognition is mainly achieved using learning-based (LE) techniques via an exhaustive training involving a huge number of face samples. However, in many applications, the number of face images available for training may be very limited. This makes LE techniques impractical for learning discriminative features and models. Thus, limited number of face samples (i.e. scarce data) degrades the recognition performance of most existing methods. To overcome this problem, the authors propose a novel approach based on two-layer collaborative representation to exploit the abundance of samples in some classes to enrich the scarce data in other classes. The first-layer collaborative representation uses the abundance of samples to construct representations for the scarce data. Then, a new face sample is recognised by computing residuals with the second-layer collaborative representation. Extensive experiments on four benchmark face databases demonstrate the effectiveness of their proposed approach which compares favourably against state-of-the-art methods.
Is Breast Surgery Necessary for Breast Carcinoma in Complete Remission Following Neoadjuvant Chemotherapy?

Abstract The likelihood of pathological complete remission (pCR) of breast cancer following neoadjuvant chemotherapy (NACT) is increasing; most of all in the triple negative and HER2 positive tumour subgroups. The question thus arises whether or not breast surgery is necessary when there is complete remission after NACT, and whether it provides any improvement of the oncological treatment result when tumour is no longer detectable. Avoiding surgery and possibly even radiotherapy would only be conceivable on the basis of a reliable diagnosis of pCR without operating. Current imaging does not achieve the necessary sensitivity and specificity to assure the diagnosis of pathological complete remission. Further studies are therefore required to determine which methods are best able to evaluate tumour response to NACT. Studies on image-guided, minimally invasive biopsies after NACT have delivered first promising results towards diagnosing pCR before surgery and could provide the basis for further studies on the possibility of avoiding surgery in this specific patient collective. Zusammenfassung Die Wahrscheinlichkeit einer pathologischen Komplettremission (pCR) bei Brustkrebs nach neoadjuvanter Chemotherapie (NACT) nimmt zu; vor allem in den Subgruppen der tripel-negativen und HER-2-positiven Tumoren. Daher stellt sich die Frage, ob bei einer Komplettremission nach NACT eine operative Therapie der Brust notwendig ist, und ob es einen Vorteil für das onkologische Behandlungsergebnis hat, wenn kein Tumor mehr nachgewiesen werden kann. Ein Verzicht auf die Operation und gegebenenfalls auch auf die Radiotherapie ist jedoch nur auf der Basis einer verlässlichen pCR-Diagnose ohne Operation denkbar. Bildgebende Verfahren erreichen derzeit nicht die nötige Sensitivität und Spezifität, um die Diagnose einer pathologischen Komplettremission sicher zu stellen. Daher sind weitere Studien nötig, um herauszufinden, welche Methode die bestmögliche Evaluation des Tumoransprechens auf NACT erlaubt. Erste vielversprechende Ergebnisse zeigen sich in Studien zu bildgebungsgesteuerten, minimalinvasiven Biopsien nach NACT. Diese evaluieren die Möglichkeit einer pCR-Diagnose vor der Operation und könnten die Grundlage für weitere Studien zu einem möglichen Verzicht auf eine Operation in diesem ausgewählten Kollektiv sein.
Auto-scale: automatic scaling of virtualised resources using neuro-fuzzy reinforcement learning approach

Auto-scaling is an indispensable mechanism which facilitates automatic provisioning and de-provisioning of computing resources on-the-fly in accordance with fluctuating cloud service demands. The success of cloud computing necessitates that auto-scaling should improve both resource utilisation and end-user's quality-of-service. The dynamic and bursty workloads, interferences among virtual machines complicate the resource scaling process. In existing literature, threshold and reinforcement learning-based approaches are employed to enforce autoscaling policy. The threshold-based approach requires expertise knowledge of service-domains and reinforcement learning based approach suffers from the problem of 'curse-ofdimensionality'. In order to address these issues, a neuro-fuzzy reinforcement learning-based resource scaling approach is proposed to automatically adapt resource-scaling process to workload dynamics by considering both SLA constraints and resource utilisation. This approach requires no prior knowledge of service domains and is implemented on Xen-virtualised environment and tested with highly dynamic benchmark workload-RUBiS. Experimental results demonstrate that the proposed approach outperforms existing approaches.
Impact of Artificial Rearing Systems on the Developmental and Reproductive Fitness of the Predatory Bug, Orius laevigatus

Abstract This study investigated the effect of several substrate types and moisture sources on the developmental and reproductive fitness of the zoophytophagous predator Orius laevigatus (Fieber) (Hemiptera: Anthocoridae) when fed a factitious prey (i.e. unnatural prey) Ephestia kuehniella (Zeller) eggs, or a meridic artificial diet based on hen's egg yolk. O. laevigatus is known to feed on plants as an alternative food source and to oviposit in plants. E. kuehniella eggs were superior to the artificial diet. Supplementary feeding on plant materials did not compensate for the nutritional shortcomings of the artificial diet. Survival rates showed that oviposition substrates such as bean pods or lipophilic surfaces such as wax paper and plastic were more suitable for rearing O. laevigatus than household paper. The use of green bean pods as a plant substrate did not have a beneficial effect on O. laevigatus. The results indicated that O. laevigatus can successfully complete its nymphal development and realize its full reproductive potential in the absence of plant material. However, plant materials would still be required for oviposition, unless a reliable and cost-effective artificial oviposition substrate were made available. The omission of plant materials from the rearing procedures may reduce production cost of this species and other heteropteran predators.
Neurovirulence of Polytropic Murine Retrovirus Is Influenced by Two Separate Regions on Opposite Sides of the Envelope Protein Receptor Binding Domain

ABSTRACT Changes in the envelope proteins of retroviruses can alter the ability of these viruses to infect the central nervous system (CNS) and induce neurological disease. In the present study, nine envelope residues were found to influence neurovirulence of the Friend murine polytropic retrovirus Fr98. When projected on a three-dimensional model, these residues were clustered in two spatially separated groups, one in variable region B of the receptor binding site and the other on the opposite side of the envelope. Further studies indicated a role for these residues in virus replication in the CNS, although the residues did not affect viral entry.
Localized Upregulation of a New Expansin Gene Predicts the Site of Leaf Formation in the Tomato Meristem

Expansins are extracellular proteins that increase plant cell wall extensibility in vitro and are thought to be involved in cell expansion. We showed in a previous study that administration of an exogenous expansin protein can trigger the initiation of leaflike structures on the shoot apical meristem of tomato. Here, we studied the expression patterns of two tomato expansin genes, LeExp2 and LeExp18. LeExp2 is preferentially expressed in expanding tissues, whereas LeExp18 is expressed preferentially in tissues with meristematic activity. In situ hybridization experiments showed that LeExp18 expression is elevated in a group of cells, called I1, which is the site of incipient leaf primordium initiation. Thus, LeExp18 expression is a molecular marker for leaf initiation, predicting the site of primordium formation at a time before histological changes can be detected. We propose a model for the regulation of phyllotaxis that postulates a crucial role for expansin in leaf primordium initiation.
A Nonlinear Model Predictive Control Algorithm for Obstacle Avoidance in Autonomous Ground Vehicles within Unknown Environments

Abstract : A nonlinear model predictive control algorithm is developed for obstacle avoidance in high-speed, large-size autonomous ground vehicles (AGVs) that perceive the environment only through information provided by on-board sensors. The mission of the AGV is to move from its initial configuration to the goal configuration safely. The resulting trajectory should be collision-free and the AGV should be dynamically safe. As a starting point, the scenario where the vehicle moves on a flat surface at a constant speed is considered. The nonlinear MPC algorithm generates steering commands for completing the mission while enforcing safety constraints. The first safety constraint is avoiding obstacles. This is fulfilled by constraining the position of the AGV inside a safe region established from sensor data. The second safety constraint is ensuring dynamical safety. This is translated into avoiding single tire lift-off, which is implemented by limiting the steering angle within a range obtained using a 14 DoF vehicle dynamics model. At each sampling time, at least one multi-phase optimal control problem (OCP) is formulated and solved on-line. The safe region is partitioned into multiple sub-regions, which can then be specified without using piecewise functions. The fact that the optimal trajectory traverses the sub-regions sequentially and hence the position constraints are different from phase to phase makes the OCP multi-phase. The multi-phase OCP is transcribed into a nonlinear programming problem using the hp-pseudospectral method, and solved using the interior-point method. Simulations of an AGV approaching multiple obstacles show the effectiveness of the proposed algorithm.
Characterisation and Testing of Multifunctional Surfaces

Surface texturing is considered an effective way for reducing friction losses and wear occurrence in mechanical systems. A large number of surfaces with textures artificially engineered has been proposed by researchers worldwide and among them lie a new developed typology: MUFU surfaces, where the acronym stands for multifunctional. Produced by hard-turning followed by a highly controllable Robot Assisted Polishing process, MUFU surfaces feature reservoirs for providing extra-lubrication between the contacting parts as well as uppermost flat regions for ensuring the bearing capability. The introduction of MUFU surfaces is however bound with a series of challenges constituting the topic of the present work. The exploration touches a number of disciplines encompassing metrology, tribology and modelling. The metrological investigation represents the core of the work as further researches are bound to a clear and comprehensive description of the surfaces analysed. Robust filtering methods are adopted, extended, coded and implemented in the commercial software SPIPTM. These methods prove to be extremely suitable in handling the raw data coming out from a measuring instrument and yield a correctly filtered and aligned roughness profile that would be unrealistically distorted if current practice methods were used. Once an aligned profile is obtained, a further operation is introduced: feature separation. The surface features are separated with a newly developed algorithm and analysed independently according to their function. In the present case, the roughness of the plateaus is investigated independently from the valleys, which on their turn are described by the amount of lubricant they can contain. These methods are applied throughout the whole experimental work in assessing the performances of MUFU surfaces in different applications. In machine elements, characterised by lower normal pressures, a new test rig is designed and developed studying the friction between bodies in pure sliding contact. Tests with this new device display how the employment of MUFU surfaces can reduce friction up to 50% compared to regularly machined surfaces. In metal forming tools, experimental tests are run in different processes. In deep drawing applications MUFU surfaces reduce the likelihood of galling occurrence compared to a highly polished surface.
PeaKDEck: a kernel density estimator-based peak calling program for DNaseI-seq data

Summary: Hypersensitivity to DNaseI digestion is a hallmark of open chromatin, and DNaseI-seq allows the genome-wide identification of regions of open chromatin. Interpreting these data is challenging, largely because of inherent variation in signal-to-noise ratio between datasets. We have developed PeaKDEck, a peak calling program that distinguishes signal from noise by randomly sampling read densities and using kernel density estimation to generate a dataset-specific probability distribution of random background signal. PeaKDEck uses this probability distribution to select an appropriate read density threshold for peak calling in each dataset. We benchmark PeaKDEck using published ENCODE DNaseI-seq data and other peak calling programs, and demonstrate superior performance in low signal-to-noise ratio datasets. Availability and implementation: PeaKDEck is written in standard Perl and runs on any platform with Perl installed. PeaKDEck is also available as a standalone application written in Perl/Tk, which does not require Perl to be installed. Files, including a user guide, can be downloaded at: www.ccmp. ox.ac.uk/peakdeck. Contact: chris.ocallaghan@ndm.ox.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
Parameter extraction procedure for a physics-based power SiC Schottky diode model

A detailed parameter extraction procedure for a simple physics-based power SiC Schottky diode model is presented. The developed procedure includes the extraction of doping concentration, active area and thickness of drift region, which are needed in the power Schottky diode model. The main advantage is that the developed procedure does not require any knowledge of device fabrication, which is usually not available to circuit designers. The only measurements required for the parameter extraction are a simple static I-V characterization and C-V measurements. Furthermore, the physics-based SiC Schottky diode model whose parameters are extracted by the proposed procedure includes temperature dependencies and is generally applicable to SiC Schottky diodes. The procedure is demonstrated for four Schottky diodes from two different manufacturers having the following ratings: 600V/50A, 1.2kV/3A, 1.2kV/7A, and 1.2kV/20A.
Reg GroupOnline PairOnline GroupFixed PairFixed GrpTr AllTr ConTr GrpTr AllTr ConTr GrpTr AllTr

We analyze the impact of atlas construction within the context of an atlas-guided segmenter applied to a morphometry study in neuroanatomy. Automatic segmenters often rely on anatomical information encoded via probabilistic atlases. These atlases are frequently constructed by registering collections of training data. In this paper, we study the impact of registration methods as well as the training data on automatic segmentation results. With respect to registration, we focus our comparison on pairwise vs. group-wise methods and fixed vs. online coordinate systems. For the training data, we consider collections of population specific and general population data. To study the impact of these factors, we revisit a previously published statistical group comparison that was based on manual segmentations. For each atlas type, we record the group differences based on automatic segmentations and compare these findings to the original ones. Furthermore, we measure the Dice overlap between manual and automatic segmentations. Our results indicate some advantages for coordinate systems that are developed in an online fashion.
Electrical characterization of reduced graphene oxide (rGO) on organic thin film transistor (OTFT)

A green method and eco-friendly solution were used to chemically reduce graphene oxide (GO) to graphene using green reductant. In this study, graphene oxide (GO) were prepared by using Tours method. Then, reduced graphene oxides (rGO) were prepared by using three typical reduction agents: L-ascorbic acid (L-AA), formamidinesulfinic acid (FAS) and sodium sulfite (Na2SO3). The reduced materials were characterized by Fourier transform infrared spectroscopy (FTIR), Thermo gravimetric analysis (TGA) and X-ray diffraction (XRD). Graphene based organic thin film transistor (G-OTFT) was prepared by a spin coating and thermal evaporation technique. The electrical characterization of G-OTFT was analyzed by using semiconductor parameter analyzer (SPA). The G-OTFT devices show p-type semiconducting behaviour. This article focuses on the synthesis and reduction of graphene oxide using three different reductants in order to maximise its electrical conductivity. The rGO product demonstrated a good electrical conductivit.
Semantic Parsing for Vietnamese Question Answering System

Based on a framework for English, we developed a Vietnamese Question Answering System. The learning paradigm in the framework reduces the burden of providing supervision during semantic parsing. Whilst taking the advantages from this mechanism, we further create our own feature calculation which is suitable for Vietnamese. A method of dynamic learning for feature computation is also presented in this work.
A Central Regulatory System Largely Controls Transcriptional Activation and Repression Responses to Phosphate Starvation in Arabidopsis

Plants respond to different stresses by inducing or repressing transcription of partially overlapping sets of genes. In Arabidopsis, the PHR1 transcription factor (TF) has an important role in the control of phosphate (Pi) starvation stress responses. Using transcriptomic analysis of Pi starvation in phr1, and phr1 phr1-like (phl1) mutants and in wild type plants, we show that PHR1 in conjunction with PHL1 controls most transcriptional activation and repression responses to phosphate starvation, regardless of the Pi starvation specificity of these responses. Induced genes are enriched in PHR1 binding sequences (P1BS) in their promoters, whereas repressed genes do not show such enrichment, suggesting that PHR1(-like) control of transcriptional repression responses is indirect. In agreement with this, transcriptomic analysis of a transgenic plant expressing PHR1 fused to the hormone ligand domain of the glucocorticoid receptor showed that PHR1 direct targets (i.e., displaying altered expression after GR:PHR1 activation by dexamethasone in the presence of cycloheximide) corresponded largely to Pi starvation-induced genes that are highly enriched in P1BS. A minimal promoter containing a multimerised P1BS recapitulates Pi starvation-specific responsiveness. Likewise, mutation of P1BS in the promoter of two Pi starvation-responsive genes impaired their responsiveness to Pi starvation, but not to other stress types. Phylogenetic footprinting confirmed the importance of P1BS and PHR1 in Pi starvation responsiveness and indicated that P1BS acts in concert with other cis motifs. All together, our data show that PHR1 and PHL1 are partially redundant TF acting as central integrators of Pi starvation responses, both specific and generic. In addition, they indicate that transcriptional repression responses are an integral part of adaptive responses to stress.
Cancer stem cells in gliomas: Identifying and understanding the apex cell in cancer's hierarchy

Neuro‐oncology research has rediscovered a complexity of nervous system cancers through the incorporation of cellular heterogeneity into tumor models with cellular subsets displaying stem‐cell characteristics. Self‐renewing cancer stem cells (CSCs) can propagate tumors and yield nontumorigenic tumor bulk cells that display a more differentiated phenotype. The ability to prospectively isolate and interrogate CSCs is defining molecular mechanisms responsible for the tumor maintenance and growth. The clinical relevance of CSCs has been supported by their resistance to cytotoxic therapies and their promotion of tumor angiogenesis. Although the field of CSC biology is relatively young, continued elucidation of the features of these cells holds promise for the development of novel patient therapies. © 2011 Wiley‐Liss, Inc.
Cooperative location verification for vehicular ad-hoc networks

Vehicular ad hoc networks (VANETs) have attracted much attention over the last few years. Localization and position information of vehicles is very significant in VANETs; this is a result of the special nature of VANETs. In this paper, we propose a location verification approach to prevent position-spoofing attacks on VANETs. Cooperative Location Verification (CLV), which is our approach, basically used two vehicles, a Verifier and a Cooperator, to complete the verification of a vehicle (Prover). The Verifier and Cooperator sent a challenge to the respective Prover; and, the Prover was required to reply with its location information immediately which was based on radio frequency. The Verifier then verified the claimed location according to the Time-of-Flight of the signals in those two challenge-response procedures. In the simulation, the results show that our approach is better than both Secure Location Verification (SLV) and Greedy Forwarding Algorithm (GFA).
Maximum power point tracking issues in control of a low power photovoltaic (PV) module

The main challenge in control of photovoltaic (PV) power system is to operate at maximum efficiency. The I-V characteristics of PV system are non-linear, which makes it difficult to track the maximum power at varying Insolation and temperature. In this paper a 50W photovoltaic module based system is investigated for its control characteristics. The operating characteristics is studied with the focus on control of boost converter to achieve maximum power point tracking by Perturbation and Observation method uniquely used with PI controller. The performance analysis of this system based model is simulated and presented for MPP operation by applying the control technique to a boost converter highlighting the issues in its control. The simulation results are obtained and plotted for varying insolation level between 50 % and 100 % for validation.