Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone-Assessed Movement Predicts Music Properties: Towards Integrating Embodied Music Cognition into Music Recommender Services via Accelerometer Motion Data

Numerous studies have shown a close relationship between movement and music [7], [17], [11], [14], [16], [3], [8]. That is why Leman calls for new mediation technologies to query music in a corporeal way [9]. Thus, the goal of the presented study was to explore how movement captured by smartphone accelerometer data can be related to musical properties. Participants (N = 23, mean age = 34.6 yrs, SD = 13.7 yrs, 13 females, 10 males) moved a smartphone to 15 musical stimuli of 20s length presented in random order. Motion features related to tempo, smoothness, size, regularity, and direction were extracted from accelerometer data to predict the musical qualities "rhythmicity", "pitch level + range" and "complexity" assessed by three music experts. Motion features selected by a 20-fold lasso predicted the musical properties to the following degrees "rhythmicity" (R2: .47), pitch level and range (R2: .03) and complexity (R2: .10). As a consequence, we conclude that music properties can be predicted from the movement it evoked, and that an embodied approach to Music Information Retrieval is feasible.
A Magnetic Field Detection and Localization Scheme for Internet of Underwater Things

The magnetic field whose intensity is changed by the magnetic permeability of the medium can be considered as a new underwater communication method. However, the magnetometer in the base station must compensate for the geomagnetic field to detect the underwater moving node for the communication and the localization. Therefore, this paper proposes a magnetic field detection and a localization scheme based on the exponential smoothing method. Simulation results show that the proposed scheme can extend the detection range and improve the localization accuracy by considering the trend of the geomagnetic field.
Deep fluid pathways beneath Mammoth Mountain, California, illuminated by migrating earthquake swarms

Earthquakes beneath a Californian volcano track fluid movement and outline a zone of partial melt in the deep and mid-crust. Although most volcanic seismicity is shallow (within several kilometers of the surface), some volcanoes exhibit deeper seismicity (10 to 30+ km) that may reflect active processes such as magma resupply and volatile transfer. One such volcano is Mammoth Mountain, California, which has also recently exhibited high rates of CO2 discharge at the surface. We perform high-resolution earthquake detection and relocation to reveal punctuated episodes of rapidly propagating seismicity at mid-crustal depths along a narrow fracture zone surrounding a body of partial melt. We infer that these earthquakes track dike intrusions or fluid pressure pulses associated with CO2 exsolution, suggesting that the deep plumbing system of Mammoth Mountain is an active conduit for fluid transport from the base of the crust to the surface.
Problems of Neural Networks Training

In this paper, we review problems B ctaTbe paccMaTpHBaIOTcR IIpoóJIeMBI, related to the training of neural networks. Modern approaches to increasing the productivity of networks of various kinds are described. Difficulties in training deep neural networks are described. Also considered the “wide” neural networks and networks of radial basis functions. The strengths and weaknesses of the represented networks where evaluated.
Distributed Statistical Estimation of High-Dimensional and Nonparametric Distributions

We consider the problem of estimating high-dimensional and nonparametric distributions in distributed networks, where each sensor in the network observes an independent sample from the underlying distribution and can communicate it to a central processor by writing at most $k$ bits on a public blackboard. We obtain matching upper and lower bounds for the minimax risk of estimating the underlying distribution under $L$ 1loss. Our results reveal that the minimax risk reduces exponentially in k. Instead of relying on strong data processing inequalities for the converse as commonly done in the literature, we build on a new representation of the communication constraint, which leads to a tight characterization of the problem.
Solution-processed gallium-tin-oxide as a new choice for indium-free active layers in TFTs

We report a newly developed solution-processed amorphous oxide semiconductor, gallium-tin-oxide (GTO), for the active layer of thin-film transistors (TFTs). The transparent GTO thin film was fabricated by sol-gel process and patterned by photo-lithography into isolated arrays. The resulting TFTs exhibit a threshold voltage around zero-volgate, an on-off ratio as 106, and the field-effect mobility as 4.1 cm2/Vs. The electrical properties is readily higher than that of ZnO and comparable to solution-processed InGaZnO. Moreover, the un-capsulated TFTs exhibit better stablility in gate-bias stressing then solution-processed IGZO TFTs as measured in ambient conditions. The study may provide a new choice for the active layers for oxide-based TFTs and display technology.
Lossy Communication Subject to Statistical Parameter Privacy

We investigate the problem of sharing (communi-cating) the outcomes of a memoryless source when some of its statistical parameters must be kept private. Privacy is measured in terms of the Bayesian statistical risk according to a desired loss function while the quality of the reconstruction is measured by the average per-letter distortion. We first bound -uniformly over all possible estimators- the expected risk from below. This information-theoretic bound depends on the mutual information between the parameters and the disclosed (noisy) samples. We then present an achievable scheme that guarantees an upper bound on the average distortion while keeping the risk above a desired threshold, even when the length of the sample increases.
Treatment of Invasive Candidiasis: A Narrative Review

Invasive candidiasis occurs frequently in hospitalized patients, and is associated with high mortality rates due to delays in recognition and initiation of appropriate antifungals. Management of invasive candidiasis must take into account multiple host, pathogen, and drug-related factors, including the site of infection, host immune status, severity of sepsis, resistance and tolerance to antifungal agents, biofilm formation, and pharmacokinetic/pharmacodynamic considerations. Recent treatment directives have been shaped by the widespread introduction of echinocandins, highly potent and safe antifungals, into clinical use, as well as important changes in drug susceptibility patterns and the emergence of known and novel drug-resistant Candida species. Advances in molecular diagnostics have the potential to guide early targeted treatment of high-risk patients.
Construction and Optimization of Three-Dimensional Disaster Scenes within Mobile Virtual Reality

Because mobile virtual reality (VR) is both mobile and immersive, three-dimensional (3D) visualizations of disaster scenes based in mobile VR enable users to perceive and recognize disaster environments faster and better than is possible with other methods. To achieve immersion and prevent users from feeling dizzy, such visualizations require a high scene-rendering frame rate. However, the existing related visualization work cannot provide a sufficient solution for this purpose. This study focuses on the construction and optimization of a 3D disaster scene in order to satisfy the high frame-rate requirements for the rendering of 3D disaster scenes in mobile VR. First, the design of a plugin-free browser/server (B/S) architecture for 3D disaster scene construction and visualization based in mobile VR is presented. Second, certain key technologies for scene optimization are discussed, including diverse modes of scene data representation, representation optimization of mobile scenes, and adaptive scheduling of mobile scenes. By means of these technologies, smartphones with various performance levels can achieve higher scene-rendering frame rates and improved visual quality. Finally, using a flood disaster as an example, a plugin-free prototype system was developed, and experiments were conducted. The experimental results demonstrate that a 3D disaster scene constructed via the methods addressed in this study has a sufficiently high scene-rendering frame rate to satisfy the requirements for rendering a 3D disaster scene in mobile VR.
Bendamustine and rituximab in elderly patients with low‐tumour burden follicular lymphoma. Results of the LYSA phase II BRIEF study

The treatment of low‐tumour burden follicular lymphoma (LTBFL) remains a challenge. Rituximab‐based strategies may be improved by adding chemotherapy. This Lymphoma Study Association multicentre phase II study assessed rituximab and bendamustine in 63 patients with untreated LTBFL who were aged over 60 years old and had a follicular lymphoma International Prognostic Index (FLIPI) score ≥2. Induction comprised 4 weekly cycles of rituximab 375 mg/m2 intravenously combined with 2 cycles of bendamustine 90 mg/m2 days 1–2 with a 28‐day interval, followed by twelve cycles of 375 mg/m2 rituximab maintenance therapy every 8 weeks. The primary endpoint was complete response (CR)/unconfirmed CR (CRu), at 12 weeks. Median age was 67·4 years and median FLIPI was 3. Ultimately, 18 patients (29%) had high tumour burden according to Groupe d'Etude des Lymphomes Folliculaires criteria. The 12‐week CR/CRu rate was 54·0% and the overall response rate was 93·7%. Surprisingly, 3 patients died during maintenance (2 sepsis, 1 neoplasm). Progression‐free survival was 85·4% at 24 months. In LTBFL patients with FLIPI ≥2, two cycles of rituximab and bendamustine result in a CR rate of 54·0%. However, the treatment‐related deaths observed do not allow this regimen to be recommended for LTBFL patients aged over 60 years.
Signal-to-Noise-Ratio-Aware Dynamic Range Compression in Hearing Aids

Fast-acting dynamic range compression is a level-dependent amplification scheme which aims to restore audibility for hearing-impaired listeners. However, when being applied to noisy speech at positive signal-to-noise ratios (SNRs), the gain function typically changes rapidly over time as it is driven by the short-term fluctuations of the speech signal. This leads to an amplification of the noise components in the speech gaps, which reduces the output SNR and distorts the acoustic properties of the background noise. An adaptive compression scheme is proposed here which utilizes information about the SNR in different frequency channels to adaptively change the characteristics of the compressor. Specifically, fast-acting compression is applied to speech-dominated time-frequency (T-F) units where the SNR is high, while slow-acting compression is used to effectively linearize the processing for noise-dominated T-F units where the SNR is low. A systematic evaluation of this SNR-aware compression scheme showed that the effective compression of speech components embedded in noise was similar to that of a conventional fast-acting system, whereas natural fluctuations in the background noise were preserved in a similar way as when a slow-acting compressor was applied.
Disturbance/Uncertainty Estimator Based Robust Control of Nonminimum Phase Systems

An open problem in disturbance/uncertainty estimator based control is to obtain explicit mathematical expressions for robust stability, performance, and bandwidth requirement. This paper offers a path based on <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_\infty$</tex-math></inline-formula> robust control to resolve this problem. Robust stability/performance is assured at the design phase. The main controller and disturbance observer are handled as a whole. The ideas are applicable to nonminimum phase systems as well. The theory is verified experimentally on a pan–tilt system and numerically on a rotary mechanical system. Comparisons with state-of-the art methods in literature show noticeable performance and robustness improvements.
An Introduction to Fault-Tolerant Quantum Computation and its Overhead Reduction Schemes

A quantum computer is a powerful machine that provides a new approach to information processing based on quantum mechanics. However, basically, qubits are very vulnerable to errors, thus a quantum error correction code (QECC) is needed to build a reliable quantum computer. Moreover, simply using QECCs is not sufficient in order that a quantum computer can tolerate errors. In quantum computation, each operation must be performed on the encoded states. Even when the gates used to perform these operations are noisy, the quantum computation must be tolerable to the errors. This is called fault-tolerant quantum computation and it is an essential condition for implementing a quantum computer. However, considering the fault tolerance, the overhead of the quantum computer increases exponentially. In this paper, we introduce fault-tolerant quantum computation and schemes to reduce the its overhead.
Impact of local administration of various doses of dexmedetomidine on ropivacaine-induced lumbar plexus-sciatic nerve block

The present study aimed to investigate the impact of various doses of dexmedetomidine (DEX) on ropivacaine (ROP)-induced lumbar plexus-sciatic nerve block (LSB). A total of 80 patients who underwent ankle surgery under LSB were divided into group R (applied with 30 ml 5% ROP), Dex1 (30 ml 0.5% ROP + 1 µg/kg DEX), Dex2 (30 ml 0.5% ROP + 1.5 µg/kg DEX) and Dex3 (30 ml 0.5% ROP + 2 µg/kg DEX), with 20 cases in each group. The onset time and duration of sensory and motor block, mean arterial pressure (MAP), heart rate (HR), oxygen saturation, Ramsay score, serum vascular endothelial growth factor (VEGF) level and adverse reactions in the four groups were observed. Results demonstrated that the durations of sensory and motor block in group R were shorter than those in groups Dex1-3 (P<0.01), followed by the sequence of group Dex1<Dex2<Dex3 (P<0.05). MAP and HR in groups Dex1-3 at T2-T5 were significantly lower than those in group R (P<0.01), and HR in group Dex3 at T3 and T4 was significantly lower than that in groups Dex1 and Dex2 (P<0.05). Ramsay scores in groups Dex1-3 at T2-T4 were significantly higher than those in group R (P<0.05). Serum VEGF levels in groups Dex2 and Dex3 at T2-T5 were significantly higher than those in group R (P<0.01). The incidences of over-sedation, bradycardia and dry mouth in group Dex3 were notably higher than those in the other groups. In conclusion, 1.5 µg/kg DEX exhibits a superior effect in improving ROP-induced LSB.
From Easy to Difficult: A Self-Paced Multi-Task Joint Sparse Representation Method

Multi-task joint sparse representation (MTJSR) is one kind of efficient multi-task learning (MTL) method for solving different problems together using a shared sparse representation. Based on the learning mechanism in human, which is a self-paced learning by gradually training the tasks from easy to difficult, I apply this mechanism into MTJSR, and propose a multi-task joint sparse representation with self-paced learning (MTJSR-SP) algorithm. In MTJSR-SP, the self-paced learning mechanism is considered as a regularizer of optimization function, and an iterative optimization is applied to solve it. Comparing with the traditional MTL methods, MTJSR-SP has more robustness to the noise and outliers. The experimental results on some datasets, i.e. two synthesized datasets, four datasets from UCI machine learning repository, an oxford flower dataset and a Caltech-256 image categorization dataset, are used to validate the efficiency of MTJSR-SP. key words: multi-task learning, sparse representation, self-paced learning, curriculum learning
BrownoutCC: Cascaded Control for Bounding the Response Times of Cloud Applications

Cloud computing has emerged as an inexpensive and powerful computing paradigm, to the point that now even applications with hard deadlines are executed in the cloud. It may happen, due to unexpected events, that an application becomes popular and receives a lot of attention and client requests in a short period of time. Provisioning computing capacity for such applications is quite a difficult task, because content popularity cannot be easily predicted. One of the main problems in case content has to be served with a hard deadline is to ensure that this deadline is respected, even in the presence of popularity spikes. To this end, partial computation and graceful degradation were exploited, originating the brownout framework. Applications would degrade the user experience in the presence of load variations, to guarantee that deadlines are met. Two different control paradigms were applied to brownout: discrete-time control of optional content percentage over a period and event-based queue management. The first one had reasonable performance providing formal guarantees about the solution. The second one was able to improve the performance and keep the response time at the setpoint better, but suffered from the drawback of not providing formally-grounded mathematical guarantees. In this work we combine the best of both worlds, providing a cascaded controller for brownout, based on a more precise model of the cloud application with respect to the original design. The BrownoutCC controller achieves performance comparable with the event-based version, without sacrificing formal guarantees.
Do Not Make the Same Mistakes Again and Again: Learning Local Recovery Policies for Navigation From Human Demonstrations

In this letter, we present a human-in-the-loop learning framework for mobile robots to generate effective local policies in order to recover from navigation failures in long-term autonomy. We present an analysis of failure and recovery cases derived from long-term autonomous operation of a mobile robot, and propose a two-layer learning framework that allows to detect and recover from such navigation failures. Employing a learning by demonstration approach, our framework can incrementally learn to autonomously recover from situations it initially needs humans to help with. The learning framework allows for both real-time failure detection and regression using Gaussian processes. Our empirical results on two different failure scenarios indicate that given 40 failure state observations, the true positive rate of the failure detection model exceeds 90%, ending with successful recovery actions in more than 90% of all detected cases.
A Gait Recognition Method for Human Following in Service Robots

In this paper, we propose a gait recognition method for service robots to conduct human following tasks. A walking sequence segmentation method is designed to extract the consecutive gait cycles from an arbitrary walking sequence. Based on the segmentation results, a novel hybrid gait feature is proposed to capture the static, dynamic, and trajectory features for each segmented key and supplementary gait cycles. A dataset of 25 human subjects is collected to evaluate the proposed method in three different walking paths with various walking directions. Experimental results show that the proposed method achieves satisfactory performance in terms of identification accuracy and ${F} _{\text {comb}}$ indexes on our dataset. Compared with five state-of-the-art gait recognition methods, the proposed method achieves the best performance on human gait recognition based on the walking sequences defined in our proposed dataset.
Quantitative Measure of Phase Noise Assessment for Quartz Oscillators

This paper considers the method of estimating the frequency instability of a quartz oscillator via measuring the Allan variance. The method is a two-sample variance and allows estimating the phase noise of an oscillator in the entire frequency-detuning region via a single numerical value, which is sometimes very important for the development of equipment. Taking into account possible sources of phase noise, in our paper the phase noise was considered by the imaginary frequency-detuning zones and sources of its origin. Temperature-stabilized quartz oscillators with artificially introduced sources of phase noise were practically studied.
Two intrauterine programming mechanisms of adult hypercholesterolemia induced by prenatal nicotine exposure in male offspring rats

Epidemiologic studies showed that low birth weight is associated with high cholesterol and an increased risk of cardiovascular diseases in adulthood. This study aimed to elucidate the intrauterine programming mechanisms of adult hypercholesterolemia. The results showed that prenatal nicotine exposure (PNE) caused intrauterine growth retardation and hypercholesterolemia in male adult offspring rats. Hepatic cholesterol synthesis and output were deceased in utero but increased in adults; hepatic reverse cholesterol transport (RCT) persistently deceased before and after birth. Meanwhile, PNE elevated serum corticosterone level and decreased hepatic IGF1 pathway activity in male fetuses, whereas converse changes were observed in male adults. The chronic stress model and cortisol‐treated HepG2 cells verified that excessive glucocorticoid (GC)‐induced GC‐IGF1 axis programming enhanced hepatic cholesterol synthesis and output. In addition, PNE decreased the expression of specific protein 1 and P300 enrichment and H3K27 acetylation at the promoter region of genes responsible for RCT both in fetal and adult, male livers and reduced expression of those genes, similar alterations were also confirmed in cortisol‐treated HepG2 cells, suggesting that excessive GC‐related programming induced continuous RCT reduction by epigenetic modification. Taken together, the “2‐programming” approach discussed above may ultimately contribute to the development of hypercholesterolemia in male adult offspring. —Zhou, J., Zhu, C. , Luo, H. , Shen, L. , Gong, J., Wu, Y. , Magdalou, J., Chen, L. , Guo, Y. , Wang, H. Two intrauterine programming mechanisms of adult hypercholesterolemia induced by prenatal nicotine exposure in male offspring rats. FASEB J. 33, 1110–1123 (2019). www.fasebj. org
Involvement of Innate and Adaptive Immune Systems Alterations in the Pathophysiology and Treatment of Depression

Major depressive disorder (MDD) is a prevalent and debilitating disorder, often fatal. Treatment options are few and often do not provide immediate relief to the patients. The increasing involvement of inflammation in the pathology of MDD has provided new potential therapeutic avenues. Cytokine levels are elevated in the blood and cerebrospinal fluid of MDD patients whereas immune cells often exhibit an immunosuppressed phenotype in MDD patients. Blocking cytokine actions in patients exhibiting MDD show some antidepressant efficacy. However, the role of cytokines, and the immune response in MDD patients remain to be determined. We reviewed here the roles of the innate and adaptive immune systems in MDD, as well as potential mechanisms whereby the immune response might be regulated in MDD.