Miracle-dz/UniFAR-FTUs huggingface dataset:
Prevalence of Illness Anxiety Disorder (IAD) and Somatic Symptom Disorder (SSD) among Medical and non Medical Students

1 Assistant Professor, Dept. of Psychiatry, Govt. Medical College Rajnandgaon (C.G.) 2 Professor and Head of department Anesthesiology, Govt. Medical College Rajnandgon (C G) 3 Associate Professor, Dept. of Community Medicine, Govt. Medical College Rajnandgaon (C.G.) 4 Statistician cum Asst. Prof. Dept. of Community Medicine, Govt. Medical College Jagdalpur, (C.G.) Correspondence Author Dr Sharad Manore Assistant Professor Dept. of Psychiatry, Govt. Medical College, Rajnandgaon (C.G.) India Email: drsharadmanore@gmail.com, Mob. No. +91 9422056440 Abstract The concept hypochondriasis is now replaced with two new entities Illness Anxiety Disorder (IAD) and Somatic Symptom Disorder (SSD) by American Psychiatric Association with new criteria of DSM V. As previously it was plagued by ambiguous definition and unreliable with DSM IV criteria. Disease phobia and disease conviction are the two main components of hypochondriasis. The person often searches for the diseases information and lives in extreme distress and anxiety. These hypochondriac characteristic often seen in medical students. Methods: The study targeted 1175 students of which 393 from medical background and 792 was from science university. Students were provided with self assessment questionnaire based on HAI (Health anxiety Inventory). Statistical analysis was done on observation on Anxiety index by using Chi – square on statistical software SPSS Version 22. Results: Prevalence of IAD and SSD and disease phobia level was slightly more seen for medical students compare to non medical as the fact they have to deal with the characteristic of disease throughout their life & has to read about it Conclusion: The overall prevalence for SSD and IAD found as 3.28. % among students. However, further clear approach is needed for its classification with DSM V criteria. This because of co morbidity categorized and vague assessment for patients symptomatic behavior. It recommends for finding prevalence on general population with large sample size also the demographic factors affecting the results with more specific categorization to avoid co morbidity and better technique for identifying the patients.
Pose-Based View Synthesis for Vehicles: A Perspective Aware Method

In this paper, we focus on the problem of novel view synthesis for vehicles. Some previous works solve the problem of novel view synthesis in a controlled 3D environment by exploiting additional 3D details (i.e., camera viewpoints and underlying 3D models). However, in real scenarios, the 3D details are difficult to obtain. In this case, we find that introducing vehicle pose to represent the views of vehicles is an alternative paradigm to solve the lack of 3D details. In novel view synthesis, preserving local details is one of the most challenging problems. To address this problem, we propose a perspective-aware generative model (PAGM). We are motivated by the prior that vehicles are made of quadrilateral planes. Preserving these rigid planes during image generation ensures that image details are kept. To this end, a classic image transformation method is leveraged, i.e., perspective transformation. In our GAN-based system, the perspective transformation is applied to the encoder feature maps, and the resulting maps are regarded as new conditions for the decoder. This strategy preserves the quadrilateral planes all the way through the network, thus shuttling the texture details from the input image to the generated image. In the experiments, we show that PAGM can generate high-quality vehicle images with fine details. Quantitatively, our method is superior to several competing approaches employing either GAN or the perspective transformation. Code is available at: https://github.com/ilvkai/view-synthesis-for-vehicles
Feature-Aware Trilateral Filter With Energy Minimization for 3D Mesh Denoising

Sharp edges and corners are crucial features for high-quality and fine-detailed 3D meshes, which we tend to treat as noises in previous tasks of mesh denoising mistakenly. A challenge arises on how to handle both surfaces and features simultaneously in 3D mesh denoising. Classical works mainly focused on surfaces, whereas features also reasonably need proper processes. In this paper, we propose a feature-aware trilateral filter under the framework of energy minimization for 3D mesh denoising and address the above challenge. Concretely, we treat the challenge as an energy minimization model, where the data and smooth terms are both carefully designed. Apart from this, we introduce a feature-aware trilateral filter for high-quality mesh guidance to the model. In this filter, features are detected and distinguished from surfaces for consequent guidance. With the help of the model and filter, more priors are involved, and we can make global optimization for better denoising performance. We perform experiments on both synthetic and scanned meshes, where both subjective and objective evaluations are displayed to show the superior performance of our method to state-of-the-art methods. Furthermore, two experiments, including ablation tests and parameter sensitivities tests, are conducted to show the robustness and efficiency of our method thoroughly. All these results demonstrate that our method is suitable for robust and efficient performance in future mesh-oriented applications.
Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model

The 2019 novel coronavirus (2019-nCoV) outbreak has been treated as a Public Health Emergency of International Concern by the World Health Organization. This work made an early prediction of the 2019-nCoV outbreak in China based on a simple mathematical model and limited epidemiological data. Combing characteristics of the historical epidemic, we found part of the released data is unreasonable. Through ruling out the unreasonable data, the model predictions exhibit that the number of the cumulative 2019-nCoV cases may reach 76,000 to 230,000, with a peak of the unrecovered infectives (22,000-74,000) occurring in late February to early March. After that, the infected cases will rapidly monotonically decrease until early May to late June, when the 2019-nCoV outbreak will fade out. Strong anti-epidemic may reduce the cumulative infected cases by 40%-49%. The improvement of medical care can also lead to about one-half transmission decrease and effectively shorten the duration of the 2019-nCoV.
A Novel Adaptive Fading Kalman Filter-Based Approach to Time-Varying Brain Spectral/Connectivity Analyses of Event-Related EEG Signals

This paper proposes a novel adaptive fading Kalman filter (AF-KF)-based approach to time-varying brain spectral and functional connectivity analyses of event-related multi-channel electroencephalogram (EEG) signals. By modeling the EEG signals as a time-varying (TV) multivariate autoregressive (MVAR) process, a new AF-KF with variable number of measurements (AF-KF-VNM) is proposed for estimating the spectra of the EEG signals and identifying their functional connectivity. The proposed AF-KF-VNM algorithm uses a new adaptive fading method to adaptively update the model parameters of the KF for improved state estimation and utilizes multiple measurements for better adaptation to the nonstationary signal observations. Experimental results on a simulated data for modeling the TV directed interactions in multivariate neural data show that the proposed AF-KF-VNM method yields better tracking performance than other approaches tested. The proposed algorithm is then integrated into a novel methodology for combined functional Magnetic Resonance Imaging (fMRI) activation maps and EEG spectrum analyses for quantifying the differences in spectrum contents and information flows between the target and standard conditions in a visual oddball paradigm. The results and findings show that the proposed methodology agrees well with the literature and is capable of revealing significant frequency components and information flow involved as well as their time variations.
A Two-Layer Network Model Reveals the Adhesion Scientist Career Stage and Research Topic in China

Despite persistent efforts in untangling the mechanism of scientists switching between research topics, little is investigated the relationship of the career stage switch and the dynamic of research topics. Here, a two-layer network model, the coauthor collaboration network(<inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-layer) and the paper similarity network(<inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-layer) with the coupled information between them, is presented, and the relationship between the scientist career stage switch and research topics is analyzed. Utilizing the data of published papers and authors with at least one at Society of Management Science and Engineering of China, it is found that the career stages in <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-layer display vivid stage switching behaviors respect to the publications, and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-layer has a clear community structure where each community represents a research topic. Computing the frequency of the state switch in <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>-layer, it is also found that scholars with a few topics in their early career have great probability to give up the research work, yet scholars with broad scopes would stay in research fields. The analysis result reveals that the coupling mechanism of the two layers is simulated by a linear correlation of the number of evolving topics with the changing career stages. The career stage switch is closely related with his/her collaborators in China, which the novice collaborating with the reputation scholar would help them find popular topics and projects, while the experienced scholars working with the novices would help to explore new knowledge frontiers.
Deep Convolutional Neural Network Based Image Spam Classification

With the tremendous growth of the internet, cyberspace is facing several threats from the attackers. Threats like spam emails account for 55% of total emails according to the Symantec monthly threat report. Over time, the attackers moved on to image spam to evade the text-based spam filters. To deal with this, the researchers have several machine learning and deep learning approaches that use various features like metadata, color, shape, texture features. But the Deep Convolutional Neural Network (DCNN) and transfer learning-based pre-trained CNN models are not explored much for Image spam classification. Therefore, in this work, 2 DCNN models along with few pre-trained ImageNet architectures like VGG19, Xception are trained on 3 different datasets. The effect of employing a Cost-sensitive learning approach to handle data imbalance is also studied. Some of the proposed models in this work achieves an accuracy up to 99% with zero false positive rate in best case.
Characterization of Radio Access Network Slicing Scenarios With 5G QoS Provisioning

5G systems are envisaged to support a wide range of application scenarios with variate requirements. To handle this heterogeneity, 5G architecture includes network slicing capabilities that facilitate the partitioning of a single network infrastructure into multiple logical networks on top of it, each tailored to a given use case and provided with appropriate isolation and Quality of Service (QoS) characteristics. Network slicing also enables the use of multi-tenancy networks, in which the same infrastructure can be shared by multiple tenants by associating one slice to each tenant, easing the cost-effective deployment and operation of future 5G networks. Concerning the Radio Access Network (RAN), slicing is particularly challenging as it implies the configuration of multiple RAN behaviors over a common pool of radio resources. In this context, this work presents a Markov model for RAN slicing capable of characterizing diverse Radio Resource Management (RRM) strategies in multi-tenant and multi-service 5G scenarios including both guaranteed and non-guaranteed bit rate services. The proposed model captures the fact that different radio links from diverse users can experience distinct spectral efficiencies, which enables an accurate modeling of the randomness associated with the actual resource requirements. The model is evaluated in a multi-tenant scenario in urban micro cell and rural macro cell environments to illustrate the impact of the considered RRM polices in the QoS provisioning.
Secured green communication scheme for interference alignment based networks

In this paper, a new security and green communication scheme is proposed to the interference-alignment (IA) based networks. To achieve a secured communication, full-duplex receivers are utilized to transmit artificial noise (AN). Both the signals and the ANs are used to harvest energy to realize green communication. For these reasons, the feasible conditions of this scheme are analyzed first. Secondly, the average transmission rate, the secrecy performance and the harvested energy are investigated. Thirdly, an optimization scheme of simultaneous wireless information and power transfer (SWIPT) is given to optimize the information transmission and the energy harvesting efficiency. Meanwhile, an improved IA iteration algorithm is designed to eliminate both the AN and the interference. Furthermore, relay cooperation is considered and its system performance is analyzed. The simulations show that the target average transmission rate is not affected by AN, while the secrecy performance can be greatly improved. The energy harvesting efficiency is also better than the traditional schemes. As expected, the average transmission rate further is improved with the relay cooperation.
Design Of A Green Iot Based Water Monitoring System For Metropolitan City.

Water is essential for life. However, waste is a major problem in the world. Although, several research work have proposed the use of IoT as a solution to curb waste and achieve effective billing. However, in developing countries like Nigeria, the application of such technology is challenging since power or energy supply is often times erratic in nature. Taking Tunga Minna, Niger state Nigeria as a case study, it is observed that the rate of power outage is high. This will however, result to large loss of water consumption data that is supposed to be uploaded to the cloud via IoT platform. To reduce the level of outage, this study presents the design of a green IoT based water monitoring system for metropolitan area. The purpose of the design is to ensure minimum probability of outage. The scope of the design is limited to the hardware design. At the end, it is expected that a design with low probability of outage is achieved.
Design of a Boost DC–DC Converter With 82-mV Startup Voltage and Fully Built-in Startup Circuits for Harvesting Thermoelectric Energy

In this letter, a boost dc–dc converter with fully built-in startup circuits has been proposed as a thermoelectric energy harvester. The proposed chip was fabricated using a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> 1P6M mixed-signal standard CMOS process. Neither post-fabrication trimming nor additional external components are needed to startup the converter. Moreover, a new type of low-leakage logic gates is proposed for lowering the minimal startup voltage, and several low-voltage startup circuits are also proposed. According to the measured results, the minimal startup voltage is only 82 mV, and the input voltage range is 60–460 mV. The measured maximal input power is 2.7 mW, the measured quiescent power with 200-mV input voltage is 8.2 <inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula>, and the measured peak efficiency is 78.55%.
A Neural Network Architecture for Information Extraction in Chinese Drug Package Insert

There is a lot of useful information in the medical photocopying materials. The correct extraction and identification of this information are of great significance for the construction of digital medical. In most previous research, researchers have been working on clinical data, and there is little discussion on the extraction of information from Chinese drug package insert. To settle this issue, a neural network model is proposed in this paper. This model uses OCR’s post-document as the data source, which can not only correct these data but also classify sentences. It is mainly composed of three layers: the first layer is employed to correct the data using the language model and the seq2seq model, the second layer is defined by convolution neural network (CNN) aiming to enrich the processed sentences, and another layer is used to determine the label of each sentence. The quantitative experimental results verify the feasibility and validity of the proposed model. In addition, the comparing experiments demonstrate that our method outperforms the regular rule-based approaches, which indicated 4%-6% higher in F1 score.
System-Level Spatiotemporal Offloading With Inter-Cell Mobility Model for Device-to-Device (D2D) Communication-Based Mobile Caching in Cellular Network

We present a new spatiotemporal offloading (STO) framework that can deal with the time-varying traffic demand in an overloaded location using proactive caching during off-peak times. Focusing on a conventional cellular network, the system-wide mobility can be captured by the inter-cell transitions along individual users’ mobility pattern. It is opposed to the existing geographical caching for content placement during off-peak times, which treats a system built around multiple base stations as a single service area, thus limiting its performance to low hit probability. We formulate a system-level STO problem to determine a content placement strategy that minimizes the access link load by delivering the requested data through device-to-device (D2D) links, rather than through a cellular link, particularly targeting potentially overloaded cells. Because its complexity is seriously attributed to the individual users’ cell-by-cell mobility and content preferences, an aggregated flow model has been proposed to reduce the search space by invoking the law of segment conservation. Simulation results clearly demonstrate the effect of the spatiotemporal offloading that can be realized through inter-cell mobility-aware proactive caching. An extremely important implication of the current framework is that a varying traffic demand can be spatiotemporally distributed in a cellular system, overcoming the traditional limitation that arises from conservative or inefficient resource allocation due to a reliance on over-dimensioning.
Deep Learning-Based mmWave Beam Selection for 5G NR/6G With Sub-6 GHz Channel Information: Algorithms and Prototype Validation

In fifth-generation (5G) communications, millimeter wave (mmWave) is one of the key technologies to increase the data rate. To overcome this technology’s poor propagation characteristics, it is necessary to employ a number of antennas and form narrow beams. It becomes crucial then, especially for initial access, to attain fine beam alignment between a next generation NodeB (gNB) and a user equipment (UE). The current 5G New Radio (NR) standard, however, adopts an exhaustive search-based beam sweeping, which causes time overhead of a half frame for initial beam establishment. In this paper, we propose a deep learning-based beam selection, which is compatible with the 5G NR standard. To select a mmWave beam, we exploit sub-6 GHz channel information. We introduce a deep neural network (DNN) structure and explain how we estimate a power delay profile (PDP) of a sub-6 GHz channel, which is used as an input of the DNN. We then validate its performance with real environment-based 3D ray-tracing simulations and over-the-air experiments with a mmWave prototype. Evaluation results confirm that, with support from the sub-6 GHz connection, the proposed beam selection reduces the beam sweeping overhead by up to 79.3 %.
Divergent strategies in faeces avoidance between two cercopithecoid primates

Parasites constitute a major selective pressure which has shaped animal behaviour through evolutionary time. One adaption to parasites consists of recognizing and avoiding substrates or cues that indicate their presence. Among substrates harbouring infectious agents, faeces are known to elicit avoidance behaviour in numerous animal species. However, the function and mechanisms of faeces avoidance in non-human primates has been largely overlooked by scientists. In this study, we used an experimental approach to investigate whether aversion to faeces in a foraging context is mediated by visual and olfactory cues in two cercopithecoid primates: mandrills (Mandrillus sphinx) and long-tailed macaques (Macaca fascicularis). Visual and olfactory cues of faeces elicited lower food consumption rates in mandrills and higher food manipulation rates in long-tailed macaques. Both results support the infection-avoidance hypothesis and confirm similar tendencies observed in other primate species. More studies are now needed to investigate the divergence of avoidance strategies observed in non-human primates regarding food contamination.
Colobops: a juvenile rhynchocephalian reptile (Lepidosauromorpha), not a diminutive archosauromorph with an unusually strong bite

Correctly identifying taxa at the root of major clades or the oldest clade-representatives is critical for meaningful interpretations of evolution. A small, partially crushed skull from the Late Triassic (Norian) of Connecticut, USA, originally described as an indeterminate rhynchocephalian saurian, was recently named Colobops noviportensis and reinterpreted as sister to all remaining Rhynchosauria, one of the earliest and globally distributed groups of herbivorous reptiles. It was also interpreted as having an exceptionally reinforced snout and powerful bite based on an especially large supratemporal fenestra. Here, after a re-analysis of the original scan data, we show that the skull was strongly dorsoventrally compressed post-mortem, with most bones out of life position. The cranial anatomy is consistent with that of other rhynchocephalian lepidosauromorphs, not rhynchosaurs. The ‘reinforced snout' region and the ‘exceptionally enlarged temporal region’ are preservational artefacts and not exceptional among clevosaurid rhynchocephalians. Colobops is thus not a key taxon for understanding diapsid feeding apparatus evolution.
Deep neural networks for waves assisted by the Wiener–Hopf method

In this work, the classical Wiener–Hopf method is incorporated into the emerging deep neural networks for the study of certain wave problems. The essential idea is to use the first-principle-based analytical method to efficiently produce a large volume of datasets that would supervise the learning of data-hungry deep neural networks, and to further explain the working mechanisms on underneath. To demonstrate such a combinational research strategy, a deep feed-forward network is first used to approximate the forward propagation model of a duct acoustic problem, which can find important aerospace applications in aeroengine noise tests. Next, a convolutional type U-net is developed to learn spatial derivatives in wave equations, which could help to promote computational paradigm in mathematical physics and engineering applications. A couple of extensions of the U-net architecture are proposed to further impose possible physical constraints. Finally, after giving the implementation details, the performance of the neural networks are studied by comparing with analytical solutions from the Wiener–Hopf method. Overall, the Wiener–Hopf method is used here from a totally new perspective and such a combinational research strategy shall represent the key achievement of this work.