Search

An experiment was conducted at the Crop Research Farm (CRF), Department of Agronomy, SHUATS, Prayagraj, U.P, India, during the Kharif season of 2023. The experiment was replicated three times according to a Randomized Block Design (RBD) with ten treatments. Three different concentrations of phosphorus (40, 50, and 60 kg/ha) and micronutrients (0.5% FeSO4, 0.5% ZnSO4, and 0.2% Borax) were administered as a topical spray to the leaves. A control group was treated with a mixture of 20:60:40 kg N:P:K/ha. Phosphorus application at a rate of 60 kg/ha and foliar spray of ZnSO₄ at 0.5% (treatment 8) resulted in the highest plant dry weight of 35.54 g, crop growth rate of 25.14 g/m²/day, number of pods per plant of 21.50, number of kernels per pod of 2.13, highest test weight of 365.33 g, seed yield of 3552.10 kg/ha, and B:C ratio of 2.43. The effect of applying a higher quantity of phosphorus, specifically 60 kg/ha, together with a foliar spray of ZnSO₄ at 0.5%, on the growth and yield of groundnut was found to be superior compared to the control treatment.

An experimental field study was carried out in the Kharif season of 2023 at the Crop Research Farm, Department of Agronomy, SHUATS, Prayagraj (U.P.). The experimental replications were conducted using a Randomized Block Design, with three levels of Nitrogen (0, 60, 90kg N/ha) and Azolla spp. (2, 4, 6t/ha), and one Control (NPK 120:60:60 kg/ha). In all, there were a total of ten treatments, each reproduced three times. The tested field had sandy loam soil texture with a neutral soil pH of 7.6, low organic carbon content of 0.372%, nitrogen content of 278.4 kg/ha, phosphorous content of 29.5 kg/ha, and potassium level of 217.3 kg/ha. The experimental results showed that treatment T₉ obtained significantly higher plant height (107.77 cm), dry weight (66.05 g), crop growth rate (57.27 g/m2/day), relative growth rate (0.0279 g/g/day), highest number of tillers per hill (22.07), panicles per m2 (282), test weight (15.23 g), grain yield (3.67 t/ha), stover yield (6.7 t/ha), and harvest index (35.74%). Transplanted Black Rice of variety BPT-2841 exhibited significantly greater maximum gross return (1,80,551.40 INR/ha), net return (1,05,496.30 INR/ha), and B:C ratio (1.41) in treatment T9. The investigation concludes that Nitrogen, being easily transportable in soil and plants, is a constituent of amino acids, nucleic acids, chlorophyll, enzymes, and hormones. These factors are essential in plant physiology and are associated with increased tillering, biomass production, protein synthesis, grain filling, yield, and quality. The availability, absorption, and utilization of nitrogen have a significant impact on these processes. Therefore, in the present experiment, the nitrogen requirements are met by applying 90kg of nitrogen through urea combined with 6t/ha of Azolla spp. This approach revealed superior performance in terms of both crop yield and economic returns .

We consider the algebra of massive fermions restricted to a diamond in two-dimensional Minkowski spacetime, and in the Minkowski vacuum state. While the massless modular Hamiltonian is known for this setting, the derivation of the massive one is an open problem. We compute the small-mass corrections to the modular Hamiltonian in a perturbative approach, finding some terms which were previously overlooked. Our approach can in principle be extended to all orders in the mass, even though it becomes computationally challenging., Comment: 31 pages, 10 figures

An estimated 2.4 million newborn infants died in 2020, 80% of them in sub-Saharan Africa and South Asia. To achieve the Sustainable Development Target for neonatal mortality reduction, countries with high mortality need to implement evidence-based, cost-effective interventions at scale. Our study aimed to estimate the cost, cost-effectiveness, and benefit-cost ratio of a participatory women's groups intervention scaled up by the public health system in Jharkhand, eastern India. The intervention was evaluated through a pragmatic cluster non-randomised controlled trial in six districts. We estimated the cost of the intervention at scale from a provider perspective, with a 42-month time horizon for 20 districts. We estimated costs using a combination of top-down and bottom-up approaches. All costs were adjusted for inflation, discounted at 3% per year, and converted to 2020 International Dollars (INT$). Incremental cost-effectiveness ratios (ICERs) were estimated using extrapolated effect sizes for the impact of the intervention in 20 districts, in terms of cost per neonatal deaths averted and cost per life year saved. We assessed the impact of uncertainty on results through one-way and probabilistic sensitivity analyses. We also estimated benefit-cost ratio using a benefit transfer approach. Total intervention costs for 20 districts were INT$ 15,017,396. The intervention covered an estimated 1.6 million livebirths across 20 districts, translating to INT$ 9.4 per livebirth covered. ICERs were estimated at INT$ 1,272 per neonatal death averted or INT$ 41 per life year saved. Net benefit estimates ranged from INT$ 1,046 million to INT$ 3,254 million, and benefit-cost ratios from 71 to 218. Our study suggests that participatory women's groups scaled up by the Indian public health system were highly cost-effective in improving neonatal survival and had a very favourable return on investment. The intervention can be scaled up in similar settings within India and other co

In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of an instrument recognition module that conditions the other two modules: a transcription module that outputs instrument-specific piano rolls, and a source separation module that utilizes instrument information and transcription results. The joint training of the transcription and source separation modules serves to improve the performance of both tasks. The instrument module is optional and can be directly controlled by human users. This makes Jointist a flexible user-controllable framework. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. Its novelty, however, necessitates a new perspective on how to evaluate such a model. In our experiments, we assess the proposed model from various aspects, providing a new evaluation perspective for multi-instrument transcription. Our subjective listening study shows that Jointist achieves state-of-the-art performance on popular music, outperforming existing multi-instrument transcription models such as MT3. We conducted experiments on several downstream tasks and found that the proposed method improved transcription by more than 1 percentage points (ppt.), source separation by 5 SDR, downbeat detection by 1.8 ppt., chord recognition by 1.4 ppt., and key estimation by 1.4 ppt., when utilizing transcription results obtained from Jointist. Demo available at \url{https://jointist.github.io/Demo}., Comment: arXiv admin note: text overlap with arXiv:2206.10805

This paper introduces a four-dimensional (4D) geometry-based stochastic model (GBSM) for polarized multiple-input multiple-output (MIMO) systems with moving scatterers. We propose a novel motion path model with high degrees of freedom based on the Brownian Motion (BM) random process for randomly moving scatterers. This model is capable of analyzing the effect of both deterministically and randomly moving scatterers on channel properties. The mixture of Von Mises Fisher (VMF) distribution is considered for scatterers resulting in a more general and practical model. The proposed motion path model is applied to the clusters of scatterers with the mixture of VMF distribution, and a closed form formula for calculating space time correlation function (STCF) is achieved, allowing the study of the behavior of channel correlation and channel capacity in the time domain with the presence of stationary and moving scatterers. To obtain numerical results for channel capacity, we employed Monte Carlo simulation method for channel realization purpose. The impact of moving scatterers on the performance of polarized MIMO systems is evaluated using 2 by 2 MIMO configurations with various dual polarizations, i.e. V/V, V/H, and slanted 45{\deg} polarizations for different signal-to-noise (SNR) regimes. The proposed motion path model can be applied to study various dynamic systems with moving objects. The presented process and achieved formula are general and can be applied to polarized MIMO systems with any arbitrary number of antennas and polarizations., Comment: 10 pages, 10 Figures

We introduce a framework that recommends music based on the emotions of speech. In content creation and daily life, speech contains information about human emotions, which can be enhanced by music. Our framework focuses on a cross-domain retrieval system to bridge the gap between speech and music via emotion labels. We explore different speech representations and report their impact on different speech types, including acting voice and wake-up words. We also propose an emotion similarity regularization term in cross-domain retrieval tasks. By incorporating the regularization term into training, similar speech-and-music pairs in the emotion space are closer in the joint embedding space. Our comprehensive experimental results show that the proposed model is effective in textless speech-to-music retrieval., Comment: To Appear IEEE ICASSP 2023

We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset consists of 1.1k human-written natural language descriptions of 706 music recordings, all publicly accessible and released under Creative Common licenses. To showcase the use of our dataset, we benchmark popular models on three key music-and-language tasks (music captioning, text-to-music generation and music-language retrieval). Our experiments highlight the importance of cross-dataset evaluation and offer insights into how researchers can use SDD to gain a broader understanding of model performance., Comment: Accepted to NeurIPS 2023 Workshop on Machine Learning for Audio

This paper investigates foundation models tailored for music informatics, a domain currently challenged by the scarcity of labeled data and generalization issues. To this end, we conduct an in-depth comparative study among various foundation model variants, examining key determinants such as model architectures, tokenization methods, temporal resolution, data, and model scalability. This research aims to bridge the existing knowledge gap by elucidating how these individual factors contribute to the success of foundation models in music informatics. Employing a careful evaluation framework, we assess the performance of these models across diverse downstream tasks in music information retrieval, with a particular focus on token-level and sequence-level classification. Our results reveal that our model demonstrates robust performance, surpassing existing models in specific key metrics. These findings contribute to the understanding of self-supervised learning in music informatics and pave the way for developing more effective and versatile foundation models in the field. A pretrained version of our model is publicly available to foster reproducibility and future research., Comment: 5 pages

Recently, multi-band spectrogram-based approaches such as Band-Split RNN (BSRNN) have demonstrated promising results for music source separation. In our recent work, we introduce the BS-RoFormer model which inherits the idea of band-split scheme in BSRNN at the front-end, and then uses the hierarchical Transformer with Rotary Position Embedding (RoPE) to model the inner-band and inter-band sequences for multi-band mask estimation. This model has achieved state-of-the-art performance, but the band-split scheme is defined empirically, without analytic supports from the literature. In this paper, we propose Mel-RoFormer, which adopts the Mel-band scheme that maps the frequency bins into overlapped subbands according to the mel scale. In contract, the band-split mapping in BSRNN and BS-RoFormer is non-overlapping and designed based on heuristics. Using the MUSDB18HQ dataset for experiments, we demonstrate that Mel-RoFormer outperforms BS-RoFormer in the separation tasks of vocals, drums, and other stems., Comment: submitted as an ISMIR 2023 late-breaking and demo paper

In the era of data-driven Music Information Retrieval (MIR), the scarcity of labeled data has been one of the major concerns to the success of an MIR task. In this work, we leverage the semi-supervised teacher-student training approach to improve MIR tasks. For training, we scale up the unlabeled music data to 240k hours, which is much larger than any public MIR datasets. We iteratively create and refine the pseudo-labels in the noisy teacher-student training process. Knowledge expansion is also explored to iteratively scale up the model sizes from as small as less than 3M to almost 100M parameters. We study the performance correlation between data size and model size in the experiments. By scaling up both model size and training data, our models achieve state-of-the-art results on several MIR tasks compared to models that are either trained in a supervised manner or based on a self-supervised pretrained model. To our knowledge, this is the first attempt to study the effects of scaling up both model and training data for a variety of MIR tasks.

We present a numerical approximation scheme for the Tomita-Takesaki modular operator of local subalgebras in linear quantum fields, working at one-particle level. This is applied to the local subspaces for double cones in the vacuum sector of a massive scalar free field in (1 + 1)- and (3 + 1)-dimensional Minkowski spacetime, using a discretization of time-0 data in position space. In the case of a wedge region, one component of the modular generator is well-known to be a mass-independent multiplication operator; our results strongly suggest that for the double cone, the corresponding component is still at least close to a multiplication operator, but that it is dependent on mass and angular momentum., Comment: 23 pages, 16 figures, source code as ancillary files

In this work, we present a packaged whispering gallery mode (WGM) device based on an optical nanoantenna as the coupler and a glass microsphere as the resonator. The microspheres were fabricated from either SiO2 fiber or Er3+-doped fiber, the latter creating a WGM laser with a threshold of 93 µW at 1531 nm. The coupler-resonator WGM device was packaged in a glass capillary. The performance of the packaged microlaser was characterized, with lasing emission both excited in and collected from the WGM cavity via the nanoantenna. The packaged system provides isolation from environmental contamination, a small size, and unidirectional coupling while maintaining a high quality (Q-) factor (∼108)., source:https://www.osapublishing.org/oe/fulltext.cfm?uri=oe-29-11-16879&id=451125

This paper introduces effective design choices for text-to-music retrieval systems. An ideal text-based retrieval system would support various input queries such as pre-defined tags, unseen tags, and sentence-level descriptions. In reality, most previous works mainly focused on a single query type (tag or sentence) which may not generalize to another input type. Hence, we review recent text-based music retrieval systems using our proposed benchmark in two main aspects: input text representation and training objectives. Our findings enable a universal text-to-music retrieval system that achieves comparable retrieval performances in both tag- and sentence-level inputs. Furthermore, the proposed multimodal representation generalizes to 9 different downstream music classification tasks. We present the code and demo online.

In this paper, we introduce Jointist, an instrument-aware multi-instrument framework that is capable of transcribing, recognizing, and separating multiple musical instruments from an audio clip. Jointist consists of the instrument recognition module that conditions the other modules: the transcription module that outputs instrument-specific piano rolls, and the source separation module that utilizes instrument information and transcription results. The instrument conditioning is designed for an explicit multi-instrument functionality while the connection between the transcription and source separation modules is for better transcription performance. Our challenging problem formulation makes the model highly useful in the real world given that modern popular music typically consists of multiple instruments. However, its novelty necessitates a new perspective on how to evaluate such a model. During the experiment, we assess the model from various aspects, providing a new evaluation perspective for multi-instrument transcription. We also argue that transcription models can be utilized as a preprocessing module for other music analysis tasks. In the experiment on several downstream tasks, the symbolic representation provided by our transcription model turned out to be helpful to spectrograms in solving downbeat detection, chord recognition, and key estimation., Comment: Submitted to ISMIR

Transformer is a successful deep neural network (DNN) architecture that has shown its versatility not only in natural language processing but also in music information retrieval (MIR). In this paper, we present a novel Transformer-based approach to tackle beat and downbeat tracking. This approach employs SpecTNT (Spectral-Temporal Transformer in Transformer), a variant of Transformer that models both spectral and temporal dimensions of a time-frequency input of music audio. A SpecTNT model uses a stack of blocks, where each consists of two levels of Transformer encoders. The lower-level (or spectral) encoder handles the spectral features and enables the model to pay attention to harmonic components of each frame. Since downbeats indicate bar boundaries and are often accompanied by harmonic changes, this step may help downbeat modeling. The upper-level (or temporal) encoder aggregates useful local spectral information to pay attention to beat/downbeat positions. We also propose an architecture that combines SpecTNT with a state-of-the-art model, Temporal Convolutional Networks (TCN), to further improve the performance. Extensive experiments demonstrate that our approach can significantly outperform TCN in downbeat tracking while maintaining comparable result in beat tracking., Comment: This paper is accepted for publication at ICASSP 2022

Exposure to trauma during childhood has been shown to have severe long-term consequences. It has been shown that art therapy promotes the reduction of trauma symptoms in children, as it allows them to easily engage in the process of therapy through non-verbal treatment. Involving the caregiver in the treatment process of children was shown to hold significant benefits for children, especially in a setting of parent-child art therapy interventions. In this capstone thesis, I present a case study in which I explored the impact of art therapy interventions performed in a parent-child dyad on the well-being of a child who has experienced trauma. The interventions were performed in family sessions over the course of two weeks in a virtual in-home therapy setting. The case study presents significant benefits of the creative interventions on the child’s experienced symptoms, as well as on the parent-child relationship. These preliminary results show a growing need for additional studies on the topic of dyadic art therapy with children who have experienced trauma, in order to improve outcomes in the child’s treatment as well as to strengthen the family relationships and the parent’s ability to support their child.

We present Music Tagging Transformer that is trained with a semi-supervised approach. The proposed model captures local acoustic characteristics in shallow convolutional layers, then temporally summarizes the sequence of the extracted features using stacked self-attention layers. Through a careful model assessment, we first show that the proposed architecture outperforms the previous state-of-the-art music tagging models that are based on convolutional neural networks under a supervised scheme. The Music Tagging Transformer is further improved by noisy student training, a semi-supervised approach that leverages both labeled and unlabeled data combined with data augmentation. To our best knowledge, this is the first attempt to utilize the entire audio of the million song dataset., Comment: International Society for Music Information Retrieval (ISMIR) 2021

Content creators often use music to enhance their stories, as it can be a powerful tool to convey emotion. In this paper, our goal is to help creators find music to match the emotion of their story. We focus on text-based stories that can be auralized (e.g., books), use multiple sentences as input queries, and automatically retrieve matching music. We formalize this task as a cross-modal text-to-music retrieval problem. Both the music and text domains have existing datasets with emotion labels, but mismatched emotion vocabularies prevent us from using mood or emotion annotations directly for matching. To address this challenge, we propose and investigate several emotion embedding spaces, both manually defined (e.g., valence/arousal) and data-driven (e.g., Word2Vec and metric learning) to bridge this gap. Our experiments show that by leveraging these embedding spaces, we are able to successfully bridge the gap between modalities to facilitate cross modal retrieval. We show that our method can leverage the well established valence-arousal space, but that it can also achieve our goal via data-driven embedding spaces. By leveraging data-driven embeddings, our approach has the potential of being generalized to other retrieval tasks that require broader or completely different vocabularies., Comment: International Society for Music Information Retrieval (ISMIR) 2021, Best Student Paper

Music classification is a music information retrieval (MIR) task to classify music items to labels such as genre, mood, and instruments. It is also closely related to other concepts such as music similarity and musical preference. In this tutorial, we put our focus on two directions - the recent training schemes beyond supervised learning and the successful application of music classification models. The target audience for this web book is researchers and practitioners who are interested in state-of-the-art music classification research and building real-world applications. We assume the audience is familiar with the basic machine learning concepts. In this book, we present three lectures as follows: 1. Music classification overview: Task definition, applications, existing approaches, datasets, 2. Beyond supervised learning: Semi- and self-supervised learning for music classification, 3. Towards real-world applications: Less-discussed, yet important research issues in practice., Comment: This is a web book written for a tutorial session of the 22nd International Society for Music Information Retrieval Conference, Nov 8-12, 2021. Please visit https://music-classification.github.io/tutorial/ for the original, web book format

Transformers have drawn attention in the MIR field for their remarkable performance shown in natural language processing and computer vision. However, prior works in the audio processing domain mostly use Transformer as a temporal feature aggregator that acts similar to RNNs. In this paper, we propose SpecTNT, a Transformer-based architecture to model both spectral and temporal sequences of an input time-frequency representation. Specifically, we introduce a novel variant of the Transformer-in-Transformer (TNT) architecture. In each SpecTNT block, a spectral Transformer extracts frequency-related features into the frequency class token (FCT) for each frame. Later, the FCTs are linearly projected and added to the temporal embeddings (TEs), which aggregate useful information from the FCTs. Then, a temporal Transformer processes the TEs to exchange information across the time axis. By stacking the SpecTNT blocks, we build the SpecTNT model to learn the representation for music signals. In experiments, SpecTNT demonstrates state-of-the-art performance in music tagging and vocal melody extraction, and shows competitive performance for chord recognition. The effectiveness of SpecTNT and other design choices are further examined through ablation studies., Comment: 6 pages

Introduction The WHO recommends community mobilisation with women's groups practising participatory learning and action (PLA) to improve neonatal survival in high-mortality settings. This intervention has not been evaluated at scale with government frontline workers. Methods We did a pragmatic cluster non-randomised controlled trial of women's groups practising PLA scaled up by government front-line workers in Jharkhand, eastern India. Groups prioritised maternal and newborn health problems, identified strategies to address them, implemented the strategies and evaluated progress. Intervention coverage and quality were tracked state-wide. Births and deaths to women of reproductive age were monitored in six of Jharkhand's 24 districts: Three purposively allocated to an early intervention start (2017) and three to a delayed start (2019). We monitored vital events prospectively in 100 purposively selected units of 10 000 population each, during baseline (1 March 2017-31 August 2017) and evaluation periods (1 September 2017-31 August 2019). The primary outcome was neonatal mortality. Results We identified 51 949 deliveries and conducted interviews for 48 589 (93.5%). At baseline, neonatal mortality rates (NMR) were 36.9 per 1000 livebirths in the early arm and 39.2 in the delayed arm. Over 24 months of intervention, the NMR was 29.1 in the early arm and 39.2 in the delayed arm, corresponding to a 24% reduction in neonatal mortality (adjusted OR (AOR) 0.76, 95% CI 0.59 to 0.98), including 26% among the most deprived (AOR 0.74, 95% CI 0.57 to 0.95). Twenty of Jharkhand's 24 districts achieved adequate meeting coverage and quality. In these 20 districts, the intervention saved an estimated 11 803 newborn lives (min: 1026-max: 20 527) over 42 months, and cost 41 international dollars per life year saved. Conclusion Participatory women's groups scaled up by the Indian public health system reduced neonatal mortality equitably in a largely rural state and were highly cost-ef

We describe numerical and analytical investigations of causal sets sprinkled into spacetime manifolds. The first part of the paper is a numerical study of finite causal sets sprinkled into Alexandrov subsets of Minkowski spacetime of dimensions $1 + 1$, $1 + 2$ and $1 + 3$. In particular we consider the rank 2 past of sprinkled causet events, which is the set of events that are two links to the past. Assigning one of the rank 2 past events as `preferred past' for each event yields a `preferred past structure', which was recently proposed as the basis for a causal set d'Alembertian. We test six criteria for selecting rank 2 past subsets. One criterion performs particularly well at uniquely selecting -- with very high probability -- a preferred past satisfying desirable properties. The second part of the paper concerns (infinite) sprinkled causal sets for general spacetime manifolds. After reviewing the construction of the sprinkling process with the Poisson measure, we consider various specific applications. Among other things, we compute the probability of obtaining a sprinkled causal set of a given isomorphism class by combinatorial means, using a correspondence between causal sets in Alexandrov subsets of $1 + 1$ dimensional Minkowski spacetime and 2D-orders. These methods are also used to compute the expected size of the past infinity as a proportion of the total size of a sprinkled causal set.

Recent advances in deep learning accelerated the development of content-based automatic music tagging systems. Music information retrieval (MIR) researchers proposed various architecture designs, mainly based on convolutional neural networks (CNNs), that achieve state-of-the-art results in this multi-label binary classification task. However, due to the differences in experimental setups followed by researchers, such as using different dataset splits and software versions for evaluation, it is difficult to compare the proposed architectures directly with each other. To facilitate further research, in this paper we conduct a consistent evaluation of different music tagging models on three datasets (MagnaTagATune, Million Song Dataset, and MTG-Jamendo) and provide reference results using common evaluation metrics (ROC-AUC and PR-AUC). Furthermore, all the models are evaluated with perturbed inputs to investigate the generalization capabilities concerning time stretch, pitch shift, dynamic range compression, and addition of white noise. For reproducibility, we provide the PyTorch implementations with the pre-trained models., Comment: 7 pages, 2 figures, Sound and Music Computing 2020 (SMC 2020)

Tag-based music retrieval is crucial to browse large-scale music libraries efficiently. Hence, automatic music tagging has been actively explored, mostly as a classification task, which has an inherent limitation: a fixed vocabulary. On the other hand, metric learning enables flexible vocabularies by using pretrained word embeddings as side information. Also, metric learning has already proven its suitability for cross-modal retrieval tasks in other domains (e.g., text-to-image) by jointly learning a multimodal embedding space. In this paper, we investigate three ideas to successfully introduce multimodal metric learning for tag-based music retrieval: elaborate triplet sampling, acoustic and cultural music information, and domain-specific word embeddings. Our experimental results show that the proposed ideas enhance the retrieval system quantitatively, and qualitatively. Furthermore, we release the MSD500, a subset of the Million Song Dataset (MSD) containing 500 cleaned tags, 7 manually annotated tag categories, and user taste profiles., Comment: 5 pages, 2 figures, submitted to ICASSP 2021

The mood of a song is a highly relevant feature for exploration and recommendation in large collections of music. These collections tend to require automatic methods for predicting such moods. In this work, we show that listening-based features outperform content-based ones when classifying moods: embeddings obtained through matrix factorization of listening data appear to be more informative of a track mood than embeddings based on its audio content. To demonstrate this, we compile a subset of the Million Song Dataset, totalling 67k tracks, with expert annotations of 188 different moods collected from AllMusic. Our results on this novel dataset not only expose the limitations of current audio-based models, but also aim to foster further reproducible research on this timely topic., Comment: Appears in Proc. of the International Society for Music Information Retrieval Conference 2020 (ISMIR 2020)

In this paper the application of two dimensional picture grammars for generating traditional Sohrai floor design of Santal is explored. Context-free picture grammars are able to yield most of the floor designs. Based on the concept of radial languages, a Q-i (quadrant-i) radial language model is proposed in this work. Further, the tiling operators are used to generate the composite two-dimensional words.

Recently, we proposed a self-attention based music tagging model. Different from most of the conventional deep architectures in music information retrieval, which use stacked 3x3 filters by treating music spectrograms as images, the proposed self-attention based model attempted to regard music as a temporal sequence of individual audio events. Not only the performance, but it could also facilitate better interpretability. In this paper, we mainly focus on visualizing and understanding the proposed self-attention based music tagging model., Comment: Machine Learning for Music Discovery Workshop (ML4MD) at ICML 2019

Self-attention is an attention mechanism that learns a representation by relating different positions in the sequence. The transformer, which is a sequence model solely based on self-attention, and its variants achieved state-of-the-art results in many natural language processing tasks. Since music composes its semantics based on the relations between components in sparse positions, adopting the self-attention mechanism to solve music information retrieval (MIR) problems can be beneficial. Hence, we propose a self-attention based deep sequence model for music tagging. The proposed architecture consists of shallow convolutional layers followed by stacked Transformer encoders. Compared to conventional approaches using fully convolutional or recurrent neural networks, our model is more interpretable while reporting competitive results. We validate the performance of our model with the MagnaTagATune and the Million Song Dataset. In addition, we demonstrate the interpretability of the proposed architecture with a heat map visualization., Comment: 13 pages, 12 figures; code: https://github.com/minzwon/self-attention-music-tagging

The automated recognition of music genres from audio information is a challenging problem, as genre labels are subjective and noisy. Artist labels are less subjective and less noisy, while certain artists may relate more strongly to certain genres. At the same time, at prediction time, it is not guaranteed that artist labels are available for a given audio segment. Therefore, in this work, we propose to apply the transfer learning framework, learning artist-related information which will be used at inference time for genre classification. We consider different types of artist-related information, expressed through artist group factors, which will allow for more efficient learning and stronger robustness to potential label noise. Furthermore, we investigate how to achieve the highest validation accuracy on the given FMA dataset, by experimenting with various kinds of transfer methods, including single-task transfer, multi-task transfer and finally multi-task learning., Multimedia Computing

In this paper, we propose a hybrid Neural Collaborative Filtering (NCF) model trained with a multi-objective function to achieve a music playlist generation system. The proposed approach focuses particularly on the cold-start problem (playlists with no seed tracks) and uses a text encoder employing a Recurrent Neural Network (RNN) to exploit textual information given by the playlist title. To accelerate the training, we first apply Weighted Regularized Matrix Factorization (WRMF) as the basic recommendation model to prelearn latent factors of playlists and tracks. These factors then feed into the proposed multi-objective optimization that also involves embeddings of playlist titles. The experimental study indicates that the proposed approach can effectively suggest suitable music tracks for a given playlist title, compensating poor original recommendation results made on empty playlists by the WRMF model., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Multimedia Computing, Intelligent Systems

The polymetallic Cu–Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu–Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated based on bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpected high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit., Validerad;2017;Nivå 2;2017-09-27 (rokbeg)

The ore of the Vergenoeg fluorite mine, South Africa, contains rare earth elements in concentrations that may be of commercial interest. To assess the distribution of the rare earth minerals in the industrial flotation process currently used to produce fluorite concentrates, one mining block, that was carefully chemically, mineralogically and petrographically characterised, was fed to the flotation plant and subsequently the plant was systematically sampled. Mineral liberation analysis was conducted on the feed and the flotation products. Monazite and xenotime are the two main rare earth minerals. They are microcrystalline and occur mostly in association with each other and with iron oxide minerals in the ground flotation feed. A Particle Tracking technique was used to process the mineral liberation analysis and to assess the flotation behaviour of monazite and xenotime. Based on the study, different flotation behaviour was proposed for liberated and locked monazite and xenotime. The highest grade of monazite and xenotime was found in the tailing sample from the second cleaner unit of the flotation plant. Monazite and xenotime show high degree of liberation in this product. The tailings, especially of the second cleaner unit, were recommended for further beneficiation of rare earth elements.

The polymetallic Cu–Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu–Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated based on bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpected high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit.

The results presented in this thesis are a case study on the polymetallic Zn–Cu volcanic-hosted massive sulphide (VHMS) deposit at Rockliden. The Rockliden mineralisation is located in north-central Sweden, about 150 km south of the Skellefte district. Exploration at Rockliden started in the 1980’s despite the relative remote location of the deposit from processing facilities in the Skellefte ore district. During the first exploration period, flotation tests wereconducted which indicated that the locally high Sb grade in the mineralisation would increase the Sb content of the Cu–Pb concentrate and lower its quality at the smelter. In 2007 exploration was resumed at the Rockliden site andmassive sulphide mineralisation was found to extend toward the depth. The average Sb grade in the mineralisation decreases with depth; it is approximately 0.13 wt% above 400m below surface and only 0.06 wt% Sb below 400m. The focus of this PhD study was to evaluate mineralogical controls on the distribution of Sb minerals in base-metal flotation, which influences the Sb grade and hence the quality of the Cu–Pb concentrates. Modelling the Sb grade of the Cu–Pb concentrates is expected to be helpful in flowsheet design, e.g. for decision making on when to usehydrometallurgical treatment to reduce the Sb grade of this product prior to submission to the smelter. The first part of the study was to characterise the massive sulphides and the immediate host rocks with special reference to Sb mineralogy. The mineralogy and mineral associations were documented by optical and scanning electron microscopy (SEM). Micro-analytical tools (such as wavelength-dispersive X-ray spectroscopy at an electron probe microanalyser) were used to study the mineralogical distribution of major, minor and trace elements (e.g., Zn, Cu, Fe, Sb). Sphalerite and chalcopyrite are the main base-metal minerals in the massive sulphides. Antimony (Sb) is a minor to trace element bound in different minerals. The Sb mineral, Godkänd; 2016; 20160205 (frimin); Nedanstående person kommer att disputera för avläggande av teknologie doktorsexamen. Namn: Friederike Elisabeth Minz Ämne: Malmgeologi/Ore Geology Avhandling: Mineralogical Controls on the Recovery of Antimony in Base-Metal Flotation Outlining the Framework of a Geometallurgical Model for the Rockliden VHMS Deposit, Sweden Opponent: Dr Julie Hunt, Minerals Engineering, Materials and Environment (GeMMe) Department, University of Liege, Belgium. Ordförande: Biträdande professor Christina Wanhainen, Avd för geovetenskap och miljöteknik, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet. Tid: Fredag 8 april 2016, kl 10.00 Plats: F341, Luleå tekniska universitet

The biology of many arthropods can only be understood when their associated microbiome is considered. The nutritional requirements of the bulb mite Rhizoglyphus robini Claparede (Acari: Astigmata: Acaridae) in the laboratory have been shown to be very easily satisfied, and in the field the mites prefer fungus-infected over uninfected plants. To test whether symbiotic bacteria facilitate the survival of R. robini on a temporarily nutritionally unbalanced diet, we investigated the composition of its microbiome. Using 454 pyrosequencing of 16S rRNA gene fragments, 3 genera were found to dominate the bacterial community: Myroides (41.4%), Serratia (11.4%), and Alcaligenes (4.5%); the latter 2 are known to include chitinase-producing species. Laboratory experiments demonstrated that mite fecundity is significantly higher (2 times) on fungus than on controls (sterilized potato dextrose agar and filter paper). Also, when mite homogenate was applied to a chitin layer, the halo produced through degradation was clearly visible, while the saline control did not produce a halo. We thus concluded that R. robini utilizes fungal chitin, at least to a certain extent, as a food source with the help of its associated bacteria. This information supports the general concept of multigenome organisms and the involvement of bacteria in the mite's nutritional ecology.

The Rockliden massive sulphide Zn-Cu deposit contains minor amounts of Sb minerals. The Sb mineralogy is complex in terms of composition, micro textures and mineral associations. The main Sb minerals comprise tetrahedrite, bournonite, gudmundite and Sb-Pb sulphides such as meneghinite. The presence of these minerals is especially critical to the quality of the Cu-Pb concentrate. To study how they are distributed in a simplified flotation circuit and what controls their process behaviour Sb-rich drill core samples were selected from the Rockliden deposit and a standard laboratory flotation test was run on the composite samples. Scanning electron microscope-based automated mineralogy was used to measure the Sb mineralogy of the test products, and the particle tracking technique was applied to mass balance the different liberation classes to finally trace the distribution of liberated and locked Sb minerals. The mineralogical factors controlling the distribution of Sb minerals are mineral grain size, the degree of liberation, and associated minerals. Similarities in the distribution of specific particle types from the tested composites point towards systematics in the behaviour of particles and predictability of their distribution which is suggested to be used in a geometallurgical model of the deposit.

Godkänd; 2014; 20141202 (andbra)

Rockliden is a polymetallic Zn-Cu volcanic-hosted massive sulphide (VHMS) deposit. It is located in north-central Sweden about 150 km south of the Skellefte ore district. Two major challenges to mining and production were identified soon after Rockliden was discovered in the 1980’s. Firstly, the geographic distance to Boliden’s main ore deposits and existing concentrator in the Skellefte district will set additional financial and logistic constrains for the processing of the ore. Secondly, the locally high Sb grade in the ore potentially lowers the quality of the Cu-Pb concentrate sent to the Cu smelter. To face the second challenge to mining and production, it is necessary to use information from three disciplines: ore geology, mineral processing and process metallurgy. The three disciplines build the geometallurgical framework of the Rockliden ore characterisation. The purpose of this investigation is to outline which mineralogical factors are the cause of the elevated Sb content in the Cu-Pb concentrate. Determining such factors will help to predict the Sb content of this product and thus outline the tools required before decisions may be reached with regard to selection between various processing alternatives for the Rockliden ore. Hydrometallurgical treatment of the Cu-Pb concentrate has previously been studied by Awe (2013) and it was found that alkaline sulphide leaching can be used to lower the Sb content.A qualitative characterisation of the ore forms the main focus of this licentiate thesis. Minerals have been identified and their chemical composition has been obtained with micro-analytical tools (SEM/EDS & EPMA/WDS) to study the mineralogical distribution of major (e.g. Zn, Cu, Fe), penalty (e.g. Sb) and bonus (e.g. Ag) metals. Furthermore, mineral textures and associations have been studied in ore samples by optical microscopy and SEM/BSE imaging. Ore types are preliminary classified based on the sulphide mineralogy, i.e. the relative content of pyri, Godkänd; 2013; 20131021 (frimin); Tillkännagivande licentiatseminarium 2013-11-22 Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Friederike Elisabeth Minz Ämne: Malmgeologi/Ore Geology Uppsats: Mineralogical Characterisation of the Rockliden Antimony-Bearing Volcanic-Hosted Massive Sulphide Deposit, Sweden Examinator: Associate Professor Christina Wanhainen, Institutionen för samhällsbyggnad och naturresurser, Luleå tekniska universitet Diskutant: Associate Professor Kurt Aasly, Department of Geology and Mineral Resources Engineering, NTNU, Trondheim, Norway Tid: Måndag den 16 december 2013 kl 10.00 Plats: F341, Luleå tekniska universitet

A geometallurgical model is currently built in two different ways. The first and most common way relies on geometallurgical testing, where a large number of samples are analysed for metallurgical response using small-scale laboratory tests, such as Davis tube testing. The second, mineralogical approach focuses on collecting mineralogical information across the orebody and basing the metallurgical model on mineralogy. At Lulea University of Technology, Sweden, the latter method has been adopted and taken further in four ongoing PhD studies. The geological model gives modal composition with the help of element-to-mineral conversion and Rietveld X-ray diffraction. Texturally, the orebody is divided into different archetypes, and liberation measurements for each of them are carried out in processing fineness using IncaMineral, a SEM-based technique. The grindability and liberation spectrum of any given geological unit (sample, ore block, domain) are extrapolated from the archetypes. The process model is taken into a liberation level by mass balancing selected metallurgical tests using the particle tracking technique. The approach is general and can be applied to any type of ores. Examples of ongoing studies on iron and massive sulphide ores are given. (Authors.), A geometallurgical model is currently built in two different ways. The first and most common way relies on geometallurgical testing, where a large number of samples are analysed for metallurgical response using small-scale laboratory tests, such as Davis tube testing. The second, mineralogical approach focuses on collecting mineralogical information across the orebody and basing the metallurgical model on mineralogy. At Lulea University of Technology, Sweden, the latter method has been adopted and taken further in four ongoing PhD studies. The geological model gives modal composition with the help of element-to-mineral conversion and Rietveld X-ray diffraction. Texturally, the orebody is divided into different archetypes, and liberation measurements for each of them are carried out in processing fineness using IncaMineral, a SEM-based technique. The grindability and liberation spectrum of any given geological unit (sample, ore block, domain) are extrapolated from the archetypes. The process model is taken into a liberation level by mass balancing selected metallurgical tests using the particle tracking technique. The approach is general and can be applied to any type of ores. Examples of ongoing studies on iron and massive sulphide ores are given. (Authors.)

The Rockliden Cu-Zn massive sulphide ore has been unprocessed since its discovery in the 1980s, due to high antimony grades within the ore. The Sb grades of the ore do not correlate directly with the Sb grades of the concentrates. This seems to be related to a variable Sb mineralogy and the differences in flotation response that these minerals show, as indicated by flotation tests. The purpose of this study is therefore to characterise the Sb mineralogy of the in situ ore as part of the geometallurgical model of the whole ore body. The Sb minerals found at Rockliden so far include boulangerite, gudmundite, bournonite and tetrahedrite. Additionally, arsenopyrite can contain up to 2.5 wt.% of Sb. Complex intergrowths of the As-, Pb- and Sb-bearing sulphide minerals are common in the upper half of the tilted and steeply dipping ore lenses, but are less frequent in samples from deeper parts of the deposit., The Rockliden Cu-Zn massive sulphide ore has been unprocessed since its discovery in the 1980s, due to high antimony grades within the ore. The Sb grades of the ore do not correlate directly with the Sb grades of the concentrates. This seems to be related to a variable Sb mineralogy and the differences in flotation response that these minerals show, as indicated by flotation tests. The purpose of this study is therefore to characterise the Sb mineralogy of the in situ ore as part of the geometallurgical model of the whole ore body. The Sb minerals found at Rockliden so far include boulangerite, gudmundite, bournonite and tetrahedrite. Additionally, arsenopyrite can contain up to 2.5 wt.% of Sb. Complex intergrowths of the As-, Pb- and Sb-bearing sulphide minerals are common in the upper half of the tilted and steeply dipping ore lenses, but are less frequent in samples from deeper parts of the deposit.

Moses Minz, Scan von Monochrom-Mikroform

not available

The polymetallic Cu-Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu-Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated on the basis of bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpectedly high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit. (Authors)., The polymetallic Cu-Zn ore of the Rockliden massive sulphide deposit in the Skellefte District in north-central Sweden contains a number of deleterious elements in relevant concentrations. Of particular concern is the amount of antimony (Sb) reporting to the Cu-Pb concentrate. The aim of this study was to compare different model options to simulate the distribution of Sb minerals in a laboratory flotation test based on different degrees of details in the mineralogical information of the flotation feed. Experimental data obtained from four composites were used for the modelling and simulation. The following different simulation levels were run (sorted from least to highest level of detail of their mineralogical information): chemical assays, unsized bulk mineralogy, sized bulk mineralogy and particle information. It was shown that recoveries simulated on the basis of bulk mineralogy are mostly within the error margin acceptable in the exploration stage of the Rockliden deposit. Unexpectedly high deviation in the simulation using particle information from the original recovery has been partly attributed to the fact that recovery of non-liberated particles cannot be modelled appropriately in the present version of the modelling and simulation software. It is expected that the implementation of full particle information in simulation will improve the Sb distribution model for the mineralogically complex Rockliden deposit. (Authors).