Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical Staffing Problem by Shift Design in Nursing Homes: A Two-stage Method

With the increasing aging population, the nursing homes have developed into the serious needs of the society. Nursing work has the characteristics of “multi shift, high time-varying demand, hierarchical and collaborative”, Multi shift means that there can be multiple shifts in a day covering the same time period; high time-varying demand means that the number of nurses needed within 24 hours of a day will change greatly according to different time periods; hierarchical means that there are different levels of nurses’ abilities; collaborative means different levels of employees cooperate to serve the elderly. Our work devotes to the shift design problem(SDP) and the hierarchical staffing problem, i.e. how to design the time window of each shift to cover the demand periods, and how to allocate the hierarchical staff to the shifts. Our work will solve the problem by the two-stage method. In the first stage, according to the characteristics of high time-varying demand, we design the schedule of shifts and determine the number of personnel needed for each shift. In the second stage, we further determine the number of personnel needed for each shift at each level according to the constraints of hierarchical coordination and cooperation. The sensitivity analysis of different demand fluctuations is carried out and some meaningful conclusions are drawn. Our research outputs can provide efficient decision tools and methodology in daily staffing to the nursing homes which faces fast-rising demands and staff shortage.
JAICOB: A Data Science Chatbot

The application of natural language to improve students’ interaction with information systems is demonstrated to be beneficial. In particular, advances in cognitive computing enable a new way of interaction that accelerates insight from existing information sources, thereby contributing to the process of learning. This work aims at researching the application of cognitive computing in blended learning environments. We propose a modular cognitive agent architecture for pedagogical question answering, featuring social dialogue (small talk), improved for a specific knowledge domain. This system has been implemented as a personal agent to assist students in learning Data Science and Machine Learning techniques. Its implementation includes the training of machine learning models and natural language understanding algorithms in a human-like interface. The effectiveness of the system has been validated through an experiment.
mcQEMU: Time-Accurate Simulation of Multi-core platforms using QEMU

Full-system emulators allow the execution of guest operating systems and applications without the need of having access to the real target hardware. For many applications, besides the correct functional modeling, the full-system emulator shall also be time-accurate. In this paper, we present a new full-system multi-core simulator that delivers time-accurate execution and preserves the functional correctness of guest application. The proposed solution is based on QEMU. We enriched QEMU with various time models of multi-core platforms. We call this new full-system simulator mcQEMU. mcQEMU supports guest CPUs with out-of-order and in-order architectures.We validated mcQEMU by emulating multi-core ARM processors in system mode. The time accuracy of mcQEMU is evaluated with the TACLeBench benchmark suite. From a timing prediction viewpoint, mcQEMU achieves an estimation error of only 15% in average when emulating the out-of-order i.MX6Quad processor by NXP. For full-system simulation, mcQEMU runs at 35 Mips for in-order architectures and 25 Mips for out-of-order ones. In user-mode simulation, mcQEMU can achieve up to 65 Mips.
Studying Microwave-Photonics-Based Super-Wide Bandwidth Transceiver for High Resolution Radar Applications

An original way of computer-aided analysis and optimization of a microwave-photonics-based super-wide bandwidth transceiver for a high-resolution linear-frequency modulated continuous-wave inverse synthetic aperture radar is proposed and demonstrated using well-known off-the-shelf microwave-electronic software tool Cadence AWRDE. To substantiate the approach, the transceiver models in the monostatic and bistatic versions are presented in the form of a set of earlier developed nonlinear electrical equivalent circuits for active optoelectronic units. Based on them, the results of simulation experiments and confirming them experimental research of a transceiver with an instantaneous bandwidth of 5 GHz in the operating range 5.5–10.5 GHz are highlighted.
Evaluation of serum MicroRNA expression profiles in patients with panic disorder

ABSTRACT BACKGROUND: Studies on the role of microRNAs (miRNA) in anxiety disorders are limited. We aimed to determine the availability of miRNAs as biomarkers in serum and to demonstrate the changes of miRNAs expression in patients with panic disorder (PD). METHODS: Thirty-five patients with PD and 35 healthy controls (HC) were evaluated with Structured Clinical Interview for DSM Disorders-I (SCID-I) and Panic Disorder Severity Scale (PDSS). In each group miRNA expression analysis was performed in venous blood by the Real-time Polymerase Chain Reaction (RT–PCR) method for genetic evaluation. RESULTS: Compared with the HC group, eight miRNA expression levels were found different in the PD group. Five of them were upregulated and three of them were downregulated. There was no correlation between the levels of miRNA expression with PDSS total score and PDSS sub-items. However, miR-1297 and miR-4465 expression levels were significantly different between the two groups. LIMITATIONS: There are some limitations in this research. Firstly the number of samples is small. Another limitation of our study is that the presence of medical illness and continuous drug use were not excluded when PD and HC groups were selected. CONCLUSIONS: Our research is the first miRNA expression study in patients with PD which excluded psychotropic use and additional psychiatric disorders. In the PD group, miR-1297 and miR-4465 expression was upregulated than compared to the HC group. miR-1297 and miR-4465 regulate the GABAA gene regions that affect GABAA receptor subtypes that thought to play a role in the aetiology of PD.
Improving the Concurrency Performance of Persistent Memory Transactions on Multicores

Persistent memory provides data persistence to in-memory transaction systems, enabling full ACID properties. However, high data persistence worsens the concurrency performance due to delayed execution of conflicted transactions on multicores. In this paper, we propose SP 3 (SPeculative Parallel Persistence) to improve the concurrency performance of persistent memory transactions. SP3 keeps the dependencies between different transactions in a DAG (direct acyclic graph) by detecting conflicts in the read/write sets, and speculatively executes conflicted transactions without waiting for the completeness of data persistence. Evaluation shows that SP3 significantly improves concurrency performance and achieves almost linear scalability in most evaluated workloads.
Development of a New Apparatus for SI Traceable Small Mass Measurements Using the Voltage Balance Method at NMIJ

Toward establishing a measurement system for small masses and forces based on the new definition of the kilogram, a measurement apparatus that uses the voltage balance method was developed. The main components of the apparatus are a mass balance mechanism, an automatic mass translation system, and a capacitance actuator for balancing the gravity force acting on the small object and the electrostatic force. The electrostatic force is calibrated by using the electrical methods that are traceable to the electrical standards. Therefore, the voltage balance method realizes SI traceable and absolute measurements of small masses. To improve the method, new technical approaches, such as the use of a monolithic flexure hinge for the balance mechanism, a double equal capacitor system for the electrical control, and a new type of buckling spring, were implemented. To evaluate the performance of the apparatus, an experiment of the mass of a 500-<inline-formula> <tex-math notation="LaTeX">$\mu \text{g}$ </tex-math></inline-formula> aluminum wire weight was conducted. A result of the weighing of the 500-<inline-formula> <tex-math notation="LaTeX">$\mu \text{g}$ </tex-math></inline-formula> weight was <inline-formula> <tex-math notation="LaTeX">$499.8~\mu \text{g}$ </tex-math></inline-formula>. The basic performance of the new apparatus for small mass measurements was confirmed.
Phase-Only Robust Minimum Dispersion Beamforming

A phase-only robust minimum dispersion (PO-RMD) beamformer is devised for non-Gaussian signals. Unlike conventional beamformers that adjust the complex-valued weights, including both amplitude and phase, of each antenna to fulfill spatial filtering, the proposed PO-RMD employs a unit-modulus constraint on the weights, which is equivalent to simply phase shifting at each antenna. Instead of the widely used minimum variance criterion, the PO-RMD adopts the minimum dispersion criterion, which minimizes the <inline-formula><tex-math notation="LaTeX">$\ell _p$</tex-math></inline-formula>-norm of the array output to utilize the non-Gaussianity of the signals. To achieve robustness against model uncertainty, the magnitude response of any steering vector within an uncertainty region is forced to exceed the threshold. A gradient projection (GP) algorithmic framework is developed to solve the resulting nonconvex optimization problem. In order to find a feasible point in the intersection of the unit-modulus, and robustness constraint sets in each iteration, an alternating projection algorithm is also devised. More importantly, the closed-form expressions of the projection onto the two sets are derived, which only need a low complexity of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(M)$</tex-math></inline-formula> with <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> being the number of antennas. Convergence of the alternating projection is analyzed, and the local linear convergence rate is established. Simulation results demonstrate the fast convergence rate, and accuracy of the GP, as well as the simplicity and robustness of the PO-RMD.
Forward and Backward Motion Planning for Autonomous Parking Using Smooth-feedback Bidirectional Rapidly-exploring Random Trees* with Pattern Cost Penalty

Nowadays, techniques of autonomous vehicles are devoted to providing convenience for people in everyday life. However, existing applications of Advanced Driver Assistance Systems (ADAS) can just be categorized into autonomous driving level 1-4 but not full automation in level 5. With the considerations of risks and environments, autonomous parking may be the first fully autonomous applications for autonomous vehicles. A sampling based motion planner is proposed in this paper to generate a human-like and collision-free parking path efficiently with any feasible start and goal configurations for parking scenarios in everyday life. Moreover, the proposed method aims to deal with the common defects of sampling based motion planners to maintain high path quality and consistency for each execution. To verify the effects of the proposed method, the simulations are conducted in common and strict parking scenarios, such as perpendicular and parallel parking. The simulation results show the potential to deal with the various parking scenarios in everyday life.
Visible Light Positioning Using Bayesian Filters

Visible light positioning has the potential to be a cost-effective technology for accurate indoor positioning. However, existing approaches often require large amounts of incoming data, usually in the form of high resolution images or dense lighting distributions. Additionally, a line of sight between transmitter and receiver is generally required at all times. In this work, we present a positioning approach that combines measurements from a camera, encoders and a gyroscope. We compare multiple algorithms for fusing these data, namely an extended Kalman filter, a particle filter and a hybrid approach. The end result is a system that provides location estimates even with sparse lighting distributions and temporary outages, yet achieves an average accuracy of 2 to 4 cm. Even in the 95th percentile of the cumulative error distribution, accuracy can be as low as 2 cm and is often lower than 10 cm. Moreover, due to the use of a low-resolution camera (640x480 pixels) and efficient fusion algorithms, the latency is relatively low on a standard laptop (between 5.6 and 21 milliseconds). Even on a low-cost embedded board, latency generally does not exceed 100 milliseconds. We validate our approach experimentally and show that it is robust under a wide range of illumination conditions.
Multiple Objects Tracking using Radar for Autonomous Driving

Object detection and tracking are the integral elements for the perception of the spatio-temporal environment. The availability and affordability of camera and lidar as the leading sensor modalities have used for object detection and tracking in research. The usage of deep learning algorithms for the object detection and tracking using camera and lidar have illustrated the promising results, but these sensor modalities are prone to weather conditions, have sparse data and spatial resolution problems. In this work, we explore the problem of detecting distant objects and tracking using radar. For the efficacy of our proposed work, extensive experimentation in different traffic scenario are performed by using our self-driving car test-bed.
A 2.0-2.9 GHz Digital Ring-Based Injection-Locked Clock Multiplier Using a Self-Alignment Frequency Tracking Loop for Reference Spur Reduction

This paper presents a 2.0-2.9 GHz digital ring-based injection-locked clock multiplier (ILCM) in a 65 nm CMOS process. A self-alignment frequency tracking loop (SA-FTL) with background delay calibration is proposed to improve the accuracy of injection timing. Since the frequency mismatch of the free-running oscillator and the target frequency is minimized without delay error, the reference spur is reduced to -55.6 dBc, exhibiting 15.9 dB improvement comparing to the case that the SA-FTL is off. The proposed ILCM achieves 545 fs RMS jitter at 2.5 GHz output frequency with a power consumption of 3.1 mW, achieving the FoM of 240.3 dB.
Periodic Structures With Higher Symmetries: Their Applications in Electromagnetic Devices

Higher symmetries frequently amaze human beings because of the illusions and incredible landscapes such symmetries can produce. For example, imagine the unearthly pictures of the Dutch graphic artist M.C. Escher. He made use of glide symmetry and reflection to produce unbelievable transitions and transformations of objects and beings, as illustrated in Figure 1(a). However, the history of higher symmetries started much earlier. Escher was partially inspired by the Moorish tessellations in the Alhambra in Granada, Spain, such as the ones pictured in Figure 1(b).
Classification of pickable and unpickable strawberries under farm conditions

In this paper, we address the problem of deformed point clouds of small targets under farm condition. Strawberry point cloud data were extracted through image detection and corresponding coordinate transformation. The deformed point cloud caused by adhesion of adjacent objects make the localization inaccurate. Therefore, multiple features in three spatial dimensions were constructed to represent the shape and distribution of the strawberry point data. Some fundamental features, such as eccentricity and least moment of inertia, were utilized, while some new features, such as 3D shape matrix and ratio of data amount in certain space, were proposed. Furthermore, classifiers that can identify unpickable strawberry data and other defined classes were trained and evaluated on constructed features and different label sets. The evaluation results show that the classifier could effectively identify unpickable cases. This work proposes a new problem and provides possible solutions that could optimize the performance of machine vision system of the strawberry harvesting robot.
Adjacent Image Correlation for Video Quality Assessment

The main aim of this paper is to analyse correlation between adjacent images in a video sequence. Adjacent images with a slow or fast changeable scene in a video have high correlation which shows consistency in the video sequence therefore it can be the proof of normal reproduction of video service. Due to QoS (Quality of service) problems, especially over lossy network, appearance of different visual degradations in frames (images) during delivery of video service to end users can happen very often. In that case, adjacent images in the video sequence have low correlation which can be used as an indicator the problem occurred in some part of the network. In addition, the paper analyses correlation correspondent to polygons i.e. parts of adjacent images in the video sequence in order to discover a degree of influence visual degradations to user’s QoE (Quality of Experience). In order to check this aim, tested degraded and non-degraded video sequence was captured using IPTV system of one significant market power provider and processed in offline mod with Python script created especially for this purpose.
Dynamic Cell-Less Radio Access Network Meta-Scheduler for High System Capacity Increase

Radio resources are limited and subject to interferences, multi-path fading, shadowing and path-loss which highly impact the transmission efficiency. Optimizing the usage of these resources is the main issue in wireless networks. The resource management can be performed at different levels: in intracellular context, it is carried out by scheduling algorithms while in multi-cellular context, it is done by interference management strategies. This paper aims to merge these two steps. The proposed solution is compatible with the most acknowledged schedulers in order to optimize spectrum usage in intra-cell domain while efficiently decreasing the interferences in multicell domain. An innovative strategy is to consider the network as an "hyper-cell" rather than a sum of cells quite independent. This strategy often called "Cell-less" is especially effective in a Cloud Radio Access Network (C-RAN) architecture because it allows to centralize the decision making. This is more suitable for interference management as it provides a better flexibility to the system by retrieving and managing data from several cells that allows an overall performance increase. The proposed solution called Dynamic Cell-less Radio Access Network Meta-Scheduler (DC-RAN-MS) dynamically handles for each cell the management of radio resources depending on the interferences potentially experienced by users. Performance evaluation shows that the DC-RAN-MS offers an increased system capacity by optimizing the usage of bandwidth while reducing the magnitude of interferences received.
A Flexible Real-Time Software-based Multi-Band Channel Sounder

We present a flexible real-time software-based multi-band channel sounder, which can flexibly be used in a variety of measurement campaigns, and fills the gap of expensive commercial channel sounders with limitations of operation frequency, sounding bandwidth, and ease of use. In general, channel sounding is a very valuable tool for designing wireless communication systems. Even though commercial channel sounders allow for thorough and detailed measurements, they are typically very expensive. In contrast, existing software-based solutions are often limited in scope and complexity of measurements. Our system closes this gap. Using GNU Radio and USRP radio front-ends, the system provides a high degree of freedom with respect to supported carrier frequencies and bandwidth. We also support features such as automatic spectrum-sweeping and transmitter gain management for dynamic channel characterization. We can use the system for both live visualization of the channel characteristics, e.g., Channel Impulse Response, Channel Frequency Response, Power Delay Profile, and Channel Phase Response, as well as for fully automated and repeatable experiments.
Navigator: Dynamic Multi-kernel Scheduling to Improve GPU Performance

Efficient GPU resource-sharing between multiple kernels has recently been a critical factor on overall performance. While previous works mainly focused on how to allocate resources to two kernels, there has been limited amount of work on determining which workloads to concurrently execute among multiple workloads. Therefore, we first demonstrate on a real GPU system how the selection of concurrent workloads can have significant impact on overall performance. We then propose GPU Navigator – a lookup-table-based dynamic multi-kernel scheduler that maximizes overall performance through online profiling. Our evaluation shows that GPU Navigator outperforms a greedy policy by 29.3% on average.
g-Mellin transform in probability theory

Some applications of the g-Mellin transform in probability theory are presented in the paper. The connection between the g-Mellin convolution and the probability density function of a product of two independent continuous random variables and one example is given. The connections between the convergence of a sequence of g-Mellin transforms of random variables and the g-weak convergence of the corresponding sequence of pseudo-probability measures is shown.
Analysis of the features of brain neuronal sources during imagery motor activity: MEG study

We analyzed the features of motor imagery (imagery arms movements) in the source level, applying the Dynamic Imaging of Coherent Sources method to MEG signals. We obtained the results that have confirmed the existence of two types of motor imagery: visual imagery and kinesthetic imagery. We have analyzed averaged distributions of normalized sources power for the visual imagery and kinesthetic imagery subgroups of volunteers and identified the main differences between these types of motor imagery in terms of the excitation of sources of neural activity.
An Intelligent Deep Learning-Based Direction-of-Arrival Estimation Scheme Using Spherical Antenna Array With Unknown Mutual Coupling

Spherical antenna array (SAA) has become highly attractive where hemispherical scan coverage is required as it can provide uniform directivity in all the scan directions. Various direction-of-arrival (DoA) estimation methods suffer from different problems, such as low accuracies in mismatched conditions, high computational complexity and poor estimation in a harsh environment. Another critical concern is mutual coupling (MC) characteristics between the array elements. These problems affect the quality of the navigation signal in harsh environments. This paper presents a robust DoA estimation and mutual coupling compensation technique based on convolutional neural network (CNN) for Spherical Array. Spherical harmonic decomposition (SHD) is used to facilitate feature extraction in two sets, which contains different features about the elevation and azimuth of the source for DoA estimation. The features serve as input to the learning technique for separate estimation of elevation and azimuth, which consequently reduce computational complexity as against the joint estimation of DoA. Learning methods for DoA estimation with few frames and dense search grids within the spherical array configuration are presented. To solve the MC error, the DoA estimation scheme is also used to obtain accurate spectrum peak in the multipath scenario with unknown MC and sharper spectrum peak via the unique structure of the MC matrix and spatial smoothing algorithms. In all, experimental results, which is the ground truth to test any procedure, show the effectiveness, validity, and potential practical application of the proposed technique.