Miracle-dz/UniFAR-FTUs huggingface dataset:
Annotating Jazz Recordings Using Lead Sheet Alignment with Deep Chroma Features

The automatic recognition of chords from jazz recordings remains largely an unsolved challenge, due to the broad harmonic vocabulary, the freedom of interpretation in performance, the rich variety of expressive techniques, and the limited availability of accurately labeled training data. In practice, many jazz recordings contain performances of popular compositions that are known as standards. We propose an approach that takes into consideration available prior information about chord changes of popular compositions known as lead sheets. Instead of estimating the exact chord symbol at each time point, we aim to identify the position in the lead sheet, thereby solving the audio-to-score alignment task (with a distinction that the score does not contain any melodic information, and the harmonic annotation is only approximate). This approach also solves the structural segmentation problem, as segment boundaries are available in the lead sheets. To achieve this goal we combine a multi-task convolutional recurrent neural architecture with an alignment algorithm based on a Hidden Markov Model. The proposed approach uses the iRealPro corpus of 1186 lead sheets and is evaluated on a test set of the 220 audio excerpts in the Weimar Jazz Database, showing that it outperforms previously published work.
Edinburgh Research Explorer A stochastic proximal alternating method for non-smooth non-convex optimization

In this work, we introduce a novel stochastic proximal alternating linearized minimization (PALM) algorithm 5 [6] for solving a class of non-smooth and non-convex optimization problems. Large-scale imaging problems are 6 becoming increasingly prevalent due to the advances in data acquisition and computational capabilities. Motivated 7 by the success of stochastic optimization methods, we propose a stochastic variant of proximal alternating linearized 8 minimization. We provide global convergence guarantees, demonstrating that our proposed method with variance-9 reduced stochastic gradient estimators, such as SAGA [16] and SARAH [27], achieves state-of-the-art oracle 10 complexities. We also demonstrate the eﬃcacy of our algorithm via several numerical examples including sparse 11 non-negative matrix factorization, sparse principal component analysis and blind image deconvolution.
Birmingham A preliminary investigation into the influence of therapist experience on the outcome of individual anger interventions for people with intellectual disabilities

Background: There is a developing literature into Anger Management interventions for people with intellectual disability. While initial reports suggest that these interventions are effective there are few evaluations examining what aspects of the therapeutic process contribute to effectiveness. Method: Individuals with an intellectual disability and anger control difﬁculties who were referred to community psychology services were allocated to either experienced clinical psychologists or a less experienced assistant psychologist who followed the same intervention framework. Results: Signiﬁcant reductions in self-reported anger intensity were reported; however, the group who were treated by more experienced therapists reported more change and more individuals reported clinically signiﬁcant change. Conclusions: While effective change was reported by both groups, these results suggest that clinical experience and training may be an important variable in determining the magnitude of change. This has implications for the design of intellectual disability mental health services.
Effects of bovine subclinical mastitis caused by Corynebacterium spp . on somatic 2 cell count , milk yield and composition by comparing contralateral quarters 3 4 5

34 Subclinical mastitis caused by Corynebacterium spp. (as a group and at the 35 species level) was investigated by evaluating contralateral (healthy and infected) 36 mammary quarters for somatic cell count (SCC), milk yield and composition. Selection 37 of cows with subclinical mastitis caused by Corynebacterium spp. was performed by 38 microbiological culture of composite samples collected from 1,242 dairy cows from 21 39 dairy herds. For each of the selected cows, milk yield was measured and milk samples 40 collected at the mammary quarter level (i.e., 1,140 mammary samples collected from 41 285 cows) for analysis of milk composition and SCC. The identification of 42 Corynebacterium spp. isolates was performed by 16S rRNA gene sequencing. 43 44 One hundred and eighty Corynebacterium spp. isolates were identified, of which 45 167 (92.77%) were C. bovis and eight (4.44%) non-C. bovis; for five of the 46 Corynebacterium spp. isolates (2.77%), sequencing of 16S rRNA genes did not allow 47
GUTJNL228254 1449..1472

INTRODUCTION Over the past decade the Improving Outcomes Guidance (IOG) document has led to service re-configuration in the NHS and there are now 41 specialist centres providing oesophageal and gastric cancer care in England and Wales. The National Oesophago-Gastric Cancer Audit, which was supported by the British Society of Gastroenterology, the Association of Upper Gastrointestinal Surgeons (AUGIS) and the Royal College of Surgeons of England Clinical Effectiveness Unit, and sponsored by the Department of Health, has been completed and has established benchmarks for the service as well as identifying areas for future improvements. The past decade has also seen changes in the epidemiology of oesophageal and gastric cancer. The incidence of lower third and oesophago-gastric junctional adenocarcinomas has increased further, and these tumours form the most common oesophago-gastric tumour, probably reflecting the effect of chronic gastro-oesophageal reflux disease (GORD) and the epidemic of obesity. The increase in the elderly population with significant co-morbidities is presenting significant clinical management challenges. Advances in understanding of the natural history of the disease have increased interest in primary and secondary prevention strategies. Technology has improved the options for diagnostic and therapeutic endoscopy and staging with cross-sectional imaging. Results from medical and clinical oncology trials have established new standards of practice for both curative and palliative interventions. The quality of patient experience has become a significant component of patient care, and the role of the specialist nurse is fully intergrated. These many changes in practice and patient management are now routinely controlled by established multidisciplinary teams (MDTs) which are based in all hospitals managing these patients.
A probabilistic estimate of global marine N‐fixation and denitrification

We determine the global rates of marine N‐fixation and denitrification and their associated uncertainties by combining marine geochemical and physical data with a new two‐dimensional box model that separates the Atlantic from the IndoPacific basins. The uncertainties are estimated using a probabilistic approach on the basis of a suite of 2500 circulation configurations of this box model. N‐fixation and denitrification are diagnosed in an inverse manner for each of these configurations usingN*, P*, and the stable nitrogen isotope composition of nitrate as data constraints. Our approach yields a median water column denitrification rate of 52 TgN yr−1 (39 to 66 TgN yr−1, 5th to 95th percentile) and a median benthic denitrification rate of 93 TgN yr−1 (68 to 122 TgN yr−1). The resulting benthic‐to‐water column denitrification ratio of 1.8 confirms that the isotopic signature of water column denitrification has a limited influence on the global mean stable isotopic value of nitrate due to the dilution of the waters with a denitrification signal with the remainder of the ocean's nitrate pool. On the basis of two different approaches, we diagnose a global N‐fixation rate of between 94 TgN yr−1 and 175 TgN yr−1, with a best estimate of 131 TgN yr−1 and 134 TgN yr−1, respectively. Most of the N‐fixation occurs in the IndoPacific suggesting a relative close spatial coupling between sources and sinks in the ocean. Our N‐fixation and denitrification estimates plus updated estimates of atmospheric deposition and riverine input yield a pre‐industrial marine N cycle that is balanced to within 3 TgN yr−1 (−38 to 40 TgN yr−1). Our budget implies a median residence time for fixed N of 4,200 yr (3,500 to 5,000 yr).
Connectivity shapes cortical thickness reductions in schizophrenia: ‘Regions that link together, thin together’

Background : Cortical thickness reductions (CTRs) in schizophrenia are irregularly distributed across multiple loci. We hypothesized that cortical connectivity networks would explain the distribution of CTRs across the cortex; namely, cortico-cortical connectivity between loci with CTRs would be exceptionally strong and form an interconnected network. We tested this hypothesis in three schizophrenia cohorts: first-episode psychosis (FEP), chronic (CSZ), and treatment-resistant (TRS) schizophrenia. Methods : Structural brain images were acquired in three cross-sectional schizophrenia cohorts (FEP: n=70, CSZ: n=153, TRS: n=47) and matching healthy controls (n=54, 168 and 54, respectively). Cortical thickness was compared between the patient and respective control groups at 148 regions spanning the cortex. Structural connectivity strength between pairs of cortical regions was quantified with structural covariance analysis. Connectivity strength between CTR regions was compared to connectivity strength between 5000 sets of randomly chosen regions to establish whether CTR regions were interconnected more strongly than expected by chance. Results : Significant (FDR <.05) and widespread CTRs were found in the CSZ (79 regions) and TRS groups (106 regions), with more circumscribed CTRs in the FEP group (34 regions). CTRs with the largest effect sizes were found in frontal, temporal, cingulate, and insular regions. In all cohorts, both the patient and control groups showed significantly increased structural covariance between regions with CTRs, compared to randomly selected regions (p <.00001 all groups).
Design and Development of a Semi-Automated Kitchen Waste Composter

Waste generation is a significant challenge exacerbated by factors such as population growth, industrialization, and urbanization, particularly in densely populated areas like Metro Manila. Consequently, effective waste management becomes increasingly crucial. This study focused on the development of a waste management device designed to address waste generation at its source, specifically targeting the residential sector. The researchers have created a semi-automated kitchen waste composter to facilitate the conversion of kitchen waste into valuable fertilizers, even for individuals with limited composting knowledge. The system incorporates a sensor network that monitors key parameters of the composting process, including temperature and moisture levels. An actuator network ensures that these parameters remain within optimal ranges. Additionally, an image processing algorithm has been implemented to detect compost maturity. By implementing this waste management device, households in urban areas can actively contribute to waste reduction efforts. It empowers individuals to participate in composting without requiring extensive expertise. This technology represents a promising solution to mitigate waste generation and promote sustainable practices in residential settings, ultimately contributing to a cleaner and more environmentally friendly urban environment.
Pathfinding Model and Lagrangian-Based Global Routing

Global routing is a critical step in VLSI physical design. This paper proposes a novel pathfinding model based on integer linear programming for VLSI global routing. The Lagrangian relaxation method combined with a direction-aware weighted A*-algorithm is developed to quickly solve the model to obtain a better initial routing solution, which is further optimized by a designed multi-stage rip-up & rerouting algorithm. In each stage of rip-up & rerouting, different routing algorithms and cost functions are used to optimize the overflow and wire length. SPRoute and CUGR are two state-of-the-art global routers. Our proposed global routing algorithm outperforms SPRoute 1.0 & 2.0 in both the wire length and the number of vias on the ISPD08 benchmarks. On the ISPD18 benchmarks, compared to CUGR, our algorithm has about 5.1% reduction in the number of vias, and the average runtime is 4.89× speedup; compared to SPRoute 2.0, our algorithm has about 1.7% reduction on the average wire length, and the number of vias is comparable.
Late Breaking Results: Fast Fair Medical Applications? Hybrid Vision Models Achieve the Fairness on the Edge

As edge devices become readily available and indispensable, there is an urgent need for effective and efficient intelligent applications to be deployed widespread. However, fairness has always been an issue, especially in edge medical applications. Compared to convolutional neuron networks (CNNs), Vision Transformer (ViT) has a better ability to extract global information, which will contribute to alleviating the unfairness problem. Typically, ViTs consume large amounts of computational and memory resources, which hinders their usage on edge. In this work, we propose a novel hardware-efficient Vision Model search framework for the fair dermatology classification, namely HeViFa. Experimental results show that HeViFa could search for a hybrid ViT model that reaches 173.1 FPS on a Samsung S21 mobile phone with 85.71% accuracy on the light skin dataset and 80.85% accuracy on the dark skin dataset. Note that HeViFa can reach both the highest accuracy and fairness under similar latency constrain on multiple edge devices (Samsung S21 mobile phone, iPhone 13 Pro and Raspberry PI).
Intermittent-Aware Neural Network Pruning

Deep neural network inference on energy harvesting tiny devices has emerged as a solution for sustainable edge intelligence. However, compact models optimized for continuously-powered systems may become suboptimal when deployed on intermittently-powered systems. This paper presents the pruning criterion, pruning strategy, and prototype implementation of iPrune, the first framework which introduces intermittency into neural network pruning to produce compact models adaptable to intermittent systems. The pruned models are deployed and evaluated on a Texas Instruments device with various power strengths and TinyML applications. Compared to an energy-aware pruning framework, iPrune can speed up intermittent inference by 1.1 to 2 times while achieving comparable model accuracy.
The Effect of Climate Change on Soil Organic Matter Decomposition

In the last few decades the climate of Síkfőkút ILTER Forest (Hungary) became warmer and dryer. Due to the climate change the species composition of forest has been changing, and the total leaf litter production has been slightly decreasing. According to our long-term litter manipulation field experiment, which is part of ILTER Detritus Input and Removal Treatments (DIRT) Project, after a 4-5 year treating period, at the No Litter, No Root and No Input treatments the soil organic C and N content, the soil bacterial and fungal count, the soil pH, the soil enzyme activity, and soil respiration decreased. Increased soil temperature raises soil respiration exponentially, and thus if the average soil temperature increased by 2oC at the dry Síkfőkút site, soil respiration would increase by 22.1%. This increase would be higher (29.9%) at a wet site, such as Harvard Forest in the USA. Increasing soil respiration can speed up global warming through a positive feedback mechanism.
High-field phase diagram of the heavy-fermion metal 
CeIn3
: Pulsed-field NMR study on single crystals up to 56 T

The heavy-fermion antiferromagnet ${\mathrm{CeIn}}_{3}$ exhibits a field-induced anomaly associated with Fermi surface reconstruction at ${H}^{*}\ensuremath{\sim}45$ T, well below the antiferromagnetic (AFM) critical field that exceeds 60 T. In order to explore the origin of this anomaly, we have measured $^{115}\mathrm{In}$ NMR spectra in pulsed magnetic fields up to 56 T using high-quality single crystals. To within experimental resolution, we do not detect any change in the NMR spectra that could suggest a modification in either the character of the hyperfine field or the electric field gradient at In sites through ${H}^{*}$. This strongly suggests that the 45 T anomaly cannot be simply ascribed to a field-induced change in the magnetic structure of the AFM state. Neither do we find any evidence of changes in the crystallographic structure or in the distribution of charge density at ${H}^{*}$. The field dependence of the NMR shift implies the existence of a field-induced anomaly in the hyperfine coupling above ${H}^{*}$.
Flexible Multichannel Speech Enhancement for Noise-Robust Frontend

This paper proposes a flexible multichannel speech enhancement system with the main goal of improving robustness of automatic speech recognition (ASR) in noisy conditions. The proposed system combines a flexible neural mask estimator applicable to different channel counts and configurations and a multichannel filter with automatic reference selection. A transform-attend-concatenate layer is proposed to handle cross-channel information in the mask estimator, which is shown to be effective for arbitrary microphone configurations. The presented evaluation demonstrates the effectiveness of the flexible system for several seen and unseen compact array geometries, matching the performance of fixed configuration-specific systems. Furthermore, a significantly improved ASR performance is observed for configurations with randomly-placed microphones.
A wind power prediction method considering wind turbine operating status based on LSTM

With the growing demand for clean energy, wind power generation has attracted increasing attention. However, traditional wind power prediction methods which rely on statistical models and physical equations have certain limitations, rendering them unable to handle nonlinear and non-stationary scenarios. To address this challenge, this paper proposes a novel wind turbine power prediction method based on the long short-term memory (LSTM) neural networks. The proposed method takes into account not only the historical power and wind speed data, but also the operational state parameters of the wind turbine are introduced as input, thereby enabling a more comprehensive consideration of the effect of the operational state on power generation. By applying the long short-term memory (LSTM) neural networks, the proposed method achieves high prediction accuracy and robustness, demonstrating its promising application prospects in wind turbine power prediction.
Dynamics of stratified regions in Saturn

Saturn has a rather peculiar magnetic field in that it is highly spin axisymmetric. Evidence for the decay time of a magnetic field on the scale of Saturn would suggest that a dynamo operates deep within its interior. As a consequence, the observed field would be in violation of Cowling’s theorem. It is believed that a stably stratified layer under the influence of a thermal shear is the reason for the observed axisymmetric field. This stable layer is believed to be formed from helium sedimentation deep within Saturn, with the thermal shear driven by pole-equator temperature differences. The combined effects of shearing and the stable layer attenuate the non-axisymmetric field components leaving only the axisymmetric field at the surface. Motivated by the influence of this stable stratification, we follow on from initial work by Stevenson (1982b) by first considering the linear problem with variable conductivity and looking at the consequences of increasing the parameter that controls the strength of the thermal wind as mentioned in his paper. In subsequent chapters the analysis concentrates on the nonlinear contributions by including the momentum equation into our calculations. We present asymptotic analysis of such a system and show that the geostrophic flow, found by satisfying Taylor’s constraint, is singular for an inviscid interior solution in the limit of small Rm, where Rm, the magnetic Reynolds number, controls the strength of the shearing effect within the layer. Numerical treatment of the system of equations for a viscous system are also considered. The results of exploring the parameter space for Rm and Ha, the Hartmann number, lead to further asymptotic analysis in which viscosity is considered. A boundary layer solution is found, which is validated by the numerical solution. The latter part of the thesis looks at the numerical solution with the inclusion of a horizontal field, the motivation for which will become apparent in the analysis of the inviscid regime.
Improving Atrial Fibrillation Detection Using Adaptive Template Matching

We develop and assess algorithms for atrial fibrillation detection in electrocardiograms acquired over long time periods. The algorithms exploit the use of adaptive templates for QRS complexes that can accommodate changes in rhythms due to factors such as measurement device movement or the onset of ventricular beats. Adaptive templates are also used to represent P wave morphology. The use of adaptive templates makes these techniques particularly valuable for automated processing in devices such as insertable cardiac monitors that support evaluation over substantial periods of time. The algorithms are compared with a baseline AF detection algorithm that is based on the consistency of RR intervals. A probabilistic model for the likelihood of AF is generated by applying logistic regression to various electrocardiogram descriptors. We use data from four MIT-BIH databases to quantify the performance of the algorithms and to show that performance is improved significantly by the use of the adaptive QRS and P wave templates.
Equity premium prediction: Are economic and technical indicators instable?

We show that technical indicators deliver economic value in predicting the U.S. equity premium. A crucial element of this value stems from the stability of return predictability over the full sample period from 1950 to 2013. Results tentatively improve over time and beat alternatives over sub-periods. By contrast, economic indicators work well only until the 1970s, thereafter they lose predictive power, even when the last crisis is considered. Translating the predictive power of technical indicators into a standard investment strategy delivers an average Sharpe Ratio of 0.6 p.a. for investors who had entered the market at any point in time.
Smart Detection of Obfuscated Thermal Covert Channel Attacks in Many-core Processors

In thermal covert channel (TCC) attacks, malicious applications seek to leak private information in a stealthy and hard-to-detect manner. State-of-the-art approaches for TCC detection employ the Discrete Fourier Transform (DFT) combined with heuristics to identify possible channels. However, as we demonstrate in this paper, these approaches are limited when detecting short-duration attacks, where an attacker intentionally halts the transmission for a time interval to avoid the detection. In order to overcome this limitation of the state-of-the-art solutions, we propose the first detection method for short-duration TCC attacks. Our solution, Dotecca, is a machine learning-based technique that employs short windows of time-domain measurements instead of the DFT to detect TCCs. To evaluate our solution, we introduce a new obfuscated short-duration attack that disguises as a regular application from the perspective of a DFT spectrum. Our experiments show that the new obfuscated attack is able to remain undetected even under advanced DFT-based state-of-the-art detection approaches, reducing their detection accuracy to about 18 %. In contrast, our smart detection approach is able to detect state-of-the-art and new obfuscated attacks with an accuracy of 99 %. Moreover, our solution reduces the overhead of the DFT-based state-of-the-art solution by more than 14 ×.