Miracle-dz/UniFAR-FTUs huggingface dataset:
Essence Vector-Based Query Modeling for Spoken Document Retrieval

Spoken document retrieval (SDR) has become a prominently required application since unprecedented volumes of multimedia data along with speech have become available in our daily life. As far as we are aware, there has been relatively less work in launching unsupervised paragraph embedding methods and investigating the effectiveness of these methods on the SDR task. This paper first presents a novel paragraph embedding method, named the essence vector (EV) model, which aims at inferring a representation for a given paragraph by encapsulating the most representative information from the paragraph and excluding the general background information at the same time. On top of the EV model, we develop three query language modeling mechanisms to improve the retrieval performance. A series of empirical SDR experiments conducted on two benchmark collections demonstrate the good efficacy of the proposed framework, compared to several existing strong baseline systems.
3D-Hog Embedding Frameworks for Single and Multi-Viewpoints Action Recognition Based on Human Silhouettes

Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-Histogram of Oriented Gradient (3D-HOG) embedding. We exploit fast-computational approaches involving Principal Component Analysis (PCA) over the local feature spaces for compactly describing actions as combinations of local gestures and L2-Regularized Logistic Regression (L2-RLR) for learning the action model from local features. Outperforming results on Weizmann and i3DPost datasets confirm efficacy of the proposed approaches as compared to the baseline method and other works, in terms of accuracy and robustness to appearance changes.
The Role of 14-MeV Neutrons in Light Element Nucleosynthesis

The 14-MeV neutron-induced nuclear reactions which might contribute to better understanding of the so-called “cosmological lithium problem” have been discussed. The big bang nucleosynthesis theory predicted <sup>7</sup>Li overestimated value could be lowered by <sup>2</sup>n-induced reactions on Li isotopes and/or <sup>7</sup>Be destruction via resonance in the d + <sup>7</sup>Be → <sup>9</sup>B process. Proposals for <sup>6</sup>Li + <sup>2</sup>n and <sup>7</sup>Li + <sup>2</sup>n reactions’ cross section evaluations and measurements are discussed in some details. In addition, we discuss also a preliminary measurements of n–n coincidences from the <sup>10</sup>B(n,2n) <sup>9</sup>B reaction with two neutron detectors placed outside the cone of 14-MeV tagged neutron beam. From the time-of-flight measurements and known geometry, we tried to deduce the neutron energies and calculate the <sup>9</sup>B missing mass spectrum. Improvements for better measurements are suggested.
TDoA and Doppler Shift Estimation Method for Passive Acoustic Location of Flying Vehicles

In this paper, the authors propose an alternative method of passive detection and tracking of flying vehicles. It consists of special hardware and software designed and developed by the authors. The software consists of an alternative procedure of time and frequency shift estimation, which is based on resampling of an acoustic signal. This makes calculations faster in comparison with traditional methods. Combination of several socalled TDoA (time difference of arrival) estimates provides the graphical representation of flying vehicle position on the coordinate plane at any time moment. In addition, the estimation of Doppler shift helps to get target velocity and to distinguish moving and stationary targets.
Optimizing the Energy Efficient VM consolidation by a Multi-Objective Algorithm

Optimizing energy efficient Virtual Machine Consolidation (VMC) in a cloud computing environment, which is a non-linear multi-objective NP-hard problem, plays a vital role in decreasing energy consumption, and increasing Quality of Service (QoS). In this paper, VMC is formulated as a multi-objective optimization problem, which has three conflicting objectives, power consumption, Service Level Agreements Violation (SLAV) and Mean Time Before Host Shutdown (MTBHS). We propose a multi-objective optimization algorithm based on Multi-Objective Sine Cosine Algorithm (MOSCA) for the VMC. We evaluate the performance of our model by applying two multi-objective algorithms, namely, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD) and Non-dominated Sorting Genetic Algorithm (NSGAII). Our research mainly focus on two tasks, i.e.,evaluating and comparing the multi-objective algorithms to find out the optimal solution and develop a MOSCA based algorithm to solve the proposed VMC model. The simulation results illustrated that the propose multi-objective model meets the optimal solutions amongst the three conflicting objectives, which significantly reduces the power consumption, SLAV and maximize the MTBHS. It got the best performance according to the Multi-objective Optimization Problem (MOP) indicators.
Object-Oriented Anomaly Detection in Surveillance Videos

Detecting and localizing anomalies in surveillance videos is an ongoing challenge. Most existing methods are patch or trajectory-based, which lack semantic understanding of scenes and may split targets into pieces. To handle this problem, this paper proposes a novel and effective algorithm by incorporating deep object detection and tracking with full utilization of spatial and temporal information. We propose a new dynamic image by fusing both appearance and motion information and feed it into object detection network, which can detect and classify objects precisely even in dim and crowd scenes. Based on the detected objects, we develop an effective and scale-insensitive feature, named histogram variance of optical flow angle (HVOFA), together with motion energy to find abnormal motion patterns. In order to further discover missing anomalies and reduce false detected ones, we conduct a post-processing step with abnormal object tracking. The proposed algorithm outperforms state-of-the-art methods on standard benchmarks.
Recognition self-awareness for active object recognition on depth images

We propose an active object recognition framework that introduces the recognition self-awareness , which is an intermediate level of reasoning to decide which views to cover during the object exploration. This is built ﬁrst by learning a multi-view deep 3D object classiﬁer; subsequently, a 3D dense saliency volume is generated by fusing together single-view visualization maps, these latter obtained by computing the gradient map of the class label on different image planes. The saliency volume indicates which object parts the classiﬁer considers more important for deciding a class. Finally, the volume is injected in the observation model of a Partially Observable Markov Decision Process (POMDP). In practice, the robot decides which views to cover, depending on the expected ability of the classiﬁer to discriminate an object class by observing a speciﬁc part. For example, the robot will look for the engine to discriminate between a bicycle and a motorbike, since the classiﬁer has found that part as highly discriminative. Experiments are carried out on depth images with both simulated and real data, showing that our framework predicts the object class with higher accuracy and lower energy consumption than a set of alternatives.
Camera Localization in Outdoor Garden Environments Using Artificial Landmarks

In this paper, we present an outdoor monocular camera localization system based on artificial markers and test its performance in one of the test gardens of the TrimBot2020 project, in Wageningen. We use ArUco markers to construct a map of the environment and to subsequently localize the camera position within it. We combine the localization algorithm based on ArUco with a Kalman filter to smooth the trajectory and improve the localization stability with respect to fast movements of the camera, and blurred or noisy images. We recorded two sequences, with resolution 480p and l080p respectively, in the TrimBot2020 garden. We compare the localization performance of ArUco with a keypoint-based approach, namely ORB-SLAM2. We analyze and discuss the strengths and problems of both marker- and keypoint-based approaches on the considered sequences. The performed comparison suggests that the two approaches might be fused to jointly improve re-localization and reduce the drift in pose estimation.
A Medal in the Olympics Runs in the Family: A Cohort Study of Performance Heritability in the Games History

Introduction: Elite performance in sports is known to be influenced by heritable components, but the magnitude of such an influence has never been quantified. Hypothesis/Objectives: We hypothesized that having a former world-class champion in the family increases the chances of an athlete to repeat the achievement of her or his kinship. We aimed to measure the heritability of a medal in the Olympic Games (OG) among Olympians and to estimate the percentage of the genetic contribution to such a heritance. Study Design: Twin-family study of a retrospective cohort. Methods: All the 125,051 worldwide athletes that have participated in the OG between 1896 and 2012 were included. The expected probability to win a medal in the OG was defined as the frequency of medallists among Olympians without any blood kinship in the OG. This expected probability was compared with the probability to win a medal for Olympians having a kinship (grandparent, aunt/uncle, parent, or siblings) with a former Olympian that was a (1) non-medallist or (2) medallist. The heritability of the genetically determined phenotype (h2) was assessed by probandwise concordance rates among dizygotic (DZ) and monozygotic (MZ) twins (n = 90). Results: The expected probability to win a medal in the OG was 20.4%. No significant difference of medal probability was found in the subgroups of Olympians with a Non-medallist kinship, except among siblings for whom this probability was lower: 13.3% (95% CI 11.2–14.8). The medal probability was significantly greater among Olympians having a kinship with a former Olympic Medallist: 44.4% for niece/nephew (33.7–54.2); 43.4% for offspring (37.4–48.6); 64.8% for siblings (61.2–68.8); 75.5% for DZ twins (63.3–86.6); and 85.7% for MZ twins (63.6–96.9); with significantly greater concordance between MZ than DZ (p = 0.01) and h2 estimated at 20.5%. Conclusion: Having a kinship with a former Olympic medallist is associated with a greater probability for an Olympian to also become a medallist, the closer an athlete is genetically to such kinship the greater this probability. Once in the OG, the genetic contribution to win a medal is estimated to be 20.5%.
Towards a Neuro-Inspired No-Reference Instrumental Quality Measure for Text-to-Speech Systems

Subjective evaluation of synthesized speech is not an easy task as various quality dimensions can be affected, including naturalness, prosody, pronunciation, and continuity, to name a few. Evaluations typically rely on naive listeners, thus more closely representing the consumers of commercial products. As such, while the results of these costly and time consuming tests may provide text-to-speech (TTS) system developers with feedback on the perceived quality and acceptability of their devices, it provides little information on what the source of the problems are and what can be done about it. In this paper, we propose the use of neuroimaging to probe the unconscious cognitive processing of naive listeners as they listen to synthesized speech generated by different systems of varying quality. The obtained neural insights have allowed us to extract a small subset of very relevant features from the speech signals and to use these features to build a simple, no-reference instrumental quality metric specifically tailored to TTS speech. The metric is tested on an unseen dataset and shown to significantly outperform a benchmark algorithm.
Downlink Power Allocation for CoMP-NOMA in Multi-Cell Networks

This paper considers the problem of dynamic power allocation in the downlink of multi-cell networks, where each cell utilizes non-orthogonal multiple access (NOMA)-based resource allocation. Also, coordinated multi-point (CoMP) transmission is utilized among multiple cells to serve users experiencing severe inter-cell interference (ICI). Under this CoMP- NOMA framework, CoMP transmission is applied to a user experiencing less distinctive channel gain with multiple base stations (BSs)/cells (i.e., severe ICI-prone user) and non-CoMP transmission (i.e., transmission without any coordination among multiple BSs) is applied to a user experiencing dominating channel gain with only one BS/cell, while NOMA is utilized at each BS to schedule CoMP and non-CoMP users over the same transmission resources, i.e., time, spectrum and space. After discussing various CoMP- NOMA models for downlink power allocation in multi-cell networks, we focus on a joint transmission CoMP- NOMA (JT-CoMP-NOMA) model. For the JT-CoMP-NOMA model, an optimal joint power allocation problem is formulated and the solution is derived for each CoMP- set consisting of multiple cooperating BSs (i.e., CoMP BSs). To avoid the huge computational complexity of the joint power optimization approach, we propose a distributed power optimization approach at each cooperating BS whose optimal solution is independent of the solution of other coordinating BSs. The distributed solution for the joint power optimization problem is validated and numerical performance evaluation is carried out for the proposed CoMP- NOMA models including JT-CoMP-NOMA and coordinated scheduling CoMP- NOMA (CS-CoMP-NOMA). The obtained results reveal significant gains in spectral and energy efficiency in comparison with conventional CoMP- orthogonal multiple access (CoMP-OMA) systems.
Optimized Environment Exploration for Autonomous Underwater Vehicles

Achieving full autonomous robotic environment exploration in the underwater domain is very challenging, mainly due to noisy acoustic sensors, high localization error, control disturbances of the water and lack of accurate underwater maps. In this work we present a robotic exploration algorithm for underwater vehicles that does not rely on prior information about the environment. Our method has been greatly influenced by many robotic exploration, view planning and path planning algorithms. The proposed method constitutes a significant improvement over our previous work [1]: Firstly, we refine our exploration approach to improve robustness; Secondly, we propose an alternative map representation based on the quadtree data structure that allows different relevant queries to be performed efficiently, reducing the computational cost of the viewpoint generation process; Thirdly, we present an algorithm that is capable of generating consistent maps even when noisy sonar data is used. The aforementioned contributions have increased the reliability of the algorithm, allowing new real experiments performed in artificial structures but also in more challenging natural environments, from which we provide a 3D reconstruction to show that with this algorithm full optical coverage is obtained.
Design of a 13.56 MHz dc-to-dc resonant converter using an impedance compression network to mitigate misalignments in a wireless power transfer system

This paper presents the design and implementation of a high-frequency resonant converter with an impedance compression network (ICN) to correct horizontal alignment variations between coils in a wireless power transfer (WPT) system. Although magnetic resonant coupling (MRC) coils provide high efficiency for charging mid-range WPT applications, any misalignment between them causes a coil-impedance change and significantly affects performance of the resonant inverters. In order to mitigate variations in coil impedance, we propose an ICN that simultaneously compresses magnitude and phase changes of coil impedance. An ICN consists of a resistance compression network (RCN) to compress magnitude variations, and phase compression network (PCN) to remove phase shifts. The main advantage of an ICN is that it requires only lossless components such as inductors or capacitors. Also, by using Smith chart, which is generally used in RF circuit design to easily calculate load variations, we can effectively reduce phase shifts in power electronics circuits with load variations. We connected the ICN between a class $\Phi_{2}$ inverter and MRC coils and then added a class DE rectifier to provide dc-to-dc operation. As a result, we maintained not only zero voltage switching (ZVS) and zero dv/dt operation in a class $\Phi_{2}$ inverter, but also constant efficiency of the whole system when the alignment between coils varied.
Position-Squeeze and Excitation Block for Facial Attribute Analysis

In this paper, we focus on multiple facial attribute recognition in a single Convolutional Neural Network (CNN). We propose a Position-Squeeze and Excitation (PSE) module, which incorporates the spatial information of different attributes into CNN training. By adding a lateral branch which computes a weight mask for each attribute, the PSE module can help the network learn features from where attributes naturally appear. More-over, the module can be added as a branch to any classical convolutional neural network to perform end-to-end multi-attribute classiﬁcation. Experiments show that, our solution has achieved high accuracy on both the CelebA dataset and the LFWA dataset.
Adaptive Oscillator-Based Control for Active Lower-Limb Exoskeleton and its Metabolic Impact

We developed a robotic lower-limb exoskeleton for those who have weakened muscle due to aging and experience difficulty in walking or getting up without help. The exoskeleton covering both limbs from the feet to the waist has 6 electric actuators in the hip abduction/adduction, hip extension/flexion and knee extension/flexion joints. For users with volitional motion, delivering assistance power according to their intention is a challenging task. We propose an adaptive oscillator-based controller to assist users walk in the lower-limb exoskeleton. To adapt to changes in walking speed and environment, motion command from the controller is modulated by estimate walking speed and walking environment recognized as one of the following categories: level ground, stairs up/down and slope up/down. Experimental results demonstrate the feasibility of the proposed environment recognition method and the impact of assistance on the metabolic cost of walking on level and inclined treadmills.
Energization and Start-up of Modular Three-stage Solid State Transformers

Grid-connection and startup of solid state transformers (SST) based on modular topologies are studied in this work. A simple solution is implemented to feed the auxiliary electronics (i.e. sensors, control boards, drivers, etc.) of each SST module. It is based on feeding this circuitry from the DC links of the module using Auxiliary Power Supplies (APS). However, this structure leads to a problematic energization of the SST due to the threshold input voltage of the APS. Before the DC link capacitors are charged to this voltage threshold, the APSs are not functional and consequently SST energization and connection to the grid are performed without monitoring. Although startup in this situation is problematic, adequate procedure can prevent unwanted transients. In this paper, a step-by-step simple procedure is proposed for energizing a three-stage SST based on a Cascaded H-Bridge (CHB) modular topology. Simulation results as well as experimental tests are presented.
The Pathological Features of Colorectal Cancer Determine the Detection Performance on Blood ctDNA

Background and Aim: Methylated SEPT9 is a novel circulating tumor DNA marker for colorectal cancer, while the effects of various colorectal cancer clinicopathological factors on its detection performance have not been fully evaluated. This study aims to investigate the significance of the clinicopathological factors on methylated SEPT9 performance in a symptomatic endoscopy cohort, with a specific focus on colorectal cancer. Methods: A total of 1160 participants were recruited in this study, including 300 patients with colorectal cancer, 122 patients with adenoma, 103 patients with hyperplastic polyps, 568 normal participants (no evidence of disease), and 67 patients with other gastrointestinal diseases. Peripheral blood samples of these participants were collected from 3 Chinese hospitals, and the methylated SEPT9 level was measured using the Epi proColon 2.0 assay. Results: Cancer stage, size, and invasion depth were positively correlated with the detection sensitivity, while no difference in sensitivity was identified among cancers at various locations. Infiltrative colorectal cancer exhibited higher sensitivity than ulcerative and protrude colorectal cancer, while no difference in sensitivity was observed among assessed histological types. The colorectal cancer differentiation showed a clear correlation with the cancer stage, and moderate and poorly differentiated colorectal cancer exhibited higher sensitivity than well-differentiated colorectal cancer. Furthermore, colorectal cancer with distal metastasis (M1) showed higher sensitivity than those without any metastasis, while colorectal cancer with lymph node metastasis (N1 and N2) did not show statistical significance compared to those without it. Finally, local vessel or nerve invasion did not affect the sensitivity. Conclusion: Factors that reflect the colorectal cancer intrinsic properties, including cancer stage, size, invasion depth, classification, differentiation, and metastasis, exhibited significant effect on the mSEPT9 detection performance.
Collaboratively Learning Latent Factors and Correlations for New Paper Influence Predication

There are an increasing number of papers published every year. It is desired for researchers to find the new high-quality papers, which is also a challenging task due to the lack of citation information. In this paper, we propose a novel method to predicate a new paper influence by collaboratively learning the latent vectors of paper features and correlations. We propose the concept topic related authority to integrate the dynamic topic model with paper citations so as to learn how content and authors influence a paper quality. We adopt the Factorization Machine method to collaboratively learn the latent vectors of correlations between different paper features. Comparing with traditional methods, it does not require the citation information to evaluate a paper quality, which is appropriate for new published papers. We conduct extensive evaluation against a real dataset crawled from ACM Digital Library. The results show that our method outperforms the other methods.