Miracle-dz/UniFAR-FTUs huggingface dataset:
Peak-to-average power ratio suppression schemes in DFT based OFDM

One of the most serious problems of orthogonal frequency division multiplexing (OFDM) is its high peak-to-average power ratio (PAPR) which substantially degrades the efficiency of a linear power amplifier. PAPR suppression schemes for OFDM are investigated in this paper. Coding schemes suppress the PAPR of an OFDM signal to a low level, but they cause considerable spectral loss and do not work efficiently when the number of subcarriers is large. Phase rotation schemes cause very small spectral loss but their suppression performance is not so good. Clipping schemes suppress the PAPR effectively but they increase the sidelobes of the signal spectrum substantially. Amplitude limiting schemes suppress the PAPR very well without any spectral loss before pulse shaping but will have an additional PAPR after pulse shaping. However, the additional PAPR can be suppressed by using an adaptive peak suppression (APS) algorithm. Thus, a new limiter plus APS scheme is introduced for bandwidth efficient PAPR suppression in OFDM. It suppresses the PAPR of 128-QPSK-OFDM from 12.7 dB to 5.2 dB with 1 dB power loss at 10/sup -3/ symbol error rate (SER) in an additive white Gaussian noise (AWGN) channel or 0.31 dB power loss at 10/sup -3/ SER in a Rayleigh fading channel. The limiter plus APS scheme shows better performance than any other schemes for DFT based BPSK or QPSK-OFDM when the number of sub-carriers is not too small (D/spl ges/32).
Adaptive Fairness-Aware Online Meta-Learning for Changing Environments

The fairness-aware online learning framework has arisen as a powerful tool for the continual lifelong learning setting. The goal for the learner is to sequentially learn new tasks where they come one after another over time and the learner ensures the statistic parity of the new coming task across different protected sub-populations (e.g. race and gender). A major drawback of existing methods is that they make heavy use of the i.i. d assumption for data and hence provide static regret analysis for the framework. However, low static regret cannot imply a good performance in changing environments where tasks are sampled from heterogeneous distributions. To address the fairness-aware online learning problem in changing environments, in this paper, we first construct a novel regret metric FairSAR by adding long-term fairness constraints onto a strongly adapted loss regret. Furthermore, to determine a good model parameter at each round, we propose a novel adaptive fairness-aware online meta-learning algorithm, namely FairSAOML, which is able to adapt to changing environments in both bias control and model precision. The problem is formulated in the form of a bi-level convex-concave optimization with respect to the model's primal and dual parameters that are associated with the model's accuracy and fairness, respectively. The theoretic analysis provides sub-linear upper bounds for both loss regret and violation of cumulative fairness constraints. Our experimental evaluation on different real-world datasets with settings of changing environments suggests that the proposed FairSAOML significantly outperforms alternatives based on the best prior online learning approaches.
Embedding fuzzy logic in content based image retrieval

This paper focuses on the possible embedding of the uncertainty regarding the colors of an image into histogram-type descriptors. The uncertainty naturally arises from both the quantization of the color components and the human perception of colors. Fuzzy histograms measure the typicality of each color within the image. We define various fuzzy color histograms following a taxonomy that classifies fuzzy techniques as crude fuzzy, fuzzy paradigm based, fuzzy aggregational and fuzzy inferential. For these fuzzy sets we must develop appropriate similarity measures and distances. We propose a class of such distances, derived from the fuzzy set equality and which we particularize according to various T-norms (fuzzy logical "or" operators). We also prove that the L1 metric naturally arises as a distance for fuzzy sets, considering the fuzzy set symmetric difference.
Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II

Humans rarely tackle every problem from scratch. Given this observation, the motivation for this paper is to improve optimization performance through adaptive knowledge transfer across related problems. The scope for spontaneous transfers under the simultaneous occurrence of multiple problems unveils the benefits of multitasking. Multitask optimization has recently demonstrated competence in solving multiple (related) optimization tasks concurrently. Notably, in the presence of underlying relationships between problems, the transfer of high-quality solutions across them has shown to facilitate superior performance characteristics. However, in the absence of any prior knowledge about the intertask synergies (as is often the case with general black-box optimization), the threat of predominantly negative transfer prevails. Susceptibility to negative intertask interactions can impede the overall convergence behavior. To allay such fears, in this paper, we propose a novel evolutionary computation framework that enables online learning and exploitation of the similarities (and discrepancies) between distinct tasks in multitask settings, for an enhanced optimization process. Our proposal is based on the principled theoretical arguments that seek to minimize the tendency of harmful interactions between tasks, based on a purely data-driven learning of relationships among them. The efficacy of our proposed method is validated experimentally on a series of synthetic benchmarks, as well as a practical study that provides insights into the behavior of the method in the face of several tasks occurring at once.
Edinburgh Research Explorer Scaling of fracture systems in geological media

Scaling in fracture systems has become an active field of research in the last 25 years motivated by practical applications in hazardous waste disposal, hydrocarbon reservoir management, and earthquake hazard assessment. Relevant publications are therefore spread widely through the literature. Although it is recognized that some fracture systems are best described by scale-limited laws (lognormal, exponential), it is now recognized that power laws and fractal geometry provide widely applicable descriptive tools for fracture system characterization. A key argument for power law and fractal scaling is the absence of characteristic length scales in the fracture growth process. All power law and fractal characteristics in nature must have upper and lower bounds. This topic has been largely neglected, but recent studies emphasize the importance of layering on all scales in limiting the scaling characteristics of natural fracture systems. The determination of power law exponents and fractal dimensions from observations, although outwardly simple, is problematic, and uncritical use of analysis techniques has resulted in inaccurate and even meaningless exponents. We review these techniques and suggest guidelines for the accurate and ob-jective estimation of exponents and fractal dimensions. Syntheses of length, displacement, aperture power law exponents, and fractal dimensions are found, after critical appraisal of published studies, to show a wide variation, frequently spanning the theoretically possible range. Extrapolations from one dimension to two and from two dimensions to three are found to be nontrivial, and simple laws must be used with caution. Directions for future research include improved techniques for gathering data sets over great scale ranges and more rigorous application of existing analysis methods. More data are needed on joints and veins to illuminate the differences between different fracture modes. The physical causes of power law scaling and variation in exponents and fractal dimensions are still poorly understood
Heterogeneity Tolerance in IoT Botnet Attack Classification

Due to the rapid adoption of Internet of Things (IoT) technologies, many networks are composed of a patchwork of devices designed by different software and hardware developers. In addition to the heterogeneity of IoT networks, the general rush-to-market produced products with poor adherence to core cybersecurity principles. Coupled together, these weaknesses leave organizations vulnerable to attack by botnets, such as Mirai and Gafgyt. Infected devices pose a threat to both internal and external devices as they attempt to add new devices to the collective or to perpetrate targeted attacks within the network or against third parties. Artificial Intelligence (AI) tools for intrusion detection are popular platforms for detecting indicators of botnet infiltration. However, when training AI tools, the heterogeneity of the network hampers detection and classification accuracy due to the differences in device architecture and network layout. To investigate this challenge, we explored the application of a Neural Network (NN) to the N-BaIoT dataset. The NN achieved 94% classification accuracy when trained using data from all devices in the network. Further, we examined the model's transferability by training on a single device and applying it to data from all devices. This resulted in a noticeable decline in classification accuracy. However, when considering cyberattack detection the model retained a very high true positive rate of 99.6%.
ATLAS probe for the study of galaxy evolution with 300,000,000 galaxy spectra

ATLAS (Astrophysics Telescope for Large Area Spectroscopy) Probe is a mission concept for a NASA probe-class space mission with primary science goal the definitive study of galaxy evolution through the capture of 300,000,000 galaxy spectra up to z=7. It is made of a 1.5-m Ritchey-Chretien telescope with a field of view of solid angle 0.4 deg2. The wavelength range is at least 1 μm to 4 μm with a goal of 0.9 μm to 5 μm. Average resolution is 600 but with a possible trade-off to get 1000 at the longer wavelengths. The ATLAS Probe instrument is made of 4 identical spectrographs each using a Digital Micro-mirror Device (DMD) as a multi-object mask. It builds on the work done for the ESA SPACE and Phase-A EUCLID projects. Three-mirror fore-optics re-image each sub-field on its DMD which has 2048 x 1080 mirrors 13.6 μm wide with 2 possible tilts, one sending light to the spectrograph, the other to a light dump. The ATLAS Probe spectrographs use prisms as dispersive elements because of their higher and more uniform transmission, their larger bandwidth, and the ability to control the resolution slope with the choice of glasses. Each spectrograph has 2 cameras. While the collimator is made of 4 mirrors, each camera is made of only one mirror which reduces the total number of optics. All mirrors are aspheric but with a relatively small P-V with respect to their best fit sphere making them easily manufacturable. For imaging, a simple mirror to replace the prism is not an option because the aberrations are globally corrected by the collimator and camera together which gives large aberrations when the mirror is inserted. An achromatic grism is used instead. There are many variations of the design that permit very different packaging of the optics. ATLAS Probe will enable ground-breaking science in all areas of astrophysics. It will (1) revolutionize galaxy evolution studies by tracing the relation between galaxies and dark matter from the local group to cosmic voids and filaments, from the epoch of reionization through the peak era of galaxy assembly; (2) open a new window into the dark universe by mapping the dark matter filaments to unveil the nature of the dark Universe using 3D weak lensing with spectroscopic redshifts, and obtaining definitive measurements of dark energy and modification of gravity using cosmic large-scale structure; (3) probe the Milky Way's dust-shrouded regions, reaching the far side of our Galaxy; and (4) characterize asteroids and other objects in the outer solar systems.
Creating safety nets through semi-parametric index based insurance: a simulation for Northern Ghana

In West Africa, farm income is highly exposed to risks from crop failure in the drier, inland areas, and from fluctuations in (world market) prices in the wetter coastal areas. As individuals and even extended families are poorly equipped to deal with these, provision of social safety nets is required Our paper reviews the situation in Ghana and the way in which the new financial instrument of index-based insurance might contribute to better it, focusing on the estimation of a crop indemnification scheme for farmers in Northern Ghana. It recalls that in a poor rural area like Northern Ghana, provision of social safety almost coincides with food security management, and must, therefore, distinguish three basic subtasks: distributing income entitlements (possibly indemnification payments from insurance) to the poor, ensuring collection of taxes (possibly insurance premiums) to fund the arrangement, and assuring delivery of staple goods, such as food to the all households, including the poor. We point out that crop insurance, in any form can at best entitle the poor, and with adequate premiums, become adequately funded, albeit that current experience suggests that farmers tend to be reluctant and to find it difficult to fulfill their obligations. Our main remark is, however, that unless the actual availability of goods is assured, the indemnification from crop insurance will under droughts only cause prices to rise and channel away scarce food from the uninsured to the insured. In short, in poor areas such as Northern Ghana co-ordinated food security management is key, particularly under severe droughts, with crop insurance possibly playing a role in the spheres of entitlement and taxation. Turning to the modalities of crop insurance, we mention the advantages of the index-based approach, which as compared to the individualized contracts of commercial insurance greatly reduces transaction costs by basing the indemnification payments on objectively and easily measurable variables, such as rainfall data collected at weather stations, and world prices of main export goods. Our contribution is an improvement of the indemnification schedules. Rather than specifying a synthetic schedule or estimating is as a parametric form, we estimate it as an optimal indemnification that minimizes farmers' risk of having their income drop below the poverty line, while restricting the indemnification to be an unknown function of index variables on weather and prices. We adapt kernel learning technique to conduct this estimation, so as to ensure that the schedule is self-financing, up to a subsidy. Our application is for Northern Ghana where poverty is highest and farming conditions are most risky. We test the scheme's performance as a social safety net in terms of its capacity to reduce basis risk and alleviate poverty. Although our schedule definitely outperforms the parametric forms, basis risk and associated poverty remain considerable.
Neuroinflammatory responses to traumatic brain injury: etiology, clinical consequences, and therapeutic opportunities

Traumatic brain injury (TBI) is a serious public health problem accounting for 1.4 million emergency room visits by US citizens each year. Although TBI has been traditionally considered an acute injury, chronic symptoms reminiscent of neurodegenerative disorders have now been recognized. These progressive neurodegenerative-like symptoms manifest as impaired motor and cognitive skills, as well as stress, anxiety, and mood affective behavioral alterations. TBI, characterized by external bumps or blows to the head exceeding the brain’s protective capacity, causes physical damage to the central nervous system with accompanying neurological dysfunctions. The primary impact results in direct neural cell loss predominantly exhibiting necrotic death, which is then followed by a wave of secondary injury cascades including excitotoxicity, oxidative stress, mitochondrial dysfunction, blood–brain barrier disruption, and inflammation. All these processes exacerbate the damage, worsen the clinical outcomes, and persist as an evolving pathological hallmark of what we now describe as chronic TBI. Neuroinflammation in the acute stage of TBI mobilizes immune cells, astrocytes, cytokines, and chemokines toward the site of injury to mount an antiinflammatory response against brain damage; however, in the chronic stage, excess activation of these inflammatory elements contributes to an “inflamed” brain microenvironment that principally contributes to secondary cell death in TBI. Modulating these inflammatory cells by changing their phenotype from proinflammatory to antiinflammatory would likely promote therapeutic effects on TBI. Because neuroinflammation occurs at acute and chronic stages after the primary insult in TBI, a treatment targeting neuroinflammation may have a wider therapeutic window for TBI. To this end, a better understanding of TBI etiology and clinical manifestations, especially the pathological presentation of chronic TBI with neuroinflammation as a major component, will advance our knowledge on inflammation-based disease mechanisms and treatments.
Norbert Wiener and the growth of negative feedback in scientific explanation : with a proposed research program of "cybernetic analysis"

Negative feedback has become ubiquitous in science both as a technique and as a conceptual tool. As a technique, negative feedback has a long history; devices based in its use were made in antiquity. It has only been during the last century, however, that rigorous quantitiative methods have become associated with the applications of negative feedback. These methods originated in communications engineering and during the World War II period spread rapidly to other areas of science where further applications were soon made. During this process of dissemination negative feedback was transformed into a powerful conceptual tool, of general application, having to do with the organization of behavior. The central figure responsible for both the dissemination and transformation of negative feedback was the American mathematician, Norbert Wiener, who, as a child prodigy, had developed graduate level proficiency in science, mathematics and philosophy before he was twenty. Wiener's multidisciplinary background and interests were critically important in allowing him to interact with professionals in many different fields and thereby to disseminate the feedback ideas. Wiener and two colleagues were the authors of the 1943 paper, "Behavior, Purpose and Teleology," which stimulated a number of interdisciplinary meetings. These meetings were important in spreading the feedback concepts to the different disciplines. Participating in these meetings were, among others, Gregory Bateson, Wolfgang Miler, Margaret Mead, Warren S. McCulloch, F. S. C. Northrop, John von Neumann and Wiener. The successful assimilation of feedback by the various disciplines in spite of the problems associated with modern discipline specialization provides a lesson in how these problems may be overcome. In the case of feedback, the climate for its assimilation was made considerably more receptive by concurrent developments in computer science and neurophysiology which mutually reinforced the robotic view. The role of negative feedback in scientific research and the significance of this role have not yet been fully identified. Such an identification must be made in order to evaluate the historical events which led to the assimilation of negative feedback. I attempt to define the role of negative feedback in scientific research in terms of a program called "cybernetic analysis. " This program develops the behavioral and functional roles of negative feedback in terms of "adaptive goal-directed behavior"; such behavior occurs when a system can maintain a certain state or tend toward a certain state even while being disturbed by external influences. This behavior is exhibited both by organisms and by mechanical devices controlled by negative feedback. Until now the idea that systems could be directed toward an end has been unacceptable because goal-directedness has been associated with the outdated notions of teleology and final cause. The ability of negative feedback to account for goal-directedness mechanistically not…
GPSLoc: Framework for Predicting Global Positioning System Quality of Service

While currently numerous existing engineering applications benefit from the global positioning system~GPS! , it is anticipated that operation of many new, emerging applications ~e.g., applications related to ubiquitous mobile computing! will rely on the information provided by this technology. Depending on the application requirement, GPS data may be collected and post-processed or collected and processed in real time. In either case, there are questions about availability, quality, and reliability of GPS data in engineering applications. To date, despite available techniques for realizing, and to some extent improving, a certain level of GPS accuracy, there is no integrated, coherent approach or technique that would provide users with solutions that combine GPS availability, quality, and reliability. To that end, we propose quality of service ~QoS! assurance for GPS. With GPS QoS, users and applications would be provided with the means for predicting GPS solutions in advance meeting the requirements in a timely and cost-effective manner. We have developed a framework for the proposed GPS QoS called GPSLoc. In this paper, we discuss the requirements, methodologies, models, and algorithms for the GPSLoc framework and the experimentation with one of the GPS QoS parameters ~visibility!
Bioconductor’s qvalue package Version 2.18.0

5 Case study: differential gene expression 4 5.1 Calculating p-values . 4 5.2 Checking the p-value histogram . 5 5.3 The qvalue function . 6 5.3. 1 The qvalue object . 6 5.3. 2 Summarizing results . 7 5.3. 3 The π0 estimate . 8 5.3. 4 The q-values . 8 5.3. 5 The local false discovery rates . 9 5.4 Visualizing results . 9
Admission Control Based on Dynamic Rate Constraints in Multi-Hop Networks

This paper presents an admission control algorithm based on dynamic constraints for multi-hop networks. Assuming each node knows the topology and flow reservations within its radio range, local constraints on flow rates can be computed. As long as these constraints are satisfied, flows are accepted. Since computing optimal constraints is not practical, existing approaches compute a system of either necessary or sufficient constraints. In practice, the approach based on necessary constraints tends to overload the network whereas in the latter approach, a significant part of the bandwidth remains unused. In addition, these works assume that the interference model and the sublayers are optimal. In this paper, we propose to take into account the channel state in the constraints computation and, thus, to adjust them according to model relevance. Therefore we give a probabilistic model to evaluate the time spent by the channel in the idle state. By comparing this estimation with the measure value, we evaluate the model accuracy and include the corresponding error rate in the constraints of the admission control. Simulations show that our admission control algorithm outperforms previous work.