Miracle-dz/UniFAR-FTUs huggingface dataset:
EEG Motor Imagery Signal Classification Using Firefly Support Vector Machine

This Study proposed Firefly-Support vector machine (FASVM) method to improve the accuracy of EEG motor imagery signals classification. Redundancy of features in EEG signal classification can affect its performance. Firefly is a metaheuristic optimization based on firefly behavior. Firefly algorithm is used to select optimal subset of features in order to increase the classification accuracy. Common Spatial Pattern (CSP) is used to extract EEG signals features before selected by firefly algorithm. The purpose of CSP is to maximize the variance for one class and to minimize the other class variance. This study used BCI Competition III data set IVa. Feature vector extracted from CSP is selected using FASVM. The accuracy of SVM is used as objective function for firefly algorithm optimisation. The proposed method FASVM produced good result with average accuracy of 93.20 %.
Refinement of breast cancer classification by molecular characterization of histological special types

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Metal Reduction at Low pH by a Desulfosporosinus species: Implications for the Biological Treatment of Acidic Mine Drainage

We isolated an acid-tolerant sulfate-reducing bacterium, GBSRB4.2, from coal mine-derived acidic mine drainage (AMD)-derived sediments. Sequence analysis of partial 16S rRNA gene of GBSRB4.2 revealed that it was affiliated with the genus Desulfosporosinus. GBSRB4.2 reduced sulfate, Fe(III) (hydr)oxide, Mn(IV) oxide, and U(VI) in acidic solutions (pH 4.2). Sulfate, Fe(III), and Mn(IV) but not U(VI) bioreduction led to an increase in the pH of acidic solutions and concurrent hydrolysis and precipitation of dissolved Al3+. Reduction of Fe(III), Mn(IV), and U(VI) in sulfate-free solutions revealed that these metals are enzymatically reduced by GBSRB4.2. GBSRB4.2 reduced U(VI) in groundwater from a radionuclide-contaminated aquifer more rapidly at pH 4.4 than at pH 7.1, possibly due to the formation of poorly bioreducible Ca-U(VI)-CO3 complexes in the pH 7.1 groundwater.
Analytical Modeling of Charge Plasma-Based Optimized Nanogap Embedded Surrounding Gate MOSFET for Label-Free Biosensing

In this paper, analytical modeling of a charge plasma-based nanogap embedded surrounding gate MOSFET biosensor for label-free biosensing has been presented and verified with extensive simulated device data. Along the channel, considering parabolic potential profile, surface potential, threshold voltage, and drain currents have been modeled. Sensitivity has been analyzed by measuring the shift in ${I} _{ \mathrm{\scriptscriptstyle ON}}/{I} _{\mathrm{\scriptscriptstyle OFF}}$ and threshold voltage when the biosensor interacts with the neutral or charged biomolecules. Less thermal budgeting scheme with simplified fabrication process flow, i.e., no ion implantation or diffusion can be achieved easily here due to new charge plasma concept. In addition, the issues related to the doping control and random dopant fluctuations have been reduced significantly to make the device immune to the process variation. Hence, the proposed device has been optimized for the enhancement in device electrostatics and sensitivity. Extensive TCAD simulations have been performed to investigate the device parameters and to verify the model data. Hence, the proposed model can be considered as an optimal model of a biosensor for the future reference.
Online Newton Step Algorithm with Estimated Gradient

Online learning with limited information feedback (bandit) tries to solve the problem where an online learner receives partial feedback information from the environment in the course of learning. Under this setting, Flaxman et al.[8] extended Zinkevich's classical Online Gradient Descent (OGD) algorithm [29] by proposing the Online Gradient Descent with Expected Gradient (OGDEG) algorithm. Specifically, it uses a simple trick to approximate the gradient of the loss function $f_t$ by evaluating it at a single point and bounds the expected regret as $\mathcal{O}(T^{5/6})$ [8], where the number of rounds is $T$. Meanwhile, past research efforts have shown that compared with the first-order algorithms, second-order online learning algorithms such as Online Newton Step (ONS) [11] can significantly accelerate the convergence rate of traditional online learning algorithms. Motivated by this, this paper aims to exploit the second-order information to speed up the convergence of the OGDEG algorithm. In particular, we extend the ONS algorithm with the trick of expected gradient and develop a novel second-order online learning algorithm, i.e., Online Newton Step with Expected Gradient (ONSEG). Theoretically, we show that the proposed ONSEG algorithm significantly reduces the expected regret of OGDEG algorithm from $\mathcal{O}(T^{5/6})$ to $\mathcal{O}(T^{2/3})$ in the bandit feedback scenario. Empirically, we further demonstrate the advantages of the proposed algorithm on multiple real-world datasets.
Identifying Pathways Between Socioeconomic Status and Language Development

Children from low-income backgrounds consistently perform below their more advantaged peers on standardized measures of language ability, setting long-term trajectories that translate into gaps in academic achievement. Our primary goals in this review are to describe how and why this is so, in order to focus attention on ways to enrich early language experiences across socioeconomic strata. We first review the literature on the relation between socioeconomic status (SES) and language ability across domains in early childhood. We then identify three potential pathways by which SES might influence language development—child characteristics, parent–child interaction, and availability of learning resources—recognizing the complicated interaction between the child's own language learning skill and his/her environmental support. Finally, we review interventions that target these three pathways with an eye toward best practice. Future research should focus on the diversity of contexts in which children acquire l.
MR-GAN: Manifold Regularized Generative Adversarial Networks

Despite the growing interest in generative adversarial networks (GANs), training GANs remains a challenging problem, both from a theoretical and a practical standpoint. To address this challenge, in this paper, we propose a novel way to exploit the unique geometry of the real data, especially the manifold information. More specifically, we design a method to regularize GAN training by adding an additional regularization term referred to as manifold regularizer. The manifold regularizer forces the generator to respect the unique geometry of the real data manifold and generate high quality data. Furthermore, we theoretically prove that the addition of this regularization term in any class of GANs including DCGAN and Wasserstein GAN leads to improved performance in terms of generalization, existence of equilibrium, and stability. Preliminary experiments show that the proposed manifold regularization helps in avoiding mode collapse and leads to stable training.
Towards a Secure Partial Reconfiguration of Xilinx FPGAs : Special Session Paper

The susceptibility of Field Programmable Gate Arrays (FPGAs) to power analysis attacks has gained more attention lately. While this was predicted a long time ago, it was not until recently that successful attacks were reported. Xilinx FPGA families before the UltraScale architecture offer little when it comes to protecting the encryption keys from being retrieved via power analysis attacks. In this paper, we propose a scheme for securing Xilinx FPGAs against these attacks by using the on-chip Xilinx Analog-to-Digital Converter (XADC) to monitor the power consumption of the chip during an encrypted partial bitstream configuration. Our proposed solution to power analysis attacks involves activating or deactivating power consumer circuits to level out the power consumption of the FPGA with the aim of masking the power signature of the encryption key. We present here our implementation strategy and preliminary power consumption measurements of FPGA primitives that can be used to achieve this objective.
Recycling of Organic Wastes for a Sustainable Agriculture

A growing number of agricultural scientists. environmentalhsts, government t)fficils, farmers, and both urban and rural laymen, have become increasingly alarmed over the potential vulerability of the energy-intensive systems of food and fiber production which now characterize U.S. agriculture. During the past 40 years, conventional agriculture has become increasingly dependent upon petroleum-based. chcmically-synthesized fertilizers and pesticides for crop protection and to supply plant nutrients. Certainly, these energy­ intensive technologies hove contributed great! y to this Nation's agricultural productivity. However, sharply escalating production costs associated with the increasing cost and uncertain availability ofenergy. i.e. fuel and fertilizers, have generated considerable interest in less expensive and more environ­ mentally compatible production alternatives such as organic farming (USDA, 1980). At a recent multi-agency workshop involving more than 100 prominent scientists and administrators, "Sustaining the Soil Productivity"-2 was unanimously selected as our foremost national agricultural research priority (Larson et aL. 1981). Today in the U.S. Corn Belt, which contains much of country's prime farmland, and where intensive row-cropping ispracticed, the average annual soil loss from erosion exceeds 8 tons/acre (18 mt/ha) (Berg, 1979). This is about twice the maximurn tolerable rate or so-cal! ed "T-value"
Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children

Objectives Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans. Study Design Cross-sectional. Setting Academic tertiary care children’s hospital. Subjects and Methods Three-dimensional models of the subglottic airway lumen were created using CT scans from 54 children undergoing imaging for indications other than airway disease. The base lumen models were deformed in software to simulate subglottic airway segments with 0%, 25%, 50%, and 75% stenoses for each subject. Statistical analysis of the airway geometry was performed using metrics extracted from the lumen centerlines. The centerline analysis was used to develop a system for subglottic stenosis assessment and classification from patient-specific airway imaging. Results The scaled hydraulic diameter gradient metric derived from intersectional changes in the lumen can be used to accurately classify and quantitate subglottic stenosis in the airway based on CT scan imaging. Classification is most accurate in the clinically relevant 25% to 75% range of stenosis. Conclusions Laryngotracheal stenosis is a complex diagnosis requiring an understanding of the airway lumen configuration, anatomical distortions of the airway framework, and alterations of respiratory aerodynamics. Using image-based airway models, we have developed a metric that accurately captures subglottis patency. While not intended to replace endoscopic evaluation and existing staging systems for laryngotracheal stenosis, further development of these techniques will facilitate future studies of upper airway computational fluid dynamics and the clinical evaluation of airway disease.
The importance of the Neutral Theory in 1968 and 50 years on: A response to Kern and Hahn 2018

A recent article reassessing the Neutral Theory of Molecular Evolution claims that it is no longer as important as is widely believed. The authors argue that “the neutral theory was supported by unreliable theoretical and empirical evidence from the beginning, and that in light of modern, genome‐scale data, we can firmly reject its universality. ” Claiming that “the neutral theory has been overwhelmingly rejected,” they propose instead that natural selection is the major force shaping both between‐species divergence and within‐species variation. Although this is probably a minority view, it is important to evaluate such claims carefully in the context of current knowledge, as inaccuracies can sometimes morph into an accepted narrative for those not familiar with the underlying science. We here critically examine and ultimately reject Kern and Hahn's arguments and assessment, and instead propose that it is now abundantly clear that the foundational ideas presented five decades ago by Kimura and Ohta are indeed correct.
Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder

Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representation is a challenging problem, because both environmental constraints (e.g., wall partitions) and temporal user dynamics should be meticulously considered and accounted for. Furthermore, traditional sequence-to-sequence autoencoders using maximum log-likelihood often require dataset covering all the possible spatiotemporal characteristics to perform well. This is infeasible or impractical in reality. We propose TREP, a practical pedestrian trajectory representation learning algorithm which captures the environmental constraints and the pedestrian dynamics without the need of any training dataset. By formulating a sequence-to-sequence autoencoder with a spatial-aware objective function under the paradigm of actor-critic reinforcement learning, TREP intelligently encodes spatiotemporal characteristics of trajectories with the capability of handling diverse trajectory patterns. Extensive experiments on both synthetic and real datasets validate the high fidelity of TREP to represent trajectories.
The left ventral premotor cortex is involved in hand shaping for intransitive gestures: evidence from a two-person imitation experiment

The ventral premotor cortex (PMv) is involved in grasping and object manipulation, while the dorsal premotor cortex (PMd) has been suggested to play a role in reaching and action selection. These areas have also been associated with action imitation, but their relative roles in different types of action imitation are unclear. We examined the role of the left PMv and PMd in meaningful and meaningless action imitation by using repetitive transcranial magnetic stimulation (rTMS). Participants imitated meaningful and meaningless actions performed by a confederate actor while both individuals were motion-tracked. rTMS was applied over the left PMv, left PMd or a vertex control site during action observation or imitation. Digit velocity was significantly greater following stimulation over the PMv during imitation compared with stimulation over the PMv during observation, regardless of action meaning. Similar effects were not observed over the PMd or vertex. In addition, stimulation over the PMv increased finger movement speed in a (non-imitative) finger–thumb opposition task. We suggest that claims regarding the role of the PMv in object-directed hand shaping may stem from the prevalence of object-directed designs in motor control research. Our results indicate that the PMv may have a broader role in ‘target-directed’ hand shaping, whereby different areas of the hand are considered targets to act upon during intransitive gesturing.
Examining Reading Comprehension Profiles of Grade 5 Monolinguals and English Language Learners Through the Lexical Quality Hypothesis Lens

This study set out to compare patterns of relationships among phonological skills, orthographic skills, semantic knowledge, listening comprehension, and reading comprehension in English as a first language (EL1) and English language learners (ELL) students and to test the applicability of the lexical quality hypothesis framework. Participants included 94 EL1 and 178 ELL Grade 5 students from diverse home-language backgrounds. Latent profile analyses conducted separately for ELLs and EL1s provided support for the lexical quality hypothesis in both groups, with the emergence of two profiles: A poor comprehenders profile was associated with poor word-reading-related skills (phonological awareness and orthographic processing) and with poor language-related skills (semantic knowledge and, to a lesser extent, listening comprehension). The good comprehenders profile was associated with average or above-average performance across the component skills, demonstrating that good reading comprehension is the result of strong phonological and orthographic processing skills as well as strong semantic and listening comprehension skills. The good and poor comprehenders profiles were highly similar for ELL and EL1 groups. Conversely, poor comprehenders struggled with these same component skills. Implications for assessment and future research are discussed.
Application of MRI Post-processing in Presurgical Evaluation of Non-lesional Cingulate Epilepsy

Background and Purpose: Surgical management of patients with cingulate epilepsy (CE) is highly challenging, especially when the MRI is non-lesional. We aimed to use a voxel-based MRI post-processing technique, implemented in a morphometric analysis program (MAP), to facilitate detection of subtle epileptogenic lesions in CE, thereby improving surgical evaluation of patients with CE with non-lesional MRI by visual inspection. Methods: Included in this retrospective study were 9 patients with CE (6 with negative 3T MRI and 3 with subtly lesional 3T MRI) who underwent surgery and became seizure-free or had marked seizure improvement with at least 1-year follow-up. MRI post-processing was applied to pre-surgical T1-weighted volumetric sequence using MAP. The MAP finding was then coregistered and compared with other non-invasive imaging tests (FDG-PET, SPECT and MEG), intracranial EEG ictal onset, surgery location and histopathology. Results: Single MAP+ abnormalities were found in 6 patients, including 3 patients with negative MRI, and 3 patients with subtly lesional MRI. Out of these 6 MAP+ patients, 4 patients became seizure-free after complete resection of the MAP+ abnormalities; 2 patients didn't become seizure-free following laser ablation that only partially overlapped with the MAP+ abnormalities. All MAP+ foci were concordant with intracranial EEG ictal onset (when performed). The localization value of FDG-PET, SPECT and MEG was limited in this cohort. FCD was identified in all patients' surgical pathology except for two cases of laser ablation with no tissue available. Conclusion: MAP provided helpful information for identifying subtle epileptogenic abnormalities in patients with non-lesional cingulate epilepsy. MRI postprocessing should be considered to add to the presurgical evaluation test battery of non-lesional cingulate epilepsy.
Study of Various Image De-Noising Methods Used for the Purpose of Traffic Sign Board Recognition in an Intelligent Advanced Driver Assistance System

As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating; slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only the recognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as removing the noise present in a particular image with the help of Arithmetic Mean Filter as well as Geometric Mean Filter. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.
Patient acceptability of cyclical etidronate and calcium therapy and symptomatic response to treatment

One hundred and seventeen patients (75 per cent women) with established osteoporosis were entered into a clinical trial assessing two regimes of cyclical etidronate and calcium (Cacit). Some subjective parameters of clinical outcome were assessed prior to, and during, treatment. Patient acceptability of therapy was also examined.
Merging genotypes: graft union formation and scion–rootstock interactions

Grafting has been utilised for at least the past 7000 years. Historically, grafting has been developed by growers without particular interest beyond the agronomical and ornamental effects, and thus knowledge about grafting has remained largely empirical. Much of the commercial production of fruit, and increasingly vegetables, relies upon grafting with rootstocks to provide resistance to soil-borne pathogens and abiotic stresses as well as to influence scion growth and performance. Although there is considerable agronomic knowledge about the use and selection of rootstocks for many species, we know little of the molecular mechanisms underlying rootstock adaptation to different soil environments and rootstock-conferred modifications of scion phenotypes. Furthermore, the processes involved in the formation of the graft union and graft compatibility are poorly understood despite over a hundred years of scientific study. In this paper, we provide an overview of what is known about grafting and the mechanisms underlying rootstock-scion interactions. We highlight recent studies that have advanced our understanding of graft union formation and outline subjects that require further development.
Event-Based Structured Light for Depth Reconstruction using Frequency Tagged Light Patterns

This paper presents a new method for 3D depth estimation using the output of an asynchronous time driven image sensor. In association with a high speed Digital Light Processing projection system, our method achieves real-time reconstruction of 3D points cloud, up to several hundreds of hertz. Unlike state of the art methodology, we introduce a method that relies on the use of frequency tagged light pattern that make use of the high temporal resolution of event based sensors. This approch eases matching as each pattern unique frequency allow for any easy matching between displayed patterns and the event based sensor. Results are show on real scenes.
Berardinelli Ã¢ÂÂ Seip Congenital Lipodystrophy

Berardinelli-Seip Congenital lipodystrophy is a rare autosomal recessive disorder characterized by acanthosis nigricans, loss of subcutaneous fat, hepatosplenomegaly, mental retardation, hypertriglyceridemia, insulin resistance. An inability to store energy in adipose tissue is one of the important pathogenetic mechanisms. In congenital lipodystrophy, insulin resistance is present from birth, resulting in hyperlipidaemia, hyperinsulinaemia, insulin resistance diabetes and acanthosis nigricans. We report here a case of congenital lipodystrophy presenting with seizure and acanthosis nigricans in absence of insulin resistance.