Miracle-dz/UniFAR-FTUs huggingface dataset:
Expanding the nitrogen regulatory protein superfamily: Homology detection at below random sequence identity

Nitrogen regulatory (PII) proteins are signal transduction molecules involved in controlling nitrogen metabolism in prokaryots. PII proteins integrate the signals of intracellular nitrogen and carbon status into the control of enzymes involved in nitrogen assimilation. Using elaborate sequence similarity detection schemes, we show that five clusters of orthologs (COGs) and several small divergent protein groups belong to the PII superfamily and predict their structure to be a (βαβ)2 ferredoxin‐like fold. Proteins from the newly emerged PII superfamily are present in all major phylogenetic lineages. The PII homologs are quite diverse, with below random (as low as 1%) pairwise sequence identities between some members of distant groups. Despite this sequence diversity, evidence suggests that the different subfamilies retain the PII trimeric structure important for ligand‐binding site formation and maintain a conservation of conservations at residue positions important for PII function. Because most of the orthologous groups within the PII superfamily are composed entirely of hypothetical proteins, our remote homology‐based structure prediction provides the only information about them. Analogous to structural genomics efforts, such prediction gives clues to the biological roles of these proteins and allows us to hypothesize about locations of functional sites on model structures or rationalize about available experimental information. For instance, conserved residues in one of the families map in close proximity to each other on PII structure, allowing for a possible metal‐binding site in the proteins coded by the locus known to affect sensitivity to divalent metal ions. Presented analysis pushes the limits of sequence similarity searches and exemplifies one of the extreme cases of reliable sequence‐based structure prediction. In conjunction with structural genomics efforts to shed light on protein function, our strategies make it possible to detect homology between highly diverse sequences and are aimed at understanding the most remote evolutionary connections in the protein world. Proteins 2002;48:75–84. © 2002 Wiley‐Liss, Inc.
Robust Monetary Policy in the New-Keynesian Framework

We study the effects of model uncertainty in a simple New-Keynesian model using robust control techniques. Due to the simple model structure, we are able to find closed-form solutions for the robust control problem, analyzing both instrument rules and targeting rules under different timing assumptions. In all cases but one, an increased preference for robustness makes monetary policy respond more aggressively to cost shocks but leaves the response to demand shocks unchanged. As a consequence, inflation is less volatile and output is more volatile than under the non-robust policy. Under one particular timing assumption, however, increasing the preference for robustness has no effect on the optimal targeting rule (nor on the economy).
A data set providing synthetic and real-world fisheye video sequences

In video surveillance as well as automotive applications, so-called fisheye cameras are often employed to capture a very wide angle of view. As such cameras depend on projections quite different from the classical perspective projection, the resulting fisheye image and video data correspondingly exhibits non-rectilinear image characteristics. Typical image and video processing algorithms, however, are not designed for these fisheye characteristics. To be able to develop and evaluate algorithms specifically adapted to fisheye images and videos, a corresponding test data set is therefore introduced in this paper. The first of those sequences were generated during the authors' own work on motion estimation for fish-eye videos and further sequences have gradually been added to create a more extensive collection. The data set now comprises synthetically generated fisheye sequences, ranging from simple patterns to more complex scenes, as well as fisheye video sequences captured with an actual fisheye camera. For the synthetic sequences, exact information on the lens employed is available, thus facilitating both verification and evaluation of any adapted algorithms. For the real-world sequences, we provide calibration data as well as the settings used during acquisition. The sequences are freely available via www.lms. lnt.de/fisheyedataset/.
In vitro investigations on the toxicity and cell death induced by tamoxifen on two non-breast cancer cell types

Tamoxifen, a potent anticancer agent known to interrupt the enhanced estrogen activity of malignant mammary gland cells, was recently approved by the Food and Drug Administration (FDA) for the treatment of breast cancer. In this investigation, the toxic effects of tamoxifen were evaluated through cell multiplication, and cytological, surface ultrastructural, and biochemical studies on human cervical carcinoma cells (HeLa) and/or murine erythroleukemic (MEL) cells (BB-88). Tamoxifen treatment demonstrated an inhibitory effect on HeLa cell multiplication at lower concentrations and toxicity at higher concentrations and longer treatment durations. The drug also triggered morphological and biochemical changes as revealed by light microscopy, scanning electron microscopy (SEM), fluorescence microscopy, Nucleosome ELISA, and the DNA smear pattern. Cytological observations showed nuclear condensation, cell shrinkage, multinucleation, and apoptotic bodies. Surface ultrastructure of tamoxifen treated cells examined under the SEM revealed abnormalities such as membrane blebbing, holes, and cytoplasmic extrusions, all of which are characteristics of programmed cell death (apoptosis). Redistribution of the membrane phospholipid phosphatidylserine (PS) from the protoplasmic surface of the plasma membrane to the cell surface was identified using annexin V-enhanced green fluorescent protein (EGFP) in tamoxifen treated MEL BB-88 cells, a general feature of cells undergoing apoptosis. Tamoxifen treated cells demonstrated internucleosomal damages of the genomic DNA and DNA fragmentations, evidenced by an increase in free nucleosomes, and distinctive DNA smear patterns on the agarose gel.
FPGAs in Industrial Current Control for PMSM

— The aim of this paper is to present the interest of implementing digital controllers using field-programmable gate array (FPGA) components. To this purpose, a variety of current control techniques, which is applied to PMSM, is designed and implemented. They consist of indirect sliding mode control and direct torque control. The quality of the regulated current is significantly improved. In this paper, we present a new contribution for the FPGA-Based implementation of controls electrical. A bench test was realized by a prototyping platform, the experimental results obtained show the effectiveness and the benefit of our contribution and the different stages of implementation for the control FPGA.
Beggar Thy Neighbour: Exchange Rate Regime Misadvice from Misunderstandings of Mundell (1961)

Economists invoke Mundell (1961) in arguing for the general policy of a flexible exchange rate regime as a means of restoring equilibria after shocks. But there is a discrepancy between the intent of the general policy and attempts at its implementation as identified by specific changes in exchange rates. When we assemble the set of specific changes called for by distinct economists operating as advocates for individual countries, these are uniformly in the form of beggar-thy-neighbour advice ie travesties of objectively identifying disequilibria and a menace to international cooperation and peace. This paper traces the unintended travesties to problems of complexity and uncertainty, problems that implicitly are assumed absent in Mundell (1961) rendering the situation so simple that equilibria are transparent. The problems remained essentially unaddressed when economists extended Mundell (1961) via expected utility theory since this theory also ignores the impossibility of maximising and the complexities of central bankers, private firms and others in doing the evaluation stage in reaching decisions. The problems can be overcome by modelling within SKAT, the Stages of Knowledge Ahead Theory. This paper points to experimental evidence in support of the view that under all sorts of disequilibrating shocks, currency unions outperform flexible currencies by eliminating the inefficiencies generated by exchange rate uncertainty.
Forecasting of Gold Prices (Box Jenkins Approach)

In recent years, the global gold price trend has attracted a lot of attention and the price of gold has frightening spike compared to historical trend. In times of uncertainty investors consider gold as a hedge against unforeseen disasters so the forecasted price of gold has been a subject of highest amongst all. In this paper an attempt has been made to develop a forecasting model for gold price. The sample data of gold price (in US$ per ounce) were taken from January 02, 2003 to March 1, 2012. Data up till January 02, 2012 were used to build the model while remaining were used to forecast the gold price and to check the accuracy of the model. We have used Box-Jenkins, Auto Regressive Integrated Moving Average (ARIMA) methodology for building forecasting model. Results suggest that ARIMA(0,1,1) is the most suitable model to be used for predicting the gold price. For testing the forecasting accuracy Root Mean Square Error, Mean Absolute Error, and Mean Absolute Percentage Error are calculated. Keywords-ARIMA, Forecasting, Stationary Time Series, Gold Price, Mean Absolute Error, Root Mean Square Error
Empirical Effectiveness Evaluation of Spectra-Based Fault Localization on Automated Program Repair

Researchers have proposed many spectra-based fault localization (SBFL) techniques in the past decades. Existing studies evaluate the effectiveness of these techniques from the viewpoint of developers, and have drawn some important conclusions through either empirical study or theoretical analysis. In this paper, we present the first study on the effectiveness of SBFL techniques from the viewpoint of fully automated debugging including the program repair of automation, for which the activity of automated fault localization is necessary. We assess the accuracy of fault localization according to the repair effectiveness in the automated repair process guided by the localization technique. Our experiment on 14 popular SBFL techniques with 11 subject programs shipping with real-life field failures presents the evidence that some conclusions drawn in prior studies do not hold in our experiment. Based on experimental results, we suggest that Jaccard should be used with high priority before some more effective SBFL techniques specially proposed for automated program repair occur in the future.
Monolithic Integrated RF Front ends for Multi-GNSS receivers

The continuing expansion of available GNSS signals is an increasing challenge for receiver designers. New signals with expanded bandwidths are demanding greater sampling rates that require careful design of the receiver RF section to maximise performance tradeoffs. A high level of integration is required to preserve signal path integrity and minimise noise while keeping power consumption to a minimum. The design of the frequency plan and the choice of IF bandwidth are critical to overall receiver performance. This paper describes the development of the monolithic RF front end chips used in the new Namuru multiGNSS receivers at UNSW. Analysis of the system requirements and architecture design are discussed including the LNA, Mixer, IF Amplifier through to the A/D converter. The re-configurable design provides frequency plan and signal selection flexibility using an integrated synthesiser and programmable bandwidth filter. The design challenges of the new front end chips are discussed including features aimed at delivering greater performance and flexibility.
A Novel Transcription Factor Binding Sites Prediction Approach

Transcription factors (TFs) and their DNA binding motifs, called transcription factor binding sites (TFBSs) play important roles in most biological processes. However, the list for TFBSs still remains largely unknown. Machine learning approaches have been intensively applied to predict TFBSs. In this paper, a novel prediction approach has been presented based on Markov Chain Monte Carlo (MCMC) method and latest discovery of TF-TFBS co-evolution. By defining and solving a problem modified from conventional TFBSs prediction problem, the paper provides a new way to predict TFBSs for poorly characterized TFs, which has been previously considered difficult. The performance of the proposed approach has been evaluated on real biological data.
Image-Like 2D Barcodes Using Generalizations of the Kuznetsov–Tsybakov Problem

In this paper, we propose a novel method for generating visually appealing two-dimensional (2D) barcodes that resemble meaningful images to human observers. The technology of 2D barcodes, currently dominated by quick response codes, is widely adopted in many applications, including product tracking, document management, and general marketing. Such barcodes typically lack user friendly appearance and do not convey any visual significance to human observers. The proposed method addresses this problem by allowing 2D barcodes to resemble an arbitrary image or a logo. Our method is based on a generalization of the Kuznetsov-Tsybakov problem that served as a foundation for wet paper codes, commonly adopted in digital steganography. We introduce weaker statistical constraints to obtain additional flexibility allowing the barcode to assume the appearance of an arbitrary pattern. This paper provides the theoretical analysis of the proposed coding framework and a practical algorithm for rapid approximation of the optimal code. We also discuss the introduction of error correction capabilities, and experimentally evaluate a prototype implementation in a smartphone-based acquisition scenario.
Variational message passing without initialization using a free energy constraint

In iterative algorithms with graphical models, it is common to initialize messages. But this approach is not suitable in certain applications. In this paper, it is conjectured that if some messages are confined to a ball around their initialized values, then initialization can be avoided. Towards this goal, for networks specified with a bipartite graph, a variant of the sum-product algorithm is derived by adding an inequality constraint on the variable free energy in the underlying Bethe optimization problem. In the binary case, this leads to an upper bound that restricts the ℓ1 norm of the extrinsic message (vector) to a ball around its (otherwise) initial value. Results are reported for binary LDPC decoders through simulations which confirm that initialization between decoding attempts can be eliminated and show that the algorithm tends to outperform the standard sum-product. A few more observations and comments on this approach, relation with other variational methods, and applications to analog processing and inference over distributed networks are outlined in the end.
Regime Type, the Fate of Leaders, and War

We propose and test a formal model of war and domestic politics, building on recent evidence on the relationship between regime type, the effect of war on the probability of losing office, and the consequences of losing office. The less the outcome of international interaction affects a leader's tenure and the less punitive are the consequences of losing office, the more a leader is willing to make concessions to strike a peaceful bargain. We demonstrate that our theory successfully predicts war involvement among nondemocratic regime types. Moreover, our theory offers an intuitive explanation for the democratic peace. Compared to nondemocratic leaders, the tenure of democratic leaders depends relatively little on the war outcome, and democratic leaders fare relatively well after losing office. Thus, democratic leaders should be more willing and able to avoid war, especially with other democrats.
Líderes Comunitarias: Evaluation of Community Workshops on Domestic Violence

Domestic violence (DV) affects communities across a variety of nations and cultures, at significant physical, psychological, and economic costs to families. In the United States Latino families affected by DV often face unique challenges influenced by changing ecologies and personal as well as political histories. Peer-led workshops are one way for communities to disseminate information about social issues like DV in a culturally relevant manner, and they have the potential to promote capacity for addressing DV within communities. The impact of peer-led DV was examined using an embedded mixed-method design, where participant feedback collected throughout the study served to enhance the nonequivalent control group survey portion of the study. It was hypothesized that sense of community would moderate the relationship between workshop participation and capacity measures of knowledge, communication, and identification with Latina community leaders. Data screening and linear regression found no effects of workshop participation for knowledge and communication. A linear regression supported the hypothesized workshop by sense of community interaction effect, where individuals with higher sense of community were more likely to connect with workshop leaders in the workshop condition. Themes that emerged from the analysis of qualitative data from individual questionnaires, researcher notes, and a group interview were: (1) connection to the larger community organization, (2) family communication about domestic violence, (3) interest in support for Latino youth and (4) community leader’s testimonies of their own experiences of violence. Together, quantitative and qualitative findings lend weight to the notion that peer interventionists can establish meaningful connections and trust with community members based on their own lived experiences. Further research is needed to link this strength in a peer-led DV program to gains in capacity domains like knowledge. Overall, the results of this study extend research on cultural specific DV community programs and provide recommendations for community organizations seeking to evaluate community-based programs. INDEX WORDS: Capacity, Community-based, Domestic Violence, Evaluation, Families, Intimate Partner Violence, Latina, Latino, Leadership, Participatory Research, Promotora LIDERES COMUNITARIAS: EVALUATION OF COMMUNITY WORKSHOPS ON DOMESTIC VIOLENCE
Exploiting contextual information for rank aggregation

This paper presents a novel rank aggregation approach based on contextual information aiming to improve the effectiveness of Content-Based Image Retrieval (CBIR) tasks. In our approach, information encoded in both distances among images and ranked lists computed by CBIR systems are used for analyzing contextual information and then re-rank collection images. We conducted several experiments involving shape, color, and texture descriptors. We also evaluated our method in comparison to other rank aggregation approaches. Experimental results demonstrate the effectiveness of our method.
AN INFORMATION BASED GENETIC ALGORITHM APPROACH TO FAST PEPTIDE DOCKING

In genetic algorithm (GA), there are two main methods of determining trial candidates: crossover and mutation. While crossover directs search between fit candidates, mutation plays a role on jumping out local optimal. In molecular docking calculations, it is desirable to chart as much unexplored search space as possible. Therefore it is desirable that mutation results in a uniform distribution of sampling points in solution space. In this work an information entropy based mutation procedure is developed. Instead of random mutation, mutation is directed to parts of the solution space that is least populated. Such a procedure is implemented in AUTODOCK and used to study the docking of Peroxisome Proliferator-Activated Receptors gamma (PPARResults show that the proposed information-based genetic algorithm is superior to conventional GA for docking both in speed and precision.
Adaptive modulation spectral filtering for improved electrocardiogram quality enhancement

Advances in portable electrocardiogram (ECG) monitoring devices has allowed for new cardiovascular applications to emerge beyond diagnostics, such as stress detection, sleep disorder characterization, mood recognition, activity surveillance, or fitness monitoring, to name a few. Such devices, however, are prone to artifacts, particularly due to movement, thus rendering heart rate and heart rate variability (HRV) metrics useless. To address this issue, this paper proposes a new ECG quality enhancement algorithm based on filtering in the so-called spectro-temporal (or modulation spectral) domain. Our experiments show that this new signal representation accurately separates ECG signal and noise components, thus allowing for adaptive filtering to improve signal quality even in extremely noisy settings. Experimental results show the proposed algorithm outperforming a state-of-the-art wavelet-based enhancement algorithm in terms of signal-to-noise ratio improvement, as well as ECG kurtosis; the latter has been widely used in the literature as an ECG quality index. The obtained findings suggest that the proposed algorithm can be used to enhance the quality of wearable ECG monitors even in extreme conditions, thus can play a key role in athletic peak performance training/monitoring.
An Efficient Fast Clustering-Based FeatureSubset Selection Algorithm for High-Dimensional Data

Feature selection is the process of identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a Fast clustering-based feature Selection algorithm (FAST) is proposed and experimentally evaluated. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. The Minimum-Spanning Tree (MST) using Prim’s algorithm can concentrate on one tree at a time. To ensure the efficiency of FAST, adopt the efficient MST using the Kruskal’s Algorithm clustering method.