Miracle-dz/UniFAR-FTUs huggingface dataset:
Total Sleep Time Interacts With Age to Predict Cognitive Performance Among Adults.

STUDY OBJECTIVES To investigate interactions between high and low amounts of sleep and other predictors of cognitive performance. METHODS We used four cognitive tests to determine whether sleep time interacted with age, personal history of a memory problem, parental history of a memory problem, or personal concerns about memory and were associated with cognitive performance. Data were collected from an internet-based cohort study. We used an ordinary least squares regression with restricted cubic splines, controlling for demographic variables and comorbidities. RESULTS We found significant nonlinear interactions between (1) total sleep time and age and (2) total sleep time and personal history of a memory problem and cognitive performance. Short and long sleep durations and self-reported memory complaints were associated with poorer performance on a test of attention and this was true to a greater degree in younger and older adults. A repeat analysis excluding subjects reporting dementia was significant only for the test of attention. CONCLUSIONS These results extend existing data on sleep duration and cognition across the lifespan by combining in a single study the results from four specific cognitive tests, both younger and older adults, and four self-reported risk factors for cognitive impairment. Longitudinal studies with biomarkers should be undertaken to determine whether causal mechanisms, such as inflammation or amyloid buildup, account for these associations.
Improving Application Launch Performance in Smartphones Using Recurrent Neural Network

Mobile phones became indispensable tools in our lives, with Android being the most used mobile OS. These devices depend on managing application lifecycles to improve launch performance, but the management of application processes is not done in an efficient way. The standard low memory killer, that is responsible for freeing memory, does not consider any user information and it frequently kills applications that are going to be launched. Context-awareness presents several possibilities to make mobile systems more efficient and user-driven in terms of user experience. In this paper, we introduce a context-based launcher using recurrent neural network (RNN), a special branch of neural networks capable of remembering dependencies, taking in consideration not just previous inputs, but also previous outputs, providing high accuracy without any extra sensor context. Our system guarantees that most of the applications are ready to use in the background, substantially improving the launch time enabling a better user experience. Experimental results demonstrate that the novel scheme can reduce application launch latency in a significant manner.
Lipidomimetic Compounds Act as HIV-1 Entry Inhibitors by Altering Viral Membrane Structure

The envelope of Human Immunodeficiency Virus type 1 (HIV-1) consists of a liquid-ordered membrane enriched in raft lipids and containing the viral glycoproteins. Previous studies demonstrated that changes in viral membrane lipid composition affecting membrane structure or curvature can impair infectivity. Here, we describe novel antiviral compounds that were identified by screening compound libraries based on raft lipid-like scaffolds. Three distinct molecular structures were chosen for mode-of-action studies, a sterol derivative (J391B), a sphingosine derivative (J582C) and a long aliphatic chain derivative (IBS70). All three target the viral membrane and inhibit virus infectivity at the stage of fusion without perturbing virus stability or affecting virion-associated envelope glycoproteins. Their effect did not depend on the expressed envelope glycoproteins or a specific entry route, being equally strong in HIV pseudotypes carrying VSV-G or MLV-Env glycoproteins. Labeling with laurdan, a reporter of membrane order, revealed different membrane structure alterations upon compound treatment of HIV-1, which correlated with loss of infectivity. J582C and IBS70 decreased membrane order in distinctive ways, whereas J391B increased membrane order. The compounds' effects on membrane order were reproduced in liposomes generated from extracted HIV lipids and thus independent both of virion proteins and of membrane leaflet asymmetry. Remarkably, increase of membrane order by J391B required phosphatidylserine, a lipid enriched in the HIV envelope. Counterintuitively, mixtures of two compounds with opposite effects on membrane order, J582C and J391B, did not neutralize each other but synergistically inhibited HIV infection. Thus, altering membrane order, which can occur by different mechanisms, constitutes a novel antiviral mode of action that may be of general relevance for enveloped viruses and difficult to overcome by resistance development.
Defect engineering by synchrotron radiation X-rays in CeO2 nanocrystals.

This work reports an unconventional defect engineering approach using synchrotron-radiation-based X-rays on ceria nanocrystal catalysts of particle sizes 4.4-10.6 nm. The generation of a large number of oxygen-vacancy defects (OVDs), and therefore an effective reduction of cations, has been found in CeO2 catalytic materials bombarded by high-intensity synchrotron X-ray beams of beam size 1.5 mm × 0.5 mm, photon energies of 5.5-7.8 keV and photon fluxes up to 1.53 × 1012 photons s-1. The experimentally observed cation reduction was theoretically explained by a first-principles formation-energy calculation for oxygen vacancy defects. The results clearly indicate that OVD formation is mainly a result of X-ray-excited core holes that give rise to valence holes through electron down conversion in the material. Thermal annealing and subvalent Y-doping were also employed to modulate the efficiency of oxygen escape, providing extra control on the X-ray-induced OVD generating process. Both the core-hole-dominated bond breaking and oxygen escape mechanisms play pivotal roles for efficient OVD formation. This X-ray irradiation approach, as an alternative defect engineering method, can be applied to a wide variety of nanostructured materials for physical-property modification.
Kernelized eigenspace based fuzzy C-means for sensing trending topics on Twitter

One of the automated methods for textual data analysis is topic detection. Eigenspace-based fuzzy c-means (EFCM) is a soft clustering-based method for topic detection. Firstly, EFCM uses truncated singular value decomposition to transform high dimensional textual data in to low dimensional textual data. Next, the clustering process is conducted in the lower dimensional space. However, that transformation process may eliminate some important features from the textual data. Therefore, the accuracy may be reduced. In this paper, we use kernel trick to overcome that weakness so that the clustering process is performed in a higher dimensional space without explicitly transforming the textual data to space. Our simulations show that this approach improves the accuracies of EFCM in term of topic recall for the problem of sensing trending topic on Twitter.
Tracking of Multiple Sources in an Acoustic Sensor Network Using an Extended Gaussian Mixture PHD Filter

We propose an approach for tracking a varying number of simultaneously active acoustic wideband signal sources in an acoustic enclosure. Relying on the assumption of W-disjoint orthogonality, the method uses narrowband position estimates of the sources for the targets. The instantaneous position estimates form clusters rather than single points, as would be required for a conventional Probability Hypothesis Density (PHD) filter. Therefore, we model the position estimates as extended targets and use a special form of the PHD filter, the extended target Gaussian mixture PHD filter, for tracking the targets. This allows to model target birth and death, which correspond to speech onset and end of utterance, respectively. With this model and by using the well-developed theory of Finite Set Statistics (FISST)-based multi-target tracking, we provide a comprehensive, strictly Bayesian treatment of the problem of tracking wideband acoustic sources using narrowband position estimates. We validate the results by tracking a varying number of targets in an enclosure simulated with the image-source method.
Illustrate your travel notes: web-based story visualization

In this paper, we present an automatic web-based framework to insert images to the proper location of travel notes without restrictions on pre-defined vocabulary and training process. Instead of learning region-entity correspondences from explicitly human labelling, we collected weakly labeled Web images and proposed a method based on clustering to further discover image regions whose visual contents are highly correlated with the corresponding weak labels, from the original whole images. Then an adaptive visual representation is constructed from the discovered image regions which can automatically link phrase-image pairs. We also release a new dataset TVN25 targeting at Web-based travel notes visualization task which consists of 25 travel notes and over 22k weakly labeled Web images about the lingual phrases in travel notes. Experiments results on travel notes visualization not only demonstrated the effectiveness of our proposed framework, but also show the potential for more real-world applications.
Throughput Control Method Between Different TCP Variants Based on SP-MAC Over WLAN

We have proposed the Media Access Control method based on the Synchronization Phenomena of coupled oscillators (SP-MAC) to improve a total throughput of wireless terminals connected to a Access Point. SP-MAC can avoid the collision of data frames that occur by applying Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) based on IEEE 802.11 in Wireless local area networks (WLAN). Furthermore, a new throughput guarantee control method based on SP-MAC has been proposed. This method enable each terminal not only to avoid the collision of frames but also to obtain the requested throughput by adjusting the parameters of SP-MAC. In this paper, we propose a new throughput control method that realizes the fairness among groups of terminals that use the different TCP versions, by taking the advantage of our method that is able to change acquired throughput by adjusting parameters. Moreover, we confirm the effectiveness of the proposed method by the simulation evaluation.
Investigations of Beamforming Designs and Millimeter Wave Channel Modeling for Multiuser MIMO Systems

Multiuser multiple input multiple output system requires a low complexity precoding processing schemes especially with massive MIMO systems. This can be achieved by reducing the number of RF chains compared to the number of antennas. In this paper, beamformers for massive MU-MIMO with lower design complexity are simulated and compared to traditional beamforming design for millimeter wave communications. Also, an accurate channel model of millimeter wave (mmWave) is applied in the system simulation. Effects of multiple user interference and antenna array types on the spectral efficiency of the system are presented and compared for both analog and digital beamformers.
Using a feature model configurator for release planning

The requirements for a system have many dependencies that can be expressed in the individual requirements managed in an issue tracker or a requirements management system. However, managing the entire body of requirements taking into account all complex dependencies is not well supported. We describe how a feature model based configurator can be used as a tool to help manage requirements data. Data transfer and constructing the needed requirements model can be carried out automatically by relying on a model generator. We implemented a prototype tool for requirements and release management that utilizes a knowledge-based configurator.
Reading Behavior Analysis with Gaze Tracking Data

Gaze tracking has become one of the most attractive human machine interfaces on consumer electronics. In addition to providing intelligent interactive functions on the consumer electronics, it also makes possible to collect the data of user attention. Furthermore, the data could be analyzed to predict the reading behavior and the information processing in the brain. In this paper, we propose an automatic tool for the gaze data analysis. A mathematic examination system is used for demonstrating the system performance. According to the gaze data, the experimental results show the feasibility of estimating the learner's ability, and the accuracy has achieved higher than 90%.
Limited ecosystem recovery from simulated chronic nitrogen deposition.

The realization that anthropogenic nitrogen (N) deposition is causing significant environmental change in many ecosystems has led to lower emissions of reactive N and deposition rates in many regions. However, the impacts of N deposition on terrestrial ecosystems can be long lasting, with significant inertia in the return of the biota and biogeochemical processes to baseline levels. To better understand patterns of recovery and the factors that may contribute to slow or no responses following declines in N deposition, we followed plant species composition, microbial abundance, N cycling rates, soil pH, and pools of NO3 - and extractable cations in an impacted alpine ecosystem following cessation of 12-yr experiment increasing N deposition rates by 0, 20, 40, and 60 kg N·ha-1 ·yr-1 . Simulated N deposition had resulted in a tripling in the cover of the nitrophilic species Carex rupestris, while the dominant sedge Kobresia myosuroides had decreased by more than half at the highest N input level. In addition, nitrification rates were elevated, soil extractable magnesium (Mg2+ ) and pH decreased, and aluminum (Al3+ ) and manganese (Mn2+ ) were elevated at the highest N treatment inputs. Over the nine years following cessation of N additions to the impacted plots, only the cover of the nitrophilic C. rupestris showed any recovery to prior levels. Abundances of both bacteria and fungi were lower with N addition in both treatment and recovery plots. Rates of nitrification and pools of NO3 - remained elevated in the recovery plots, likely contributing to the lack of biotic response to the cessation of N inputs. In addition, nutrient base cations (Ca2+ and Mg2+ ) and soil pH remained depressed, and the toxic metal cations (Al3+ and Mn2+ ) remained elevated in recovery plots, also potentially influencing biotic recovery. These results emphasize the importance of considering long-term environmental impacts of N deposition associated with legacy effects, such as elevated N cycling and losses of base cations, in determining environmental standards such as the metrics used for critical loads.
Observing Friendship Patterns Through Smart Phone Radios

Nowadays, social networks are not used only to analyze the human society but also to rebuild it. Social activities and interactions increase dramatically. A common way to construct the networks is to generalize a single dataset, such as location or proximity data. It, however, is very hard to find a universal method obtaining highly accurate networks which represent the social relationships. The structures of the networks may also vary depending on definitions of nodes and edges. Previously, the relations between human mobility and human relationships were mainly studied so far. In this paper, $w$ e design a combined network model with multiple datasets to provide a highly efficient way for constructing social networks. Specifically, the networks are friendship networks and individual activity networks. The effect of friendship on social activities and interactions is analyzed as well. The performance of the model is evaluated using centralities and coefficients. Finally, relationships among the networks are also shown.
Training Children to Perceive Non-native Lexical Tones: Tone Language Background, Bilingualism, and Auditory-Visual Information

This study investigates the role of language background and bilingual status in the perception of foreign lexical tones. Eight groups of participants, consisting of children of 6 and 8 years from one of four language background (tone or non-tone) × bilingual status (monolingual or bilingual)—Thai monolingual, English monolingual, English-Thai bilingual, and English-Arabic bilingual were trained to perceive the four Mandarin lexical tones. Half the children in each of these eight groups were given auditory-only (AO) training and half auditory-visual (AV) training. In each group Mandarin tone identification was tested before and after (pre- and post-) training with both auditory-only test (ao-test) and auditory-visual test (av test). The effect of training on Mandarin tone identification was minimal for 6-year-olds. On the other hand, 8-year-olds, particularly those with tone language experience showed greater pre- to post-training improvement, and this was best indexed by ao-test trials. Bilingual vs. monolingual background did not facilitate overall improvement due to training, but it did modulate the efficacy of the Training mode: for bilinguals both AO and AV training, and especially AO, resulted in performance gain; but for monolinguals training was most effective with AV stimuli. Again this effect was best indexed by ao-test trials. These results suggest that tone language experience, be it monolingual or bilingual, is a strong predictor of learning unfamiliar tones; that monolinguals learn best from AV training trials and bilinguals from AO training trials; and that there is no metalinguistic advantage due to bilingualism in learning to perceive lexical tones.
Ultrafast X-ray imaging of laser–metal additive manufacturing processes

The high-speed synchrotron X-ray imaging technique was synchronized with a custom-built laser-melting setup to capture the dynamics of laser powder-bed fusion processes in situ. Various significant phenomena, including vapor-depression and melt-pool dynamics and powder-spatter ejection, were captured with high spatial and temporal resolution.
Elastic Net Representation-based Projections for Bearing Fault Classification

In this paper, a novel dimensionality reduction algorithm named elastic net representation-based projections (ENRP) has been proposed for bearing fault diagnosis, which effectively excavates useful features from highly-correlated and strongly-disturbed vibration signals. An elastic net model is integrated into the algorithm, which absorbs the merits and also lower the limitations of applying sparse representation (SR) or collaborative representation (CR) alone. Moreover, maximum margin criterion (MMC) is also incorporated to take the global structures of data into consideration. The effectiveness of the proposed method has been demonstrated with experiments on CWRU bearing data using l-Nearest-Neighbor (lNN) classifier. The ENRP method outperforms the other compared approaches with greater classification accuracy and stability, and shows its superiority especially when dealing with vibration signals interfered by strong background noise.
Demo: A Delay-Tolerant Payment Scheme on the Ethereum Blockchain

Cash-less payment via a variety of credit, debit or prepaid cards is pervasive in our interconnected society, but not so ubiquitous in remote rural regions where network connectivity is intermittent. We proposed a cash-less payment scheme for remote villages based on blockchains that allow maintaining a record of verifiable transactions in a distributed manner. We overcome the limitations of intermittent network connectivity by solely relying on blockchain mining nodes in the village for transaction processing and verification. The bank joins as a peer and monitors node behaviors, rewards miners and processes currency exchanges whenever the connectivity is available. We take advantage of the Ethereum network to develop our solution and demonstrate the feasibility of the proposed system on off-the-shelf computing devices. We emulate a remote village scenario with intermittent network connectivity and show the robustness and reliability of the proposed system.
Constant On-Time Variable Frequency Control for a Novel Switched-Capacitor Converter

Due to the requirement for high power density and high integration, the development of traditional inductive switching power converter has encountered bottlenecks. Switch-capacitor (SC) converter is the development tendency for the DC-DC converter. This paper proposed a novel two-stage SC converter. It is designed to convince the flying capacitors sustain lower voltage stress. Meanwhile, the control method adopts a constant “on-time” of the switches that is less than the time constant τ of charging circuit. It does not need to adjust duty-ratio to control output power. The simulation results show that the control method could reject external interference of the two-stage SC converter immediately. The conclusions can be can be made from simulation results that the constant on-time variable frequency (COTVF) control method could improve dynamic response speed which enable the converter works in a wide range.
Randomness of Binary Sequences with Long Period by Combining M-Sequence and Knuth's Quadratic Congruential Sequence

Cryptography is one of the most important elements on the information security. In this paper, we propose a construction method of a long-period binary sequence with good randomness which plays an important role in cryptography. We generate the sequence by combining two types of binary sequences; one is the m-sequence and the other is a quadratic congruential sequence proposed by Knuth. It is well known that the m-sequence has good statistical properties, and Knuth's method can generate a long period sequence. However, both of these sequences cannot pass the almost NIST statistical tests by stand-alone. We derive a binary sequence combining these two sequences. In addition; we also consider the reasons why the proposed sequence has good randomness properties.
Multi-interactive deep matrix factorization model based on auxiliary information

In the traditional recommendation systems, the approach of matrix factorization collaborative filtering only just considers the single information of rating, it as a shallow model, can hardly learn deeper feature information. This paper propose a multi-interaction deep matrix factorization model based on auxiliary information, firstly through deep learning model and merge more auxiliary information as input, effectively alleviate the problem of data sparsity. Then, we leverage the structure of multi-interactive nonlinear network to learn the deep feature representation of more abstract and dense; through an inner product interactions on the latent features of users and items repeatedly, to obtain the different layers of feature representation results; the finally aggregating all the interaction results to predict. The experiment results on the Movielens latest 100K dataset shown that the proposed model over the state-of-the-art methods in RMSE.