Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation criteria for closed-loop adaptive dynamic discrete-continuous brain-computer interfaces: clinical study case with tetraplegic patient.

Quantifying and evaluating properly the performances is a critical issue in BCI experiments. The choice of the most adapted metrics can be difficult because they are specific to the experimental paradigm, task control, and data. In the current study evaluation criteria for closed-loop adaptive dynamic and hierarchical discrete-continuous brain-computer interfaces are examined. The challenges such as imbalanced multi-class bias for discrete decoding (classification), online computing cost and dynamic analysis of results in time are considered. There are two main objectives of the study: identifying the best suited performances metrics according to the requirements and combining several levels of evaluation in the whole BCI system (decoder and patient oriented). The main novelty of the study is the combination of several common metrics with new temporal metrics and a time dynamic approach which allows adequately reflect the performance of adaptive dynamic and hierarchical discrete-continuous brain-computer interfaces BCI systems. Additional response time and blocking error patterns reveal complementary information for BCI system performance evaluation. Developed criteria are applied to performance evaluation of 8-dimensional exoskeleton control by tetraplegic patient for both decoder and user-centered metrics.
Coordinating storage and grid : efficient regulation in a multilevel system with strategic actors

With declining cost for renewable generation technologies and chemical storage there is an increasing number of integrated systems that allow for a co-optimization of power production and consumption using storage (prosumage). In addition to temporal arbitrage and portfolio optimization, electricity storage has great potential to be used for network services in both the distribution and the transmission grid (Schill et al. 2017). However, these usages depend on storage properties like its location and size as well as its operation (dispatch) and the different objectives might counteract with each other. Moreover, storage owners are likely to neglect system effects and potential services that their storage can provide, but rather focus on other purposes like maximization of arbitrage, reduction of wind power curtailment or the increase of self-consumption.
An hardware-aware image polarity detector enhanced with visual attention

Providing user customized experience is one of the main goals for present-day electronic smart devices. Image polarity detection plays a crucial role in understanding users’ preferences due to the fact that information is massively represented by means of pictures. State-of-the-art frameworks are based on deep learning networks and continue evolving adding sophisticated structures to enhance generalization performances of the inference systems. Recent works proved that image analysis can be enhanced exploiting the information about salient regions. However, better performances are obtained at the cost of a higher computational load. This paper presents a hardware-friendly deep learning framework for image polarity detectors based on salient regions of an image. Experimental results show the reliable performances of the proposed solution on real-world data.
Multi-level Visual Fusion Networks for Image Captioning

Image captioning is a multi-modal complex task in machine learning. Traditional methods focus only on entities in visual strategy networks, and can’t reason about the relationship between entities and attributes. There are problems of exposure bias and error accumulation in language strategy networks. To this end, this paper proposes a multi-level visual fusion network model based on reinforcement learning. In the visual strategy network, multi-level neural network modules are used to transform visual features into feature sets of visual knowledge. The fusion network generates function words that make the description more fluent, and is used for the interaction between the visual strategy network and the language strategy network. The self-criticism strategy gradient algorithm based on reinforcement learning in language strategy networks is used to achieve end-to-end optimization of visual fusion networks. We evaluated our model on the Flickr 30K and MS-COCO datasets, and verified the accuracy of the model and the diversity of model learning subtitles through experiments. Our model achieves better performance over state-of-the-art methods.
Dependency Guided Graph Convolutional Network for Aspect-Based Sentiment Analysis

—Aspect-based sentiment analysis(ABSA) is a task to identify the sentiment polarity of the given aspect. Usually, attention mechanisms, Convolution Neural Networks(CNN) and Recurrent Neural Network(RNN) are applied to this ﬁeld. These models can learn the semantic information of sentences but ignore the syntactic information. Dependency tree can help improve this. But in the previous model, it is not treated seriously. In this paper, we propose a model named Dependency Guided Graph Convolutional Network( DGGCN ) to tackle the shortcomings of previous models. The model uses different types of dependency information to guide the operation of graph convolutional network and employ a ﬁlter layer to enhance the important parts while ﬁltering out the irrelevant parts of the sentence. Experiments on four benchmark datasets demonstrate our model’s effectiveness, which signiﬁcantly outperforms previous state-of-the-art baselines.
Improved Learning of Word Embeddings with Word Definitions and Semantic Injection

Recently, two categories of linguistic knowledge sources, word deﬁnitions from monolingual dictionaries and linguistic relations (e.g. synonymy and antonymy), have been leveraged separately to improve the traditional co-occurrence based methods for learning word embeddings. In this paper, we investigate to leverage these two kinds of resources together. Speciﬁcally, we propose a new method for word embedding specialization, named Deﬁnition Autoencoder with Semantic Injection (DASI). In our experiments 1 , DASI outperforms its single-knowledge-source counterparts on two semantic similarity benchmarks, and the improvements are further justiﬁed on a downstream task of dialog state tracking. We also show that DASI is superior over simple combinations of existing methods in incorporating the two knowledge sources.
Enhancing Question Answering over Knowledge Base Using Dynamical Relation Reasoning

The task of Knowledge Base Question Answering(KBQA) is to provide a convenient way for the human to more efficiently and easily answer natural language questions using the substantial and valuable knowledge in the KB. Predicate generation is the most important sub-task of KBQA, which aims to generate the predicate paths from head entity to tail entity. Existing models mostly employ a seq2seq method to handle this task. However, the seq2seq method essentially is a classification model, which needs to know the number of the categories in advance. Meanwhile, KBs often are incompleteness and questions are always unbounded, which would cause the problem that the predicates of the questions would be beyond predefined categories. Obviously, the seq2seq model cannot handle this problem. In this paper, to solve the problem above, we explore to build an scoring module with strong genelization to score the predicates not in predefined categories, and also to improve the performance of the seq2seq method.To bridge the gap, we carefully design a reasoning module to score the predicates through reasonably employing the attention mechanism with memory ability. Simultaneously, in order to improve the generalization of the reasoning module, we try to use multi-task learning to enrich the represent of the reasoning module based on the idea of transfer learning. Massive experiments are conducted on two popular benchmark datasets—SimpleQuestion(SimQ) and WebQuestion(WebQ). The experimental results demonstrate that the proposed relation reasoning framework outperforms the state-of-the-art methods.
Constant-Q magnitude-phase coefficients extraction for synthetic speech detection

Previous works in synthetic speech detection have focused on features based on magnitude or phase spectrum. In this study, to extract useful discriminative information for synthetic speech detection, the authors propose a feature based on magnitude-phase spectrum (MPS), combining magnitude- and phase-spectra information. The proposed feature is termed as constant-Q magnitude-phase coefficient (CMPC), which is obtained by combining constant-Q transform (CQT), MPS, uniform resampling, and discrete cosine transform. The CQT used in this study is a long-term window transform, which can provide the basis for CMPC to capture important artefacts of synthetic speech. Such artefacts are obtained using a unit selection algorithm, which have difficulties when based on the short-term window transform. Uniform resampling aims to convert MPS from the octave domain into the linear domain. The discrete cosine transform is used when extracting principal components to remove correlations among the feature dimensions. The experimental results on AVspoof and ASVspoof 2015 corpora show that CMPC performs better than some commonly used features based on magnitude or phase spectrum alone. Their system based on CMPC outperforms many known systems.
Encryption scheme against eavesdropper collusion in wireless multi-user network

In order to resist eavesdropper collusion attack in wireless multi-user network, the authors propose a distributed physical layer encryption (DPLE) scheme with energy-efficient design. The encryption is embedded into rateless coding at both user nodes and relay node. Meanwhile, data frame permutation is added at the relay for increasing randomness. It is noteworthy that the secret key is used as the seed of random number generator, which controls the encoding process and permutation. At receiver, the decryption is united with decoding process without any extra cost, which makes DPLE special in the field of lightweight security. There are two cases considered in security analysis for resisting collusion attack: without error and with error. Typical results are time complexity and attack success probability, respectively. Furthermore, the evaluation for algorithm implementation is investigated. The theoretic result and simulation show that the proposed scheme can combat colluding eavesdroppers by high complexity and very low attack success probability. Moreover, lower computation cost is achieved.
Non-invasive intervention for motor signs of Parkinson’s disease: the effect of vibratory stimuli

In recent years there has been a renewed interest in the use of peripheral vibration to ameliorate some of the motor symptoms of Parkinson’s disease (PD). The possibility that peripheral vibration can improve patients’ motor symptoms is exciting as the intervention is non-invasive and of low cost. However, there remains little published scientific research to support some of the claims. Previously we demonstrated that vibration at 80 Hz at the wrist for 30 s prior to movement onset improved motor performance in a variety of manual tasks both in healthy controls and in patients with PD.1 Here we report the results of testing whether a wearable haptic device (the ‘Emma Watch’) developed by Microsoft Research,2 which delivers constant vibratory stimuli at the wrist, significantly improves motor function of the stimulated upper limb in patients with PD. The Emma Watch delivers constant vibration at 200 Hz through six small electromagnetic mechanical stimulators, three on each side of the wrist. The vibration frequency is modulated by a lower frequency, either 20 bpm (beats per minute) or 60 bpm. These modulation parameters were based on the parameter that improved motor function in the first tested patient with PD (60 bpm) and on a parameter that did not (20 bpm). Here we tested whether the Emma Watch could improve motor function in 16 patients with PD (11 women, mean age=63 years, range 52–72 years, Unified Parkinson’s …
Extracellular matrix stiffness determines DNA repair efficiency and cellular sensitivity to genotoxic agents

Extracellular matrix stiffness controls DNA damage response. DNA double-strand breaks (DSBs) are highly toxic lesions that can drive genetic instability. These lesions also contribute to the efficacy of radiotherapy and many cancer chemotherapeutics. DNA repair efficiency is regulated by both intracellular and extracellular chemical signals. However, it is largely unknown whether this process is regulated by physical stimuli such as extracellular mechanical signals. Here, we report that DSB repair is regulated by extracellular mechanical signals. Low extracellular matrix (ECM) stiffness impairs DSB repair and renders cells sensitive to genotoxic agents. Mechanistically, we found that the MAP4K4/6/7 kinases are activated and phosphorylate ubiquitin in cells at low stiffness. Phosphorylated ubiquitin impairs RNF8-mediated ubiquitin signaling at DSB sites, leading to DSB repair deficiency. Our results thus demonstrate that ECM stiffness regulates DSB repair efficiency and genotoxic sensitivity through MAP4K4/6/7 kinase–mediated ubiquitin phosphorylation, providing a previously unidentified regulation in DSB-induced ubiquitin signaling.
Analysis of tilted subwavelength periodical waveguides: polarization effects

Birefringence behavior has been frequently obtained when subwavelength waveguides are used on devices construction. In order to explore this property, we analyze a tilted silicon-on-insulator periodically segmented waveguides (PSW). The propagation characteristics are analyzed for different values of duty cycle and tilt. The degree of the birefringence can be tailored only by changing SWGs structural parameters such as the grating period, the filling factor, and the tilting. Also the tilted PSW can works as a birefringent waveguide for both TE and TM modes polarization, an important and desired requisite in photonics circuit devices. The approach is easily adaptable to a wide range of waveguide dimension keeping the single-etch-step fabrication.
Current-driven optical response of plasmonic crystal: From dissipation to amplification

We study optical response of a plasmonic crystal based on multi-gated 2D structure with periodic modulation of the electron density in the device channel. In such a structure, the plasma wave velocity is periodically modulated as well. We consider the simplest model of periodically alternating stripes of the electron density and plasma wave velocity: active regions with high plasma wave velocity and passive regions with low plasma wave velocity. Terahertz radiation applied to such a structure excites plasmonic resonances both in the active and passive stripes. The width of the resonances is determined by the momentum relaxation rate. For sufficiently large relaxation rates, the resonances in the passive regions strongly overlap and only “active resonances” survive. In this regime, the plasmonic oscillations in the active regions exponentially decay into the passive regions, so that different active regions are disconnected at plasmonic frequencies but connected at zero dc frequency. We assume that dc current is applied to this plasmonic crystal and calculate radiation-induced correction to the dissipation in the channel. We demonstrate that with increasing the dc current this correction changes sign, which results in amplification of the optical signal.
Paleozoic ammonoid ecomorphometrics test ecospace availability as a driver of morphological diversification

Paleozoic ammonoid ecomorphometrics show no support for early bursts at origination or during mass extinction recoveries. The early burst model suggests that disparity rises rapidly to fill empty ecospace following clade origination or in the aftermath of a mass extinction. Early bursts are considered common features of fossil data, but neontological studies have struggled to identify them. Furthermore, tests have proven difficult because factors besides ecology can drive changes in morphology. Here, we document the ecomorphometric evolution of the extinct Ammonoidea at 1-million-year resolution, from their origination in the Early Devonian (Emsian) to the Early Triassic (Induan), over ~156 million years. This time interval encompasses six global extinction events, including two of the Big Five, and incorporates multiple ammonoid radiations. However, we find no evidence for early bursts of ecomorphological disparity. This contradicts arguments that the temporal scope, or traits measured in genomic data, conceal evidence of early bursts. Rather, early bursts may be less prevalent in fossil data than is often assumed.
Modes interplay controls the second harmonic generation dynamics of plasmonic nanostructures

A large amount of experimental and theoretical works deals with the second harmonic generation from different plasmonic geometries. Since they often consider relatively long optical pulses, many of these studies are focused on the investigation of a quasi-monochromatic response of the system and can be understood through the excitation of one, possibly two, optical modes. On the other hand, when the excitation pulse duration is short (say, below several tens of fs), the excitation spectrum becomes broader and a very interesting dynamics emerges from the interplay between several optical modes. In this work, the dynamics of modes at the second harmonic frequency for two silver spheres of different diameters and a nanorod is investigated numerically and shown to be quite different. For the pulsed illumination with length close to the modes lifetime, apart from different relative contributions of dipolar and quadrupolar multipoles in the far-field, we have been able to observe and explain non constant phase difference between multipoles, which is not accessible in continuous wave regime. Short pulse durations also allow us to observe only one mode, while another one has already decayed. For the case of the nanorod we also perform an eigenmode analysis, which allows to understand the modes interplay that explains the observed spectra. In the paper, we also show a method allowing a significant reduction of required computational steps to find the response of a plasmonic nanostructure to a pulsed illumination with arbitrary frequency-domain method.
Automated material identification and segmentation using deep learning for laser powder bed fusion

In-situ Laser Powder Bed Fusion (LPBF) sensor packages seek to enable both the commercial and Department of Defense (DoD) supply chains via process monitoring for qualification and machine feedback. An automated material identification and geometric segmentation would be valuable for LPBF process monitoring. In this paper, various segmentation approaches are presented and discussed to determine the best approach. Later, deep learning method(s) to classify the materials as either AlSi10Mg or IN718 are presented. Diverse videos (in terms of shape, size, structure, and camera angle) of both materials are captured and labeled as either AlSi10Mg (24357 frames) or IN718 (9222 frames). A given frame can contain single or multiple parts of a material. The segmentation approach is applied to extract each part and 121,036 images are obtained. The dataset is randomly split into groups of 72%, 8% and 20% for training, validation, and testing respectively. Classification performance(s) using the proposed Convolutional Neural Network (CNN) in addition to transfer learning approaches using established networks such as AlexNet, ResNet, and SqueezeNet are studied. An overall accuracy of 99.6% is obtained on a set of 24,214 test images. In addition, efficacy of the proposed classification model is demonstrated by testing the algorithm on a completely different variant (in terms of shape, size, structure, or camera angle) of either material. The class activation mapping results of these networks are presented, yielding an insight into the network’s decision, and assisting the manufacturers in their decision-making process.
Hyperspectral Image Classification Based on Multilayer Perceptron Trained with Eigenvalue Decay

Abstract Hyperspectral Images (HSI) require sufficient labeled samples and a complex classifier to identify an area. Support Vector Machine (SVM) is one of the most competent algorithms in this field. Neural Networks (NN) is another approach used for classification problems, and both have been widely proposed in the literature. The Convolutional Neural Network (CNN) method has also received significant attention in the deep learning field recently. Nevertheless, during NN training, the overfitting problem may cause continuous dragging of the algorithm toward larger error. In this case, a regularization technique is needed to constitute the most useful decision boundary. The Eigenvalue Decay method is one of the regularization techniques that may be applied for HSI. This study investigates the performance of Multilayer Perceptron trained with an Eigenvalue Decay (MLP-ED) algorithm for HSI classification. The SVM, CNN with Pixel-Pair and CNN-Ensemble methods are used as comparison algorithms for MLP-ED performance assessment. All methods were tested with 3 different high-resolution HSI datasets. While SVM is one of the classic classifiers, and the 2 new CNN algorithms show high performance, the proposed MLP-ED method has more computational efficiency and achieves higher success than the others do.
Generating multifunctional acoustic tweezers in Petri dishes for contactless, precise manipulation of bioparticles

Acoustic tweezers enable contactless, dynamic, precise, and multifunctional manipulation of bioparticles in Petri dishes. Acoustic tweezers are a promising technology for the biocompatible, precise manipulation of delicate bioparticles ranging from nanometer-sized exosomes to millimeter-sized zebrafish larva. However, their widespread usage is hindered by their low compatibility with the workflows in biological laboratories. Here, we present multifunctional acoustic tweezers that can manipulate bioparticles in a disposable Petri dish. Various functionalities including cell patterning, tissue engineering, concentrating particles, translating cells, stimulating cells, and cell lysis are demonstrated. Moreover, leaky surface acoustic wave–based holography is achieved by encoding required phases in electrode profiles of interdigitated transducers. This overcomes the frequency and resolution limits of previous holographic techniques to control three-dimensional acoustic beams in microscale. This study presents a favorable technique for noncontact and label-free manipulation of bioparticles in commonly used Petri dishes. It can be readily adopted by the biological and medical communities for cell studies, tissue generation, and regenerative medicine.
Tricontrollable pixelated metasurface for stopband for terahertz radiation

Transmission of a normally incident plane wave through a pixelated metasurface with meta-atoms made up of graphene-patched and InSb-patched pixels on a silicon substrate was simulated using CST Microwave StudioTM2019 software. This metasurface is electrically controllable due to the graphene-patched pixels, and it is magnetically as well as thermally controllable due to the InSb-patched pixels. A tricontrollable stopband with minimum transmittance as low as 0.05 can be achieved for operation in the terahertz gap.