Miracle-dz/UniFAR-FTUs huggingface dataset:
Harmonic Recomposition using Conditional Autoregressive Modeling

We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den Oord et al.(2017). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.
Sensor Fusion for Recognition of Activities of Daily Living

Activity of daily living (ADL) is a significant predictor of the independence and functional capabilities of an individual. Measurements of ADLs help to indicate one’s health status and capabilities of quality living. Recently, the most common ways to capture ADL data are far from automation, including a costly 24/7 observation by a designated caregiver, self-reporting by the user laboriously, or filling out a written ADL survey. Fortunately, ubiquitous sensors exist in our surroundings and on electronic devices in the Internet of Things (IoT) era. We proposed the ADL Recognition System that utilizes the sensor data from a single point of contact, such as smartphones, and conducts time-series sensor fusion processing. Raw data is collected from the ADL Recorder App constantly running on a user’s smartphone with multiple embedded sensors, including the microphone, Wi-Fi scan module, heading orientation of the device, light proximity, step detector, accelerometer, gyroscope, magnetometer, etc. Key technologies in this research cover audio processing, Wi-Fi indoor positioning, proximity sensing localization, and time-series sensor data fusion. By merging the information of multiple sensors, with a time-series error correction technique, the ADL Recognition System is able to accurately profile a person’s ADLs and discover his life patterns. This paper is particularly concerned with the care for the older adults who live independently.
Photoperiod–temperature phase lag: A universal environmental context of seasonal developmental plasticity

Seasonal developmental plasticity, which consists of season‐dependent alternations of developmental processes, has evolved to produce optimal phenotypes depending on specific periods in a year. For example, many phenological events in plants, such as flowering, fruiting, bud blast, bud formation, and growth cessation, are often controlled seasonally. Although temperature and photoperiod are the two major seasonal cues for such responses, the importance of phase lag between annual oscillations of the two signals has been unexplored, despite its universal nature in the context of seasonal environments. In this article, the phase‐lag calendar hypothesis (New Phytologist, 210, 2016, 399), especially the one between temperature and photoperiod, is explained using meteorological data obtained from central Japan as an example. We set forth to show how, for a narrow window in time of a couple of weeks in a year, simple threshold responses to these two signals that differ in annual oscillation phases are enough to make developmental plasticity to be expressed as phenological events. The properties of the underlying mechanisms of the events in different seasons are further predicted, and the responses are compared with reported empirical examples. Because many organisms have evolved under the phase lag between photoperiod and temperature, the developmental plasticity in response to the phase lag should be evaluated for diverse organisms.
A Message Sharing System that Enables Highly Accurate Space Attachment Linked with the Web

Services using Augmented Reality (AR) are widely used with the spread of smartphones. More specifically, there are services that put messages and images into the AR space for sharing them among users who visit the place as well as services that support the user's work by superimposing the work procedure into a real object. In these services, several pieces of information are tied to the corresponding objects or places. However, these services do not accommodate dynamic editing the information in a real-time fashion and the access to the information remotely. Based on the background, we propose a system called Flexibly Configurable Augmented Reality (FCAR) that uses spatial recognition to attach messages to an arbitrary place in the AR space corresponding to the structure of the real environment and allows information to be read and written without being in the location by linking to the Web. We also evaluate the usefulness of the system.
Effects of Ultraviolet Radiation (UVA + UVB) on Germination of Carpospores of the Red Macroalga Pyropia acanthophora var. brasiliensis (Rhodophyta, Bangiales): Morphological Changes

Carpospores of Pyropia acanthophora var. brasiliensis are dispersion and reproduction units responsible for giving rise to the diploid filamentous structure of this alga's life cycle. The present study assesses the anthropogenic impact of ultraviolet radiation (UVR) on morphology and ultrastructure, spore viability, autofluorescence of chloroplasts and the amount of intensity of ROS during the germination of carpospores. Carpospores were cultivated at 24 ± 1°C, 40 ± 10 μmol photons m−2 s−1 with photoperiod of 12 h and exposed to UVAR + UVBR for 3 h a day for 2 days with a daily dose of 5.05 J cm−2 for UVAR and 0.095 J cm−2 for UVBR. Samples were cultured for another five days exposed only to PAR in order to confirm their viability after the initial 2‐day exposure. Carpospores showed significant sensitivity to UVR exposure after only 48 h, including changes in developmental rate, overall morphology, cell organization and chloroplast autofluorescence. UVR exposure inhibited germ tube formation in carpospores, which were mostly nonviable and/or altered, showing retracted cytoplasm and disorganized cytoplasmic content. Even in the absence of UVR exposure, carpospores remained collapsed, indicating irreversible damage. It can be concluded that UVR is a limiting factor for the development of P. acanthophora.
Asiatic acid ameliorates CCl4-induced liver fibrosis in rats: involvement of Nrf2/ARE, NF-κB/IκBα, and JAK1/STAT3 signaling pathways

Purpose Currently, there are no effective therapies for liver fibrosis; hence, the development of anti-liver fibrosis agents is urgently needed. Here, we attempted to investigate the therapeutic effect and mechanism of asiatic acid (AA) on liver fibrosis, mainly focusing on the impact of AA on nuclear erythroid 2-related factor 2/antioxidant response element (Nrf2/ARE), nuclear factor-kappa B (NF-κB)/IκBα, and JAK1/signal transducer and activator of transcription 3 (STAT3) signaling pathways. Methods Rats were induced liver fibrosis by carbon tetrachloride (CCl4) for 6 weeks and concomitantly treated with AA (5 and 15 mg/kg) or vehicle by daily gavage. After AA treatment, the morphology of liver tissue was analyzed by H&E and Masson’s trichrome staining, and serum biochemical indicators were also assayed. Thereafter, the protein levels of Nrf2, HO-1, NQO-1, GCLC, NF-κB, IκBα, JAK1, p-JAK1, STAT3, and p-STAT3 were determined by Western blotting. Results Our results showed that AA treatment dramatically ameliorated CCl4-induced oxidative stress, inflammation, and fibrosis in rats. The expression of nuclear Nrf2 was increased after AA treatment, whereas cytoplasm Nrf2 levels were decreased. The protein expression of Nrf2 target proteins including HO-1, NQO-1, and GCLC was significantly increased by AA treatment. Furthermore, AA treatment decreased the levels of nuclear NF-κB to inhibit NF-κB/IκBα signaling pathway. In addition, we also found that AA treatment regulated JAK1/STAT3 signaling by decreasing the phosphorylation levels of JAK1 and STAT3. Conclusion These results demonstrate that AA ameliorates CCl4-induced liver fibrosis in rats by regulating Nrf2/ARE, NF-κB/IκBα, and JAK1/STAT3 signaling pathways, which suggests that AA might be a new antifibrosis agent that improves liver fibrosis.
PCEX: Interactive Program Construction Examples for Learning Programming

A sizable body of research on instructional practices supports the use of worked examples for acquiring cognitive skills in domains such as mathematics and physics. Although examples are also important in the domain of programming, existing research on programming examples is limited. Program examples are used by instructors to achieve two important goals: to explain program behavior and to demonstrate program construction patterns. Program behavior examples are used to demonstrate the semantics of various program constructs (i.e., what is happening inside a program or an algorithm when it is executed). Program construction examples illustrate how to construct a program that achieves a specific purpose. While both functions of program examples are important for learning, most of the example-focused research in computer science education focused on technologies for augmenting program behavior examples such as program visualization, tracing tables, etc. In contrast, advanced technologies for presenting program construction examples were rarely explored. This work introduces interactive Program Construction Examples (PCEX) to begin a systematic exploration of worked-out program construction examples in the domain of computer science education. A classroom evaluation and analysis of the survey data demonstrated that the usage of PCEX examples is associated with better student's learning and performance.
On computer science major students' motivation in a practically oriented robotics course

Educational robotics is considered to motivate students to learn principles of computing and computational thinking in many contexts. In this paper, we present the first experiences from our recent multimodal robotics course, that was given to more than 100 computer science major students at the University of Eastern Finland. Preliminary results show, aligned with suggestions from the literature, that hands-on robotics exercises and the course project work motivated students. Furthermore, results indicate that robotics would have a stronger role in our computer science curriculum.
Reducing multi-hop communication latency of schedule-based asynchronous duty cycle mechanisms through low-resolution synchronization

-In Wireless Sensor Networks, nodes typically employ batteries that cannot be easily recharged or replaced. Hence, optimizing energy consumption is a major concern. Among the several proposals in the literature, schedule-based asynchronous duty cycle methods are the simplest because they do not require mechanisms, protocols or specific hardware for clock synchronization between nodes. These solutions, however, result in high latency for multi-hop communication. In this work, we show how existing asynchronous mechanisms can benefit from a low level of synchronism, with resolution of slots. Assuming this possibility, we show through mathematical models, numerical and network simulations that the use of specific offsets between clocks of neighboring nodes, according to their distances to the sink node, significantly reduces latency.
Epilepsy detection from EEG signals: a review

Abstract Over many decades, research is being attempted for the detection of epileptic seizure to support for automatic diagnosis system to help clinicians from burdensome work. In this respect, an enormous number of research papers is published for identification of epileptic seizure. It is difficult to present a detailed review of all these literature. Therefore, in this paper, an attempt has been made to review the detection of an epileptic seizure. More than 100 research papers have been discussed to discern the techniques for detecting the epileptic seizure. Further, the literature survey shows that the pattern recognition required to detect epileptic seizure varies with different conditions of EEG datasets. This is mainly due to the fact that EEG detected under different conditions has different characteristics. This is, in turn, necessitates the identification of pattern recognition technique to effectively distinguish EEG epileptic data from a various condition of EEG data.
Polyphonic audio tagging with sequentially labelled data using CRNN with learnable gated linear units

Audio tagging aims to detect the types of sound events occurring in an audio recording. To tag the polyphonic audio recordings, we propose to use Connectionist Temporal Classification (CTC) loss function on the top of Convolutional Recurrent Neural Network (CRNN) with learnable Gated Linear Units (GLUCTC), based on a new type of audio label data: Sequentially Labelled Data (SLD). In GLU-CTC, CTC objective function maps the frame-level probability of labels to clip-level probability of labels. To compare the mapping ability of GLU-CTC for sound events, we train a CRNN with GLU based on Global Max Pooling (GLU-GMP) and a CRNN with GLU based on Global Average Pooling (GLU-GAP). And we also compare the proposed GLU-CTC system with the baseline system, which is a CRNN trained using CTC loss function without GLU. The experiments show that the GLU-CTC achieves an Area Under Curve (AUC) score of 0.882 in audio tagging, outperforming the GLU-GMP of 0.803, GLU-GAP of 0.766 and baseline system of 0.837. That means based on the same CRNN model with GLU, the performance of CTC mapping is better than the GMP and GAP mapping. Given both based on the CTC mapping, the CRNN with GLU outperforms the CRNN without GLU.
Release of Graphene and Carbon Nanotubes from Biodegradable Poly(Lactic Acid) Films during Degradation and Combustion: Risk Associated with the End-of-Life of Nanocomposite Food Packaging Materials

Nanoparticles of graphene and carbon nanotubes are attractive materials for the improvement of mechanical and barrier properties and for the functionality of biodegradable polymers for packaging applications. However, the increase of the manufacture and consumption increases the probability of exposure of humans and the environment to such nanomaterials; this brings up questions about the risks of nanomaterials, since they can be toxic. For a risk assessment, it is crucial to know whether airborne nanoparticles of graphene and carbon nanotubes can be released from nanocomposites into the environment at their end-life, or whether they remain embedded in the matrix. In this work, the release of graphene and carbon nanotubes from the poly(lactic) acid nanocomposite films were studied for the scenarios of: (i) biodegradation of the matrix polymer at the disposal of wastes; and (ii) combustion and fire of nanocomposite wastes. Thermogravimetric analysis in air atmosphere, transmission electron microscopy (TEM), atomic force microscopy (AFM) and scanning electron microscope (SEM) were used to verify the release of nanoparticles from nanocomposite films. The three factors model was applied for the quantitative and qualitative risk assessment of the release of graphene and carbon nanotubes from nanocomposite wastes for these scenarios. Safety concern is discussed in respect to the existing regulations for nanowaste stream.
A Guidance System for Blind and Visually Impaired People via Hybrid Data Fusion

This paper presents an indoor navigation system built in a wearable device. The system allows visually impaired users to perform guided audio navigation safe, fast and has a low computational complexity. The methodological approach chosen divides the process into two phases: offline and online. In offline phase, the indoor mapping is done by data fusing of radio frequency and visual markers, constructing a unique and consistent representation. In the online phase navigation and recognition of each of the internal positions are performed through the fusion representation or only by the wi-fi or visual signals when one of the sensors is strongly affected by noises or other interferences. The results showed that the recognition levels of wi-fi, visual and fusion markers were 87.59%, 90.92%, and 92.03%, respectively. The error margin after the data fusion application was 0.8 m, with an average time of 0.62 ms.
Adaptation to DNA damage checkpoint in senescent telomerase-negative cells promotes genome instability

Here, Coutelier et al. used a microfluidic-based approach and live-cell imaging in yeast to capture early mutation events during replicative senescence and observed that prolonged checkpoint arrests occurred frequently in telomerase-negative lineages. Their results demonstrate that the adaptation pathway is a major contributor to the genome instability induced during replicative senescence.
Scaling Byzantine Consensus: A Broad Analysis

Blockchains and distributed ledger technology (DLT) that rely on Proof-of-Work (PoW) typically show limited performance. Several recent approaches incorporate Byzantine fault-tolerant (BFT) consensus protocols in their DLT design as Byzantine consensus allows for increased performance and energy efficiency, as well as it offers proven liveness and safety properties. While there has been a broad variety of research on BFT consensus protocols over the last decades, those protocols were originally not intended to scale for a large number of nodes. Thus, the quest for scalable BFT consensus was initiated with the emerging research interest in DLT. In this paper, we first provide a broad analysis of various optimization techniques and approaches used in recent protocols to scale Byzantine consensus for large environments such as BFT blockchain infrastructures. We then present an overview of both efforts and assumptions made by existing protocols and compare their solutions.
MEK Inhibition Induces Therapeutic Iodine Uptake in a Murine Model of Anaplastic Thyroid Cancer

Anaplastic thyroid carcinoma (ATC) is refractory to radioiodine therapy in part because of impaired iodine metabolism. We targeted the mitogen-activated protein kinase and phosphatidylinositol 3-kinase (PI3′K) pathways with the intent to induce radioiodine uptake for radioiodine treatment of ATC. Methods: Human ATC cells were used to evaluate the ability of pharmacologic inhibition of the mitogen-activated protein kinase and PI3′K pathways to induce radioiodine uptake. Thyrocyte-specific double-mutant BRAFV600E PIK3CAH1047R mice were treated with a MEK inhibitor followed by radioiodine treatment, and tumor burden was monitored by ultrasound imaging. Results: ATC cell lines showed an increase in sodium-iodine symporter transcription when treated with a MEK or BRAFV600E inhibitor alone and in combination with PI3′K inhibitor. This translated into a dose-dependent elevation of iodine uptake after treatment with a MEK inhibitor alone and in combination with a PI3′K inhibitor. In vivo, MEK inhibition but not BRAF or PI3′K inhibition upregulated sodium-iodine symporter transcription. This translated into a stable reduction of tumor burden when mice were treated with a MEK inhibitor before radioiodine administration. Conclusion: This study confirms the ability of MEK inhibition to induce iodine uptake in in vitro and in vivo models of ATC. The approach of using a MEK inhibitor before radioiodine treatment could readily be translated into clinical practice and provide a much-needed therapeutic option for patients with ATC.
Longer Prehospital Time was not Associated with Mortality in Major Trauma: A Retrospective Cohort Study

Abstract Objective: The objective of this study was to determine the association between prehospital time and outcomes in adult major trauma patients, transported by ambulance paramedics. Methods: A retrospective cohort study of major trauma patients (Injury Severity Score >15) attended by St John Ambulance paramedics in Perth, Western Australia, who were transported to hospital between January 1, 2013 and December 31, 2016. Inverse probability of treatment weighting (IPTW) using the propensity score was performed to limit selection bias and confounding. The primary outcome was 30-day mortality and the secondary outcome was the length of hospital stay (LOS) for 30-day survivors. Multivariate logistic and log-linear regression analyses with IPTW were used to determine if prehospital time of more than the one hour (from receipt of the emergency call to arrival at hospital) or any individual prehospital time interval (response, on-scene, transport, or total time) was associated with 30-day mortality or LOS. Results: A total of 1,625 major trauma patients were included and 1,553 included in the IPTW sample. No significant association between prehospital time of one hour and 30-day mortality was found (adjusted odds ratio 1.10, 95% confidence interval (CI) 0.71–1.69). No association between any individual prehospital time interval and 30-day mortality was identified. In the 30-day survivors, one-minute increase of on-scene time was associated with 1.16 times (95% CI 1.03–1.31) longer LOS. Conclusion: Longer prehospital times were not associated with an increased likelihood of 30-day mortality in major trauma patients transported to hospital by ambulance paramedics. We found no evidence to support the hypothesis that prehospital time longer than one hour resulted in an increased risk of 30-day mortality. However, longer on-scene time was associated with longer hospital LOS (for 30-day survivors). Our recommendation is that prehospital care is delivered in a timely fashion and delivery of the patient to hospital is reasonably prompt.
Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS)

Neurons use sequences of action potentials (spikes) to convey information across neuronal networks. In neurophysiology experiments, information about external stimuli or behavioral tasks has been frequently characterized in term of neuronal firing rate. The firing rate is conventionally estimated by averaging spiking responses across multiple similar experiments (or trials). However, there exist a number of applications in neuroscience research that require firing rate to be estimated on a single trial basis. Estimating firing rate from a single trial is a challenging problem and current state-of-the-art methods do not perform well. To address this issue, we develop a new method for estimating firing rate based on kernel smoothing technique that considers the bandwidth as a random variable with prior distribution that is adaptively updated under a Bayesian framework. By carefully selecting the prior distribution together with Gaussian kernel function, an analytical expression can be achieved for the kernel bandwidth. We refer to the proposed method as Bayesian Adaptive Kernel Smoother (BAKS). We evaluate the performance of BAKS using synthetic spike train data generated by biologically plausible models: inhomogeneous Gamma (IG) and inhomogeneous inverse Gaussian (IIG). We also apply BAKS to real spike train data from non-human primate (NHP) motor and visual cortex. We benchmark the proposed method against the established and previously reported methods. These include: optimized kernel smoother (OKS), variable kernel smoother (VKS), local polynomial fit (Locfit), and Bayesian adaptive regression splines (BARS). Results using both synthetic and real data demonstrate that the proposed method achieves better performance compared to competing methods. This suggests that the proposed method could be useful for understanding the encoding mechanism of neurons in cognitive-related tasks. The proposed method could also potentially improve the performance of brain-machine interface (BMI) decoder that relies on estimated firing rate as the input.
Kinder partizipativ in Forschung einbeziehen – ein kinderrechtsbasiertes Stufenmodell

B ibliographie: Buker, Petra/Hupping, Birgit/Mayne, Fiona/Howitt, Christine: Kinder partizipativ in Forschung einbeziehen – ein kinderrechtsbasiertes Stufenmodell, Diskurs Kindheits- und Jugendforschung, 1-2018, S. 109-114. https://doi.org/10.3224/diskurs.v13i1. 10