Miracle-dz/UniFAR-FTUs huggingface dataset:
Motif Transformer: Generating Music With Motifs

Music is composed of a set of regular sound waves, which are usually ordered and have a large number of repetitive structures. Important notes, chords, and music fragments often appear repeatedly. Such repeated fragments (referred to as motifs) are usually the soul of a song. However, most music generated by existing music generation methods can not have distinct motifs like real music. This study proposes a novel multi- encoders model called Motif Transformer to generate music containing more motifs. The model is constructed using an encoder-decoder framework that includes an original encoder, a bidirectional long short term memory-attention encoder (abbreviated as bilstm-attention encoder), and a gated decoder. Where the original encoder is taken from the transformer’s encoder and the bilstm-attention encoder is constructed from the bidirectional long short-term memory network (BILSTM) and the attention mechanism; Both the original encoder and the bilstm-attention encoder encode the motifs and input the encoded information representations to the gated decoder; The gated decoder decodes the entire input of the music and the information passed by the encoders and enhances the model’s ability to capture motifs of the music in a gated manner to generate music with significantly repeated fragments. In addition, in order to better measure the model’s ability of generating motifs, this study proposes an evaluation metric called used motifs. Experiments on multiple music field metrics show that the model proposed in this study can generate smoother and more beautiful music, and the generated music contains more motifs.
Detection and Classification of Early Stages of Parkinson’s Disease Through Wearable Sensors and Machine Learning

Parkinson’s disease (PD) is a neurodegenerative disease which is among the most spread and growing common neurodegenerative disorders. It significantly limits the physical and social activities of patients if not diagnosed timely. However, diagnosis of PD at an early stage is not a trivial task as a number of neurodegenerative diseases are characterized by similar symptoms. One of the more pressing concerns is the detection of early stages of PD, namely, stages 1 and 2 for assigning a proper therapy helping to notably reduce the disease progression. In these stages, symptoms might not be as distinguishable and might be more easily confused with other diseases. We report on an approach based on wearable sensors and machine learning to differentiate healthy controls from patients with stages 1 and 2 PD. For this reason, we designed 11 common exercises and collect the data via a wearable commercial off-the-shelf accelerometer, gyroscope, and magnetometer sensors. In total, we collected the data from 113 subjects. Data analysis using machine learning methods (feature extraction, dimensionality reduction, classification) helps greatly improve the accuracy of the PD diagnosis at an early stage. Our best results demonstrate f1-micro scores of 0.78 and 0.88 for PD stages 1 and 2, respectively.
A 270 fJ/op 5.8 GHz MOS Current Mode Logic D-Latch for High-Speed Application

In this paper, we present the design of a new low-power, high-performance MOS Current Mode Logic (MCML) D-Latch. The proposed design consists of cross-coupled transistors which dynamically control the load resistance and eliminate static power dissipation. The performance of the design was improved by reducing the threshold voltage of the input transistors at the critical phase to switch them ON faster using the clocked-driven forward body biasing technique. The proposed design achieves an energy improvement of 54% and 49% and a performance improvement of 20% and 43% compared to the Folded and Folded (DTMOS) D-Latches, respectively. The designs were simulated on Cadence Virtuoso ADE tool using 40 nm technology TSMC PDK. Moreover, the proposed design provides higher output voltage swing and is less sensitive to the change of load capacitance compared to the other designs.
RFID Inventory Management System Sampling Optimization Based on Zebra Android Framework

In services with a high rate of input and output of items, the inventory data collection process often imposes high demands on staff in terms of reliable evidencing and overall repeatability. Certain business services, such as on-site laundry facilities in public hospitals, provide a good platform for the ad-hoc implementation of custom ICT (Information and Communication Technology) inventory management solutions, especially if they have already partially implemented proven technologies like RFID. Considering the relatively low cost and ease of design, development, and implementation of such systems in public institutions or other industry segments where accurate and reliable daily-based item inventory is crucial, it offers a viable alternative to commercially available inventory management systems. Processing operations for data elements are essential components of any information system. These operations are optimized with a focus on speed and reliability to meet the timing requirements of RFID sampling. This includes improvements to the database scheme, as well as the application and adaptation of existing algorithms for retrieving duplicate elements from data sets.
NISB-Map: Scalable Mapping With Neural Implicit Spatial Block

Recently, neural implicit representations have been applied in the mapping process of simultaneous localization and mapping (SLAM), accompanied by less storage overhead and continuous representation. Nevertheless, related methods use a single neural network to represent the whole scene, resulting in forgetting the observed regions caused by the limited capacity of a single network in the large-scale scene. Several methods encode the scene into implicit voxels to avoid parameter forgetting while the memory is sacrificed. In this letter, we introduce a scalable mapping framework that utilizes extensible Neural Implicit Spatial Blocks (NISB) with fixed size to cover the entire scene by incrementally creating multiple Multi-Layer Perceptron (MLP) networks. In evaluations against alternative methods on 3 datasets of indoor environments, our method Avoids forgetting the observed areas during the mapping process with a small memory footprint and smoothly updates the global map at 2 Hz.
ROS Framework for Distributed Control of Networks of Dynamical Systems

In this paper we present a software package developed to accommodate flexible and efficient modelling and simulation of networks of dynamical systems with distributed control structures. The package is implemented within the Robot Operating System (ROS), which is becoming a standard software implementation tool in robotics, mechatronics and wider. The developed package is characterized with the following features: implementation of distributed controllers with both off-line predefined control laws in closed form and with on-line iterative solving of an optimisation problem (distributed model predictive control); modular structure which allows the user to easily modify the subsystems in the network, as well as the number of subsystems; allows for straightforward implementation of controllers onto real physical systems.
The Relationship Between Athletics and Higher Education Fund Raising : The Myths Far Outweigh the Facts A Report Prepared for the United States Department of Education Commission on Opportunities in Athletics

Since the formalization of intercollegiate athletics within institutions of higher learning in the late 1800s/early 1900s, college presidents, legislators, alumni/nae, faculty, athletic directors, and average citizens have speculated about the relationship between the success of athletic programs and the generosity of donors. As a matter of first impression, it is easy to understand why most people assume that big time college sport has a positive influence on donation levels to institutions. Given the multi-billion dollar industry which is college sport and the degree to which the public is exposed to March Madness and the Bowl Championship Series via television, print media, and the internet, circumstantial evidence reinforces a common sense belief that big time college sport possesses the potential to generate big time revenue for colleges and universities.
Monte Carlo simulations of Fully Depleted CMOS pixel sensors for radiation detection applications

The ARCADIA (Advanced Readout CMOS Architectures with Depleted Integrated sensor Arrays) collaboration has developed $25 \mu m$ pitch Fully Depleted Monolithic Active Pixel Sensors (MAPS) based on a modified 110 nm CMOS process in collaboration with LFoundry. This work illustrates a set of simulations performed on this detector technology, with the aim of investigating its capabilities for radiation detection applications. Three-dimensional Technology Computer Aided Design (TCAD) simulations have been performed in order to extract the electric field and electrostatic potential maps suitable to execute Monte Carlo simulations, providing high statistics of particle-sensor interaction in a reasonable computing time. The simulation flow will be described in detail and the main results will be pointed out.
A Reliable Anti-Interference Temperature Sensor Based on a Four-Hole Microstructure Optical Fiber Mach–Zehnder Interferometer

In this article, an in-line Mach–Zehnder interferometer (ILMZI)-based temperature sensor of a sandwich-like composite structure was proposed and experimentally demonstrated. Using simple cleaving and splicing operations, ILMZI was formed by compactly sandwiching a Section of four-hole microstructure optical fiber (FHMF) of ultrashort length between two segments of multimode fibers and single-mode fibers in turn. Due to the large thermo-optic coefficient difference between the silica and air modes in FHMF, the sensor achieves a comparable sensitivity of 67.29 pm/°C over a wide dynamic range of 20.8 °C–100.4 °C. Because the sensing area length is only a few hundred micrometers, the long-term stability and anti-interference of the sensor are greatly enhanced. In addition, the large airhole design confines the propagation mode in silica medium by total internal reflection, reducing the interaction between the liquid sample and the sensing element, making the sensor inert to the ambient refractive index. The maximum response deviation of the sensor under microvibration disturbances is $3.51\times 10^{-4}$ nm/°C, and the maximum cross-sensitivity under strain disturbances is only 0.0018 $^{\circ }\text{C}/\mu \varepsilon $ . The proposed sensor is excellent in performance, easy in fabrication, simple in structure, and low in cost, which can be expected to be used in practical temperature sensing applications without any additional packaging.
Investigation of RF-based networking for underwater wireless sensor networks using dynamic cluster head selection strategy

Communication between two underwater wireless nodes or two underwater vehicles is possible with RF communication, provided the aspects of low transmission distance and high attenuation are taken care of in the communication system design. Notably, a cluster-based communication architecture is better suited than direct communication architecture when an RF signal is considered. This paper presents a cluster-based two-hop communication mechanism between two underwater nodes or two underwater vehicles. Particularly, a novel dynamic cluster head selection mechanism based on the unused energy and distance from cluster head to buoy has been proposed. This mechanism ensures uniform power consumption among all the underwater nodes or vehicles in a cluster. Moreover, it eliminates the necessity for a dedicated cluster head. The network performance using the proposed model has been analyzed in terms of the total network’s life and the time after which a node dies first in the network. The results have been compared with the existing communication mechanisms like communication via a fixed cluster head and direct communication.
Examining the Relationship Between Students’ Social Media Usage, Smartphone Checking During Lessons/Studying, and Academic Achievement (GPA)

The interaction of young people with modern technology, specifically social media, is being studied in various contexts. This study aimed to examine the relationship between the students’ social media usage (SMU), smartphone checking during lessons or while studying, and their academic achievement (GPA). In total, 225 participants took part in the study, with a mean age of 17.22 years (SD=1.57). To examine the role of time spent on Facebook (FB) and Instagram (IG), frequency of checking FB and IG, and smartphone checking during lessons/while studying, a hierarchical regression analysis was made. In the first step of the analysis, the participants’ age and gender were controlled for, and both were significant predictors of the students’ GPA. According to the findings, younger age, and female gender both predict a higher GPA. The amount of time spent on IG was a negative predictor of the student’s GPA. Furthermore, the frequency of FB checking was also a predictor of the GPA, students who checked FB more frequently had higher GPAs. The two different measures of SMU (time spent and frequency of checking) were moderately congruent. The limitations and contributions of this study were discussed.
Connectivity-Preserving Formation Tracking for Multiple Double Integrators by a Self-Tuning Adaptive Distributed Observer

In this letter, we study the distributed formation tracking problem for multiple double-integrator systems with connectivity preservation over a state-dependent communication network. In particular, we employ an adaptive distributed observer for the leader system that can estimate both the state and the system matrix of the leader. As a result, unlike the existing results, we do not require all vehicles to know the system matrix of the leader. Furthermore, the adaptive distributed observer incorporates a self-tuning dynamic observer gain, which eliminates the need of computing the observer gain in advance. The effectiveness of our approach is illustrated by an example.
A 28 GHz and 38 GHz Dual-Band Up-Conversion Mixer for 5G Applications in 22 nm FD-SOI CMOS

This paper investigates a dual-band direct up-conversion mixer operating at 5G millimeter-wave (mmW) bands. To prove the concept, the mixer is implemented in a 22nm FDSOI technology. A Gilbert cell was applied for the mixer core to achieve a high local oscillator signal (LO) suppression. An active balun was integrated on-chip to manage and optimize the LO phase- and magnitude-mismatch. Dual-band and broadband matching networks enable dual-band operation. The mixer has a total power consumption of 38mW including the LO active balun, mixer core and RF output buffer. The measurements show a conversion gain of −2.6 dB and −1 dB and a 1-dB output compression point ($\mathrm{oP}_{1\mathrm{d}\mathrm{B}}$) of −2.5 dBm and −0.6 dBm at 28 GHz and 38 GHz bands, respectively. The core area of the design is 0.24 mm2. The mixer compares well against other designs operating at 5GmmW bands by showing one of the highest $\mathrm{oP}_{1\mathrm{d}\mathrm{B}}$, a comparable conversion gain and power consumption. To the best knowledge of the authors, this is the first 5G dual-band up-conversion mixer from technology node 22nm and below.
Graphical user interface to perform glacier simulations with PISM

Computer simulations of glacial dynamics help us understand how glaciers respond to changes in climate, such as warming temperatures and increased precipitation. Simulations are performed with PISM (Parallel Ice Sheet Model). To tackle the complexities of ice dynamics, PISM employs several sub-models for various aspects and thus exposes a plethora of parameters. There are also several options for setting parameter values, i.e. through config files, command line options or input files. To get a good handle on all the provided buttons and knobs, we developed a user interface for setting parameters of out simulations and for executing PISM with the selected set of parameters on a remote workstation.In this paper we present the GUI developed in Jupyter and deployed in a local JupyterLab installation. The environment contains DEM files, Python scripts and other inputs locally but connects to a 128-core workstation for simulation execution. The simulation inputs and outputs are transferred between the computers over an SSH connection. The GUI is publicly exposed in a single-user environment, only for use, while development is performed on a git repository which is then synchronized to the public through git. This approach is general enough to be used on other software as well.
Meta-Learning for Subject Adaptation in Low-Data Environments for EEG-Based Motor Imagery Brain-Computer Interfaces

Motor imagery classification from Electroencephalogram (EEG) signals involves decoding information during the imagination of specific movements. However, learning representations for EEG-based motor imagery classification is challenging due to inter-subject variability and differences in mental imagery, resulting in poor generalization of deep learning models to new subjects. While pre-trained deep learning models achieve high accuracy on subjects with similar domains, they fail on subjects with dissimilar domains. Optimization-based meta-learning algorithms can address this limitation by learning a good initialization for the model, enabling quick adaptation to new subjects with limited fine-tuning examples. We demonstrate that our Meta Learning approach consistently outperforms Transfer Learning on the BCI Competition IV 2a dataset. Although accuracy varies depending on domain similarity, meta-learning demonstrates efficient adaption to unseen subjects with limited data. By improving generalization across subjects with different domains under low-data environments, we can enhance the reliability and practicality of brain-computer interfaces for real-world applications.
An Experimental Investigation of Whispering Gallery Mode Microsphere Resonators for Sensing, Lasing, and Nonlinear Optical Applications

Microsphere resonators are gaining popularity as a key component in photonic circuit devices, optical communications, sensing, lasing, and nonlinear optical applications. As one of the essential categories of optical microcavities, microsphere resonators based on whispering gallery modes (WGMs) have benefits of ultra-high Q factor, small mode volume, ease of fabrication, and low cost. In this paper, we will present experimental results achieved by our group in regards to different application of microsphere resonators:1)We studied the refractometric gas sensing sensitivity and the effect of thickness of the sol-gel silica layer coated to the microsphere to detect small traces of ammonia.2)Rare earth doped microsphere resonators are promising candidates for lasing applications. Tellurite, fluoride, phosphate, and silicate glasses are good hosts for doping with rare earth elements for broadband applications. Tellurite doping with various rare earth elements can provide a wide stimulated emission cross section in the communication band.3)High Q factor and low mode volume of WGM microresonators is a perfect host for non-linear optics application. We will present some non-linear effects which are present in silica spherical microresonators which include stimulated Raman scattering and frequency comb generation via degenerative four wave mixing.
The Impact of Short and Very Short Videos on the Effectiveness of Teaching and the Principles of Their Development

Short video content for education is increasingly appearing as part of educational content. This was especially evident during the COVID19 crisis. Not only have they persisted, but they are taking an ever-increasing share in classical education Video material not only needs to be developed in accordance with the pedagogical principles, it also needs to keep students’ engagement and promote active learning. The length of video material is an important element that needs to be considered in development of teaching contents - how long the video should be. The article outlines active learning guidelines for teaching with video materials and the impact of the length of video materials (short and even shorter) on learning effectiveness. This article reviews previously published research and the results of the research projects conducted in three sixth grade classes in primary schools in Juršići, Marčana and Rovinj. The research included 55 elementary school students and video materials for the subject of Informatics. Students in each class were divided into two groups: One group of students was presented with a short video of 13:27 minutes, and the other group was presented with ten very short videos, ranging from 0:27 to 2:59 minutes.
Title Generic initial ideals and fibre products

We study the behavior of generic initial ideals with respect to fibre products. In our main result we determine the generic initial ideal of the fibre product with respect to the reverse lexicographic order. As an application we compute explicitly the generic initial ideal of a fibre product in a special case. We a lso prove that the fibre product of two graded ideals is componentwise linear if and o ly if both ideals have this property.
Effects of Argon (Ar) on Synthesis and Photocatalytic Activities of Graphene

The effects of Argon (Ar) on the Synthesis and Photocatalytic activities of monolayer Graphene under the flow of Methane (CH4) and Hydrogen (H2) gas has been studied. The Chemical Vapor Deposition (CVD) method was used to create a large area of monolayer graphene with features that meet the needs of a wide variety of applications. With this, the possibility of varying the dimensions of graphene by differing the argon gas while keeping other parameters constant was demonstrated. This helps with the ability to control the size and number of layers of graphene for different applications. Further, the photocatalytic activities of produced graphene were examined during CVD at various argon gas flow rates to observe their effects on the performances of graphene. The result shows that the synthesized graphene at Ar = 80 sccm is a monolayer with perfectly shaped edges. After exfoliation, the graphene obtained was properly formed with a large surface area, which helped achieve the results of higher photocatalytic activity.
A 60 μW front-end for 10 ps resolution monolithic pixel sensors in a 130nm SiGe BiCMOS process

This paper presents the development and optimization of a front-end circuit and the design of a monolithic sensor demonstrator with a high spatial resolution (hexagonal pixels with 50 μ m pitch) and sub-10 picosecond timing capability for the detection of ionizing radiation. The system combines a monolithic sensor in a SiGe BiCMOS process with a front-end architecture based on a SiGe Heterojunction Bipolar Transistor (HBT). The design of the prototype has been optimized to achieve improved timing performance while maintaining low power consumption by analyzing the trade-off between sensor input capacitance and power consumption. The goal is to achieve a timing resolution below 10 ps, a significant improvement over the previous prototype, which demonstrated a time resolution of 20 ps. This prototype has been developed in the framework of the MONOLITH H2020 ERC project.