Miracle-dz/UniFAR-FTUs huggingface dataset:
Antimicrobial Susceptibility Breakpoints and First-Step parC Mutations in Streptococcus pneumoniae: Redefining Fluoroquinolone Resistance

Clinical antimicrobial susceptibility breakpoints are used to predict the clinical outcome of antimicrobial treatment. In contrast, microbiologic breakpoints are used to identify isolates that may be categorized as susceptible when applying clinical breakpoints but harbor resistance mechanisms that result in their reduced susceptibility to the agent being tested. Currently, the National Committee for Clinical Laboratory Standards (NCCLS) guidelines utilize clinical breakpoints to characterize the activity of the fluoroquinolones against Streptococcus pneumoniae. To determine whether levofloxacin breakpoints can identify isolates that harbor recognized resistance mechanisms, we examined 115 S. pneumoniae isolates with a levofloxacin MIC of >2 μg/mL for first-step parC mutations. A total of 48 (59%) of 82 isolates with a levofloxacin MIC of 2 μg/mL, a level considered susceptible by NCCLS criteria, had a first-step mutation in parC. Whether surveillance programs that use levofloxacin data can effectively detect emerging resistance and whether fluoroquinolones can effectively treat infections caused by such isolates should be evaluated.
Stuttering and natural speech processing of semantic and syntactic constraints on verbs.

PURPOSE Previous findings from event-related brain potentials (ERPs) indicate that adults who stutter (AWS) exhibit processing differences for visually presented linguistic information. This study explores how neural activations for AWS may differ for a linguistic task that does not require preparation for overt articulation or engage the articulatory loop for silent speech. METHOD Syntactic and semantic processing constraints were examined in AWS and adults who are normally fluent (AWNF) by assessment of their behavioral performance and ERPs in a natural speech listening task. RESULTS AWS performed similarly to AWNF in identifying verb-agreement violations and semantic anomalies, but ERPs elicited by syntactic and semantic constraints indicated atypical neural functions for AWS. ERPs of the AWNF displayed an expected N400 for reduced semantic expectations and a typical P600 for verb-agreement violations. In contrast, both N400s and P600s for the semantic and verb-agreement conditions were observed in the ERPs of the AWS. CONCLUSIONS The findings suggest that AWS may engage semantic-syntactic mechanisms more generally for semantic and syntactic processing. These findings converge with earlier studies using visual stimuli to indicate that whereas linguistic abilities are normal in AWS, underlying brain activity mediating some aspects of language processing may function differently.
GSAEC — Acoustic echo cancellation embedded into the generalized sidelobe canceller

In this paper, we examine the integration of acoustic echo cancellation (AEC) with a robust generalized side-lobe canceller (GSC) [2, 3] with regard to exploitable synergies. Since the AEC is located behind the fixed beamformer (FBF) of the GSC only one AEC is required for an arbitrary number of array elements. System analysis shows that ideal suppression of local interferers and of acoustic echoes cannot be achieved simultaneously. However, experiments confirm that the interference reduction (IR) is degraded by less than 3 dB compared to a cascade of AEC and GSC and that the echo reduction is improved by more than 13 dB relative to the GSC in echoic environments.
A Hybrid Method Based on Cuckoo Search and Krill Herd Optimization with Differential Evolution

A novel hybrid Krill Herd (KH) and Cuckoo Search(CS) with Differential Evolution(DE), called DECSKH, is proposed for the global optimization. DECSKH is designed for enhancing the ability of the local search and increasing the individual diversity in the population. In DECSKH, all individuals are divided into two parts, denoted as part I and part II, respectively. Two different optimization algorithm combined with DE, DECS and DEKH, are incorporated into these two parts, respectively. DECSKH, therefore, is expected to obtain an efficient balance between exploration and exploitation. DECSKH can make all the individuals proceed to the true global optimum without introducing additional operators to the basic CS, KH and DE algorithms. The effectiveness of these improvements is tested by 18 standard benchmark functions. Based on the results, we can easily infer that DECSKH is more effective and efficient than the original CS, KH, DE and other approaches.
A Taylor Function Calculus for Hybrid System Analysis: Validation in Coq

We present a framework for the verification of the numerical algorithms used in Ariadne, a tool for analysis of nonlinear hybrid system. In particular, in Ariadne, smooth functions are approximated by Taylor models based on sparse polynomials. We use the Coq theorem prover for developing Taylor models as sparse polynomials with floating-point coefficients. This development is based on the formalisation of an abstract data type of basic floating-point arithmetic . We show how to devise a type of continuous function models and thereby parametrise the framework with respect to the used approximation, which will allow us to plug in alternatives to Taylor models.
Supporting flexible roles in a shared space

People in a group play various roles. Manager, intern, department chair, guest speaker, the woman who knows how the fax machine works, the guy who got the pizza, the person speaking now, the person capturing this on the whiteboard; roles can be formal or informal, long-lived or ephemeral. They can be bestowed by ceremony, or assumed with the invisibility of the taken-for-granted. But it is the interaction of minds that creates and sustains a role: a role is a human construct. Even roles like “mother” or “father”, which would seem to demand certain biological characteristics, cannot be defined at any depth without appeal to social convention. The largely hands-off approach taken by nature seems to work fairly well for groups of humans: when there is confusion or concern about who is playing what role, it is generally good for a group to work it out as a social issue. If roles were encoded solely in human biology or in physical law, life might be simpler but certainly more stulfiying.
American Politics and the African American Quest for Universal Freedom

I. FOUNDATIONS. 1. Universal Freedom Declared, Universal Freedom Denied: Racism, Slavery, and the Ideology of White Supremecy in the Founding of the Republic. 2. Federalism and the Limits of Universal Freedom. II. POLITICAL BEHAVIORISM. 3. Political Culture. 4. Political Socialization. 5. Public Opinion. 6. African Americans and the Media. III. COALITIONS, MOVEMENTS, INTEREST GROUPS, PARTIES, AND ELECTIONS. 7. Social Movements and a Theory of African American Coalition Politics. 8. Interest Groups. 9. Political Parties. 10. Voting Behavior and Elections. IV. INSTITUTIONS. 11. The Congress and the African American Quest for Universal Freedom. 12. The Presidency and the African American Quest for Universal Freedom. 13. The Supreme Court and the African American Quest for Universal Freedom. 14. The Bureaucracy and the African American Quest for Universal Freedom. V. PUBLIC POLICY. 15. Domestic Policy and the African American Quest for Social and Economic Justice. 16. The African American Quest for Universal Freedom and U.S. Foreign Policy. Appendix. The Declaration of Independence. The Constitution of the United States of America. Martin Luther King, Jr.'s "I Have a Dream" Speech. Credits. Index.
Spectral Integration in the Inferior Colliculus of the Mustached Bat

Acoustic behaviors including orientation and social communication depend on neural integration of information across the sound spectrum. In many species, spectral integration is performed by combination-sensitive neurons, responding best when distinct spectral elements in sounds are combined. These are generally considered a feature of information processing in the auditory forebrain. In the mustached bat's inferior colliculus (IC), they are common in frequency representations associated with sonar signals but have not been reported elsewhere in this bat's IC or the IC of other species. We examined the presence of combination-sensitive neurons in frequency representations of the mustached bat's IC not associated with biosonar. Seventy-five single-unit responses were recorded with the best frequencies in 10–23 or 32–47 kHz bands. Twenty-six displayed single excitatory tuning curves in one band with no additional responsiveness to a second signal in another band. The remaining 49 responded to sounds in both 10–23 and 32–47 kHz bands, but response types varied. Sounds in the higher band were usually excitatory, whereas sounds in the lower band either facilitated or inhibited responses to the higher frequency signal. Interactions were usually strongest when the higher and lower frequency stimuli were presented simultaneously, but the strength of interactions varied. Over one-third of the neurons formed a distinct subset; they responded most sensitively to bandpass noise, and all were combination sensitive. We suggest that these combination-sensitive interactions are activated by elements of mustached bat social vocalizations. If so, neuronal integration characterizing analysis of social vocalizations in many species occurs in the IC.
A Company of Leaders: Five Disciplines for Unleashing the Power in Your Workforce

To be successful in today's business environment, organizations need the knowledge, ideas, energy, and creativity of every employee. The best companies accomplish this by turning themselves into a company of leaders--an organization in which employees at every level take the initiative and act as though the business where their own. Gretchen M. Spreitzer and Robert E. Quinn draw on ten years of empirical research to show how employee empowerment can transform a workforce and create a very real competitive advantage. They describe five key disciplines that help empower employees to take initiative, be more innovative, engage in transformational change, and act as leaders. And they provide real-life examples specific tools and strategies that will help you to put those disciplines to work in your own organization. A Book in the University of Michigan Business School Series
Assessment of vocal correlates of clinical depression in female subjects with probabilistic mixture modeling of speech cepstrum

The acoustical properties of speech have been reported to relate to the mental state of speaker while speaking. This proposed work describes way to address the issue of distinguishing between female depressed patients and female remitted subjects based on the measurable change in the cepstral parameters extracted from their sound record. The cepstral coefficients corresponding to the filter response characteristics, affectively mediated by the emotionally depressive illness or even in particular case of the elevated suicidal risk into the speech production system of depressed speaker, are analyzed via the speech cepstral estimation in conjunction with the GMM fitting approximation. The results of pairwise classification in combinations with SVM, cross-validation, training and testing the cepstral coefficients provide the fairly high accuracy in class separation, when evaluating the testing datasets of coefficients extracted from speech segmentations which are highly corresponding to individual female speakers.
Anti-vibration PID control for a robot manipulator experiments

Vibration becomes a major control issue as industrial robots get lighter and slimmer. Proportional- Integral-Derivative (PID) control is simple and easy to regulate the motion of robots. If gains of PID can be tuned precisely for the given purpose, it will be more beneficial than heavy and complicated control algorithm. The paper introduces the feasibility of PID tip position control to suppress oscillation at the end of the effecter (EOF) by experiments and leads to controller synthesis and design for better performance.
Environments for Multiagent Systems Report

There is a growing awareness in the multiagent systems research community that the environment plays a prominent role in multiagent systems. Originating from research on behavior-based agent systems and situated multiagent systems, the importance of the environment in multiagent systems is now gradually accepted in the multiagent system community in general. The aim of the AgentLink Technical Forum Group (TFG) on Environments for Multiagent Systems is to put forward the environment as a strategic research direction for multiagent systems. The TFG considers the environment as a first-order abstraction in multiagent systems. This claim is motivated by the fact that several aspects of multiagent systems that conceptually do not belong to agents themselves should not be assigned to, or hosted inside the agents. Examples are infrastructure for communication, the topology of a spatial domain or support for the action model. These (and other) aspects should be considered explicitly. The environment is the natural candidate to encapsulate these aspects. During the first meeting of the TFG, the group had an open discussion on a number of core topics regarding environments. Discussion topics ranged from the fundamental question “what is an environment” over “how to engineer environments” to the discussion of a number of real-world applications in which the environment plays a central role. This report elaborates on the different topics discussed during the TFG meeting.
Accuracy Analysis Mechanism for Agriculture Data Using the Ensemble Neural Network Method

With the rise and development of information technology (IT) services, the amount of data generated is rapidly increasing. Data from many different places are inconsistent. Data capture, storage and analysis have major challenges. Most data analysis methods are unable to handle such large amounts of data. Many studies employ neural networks, mostly specifying the number of hidden layers and neurons according to experience or formula. Different sets of network topologies have different results, and the best network model is selected. This investigation proposes a system based on the ensemble neural network (ENN). It creates multiple network models, each with different numbers of hidden layers and neurons. A model that does not achieve the accuracy rate is discarded. The proposed system derives the weighted average of all remaining network models to improve the accuracy of the prediction. This study applies the proposed method to generate agricultural yield predictions. The agricultural production process in Taiwan is more complex than those of manufacturing or other industries. The Council of Agriculture provides agricultural forecasting primarily based on the planted area and experience to predict the yield, but without consideration of the overall planting environment. This work applies the proposed data analysis method to agriculture. The method based on ENN has a much lower error rate than traditional back-propagation neural networks, while multiple regression analysis has an error rate of 12.4%. Experimental results reveal that the ENN method is better than traditional back-propagation neural networks and multiple regression analysis.
Using Field of Research Codes to Discover Research Groups from Co-authorship Networks

Nowadays, academic collaboration has become more prevalent and crucial than ever before and many studies of academic collaboration analysis are implemented based on coauthor ship networks. This paper aims to build a novel coauthor ship network by importing field of research codes based on Newman's model, and then analyze and extract research groups via spectral clustering. In order to explain the effectiveness of this revised network, we take the academic collaboration at the University of Technology, Sydney (UTS) as an example. The result of this study advances methods for selecting the most prolific research groups and individuals in research institutions, and provides scientific evidence for policymakers to manage laboratories and research groups more efficiently in the future.
An MBoC Favorite: Ezrin self-association involves binding of an N-terminal domain to a normally masked C-terminal domain that includes the F-actin binding site

The ezrin, radixin, and moesin (ERM) proteins have emerged as important regulators of the cortical actin cytoskeleton, particularly in the apical domain of polarized epithelial cells. In this paper (Gary and Bretscher, 1995), which has been cited more than 250 times since publication, the authors showed that head-to-tail binding regulates interactions with actin and other binding partners at the cell cortex. Intramolecular head-to-tail binding has subsequently been shown to be critical for regulation of ERM proteins, as well as for the closely related protein Merlin, a component of the Hippo growth control pathway. ERM folding has also served as a model for autoinhibition of a number of other cortical cytoskeletal proteins. Interestingly, although the authors’ data suggested the existence of both intramolecular and intermolecular interactions, little is known about the importance of the latter in living cells, suggesting that the full importance of this work remains to be discovered.
Regularized LAD algorithms for sparse time-varying system identification with outliers

Two regularized least mean absolute deviation (LAD) algorithms are proposed for sparse system identification, which are referred to as zero-attracting LAD (ZA-LAD) and re-weighted zero-attracting LAD (RZA-LAD), respectively. The LAD type algorithms are robust to the impulsive noises. Furthermore, ℓ1-norm penalty is imposed on the filter coefficients to exploit sparsity of the system. The performance of ZA-LAD type algorithms is evaluated for linear time varying system identification under impulsive noise environments through computer simulations.
Extreme Scaling of Supercomputing with Stranded Power: Costs and Capabilities

Power consumption (supply, heat, cost) and associated carbon emissions (environmental impact) are increasingly critical challenges in scaling supercomputing to Exascale and beyond. We proposes to exploit stranded power, renewable energy that has no value to the power grid, for scaling supercomputers, Zero-Carbon Cloud (ZCCloud), and showing that stranded power can be employed effectively to expand computing [1]. We build on those results with a new analysis of stranded power, characterizing temporal, geographic, and interval properties. We simulate production supercomputing workloads and model datacenter total-cost-of-ownership (TCO), assessing the costs and capabilities of stranded-power based supercomputing. Results show that the ZCCloud approach is cost-effective today in regions with high cost power. The ZCCloud approach reduces TCO by 21-45%, and improves cost-effectiveness up to 34%. We study many scenarios. With higher power price, cheaper computing hardware and higher system power density, benefits rise to 55%, 97% and 116% respectively. Finally, we study future extreme-scale systems, showing that beyond terascale, projected power requirements in excess of 100MW make ZCCloud up to 45% lower cost, for a fixed budget, increase peak PFLOPS achievable by 80%.
Balancing the Costs and Benefits of Learning Ability

We study the costs and benefits of plasticity by evolving agents in environments with different rates of environmental change. Evolution allows both hard-coded strategies and learned strategies, with learning rates varying throughout life. We observe a range of change rates where the balance of costs and benefits are just right for evolving learning. Inside this range, we see two separate strategies evolve: lifelong plasticity and sensitive periods of plasticity. Sensitive periods of plasticity are found to reduce the learning cost while retaining the benefits of learning. This affects the evolutionary process, by limiting genetic assimilation of learned characteristics, making agents able to remain adaptive after relatively long periods of environmental stability.
Conversations with internal auditors: The power of ambiguity

Purpose – Aims to examine the New Zealand internal auditor role and conceptualise on the auditor's influence over that role. At its heart lies the question of how an effective internal auditor can overcome the tension of working with management to improve performance while also remaining sufficiently distant from management in order to report on their performance.Design/methodology/approach – An Eisenhardt‐inspired multiple case‐based approach in which the researcher made observations, examined documents and interviewed senior internal auditors in six New Zealand organisations.Findings – Three concepts characteristic of those who best balanced their role: the internal auditor's external professional status, the presence of a formal and informal communication network, and the internal auditor's place in determining their own role. Informing these concepts is the auditor's ability to manage ambiguity.Research limitations/implications – Limitations of the research include those typical of qualitative study g.
Real-time road traffic density estimation using block variance

The increasing demand for urban mobility calls for a robust real-time traffic monitoring system. In this paper we present a vision-based approach for road traffic density estimation which forms the fundamental building block of traffic monitoring systems. Existing techniques based on vehicle counting and tracking suffer from low accuracy due to sensitivity to illumination changes, occlusions, congestions etc. In addition, existing holistic-based methods cannot be implemented in real-time due to high computational complexity. In this paper we propose a block based holistic approach to estimate traffic density which does not rely on pixel based analysis, therefore significantly reducing the computational cost. The proposed method employs variance as a means for detecting the occupancy of vehicles on pre-defined blocks and incorporates a shadow elimination scheme to prevent false positives. In order to take into account varying illumination conditions, a low-complexity scheme for continuous background update is employed. Empirical evaluations on publicly available datasets demonstrate that the proposed method can achieve real-time performance and has comparable accuracy with existing high complexity holistic methods.