Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing secrecy of delay-constrained system in cooperative relay environment using rateless codes

We analyze secrecy of a dual-hop cooperative system consisting of multiple decode-and-forward relays in delay-constrained environment using rateless code, where there exists a separate time limit for both the hops. During the first hop, source carries out rateless coded transmission in the allotted time. Relay nodes which have successfully acquired target entropy forwards the information to the destination in the given time limit, using same rateless code at each node. Simultaneous transmissions by these relay nodes leads to faster accumulation of energy at the destination thereby reducing the decoding time. We have derived a closed-form expression of the average time for the first phase which allows fixed number of relays to decode the information. Analysis for achieving the time limit by these relays in the second phase has also been evaluated. Simulation results show that there exist an optimal number of decoding relays that leads to the lowest expenditure of the total time. Further, a closed-form expression for secrecy outage probability of this delay-constrained dual hop system has been derived.
pseudospectral-hybrid ﬁnite

— Chebyshev pseudospectral-hybrid finite element schemes are proposed for two-dimensional vorticity équation. Some approximation results in non-isotropic Sobolev spaces are presented. The generalized stability and the convergence are proved. The hybrid finite element approximation provides the optimal convergence rate. The numerical results show the advantages of the approach. The technique in this paper is also applicable to other nonlinear problems in computational fluid dynamics.
A statistical approach for offline signature verification using local gradient features

Signature is widely used as a means of personal verification which emphasizes the need for a signature verification system. Often the single signature feature may produce unacceptable error rates. Features used in this method are mainly local key-point feature that deals with the orientation around each key-point. Before extracting the features, preprocessing of a scanned image is necessary to isolate the region of interest part of a signature and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures are to be authenticated by the system. For extracting the feature, key-points of the image are detected. For each point, orientation around the point is calculated as the feature. By matching the features of sample signature and testing signature decision is taken. If a query signature is in the acceptance range then it is an authentic signature, otherwise it is a forged signature.
Human Neuroscience

Studies have also implicated the premo-tor cortex (PMC) in learning new visual categories, especially for categories that require a stereotyped motor response (Halsband and Freund, 1990; Boettiger and D'Esposito, 2005). Though PFC, PMC, BG, and MTL have shown to be important to initially learning visual categories, damage to these regions does not consistently produce defi cits in the automatic recognition of well-established categories (Freedman et al., 2001; Zgaljardic et al., 2003; Squire et al., 2004). There are some reports, however, of patients with MTL damage that present with object discrimination diffi culties, suggesting that the MTL may facilitate higher-level object processing (Lee et al., 2005). Additionally, lateral frontal regions have feedback connections to object recognition regions in ITC and studies have shown that lateral frontal regions modulate ITC to help facilitate successful recognition (Fuster et al., 1985; Barcelo et al., 2000; Gazzaley et al., 2007). A goal of the current study is to further characterize the supporting roles that the PFC, PMC, BG, and MTL play in well-established categorization and how the interactions between these regions and ITC change with category learning. In contrast to patients with PFC, PMC, BG, and MTL lesions, patients with ITC lesions present with severe defi cits in recognizing well-established categories (Warrington, 1982). These patients can often describe an object in their visual fi eld in great detail, including color, texture, and shape but are unable to integrate this information to identify the object. Thus, ITC has a crucial role in organizing perceptual features into an integrated percept, essentially linking perception with recognition. In addition to this role in representing and recognizing well-established categories, ITC has also been shown
Multi-constrained routing based on simulated annealing

Multi-constrained quality-of-service routing (QoSR) is to find a feasible path that satisfies multiple constraints simultaneously, as an NPC problem, which is also a big challenge for the upcoming next-generation networks. In this paper, we propose SA/spl I.bar/MCP, a novel heuristic algorithm, by applying simulated annealing to Dijkstra's algorithm. This algorithm first uses a nonlinear energy function to translate multiple QoS weights into a single metric and then seeks to find a feasible path by simulated annealing. The paper outlines simulated annealing algorithm and analyzes the problems met when we apply it to QoSR. Extensive simulations demonstrate that SA/spl I.bar/MCP has good scalability regarding both network size and the number of QoS constraints with high performance. Furthermore, when most QoS requests are feasible, the running time of SA/spl I.bar/MCP is about O(k(m+nlogn)), which is only k times that of the traditional Dijkstra's algorithm, where k is the number of QoS constraints.
A Study on 5G Multiband and Wideband MIMO Antenna for Wireless Application

Multiple-input, multiple-output (MIMO) technology has become a very promising wireless communication method. It presents a possible solution for solving traffic capacity constraints in high-speed wireless broadband network access. MIMO systems enhance the efficiency and performance of wireless communication systems through the use of multiple antennas at both the transmitting and receiving ends. This paper provides a comprehensive analysis of multiple-input multiple-output (MIMO) antenna design methodologies for fifth-generation (5G) and beyond wireless communication systems. This paper provides a thorough analysis of the performance characteristics of Multiple-Input Multiple-Output (MIMO) systems, focusing on isolation, efficiency, gain, antenna type, and Envelope Correlation Coefficient (ECC). In addition, the benefits of MIMO technology over traditional Single-Input Single-Output (SISO) systems are discussed.
DOR-SLAM: A Visual SLAM Based on Dynamic Object Removal for Dynamic Environments

vSALM is a system that enables precise positioning of a mobile robot in an unknown environment and generates corresponding 3D maps. However, the feature matching process of the visual odometry in the SLAM system is easily disturbed by dynamic objects in real-world scenarios, such as moving pedestri-ans and vehicles, which will reduce the accuracy of positioning and mapping. To address this issue. , a visual SLAM system with dyna-mic object removal. , called as Dynamic Object Removal SLAM (DOR-SLAM). , is proposed based on the ORB-SLAM2 system. Firstly. , a dynamic probability calculation formula of pixels based on optical flow and semantic information has been proposed to determine the motion state of object. Then. , a process of instance segmentation is used to segment the recognized dynamic objects in the image frames. Next. , these dynamic objects will be removed by using a flow-guided video inpainting network. , while inpainting the background. To improve system efficiency. , a simplified bidirecti-onal optical flow calculation method is proposed and applied to the video inpainting network. The effectiveness of the proposed meth-od is evaluated with dynamic RGB-D sequences from the TUM dataset. The results of the experiments indicate that the proposed DOR-SLAM can improve the accuracy of localization in dynamic environments while maintaining high computational efficiency.
Cross-Modal and Intra-Modal Characteristics of Visual Function and Speech Perception Performance in Postlingually Deafened, Cochlear Implant Users

Evidence of visual-auditory cross-modal plasticity in deaf individuals has been widely reported. Superior visual abilities of deaf individuals have been shown to result in enhanced reactivity to visual events and/or enhanced peripheral spatial attention. The goal of this study was to investigate the association between visual-auditory cross-modal plasticity and speech perception in post-lingually deafened, adult cochlear implant (CI) users. Post-lingually deafened adults with CIs (N = 14) and a group of normal hearing, adult controls (N = 12) participated in this study. The CI participants were divided into a good performer group (good CI, N = 7) and a poor performer group (poor CI, N = 7) based on word recognition scores. Visual evoked potentials (VEP) were recorded from the temporal and occipital cortex to assess reactivity. Visual field (VF) testing was used to assess spatial attention and Goldmann perimetry measures were analyzed to identify differences across groups in the VF. The association of the amplitude of the P1 VEP response over the right temporal or occipital cortex among three groups (control, good CI, poor CI) was analyzed. In addition, the association between VF by different stimuli and word perception score was evaluated. The P1 VEP amplitude recorded from the right temporal cortex was larger in the group of poorly performing CI users than the group of good performers. The P1 amplitude recorded from electrodes near the occipital cortex was smaller for the poor performing group. P1 VEP amplitude in right temporal lobe was negatively correlated with speech perception outcomes for the CI participants (r = -0.736, P = 0.003). However, P1 VEP amplitude measures recorded from near the occipital cortex had a positive correlation with speech perception outcome in the CI participants (r = 0.775, P = 0.001). In VF analysis, CI users showed narrowed central VF (VF to low intensity stimuli). However, their far peripheral VF (VF to high intensity stimuli) was not different from the controls. In addition, the extent of their central VF was positively correlated with speech perception outcome (r = 0.669, P = 0.009). Persistent visual activation in right temporal cortex even after CI causes negative effect on outcome in post-lingual deaf adults. We interpret these results to suggest that insufficient intra-modal (visual) compensation by the occipital cortex may cause negative effects on outcome. Based on our results, it appears that a narrowed central VF could help identify CI users with poor outcomes with their device.
Guidelines for the successful treatment of lymphangioma with OK-432.

BACKGROUND/PURPOSE Cystic hygroma or lymphangioma (LA) is a disfiguring benign lesion commonly seen in the neck and face regions in children. Extensive neck/face resectional surgery is frequently performed for this condition, often with disappointing results. An attractive alternative to surgery is injection, sclerosing therapy. OK-432 injection therapy has been characterised as a novel treatment of LA since 1987. Since this first report, there have been a number of articles from different institutions reporting variable success rates with its use. This has resulted in uncertainty and confusion among physicians, surgeons and parents alike as to in whom, when, and how to treat patients with this modality. METHOD A prospective study over a five-year period. Thirty-five patients were injected with this agent, 1 to 4 times, depending on response. RESULT In patients with macrocystic LA, complete regression was achieved in 96 %. Patients with microcystic LA responded poorly or not at all. CONCLUSION The anatomical appearance of the malformation on computerised tomography (or sonar) scan is crucial in determining the treatment strategy taken; macrocystic lesions respond almost universally to OK-432 injections, whereas patients with microcystic disease generally do not respond and should therefore not be injected with this agent.
Comparison between ferroresonance suppression techniques on distribution transformers

Maintaining the continuity of electrical power delivery is of utmost importance to customers. Therefore, maintaining the installation of the electrical network and its parts, especially the transformer, is critical. The ferromagnetic resonance resulted in catastrophic failure to the power system and its component. This is because ferromagnetic resonance leads to a doubling of the current or/and voltage values in the electrical system to several times the rated value. This article presents the investigation of ferromagnetic resonance in distribution transformers. Also, the effect of changing the load on the transformer terminal on the response of ferromagnetic resonance investigation is presented. Furthermore, it is surveyed a comparison of ferromagnetic resonance suppression techniques on distribution transformers. The best of these techniques has been nominated to reduce ferromagnetic resonance. The ferromagnetic resonance is simulated with the employing of PSCAD/EMTDC program.
Liver tumor segmentation method based on U-Net architecture: a review

Liver cancer is a disease with a high incidence and high probability of deterioration, and for the rapid diagnosis of liver disease, CT scans must be used to segment the liver tumors. For the past few years, with the rapid development of deep learning, many deep learning methods for liver tumor segmentation using abdominal computed tomography (CT) images have appeared, and the clinical application of these methods is of important significance for computer-aided diagnosis of liver tumors. The U-Net, with its unique U-shape network structure, exhibits excellent performance in medical image segmentation field and has been extensively utilized in various medical image segmentation applications. In this paper, we summarize the researches of U-Net and its improved networks in CT image segmentation of liver tumors by deep learning methods and classify various U-Net-based convolutional neural networks (CNNs) into 2D (two-dimensional), 3D (three-dimensional), and 2.5D (2.5-dimensional). In this paper, 2D, 3D, and 2.5D convolutional neural networks are summarized. In addition, this paper summarizes the advantages and disadvantages as well as the improvement methods of each type of network, which provides a useful reference for the studies of deep learning based on liver tumor segmentation field. Finally, this paper envisions future research trends for deep learning segmentation methods in the context of liver tumors.
Anomaly Detection in Graph-Based Data Utilizing Graph Topology

This paper presents a novel framework for anomaly detection in complex networks using Graph Neural Networks (GNNs). By incorporating graph topology and node attributes, the proposed model offers improved accuracy and detection capabilities compared to traditional methods. The proposed framework utilizes a graph autoencoder with decoders for node features and the adjacency matrix, capturing the underlying structure of the graph. Topological features such as centrality and clustering coefficients are integrated to enhance the model's performance. To address the complexities of the model, a generic algorithm is employed for efficient optimization. Our approach demonstrates superior performance compared to traditional autoencoders and other machine learning models. The model's effectiveness in identifying anomalous nodes and subgraphs highlights the importance of considering both structural components and node attributes in anomaly detection with GNNs. One of the key advantages of the proposed model is its adaptability to various datasets and node features. Training the model with multiple graphs or instances allows for handling diverse working conditions and uncertainties.
Impacts of Deforestation and Climate Variability on Terrestrial Evapotranspiration in Subarctic China

Although deforestation affects hydrological and climatic variables over tropical regions, its actual contributions to changes in evapotranspiration (ET) over subarctic China remain unknown. To establish a quantitative relationship between deforestation and terrestrial ET variations, we estimated ET using a semi-empirical Penman (SEMI-PM) algorithm driven by meteorological and satellite data at both local and regional scales. The results indicate that the estimated ET can be used to analyse the observed inter-annual variations. There is a statistically significant positive relationship between local-scale forest cover changes (∆F) and annual ET variations (∆ET) of the following form: ∆ET = 0.0377∆F – 2.11 (R2 = 0.43, p < 0.05). This relationship may be due to deforestation-induced increases in surface albedo and a reduction in the fractional vegetation cover (FVC). However, the El Nino/Southern Oscillation (ENSO), rather than deforestation, dominates the multi-decadal ET variability due to regional-scale wind speed changes, but the exact effects of deforestation and ENSO on ET are challenging to quantify.
Multi-Sample Cooperative Training SDN Intrusion Detection System Based on Transformer

Nowadays, there is a relatively low performance i n the intrusion detection of SDN. In this paper, a multi-data ba sed cooperative training strategy and a weighted loss function s trategy are proposed, which is based on Transformer model an d InSDN dataset. Based on the proposed strategies a ADMS-Tr ansformer(Add Dimension Multi-Sample Transformer) model is designed for intrusion detection in SDN network. Through m odel designing, data processing and loss function optimization, a novel loss function with constraining factor is derived and ve rified. In the experiment, the accuracy, recall, and F1 scores of ADMS Transformer were 99.78%, 99.68%, 99.17%, and 99.7 3%, respectively. The ablation experiment was designed to de monstrate that multi-sample collaborative training, sample up dimensioning, and weighted loss functions have improved the e valuation indicators of the model. Compared to other models, i t has increased by 1%-3% percentage points.
The affective dimension of laboratory dyspnea: air hunger is more unpleasant than work/effort.

RATIONALE It is hypothesized that the affective dimension of dyspnea (unpleasantness, emotional response) is not strictly dependent on the intensity of dyspnea. OBJECTIVES We tested the hypothesis that the ratio of immediate unpleasantness (A(1)) to sensory intensity (SI) varies depending on the type of dyspnea. METHODS Twelve healthy subjects experienced three stimuli: stimulus 1: maximal eucapnic voluntary hyperpnea against inspiratory resistance, requiring 15 times the work of resting breathing; stimulus 2: Pet(CO(2)) 6.1 mm Hg above resting with ventilation restricted to less than spontaneous breathing; stimulus 3: Pet(CO(2)) 7.7 mm Hg above resting with ventilation further restricted. After each trial, subjects rated SI, A(1), and qualities of dyspnea on the Multidimensional Dyspnea Profile (MDP), a comprehensive instrument tested here for the first time. MEASUREMENTS AND MAIN RESULTS Stimulus 1 was always limited by subjects failing to meet a higher ventilation target; none signaled severe discomfort. This evoked work and effort sensations, with relatively low unpleasantness (mean A(1)/SI = 0.64). Stimulus 2, titrated to produce dyspnea ratings similar to those subjects gave during stimulus 1, evoked air hunger and produced significantly greater unpleasantness (mean A(1)/SI = 0.95). Stimulus 3, increased until air hunger was intolerable, evoked the highest intensity and unpleasantness ratings and high unpleasantness ratio (mean A(1)/SI = 1.09). When asked which they would prefer to repeat, all subjects chose stimulus 1. CONCLUSIONS (1) Maximal respiratory work is less unpleasant than moderately intense air hunger in this brief test; (2) unpleasantness of dyspnea can vary independently from perceived intensity, consistent with the prevailing model of pain; (3) separate dimensions of dyspnea can be measured with the MDP.
Forgiver Triumphs in Alternating Prisoner's Dilemma

Cooperative behavior, where one individual incurs a cost to help another, is a wide spread phenomenon. Here we study direct reciprocity in the context of the alternating Prisoner's Dilemma. We consider all strategies that can be implemented by one and two-state automata. We calculate the payoff matrix of all pairwise encounters in the presence of noise. We explore deterministic selection dynamics with and without mutation. Using different error rates and payoff values, we observe convergence to a small number of distinct equilibria. Two of them are uncooperative strict Nash equilibria representing always-defect (ALLD) and Grim. The third equilibrium is mixed and represents a cooperative alliance of several strategies, dominated by a strategy which we call Forgiver. Forgiver cooperates whenever the opponent has cooperated; it defects once when the opponent has defected, but subsequently Forgiver attempts to re-establish cooperation even if the opponent has defected again. Forgiver is not an evolutionarily stable strategy, but the alliance, which it rules, is asymptotically stable. For a wide range of parameter values the most commonly observed outcome is convergence to the mixed equilibrium, dominated by Forgiver. Our results show that although forgiving might incur a short-term loss it can lead to a long-term gain. Forgiveness facilitates stable cooperation in the presence of exploitation and noise.
Simultaneous information and power transfer under a non-linear RF energy harvesting model

In the design of simultaneous wireless information and power transfer (SWIPT) systems, it has been typically assumed that energy conversion efficiency is independent from the level of the input power at the energy receiver. On the other hand, in practice the energy conversion efficiency exhibits a nonlinear behavior and highly depends on the input power. This leads to a discrepancy between the practical energy harvesting (EH) hardware available and the resource allocation designs made for the SWIPT systems. This work is concerned with this issue. In particular, we propose a practical quadratic model for the power conversion efficiency in EH circuitry. Comparisons with the constant efficiency models used in conventional SWIPT system design as well as another non-linear model proposed in the literature are made. With its convexity properties together with the good match it provides for the measurement data from practical EH circuitry, the proposed model is shown to be a promising alternative to the existing EH approaches. Using the proposed model, the problem of resource allocation for a multiuser Orthogonal Frequency-Division Multiple Access (OFDMA) system is investigated. The performance improvement due to the usage of the proposed non-linear model is illustrated.