Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystal structures of thermostable xylose isomerases from Thermus caldophilus and Thermus thermophilus: possible structural determinants of thermostability.

The crystal structures of highly thermostable xylose isomerases from Thermus thermophilus (TthXI) and Thermus caldophilus (TcaXI), both with the optimum reaction temperature of 90 degrees C, have been determined by X-ray crystallography. The model of TcaXI has been refined to an R-factor of 17.8 % for data extending to 2.3 A and that of TthXI to 17.1 % for data extending to 2.2 A. The tetrameric arrangement of subunits characterized by the 222-symmetry and the tertiary fold of each subunit in both TcaXI and TthXI are basically the same as in other xylose isomerases. Each monomer is composed of two domains. Domain I (residues 1 to 321) folds into the (beta/alpha)8-barrel. Domain II (residues 322 to 387), lacking beta-strands, makes extensive contacts with domain I of an adjacent subunit. Each monomer of TcaXI contains ten beta-strands, 15 alpha-helices, and six 310-helices, while that of TthXI contains ten beta-strands, 16 alpha-helices, and five 310-helices. Although the electron density does not indicate the presence of bound metal ions in the present models of both TcaXI and TthXI, the active site residues show the conserved structural features. In order to understand the structural basis for thermostability of these enzymes, their structures have been compared with less thermostable XIs from Arthrobacter B3728 and Actinoplanes missouriensis (AXI and AmiXI), with the optimum reaction temperatures of 80 degrees C and 75 degrees C, respectively. Analyses of various factors that may affect protein thermostability indicate that the possible structural determinants of the enhanced thermostability of TcaXI/TthXI over AXI/AmiXI are (i) an increase in ion pairs and ion-pair networks, (ii) a decrease in the large inter-subunit cavities, (iii) a removal of potential deamidation/isoaspartate formation sites, and (iv) a shortened loop.
Plane segment finder: algorithm, implementation and applications

This paper describes the development of a plane segment finder, which is able to detect three-dimensional planar surfaces from input images in real-time. We propose an algorithm for detecting plane segments, that includes: 1) plane segment candidate extraction using the 3D Hough transform from depth map information; and 2) fitting the plane segment candidates to the depth map in order to detect the partial plane segment, since the extracted plane segment candidates are general planes, with no boundary. To achieve real-time plane segment finding system, we apply: 1) the recursive correlation method for depth map generation; and 2) the randomized Hough transform for plane segment extraction. Finally, experimental results using an implementation of our system along with a humanoid robot are shown.
Incremental learning of human social behaviors with feature-based spatial effects

In the near future, robots will exist in our home, office, hospital, school and all other public places. In other words, robots have to work and share environments with human. However, there exist many invisible social rules or social protocols in human society. For example, people usually stand in a line to access certain services. It seems that there is a social force or a “spatial effect” which forces people to perform the behavior of standing in a line. Unfortunately, these spatial effects are usually invisible and immeasurable. It makes social behavior understanding to be a difficult problem. The robot that does not comprehend these spatial effects might harm people or itself. Previous methods of behavior understanding mainly only targeted certain scenarios or environments. This paper follows the framework of SBCM (Spatial Behavior Cognition Model)[3] and extends to feature-based spatial effects. By utilizing feature to represent the spatial effect, it is easier to apply known spatial effects to different environments. This paper also demonstrates that the robot is able to learn and execute some common social behaviors under the proposed method. Several simulations and experiments are conducted to verify the proposed idea in this paper.
Relay Aided Wireless Multicast Utilizing Network Coding: Outage Behaviour and Diversity Gain

Broadcast nature of wireless networks can be exploited to provide a flexible transmission, especially in multicast service. The potential relay then is capable to participate in message forwarding. This paper presents a network coding based cooperative (NCBC) multicast scheme exploiting limited feedback. In the developed NCBC multicast scheme, the source transmits two separate signals to multiple destinations in two successive half slots. The relay may combine the signals if it received two signals correctly and forward it to destinations in the next half slot. The destinations, therefore, can recover signals either from direct transmission or the relay forwarding. In this paper, performance analysis is presented in terms of outage probability and diversity order. It is demonstrated that the NCBC multicast scheme can work better than direct source multicast in terms of outage probability. Meanwhile, the NCBC multicast scheme can achieve full diversity gain (diversity two for one relay case). Comparisons with amplify-and-forward (AF) and selection decode-and-forward cooperation schemes shows that the NCBC multicast scheme achieves almost the same outage performance as AF and selection decode and forward while consuming less bandwidth and energy resources.
Assessing the Applicability of a Combinatorial Testing tool within an Industrial Environment

This paper describes a case study executed to evaluate a combinatorial test design approach within the industrial setting of IBM Research. An existing combinatorial test suite was compared against a prioritized combinatorial test suite that was generated with the Combi-natorial Tree Editor XL Profesional (CTE). The prioritization technique was recently developed and added to the CTE in the context of the FITTEST project. Test design for the new test suite was carried out by the developers of the prioritization technique. Test implementation and execution was done by the industrial partner of the System Under Test. This case study has investigated whether the prioritized combinatorial technique is useful to complement current testing practices at IBM Research. The focus of this study is on fault finding capability of artificially injected faults that have been selected and prioritized using domain knowledge and expertise, and efficiency of test case execution. Conclusions of this study are that for the testing of the target product in a simulated environment, the improved combinatorial testing tools do qualify as useful and this type of testing will be included in current practices.
Three‐dimensional numerical simulation of MHD waves observed by the Extreme Ultraviolet Imaging Telescope

We investigate the global large amplitude waves propagating across the solar disk as observed by the SOHO/Extreme Ultraviolet Imaging Telescope (EIT). These waves appear to be similar to those observed in Hα in the chromosphere and which are known as “Moreton waves,” associated with large solar flares [Moreton, 1960, 1964]. Uchida [1968] interpreted these Moreton waves as the propagation of a hydromagnetic disturbance in the corona with its wavefront intersecting the chromosphere to produce the Moreton wave as observed in movie sequences of Hα images. To search for an understanding of the physical characteristics of these newly observed EIT waves, we constructed a three-dimensional, time-dependent, numerical magnetohydrodynamic (MHD) model. Measured global magnetic fields, obtained from the Wilcox Solar Observatory (WSO) at Stanford University, are used as the initial magnetic field to investigate hydromagnetic wave propagation in a three-dimensional spherical geometry. Using magnetohydrodynamic wave theory together with simulation, we are able to identify these observed EIT waves as fast mode MHD waves dominated by the acoustic mode, called magnetosonic waves. The results to be presented include the following: (1) comparison of observed and simulated morphology projected on the disk and the distance-time curves on the solar disk; (2) three-dimensional evolution of the disturbed magnetic field lines at various viewing angles; (3) evolution of the plasma density profile at a specific location as a function of latitude; and (4) computed Friedrich's diagrams to identify the MHD wave characteristics.
A UNIFICATION OF COMPONENT ANALYSIS METHODS

Over the last century Component Analysis (CA) methods such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Canonical Correlation Analysis (CCA), k-means and Spectral Clustering (SC) have been extensively used as a feature extraction step for modeling, classification, visualization and clustering. CA techniques are appealing because many can be formulated as eigen-problems, offering great potential for learning linear and non-linear representations of data without local minima. However, the eigen-formulation often conceals important analytic and computational drawbacks of CA techniques, such as solving generalized eigen-problems with rank deficient matrices, lacking intuitive interpretation of normalization factors, and understanding relationships between CA methods. This chapter proposes a unified framework to formulate many CA methods as a leastsquares estimation problem. We show how PCA, LDA, CCA, k-means, spectral graph methods and kernel extensions correspond to a particular instance of a least squares weighted kernel reduced rank regression (LS-KRRR). The least-squares formulation allows better understanding of normalization factors, provides a clean framework to understand the communalities and differences between many CA methods, yields efficient optimization algorithms for many CA algorithms, suggest easy derivation for on-line learning methods, and provides an easier generalization of CA techniques. In particular, we derive the matrix expressions for weighted generalizations of PCA, LDA, SC and CCA (including kernel extensions).
A European carotenoid database to assess carotenoid intakes and its use in a five-country comparative study

A food frequency questionnaire (FFQ) and carotenoid database with information on α- and β-carotene, lutein, lycopene and β-cryptoxanthin was prepared and used to compare the carotenoid intakes in five European countries: UK, Republic of Ireland, Spain, France and The Netherlands. Eighty, age- (25–45 years) and sex-matched volunteers were recruited in each of the five countries. A FFQ and carotenoid database was prepared of the most commonly consumed carotenoid rich foods in the participating countries and the information was used to calculate frequency and intake of carotenoid-rich foods. The median total carotenoid intake based on the sum of the five carotenoids, was significantly higher (P<0.05) in France (16.1 mg/day) and lower in Spain (9.5 mg/day,) than the other countries, where the average intake was approximately 14 mg/day. Comparison of dietary source of carotenoids showed that carrots were the major source of β-carotene in all countries except Spain where spinach was most important. Likewise, carrots were also the main source of α-carotene. Tomato or tomato products, were the major source of lycopene. Lutein was mainly obtained from peas in Republic of Ireland and the UK, however, spinach was found to be the major source in other countries. In all countries, β-cryptoxanthin was primarily obtained from citrus fruit. Comparing the data with that from specific European country studies suggests that the FFQ and carotenoid database described in the present paper can be used for comparative dietary intake studies within Europe. The results show that within Europe there are differences in the specific intake of some carotenoids which are related to different foods consumed by people in different countries.
Exploring architectural heterogeneity in intelligent vision systems

Limited power budgets and the need for high performance computing have led to platform customization with a number of accelerators integrated with CMPs. In order to study customized architectures, we model four customization design points and compare their performance and energy across a number of computer vision workloads. We analyze the limitations of generic architectures and quantify the costs of increasing customization using these micro-architectural design points. This analysis leads us to develop a framework consisting of low-power multi-cores and an array of configurable micro-accelerator functional units. Using this platform, we illustrate dataflow and control processing optimizations that provide for performance gains similar to custom ASICs for a wide range of vision benchmarks.
MOO: A Methodology for Online Optimization through Mining the Offline Optimum

Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized). These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decision-making? MOO is a novel application of data mining to online optimization. The idea is to mine (logged) expert decisions or the offline optimum for rules that can be used for online decisions. It requires little knowledge about the task distribution and cost structure, and is applicable to a wide range of problems. This paper presents a feasibility study of the methodology for the well-known k-server problem. Experiments with synthetic data show that optimization can be recast as classification of the optimum decisions; the resulting heuristic can achieve the optimum for strong request patterns, consistently outperforms other heuristics for weak patterns, and is robust despite changes in cost model.
Performance Analysis of Network-Coding-Based P2P Live Streaming Systems

Peer-to-peer (P2P) video streaming is a scalable and cost-effective technology to stream video content to a large population of users and has attracted a lot of research for over a decade now. Recently, network coding has been introduced to improve the efficiency of these systems and to simplify the protocol design. There are already some successful commercial applications that utilize network coding. However, previous analytical studies of network-coding-based P2P streaming systems mainly focused on fundamental properties of the system and ignored the influence of the protocol details. In this study, a unique stochastic model is developed to reveal how segments of the video stream evolve over their lifetime in the buffer before they go into playback. Different strategies for segment selection have been studied with the model, and their performance has been compared. A new approximation of the probability of linear independence of coded blocks has been proposed to study the redundancy of network coding. Finally, extensive numerical results and simulations have been provided to validate our model. From these results, in-depth insights into how system parameters and segment selection strategies affect the performance of the system have been obtained.
Cascade adaptive predictive controller design based on multiple models (Case study: GILAN combined cycle)

This paper presents a cascade adaptive predictive controller (APC) for an industrial application, namely, drum of GILAN combine cycle steam power plant. In order to achieve an accurate model for the drum, real data from GILAN power plant is used. The main objective of the controller design is to achieve an acceptable performance for regulating water level deviation of the drum. APC outer loop of the designed cascade controller adapts with the process dynamics variations, namely, load changes (as a measured disturbance) as well as pressure changes (as an unmeasured disturbance) effectively. The drum water level is maintained in the safe region. In order to achieve the above features, a bank of possible nonlinear models is employed. Each model is used to re-initialize the adaptive predictive controller every time. Simulation results for nonlinear drum model of GILAN combine cycle power plant show that the designed cascade adaptive predictive controller has better performance compared to the well tuned cascade proportional integral controllers.
The mircrine mechanism controlling cardiac stem cell fate

The recent identification of c-kit-positive cardiac stem cells revealed the great growth reserve of the heart, in which connection among cells might be essential in regulating their fate. Especially, the mircrine mechanism, translocation of microRNAs (miRs) from a cell to another via gap junctions, appeared to be important in controlling the differentiation of cardiac stem cells. The modification on miR expression and/or translocation may be able to enhance further the clinical efficacy of cellular therapy.
A virtual network solution for service mobility

In the Cloud Computing Context, a virtual Machine (VM) can be migrated from one physical host to a remote physical host while keeping it's address to avoid service discontinuity. The service application is so no more hosted on a particular physical machine but can move from one to other. We define therefore a new mobility context where the services provided by VMs move from one location to another during the continuous execution of the service application. This mobility requires the establishment of Layer-2 pipes to ensure that the VMs keep their initial addresses. We propose SMVN (Service Mobility Virtual Network), an MPLS/VPLS virtual network for the seamless VMs mobility. We state a MILP formulation of the proposed problem and solve it optimally using the TOMLAB/CPLEX solver. To deal with the latency constraints of VMs mobility, we propose also a Multi-Weighted Pareto Heuristic (MWPH) to solve the SMVN problem.
The modal age at death and the shifting mortality hypothesis.

The modal age at death is used to study the shifting mortality scenario experienced by low mortality countries. The relations of the life table functions at the modal age are analyzed using mortality models. In the models the modal age increases over time, but there is an asymptotic approximation towards a constant number of deaths and standard deviation from the mode. The findings are compared to the changes observed in populations with historical mortality data. During the transition period to a shifting mortality era the population becomes highly heterogeneous and the rate of improvement in mortality is highly sensitive to these changes. By focusing in the modal age at death, a new perspective on the analysis of human longevity is revealed.
ANFIS Based Terminal Sliding Mode Control Design for Nonlinear Systems

In this paper, an adaptive network based fuzzy inference system (ANFIS) terminal sliding (TS) mode (ANFISTS) controller is developed to deal with the disturbed nonlinear system. This ANFISTS control system is composed of an ANFIS controller and a TS controller. The ANFIS controller is designed to do as a main controller and the TS controller is designed to cope with disturbances and uncertainties. In simulations, the nonlinear inverted pendulum system is illustrated to verify this proposed ANFISTS control methodology. The results possess better performances and robustness by comparisons with fuzzy control.
Multi-view common space learning for emotion recognition in the wild

It is a very challenging task to recognize emotion in the wild. Recently, combining information from various views or modalities has attracted more attention. Cross modality features and features extracted by different methods are regarded as multi-view information of the sample. In this paper, we propose a method to analyse multi-view features of emotion samples and automatically recognize the expression as part of the fourth Emotion Recognition in the Wild Challenge (EmotiW 2016). In our method, we first extract multi-view features such as BoF, CNN, LBP-TOP and audio features for each expression sample. Then we learn the corresponding projection matrices to map multi-view features into a common subspace. In the meantime, we impose l_2,1-norm penalties on projection matrices for feature selection. We apply both this method and PLSR to emotion recognition. We conduct experiments on both AFEW and HAPPEI datasets, and achieve superior performance. The best recognition accuracy of our method is 0.5531 on the AFEW dataset for video based emotion recognition in the wild. The minimum RMSE for group happiness intensity recognition is 0.9525 on HAPPEI dataset. Both of them are much better than that of the challenge baseline.
Exploiting Dark Silicon for Energy Efficiency EXECUTIVE

EXECUTIVE SUMMARY Application datasets grow faster than Moore’s Law [7,8], both in personal and desktop computing, as well as in the scientific domain. For example, during March 2011, over 1.6PB of data were generated and transferred for processing among the Tier-1 sites of CERN’s Large Hadron Collider computing grid [3]. Processing all these data requires unprecedented amounts of computation, with commensurate energy demands. Computing’s insatiable appetite for energy has created an IT industry with approximately the same carbon footprint as the airline industry, accounting for 2% of the global greenhouse gas emissions. Computers consumed 408 TWh globally in 2010, and with a forecasted 10% annual growth on installed computing systems, this figure will rise, negatively impacting the environment and the economics of computing [1]. Unfortunately, a large fraction of this energy is wasted. Modern processors account for 38% of the power consumption of a computing system [5], but the general-purpose computing substrate they provide is hugely power inefficient. For example, compared to an optimized ASIC design, conventional multicores consume 157–707 times more energy when executing various phases of an H.264 encoder [6]. We postulate that a promising path towards energy-efficient data processing is the development of multicore processors with specialized cores. To implement these cores, we propose to utilize silicon that would otherwise remain powered-off, or “dark”, due to the physical constraints of processor chips. Specialized multicores can leverage the underutilized die area to overcome the power barrier, delivering significantly higher performance for the same power envelope, thereby significantly reducing energy consumption. In the remaining of the paper we describe these constraints and how they give rise to dark silicon, explain why utilizing dark silicon is a viable solution, estimate the possible energy benefits that could be achieved by specialized computing on commercial workloads (e.g., online transactional processing, decision support, and web server workloads), and conclude with a set of research directions for the software community.
Discussion of Faruqee, Laxton, Muir, and Pesenti, "Smooth Landing or Crash? Model-based Scenarios of Global Current Account Rebalancing" ∗

I enjoyed reading this fine paper, Faruqee, Laxton, Muir, and Pesenti [2] (FLMP). I believe it is worthwile to see the paper in the context of the lively debate in the recent literature on current-account developments, especially given the the large US current-account deficit. The recent literature includes much-noted contributions of Gourinchas and Rey [3] and of Lane and Milesi-Feretti [4] on the role of the currency composition of gross assets and liabilities, revaluation effects, and return differences on home and foreign debt and assets. Obstfeld and Rogoff [6] and [7] have recently presented a more formal model of the relation between the current account and the real exchange rate for the US economy, available in 2-country and 3-country versions. They emphasize the role of home bias in consumption and develop a static relation between the current account and the real exchange rate. The following ratios are defined: