Miracle-dz/UniFAR-FTUs huggingface dataset:
Megalakes in the Sahara? A Review

Abstract The Sahara was wetter and greener during multiple interglacial periods of the Quaternary, when some have suggested it featured very large (mega) lakes, ranging in surface area from 30,000 to 350,000 km2. In this paper, we review the physical and biological evidence for these large lakes, especially during the African Humid Period (AHP) 11–5 ka. Megalake systems from around the world provide a checklist of diagnostic features, such as multiple well-defined shoreline benches, wave-rounded beach gravels where coarse material is present, landscape smoothing by lacustrine sediment, large-scale deltaic deposits, and in places, tufas encrusting shorelines. Our survey reveals no clear evidence of these features in the Sahara, except in the Chad basin. Hydrologic modeling of the proposed megalakes requires mean annual rainfall ≥1.2 m/yr and a northward displacement of tropical rainfall belts by ≥1000 km. Such a profound displacement is not supported by other paleo-climate proxies and comprehensive climate models, challenging the existence of megalakes in the Sahara. Rather than megalakes, isolated wetlands and small lakes are more consistent with the Sahelo-Sudanian paleoenvironment that prevailed in the Sahara during the AHP. A pale-green and discontinuously wet Sahara is the likelier context for human migrations out of Africa during the late Quaternary.
Effects of recreational football performed once a week (1 h per 12 weeks) on cardiovascular risk factors in middle-aged sedentary men

ABSTRACT Objectives: It is well established that there is a strong relationship between physical activity, cardiovascular diseases and mortality. Regular recreational football training can lower blood pressure, heart rate at rest, fat percentage, LDL cholesterol and increase maximal aerobic power (VO2max). This study analyzed the effect of one recreational football training per week on middle-aged men. Design: Randomized controlled trial. Methods: Twenty-four participants (mean ± SDs; age 44.5 ± 4.7 years, weight 81.9 ± 10.4 kg, height 175.0 ± 7.3 cm) were randomized in a football group (FG = 10) and control group (CG = 14). FG performed supervised recreational football training (five-a-side futsal match) on 36 × 18.5 m synthetic indoor and outdoor field, 60 min per week over 12 weeks. Results: After training, VO2max and maximal aerobic speed improved in FG respectively of 4.4% (+1.89 mL O2⋅ kg−1.min−1, P = 0.002) and 5.9% (P = 0.01). Systolic and mean blood pressure decreased respectively of 2.5% (−3.18 mmHg, P = 0.04) and 2.2% (−2.28 mmHg, P = 0.04) in FG, while diastolic blood pressure did not change (−1.84 mmHg, P = 0.09). Conclusions: Recreational football activity produces health benefits by improving VO2 max and lowering blood pressure parameters in middle-aged men.
Semantic Web of Things for Industry 4.0

Industry 4.0 which is also referred to as the fourth industrial revolution aims at mass customized production with low-cost and shorter production life-cycle, by digitalizing and automating the manufacturing process. The Automation Systems (AS) used in the manufacturing process should be flexible in order to achieve this goal. But, lack of interoperability between AS devices from different domains makes it harder to achieve this goal. In this study, we employ Web of Things and Semantic Web Technologies to address the cross-domain interoperability problems in AS. Then, we propose an approach to engineer and configure an AS with minimum effort; and show the preliminary results as a demonstration on a process automation workstation to show the feasibility of our
On the other side of the mean : The perception of dissimilarity in human faces

We created a `face space' using a laser-scan representation of faces. In this space, a caricature can be made by moving a face away from the average face, along the line connecting the particular face to the average face. Here, we move the face along this line in the other direction, proceeding through the mean and `out the other side'. This results in a face that is `opposite', in a computational sense, to the original face. We morphed several faces into their anti-faces and sampled the morph trajectory in five discrete steps. We then collected similarity ratings from human participants for all possible pairs of morphed faces to determine how the distances in the `physical face space' related to the distances in the `psychological face space'. The data indicate that there is a perceptual discontinuity of face identity as the face crosses over to the `other side of the mean'. We consider these results in the context of face-space models of human face processing. DOI:10.1068/p2851
QPM-GaAs for Mid-Infrared Applications

Powerful coherent laser sources are needed throughout the mid-infrared region for a number of civilian or defense applications, exploiting either the atmospheric transmission windows, or the fingerprint of common molecules. Nonlinear optical materials play a key role as they permit the frequency down-conversion of mature near-infrared solid-state lasers into the mid-IR, where few direct laser solutions exist. Gallium arsenide (GaAs) has excellent characteristics for parametric frequency conversion and is potentially one of the most attractive mid-IR nonlinear-optical materials. It has an extremely large second-order nonlinear optical coefficient d
Cost-Efficient Dragonfly Topology for Large-Scale Systems

It is more efficient to use increasing pin bandwidth by creating high-radix routers with a large number of narrow ports instead of low-radix routers with fewer wide ports. building networks using high-radix routers lowers cost and improves performance, but also presents many challenges. the dragonfly topology minimizes network cost by reducing the number of global channels required.
Scholarship @ Claremont Scholarship @ Claremont

This paper argues that using Socio-Technical Interaction Networks to build on extensively-used Digital Library infrastructures for supporting Open Science knowledge environments. Using a more social -technical approach could lead to an evolutionary reconceptualization of Digital Libraries. Digital Libraries being used as knowledge environments, built upon on the document repositories, will also emphasize the importance of user interaction and collaboration in carrying out those activities. That is to say, the primary goal of Digital Libraries is to help users convert information into knowledge; therefore, Digital Libraries examined in light of socio-technical interaction networks have the potential to shift Digital Libraries from individual, isolated collections to more interoperable, interconnected knowledge-creating repositories that support an evolving relationship between open science users and the Digital Library environment.
Quantifying ChIP-seq data: a spiking method providing an internal reference for sample-to-sample normalization

Chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) experiments are widely used to determine, within entire genomes, the occupancy sites of any protein of interest, including, for example, transcription factors, RNA polymerases, or histones with or without various modifications. In addition to allowing the determination of occupancy sites within one cell type and under one condition, this method allows, in principle, the establishment and comparison of occupancy maps in various cell types, tissues, and conditions. Such comparisons require, however, that samples be normalized. Widely used normalization methods that include a quantile normalization step perform well when factor occupancy varies at a subset of sites, but may miss uniform genome-wide increases or decreases in site occupancy. We describe a spike adjustment procedure (SAP) that, unlike commonly used normalization methods intervening at the analysis stage, entails an experimental step prior to immunoprecipitation. A constant, low amount from a single batch of chromatin of a foreign genome is added to the experimental chromatin. This “spike” chromatin then serves as an internal control to which the experimental signals can be adjusted. We show that the method improves similarity between replicates and reveals biological differences including global and largely uniform changes.
Sparse distance-based learning for simultaneous multiclass classification and feature selection of metagenomic data

MOTIVATION Direct sequencing of microbes in human ecosystems (the human microbiome) has complemented single genome cultivation and sequencing to understand and explore the impact of commensal microbes on human health. As sequencing technologies improve and costs decline, the sophistication of data has outgrown available computational methods. While several existing machine learning methods have been adapted for analyzing microbiome data recently, there is not yet an efficient and dedicated algorithm available for multiclass classification of human microbiota. RESULTS By combining instance-based and model-based learning, we propose a novel sparse distance-based learning method for simultaneous class prediction and feature (variable or taxa, which is used interchangeably) selection from multiple treatment populations on the basis of 16S rRNA sequence count data. Our proposed method simultaneously minimizes the intraclass distance and maximizes the interclass distance with many fewer estimated parameters than other methods. It is very efficient for problems with small sample sizes and unbalanced classes, which are common in metagenomic studies. We implemented this method in a MATLAB toolbox called MetaDistance. We also propose several approaches for data normalization and variance stabilization transformation in MetaDistance. We validate this method on several real and simulated 16S rRNA datasets to show that it outperforms existing methods for classifying metagenomic data. This article is the first to address simultaneous multifeature selection and class prediction with metagenomic count data. AVAILABILITY The MATLAB toolbox is freely available online at http://metadistance.igs. umaryland.edu/. CONTACT zliu@umm.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
A Lossy Compression Technique Enabling Duplication-Aware Sequence Alignment

In spite of the recognized importance of tandem duplications in genome evolution, commonly adopted sequence comparison algorithms do not take into account complex mutation events involving more than one residue at the time, since they are not compliant with the underlying assumption of statistical independence of adjacent residues. As a consequence, the presence of tandem repeats in sequences under comparison may impair the biological significance of the resulting alignment. Although solutions have been proposed, repeat-aware sequence alignment is still considered to be an open problem and new efficient and effective methods have been advocated. The present paper describes an alternative lossy compression scheme for genomic sequences which iteratively collapses repeats of increasing length. The resulting approximate representations do not contain tandem duplications, while retaining enough information for making their comparison even more significant than the edit distance between the original sequences. This allows us to exploit traditional alignment algorithms directly on the compressed sequences. Results confirm the validity of the proposed approach for the problem of duplication-aware sequence alignment.
Reducing reconfiguration overheads of a reconfigurable dynamic system using active run- time prediction

The performance of the Field-Programmable Gate Array (FPGA) is largely affected by its reconfiguration overheads. For a dynamic system in which the nature of the system is unpredictable at design-time, these overheads are expensive in terms of performance. To reduce these overheads, architecture along with two algorithms is proposed which dynamically predicts and schedules the configuration. As a result, the time reconfiguration overhead is reduced to improve the performance. In most cases, the scheduling result obtained exactly matches with the system whose configurations are fetched from HS memory. This could be the near maximum achievable performance for any FPGA architecture, executing a dynamic application. Keywords—Dynamic systems; Prediction based algorithm; Reconfiguration overheads; Configuration mapping; fieldprogrammable gate array (FPGA)
Modeling Duration Patterns for Luciana

We present a method for speaker recognition that uses the duration patterns of speech units to aid speaker classification. The approach represents each word and/or phone by a feature vector comprised of either the durations of the individual phones making up the word, or the HMM states making up the phone. We model the vectors using mixtures of Gaussians. The speaker specific models are obtained through adaptation of a “background” model that is trained on a large pool of speakers. Speaker models are then used to score the test data; they are normalized by subtracting the scores obtained with the background model. We find that this approach yields significant perfomance improvement when combined with a state-of-the-art speaker recognition system based on standard cepstral features. Furthermore, the improvement persists even after combination with lexical features. Finally, the improvement continues to increase with longer test sample durations, beyond the test duration at which standard system accuracy level off.
Printed in Great Britain

Tills are a distinctive geological and engineering group of soils which are characterized by a wide range of behaviourai patterns and a high degree of variability. As a means of identifying the various geological types of tills, a classification system based on their modes of formation, transportation and deposition has been developed. To accommodate the inherent variability of tills, sub-division of these classifications has been carried out on the basis of their gradational characteristics. Further, the fabric features contributing to variability in engineering bebaviour are identified. From this, extended classifications are tentatively suggested to aid the prediction of the engineering properties of tills and the selection of the most appropriate sampling and testing techniques for them.
A Learning Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases

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RETRACTED: Urban Sound Classification Using Convolutional Neural Network Model

The programmed content-based order of urban sound classes is a significant part of different developing methods and applications, for example, observation, urban soundscape comprehension and commotion source distinguishing proof, along these lines the exploration subject has increased a great deal of consideration lately. The objective of this paper is to create a proficient AI based plan for urban sound classification. Ongoing fruitful utilizations of convolutional neural systems (CNNs) to sound order and discourse acknowledgment have spurred the quest for better information portrayals for progressively proficient preparation. Visual presentations of a sound signal, through different time-recurrence portrayals, for example, spectrograms offer a very good representation of the worldly picture of the original signal. Utilizing a spectrogram picture of the sound and afterward changing over the equivalent to information focuses (As is accomplished for pictures). This is effortlessly done utilizing mel_spectogram a function of Librosa. At the approval stage, we lead tests on Urban Sound 8K database which comprises 10 classes of urban sound happenings with 8732 real-world sound clips. As a result, we see how convolutional neural network (CNN) frameworks with raw sound waveforms improve the exactness in urban sound classification and clearly shows the structure concerning the number of parameters.
Design Futures: A New Discipline, Tool and Medium

While digital media are providing a valuable resource to keep the world connected, their intrinsic channel speed has made information extremely transient and present-oriented in its nature, often lacking overall quality in an era of data over-abundance. If rear-view mirrorism has been characterizing our attitude towards the future, nowadays our speed of technological development is making it unfeasible to face what is next while running backwards. At the crossroads of design fiction, interaction design, ethnographic research and future foresights, the design futures discipline aims to create engaging future-projected experiences, with the goal of inspiring people, increasing their awareness on contemporary challenges and their willingness to act, responsibly impacting the future and consciously choosing among possible alternatives. In this paper we trace the origins and the role of the new discipline, we provide a definition, we introduce some examples and we analyze design futures’ role as a multidisciplinary platform for discussion and convergent media experimentation and development.
The University of Hong Kong

On the Instructor: Ernest is a Designer with a PhD in Electrical Engineering, a serial entrepreneur and founder of Future Impact Lab (FILL). FILL adopts Design Thinking to solve business issues with empathy, technology, and creating social impact. Current projects include AI-driven energy-saving system, safe farming in Vietnam adopting IoT, AI, and Blockchain, and smart community design in Hong Kong and Malaysia, etc. Prior to that, Ernest was the Chief Representative in HK of an advanced Catalonian telecommunication R&D institution, bringing lean startup culture into traditional R&D operation which led to invention and commercialization of new technology, e.g. LiFi.
Decoding the origins of vertical land motions observed today at coasts

In recent decades, geodetic techniques have allowed detecting vertical land motions and sea-level changes of a few millimetres per year, based on measurements taken at the coast (tide gauges), on board of satellite platforms (satellite altimetry) or both (Global Navigation Satellite System). Here, contemporary vertical land motions are analysed from January 1993 to July 2013 at 849 globally distributed coastal sites. The vertical displacement of the coastal platform due to surface mass changes is modelled using elastic and viscoelastic Green's functions. Special attention is paid to the effects of glacial isostatic adjustment induced by past and present-day ice melting. Various rheological and loading parameters are explored to provide a set of scenarios that could explain the coastal observations of vertical land motions globally. In well-instrumented regions, predicted vertical land motions explain more than 80 per cent of the variance observed at scales larger than a few hundred kilometres. Residual vertical land motions show a strong local variability, especially in the vicinity of plate boundaries due to the earthquake cycle. Significant residual signals are also observed at scales of a few hundred kilometres over nine well-instrumented regions forming observation windows on unmodelled geophysical processes. This study highlights the potential of our multitechnique database to detect geodynamical processes, driven by anthropogenic influence, surface mass changes (surface loading and glacial isostatic adjustment) and tectonic activity (including the earthquake cycle, sediment and volcanic loading, as well as regional tectonic constraints). Future improvements should be aimed at densifying the instrumental network and at investigating more thoroughly the uncertainties associated with glacial isostatic adjustment models.
On the Evaluation of Seasonal Variability of the Ocean Kinetic Energy

AbstractThe seasonal cycles of the mean kinetic energy (MKE) and eddy kinetic energy (EKE) are compared in an idealized flow as well as in a realistic simulation of the Gulf Stream (GS) region based on three commonly used definitions: orthogonal, nonorthogonal, and moving-average filtered decompositions of the kinetic energy (KE). It is shown that only the orthogonal KE decomposition can define the physically consistent MKE and EKE that precisely represents the KEs of the mean flow and eddies, respectively. The nonorthogonal KE decomposition gives rise to a residual term that contributes to the seasonal variability of the eddies, and therefore the obtained EKE is not precisely defined. The residual term is shown to exhibit more significant seasonal variability than EKE in both idealized and realistic GS flows. Neglecting its influence leads to an inaccurate evaluation of the seasonal variability of both the eddies and the total flow. The decomposition using a moving-average filter also results in a nonneg.
Polynomial encoding of ORM conceptual models in CFDI

The use of conceptual models has long been confined to the data analysis stage of software development. In recent years, this has been extended to use them at run-time as well, for, among others, querying large amounts of data. This brings afore the need to have tractable logicbased reconstructions of the conceptual models, i.e., in at most PTIME. We provide such a logic-based reconstruction for most of ORM using the Description Logic language CFDI∀− nc , which has several features important for conceptual models, notably n-ary relationships, complex identification constraints, and role subsumption. The encoding captures over 96% of the constructs used in practice in the set of 33 ORM diagrams analysed. The results are easily transferable to EER and UML Class diagrams, with an even greater coverage.
A severe weather climatology for the Charleston, South Carolina, WFO county warning area

The operational staff of the National Weather Service (NWS) Weather Forecast Office (WFO) in Charleston, South Carolina issues severe weather warnings for 20 counties in eastern South Carolina and Georgia (Fig 1). New technologies, such as the AWIPS (Advanced Weather Interactive Processing System) and the WSR-88D (Weather Surveillance Radar-1988 Doppler) has enhanced the analysis of the potential for, and occurrence of, severe weather. The forecast process employed by local forecast offices and the Storm Prediction Center (SPC) of the National Centers for Environmental Prediction (NCEP), depends upon the scientific evaluation of the evolving pre-storm environment, severe weather pattern recognition, and climatology (Johns and Doswell 1992). This study provides one of these necessary tools, a detailed severe weather climatology for the Charleston, SC, County Warning Area (CWA).
Consistency and set intersection

We propose a new framework to study properties of consistency in a Constraint Network from the perspective of properties of set intersection. Our framework comes with a proof schema which gives a generic way of lifting a set intersection property to one on consistency. Various well known results can be derived with this framework. More importantly, we use the framework to obtain a number of new results. We identify a new class of tree convex constraints where local consistency ensures global consistency. Another result is that in a network of arbitrary constraints, local consistency implies global consistency whenever there arc certain m-tight constraints. The most interesting result is that when the constraint on every pair of variables is properly m-tight in an arbitrary network, global consistency can be achieved by enforcing relational m=1 -consistency. These results significantly improve our understanding of convex and tight constraints. This demonstrates that our framework is a promising and powerful tool for studying consistency.