Miracle-dz/UniFAR-FTUs huggingface dataset:
An Automated Approach for Supporting Application QoS in Shared Resource Pools

Many enterprises are beginning to exploit shared resource pool environments. In these environments, the application workloads exploit a common set of hardware resources. These are complex environments where selecting per-application scheduler parameter settings is a challenging task. It is a challenge because i) the capacity of resource pools are generally overbooked, i.e., the sum of per-application peak demands are greater than the capacity of the pool, and ii) because different applications can have different quality of service (QoS) requirements that are affected by the applications’ ability to obtain capacity when needed. This paper presents a new method that automates the computation of appropriate values for scheduler parameters. The method takes as input a characterization of an application’s workload demands, an application QoS requirement, and a measure of resource access QoS for resources that governs overbooking within the pool. As output, the method automatically specifies resource scheduler parameter settings that are expected to realize the application’s requirements. We assume that the pool’s resource schedulers can support at least two service priorities and can also manage the relationship between resource demand and allocation. A case study demonstrates the method and illustrates the per-application risks associated with resource sharing.
INTEGRABLE STRUCTURE OF 5d$\mathcal{N} = 1$ SUPERSYMMETRIC YANG-MILLS AND MELTING CRYSTAL

We study loop operators of 5d SYM in Ω background. Computation of their correlation functions is described. For the case of U(1) theory, the generating function reproduces the partition function of melting crystal model with external potential. We argue the common integrable structure of 5d SYM in Ω background and melting crystal model. An extension of the Seiberg-Witten geometry of the U(1) theory is presented.
CLARCS, a C++ Library for Automated Registration and Comparison of Surfaces: Medical Applications

In this paper we present the methods implemented in the CLARCS (C++ Library for Automated Registration and Comparison of Surfaces) library. This library allows some basic and high level processing on free-form surfaces, represented as point sets or meshes. Three methods are the "building bricks" of CLARCS; they allow (i) the rigid/affine/non-linear registration of two point sets, (ii) the computation of the mid-sagittal plane of one point set, (iii) the computation of a mean point set from several point sets, and the variability around this mean. These methods are all based on a common methodological framework, in which the point sets/meshes are represented either as a Gaussian mixture model or as a draw of such a model. We propose some applications of the methods implemented in CLARCS on different sets of medical data.
Crystal Structure of the Saccharomyces cerevisiae Ubiquitin-conjugating Enzyme Rad6 at 2.6 Å Resolution*

The Saccharomyces cerevisiaeubiquitin-conjugating enzyme (UBC) Rad6 is required for several functions, including the repair of UV damaged DNA, damage-induced mutagenesis, sporulation, and the degradation of cellular proteins that possess destabilizing N-terminal residues. Rad6 mediates its role in N-end rule-dependent protein degradation via interaction with the ubiquitin-protein ligase Ubr1 and in DNA repair via interactions with the DNA binding protein Rad18. We report here the crystal structure of Rad6 refined at 2.6 Å resolution to anR factor of 21.3%. The protein adopts an α/β fold that is very similar to other UBC structures. An apparent difference at the functionally important first helix, however, has prompted a reassessment of previously reported structures. The active site cysteine lies in a cleft formed by a coil region that includes the 310 helix and a loop that is in different conformations for the three molecules in the asymmetric unit. Residues important for Rad6 interaction with Ubr1 and Rad18 are on the opposite side of the structure from the active site, indicating that this part of the UBC surface participates in protein-protein interactions that define Rad6 substrate specificity.
18FDG‐PET in different subtypes of temporal lobe epilepsy: SEEG validation and predictive value

The objective of the study was to characterize interictal 18‐fluorodeoxyglucose–positron emission tomography (18FDG‐PET) whole‐brain voxel‐based metabolic patterns among distinct subtypes of temporal lobe epilepsy (TLE), as defined by stereo–electroencephalography (SEEG) and to determine predictive value of PET result on postoperative outcome.
A computational paradigm for the simulation of complex epidemic diseases

In this work, we present a paradigm to build a decision support tool which enable users to "play" with the model, change the input parameters, test different intervention scenarios,. in case of infectious diseases. The proposed paradigm involves modeling of closed to realistic social contact networks through which the disease transmissions take place as well as the social influence networks which take part in determining individual decisions and hence, lead to impacts on the propagation of the disease. The proposed model follows agent based approach and uses statecharts for the modeling of agent behaviors focusing on the behaviors of each individual when he/she is being sick or when hearing of the sickness of the others. The dynamics of attitude of a person (which affects that agent's decision) is characterized using opinion dynamics model that take place on those influence networks. Different interventions strategies are also investigated that take into account the fact that agent is incentive. Our preliminary simulation results shows that, with the making best use of available (demographic, transportation,.) data and considering of behaviors at individual level, the proposed model can capture the spreading characteristics of the disease as well as the impacts of intervention strategies on its propagation process at the whole population level.
An Architecture for Affective Behaviours Based on Conservation of Resources

This paper presents a model for autonomous virtual agents that enables them to display affective behaviours. Our goal is to obtain believable behaviours, i.e. behaviours that are similar to those of human beings, for various simulation contexts in an urban environment. The proposed architecture is based on a principle of conservation and acquisition of resources.
Virtual Topology Reconfiguration Problem on survivable WDM networks

In this paper, the virtual topology reconfiguration problem (VTRP) on survivable WDM networks with reconfiguration constraint is studied. Given a physical topology, a dedicated path-protected virtual topology and a new traffic demand matrix, the goal of VTRP is to reconfigure the current virtual topology under the pre-specified reconfiguration constraint such that the objective value can be minimized. The objective value considered in this paper is the average weighted propagation delay (AWPD). Because designing a polynomial time algorithm to find the optimal solution of VTRP is impractical when the reconfiguration constraint is larger. In this paper, a simulated annealing (SA) and a genetic algorithm (GA) are proposed to solve this problem. Experiment results of these algorithms are also given.
Sensitivity to Constraints in Blackbox Optimization

The paper proposes a framework for sensitivity analyses of blackbox constrained optimization problems for which Lagrange multipliers are not available. Two strategies are developed to analyze the sensitivity of the optimal objective function value to general constraints. These are a simple method which may be performed immediately after a single optimization, and a detailed method performing biobjective optimization on the minimization of the objective versus the constraint of interest. The detailed method provides points on the Pareto front of the objective versus a chosen constraint. The proposed methods are tested on an academic test case and on an engineering problem using the mesh adaptive direct search algorithm.
Revealing the Changing Spatial Structure of Cities from Human Activity Patterns

Identifying changes in the spatial structure of cities is a prerequisite for the development and validation of adequate planning strategies. Nevertheless, current methods of measurement are becoming ever more challenged by increasingly dispersed forms of urban organisation. Being motivated by the recent availability of large-scale data on human activities, this paper proposes a new quantitative measure for the centrality of locations, taking into account both the number of people attracted to different locations and the diversity of their activities. This ‘centrality index’ allows for the identification of functional urban centres and for a systematic tracking of their relative importance over time. As a case study, the proposed index is applied to extensive travel survey data in Singapore for the years 1997, 2004 and 2008. It is shown that, on the one hand, the city-state has been developing rapidly towards a polycentric urban form that compares rather closely with the official urban development plan. On the other hand, however, the downtown core has strongly gained in importance, and this can be largely attributed to the extension of the public transit system.
On arbitrarily varying bidirectional broadcast channels with constraints on input and states

The concept of bidirectional relaying is a key technique to improve the performance in future wireless networks. It applies to three-node networks, where a relay node establishes a bidirectional communication between two other nodes using a decode-and-forward protocol. It divides the whole communication into two phases, namely the multiple access and bidirectional broadcast phase. Here, we concentrate on the second phase, which is also known as the bidirectional broadcast channel, and assume that the transmission is affected by arbitrarily varying channels. We impose constraints on the permissible input and state sequences and derive the capacity regions for random and deterministic coding.
Financing higher education in Africa

This report demonstrated that the key for success in a globalized world lies increasingly in how effectively a country can assimilate the available knowledge and build comparative advantages in areas with good growth prospects and how it can use technology to address the most pressing environmental challenges. Higher-level institutions in Sub-Saharan Africa that is equipped to impart quality education and conduct relevant applied research can play a critical role in producing workers with the skills to assimilate technology and make effective decisions that help industry to diversify into a broader range of products. Good-quality and relevant higher education is also a key to stimulating innovations in new varieties of crops, new materials, or sources of energy that would facilitate progress toward reducing poverty, achieving food security, and improving health. This report is a follow-up to the 2008 study. It examines current practices in financing higher education in Sub-Saharan Africa, taking into account the significant differences that exist among countries. Drawing on experience from around the world, the report also examines the range of policy options that could be considered in tackling the financing issue. The report also carries an encouraging message. It shows that a full range of options do exist and that some African countries and institutions have started implementing them. Private higher education is experiencing spectacular growth in Africa. Cost-sharing programs are being implemented in many universities, accompanied by student loans and financial aid for low-income students. Higher education is being diversified to offer lower cost and more effective delivery alternatives. In a few cases, impressive reforms to improve internal efficiency have been implemented, and governments are increasingly adopting more effective budget management practices.
Traffic Sign Recognition Using Extreme Learning Classifier with Deep Convolutional Features

Traffic sign recognition is an important but challenging task, especially for automated driving and driver assistance. Its accuracy depends on two aspects: feature extractor and classifier. Current popular algorithms mainly use convolutional neural networks (CNN) to execute both feature extraction and classification. Such methods could achieve impressive results but usually on the basis of an extremely huge and complex network. What’s more, since the fully-connected layers in CNN form a classical neural network classifier, which is trained by gradient descent-based implementations, the generalization ability is limited and sub-optimal. The performance could be further improved if other favorable classifiers are used and extreme learning machine (ELM) classifier is just the candidate. In this paper, ELM classifier equipped with deep convolutional features is utilized, which integrates the terrific discriminative capability of deep convolutional features learnt by CNN with the outstanding generalization performance of ELM classifier. Firstly CNN learns deep and robust features, followed by the removing of the fully-connected layers, which turns CNN to be the feature extractor. Then ELM fed with the CNN features is used as the classifier to conduct an excellent classification. Experiments on German traffic sign recognition benchmark (GTSRB) demonstrate that the proposed method can obtain competitive results with state-of-the-art algorithms with much less complexity.
Frontal Cortex Subregions Play Distinct Roles in Choices between Actions and Stimuli

The orbitofrontal cortex (OFC) has been implicated in reinforcement-guided decision making, error monitoring, and the reversal of behavior in response to changing circumstances. The anterior cingulate cortex sulcus (ACCS), however, has also been implicated in similar aspects of behavior. Dissociating the unique functions of these areas would improve our understanding of the decision-making process. The effect of selective OFC lesions on how monkeys used the history of reinforcement to guide choices of either particular actions or particular stimuli was studied and compared with the effects of ACCS lesions. Both lesions disrupted decision making, but their effects were differentially modulated by the dependence on action– or stimulus–value contingencies. OFC lesions caused a deficit in stimulus but not action selection, whereas ACCS lesions had the opposite effect, disrupting action but not stimulus selection. Furthermore, OFC lesions that have previously been found to impair decision making when deterministic stimulus–reward contingencies are switched were found to cause a more general learning impairment in more naturalistic situations in which reward was stochastic. Both OFC and ACCS are essential for reinforcement-guided decision making rather than just error monitoring or behavioral reversal. The OFC and ACCS are both, however, more concerned with learning and making decisions, but their roles in selecting between stimulus and action values are distinct.
Discovering Features Towards Recognising Textual Entailment

Recognising Textual Entailment (RTE) has the objective of judging whether one piece of short text, normally a single sentence, follows from another piece of longer text. Such a task has been the focus of three consecutive challenges since 2005. Current approaches have explored the more sophisticated deep Natural Language Processing (NLP) techniques. However, the capability of shallow features are yet to be fully exploited. Furthermore, since the data for the task are extracted from different NLP categories, namely Question Answering (QA), Text Summarisation (SUM), Information Retrieval (IR) and Information Extraction (IE), further data/feature analysis can be done on these categories individually to search for differences between these categories in terms of Recognising Textual Entailment. This thesis sets to analyse the RTE datasets for features that can be used in classification, to examine the effectiveness of using only shallow NLP techniques and to investigate whether data from different NLP categories possess different characteristics. The practicality of the shallow features and the performance of the RTE system are analysed in several experiments. The results of the experiments indicate that shallow features are candidates for feature-based RTE systems though further improvements require the extraction of deeper features. Moreover, a system that uses a different set of features on each individual NLP category has the potential of beating a single classifier that is trained on all the training data and all the features in accuracy. Future work include improving the current matching and alignment techniques, better relation detection and adding in temporal normalisation.
Aggregation Pheromone Density Based Clustering

Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a new algorithm (called APC) for clustering data set based on this property of aggregation pheromone found in ants. At each location of data point an ant is placed, and the ants are allowed to move in the search space to find points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. If the pheromone density is more at a point, more ants are attracted towards this point. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of well-known benchmark data sets using different cluster validity measures. Results are compared with those obtained using average linkage clustering algorithm and are found to be better and robust.
Observer-Based State-Space Current Control for a Three-Phase Grid-Connected Converter Equipped With an LCL Filter

This paper presents a state-space current control method for active damping of the resonance frequency of the LCL filter and setting the dominant dynamics of the converter current through direct pole placement. A state observer is used, where upon additional sensors are not needed in comparison with the conventional L filter design. The relationship between the system delay and instability caused by the resonance phenomenon is considered. Nyquist diagrams are used to examine the parameter sensitivity of the proposed method. The method is validated with simulations and experiments.
Identification and functional assay of the interaction motifs in the partner protein OsNAR2.1 of the two-component system for high-affinity nitrate transport

A partner protein, NAR2, is essential for high-affinity nitrate transport of the NRT2 protein in plants. However, the NAR2 motifs that interact with NRT2s for their plasma membrane (PM) localization and nitrate transporter activity have not been functionally characterized. In this study, OsNAR2.1 mutations with different carbon (C)-terminal deletions and nine different point mutations in the conserved regions of NAR2 homologs in plants were generated to explore the essential motifs involved in the interaction with OsNRT2.3a. Screening using the membrane yeast two-hybrid system and Xenopus oocytes for nitrogen-15 (15N) uptake demonstrated that either R100G or D109N point mutations impaired the OsNAR2.1 interaction with OsNRT2.3a. Western blotting and visualization using green fluorescent protein fused to either the N- or C-terminus of OsNAR2.1 indicated that OsNAR2.1 is expressed in both the PM and cytoplasm. The split-yellow fluorescent protein (YFP)/BiFC analyses indicated that OsNRT2.3a was targeted to the PM in the presence of OsNAR2.1, while either R100G or D109N mutation resulted in the loss of OsNRT2.3a-YFP signal in the PM. Based on these results, arginine 100 and aspartic acid 109 of the OsNAR2.1 protein are key amino acids in the interaction with OsNRT2.3a, and their interaction occurs in the PM but not cytoplasm.
Ja n 20 06 Plane waves in metric-affine gravity

We describe plane-fronted waves in the Yang-Mills type quadratic metricaffine theory of gravity. The torsion and the nonmetricity are both nontrivial, and they do not belong to the triplet ansatz. PACS: 04.50. +h, 04.30. -w, 04.20. Jb.
Improvement of Page Ranking Algorithm Based on Timestamp and Link

The traditional ranking algorithm favors the old pages, which makes old pages always appear on the top of the ranking results when pages are ranked according to the dynamic web by the static ranking algorithm. Therefore, this paper introduces temporal-link-analysis algorithm to resolve the problem. This algorithm uses the last modification time that returned by the HTTP response as the timestamp of nodes and links concerned. And the weight of the in-link and out-link are also integrated to compute the overall weight of the pages. Using the WTPR algorithm can make the old pages decline and new pages raise in the ranking result, meanwhile it can help the old pages which have high-quality get higher rank value than common old pages.
LMS-based calibration of pipelined ADCs including linear and nonlinear errors

A least mean square (LMS) based calibration algorithm is proposed to calibrate most known error sources in 1.5 bit/stage pipelined ADCs, known to be immune to moderate comparator offsets. The error sources include linear gain errors, reference voltage errors, systematic offset errors and amplifier non-linear errors of each pipeline stage. LMS is used to estimate the error parameters in the digital domain. After estimation the proposed algorithm calibrate the pipelined ADC using the estimated parameter errors. Simulation results show that the proposed algorithm can improve the ENOB from 6.6 bits to 13.9 bits for a 14 bit 1.5 bits/stage pipelined ADC.
A novel and practical algorithm for generating 3D volumetric clouds

Aimed at actual demand for simulating the real sense clouds and based on the basic idea of Diamond-Square algorithm, this paper proposes a novel and practical algorithm for generating 3D Volumetric clouds, i.e. Cube-Diamond-Square algorithm. The algorithm solves the problem that Diamond-Square can only simulate 2D clouds or the surface of 3D clouds. Although without complicated numerical models, it can effectively generate true 3D volumetric clouds, and can bring the user the sense of passing through real clouds. The algorithm supports hierarchical transmission of simulation data, which effectively solves the problem of real-time performance of the simulation data transmission and visualization due to too large simulation data. Experiments prove that Cube-Diamond-Square algorithm is simple and efficient, and has good applicability.