Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy of online survey assessment of mental disorders and suicidal thoughts and behaviors in Spanish university students. Results of the WHO World Mental Health- International College Student initiative

Objective To assess the accuracy of WMH-ICS online screening scales for evaluating four common mental disorders (Major Depressive Episode[MDE], Mania/Hypomania[M/H], Panic Disorder[PD], Generalized Anxiety Disorder[GAD]) and suicidal thoughts and behaviors[STB] used in the UNIVERSAL project. Methods Clinical diagnostic reappraisal was carried out on a subsample of the UNIVERSAL project, a longitudinal online survey of first year Spanish students (18–24 years old), part of the WHO World Mental Health-International College Student (WMH-ICS) initiative. Lifetime and 12-month prevalence of MDE, M/H, PD, GAD and STB were assessed with the Composite International Diagnostic Interview-Screening Scales [CIDI-SC], the Self-Injurious Thoughts and Behaviors Interview [SITBI] and the Columbia-Suicide Severity Rating Scale [C-SSRS]. Trained clinical psychologists, blinded to responses in the initial survey, administered via telephone the Mini-International Neuropsychiatric Interview [MINI]. Measures of diagnostic accuracy and McNemar χ2 test were calculated. Sensitivity analyses were conducted to maximize diagnostic capacity. Results A total of 287 students were included in the clinical reappraisal study. For 12-month and lifetime mood disorders, sensitivity/specificity were 67%/88.6% and 65%/73.3%, respectively. For 12-month and lifetime anxiety disorders, these were 76.8%/86.5% and 59.6%/71.1%, and for 12-month and lifetime STB, 75.9%/94.8% and 87.2%/86.3%. For 12-month and lifetime mood disorders, anxiety disorders and STB, positive predictive values were in the range of 18.1–55.1% and negative predictive values 90.2–99.0%; likelihood ratios positive were in the range of 2.1–14.6 and likelihood ratios negative 0.1–0.6. All outcomes showed adequate areas under the curve [AUCs] (AUC>0.7), except M/H and PD (AUC = 0.6). Post hoc analyses to select optimal diagnostic thresholds led to improved concordance for all diagnoses (AUCs>0.8). Conclusion The WMS-ICS survey showed reasonable concordance with the MINI telephone interviews performed by mental health professionals, when utilizing optimized cut-off scores. The current study provides initial evidence that the WMS-ICS survey might be useful for screening purposes.
Non-local Weighted Median Regularization for PET Reconstruction

An improved median-regularized reconstruction method is presented for positron emission tomography (PET). Our motivation of improving the median regularizer lies in the fact that, though it can preserve edges while suppressing the noise, it suffers from the difficulty in selecting the median weights for its optimal performance. In this work, we propose an adaptive method of choosing the median weights for penalized-likelihood reconstruction. Our work is inspired by the non-local means denoising approach, which exploits the patch similarity in the image to reduce noise. In this work, instead of applying the measure of similarity between patch neighborhoods to weighted smoothing, we apply it to the weights for the median regularization. The experimental results indicate that our non-local median regularization method improves both the overall and the regional accuracies of median regularized PET reconstruction.
FLASH: Content-based Power-saving Design for Scrolling Operations in Browser Applications on Mobile OLED Devices

As modern people become highly dependent on mobile applications and services, saving the power consumption of browser applications plays an important role for the battery lifetime of mobile devices. This paper investigates how to reduce the power consumption of mobile OLED displays under scrolling operations when displaying web pages. We first propose a content-based analysis to efficiently determine the visual appeal in a web page. Then, two algorithms are presented to dynamically generate power-saving transformation programs for a web page. We next propose a scrolling operation detection to activate the transformation without reducing the user experience. Finally, the results of the extensive experiments conducted on a commercial smart-phone with four real-world web pages to evaluate the performance of the proposed design are very encouraging.
Photon-Counting Underwater Wireless Optical Communication by Recovering Clock and Data From Discrete Single Photon Pulses

To realize a long-distance underwater communication by using low-cost light emitting diode (LED) and single photon avalanche diode (SPAD), we proposed a method for recovering clock and data directly from discrete random pulse sequences output by SPAD. On this basis, a new communication model is established, taking into account not only fluctuations in photon flux and quantum efficiency of photon detection in a time-slot, but also the phase difference of the recovered clock caused by it. A photon-counting underwater optical wireless communication (UOWC) system was built. The effects of the system setting parameters such as the number of gating signals, synchronous character length and baud rate on the average bit error rate (BER) were verified by experiments. The experimental results show that under condition of only average 10 photons in one time-slot, photon-counting UOWC with a BER of 3.51 × 10−4 and a baud rate of 1 Mbps can be achieved.
Rethinking Last-level-cache Write-back Strategy for MLC STT-RAM Main Memory with Asymmetric Write Energy

To meet the requirement of low-power consumption, multi-level-cell STT-RAM (MLC STT-RAM) has been widely regarded as a potential candidate for replacing DRAM-based main memory in the next generation computer architectures because of its high memory cell density, fast read/write performance and zero refresh power consumption. However, MLC STT-RAM has higher power consumption than DRAM while a write operation is performed because MLC STT-RAM sometimes needs to perform a two-step transition to change the originally stored bits to another specifically written bit patterns. As a result, MLC STT-RAM has different power consumption while different bit patterns are written to a memory cell. To the best of our knowledge, a few or none of the previous studies rethink a cache replacement policy to overcome the asymmetric write energy issue of MLC STT-RAM-based main memory. Thus, this study proposes an energy-aware cache replacement policy, namely E-cache, which considers asymmetric write-back power consumption on MLC STT-RAM-based main memory to evict a proper cached data from the last-level cache, so as to minimize system power consumption. The experimental results show that the proposed solution reduces the energy consumption by 36% on average, compared with the LRU.
Human Comfort in Indoor Environment: A Review on Assessment Criteria, Data Collection and Data Analysis Methods

Occupants’ comfort perception about the indoor environment is closely linked to their health, wellbeing and productivity. Improvement of comfort level in office buildings has significant positive impacts on both employers and employees. Human comfort in indoor environment usually can be assessed in four aspects: thermal comfort, visual comfort, acoustic comfort and respiratory comfort. In this paper, we present a literature review on the previous research contributions towards studying various aspects of human comfort with a special focus on the respective assessment criteria, data collection methods and data analysis approaches employed by former studies. Previous review work has covered the fundamental concepts associated with human comfort. However, their studies mainly focus on thermal comfort and there is limited work that covers other aspects of comfort. Moreover, few of them discuss how the data is obtained, how to extract useful information from the data and how the data is analyzed. To fill up this gap, this paper conducts the survey from the data-driven point of view. Through the survey, we find that sensor technology has been widely used in the data collection for various types of comfort, while so far the machine learning approaches are mainly applied in the area of thermal comfort study. Finally, some potential future research areas are proposed based on the current status of the research work. The established knowledge in this paper would provide useful insights for engineers or researchers who embark on their research in this area.
Short-Term Load Forecasting of Virtual Machines Based on Improved Neural Network

Cloud computing allows applications to share resources on physical machines in an independent manner so as to effectively increase the utilization of physical machines. To enable cloud service users to obtain high-quality services, it is significant for cloud service providers to reduce the time required to allocate and deploy virtual machines, as well as to provide time windows for the deployment of physical servers. To ensure demand is met, one effective implementation method is to predict the workload of virtual machines in the future, as this contributes to the efficient deployment of a data center’s requests from virtual machines to dynamically changing physical servers. However, due to the complexity of virtual machine requests, virtual machine workload prediction remains a significant challenge at present; on the one hand, it is difficult for standard recurrent neural networks to capture long-term dependencies because of the disappearance of gradients, while on the other hand, the long short-term memory method cannot handle irregular intervals. Accordingly, a new virtual machine workload prediction method that is capable of managing irregular time intervals is proposed in this paper. The proposed method combines the amount and time intervals of historical virtual machine work requests in order to accurately predict the virtual machine’s future workload using a fixed length of time as a unit. Experiments show that the proposed model can generate more accurate prediction results than the long short-term memory method.
A Decentralized Lightweight Authentication and Privacy Protocol for Vehicular Networks

Recently many security protocols have been proposed for road safety applications in Vehicle-to-everything (V2X) communications. Most of them, however, do not fully satisfy the requirements of light-weight and fast processing, which are special properties for V2X. Most of the previous authentication protocols assume that a Certificate Authority (CA) is present within the communication range from all the vehicles, which is not practical for moving vehicles. We propose a light-weight security protocol for authentication and privacy protection for V2X. It employs two security hardware devices, Biometric Device (BD) and Tamper Proof Device (TPD), which verifies the driver and securely keeps the keys, respectively. It decentralizes the CA’s tasks by locally generating pseudo-identity and private keys to preserve privacy and provide authentication in Vehicle-to-Vehicle (V2V) communication. In addition, we propose an authentication signature protocol using a notion of hash-chain key generation. We implemented the proposed key generation and authentication protocol using NS-3 simulator. Our extensive simulations demonstrated that the proposed authentication protocol significantly enhances the security level while protecting the conditional privacy of vehicles by utilizing anonymous identities. The proposed protocol has a 20% ~ 85% less communication overhead compared with the previous protocols.
Performance of Multi-Pair Two-Way Full-Duplex Massive MIMO Relay Systems with Direct Lin

In this paper, we consider a multi-pair two-way full-duplex relaying system with multiple-input-multiple-output (MIMO) users. Each pair of users exchange information with the aid of a massive MIMO amplify-and-forward (AF) relay, and correlation between the antennas both at the users as well as the relay is considered. The direct link between all user nodes is assumed to be non-negligible, which is suitable for practical urban scenarios. The low-complexity transceiver design at the relay based on maximum ratio combining/maximum ratio transmission (MRC[MRT) processing is presented. The performance of the system is evaluated in terms of the achievable sum-spectral efficiency under two communication schemes. The first scheme attempts to make use of the joint benefits of the relayed and direct links, while in the second scheme the direct link is considered as interference. Comparison analysis show that the first scheme outperforms the second in the presence of a strong direct link. Moreover, the detrimental effect of spatial correlation between the antennas at each user node is investigated.
SAR Signal Processing Architecture and Effects of Motion Errors for mmWave and THz Frequencies

Most commonly used frequencies in Synthetic Aperture Radar (SAR) imaging are from hundreds of MHz to tens of GHz. Nowadays, the unexplored mmWave and THz regimes are being investigated for SAR imaging to have higher resolution. Two research challenges for this technology motion compensation and real-time processing are addressed in this paper. SAR image reconstruction algorithms lack data dependencies. Therefore, it can be processed in a parallel computing environment to accelerate the image reconstruction. This paper presents the theoretical model of a distributed signal processing testbed. The performance enhancement with the inclusion of high performance computing for image processing is also explained. Another challenge for this technology comes from a motion compensation point of view. The SAR signal processing assumption is that the radar-carrying platform follows an ideal path. However, in reality, the platform deviates from this path. Especially at THz, very small deviations in the range of sub-mm produce motion errors because of wavelengths in the range of deviations. It degrades the image quality and provides wrong estimations of range and azimuth scatters. The paper summarizes motion error types, their characterization, and constraint on the amplitude of deviation. Also, the paper presents effects of these errors at 100 GHz with simulation results.
Deep Learning-Based Motion Estimation for Uninterrupted Tracking of Awake Rodents in PET

The ability to image the brain of awake rodents using motion-compensated positron emission tomography (PET) presents many exciting possibilities for exploring the links between brain function and behavior. A key requirement of this approach is obtaining accurate estimates of animal pose throughout a scan. Our present motion tracking approach suffers crucial line-of-sight limitations which leads to tracking "dropout" and subsequent loss of motion information that can be used for motion correction. The proportion of a scan affected can range anywhere from 5% for sedentary subjects up to >50% for highly active subjects. The aim of this work was to investigate the feasibility of augmenting optical motion tracking with a video-based deep learning motion estimation method to mitigate the impact of tracking drop-out.A deep convolutional neural network (CNN) based regression approach for estimating six rigid-body motion parameters is proposed. We tested our model using multi-view camera images of a rat phantom under robotic control. The commanded robot motion provided the labels for our data. We compared the performance of deep learning-based motion estimation for simulated gaps in the motion sequence against the robot ground truth. We also compared deep learning to naïve linear interpolation of motion across the gaps. Deep learning provided promising alignment with the ground truth motion, in many cases sub-degree/sub-mm. The root mean square error for the deep learning and interpolation methods versus ground-truth was 1.26° and 23.64° (y-axis rotation) and 0.77 mm and 6.57 mm (z-position), respectively.Deep learning-based rigid-body motion estimation from multi-view video appears promising as a solution for augmenting optical tracking. Future work will focus on (i) the use of a Long Short-Term Memory (LSTM) unit to better model temporal information in the motion trace and (ii) incorporation of the known camera calibration to further constrain pose estimates.
AdaComm: Tracing Channel Dynamics for Reliable Cross-Technology Communication

Cross-Technology Communication (CTC) is an emerging technology to support direct communication between wireless devices that follow different standards. In spite of the many different proposals from the community to enable CTC, the performance aspect of CTC is an equally important problem but has seldom been studied before. We find this problem is extremely challenging, due to the following reasons: on one hand, a link for CTC is essentially different from a conventional wireless link. The conventional link indicators like RSSI (received signal strength indicator) and SNR (signal to noise ratio) cannot be used to directly characterize a CTC link. On the other hand, the indirect indicators like PER (packet error rate), which is adopted by many existing CTC proposals, cannot capture the short-term link behavior. As a result, the existing CTC proposals fail to keep reliable performance under dynamic channel conditions. In order to address the above challenge, we in this paper propose AdaComm, a generic framework to achieve self-adaptive CTC in dynamic channels. Instead of reactively adjusting the CTC sender, AdaComm adopts online learning mechanism to adaptively adjust the decoding model at the CTC receiver. The self-adaptive decoding model automatically learns the effective features directly from the raw received signals that are embedded with the current channel state. With the lossless channel information, AdaComm further adopts the fine tuning and full training modes to cope with the continuous and abrupt channel dynamics. We implement AdaComm and integrate it with two existing CTC approaches that respectively employ CSI (channel state information) and RSSI as the information carrier. The evaluation results demonstrate that AdaComm can significantly reduce the SER (symbol error rate) by 72.9% and 49.2%, respectively, compared with the existing approaches.
Gridvoronoi: An Efficient Spatial Index for Nearest Neighbor Query Processing

In this paper, based upon Voronoi Diagram, we propose GridVoronoi which is a novel spatial index that enables users to find the spatial nearest neighbour (NN) from two-dimensional (2D) datasets in almost O(1) time. GridVoronoi augments the Voronoi Diagram with a virtual grid to promptly find out (in a geometric space) which Voronoi cell contains the query point. It consists of an off-line data pre-processing phase and an on-line query processing phase. In the off-line phase, the digital geographical space is partitioned with a Voronoi Diagram and a virtual grid, respectively. Next, for each square unit (i.e., grid cell), the corresponding Voronoi cells that contain or intersect with this square are derived and kept in a hashmap-like structure. In the on-line phase, for each real-time spatial NN query, the algorithm first identifies which virtual square(s) contain(s) this query; then looks up the hashmap structure to find the corresponding Voronoi cell(s) for this grid cell and the final result for the query. Overall, GridVoronoi significantly reduces the time complexity in finding spatial NN in 2D space, thus improves the efficiency of real-time spatial NN queries and Location Based Services.
Resonant Frequency Selection Method for Wireless Power Transfer System Considering Electromagnetic Interference Reduction

In typical serise-serise resonant WPT systems, if the resonance frequency of transmitting coil is too close to switching frequency of inverter, the inverter is not allowed to operate with zero-voltage switching (ZVS), which degrades the power transfer efficiency and increases the EMI. In this paper, we propose the resonance frequency selection method for WPT system to reduce the EMI. The proposed method guarantee the ZVS operation and select the resonance frequency that can minimize the EMI. The selection method of optimum transmitting resonance frequency is proposed as a function of mutual inductance, transmitting, receiving coil property and load condition. The proposed method is verified using circuit simulation.
Fabrication of a PVC-based solid-state Ag/AgCl reference electrode

A solid-state reference electrode (SSRE) is similar to a traditional reference electrode in configuration but has no inner solution. Its layer-by-layer structure influences the stability of an SSRE because of the difference of junction potential between the surfaces. In this work, a SSRE of PVC/PVC-KCl/AgCl/Ag was fabricated and studied. The results confirmed that the proportion of AgCl on the electrode surface affected the stability of reference potential. Besides, PVC membrane coated on the top of electrode controlled the entrance of different ions from the analyte solution. Finally, the optimized SSRE was applied in a electrochemical three-electrode system. In sum, we have built the fabrication process of a PVC-based SSRE and demonstrated its feasibility for sensor applications.
A Ka-Band Circularly Polarized Substrate Integrated Cavity-Backed Antenna Array

A high-gain 4 × 4 substrate integrated waveguide (SIW) circularly polarized (CP) antenna array at a Ka-band with a quadruple-ridge waveguide polarizer is proposed in this letter. The antenna array consists of 16 cavity-backed slot antenna elements, quadruple ridge waveguide polarizers, and SIW T-type power dividers. Dominant resonant mode TE110 is excited in the cavity-backed slot antenna element. The measured impedance bandwidth (S11 < –10 dB) is from 35.3 to 35.55 GHz, and 3 dB axial-ratio bandwidth is from 35.24 to 35.57 GHz. In addition, the maximum measured gain of 18.14 dBi at the boresight is experimentally obtained at 35.42 GHz. The antenna prototype was fabricated by a multilayer printed circuit board technology. To the best of our knowledge, the quadruple-ridge waveguide is used as polarizer to produce CP signals for the first time. Compared with an aperture antenna with a conventional rectangle or a circular waveguide polarizer, this work has high aperture radiation efficiency as well as compact size. This design idea may open a new way for development of millimeter-wave high-efficiency arrays.
Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity

Caching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from caching. In this paper, we resort to a hybrid proactive and reactive policy to deal with the dynamics of popularity, in particular the hybrid of proactive probabilistic caching policy and least recently used policy, which are appropriate respectively for the contents with low and high dynamic popularity. We divide the contents requested in a region into two classes, where one can be modeled by independent reference model (IRM) and the other can be modeled by shot noise model (SNM). To maximize the total successful offloading ratio achieved by caching the two classes of contents, we optimize the hybrid caching policy including both the cache resource allocation to each class of contents and the probability of caching each IRM content. We find the optimal solution to the problem for general case, and provide a closed-form solution in a special case to gain insights. To provide a viable solution for practical use, we propose a heuristic method to obtain the optimal allocation fraction, and predict the popularity distribution and the allocation fraction using neural networks with historical data. We validate our analytical results by simulation results via synthetic datasets. We evaluate the performance of the proposed hybrid caching policy via synthetic data generated by SNM and two real datasets, and compare it with the proactive policy, the reactive policy and the existing hybrid proactive and reactive policy.