Miracle-dz/UniFAR-FTUs huggingface dataset:
The crystal structure of XG‐34, an evolved xyloglucan‐specific carbohydrate‐binding module

This thesis presents the application of different protein engineering methods on enzymes and non-catalytic proteins that act upon xyloglucans. Xyloglucans are polysaccharides found as storage polymers in seeds and tubers, and as cross-linking glucans in the cell wall of plants. Their structure is complex with intricate branching patterns, which contribute to the physical properties of the polysaccharide including its binding to and interaction with other glucans such as cellulose. One important group of xyloglucan-active enzymes is encoded by the GH16 XTH gene family in plants, including xyloglucan endo-transglycosylases (XET) and xyloglucan endo-hydrolases (XEH). The molecular determinants behind the different catalytic routes of these homologous enzymes are still not fully understood. By combining structural data and molecular dynamics (MD) simulations, interesting facts were revealed about enzyme-substrate interaction. Furthermore, a pilot study was performed using structure-guided recombination to generate a restricted library of XET/XEH chimeras. Glycosynthases are hydrolytically inactive mutant glycoside hydrolases (GH) that catalyse the formation of glycosidic linkages between glycosyl fluoride donors and glycoside acceptors. Different enzymes with xyloglucan hydrolase activity were engineered into glycosynthases, and characterised as tools for the synthesis of well-defined homogenous xyloglucan oligo- and polysaccharides with regular substitution patterns. Carbohydrate-binding modules (CBM) are non-catalytic protein domains that bind to polysaccharidic substrates. An important technical application involves their use as molecular probes to detect and localise specific carbohydrates in vivo. The three-dimensional structure of an evolved xyloglucan binding module (XGBM) was solved by X-ray diffraction. Affinity-guided directed evolution of this first generation XGBM resulted in highly specific probes that were used to localise non-fucosylated xyloglucans in plant tissue sections.
A study of statistical query expansion strategies for sentence retrieval

retrieval of sentences that are relevant to a given in- formation need is a challenging passage retrieval task. In this context, the well-known vocabulary mismatch prob- lem, present in most Information Retrieval processes, arises severely because of the fine granularity of the task. Short queries, which are usually the rule rather than the excep- tion, come to aggravate the problem. Consequently, effec- tive sentence retrieval methods tend to apply some form of query expansion, usually based on pseudo-relevance feed- back. Nevertheless, there are no extensive studies compar- ing different expansion strategies for sentence retrieval prob- lems. In this work we aim to fill this gap. We start from a set of retrieved documents in which relevant sentences have to be found. In our experiments we test different term selec- tion strategies and we also check whether expansion before sentence retrieval can yield reasonable performance. This is particularly novel because expansion techniques for sentence retrieval are often applied after a first retrieval of sentences and there are no comparative results available between ex- pansion before and after sentence retrieval. This compari- son is valuable not only for testing distinct expansion-based methods but also because there are important implications in time efficiency.
Vocabulary-based hashing for image search

This paper proposes a hash function family based on feature vocabularies and investigates the application in building indexes for image search. Each hash function is associated with a set of feature points, i.e. a vocabulary, and maps an input point to the ID of the nearest one in the vocabulary. The function family can be employed to build a high-dimensional index for approximate nearest neighbor search. Then we concentrate on its application in image search. Guiding rules for the construction of the vocabularies are derived, which improve the effectiveness of the approach in this context by taking advantage of the data distribution. The rules are applied to design an algorithm for vocabulary construction in practice. Experiments show promising performance of the approach and the effectiveness of the guiding rules. Comparison with the popular Euclidean locality-sensitive hashing also shows the advantage of our approach in image search.
Throughput Maximization in Multiuser MIMO Downlink with Individual QoS Constraints

This paper addresses the problem of maximizing the throughput with a total power and individual rate constraints for a multiuser multiple-input multiple-output (MIMO) antenna system in the downlink using generalized beamforming. Channel state information at the transmitter (CSIT) and the receivers (CSIR) is assumed available. An iterative approach is proposed which alternates between optimizing the power allocation, transmit antenna vectors and receive antenna vectors. Moreover, we also show that the throughput in the proposed algorithm has a monotonic non-decreasing property and hence the algorithm is convergent. Simulation results demonstrate that this algorithm can substantially increase the throughput compared with a zero-forcing solution (GZF) and at the same time it can guarantee the individual users' transmission rates
Applying Gap Model for Bringing Effectiveness to e-Government Services: A Case of NeGP Deployment in India

Taking the means-ends approach to e-governance service quality the authors adopt the Parasuramanâ€™s â€˜Gap Modelâ€™ to evaluate the antecedents of service performance in an Indian context of government-to-citizen (G2C) service deployment under the national e-governance plan (NeGP) of India. This e-governance initiative in India has been implemented at multiple tiers of the government that integrates administration and service processes at different levels that includes center, state, district, block, and further to the lowest level of governance unit (Panchayat). The authors acknowledge five levels of potential service discrepancies across the service delivery chain, from designing the service policy to achieving citizen satisfaction. These are service conceptualization, service design, service capacity, service offering, and service consumption. Corresponding to these discrepancies, the authors explain six types of potential gaps in e-governance G2C service context: Assessment Gap, Design Gap, Capacity Gap, External Communication Gap, Delivery Gap, and Service Gap. Preliminary strategies to close these gaps are also proposed.
Proportionate normalized least-mean-squares adaptation in echo cancelers

On typical echo paths, the proportionate normalized least-mean-squares (PNLMS) adaptation algorithm converges significantly faster than the normalized least-mean-squares (NLMS) algorithm generally used in echo cancelers to date. In PNLMS adaptation, the adaptation gain at each tap position varies from position to position and is roughly proportional at each tap position to the absolute value of the current tap weight estimate. The total adaptation gain being distributed over the taps is carefully monitored and controlled so as to hold the adaptation quality (misadjustment noise) constant. PNLMS adaptation only entails a modest increase in computational complexity.
Test-Retest Reliability of the Stair Test in Patients with Total Hip Arthroplasty

Purpose Stair climbing is one of the important functional activities of daily living to maintain mobility and independence. Walking and stair climbing have been identified by clinicians and patients as critical functional activities before and after total hip arthroplasty (THA). Testing the ability to manage steps has been commonly used in clinical and research settings because it is an inexpensive and simple way to measure functional status after THA. The frequent use of this test supports studies seeking evidence to validate stair test (ST) as a measure of physical performance in subjects with THA. The aim of this study is to determine the test-retest reliability of the 9-step ST in patients with THA. Methods Patients performed twice trials for the ST on the same day with one hour interval. To assess test-retest reliability, the intra-class correlation coefficient – ICC (2,1), standard error of measurement (SEM), minimal detectable change at the 90% confidence level (MDC90) were calculated. Results A total of 37 patients with THA participated in this study. The ST showed very high reliability (ICC = 0.98 (95% CI, 0.96 to 0.99)). The SEM was 0.3 seconds and the MDC90 was 0.7 seconds. Conclusions Test-retest reliability of the ST was very high for the patients with THA, and the SEM and MDC90 were adequate for clinical use. The ST is less time consuming and has very high reliability to measure the functional level after THA in the clinical setting.
ViewFocus: explore places of interests on Google maps using photos with view direction filtering

This paper presents a novel system to explore places of interests based on the large amount of photos that are placed on Google Maps. The system, named ViewFocus, estimates the view directions of photos via robust object matching and camera reconstruction techniques, and geo-registers the directions on the map. Thus users are able to select the places they are interested in, and the system automatically returns a set of precise photos of the target places for users to focus their exploration, by filtering out photos that are pointing to other directions.
Separating inherent asymmetries from high sensitivity rotor bar fault indicator

Inverter operation significantly reduces sensitivity of rotor bar detection methods based on current signature analysis (CSA). Considering the high frequency and transient electrical properties of the machine a very sensitive detection was proven possible by different authors recently. These methods apply high frequency or transient voltage signals to the machine terminals using the inverter and measure the current reaction. For on-line detection however these high frequency and transient electrical properties are usually superposed with inherent asymmetries like slotting or spatial saturation. To ensure a fault detection in all operating states and to keep sensitivity high the flux/load dependencies of these inherent asymmetries have to be identified during a pre commissioning procedure using for example neural network approach. After this procedure an accurate compensation is possible in all operating states. In this paper a measurement and signal processing chain is described to directly separate the different inherent asymmetries from the fault induced asymmetry without pre commissioning. This signal processing chain is based on repetitive data sampling during normal operation of the drive and applying a set of Fourier transforms. Measurements on a special manufactured machine are presented. The cage of the test rotor allows a single replacement of each individual bar thus enabling to investigate the effect of even partially broken bars. Measurement results prove the efficiency of the proposed method to accurately eliminate the inherent asymmetries caused by slotting and spatial saturation in various points of operation.
On Mechanism, Process and Polity: An Agent-Based Modeling and Simulation Approach

The present approach provides a theoretical account of political culture-based modeling of political change phenomena. Our approach is an agent-based simulation model inspired by a social-psychological account of the relation between the individual agents (citizens) and the polity. It includes political culture as a fundamental modeling dimension. On this background, we reconsider the operational definitions of agent, mechanism, process, and polity so as to specify the role they play in the modeling of political change phenomena. We evaluate our previous experimental simulation experience in corruption emergence and political attitude change. The paper approaches the artificial polity as a political culture-based model of a body politic. It involves political culture concepts to account for the complexity of domestic political phenomena, going from political attitude change at the individual level up to major political change at the societal level. Architecture, structure, unit of interaction, generative mechanisms and processes are described. Both conceptual and experimental issues are described so as to highlight the differences between the simulation models of society and polity.
Analysis of control strategies for systems with input-induced nonlinearities

In this paper we consider the design of a nonlinear predictive controller based on a nonlinear dynamic model of the system so as to achieve tracking control for systems with input-induced nonlinearities. The economic performance under nonlinear dynamic behavior and changing operating conditions is discussed. The closed-loop properties are analyzed under suitable assumptions on the model of the plant. Control strategies are discussed and compared to different implementations in the literature. Simulation examples show the effectiveness of the proposed approaches. The approach has been validated on the example of an industrial printing system with large variations in the input print queue.
MAXIMAL POWER, BUT NOT FATIGABILITY, IS GREATER DURING REPEATED SPRINTS PERFORMED IN THE AFTERNOON

The present study was designed to investigate if the suggested greater fatigability during repeated exercise in the afternoon, compared to the morning, represents a true time-of-day effect on fatigability or a consequence of a higher initial power. In a counterbalanced order, eight subjects performed a repeated-sprint test [10 × (6 s of maximal cycling sprint + 30 s of rest)] on three different occasions between: 08:00–10:00, 17:00–19:00, and 17:00-19:00 h controlled (17:00–19:00 hcont, i.e., initial power controlled to be the same as the two first sprints of the 08:00–10:00 h trial). Power output was significantly (p < 0.05) higher for sprints 1, 2, and 3 in the afternoon than in the morning (e.g., sprint 1: 23.3 ±1 versus 21.2 ±1 W·kg−1), but power decrement for the 10 sprints was also higher in the afternoon. Based on the following observations, we conclude that this higher power decrement is a consequence of the higher initial power output in the afternoon. First, there was no difference in power during the final five sprints (e.g., 20.4 ±1 versus 19.7 ±1 W·kg−1 for sprint 10 in the afternoon and morning, respectively). Second, the greater decrement in the afternoon was no longer present when participants were producing the same initial power output in the afternoon as in the morning. Third, electromyographic activity of the vastus lateralis decreased during the exercise (p < 0.05), but without a time-of-day effect. (Author correspondence: sebastien.racinais@aspetar.com)
Using Avida to Test the Effects of Natural Selection on Phylogenetic Reconstruction Methods

Phylogenetic trees group organisms by their ancestral relationships. There are a number of distinct algorithms used to reconstruct these trees from molecular sequence data, but different methods sometimes give conflicting results. Since there are few precisely known phylogenies, simulations are typically used to test the quality of reconstruction algorithms. These simulations randomly evolve strings of symbols to produce a tree, and then the algorithms are run with the tree leaves as inputs. Here we use Avida to test two widely used reconstruction methods, which gives us the chance to observe the effect of natural selection on tree reconstruction. We find that if the organisms undergo natural selection between branch points, the methods will be successful even on very large time scales. However, these algorithms often falter when selection is absent.
Class II MHC Self-Antigen Presentation in Human B and T Lymphocytes

Human CD4+ T cells process and present functional class II MHC-peptide complexes, but the endogenous peptide repertoire of these non-classical antigen presenting cells remains unknown. We eluted and sequenced HLA-DR-bound self-peptides presented by CD4+ T cells in order to compare the T cell-derived peptide repertoire to sequences derived from genetically identical B cells. We identified several novel epitopes derived from the T cell-specific proteome, including fragments of CD4 and IL-2. While these data confirm that T cells can present peptides derived from the T-cell specific proteome, the vast majority of peptides sequenced after elution from MHC were derived from the common proteome. From this pool, we identified several identical peptide epitopes in the T and B cell repertoire derived from common endogenous proteins as well as novel endogenous epitopes with promiscuous binding. These findings indicate that the endogenous HLA-DR-bound peptide repertoire, regardless of APC type and across MHC isotype, is largely derived from the same pool of self-protein.
Optimal Power Allocation for Enhancing Physical Layer Security in Opportunistic Relay Networks in the Presence of Co-Channel Interference

In this paper, the impact of co-channel interference (CCI) on the secrecy performance of the dual-hop decode-and-forward relaying network is investigated. First, the security-reliability trade-off (SRT) analysis is studied for conventional single-hop networks under a single passive eavesdropper attack. Then, the SRT analysis is investigated to the opportunistic relay selection (ORS) model in the presence of independent and identically distributed (i.i.d.) interfering signals. Moreover, the proposed work enhances physical layer (PHY) security performance of ORS model using cooperative jamming techniques (CJ-ORS). For this purpose, new closed-form expressions are derived for intercept probability and outage probability of CJ-ORS model in the presence of interference over Rayleigh fading channels. A power allocation optimization problem is formulated and solved for enhancing the system security. The derived analytical formulas herein are supported by numerical and simulation results to clarify the main contributions of the paper.
A new two-stage method for restoration of images corrupted by Gaussian and impulse noises using local polynomial regression and edge preserving regularization

This paper proposes a new two-stage method for restoring image corrupted by additive impulsive and Gaussian noise based on local polynomial regression (LPR) and edge preserving regularization. In LPR, the observations are modeled locally by a polynomial using least-squares criterion with a kernel controlled by a certain bandwidth matrix. A refined intersection confidence intervals (RICI) adaptive scale selector for symmetric kernel is applied in LPR to achieve a better bias-variance tradeoff. The method is further extended to steering kernel with local orientation to adapt better to local characteristics of images. The resulting steering-kernel-based LPR with RICI method (SK-LPR-RICI) is applied to smooth images contaminated with Gaussian noise. Furthermore, to remove the impulsive noise in images, an edge-preserving regularization method is employed prior to SK-LPR-RICI and it gives rise to a two-stage method for suppressing both additive impulsive and Gaussian noises. Simulation results show that the proposed method performs satisfactorily and the SK-LPR-RICI method significantly improves the performance after edge-preservation regularization in suppressing the impulsive noise.
Analysing convergence of Quantized Congestion Notification in Data Center Ethernet

Enhancing Ethernet as the unified data center fabric to concurrently handle the traffic of Local Area Network (LAN), Storage Area Network (SAN), and High Performance Computing (HPC) has attracted much attention. Congestion management is one critical enhancement to fill the performance gap between traditional Ethernet and the unified data center fabric. Currently, Quantized Congestion Notification (QCN) has been approved as the standard congestion management mechanism. However, lots of work pointed out that QCN suffers from the problem of unfairness among different flows. In this paper, we found that QCN could achieve fairness, merely the convergence time to fairness is quite long. Thus, we build a convergence time model to investigate the reasons of the slow convergence process of QCN. The model indicates that the convergence time of QCN can be decreased if RPs have the same rate increase probability or the rate increase step becomes larger at steady state. We validate the precise of our model by comparing with experimental data on the NetFPGA platform. The results show that it well characterizes the convergence time to fairness of QCN. Based on the proposed model, the impact of QCN parameters, network parameters, and QCN variants on the convergence time is analysed. Finally, enlightened by the analysis, we proposed a mechanism, called QCN-T, which replaces the Byte Counter and Timer at sources with a single modified Timer, to reduce the convergence time of QCN.
QRNN: $q$ -Generalized Random Neural Network

Artificial neural networks (ANNs) are widely used in applications with complex decision boundaries. A large number of activation functions have been proposed in the literature to achieve better representations of the observed data. However, only a few works employ Tsallis statistics, which has successfully been applied to various other fields. This paper presents a random neural network (RNN) with <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-Gaussian activation functions [<inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula>-generalized RNN (QRNN)] based on Tsallis statistics. The proposed method employs an additional parameter <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> (called the entropic index) which reflects the degree of nonextensivity. This approach has the flexibility to model complex decision boundaries of different shapes by varying the entropic index. We conduct numerical experiments to analyze the efficiency of QRNN compared with RNNs and several other classical methods. Statistical tests (Wilcoxon and Friedman) are used to validate our results and show that the QRNN performs significantly better than RNNs with different activation functions. In addition, we find that QRNN outperforms many of the compared classical methods, with the exception of support vector machines, in which case it still exhibits a substantial advantage in terms of implementation simplicity and speed.
Adder and comparator synthesis with exclusive-OR transform of inputs

An exclusive-OR transform of input variables significantly reduces the size of the PLA implementation for adder and comparator circuits. For n bit adder circuits, the size of PLA for transformed functions is O(n/sup 2/). In comparison, when the complete truth-table of an adder is minimized, the PLA size will be O(2/sup n+2/). Similarly, for an n bit comparator, the size of the PLA is reduced from O(2/sup n+1/) to O(n). These implementations require additional transform logic of complexity O(n), consisting of exclusive-OR gates.
Regression mutation testing

Mutation testing is one of the most powerful approaches for evaluating quality of test suites. However, mutation testing is also one of the most expensive testing approaches. This paper presents Regression Mutation Testing (ReMT), a new technique to speed up mutation testing for evolving systems. The key novelty of ReMT is to incrementally calculate mutation testing results for the new program version based on the results from the old program version; ReMT uses a static analysis to check which results can be safely reused. ReMT also employs a mutation-specific test prioritization to further speed up mutation testing. We present an empirical study on six evolving systems, whose sizes range from 3.9KLoC to 88.8KLoC. The empirical results show that ReMT can substantially reduce mutation testing costs, indicating a promising future for applying mutation testing on evolving software systems.