Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Mechanism to Detect Paroxysmal Stage of Atrial Fibrillation Using Adaptive Threshold-Based Algorithm with Artificial Neural Network

SUMMARY Automatic detection of heart cycle abnormalities in a long duration of ECG data is a crucial technique for diagnosing an early stage of heart diseases. Concretely, Paroxysmal stage of Atrial Fibrillation rhythms (ParAF) must be discriminated from Normal Sinus rhythms (NS). The both of waveforms in ECG data are very similar, and thus it is di ﬃ cult to completely detect the Paroxysmal stage of Atrial Fibrillation rhythms. Previous studies have tried to solve this issue and some of them achieved the discrimination with a high degree of accuracy. However, the accuracies of them do not reach 100%. In addition, no research has achieved it in a long duration, e.g. 12 hours, of ECG data. In this study, a new mechanism to tackle with these issues is proposed: “Door-to-Door” algorithm is introduced to accurately and quickly detect signiﬁcant peaks of heart cycle in 12 hours of ECG data and to discriminate obvious ParAF rhythms from NS rhythms. In addition, a quantitative method using Artiﬁcial Neural Network (ANN), which discriminates unobvious ParAF rhythms from NS rhythms, is investigated. As the result of Door-to-Door algorithm performance evaluation, it was revealed that Door-to-Door algorithm achieves the accuracy of 100% in detecting the signiﬁcant peaks of heart cycle in 17 NS ECG data. In addition, it was veriﬁed that ANN-based method achieves the accuracy of 100% in discriminating the Paroxysmal stage of 15 Atrial Fibrillation data from 17 NS data. Furthermore, it was conﬁrmed that the computational time to perform the proposed mechanism is less than the half of the previous study. From these achievements, it is concluded that the proposed mechanism can practically be used to diagnose
Hydraulic Strategy of Cactus Root–Stem Junction for Effective Water Transport

Cactus roots function as a hydraulic safety valve by conducting available water quickly and preventing water loss under drought condition. In particular, the root–stem (R–S) junction is responsible for effective water transport by direct coupling of the water absorptive thin roots and the moisture-filled bulky stem. In this study, the morphological features of the R–S junction were observed by using X-ray micro-imaging technique. Their structural and functional characteristics were also elucidated according to a hydrodynamic viewpoint. With regard to the axial water transport through xylem, the R–S junction prevents water leakage by embolizing large-scale vessels with relatively high hydraulic conductivity. In addition, the axial theoretical hydraulic conductivity of xylem vessels from the roots to the stem drastically increases to facilitate water absorption and prevent water loss. The cortex cell layer of a cactus is thinner than that of other plant species. In the viewpoint of radial conductivity, this property can be the hydraulic strategy of the cactus R–S junction to transport water quickly from the root surface into the xylem. These results suggest that the R–S junction functions as a hydraulic safety valve that can maximize water uptake in axial and radial directions at limited rainfall. This junction can also prevent the stem from leaking water under drought condition.
Mechanical articulated arm installation method for the largest equipment of the spacecraft sealed cabin

The structure of manned spacecraft is sealed cabin, the passage of which is narrow. Installation location of the largest equipment in cabin longitudinal depth and transverse width, it is difficult to install the largest equipment. First, the best installation path is obtained by simulating the joint position. Reach to the installation position with mechanical articulated arm, through small hanger flexible installation the equipment. After experimental the results show that the method can complete the largest equipment installation of narrow space in sealed cabin, position adjustment work is finished by only one person, equipment installation work is finished by two people, is superior to traditional methods. This method can offer reference for similar installation conditions.
When and where do feed-forward neural networks learn localist representations?

According to parallel distributed processing (PDP) theory in psychology, neural networks (NN) learn distributed rather than interpretable localist representations. This view has been held so strongly that few researchers have analysed single units to determine if this assumption is correct. However, recent results from psychology, neuroscience and computer science have shown the occasional existence of local codes emerging in artificial and biological neural networks. In this paper, we undertake the first systematic survey of when local codes emerge in a feed-forward neural network, using generated input and output data with known qualities. We find that the number of local codes that emerge from a NN follows a well-defined distribution across the number of hidden layer neurons, with a peak determined by the size of input data, number of examples presented and the sparsity of input data. Using a 1-hot output code drastically decreases the number of local codes on the hidden layer. The number of emergent local codes increases with the percentage of dropout applied to the hidden layer, suggesting that the localist encoding may offer a resilience to noisy networks. This data suggests that localist coding can emerge from feed-forward PDP networks and suggests some of the conditions that may lead to interpretable localist representations in the cortex. The findings highlight how local codes should not be dismissed out of hand.
Oenococcus oeni Exopolysaccharide Biosynthesis, a Tool to Improve Malolactic Starter Performance

Oenococcus oeni is the lactic acid bacterium that most commonly drives malolactic fermentation (MLF) in wine. Though the importance of MLF in terms of wine microbial stability and sensory improvement is well established, it remains a winemaking step not so easy to control. O. oeni displays many adaptation tools to resist the harsh wine conditions which explain its natural dominance at this stage of winemaking. Previous findings showed that capsular polysaccharides and endogenous produced dextran increased the survival rate and the conservation time of malolactic starters. In this paper, we showed that exopolysaccharides specific production rates were increased in the presence of single stressors relevant to wine (pH, ethanol). The transcription of the associated genes was investigated in distinct O. oeni strains. The conditions in which eps genes and EPS synthesis were most stimulated were then evaluated for the production of freeze dried malolactic starters, for acclimation procedures and for MLF efficiency. Sensory analysis tests on the resulting wines were finally performed.
Air-Ground Integrated Vehicular Network Slicing With Content Pushing and Caching

In this paper, an Air-Ground Integrated VEhicular Network (AGIVEN) architecture is proposed, where the aerial high-altitude platforms (HAPs) proactively push contents to vehicles through large-area broadcast, while the ground roadside units (RSUs) provide high-rate unicast services on demand. To efficiently manage the multi-dimensional heterogeneous resources, a service-oriented network slicing approach is introduced, where the AGIVEN is virtually divided into multiple slices and each slice supports a specific application with guaranteed quality of service (QoS). Specifically, the fundamental problem of multi-resource provisioning in AGIVEN slicing is investigated by taking into account the typical vehicular applications of location-based map and popularity-based content services. For the location-based map service, the capability of HAP-vehicle proactive pushing is derived with respect to the HAP broadcast rate and vehicle cache size, wherein a saddle point exists, indicating the optimal communication-cache resource trading. For the popular contents of common interests, the average on-board content hit ratio is obtained with HAPs pushing newly generated contents to keep on-board cache fresh. Then, the minimal RSU transmission rate is derived to meet the average delay requirements of each slice. The obtained analytical results reveal the service-dependent resource provisioning and trading relationships among RSU transmission rate, HAP broadcast rate, and vehicle cache size, which provides guidelines for multi-resource network slicing in practice. Simulation results demonstrate that the proposed AGIVEN network slicing approach matches the multi-resources across slices, whereby the RSU transmission rate can be saved by 40% while maintaining the same QoS.
Context Modulates Congruency Effects in Selective Attention to Social Cues

Head and gaze directions are used during social interactions as essential cues to infer where someone attends. When head and gaze are oriented toward opposite directions, we need to extract socially meaningful information despite stimulus conflict. Recently, a cognitive and neural mechanism for filtering-out conflicting stimuli has been identified while performing non-social attention tasks. This mechanism is engaged proactively when conflict is anticipated in a high proportion of trials and reactively when conflict occurs infrequently. Here, we investigated whether a similar mechanism is at play for limiting distraction from conflicting social cues during gaze or head direction discrimination tasks in contexts with different probabilities of conflict. Results showed that, for the gaze direction task only (Experiment 1), inverse efficiency (IE) scores for distractor-absent trials (i.e., faces with averted gaze and centrally oriented head) were larger (indicating worse performance) when these trials were intermixed with congruent/incongruent distractor-present trials (i.e., faces with averted gaze and tilted head in the same/opposite direction) relative to when the same distractor-absent trials were shown in isolation. Moreover, on distractor-present trials, IE scores for congruent (vs. incongruent) head-gaze pairs in blocks with rare conflict were larger than in blocks with frequent conflict, suggesting that adaptation to conflict was more efficient than adaptation to infrequent events. However, when the task required discrimination of head orientation while ignoring gaze direction, performance was not impacted by both block-level and current trial congruency (Experiment 2), unless the cognitive load of the task was increased by adding a concurrent task (Experiment 3). Overall, our study demonstrates that during attention to social cues proactive cognitive control mechanisms are modulated by the expectation of conflicting stimulus information at both the block- and trial-sequence level, and by the type of task and cognitive load. This helps to clarify the inherent differences in the distracting potential of head and gaze cues during speeded social attention tasks.
Identifying Malicious Applications by Behavioral Similarity on Android Platforms

As mobile applications become popular, they become the new target of attackers. For Android platforms, adversaries can easily repackage the malicious code into the different benign applications for distribution. The work of detecting and analyzing the malicious application becomes a challenge of Android. Though, the repackaged applications have different outward appearances, the same malicious behaviors still appear during runtime. Therefore, we propose a behavior-based detection mechanism based on system call sequences. We extract the common system call subsequences of malicious applications and purpose a comparison approach to deal with multiple threads produced by the applications. We also utilize the Bayes probability model to filter subsequences which have lower probability of appearance in the repackaged applications. Finally, we can detect repackaged applications by those extracted subsequences. In our experiment, we use five different types of repackaged applications and 100 benign applications to evaluate the accuracy rate. The detection result demonstrates that our approach has 97.6% high accuracy. We evaluate 25 repackaged applications and miss only one evaluated target.
Preventing Wine Counterfeiting by Individual Cork Stopper Recognition Using Image Processing Technologies

Wine counterfeiting is a major problem worldwide. Within this context, an approach to the problem of discerning original wine bottles from forged ones is the use of natural features present in the product, object and/or material (using it “as is”). The proposed application uses the cork stopper as a unique fingerprint, combined with state of the art image processing techniques to achieve individual object recognition and smartphones as the authentication equipment. The anti-counterfeiting scheme is divided into two phases: an enrollment phase, where every bottle is registered in a database using a photo of its cork stopper inside the bottle; and a verification phase, where an end-user/retailer captures a photo of the cork stopper using a regular smartphone, compares the photo with the previously-stored one and retrieves it if the wine bottle was previously registered. To evaluate the performance of the proposed application, two datasets of natural/agglomerate cork stoppers were built, totaling 1000 photos. The worst case results show a 100% precision ratio, an accuracy of 99.94% and a recall of 94.00%, using different smartphones. The perfect score in precision is a promising result, proving that this system can be applied to the prevention of wine counterfeiting and consumer/retailer security when purchasing a wine bottle.
Relative Importance of Habitat Characteristics and Interspecific Relations in Determining Terrestrial Carnivore Occurrence

Carnivores act as top-down regulators in terrestrial ecosystems, and their occurrence and relative abundance is a result of complex interactions between food and habitat availability, human pressure (e.g., trapping, hunting, roadkill) and intraguild interactions (competition, predation). Eastern United States has a long history of human impact, which resulted in an altered carnivore community. Specifically, Ohio presents an interesting case for evaluating the relative roles of interspecific relations and habitat characteristics for shaping the carnivore community, as its carnivore community has a unique dynamics and composition: invasive coyote and red fox (Vulpes vulpes), and native bobcat (Lynx rufus), currently recovering and expanding its range, gray fox (Urocyon cinereoargenteus) declining at a fast pace, and the generalist raccoon (Procyon lotor) and Virginian opossum (Didelphis virginiana). We used 50 camera traps to collect presence/absence data in southeastern Ohio, USA. We hypothesized potential interactions between the six carnivores, and used land cover variables, as well as occupancy probabilities of interacting species, to parameterize to single-species occupancy models. We found that landscape composition at three different scales (500m and 1000 m buffer around camera locations, and 3x3 km grid cell) had little effect on species occurrence. We identified strong negative interspecific relations between carnivores, with bobcat occurrence being influenced by presence of coyotes, red fox occurrence by gray foxes, and raccoon occurrence by Virginia possums. While these findings cannot discriminate between habitat partitioning (spatial or temporal) and competition (direct or interference), they lend support to complex dynamics between invasive coyotes and red foxes and recovering (bobcat) and declining (gray fox) native carnivore species. In particular, the negative relation between the apex predator in our system, C. latrans, and L. rufus, raise further questions on whether direct competition from coyotes has the potential to slow bobcat population recovery. In the context of regulated trapping (ongoing for gray fox and proposed season for bobcat), a better understanding of the carnivore intraguild relations can inform management and conservation actions targeted at minimizing the impact of competition on at-risk native species from non-native species.
Multi-Task Deep Networks for Depth-Based 6D Object Pose and Joint Registration in Crowd Scenarios

In bin-picking scenarios, multiple instances of an object of interest are stacked in a pile randomly, and hence, the instances are inherently subjected to the challenges: severe occlusion, clutter, and similar-looking distractors. Most existing methods are, however, for single isolated object instances, while some recent methods tackle crowd scenarios as post-refinement which accounts multiple object relations. In this paper, we address recovering 6D poses of multiple instances in bin-picking scenarios in depth modality by multi-task learning in deep neural networks. Our architecture jointly learns multiple sub-tasks: 2D detection, depth, and 3D pose estimation of individual objects; and joint registration of multiple objects. For training data generation, depth images of physically plausible object pose configurations are generated by a 3D object model in a physics simulation, which yields diverse occlusion patterns to learn. We adopt a state-of-the-art object detector, and 2D offsets are further estimated via a network to refine misaligned 2D detections. The depth and 3D pose estimator is designed to generate multiple hypotheses per detection. This allows the joint registration network to learn occlusion patterns and remove physically implausible pose hypotheses. We apply our architecture on both synthetic (our own and Sileane dataset) and real (a public Bin-Picking dataset) data, showing that it significantly outperforms state-of-the-art methods by 15-31% in average precision.
SciDTB: Discourse Dependency TreeBank for Scientific Abstracts

Annotation corpus for discourse relations benefits NLP tasks such as machine translation and question answering. In this paper, we present SciDTB, a domain-specific discourse treebank annotated on scientific articles. Different from widely-used RST-DT and PDTB, SciDTB uses dependency trees to represent discourse structure, which is flexible and simplified to some extent but do not sacrifice structural integrity. We discuss the labeling framework, annotation workflow and some statistics about SciDTB. Furthermore, our treebank is made as a benchmark for evaluating discourse dependency parsers, on which we provide several baselines as fundamental work.
More than a sum of parts: robust face recognition by integrating variation

Familiarity incrementally improves our ability to identify faces. It has been hypothesized that this improvement reflects the refinement of memory representations which incorporate variation in appearance across encounters. Although it is established that exposure to variation improves face identification accuracy, it is not clear how variation is assimilated into internal face representations. To address this, we used a novel approach to isolate the effect of integrating separate exposures into a single-identity representation. Participants (n = 113) were exposed to either a single video clip or a pair of video clips of target identities. Pairs of video clips were presented as either a single identity (associated with a single name, e.g. Betty-Sue) or dual identities (associated with two names, e.g. Betty and Sue). Results show that participants exposed to pairs of video clips showed better matching performance compared with participants trained with a single clip. More importantly, identification accuracy was higher for faces presented as single identities compared to faces presented as dual identities. This provides the first direct evidence that the integration of information across separate exposures benefits face matching, thereby establishing a mechanism that may explain people's impressive ability to recognize familiar faces.
Perfusion and Diffusion in Preclinical Models

Introduction Magnetic resonance imaging is a well established tool in clinical practice and in research on human brain. In 2010, about 18,000 MRI scanners were available worldwide (OECD Health database) and more than 10,000 papers related to 'Brain and MRI' related to human studies were published. With the increased availability of MRI systems dedicated to small animals (about 700 worldwide, i.e. less than 4% of the total number of MRI scanners), MRI research utilizing animal models of central nervous system (CNS) diseases is developing. This can be observed from the evolution of the ratio between animal and human studies related to brain and MRI (Fig. 1). 0 2 4 6 8 10 12
Gear Training: A new way to implement high-performance model-parallel training

The training of Deep Neural Networks usually needs tremendous computing resources. Therefore many deep models are trained in large cluster instead of single machine or GPU. Though major researchs at present try to run whole model on all machines by using asynchronous asynchronous stochastic gradient descent (ASGD), we present a new approach to train deep model parallely -- split the model and then seperately train different parts of it in different speed.
Sarcopenic Dysphagia as a New Concept

Dysphagia (swallowing difficulties) is a serious problem associated with malnutrition, dehydration, aspiration pneumonia, and death. Its well‐known causes include stroke, neuromuscular disease, and head and neck cancer, and these affect muscles and sensa‐ tion during deglutition. In recent years, dysphagia due to sarcopenia (i.e. “sarcopenic dysphagia”) has been reported as a new concept. Sarcopenic dysphagia results from low swallowing and general skeletal muscle mass and strength. The characteristic changes in swallowing muscles occur primarily in oral and pharyngeal muscles along with other associated factors. With a rapidly aging population, the number of older adults with sar‐ copenic dysphagia is expected to increase. Therefore, it is necessary to investigate the pathophysiology and treatment strategies for sarcopenic dysphagia. In this chapter, we summarize previous studies related to sarcopenic dysphagia.
Lean Multiclass Crowdsourcing

We introduce a method for efficiently crowdsourcing multiclass annotations in challenging, real world image datasets. Our method is designed to minimize the number of human annotations that are necessary to achieve a desired level of confidence on class labels. It is based on combining models of worker behavior with computer vision. Our method is general: it can handle a large number of classes, worker labels that come from a taxonomy rather than a flat list, and can model the dependence of labels when workers can see a history of previous annotations. Our method may be used as a drop-in replacement for the majority vote algorithms used in online crowdsourcing services that aggregate multiple human annotations into a final consolidated label. In experiments conducted on two real-life applications we find that our method can reduce the number of required annotations by as much as a factor of 5.4 and can reduce the residual annotation error by up to 90% when compared with majority voting. Furthermore, the online risk estimates of the models may be used to sort the annotated collection and minimize subsequent expert review effort.
Schlaf und Atmung

Erwachsene atmen sowohl im Wach- als auch im Schlafzustand zirka zwölf- bis zwanzigmal pro Minute. In allen Phasen gelingt es dem gesunden Körper, ein Gleichgewicht von Kohlendioxidproduktion und -ausscheidung sowie eine regelrechte Versorgung mit Sauerstoff zu erhalten. Dennoch treten charakteristische Veränderungen des Atmungsmusters sowohl beim Übergang vom Wach- in den Schlafzustand als auch während der unterschiedlichen Schlafstadien auf. Dieser Artikel soll einen Überblick über die schlafbezogenen Atmungsstörungen, ihre Auswirkungen auf das Herz-Kreislauf-System und den Stoffwechsel sowie die therapeutischen Möglichkeiten bieten.