Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of short-term language-switching training on the plasticity of the cognitive control mechanism in bilingual word production

This study examined the effect of short-term language-switching training on the cognitive control mechanism in bilingual word production. In two experiments, two groups of relatively proficient but unbalanced Chinese–English bilinguals performed a cued picture-naming task, in which they switched between their two languages. On two consecutive days, the participants took part in four sessions. The same procedure was employed on 2 days in Experiment 1, whereas the cue-language mapping was reversed on Day 2 in Experiment 2. In both experiments, picture naming in the dominant language (L1, Chinese) was slower than that in the weaker second language (L2, English) in all sessions. In addition, the reversed language dominance effect was enhanced with training, suggesting that training proactively increases the amount of inhibition of the dominant L1 at the global level. Furthermore, switching costs in the L1 were reduced with training in Experiment 1, but not in Experiment 2. These results indicate that language-switching training improves the efficiency of reactively exerting inhibitory control over the dominant L1 at the local level. However, when a cue matches with different target languages, the effect of training is absent at the local level. These findings reveal the plasticity and complexity of the cognitive control mechanism as a function of bilingual experience, particularly in language switching.
Learning Place-and-Time-Dependent Binary Descriptors for Long-Term Visual Localization

Vision-based navigation is extremely susceptible to natural scene changes. This can result in localization failures in less than a few hours after map creation. To combat short-term illumination changes as well as long-term seasonal variations, we propose using a place-and-time-dependent binary descriptor that adapts to different scenarios in an online fashion. This is achieved by extending the GRIEF [6] evolution algorithm in two ways: correspondence generation using a known pose change and the inclusion of LATCH triplets in addition to BRIEF comparisons for descriptor generation. We show the adaptive descriptor outperforms a single descriptor scheme for localization within a single-experience Visual Teach and Repeat (VT&R) system while maintaining the efficiency of binary descriptors. By adapting the description function to different environmental conditions, it allows the system to operate for a longer period before a new experience is required. In the presence of extreme illumination changes from day to night, we obtain 40% more inlier matches compared to SURF. In the case of seasonal variations, a 70% increase is demonstrated. The increased correspondences result in more localizable sections along the paths, amounting to a 25% and 150% increase in the lighting and seasonal cases, respectively.
Differential Flatness Transformations for Aggressive Quadrotor Flight

Aggressive maneuvering amongst obstacles could enable advanced capabilities for quadrotors in applications such as search and rescue, surveillance, inspection, and situations where rapid flight is required in cluttered environments. Previous works have treated quadrotors as differentially flat systems, and this property has been exploited widely to design simple algorithms that generate dynamically feasible trajectories and to enable hierarchical control. The differentially flat property allows the full state of the quadrotor to be extracted from the reduced dimensional space of x, y, z, yaw and their derivatives. This differential flatness transformation has a number of singularities, however, as well as stability issues when controlling near these singularities. Many methods have been described in the literature to address these; however, they all have limitations when exploring the full flight envelope of a quadrotor, including roll or pitch angles past 90°, and during inverted flight. In this paper, we review these existing methods and then introduce our method, which combines multiple methods to provide a highly-robust differential flatness transformation that addresses most of these issues. Our approach is demonstrated enabling highly-aggressive quadrotor flight in both simulations and real-world experiments.
A Survey of Link Prediction in Information Networks

Most information networks containing more and more information consist of a large number of interacting objects, while more and more researchers pay their attentions to information mining in networks. As an important topic of information mining in networks, link prediction is intended to predict the possibility of future links between two objects in the network by analyzing existing network information. Since the Internet of Things (IoT) is a special information network, research on link prediction in information networks has important implications for the topology and extension of IoT. In this paper, we provide a survey of link prediction in information networks. We will introduce basic concepts and definitions of information networks, summarize the latest link prediction methods in information networks, including link prediction methods in single layer networks, link prediction methods in multiplex networks, and link prediction methods in heterogeneous networks, point out the existing problems and the future research direction.
Myelination of Axons Corresponds with Faster Transmission Speed in the Prefrontal Cortex of Developing Male Rats

Visual Abstract Myelination of prefrontal circuits during adolescence is thought to lead to enhanced cognitive processing and improved behavioral control. However, while standard neuroimaging techniques commonly used in human and animal studies can measure large white matter bundles and residual conduction speed, they cannot directly measure myelination of individual axons or how fast electrical signals travel along these axons. Here we focused on a specific population of prefrontal axons to directly measure conduction velocity and myelin microstructure in developing male rats. An in vitro electrophysiological approach enabled us to isolate monosynaptic projections from the anterior branches of the corpus callosum (corpus callosum-forceps minor, CCFM) to the anterior cingulate subregion of the medial prefrontal cortex (Cg1) and to measure the speed and direction of action potentials propagating along these axons. We found that a large number of axons projecting from the CCFM to neurons in Layer V of Cg1 are ensheathed with myelin between pre-adolescence [postnatal day (PD)15] and mid-adolescence (PD43). This robust increase in axonal myelination is accompanied by a near doubling of transmission speed. As there was no age difference in the diameter of these axons, myelin is likely the driving force behind faster transmission of electrical signals in older animals. These developmental changes in axonal microstructure and physiology may extend to other axonal populations as well, and could underlie some of the improvements in cognitive processing between childhood and adolescence.
Development of deep learning algorithms to categorize free-text notes pertaining to diabetes: convolution neural networks achieve higher accuracy than support vector machines

Health professionals can use natural language processing (NLP) technologies when reviewing electronic health records (EHR). Machine learning free-text classifiers can help them identify problems and make critical decisions. We aim to develop deep learning neural network algorithms that identify EHR progress notes pertaining to diabetes and validate the algorithms at two institutions. The data used are 2,000 EHR progress notes retrieved from patients with diabetes and all notes were annotated manually as diabetic or non-diabetic. Several deep learning classifiers were developed, and their performances were evaluated with the area under the ROC curve (AUC). The convolutional neural network (CNN) model with a separable convolution layer accurately identified diabetes-related notes in the Brigham and Womens Hospital testing set with the highest AUC of 0.975. Deep learning classifiers can be used to identify EHR progress notes pertaining to diabetes. In particular, the CNN-based classifier can achieve a higher AUC than an SVM-based classifier.
Symmetric Upwind Scheme for Discrete Weighted Total Variation

This paper is devoted to the study of the discrete formulations of the weighted Total Variation (TV) based on upwind schemes that have been proposed for imaging problems in a local setting in [1] and in a non-local setting for graphs and point-clouds in [2]. We focus on two new symmetric formulations based on the $\ell^{2}$ and $\ell^{\infty}$ norms respectively and propose a dedicated optimization algorithm to solve convex problems based on such TV penalties. We demonstrate the theoretical and practical interest of such formulations for image processing tasks.
Local Pairwise Linear Discriminant Analysis for Speaker Verification

Linear discriminant analysis—probabilistic linear discriminant analysis (LDA-PLDA) is a standard and effective backend in the field of speaker verification. The object of LDA is to perform dimensionality reduction while minimizing within-class covariance and maximizing between-class covariance. For a target class (or speaker), our task is to make a binary decision about whether a test utterance is from a specific target speaker. Generally, the nontarget test utterances that are close to the target speaker are easily misjudged. Inspired by this idea, we propose a local pairwise linear discriminant analysis (LPLDA) algorithm. This new method focuses on maximizing the local pairwise covariance, which represents the local structure between the target class samples and neighboring nontarget class samples, instead of the between-class covariance, which represents the global structure of the data. Experiments on the NIST SRE 2010, 2014, and 2016 database show that, the proposed LPLDA-PLDA backend has significant performance improvements over the LDA-PLDA backend.
Mechanisms of the anti-aging and prolongevity effects of caloric restriction: evidence from studies of genetically modified animals

It is widely accepted that caloric restriction (CR) extends lifespan and suppresses various pathophysiological changes. CR suppresses growth hormone/insulin-like growth factor signaling and mechanistic target of rapamycin complex 1 activity, activates sirtuin and enhances mitochondrial redox regulation, but the exact mechanisms are still under debate. In this review, we discuss the mechanisms of CR using evidence from studies of animals that were genetically modified according to recent advances in molecular and genetic technologies, from the viewpoint of the adaptive response hypothesis proposed by Holliday (1989). We then explain the beneficial actions of CR, classified according to whether they operate under feeding or fasting conditions.
Extrinsic Camera Calibration Without Visible Corresponding Points Using Omnidirectional Cameras

This paper proposes a novel algorithm that calibrates multiple cameras scattered across a broad area. The key idea of the proposed method is “using the position of an omnidirectional camera as a reference point. ” The common approach to calibrating multiple cameras assumes that the cameras capture at least some common points. This means calibration becomes quite difficult if there are no shared points in each camera’s field of view (FOV). The proposed method uses the position of an omnidirectional camera to determine point correspondence. The position of an omnidirectional camera relative to the calibrated camera is estimated by the theory of epipolar geometry, even if the omnidirectional camera is placed outside the camera’s FOV. This property makes our method applicable to multiple cameras scattered across a broad area. Qualitative and quantitative evaluations using synthesized and real data, e.g., a sports field, demonstrate the advantages of the proposed method.
A fatal diarrhoea outbreak in farm‐raised Deinagkistrodon acutus in China is newly linked to potentially zoonotic Aeromonas hydrophila

Deinagkistrodon acutus is a venomous pit viper commonly used in traditional Chinese medicine; farming these snakes has become a major industry. In 2017, an outbreak of fatal diarrhoea among farm-raised D. acutus in Hunan Province caused the deaths of 5,600 snakes within 3 weeks. We isolated a brand-new sequence type of Aeromonas hydrophila (ST516) from lesions and confirmed that this bacterium was the causal agent of the outbreak. Snakes infected with the bacterium in the laboratory showed similar clinical symptoms to those of snakes in the original outbreak. We also tested bacterial virulence in Kunming mice to examine the likelihood of zoonosis. Isolates were pathogenic to mice, causing diarrhoea within 4 hr post-challenge, which indicates that the bacterium can potentially infect mammals. Environmental analysis showed that polluted spring water likely caused the diarrhoea in snakes. This study is the first to report on a large-scale outbreak of fatal diarrhoea in farm-raised snakes, originating in a pathogen that can infect mammals. These results should raise awareness regarding potential anthropozoonosis among poikilotherms, mammals, and humans; appropriate prevention or control methods should be developed.
VeriTracer: Context-enriched tracer for floating-point arithmetic analysis

VeriTracer automatically instruments a code and traces the accuracy of floating-point variables over time. VeriTracer enriches the visual traces with contextual information such as the call site path in which a value was modified. Contextual information is important to understand how the floating-point errors propagate in complex codes. VeriTracer is implemented as an LLVM compiler tool on top of Verificarlo. We demonstrate how VeriTracer can detect accuracy loss and quantify the impact of using a compensated algorithm on ABINIT, an industrial HPC application for Ab Initio quantum computation.
Surface-Based Exploration for Autonomous 3D Modeling

In this study, we addressed a path planning problem of a mobile robot to construct highly accurate 3D models of an unknown environment. Most studies have focused on exploration approaches, which find the most informative viewpoint or trajectories by analyzing a volumetric map. However, the completion of a volumetric map does not necessarily describe the completion of a 3D model. A highly complicated structure sometimes cannot be represented as a volumetric model. We propose a novel exploration algorithm that considers not only a volumetric map but also reconstructed surfaces. Unlike previous approaches, we evaluate the model completeness according to the quality of the reconstructed surfaces and extract low-confidence surfaces. The surface information is used to guide the computation of the exploration path. Experimental results showed that the proposed algorithm performed better than other state-of-the-art exploration methods and especially improved the completeness and confidence of the 3D models.
Inhibition of α-Glucosidase, Intestinal Glucose Absorption, and Antidiabetic Properties by Caralluma europaea

Many medicinal plants around the world are used for therapeutic purposes against several diseases, including diabetes mellitus. Due to their composition of natural substances that are effective and do not represent side effects for users, unlike synthetic drugs, in this study, we investigated the inhibitory effect of Caralluma europaea (CE) on α-glucosidase activity in vitro; then the kinetics of the enzyme were studied with increasing concentrations of sucrose in order to determine the inhibition type of the enzyme. In addition, this effect of Caralluma europaea (CE) was confirmed in vivo using rats as an experimental animal model. Among the five fractions of CE, only the ethyl acetate fraction of C. europaea (EACe) induced a significant inhibition of α-glucosidase and its inhibition mode was competitive. The in vivo studies were conducted on mice and rats using glucose and sucrose as a substrate, respectively, to determine the oral glucose tolerance test (OGTT). The results obtained showed that the EACe and the aqueous extract of C. europaea (AECe) have significantly reduced the postprandial hyperglycemia after sucrose and glucose loading in normal and diabetic rats. AECe, also, significantly decreased intestinal glucose absorption, in situ. The results obtained showed that Caralluma europaea has a significant antihyperglycemic activity, which could be due to the inhibition of α-glucosidase activity and enteric absorption of glucose.
Historical Data is Useful for Navigation Planning: Data Driven Route Generation for Autonomous Ship

This work presents a method for automated generation of navigation plan for autonomous or robotic surface vessel. Historical Automatic Identification System (AIS) data is of significant value to this problem. The method joins AIS locations of a same vessel at different time and locations in a region into a route. Next, it automatically computes navigation plans using nearest neighbour based path retrieval relying on two representations, Ship Feature and Navigation Feature. Before starting service, existing AIS records in the form of ship properties and corresponding route are preprocessed and stored in the form of Ship and Navigation Feature. During online retrieval, given input constraints in vector form, nearest neighbour of this query vector in the same space is found and corresponding path of the neighbour is returned as recommended path. Analysis was done in four and two dimensional spaces for Ship and Navigation Feature respectively. Application of the method is demonstrated in two regions of Australian, covering Bass Strait and Great Australian Bight.
Improving Mandarin Tone Mispronunciation Detection for Non-Native Learners with Soft-Target Tone Labels and BLSTM-Based Deep Models

We propose three techniques to improve mispronunciation detection of Mandarin tones of second language (L2) learners using tone-based extended recognition network (ERN). First, we extend our model from deep neural network (DNN) to bidirectionallon-short-term memory (BLSTM) in order to model tone-level co-articulation influenced by a broader temporal context (e.g., two or three consecutive Mandarin syllables). Second, we relax the hard labels to characterize the situations when a single tone class label is not enough because L2 learners' pronunciations are often between two canonical tone categories. Therefore, soft targets (a probabilistic transcription) are proposed for acoustic model training in place of conventional hard targets (one-hot targets). Third, we average tone scores produced by BLSTM models trained with hard and soft targets to seek the complementarity from modeling at the tone-target levels. Compared to our previous system based on the DNN-trained ERNs, the BLSTM-trained system with soft targets reduces the equal error rate (ERR) from 5.77% to 4.86%, and system combination decreases EER further to 4.34%, achieving a 24.78% relative error reduction.
Multiple Peer-to-Peer Bidirectional Cooperative Communications Using Massive MIMO Relays

We study a two-way relay network where multiple multi-antenna relays facilitate two-way communications between multiple pairs of transceivers. Each relay is equipped with a massive number of antennas. As a result, we can assume that the transceiver-relay channel vectors are approximately orthogonal, and thus, intra-and inter-pair interference will be negligible. Aiming to maintain the signal-to-noise ratio (SNR) at receiver front-end of each transceiver above a certain threshold, we obtain the relay beamforming matrices and the transceiver powers such that the total transmit power consumed in the entire network is minimized. To do so, we assume that the channel vectors between each relay and different transceivers are asymptotically orthogonal. For such power minimization problem, we derive computationally efficient solutions.
Effect of Entropy in Icons and Background on Selection Accuracy

The purpose of this research is to study the effect of icon and background entropy on selection accuracy under differing cognitive conditions. A series of two experiments are reported here. Both experiments used a 4x2 design (very high/ high/ medium/ low entropy background x high/ low entropy icon), measuring selection accuracy under two different conditions (untimed/3 second timed). The sample included undergraduate students at a university in Thailand (n = 100). Results were analysed using chi-square. The analysis showed that under untimed conditions, there were no significant differences in selection accuracy between different background and icon entropy levels. However, under timed conditions, very high entropy background/high entropy icon selection was significantly lower than other conditions, while low entropy background/low entropy icon selection was higher. This implies that while under normal conditions, visual entropy in field or figure does not impede distinguishing different figures, under conditions of cognitive stress entropy could reduce ability to distinguish such images.
On the Performance of Wireless Powered Cognitive Relay Network With Interference Alignment

In this paper, a two-hop decode-and-forward wireless powered relaying cognitive radio network with interference alignment over Rayleigh fading channels is investigated. The energy-constrained secondary relay node harvests energy from both the information and interference signals. Then, the harvested energy is used by the relay to forward the information signal from the source to the destination node. By applying beamforming matrices to primary and secondary networks, the performance metrics, such as outage probability, capacity, and bit error rate are studied under perfect and imperfect channel state information scenarios for both power-splitting relaying (PSR) and time-switching relaying (TSR) protocols. In addition, the optimal network performance is achieved by calculating the optimal energy harvesting time-switching and power-splitting coefficients. Finally, closed-form expressions for the outage probability of primary and secondary users are derived. Monte Carlo simulation results corroborate the analytical ones and show that the PSR technique outperforms TSR in the considered system model.
Investigating Blueshift in Peak of Modulation Response Curve at Quantum Well Laser Diode under Small-Signal Analysis

Modulation response of GaAs quantum well laser is analytically investigated at microwave to millimeterwave operative frequency region. Effect of gain compression parameter is taken into account for calculating the response function, and the work is centered around $1.55 \mu \mathrm{m}$. Confinement factor, mirror reflectivity and scattering effect inside the cavity are varied within practical limit to determine the blueshift of peak of response curve. Width of quantum well changes the wavelength of emitted wavelength, and that changes the modulation feature of the device. Threshold of all the tuning parameters are obtained after which the curve becomes flat, which is the limiting condition. Result will play key role in determining its efficiency as optical transmitter when coupled with optical fiber.