nils-herrmann/clean_scirepeval huggingface dataset:
Jaya a novel optimization algorithm: What, how and why?

This paper presents a comprehensive discussion of the Jaya algorithm, a novel approach for the optimization. There exist two broad categories of heuristic algorithms are: evolutionary algorithms and swarm intelligence. These algorithms' performance vastly depends on the parameters used need extensive tuning during the computational experiments to achieve the superior performance. The Jaya algorithm is a new optimization algorithm has been proposed recently is parameter less and therefore parameters tuning is not needed for it. The primary aim of this paper is to discuss the Jaya algorithm based on the rational aspects outlined as: (a) what is Jaya algorithm; (b) How it works and (c) why one should use it. The author believes that this discussion might be useful to explore the potential of the Jaya to the general audience working for the optimization.
Climate Change Adaptation and Development : Exploring the Linkages �

Please note that Tyndall working papers are "work in progress". Whilst they are commented on by Tyndall researchers, they have not been subject to a full peer review. The accuracy of this work and the conclusions reached are the responsibility of the author(s) alone and not the Tyndall Centre. Manuscript has also been submitted to a peer-reviewed journal 1 Summary Successful human societies are characterised by their adaptability, evidenced throughout human existence. However, climate change introduces a new challenge, not only because of the expected rise in temperature and sea-levels, but also due to the current context of failure to address the causes of poverty adequately. As a result, policy supporting adaptation has been cast as a necessary strategy for responding to both climate change and supporting development, making adaptation the focus of much recent scholarly and policy research. This paper addresses this new adaptation discourse, arguing that work on adaptation so far has focused on responding to the impacts of climate change, rather than sufficiently addressing the underlying factors that cause vulnerability. While there is a significant push all around for adaptation to be better placed in development planning, the paper finds this to be putting the cart before the horse. A successful adaptation process will require adequately addressing the underlying causes of vulnerability: this is the role that development has to play. This work results from research aimed at exploring the international discourse on adaptation to climate change and the meaning of adaptation to climate change in the context of development. 1.  As a result of evidence that human-induced global climate change is already occurring and will continue to affect society over the coming decades, a surge in interest in impact-oriented action is discernable since the beginning of the century, in contrast to efforts centred on prevention (Burton et al., 2002). Frustration over the lack of progress and effectiveness of policy to reduce greenhouse gas emissions has contributed to this shift. Adapting to the changes has consequently emerged as a solution to address the impacts of climate change that are already evident in some regions. However, this course of action has not always been considered relevant within science and policy (Schipper, 2006a;  2003). Adaptation responds directly to the impacts of the increased concentrations of greenhouse gases in both precautionary and reactive ways, rather than through the preventative approach of limiting the source of the gases (this …
Mass Imprisonment and the Life Course : Race and Class Inequality in U . S . Incarceration

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Complex cellular logic computation using ribocomputing devices

Synthetic biology aims to develop engineering-driven approaches to the programming of cellular functions that could yield transformative technologies. Synthetic gene circuits that combine DNA, protein, and RNA components have demonstrated a range of functions such as bistability, oscillation, feedback, and logic capabilities. However, it remains challenging to scale up these circuits owing to the limited number of designable, orthogonal, high-performance parts, the empirical and often tedious composition rules, and the requirements for substantial resources for encoding and operation. Here, we report a strategy for constructing RNA-only nanodevices to evaluate complex logic in living cells. Our ‘ribocomputing’ systems are composed of de-novo-designed parts and operate through predictable and designable base-pairing rules, allowing the effective in silico design of computing devices with prescribed configurations and functions in complex cellular environments. These devices operate at the post-transcriptional level and use an extended RNA transcript to co-localize all circuit sensing, computation, signal transduction, and output elements in the same self-assembled molecular complex, which reduces diffusion-mediated signal losses, lowers metabolic cost, and improves circuit reliability. We demonstrate that ribocomputing devices in Escherichia coli can evaluate two-input logic with a dynamic range up to 900-fold and scale them to four-input AND, six-input OR, and a complex 12-input expression (A1 AND A2 AND NOT A1*) OR (B1 AND B2 AND NOT B2*) OR (C1 AND C2) OR (D1 AND D2) OR (E1 AND E2). Successful operation of ribocomputing devices based on programmable RNA interactions suggests that systems employing the same design principles could be implemented in other host organisms or in extracellular settings.
Modeling and simulation of electronic differential system for an electric vehicle with two-motor-wheel drive

In-wheel-motor drive electric vehicle (EV) is an innovative configuration, in which each wheel is driven individually by an electric motor. It is possible to use an electronic differential (ED) instead of the heavy mechanical differential because of the fast response time of the motor. A new ED control approach for a two-in-wheel-motor drive EV is devised based on the fuzzy logic control method. The fuzzy logic method employs to estimate the slip rate of each wheel considering the complex and nonlinear of the system. Then, the ED system distributes torque and power to each motor according to requirements. The effectiveness and validation of the proposed control method are evaluated in the Matlab/Simulink environment. Simulation results show that the new ED control system can keep the slip rate within the optimized range, ensuring the stability of the vehicle either in a straight or a curve lane.
Virtual Team Networks: How Group Social Capital Affects Team Success in a Massively Multiplayer Online Game

Virtual teams have become a ubiquitous form of organizing, but the impact of social structures within and between teams on group performance remains understudied. This paper uses the case study of a massively multiplayer online game and server log data from over 10,000 players to examine the connection between group social capital (operationalized through guild network structure measures) and team effectiveness, given a variety of in-game social networks. Three different networks, social, task, and exchange networks, are compared and contrasted while controlling for group size, group age, and player experience. Team effectiveness is maximized at a roughly moderate level of closure across the networks, suggesting that this is the optimal level of the groupâs network density. Guilds with high brokerage, meaning they have diverse connections with other groups, were more effective in achievement-oriented networks. In addition, guilds with central leaders were more effective when they teamed up with other guild leaders.
HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm

Cardinality estimation has a wide range of applications and is of particular importance in database systems. Various algorithms have been proposed in the past, and the HyperLogLog algorithm is one of them. In this paper, we present a series of improvements to this algorithm that reduce its memory requirements and significantly increase its accuracy for an important range of cardinalities. We have implemented our proposed algorithm for a system at Google and evaluated it empirically, comparing it to the original HyperLogLog algorithm. Like HyperLogLog, our improved algorithm parallelizes perfectly and computes the cardinality estimate in a single pass.
Efficient Bayesian Clustering for Reinforcement Learning

A fundamental artificial intelligence challenge is how to design agents that intelligently trade off exploration and exploitation while quickly learning about an unknown environment. However, in order to learn quickly, we must somehow generalize experience across states. One promising approach is to use Bayesian methods to simultaneously cluster dynamics and control exploration; unfortunately, these methods tend to require computationally intensive MCMC approximation techniques which lack guarantees. We propose Thompson Clustering for Reinforcement Learning (TCRL), a family of Bayesian clustering algorithms for reinforcement learning that leverage structure in the state space to remain computationally efficient while controlling both exploration and generalization. TCRL-Theoretic achieves near-optimal Bayesian regret bounds while consistently improving over a standard Bayesian exploration approach. TCRLRelaxed is guaranteed to converge to acting optimally, and empirically outperforms state-of-the-art Bayesian clustering algorithms across a variety of simulated domains, even in cases where no states are similar.
Security and Privacy in Sensor Networks

S ensor networks offer economically viable solutions for a variety of applications. For example, current implementations monitor factory instrumentation, pollution levels, freeway traffic, and the structural integrity of buildings. Other applications include climate sensing and control in office buildings and home environmental sensing systems for temperature, light, moisture, and motion. Sensor networks are key to the creation of smart spaces, which embed information technology in everyday home and work environments. The miniature wireless sensor nodes, or motes, developed from low-cost offthe-shelf components at the University of California, Berkeley, as part of its smart dust projects, establish a selforganizing sensor network when dispersed into an environment. The privacy and security issues posed by sensor networks represent a rich field of research problems. Improving network hardware and software may address many of the issues, but others will require new supporting technologies.
Latent variable decomposition of spectrograms for single channel speaker separation

In this paper we present an algorithm for the separation of multiple speakers from mixed single-channel recordings by latent variable decomposition of the speech spectrogram. We model each magnitude spectral vector in the short-time Fourier transform of a speech signal as the outcome of a discrete random process that generates frequency bin indices. The distribution of the process is modelled a mixture of multinomial distributions, such that the mixture weights of the component multinomials vary from analysis window to analysis window. The component multinomials are assumed to be speaker specific and are learnt from training signals for each speaker. The distributions representing magnitude spectral vectors for the mixed signal are decomposed into mixtures of the multinomials for all component speakers. The frequency distribution, i.e. the spectrum for each speaker is reconstructed from this decomposition. Experimental results show that the proposed method is very effective at separating mixed signals.
Effect of a combination of flip and zooming stimuli on the performance of a visual brain-computer interface for spelling.

Brain-computer interface (BCI) systems can allow their users to communicate with the external world by recognizing intention directly from their brain activity without the assistance of the peripheral motor nervous system. The P300-speller is one of the most widely used visual BCI applications. In previous studies, a flip stimulus (rotating the background area of the character) that was based on apparent motion, suffered from less refractory effects. However, its performance was not improved significantly. In addition, a presentation paradigm that used a "zooming" action (changing the size of the symbol) has been shown to evoke relatively higher P300 amplitudes and obtain a better BCI performance. To extend this method of stimuli presentation within a BCI and, consequently, to improve BCI performance, we present a new paradigm combining both the flip stimulus with a zooming action. This new presentation modality allowed BCI users to focus their attention more easily. We investigated whether such an action could combine the advantages of both types of stimuli presentation to bring a significant improvement in performance compared to the conventional flip stimulus. The experimental results showed that the proposed paradigm could obtain significantly higher classification accuracies and bit rates than the conventional flip paradigm (p<0.01).
Sub-MICs of Mentha piperita essential oil and menthol inhibits AHL mediated quorum sensing and biofilm of Gram-negative bacteria

Bacterial quorum sensing (QS) is a density dependent communication system that regulates the expression of certain genes including production of virulence factors in many pathogens. Bioactive plant extract/compounds inhibiting QS regulated gene expression may be a potential candidate as antipathogenic drug. In this study anti-QS activity of peppermint (Mentha piperita) oil was first tested using the Chromobacterium violaceum CVO26 biosensor. Further, the findings of the present investigation revealed that peppermint oil (PMO) at sub-Minimum Inhibitory Concentrations (sub-MICs) strongly interfered with acyl homoserine lactone (AHL) regulated virulence factors and biofilm formation in Pseudomonas aeruginosa and Aeromonas hydrophila. The result of molecular docking analysis attributed the QS inhibitory activity exhibited by PMO to menthol. Assessment of ability of menthol to interfere with QS systems of various Gram-negative pathogens comprising diverse AHL molecules revealed that it reduced the AHL dependent production of violacein, virulence factors, and biofilm formation indicating broad-spectrum anti-QS activity. Using two Escherichia coli biosensors, MG4/pKDT17 and pEAL08-2, we also confirmed that menthol inhibited both the las and pqs QS systems. Further, findings of the in vivo studies with menthol on nematode model Caenorhabditis elegans showed significantly enhanced survival of the nematode. Our data identified menthol as a novel broad spectrum QS inhibitor.
Practical SVBRDF capture in the frequency domain

Spatially-varying reflectance and small geometric variations play a vital role in the appearance of real-world surfaces. Consequently, robust, automatic capture of such models is highly desirable; however, current systems require either specialized hardware, long capture times, user intervention, or rely heavily on heuristics. We describe an acquisition setup that utilizes only portable commodity hardware (an LCD display, an SLR camera) and contains no moving parts. In particular, a laptop screen can be used for illumination. Our setup, aided by a carefully constructed image formation model, automatically produces realistic spatially-varying reflectance parameters over a wide range of materials from diffuse to almost mirror-like specular surfaces, while requiring relatively few photographs. We believe our system is the first to offer such generality, while requiring only standard office equipment and no user intervention or parameter tuning. Our results exhibit a good qualitative match to photographs taken under novel viewing and lighting conditions for a range of materials.
Bench-to-bedside review: Microvascular dysfunction in sepsis –hemodynamics, oxygen transport, and nitric oxide

The microcirculation is a complex and integrated system that supplies and distributes oxygen throughout the tissues. The red blood cell (RBC) facilitates convective oxygen transport via co-operative binding with hemoglobin. In the microcirculation oxygen diffuses from the RBC into neighboring tissues, where it is consumed by mitochondria. Evidence suggests that the RBC acts as deliverer of oxygen and 'sensor' of local oxygen gradients. Within vascular beds RBCs are distributed actively by arteriolar tone and passively by rheologic factors, including vessel geometry and RBC deformability. Microvascular oxygen transport is determined by microvascular geometry, hemodynamics, and RBC hemoglobin oxygen saturation. Sepsis causes abnormal microvascular oxygen transport as significant numbers of capillaries stop flowing and the microcirculation fails to compensate for decreased functional capillary density. The resulting maldistribution of RBC flow results in a mismatch of oxygen delivery with oxygen demand that affects both critical oxygen delivery and oxygen extraction ratio. Nitric oxide (NO) maintains microvascular homeostasis by regulating arteriolar tone, RBC deformability, leukocyte and platelet adhesion to endothelial cells, and blood volume. NO also regulates mitochondrial respiration. During sepsis, NO over-production mediates systemic hypotension and microvascular reactivity, and is seemingly protective of microvascular blood flow.
Deformation-driven topology-varying 3D shape correspondence

We present a deformation-driven approach to topology-varying 3D shape correspondence. In this paradigm, the best correspondence between two shapes is the one that results in a minimal-energy, possibly topology-varying, deformation that transforms one shape to conform to the other while respecting the correspondence. Our deformation model, called GeoTopo transform, allows both geometric and topological operations such as part split, duplication, and merging, leading to fine-grained and piecewise continuous correspondence results. The key ingredient of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation, and simultaneously allows topology changes. This is accomplished by connecting shape parts using structural rods, which behave similarly to virtual springs but simultaneously allow the encoding of energies arising from geometric, structural, and topological shape variations. Driven by the combined deformation energy, an optimal shape correspondence is obtained via a pruned beam search. We demonstrate our deformation-driven correspondence scheme on extensive sets of man-made models with rich geometric and topological variation and compare the results to state-of-the-art approaches.
PSCAD simulation of grid-tied photovoltaic systems and Total Harmonic Distortion analysis

With the increasing fears of the impacts of the high penetration rates of Photovoltaic (PV) systems, a technical study about their effects on the power quality metrics of the utility grid is required. Since such study requires a complete modeling of the PV system in an electromagnetic transient software environment, PSCAD was chosen. This paper investigates a grid-tied PV system that is prepared in PSCAD. The model consists of PV array, DC link capacitor, DC-DC buck converter, three phase six-pulse inverter, AC inductive filter, transformer and a utility grid equivalent model. The paper starts with investigating the tasks of the different blocks of the grid-tied PV system model. It also investigates the effect of variable atmospheric conditions (irradiation and temperature) on the performance of the different components in the model. DC-DC converter and inverter in this model use PWM and SPWM switching techniques, respectively. Finally, total harmonic distortion (THD) analysis on the inverter output current at PCC will be applied and the obtained THD values will be compared with the limits specified by the regulating standards such as IEEE Std 519-1992.
Towards a Formal Modeling of Affective Agents in a BDI Architecture

Affective characteristics are crucial factors that influence human behavior, and often, the prevalence of either emotions or reason varies on each individual. We aim to facilitate the development of agents’ reasoning considering their affective characteristics. We first identify core processes in an affective BDI agent, and we integrate them into an affective agent architecture (GenIA3). These tasks include the extension of the BDI agent reasoning cycle to be compliant with the architecture, the extension of the agent language (Jason) to support affect-based reasoning, and the adjustment of the equilibrium between the agent’s affective and rational sides.
Whipple's disease

A typical case of Whipple's disease is reported, substantiated by histopathologic study. Treatment with steroids has been used with good results. A prolonged clinical remission has occurred, although persistance of pathologic changes in the small intestine has been demonstrated. This disease is a generalized one with polyserositis and malabsorption, the etiology of which is unknown. A genetic factor has been suggested, with the presence of a basic defect in intestinal cell absorption. That it may represent a general sensitivity reaction with inflammation, is possible. It may be amenable to steroid therapy.
Moving Average Convergence Divergence filter preprocessing for real-time event-related peak activity onset detection : Application to fNIRS signals

Real-time solutions for noise reduction and signal processing represent a central challenge for the development of Brain Computer Interfaces (BCI). In this paper, we introduce the Moving Average Convergence Divergence (MACD) filter, a tunable digital passband filter for online noise reduction and onset detection without preliminary learning phase, used in economic markets analysis. MACD performance was tested and benchmarked with other filters using data collected with functional Near Infrared Spectoscopy (fNIRS) during a digit sequence memorization task. This filter has a good performance on filtering and real-time peak activity onset detection, compared to other techniques. Therefore, MACD could be implemented for efficient BCI design using fNIRS.
An offset-canceling low-noise lock-in architecture for capacitive sensing

We describe an offset-canceling low-noise lock-in architecture for capacitive sensing. We take advantage of the properties of modulation and demodulation to separate the signal from the dc offset and use nonlinear multiplicative feedback to cancel the offset. The feedback also attenuates out-of-band noise and further enhances the power of a lock-in technique. Experimentally, in a 1.5m BiCMOS chip, a fabrication dc offset of 2 mV and an intentional offset of 100 mV were attenuated to 9 V. Our offsetcanceling technique could also be useful for practical multipliers that need tolerance to fabrication errors. We present a detailed theoretical noise analysis of our architecture that is confirmed by experiment. As an example application, we demonstrate the use of our architecture in a simple capacitive surface-microelectromechanical-system vibration sensor where the performance is limited by mechanical Brownian noise. However, we show that our electronics limits us to 30 g Hz, which is at least six times lower than the noise floor of commercial state-of-the-art surface-micromachined inertial sensors. Our architecture could, thus, be useful in high-performance inertial sensors with low mechanical noise. In a 1–100-Hz bandwidth, our electronic detection threshold corresponds to a one-part-per-eight-million change in capacitance.