nils-herrmann/clean_scirepeval huggingface dataset:
Microglia-mediated recovery from ALS-relevant motor neuron degeneration in a mouse model of TDP-43 proteinopathy

Though motor neurons selectively degenerate in amyotrophic lateral sclerosis, other cell types are likely involved in this disease. We recently generated rNLS8 mice in which human TDP-43 (hTDP-43) pathology could be reversibly induced in neurons and expected that microglia would contribute to neurodegeneration. However, only subtle microglial changes were detected during disease in the spinal cord, despite progressive motor neuron loss; microglia still reacted to inflammatory triggers in these mice. Notably, after hTDP-43 expression was suppressed, microglia dramatically proliferated and changed their morphology and gene expression profiles. These abundant, reactive microglia selectively cleared neuronal hTDP-43. Finally, when microgliosis was blocked during the early recovery phase using PLX3397, a CSF1R and c-kit inhibitor, rNLS8 mice failed to regain full motor function, revealing an important neuroprotective role for microglia. Therefore, reactive microglia exert neuroprotective functions in this amyotrophic lateral sclerosis model, and definition of the underlying mechanism could point toward novel therapeutic strategies. Using an inducible mouse model of sporadic ALS, Spiller et al. show that spinal microgliosis is not a major feature of TDP-43-triggered disease. Instead, microglia mediate TDP-43 clearance and motor recovery, suggesting a neuroprotective role in ALS.
Fucoxanthin, a Marine Carotenoid Present in Brown Seaweeds and Diatoms: Metabolism and Bioactivities Relevant to Human Health

The marine carotenoid fucoxanthin can be found in marine brown seaweeds, the macroalgae, and diatoms, the microalgae, and has remarkable biological properties. Numerous studies have shown that fucoxanthin has considerable potential and promising applications in human health. In this article, we review the current available scientific literature regarding the metabolism, safety, and bioactivities of fucoxanthin, including its antioxidant, anti-inflammatory, anticancer, anti-obese, antidiabetic, antiangiogenic and antimalarial activities, and its protective effects on the liver, blood vessels of the brain, bones, skin, and eyes. Although some studies have shown the bioavailability of fucoxanthin in brown seaweeds to be low in humans, many studies have suggested that a dietary combination of fucoxanthin and edible oil or lipid could increase the absorption rate of fucoxanthin, and thus it might be a promising marine drug.
The Value Function Polytope in Reinforcement Learning

We establish geometric and topological properties of the space of value functions in finite stateaction Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope (Aigner et al., 2010). To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line theorem, which shows that the value functions of policies constrained on all but one state describe a line segment. Finally, we use this novel perspective to introduce visualizations to enhance the understanding of the dynamics of reinforcement learning algorithms.
Mobile Food Recognition with an Extreme Deep Tree

Food recognition is an emerging topic in the field of computer vision. The recent interest of the research community in this area is justified by the rise in popularity of food diary applications, where the users take note of their food intake for self-monitoring or to provide useful statistics to dietitians. However, manually annotating food intake can be a tedious task, thus explaining the need of a system that automatically recognizes food, and possibly its amount, from pictures acquired by mobile devices. In this work we propose an approach to food recognition which combines the strengths of different state-of-the-art classifiers, namely Convolutional Neural Networks, Extreme Learning Machines and Neural Trees. We show that the proposed architecture can achieve good results even with low computational power, as in the case of mobile devices.
Seeing with Humans: Gaze-Assisted Neural Image Captioning

Gaze reflects how humans process visual scenes and is therefore increasingly used in computer vision systems. Previous works demonstrated the potential of gaze for object-centric tasks, such as object localization and recognition, but it remains unclear if gaze can also be beneficial for scene-centric tasks, such as image captioning. We present a new perspective on gaze-assisted image captioning by studying the interplay between human gaze and the attention mechanism of deep neural networks. Using a public large-scale gaze dataset, we first assess the relationship between state-of-the-art object and scene recognition models, bottom-up visual saliency, and human gaze. We then propose a novel split attention model for image captioning. Our model integrates human gaze information into an attention-based long short-term memory architecture, and allows the algorithm to allocate attention selectively to both fixated and non-fixated image regions. Through evaluation on the COCO/SALICON datasets we show that our method improves image captioning performance and that gaze can complement machine attention for semantic scene understanding tasks.
Network Forensic Investigation of HTTPS Protocol

Nowadays a large amount of personal and business transactions are done electronically through secured internet communication with HTTPS Protocol. The internet offers computer users access to a wealth of information and reaches in to the heart of many organizations. In this context, there are many possibilities for having different malicious activities or attacks that may occur through the HTTPS protocol. Usually it is very difficult to see or recreate HTTPS network sessions to verify its content as part of the forensic analysis. Network analysts must be able to see and test the packet data when a malicious network usage is identified and produce actual session by recreating the original data between users as part of forensic analysis. So we need an efficient forensic system to perform this kind of content analysis. The proposed novel technique can be used for content level observation of HTTPS protocol and regenerate original malicious HTTPS session between users for network forensic investigations.
Direct visibility of point sets

This paper proposes a simple and fast operator, the "Hidden" Point Removal operator, which determines the visible points in a point cloud, as viewed from a given viewpoint. Visibility is determined without reconstructing a surface or estimating normals. It is shown that extracting the points that reside on the convex hull of a transformed point cloud, amounts to determining the visible points. This operator is general - it can be applied to point clouds at various dimensions, on both sparse and dense point clouds, and on viewpoints internal as well as external to the cloud. It is demonstrated that the operator is useful in visualizing point clouds, in view-dependent reconstruction and in shadow casting.
Security advances and challenges in 4G wireless networks

This paper presents a study of security advances and challenges associated with emergent 4G wireless technologies. The paper makes a number of contributions to the field. First, it studies the security standards evolution across different generations of wireless standards. Second, the security-related standards, architecture and design for the LTE and WiMAX technologies are analyzed. Third, security issues and vulnerabilities present in the above 4G standards are discussed. Finally, we point to potential areas for future vulnerabilities and evaluate areas in 4G security which warrant attention and future work by the research and advanced technology industry.
A complexity theory based on Boolean algebra

A projection of a Boolean function is a function obtained by substituting for each of its variables a variable, the negation of a variable, or a constant. Reducibilities among computational problems under this relation of projection are considered. It is shown that much of what is of everyday relevance in Turing-machine-based complexity theory can be replicated easily and naturally in this elementary framework. Finer distinctions about the computational relationships among natural problems can be made than in previous formulations and some negative results are proved.
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

In recent years, research in artificial neural networks has resurged, now under the deep-learning umbrella, and grown extremely popular. Recently reported success of DL techniques in crowd-sourced QSAR and predictive toxicology competitions has showcased these methods as powerful tools in drug-discovery and toxicology research. The aim of this work was dual, first large number of hyper-parameter configurations were explored to investigate how they affect the performance of DNNs and could act as starting points when tuning DNNs and second their performance was compared to popular methods widely employed in the field of cheminformatics namely Naïve Bayes, k-nearest neighbor, random forest and support vector machines. Moreover, robustness of machine learning methods to different levels of artificially introduced noise was assessed. The open-source Caffe deep-learning framework and modern NVidia GPU units were utilized to carry out this study, allowing large number of DNN configurations to be explored.



We show that feed-forward deep neural networks are capable of achieving strong classification performance and outperform shallow methods across diverse activity classes when optimized. Hyper-parameters that were found to play critical role are the activation function, dropout regularization, number hidden layers and number of neurons. When compared to the rest methods, tuned DNNs were found to statistically outperform, with p value <0.01 based on Wilcoxon statistical test. DNN achieved on average MCC units of 0.149 higher than NB, 0.092 than kNN, 0.052 than SVM with linear kernel, 0.021 than RF and finally 0.009 higher than SVM with radial basis function kernel. When exploring robustness to noise, non-linear methods were found to perform well when dealing with low levels of noise, lower than or equal to 20%, however when dealing with higher levels of noise, higher than 30%, the Naïve Bayes method was found to perform well and even outperform at the highest level of noise 50% more sophisticated methods across several datasets.
An Extension of Particle Swarm Optimization (E-PSO) Algorithm for Solving Economic Dispatch Problem

Producing the energy power that meets the load demands at a minimum cost while satisfying the constraints is known as economic dispatch. Economic dispatch becomes one of the most complex problems in the planning and operation of a power system that aims to determine the optimal generation scheduling at minimum cost. For that reason many optimization researches on finding an optimal solution regarding the total cost of generation have been carried out. This paper presents the implementation of the extension of the PSO (E-PSO) system in solving the continuous nonlinear function of the cost curves of the generator. In this paper, a 6-unit generation system has been applied to show the effectiveness of the E-PSO compared to the standard PSO. The results show that E-PSO is capable in solving the economic dispatch problem in term of minimizing the total cost of generation while considering the generator limits and transmission losses.
Piecewise linear spine for speed-energy efficiency trade-off in quadruped robots

We compare the effects of linear and piecewise linear compliant spines on locomotion performance of quadruped robots in terms of energy efficiency and locomotion speed through a set of simulations and experiments. We first present a simple locomotion system that behaviorally resembles a bounding quadruped with flexible spine. Then, we show that robots with linear compliant spines have higher locomotion speed and lower cost of transportation in comparison with those with rigid spine. However, in linear case, optimal speed and minimum cost of transportation are attained at very different spine compliance values. Moreover, it is verified that fast and energy efficient locomotion can be achieved together when the spine flexibility is piecewise linear. Furthermore, it is shown that the robot with piecewise linear spine is more robust against changes in the load it carries. Superiority of piecewise linear spines over linear and rigid ones is additionally confirmed by simulating a quadruped robot in Webots and experiments on a crawling two-parts robot with flexible connection.
Arbitrary Analog/RF Spatial Filtering for Digital MIMO Receiver Arrays

Traditional digital multiple-input multiple-output (MIMO) receivers that feature element-level digitization face high instantaneous dynamic range challenges in the analog/RF domain due to the absence of analog/RF spatial filtering. Existing analog/RF spatial notch filtering techniques are limited in their noise, linearity, and spatial filtering bandwidth performance. More importantly, only single spatial notches have been demonstrated, providing insufficient filtering in practical scenarios. We propose a frequency-translational arbitrary spatial filtering technique that features not only arbitrary spatial filtering response generation at baseband for the protection of the following analog-to-digital converters, but modulated baseband input impedance that can be translated by passive mixers to achieve arbitrary spatial filtering at RF as well. This technique allows the synthesis and independent steering of an arbitrary number of spatial notches, and the independent adjustment of notch depths. Current-mode operation leads to superior linearity performance and ultra-wideband rejection. A four-element 65-nm CMOS  GHz prototype MIMO receiver array shows arbitrary spatial response formation, more than 50-dB spatial rejection across all measured directions, and an ultra-wide 320-MHz 20-dB rejection bandwidth for a single-notch setting at 500-MHz local oscillator (LO) frequency. Formation of a single spatial notch only moderately degrades the equivalent single-element double-sideband noise figure from 2.1–3.7 dB to 3.4–5.8 dB. In the notch direction, +34 dBV in-band output-referred IP3 is measured, an improvement of 33 dB compared with outside-notch directions. A wireless demonstration shows the receiver array demodulating a weak spatial signal in the presence of two strong in-band spatial signals, verifying the arbitrary spatial filtering functionality.
Online education today.

Online education is established, growing, and here to stay. It is creating new opportunities for students and also for faculty, regulators of education, and the educational institutions themselves. Much of what is being learned by the practitioners will flow into the large numbers of blended courses that will be developed and delivered on most campuses. Some of what is being learned will certainly improve pedagogical approaches and possibly affect other important problems, such as the lengthening time to completion of a degree. Online education is already providing better access to education for many, and many more will benefit from this increased access in the coming years.
SALICON: Saliency in Context

Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. This paper presents a new method to collect large-scale human data during natural explorations on images. While current datasets present a rich set of images and task-specific annotations such as category labels and object segments, this work focuses on recording and logging how humans shift their attention during visual exploration. The goal is to offer new possibilities to (1) complement task-specific annotations to advance the ultimate goal in visual understanding, and (2) understand visual attention and learn saliency models, all with human attentional data at a much larger scale. We designed a mouse-contingent multi-resolutional paradigm based on neurophysiological and psychophysical studies of peripheral vision, to simulate the natural viewing behavior of humans. The new paradigm allowed using a general-purpose mouse instead of an eye tracker to record viewing behaviors, thus enabling large-scale data collection. The paradigm was validated with controlled laboratory as well as large-scale online data. We report in this paper a proof-of-concept SALICON dataset of human “free-viewing” data on 10,000 images from the Microsoft COCO (MS COCO) dataset with rich contextual information. We evaluated the use of the collected data in the context of saliency prediction, and demonstrated them a good source as ground truth for the evaluation of saliency algorithms.
Water Quality Measurement and Control from Remote Station for Pisiculture Using NI myRIO Beena

A Wireless sensor network is designed and implemented to automatically and remotely monitor and control the water quality parameters such as temperature, turbidity, pH and dissolved oxygen required for pisiculture. The measurements of the system which is at base station are communicated to the remote farmer through built-in Wi-Fi of NI-myRIO and displayed on user’s computer screen from where he can monitor and change the settings of the system. An audio output is also produced from speakers of the computer. SMS alert is sent to farmer’s mobile for variations in the monitored parameters of the system at the base station via GSM modem. The programming of the entire system is done using LabVIEW software and NI myRIO which has inbuilt processor, FPGA, Wi-Fi and web servicing capabilities. Web publishing tool of LabVIEW allows internet access to the front panel of the system to any remote user.
Operability of Folded Microstrip Patch-Type Tag Antenna in the UHF RFID Bands Within 865-928 MHz

The emerging use of passive radio frequency identification (RFID) systems at ultra-high-frequency (UHF) spectrum requires application specific tag antenna designs for challenging applications. Many objects containing conductive material need novel tag antenna designs for reliable identification. The operability of folded microstrip patch-type tag antenna for objects containing conductive material is analyzed and tested within the UHF RFID bands used at the moment mainly in Europe and in North and South America. First the operability of the tag antenna design and the effects of conductive material are modeled with simulations based on finite element method (FEM). The performance of the tag antenna design affixed to a package containing metallic foil is verified with read range measurements. The results show that the antenna design is operable in both of the UHF RFID bands within 865-928 MHz
Hierarchical Discrete Distribution Decomposition for Match Density Estimation

Existing deep learning methods for pixel correspondence output a point estimate of the motion field, but do not represent the full match distribution. Explicit representation of a match distribution is desirable for many applications as it allows direct representation of the correspondence probability. The main difficulty of estimating a full probability distribution with a deep network is the high computational cost of inferring the entire distribution. In this paper, we propose Hierarchical Discrete Distribution Decomposition, dubbed HD, to learn probabilistic point and region matching. Not only can it model match uncertainty, but also region propagation. To achieve this, we estimate the hierarchical distribution of pixel correspondences at different image scales without multi-hypotheses ensembling. Despite its simplicity, our method can achieve competitive results for both optical flow and stereo matching on established benchmarks, while the estimated uncertainty is a good indicator of errors. Furthermore, the point match distribution within a region can be grouped together to propagate the whole region even if the area changes across images.
Anti-falling tree climbing mechanism optimization

This paper presents an improved anti-falling tree climbing mechanism. A study and comparison was made on an original tree climbing robotic system published in reference. This optimized robotic system only relies on one stepper to achieve the anti-falling and anti-jamming functionality under either static or dynamic situations. This improved design feature reduces the total robot weight and enhances the control system robustness. Moreover, to achieve the robot spiral climbing morphology and decrease the servomotor rotational frictional forces, a special servomotor module and a bearing supporting mechanism are designed and implemented. The optimized mechanism design of the climbing robot is modelled in SolidWorks and the climbing process is simulated using SimMechanics. The simulation outcome of the initial and improved design shows that the new design has higher climbing speed and lighter weight. Physical prototype experiment matches with the simulation outcome and verifies the feasibility and effectiveness of this redesigned tree climbing mechanism.
MOBILE BANKING ADOPTION : APPLICATION OF DIFFUSION OF INNOVATION THEORY

Many banks in Saudi Arabia are starting to offer banking services through mobile phones. However, not many studies investigate the factors that may help the bankers to design mobile services, which are suitable for and adoptable by bank customers. This study fills this gap and examines a number of factors affecting the mobile banking adoption. Using Diffusion of Innovation as a baseline theory, data are obtained from 330 actual mobile banking users. It is found that relative advantage, compatibility, and observability have positive impact on adoption. Contrary to the findings in extant literature, trialability and complexity have no significant effect on adoption. Perceived risk has a negative impact on adoption. The findings of this study will have practical implications for banking industry in Saudi Arabia.
THIRTY YEARS OF RESEARCH ON RACE DIFFERENCES IN COGNITIVE ABILITY

The culture-only (0% genetic–100% environmental) and thereditarian (50% genetic–50% environmental) models of the causes of mean Black–White differences in cognitive ability are compared and contrasted across 10 categories of evidence: the worldwide distribution of test scores, g factor of mental ability, heritability, brain size and cognitive ability, transracial adoption, racial admixture, regression, related life-history traits, human origins research, and hypothesized environmental variables. The new evidence reviewed here points to some genetic component in Black–White differences in mean IQ. The implication for public policy is that the discrimination model (i.e., Black–White differences in socially valued outcomes will be equal barring discrimination) must be tempered by a distributional model (i.e., Black–White outcomes reflect underlying group characteristics).