Miracle-dz/UniFAR-FTUs huggingface dataset:
Patch-Gated CNN for Occlusion-aware Facial Expression Recognition

Facial expression recognition in the wild is challenging due to various un-constrained conditions. Although existing facial expression classifiers have been almost perfect on analyzing constrained frontal faces, they fail to perform well on partially occluded faces that are common in the wild. In this paper, we propose an end-to-end trainable Patch-Gated Convolution Neutral Network (PG-CNN) that can automatically percept the occluded region of the face and focus on the most discriminative un-occluded regions. To determine the possible regions of interest on the face, PG-CNN decomposes an intermediate feature map into several patches according to the positions of related facial landmarks. Then, via a proposed Patch-Gated Unit, PG-CNN reweighs each patch by the unobstructed-ness or importance that is computed from the patch itself. The proposed PG-CNN is evaluated on two largest in-the-wild facial expression datasets (RAF-DB and AffectNet) and their modifications with synthesized facial occlusions. Experimental results show that PG-CNN improves the recognition accuracy on both the original faces and faces with synthesized occlusions. Visualization results demonstrate that, compared with the CNN without Patch-Gated Unit, PG-CNN is capable of shifting the attention from the occluded patch to other related but unobstructed ones. Experiments also show that PG-CNN outperforms other state-of-the-art methods on several widely used in-the-lab facial expression datasets under the cross-dataset evaluation protocol.
Structure Learning of Bayesian Networks by Finding the Optimal Ordering

Ordering-based search methods have advantages over graph-based search methods for structure learning of Bayesian networks in terms of both efficiency and accuracy. With the aim of further increasing the accuracy of ordering-based search methods, we propose to increase the search space, which can facilitate escaping from local optima. We present our search operators with majorizations, which are easy to implement. Experiments demonstrate that the proposed algorithm achieves significant accuracy improvement and exhibits high efficiency at the same time on both synthetic and real data sets. With regard to further improve the algorithm efficiency on learning large scale networks, we discuss a solution at the end of the paper.
Transport efficiency imaging in multi-junction solar cells by luminescence analysis

Multi-junction solar cells are currently the efficiency leaders among all types of solar cells. However, efficiencies of multi-junction solar cells are limited by various series resistance when they are illuminated under high concentrations. In this paper, we provide a robust method to obtain the current transport efficiency mapping for a precise and rapid measurement of resistance effect using luminescence characterization. We verified the method on a triple-junction solar cell with illuminations on all subcells. The results successfully matched with both electrical measurement and simulation results. Light beam induced current (LBIC) measurement is used for the first time to compare with the luminescence mapping. Both methods show a similar profile of transport efficiency.
Impact of Occupancy Modeling and Horizon Length on HVAC Controller Efficiency

Controlling heating, ventilation, and air conditioning (HVAC) to improve its energy efficiency or implement ancillary services (e.g., demand response or frequency regu- lation) requires compensating for occupants and the computers/equipment they use, which significantly impact the thermal dynamics of buildings. Several studies have explored the use of either binary models or online learning for occupancy within model predictive control (MPC) frameworks, but the use of more fine-grained predictive models for occupancy has been less well-studied. This paper uses real data from an HVAC testbed to develop predictive models of occupancy and heating load, evaluate the predictive accuracy of the occupancy model, and then quantity its efficacy in achieving energy reductions in conjunction with a learning-based MPC (LBMPC) controller. We conclude by conducting a simulation study to determine the optimal horizon for an LBMPC controller. In particular, we find there is a trade-off with increasing the horizon length between decreasing prediction accuracy of occupancy heating load and the ability of the MPC to better anticipate thermal dynamics due to occupancy and weather.
Enhanced VFDM Spectrum-Sharing Technique for Smart Platform

The growth of connected devices has recently forced the demand of its corresponding spectrum to match their communication system needs. The challenge now is to design network architecture to be more energy-efficient by the quality of services they render. This paper introduces Vandermonde subspace Frequency Division Multiplexing (VFDM), as a recently proposed technique for spectrum-sharing and as candidate to manage the exchange of messages between devices in a smart platform. We also extend our investigations to the coexistence of a two-tier network system for interference management through spectrum-sharing in a time duplex mode (TDM). After comparing the simulation result of both cases, it is shown that the spectrum efficiency performance of multi-user case is higher than the single user case, and this led to the conclusion of the proposed VFDM technique as an efficient real-time solution for next generation mobile technology.
Ship-to-Shore Versus Shore-to-Ship Synchronization Strategy

A novel approach to synchronization of shore and ship power systems is presented in this paper, providing a blackout-free supply from the shore. An application of the shore-to-ship synchronization strategy, proposed in the paper, instead of the commonly used ship-to-shore synchronization, enables a fuller exploitation of the high dynamic offered by power electronic converters. Converters are increasingly being used in shore-to-ship supply arrangements in order to ensure the required levels of voltages and frequencies, specified in shore-supply related standards. Simulation results, supported by preliminary tests in small-scale experimental arrangements, have confirmed that shore-to-ship applications might significantly improve the dynamics of the synchronization as well as the parameters of the load transferring process, especially under distorted ship voltage conditions.
Cross-Modulation Networks for Few-Shot Learning

A family of recent successful approaches to few-shot learning relies on learning an embedding space in which predictions are made by computing similarities between examples. This corresponds to combining information between support and query examples at a very late stage of the prediction pipeline. Inspired by this observation, we hypothesize that there may be benefits to combining the information at various levels of abstraction along the pipeline. We present an architecture called Cross-Modulation Networks which allows support and query examples to interact throughout the feature extraction process via a feature-wise modulation mechanism. We adapt the Matching Networks architecture to take advantage of these interactions and show encouraging initial results on miniImageNet in the 5-way, 1-shot setting, where we close the gap with state-of-the-art.
Comparison of First Order Differential Algorithm, Perturb and Observe (P&O) and Newton Raphson Methods for PV Application in DC Microgrid Isolated System

The necessary for energy always increases every year, so it is necessary energy alternative to overcome the phenomenon. From some alternative energy that is currently widely developed is solar energy. Unfortunately the use of solar energy with the help of solar panels produces power that varies according to the irradiation received and the temperature on the solar panel. Irradiation is the emission of energy coming from the sun. This parameter causes the output power characteristic curve to be non-linear. And the power generated by PV has not reached the maximum power point. To overcome this problem requires an arrangement to find the maximum power point and make it stable at that point. Therefore, this paper proposes “Comparison of First Order Differential Algorithms, Perturb and Observe(P&O) and Newton Raphson Methods for PV Application in DC Microgrid Isolated System” so that the maximum power point can be achieved quickly and accurately. In this paper we tried to compare 3 methods of First-order Differential, P & O (Perturb and Observe) and Newton Raphson by using ZETA Converter as its object in switching settings. By using the simulation can be obtained the results of tracking speed and maximum power level. The results of the simulation show that error power the First-order Differential is 4%, P & O is 0.26389% and Newton Raphson is 0.45178%. It is evident that Raphson's newton algorithm is superior to speed and accuracy.
Classification of Cetacean Whistles Based on Convolutional Neural Network

Vocal communication is a primary communication method of cetaceans. Learning the language of them is of great significance for the protection of cetaceans, and it also provides support for acoustic study of cetaceans’ biological behavior. In this study, a classification method based on deep learning is proposed for the classification of cetacean whistles. Firstly, the method performs short-time Fourier transform on whistles to obtain the time-frequency distribution, and then uses the deep learning model, convolutional neural network, to classify the signal based on time-frequency characteristics. The simulation results show that this method has a relatively high classification accuracy when choosing the appropriate parameters, indicating the method is effective.
A Benchmark Dataset and Learning High-Level Semantic Embeddings of Multimedia for Cross-Media Retrieval

The selection of semantic concepts for modal construction and data collection remains an open research issue. It is highly demanding to choose good multimedia concepts with small semantic gaps to facilitate the work of cross-media system developers. However, very little work has been done in this area. This paper contributes a new, real-world web image dataset for cross-media retrieval called FB5K. The proposed FB5K dataset contains the following attributes: 1) 5130 images crawled from Facebook; 2) images that are categorized according to users’ feelings; 3) images independent of text and language rather than using feelings for search. Furthermore, we propose a novel approach through the use of Optical Character Recognition and explicit incorporation of high-level semantic information. We comprehensively compute the performance of four different subspace-learning methods and three modified versions of the Correspondence Auto Encoder, alongside numerous text features and similarity measurements comparing Wikipedia, Flickr30k, and FB5K. To check the characteristics of FB5K, we propose a semantic-based cross-media retrieval method. To accomplish cross-media retrieval, we introduced a new similarity measurement in the embedded space, which significantly improved system performance compared with the conventional Euclidean distance. Our experimental results demonstrated the efficiency of the proposed retrieval method on three different datasets to simplify and improve general image retrieval.
Sequence Searching Allowing for Non-Overlapping Adjacent Unbalanced Translocations

Unbalanced translocations are among the most frequent chromosomal alterations, accounted for 30\% of all losses of heterozygosity, a major genetic event causing inactivation of tumor suppressor genes. Despite of their central role in genomic sequence analysis, little attention has been devoted to the problem of matching sequences allowing for this kind of chromosomal alteration. In this paper we investigate the \emph{approximate string matching} problem when the edit operations are non-overlapping unbalanced translocations of adjacent factors. In particular, we first present a $O(nm^3)$-time and $O(m^2)$-space algorithm based on the dynamic-programming approach. Then we improve our first result by designing a second solution which makes use of the Directed Acyclic Word Graph of the pattern. In particular, we show that under the assumptions of equiprobability and independence of characters, our algorithm has a $O(n\log^2_{\sigma} m)$ average time complexity, for an alphabet of size $\sigma$, still maintaining the $O(nm^3)$-time and the $O(m^2)$-space complexity in the worst case. To the best of our knowledge this is the first solution in literature for the approximate string matching problem allowing for unbalanced translocations of factors.
Exploring Spanish Corpora for Portuguese Coreference Resolution

The task of coreference resolution has attracted a great deal of attention in the literature due to its importance in deep language understanding, and its potential as a subtask in a variety of complex natural language processing problems. We experiment with different methods for generating training data and architectures for extracting meaningful mention representations. Coreference resolution in lesser-resourced languages is challenging, and transfer learning is a promising technique to overcome the comparatively smaller available corpora. We explore direct transfer learning from Spanish to Portuguese. We present state-of-the-art systems on both Spanish and Portuguese, and report promising results in a cross-language setting.
Application of Two-Constant Feedback Quasi-Orthogonal Space Time Block Coding in MIMO Communication Systems

This paper presents the application of quasi-orthogonal space time block coding (QO-STBC) with two constant feedback scheme. The feedback scheme employs two-constant feedback paths. This is applied to four transmit and N receiver antennas to achieve full transmit diversity gain and full data transmission rate with simple-wise decoding complexity at the receiver side. This proposed feedback scheme achieves full diversity order of 4 ×N at the receiver side by canceling the self-interference from adjacent symbols in QO-STBC codeword. The simulation results confirm that the feedback scheme significantly improves the performance over all existing feedback methods with reduced computational complexity and is based on the channel state information (CSI) available at the receiver side.
Estimating the Topology of Neural Networks from Distributed Observations

We address the problem of estimating the effective connectivity of the brain network, using the input stimulus model proposed by Izhikevich in [1], which accurately reproduces the behaviour of spiking and bursting biological neurons, whilst ensuring computational simplicity. We first analyse the temporal dynamics of neural networks, showing that the spike propagation within the brain can be modelled as a diffusion process. This helps prove the suitability of NetRate algorithm proposed by Rodriguez in [2] to infer the structure of biological neural networks. Finally, we present simulation results using synthetic data to verify the performance of the topology estimation algorithm.
Multi-Resolution Reconstructions from Compressive Spectral Coded Projections

Compressive spectral coded projections are attained by an imaging detector as a spatial-spectral field traverses diverse optical elements such as a coded aperture and a dispersive element. Compressed sensing reconstruction algorithms are used to recover the underlying data cube at the resolution enabled by the captured projections. Such reconstructions, however, are computationally expensive because of the data dimensions. In this paper, a multi-resolution (MR) reconstruction approach is presented, such that several versions of the data cube can be recovered at different spatial resolutions, by employing gradient intensity maps. Simulations show that this approach overcomes interpolation results in up to 3dB of PSNR in noisy scenarios.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome in the Era of the Human Microbiome: Persistent Pathogens Drive Chronic Symptoms by Interfering With Host Metabolism, Gene Expression, and Immunity

The illness ME/CFS has been repeatedly tied to infectious agents such as Epstein Barr Virus. Expanding research on the human microbiome now allows ME/CFS-associated pathogens to be studied as interacting members of human microbiome communities. Humans harbor these vast ecosystems of bacteria, viruses and fungi in nearly all tissue and blood. Most well-studied inflammatory conditions are tied to dysbiosis or imbalance of the human microbiome. While gut microbiome dysbiosis has been identified in ME/CFS, microbes and viruses outside the gut can also contribute to the illness. Pathobionts, and their associated proteins/metabolites, often control human metabolism and gene expression in a manner that pushes the body toward a state of illness. Intracellular pathogens, including many associated with ME/CFS, drive microbiome dysbiosis by directly interfering with human transcription, translation, and DNA repair processes. Molecular mimicry between host and pathogen proteins/metabolites further complicates this interference. Other human pathogens disable mitochondria or dysregulate host nervous system signaling. Antibodies and/or clonal T cells identified in patients with ME/CFS are likely activated in response to these persistent microbiome pathogens. Different human pathogens have evolved similar survival mechanisms to disable the host immune response and host metabolic pathways. The metabolic dysfunction driven by these organisms can result in similar clusters of inflammatory symptoms. ME/CFS may be driven by this pathogen-induced dysfunction, with the nature of dysbiosis and symptom presentation varying based on a patient's unique infectious and environmental history. Under such conditions, patients would benefit from treatments that support the human immune system in an effort to reverse the infectious disease process.
SequentialSegNet: Combination with Sequential Feature for Multi-Organ Segmentation

Multi-organ segmentation from computed tomography (CT) images is essential for computer aided diagnosis (CAD), and recent advances in fully convolutional networks (FCNs) for volumetric image segmentation have demonstrated the importance of leveraging spatial information. In this paper, we propose a novel framework called SequentialSegNet, which efficiently combines features within a single CT image (intra-slice) and among multiple adjacent images (inter-slice) for a multi-organ segmentation. Experimental results show that our approach can effectively improve the segmentation performance on both large-size and small-size abdominal organs including liver, spleen and gallbladder.
UAV-Aided NOMA Networks with Optimization of Trajectory and Precoding

The explosive data traffic and connections in 5G networks require the use of non-orthogonal multiple access (NOMA) to accommodate more users. Unmanned aerial vehicle (UAV) can be exploited with NOMA to improve the situation further. In this paper, we propose a UAV-assisted NOMA network, in which the UAV and base station (BS) cooperate with each other to serve ground users simultaneously. First, the sum rate of the UAV-served users is maximized via alternate user scheduling and UAV trajectory, with its interference to the BS-served users below a threshold. Then, the optimal NOMA precoding vectors are obtained to cancel the interference from the BS to the UAV-served user. Numerical results are provided to evaluate the effectiveness of the proposed algorithms for the hybrid NOMA and UAV network.
Dedicated Beam-based Channel Training Technique for Millimeter Wave Communications with high Mobility

In this paper, we propose a new beam training framework to cope with mobility scenarios in millimeter wave communications. When a position of the mobile changes, the basestation needs to perform beam training frequently to track the time-varying channel, which leads to significant training overhead in radio resources. In order to alleviate this problem, we propose a “dedicated beam training” which serves only users under high mobility. Combined with conventional common beam training, the proposed dedicated beam training can allow the high mobility users to acquire channels with a small number of training beams exploiting the location information of the target user. The optimal selection of the training beams is formulated such that the lower bound of the angle of departure (AoD) estimate is minimized over the beam codebook indices given the estimate of the previous AoD state. Our numerical evaluation demonstrates that the proposed beam training scheme can maintain good channel estimation performance with less training overhead than the conventional beam training protocol.
Vector Field Analysis for optimization of the Tool Path of the Five-Axis Milling Machine

The paper presents a method to increase the production rate of a five axis milling machine through improving how to change the coordinates of the to-be-milled points from the “workpiece” to the “machine” coordinate system, and then deciding the cutting path by maximizing the material removal rate. The method to optimize the tool path is based on the analysis of a vector field of optimal directions which can be pre-computed for a particular machine configuration. The method includes grid generation, space filling curves and vector field decomposition using rotationally invariant moments. The proposed tool path performs the machining 1.2-6 times faster than conventional methods.
Piano Legato-Pedal Onset Detection Based on a Sympathetic Resonance Measure

In this paper, the problem of legato pedalling technique detection in polyphonic piano music is addressed. We propose a novel detection method exploiting the effect of sympathetic resonance which can be enhanced by a legato-pedal onset. To measure the effect, a specific piano transcription was performed using the templates of pre-recorded isolated notes, from which partial frequencies were estimated. This promotes the acquisition of residual components associated to the weak co-excitation of damped notes due to the legato pedalling technique. Features that represent the sympathetic resonance measure were extracted from residuals. We finally used a logistic regression classifier to determine the existence of legato-pedal onsets.
Practical and Fast Causal Consistent Partial Geo-Replication

Distributed storage systems are a fundamental component of large-scale Internet services. To keep up with the increasing expectations of users regarding availability and latency, the design of data storage systems has evolved to achieve these properties by exploiting techniques such as partial replication, geo-replication, and weaker consistency models. How to combine all these techniques in a single solution in a practical and efficient way is highly challenging. In this paper we propose a novel replication scheme that can offer causal+ consistency in a geo-distributed scenario with partial replication, where datacenters replicate different portions of the entire database. We leverage on a recently proposed methodology that decouples the propagation of data and causality-tracking metadata. Our solution presents a novel causal consistency tracking and enforcing algorithm, focusing on maximizing parallelism in the execution of remote operations which, as we show, has a significant influence on the performance of a partially replicated system. We also propose and implement a design to integrate our solution in the popular Cassandra database. Experimental results show that, by exploring a new position in the trade-off between throughput and data visibility (by balancing the execution of local and remote operations, respectively), our solution presents overall good performance.