Miracle-dz/UniFAR-FTUs huggingface dataset:
Qualitative judgements of urban soundscapes : Questionning questionnaires and semantic scales

The present work aims at a qualitative analysis of various soundscapes in two French cities in order to evaluate and potentially improve urban development. The method employs an analysis of open-ended questions and of verbal comments and judgments on a semantic differential grid. On-site experiments were carried out in various urban locations of two French cities, such as main thoroughfares, pedestrian districts, playgrounds and market squares. Differential patterns were clearly seen from a histogram analysis of the differential grid, which points to the limits of an average calculation of the semantic scale. A psycholinguistic analysis of the verbal comments was carried out to explicate the heterogeneous judgements across people about temporal, spatial and activity appraisals that the quantitative data obscure. Such an analysis allowed us to differentiate two cognitive modes for representing urban soundscapes: (i) a "descriptive listening" mode which refers to the identification of acoustic sources or events; and (ii) a "holistic hearing" mode which refers to the soundscape as a whole, without semantic processing of any specific sources. Since the same physical phenomenon may be interpreted by two distinct cognitive representations, the results highlight the need for employing differential scales with a semantic analysis to improve qualitative judgement and to further elaborate new semantic as well as physical indicators based on community noise measurements.
Coexistence of Symmetry Properties for Bayesian Confirmation Measures

Many Bayesian Confirmation Measures have been proposed so far. They are used to assess the degree to which an evidence (or premise) E supports or contradicts an hypothesis (or conclusion) H, making use of prior probability P(H), posterior probability P(H|E) and of probability of evidence P(E). Many kinds of comparisons of those measures have already been made. Here we focus on symmetry properties of confirmation measures, which are partly inspired by classical geometric symmetries. We define symmetries relating them to the dihedral group of symmetries of the square, determining the symmetries that can coexist and reconsidering desirable/undesirable symmetry properties for a Bayesian Confirmation Measure.
Explicit/multi-parametric moving horizon estimation and model predictive control & their application to small unmanned aerial vehicles

Moving horizon estimation (MHE) is a class of estimation methods in which the system state and disturbance estimates are obtained by solving a constrained optimization problem. The main advantage of MHE is that information about the system can be explicitly considered in the form of constraints and hence improve the estimates. In stochastic systems the estimation error will inevitably be non-zero and the controller needs to explicitly account for it to prevent constraint violations. In order for the controller to be robustified against the estimation error, bounds on the error need to be known. These bounds can be calculated if the dynamics that govern the estimation error are known. This work presents those dynamics for the unconstrained and the constrained case of the moving horizon estimator with a linear timeinvariant model, and also discusses how the bounds on the estimation error can be obtained with set-theoretical methods. Those bounds are then used for robust output-feedback model predictive control (MPC). The MHE and the MPC are derived explicitly through multi-parametric programming. The complete framework is demonstrated using simultaneous MHE and tubebased MPC. The possibility of solving MPC explicitly is very appealing for flight control of small unmanned aerial vehicles (UAVs) because the behaviour of the controller is known in advance and can be guaranteed. Flight control is a challenging task that involves a multi-layer control structure where each decision influences the other layers and the overall performance. This work investigates the requirements on the different layers and their cross-effects. A linear model of the UAV is derived such that it captures the wind which is the most challenging disturbance for UAV flight. Particular focus is placed on the design of a model predictive controller as the autopilot and on in-flight wind estimation.
Price volatility trends and price transmission for major staples in India

India is one of the largest producers as well as exporters of some major food commodities, and is often criticized for its protectionist measures to curb transmission of price shocks from international markets. This paper examines if such policies are necessary to protect domestic consumers from price fluctuations and what are their implications on international prices. To understand this, in this paper we have examined the patterns, trends and volatility in domestic and international prices of rice and wheat, and found that although both the international and domestic prices are volatile, the degree of volatility is higher in the international prices. The volatility in domestic prices is mainly due to internal production shocks and is not influenced much by the international prices. This disconnect is attributed to domestic policy measures, such as market support to farmers and public stockholding of food grains for public distribution and price stabilization.
Ray-based Elastic Distributed Data Parallel Framework with Distributed Data Cache

With the development of large-scale machine learning, distributed data parallel has become the de facto standard strategy for model training. However, when training model using distributed data parallel on large-scale clusters, some unexpected factors may lead to the failure of the training tasks. Thus, a high-performance, scalabe, yet fault-tolerant distributed training framework is urgently needed. Most commonly used open-sourced distributed training frameworks (e.g., PyTorch) do not fully meet this need. In this paper, we have designed an elastic distributed training framework based on Ray (a high-performance distributed framework). Our framework takes advantage of Ray’s fault-tolerant store, scalability, and the stateful actor. In our framework, training tasks will not be terminated when the number of training processes changes. Moreover, we have designed an elastic distributed data cache using Ray’s object store and provided an efficient dataloader (called elastic_dataloader). Performance evaluation shows that elastic_dataloader is more than 2 times faster than PyTorch’s Dataloader on a cluster equipped with 10 Gigabit Ethernet.
Adaptive Monte Carlo Sampling for Cloud and Microphysics Calculations

Adaptive Monte Carlo sampling for cloud and microphysics calculations by Thomas Franz-Peter Roessler The University of Wisconsin–Milwaukee, 2017 Under the Supervision of Professor Vincent E. Larson An important problem in large-scale modeling of the atmosphere is the parametrization of clouds and microphysics on subgrid scales. The framework Cloud Layers Unified By Binormals (CLUBB) was developed to improve the parametrization of subgrid variability. Monte Carlo sampling is used to couple the different physical processes, which improves the grid average of subgrid tendencies. In this thesis we develop an adaptive Monte Carlo sampling algorithm that re-uses sample points of the previous time step by re-weighting them according to the change of the underlying distribution. This process is called “what-if sampling” and is an application of importance sampling. An example illustrates that what-if sampling converges slowly when the atmospheric conditions change too much. Therefore, the algorithm was extended by adaptive criteria. These prohibit re-weighting if the atmospheric conditions change too fast and allow the re-weighting method to converge to the right solution. We studied five test cases for different atmospheric conditions and found that the computation of the what-if weights is too expensive and suffers from bad importance sampling. The high-dimensional distribution of CLUBB that evolves in time makes re-weighting difficult. The simulation results of what-if sampling are similar to the standard Monte Carlo method or even worse considering the higher computational costs. Therefore, the algorithm was simplified such that old tendencies are re-used without any re-weighting. This approximation removes the overhead and reduces the extra noise. However, simple re-using does not improve the accuracy of the model for the same computation time for general appliii cation. Only in the special case of very few sample points, this method can improve the performance without increasing the error significantly. The standard Monte Carlo sampler of CLUBB works very efficiently by applying well suited importance sampling. For normal simulations, using fewer sample points is better than applying any re-using algorithm to a larger number of sample points.
Analysis of correlations between electric arc furnace model coefficients

Electric arc furnaces (EAFs) are the largest loads in power systems and are characterized by a significant negative influence on power quality. The modeling of EAFs provides the knowledge necessary for the proper design of power systems and power quality improvement systems. This paper introduces a new EAF model based on the Random Differential Equation resulting from an extension of the power balance arc model with stochastic processes. The proposed solution considers correlations between the coefficients of the equation, resulting in improved accuracy and simplification of the model. The developed solution is based on real measurement data originating from the melting stage of a large industrial EAF.
Pure Exploration of Continuum-Armed Bandits under Concavity and Quadratic Growth Conditions

The traditional setting for pure exploration of multi-armed bandits is to identify an optimal arm in a decision set, which contains a finite number of stochastic slot machines. The finite-arm setting restricts classic bandit algorithms, because the decision set for optimal selection can be continuous and infinite in many practical applications, e.g., determining the optimal parameter in communication networks. In this paper, to generalize bandits into wider real scenarios, we focus on the problem of pure exploration of Continuum-Armed Bandits (CAB), where the decision set is a compact and continuous set. Compared to the traditional setting of pure exploration, identifying the optimal arm in CAB raises new challenges, of which the most notorious one is the infinite number of arms. By fully taking advantage of the structure information of payoffs, we successfully solve the challenges. In particular, we derive an upper bound of sample complexity for pure exploration of CAB with concave structures via gradient methodology. More importantly, we develop a warm-restart algorithm to solve the problem where a quadratic growth condition is further satisfied, and derive an improved upper bound of sample complexity. Finally, we conduct experiments with real-world oracles to demonstrate the superiority of our warm-restart algorithm.
A Novel Metaheuristic Algorithm Optimized Deep CNN-LSTM Model for Amla Disease Identification

The quality evaluation of agricultural goods are vital components in improving their vendibility and managing wastes. Fruit diseases present a persistent risk to producers and consumers. Precision agriculture can assist in making decisions on the management of fruit diseases by using computer vision-based approaches. Manual disease detection is highly challenging and it takes an excessive amount of effort, knowledge, and processing time. Deep learning is therefore used to better accurately diagnose diseases. In this work, it is aimed to present deep learning framework approach for quickly identifying the apparent defects in amla fruit. In the proposed algorithm, deep convolutional neural network model with marine predator optimization algorithm is used for optimizing the accuracy of disease detection in amla fruit. The structure of the deep convolutional neural network comprises components for feature extraction and optimum classification. The hyperparameters for training convolutional neural network layers are tuned by marine predator optimization algorithm. The efficiency of proposed model is evaluated and compared to other optimization strategies. The optimized deep learning model outperforms by providing greater classification accuracy when compared to the current approaches, according to numerical analysis, demonstrating its potential for accurate disease detection and classification.
An Empirical Analysis of Collaborative and Content Based Recommender System by using AI

A system that attempts to predict our preferred movies based on our own past selections and preferences is referred to as a “movie recommendation system. ” Machine Learning algorithms like content-based filtering, feature selection with its subset method Vectorization is one of those key components which acts as the base of this movie recommendation system. Collaborative filtering mechanism is also used widely in recommender systems. In this paper, we have presented an analysis of Collaborative and Content based recommender system which will help in selection of an algorithm based on the requirements of the developer.
Highly Compact and High Gain 2 x 2 Patch Array Antenna with Slotted Meanderline Loading

This paper presents a highly compact and high gain patch array antenna for Citizens Broadband Radio Service (CBRS) applications at 3.5 GHz. The meander line (ML) is etched on each patch and placed diagonally on top of the patch antenna, resulting in a large current density near the ML slot lines. The proposed antenna design offers advantages through the characteristic of these current flows: a significant size reduction of a 2 x 2 patch antenna, where the gap between neighboring patches is 2 mm (0.023 of free space wavelength), the smallest among reported, and a very high gain of 5.67 dB. The inserted meander slot structure, which increases the electrical length of the resonance circuit, contributes to a 51% reduction in the total area of the patch array antenna. The simulation exhibit that the proposed antenna has excellent antenna performance, and thus it is expected to be useful for multiple compact wireless RF systems.
Research on Seismic Data Denoising Based on Dual Channel Residual Attention Network

In recent years, seismic data denoising has attracted more and more scholars' attention and research, and the suppression of random noise is the key to improving the signal-to-noise ratio of seismic data. Aiming at the problem that traditional denoising methods are difficult to effectively remove a large amount of random noise and retain effective signals, we propose a neural network model based on dual channel residual attention network (DCRANet). Specifically, the model consists of a residual attention block (RAB), a dilated convolution sparse block (DCSB) and a feature enhancement block (FEB). The residual blocks in RAB can avoid some problems such as gradient vanishing and gradient exploding when the network is too deep, and the use of attention mechanism can guide the network to effectively extract complex noise information. The DCSB recovers the useful details from complex noise information by expanding the receptive field, fully acquiring important structural information and edge features of seismic data. The FEB integrates the noise features extracted by RAB and DCSB, it uses convolutional layers to extract the noise information of seismic data, and finally reconstructs clean seismic data image by the residual learning strategy. Compared with NL-Bayes, BM3D, DnCNN, CBDNet and DudeNet, DCRANet effectively suppresses random noise while retaining more local details and obtains a higher average peak signal-to-noise ratio (PSNR) and average structural similarity (SSIM).
Supplement 1 : Ensuring the Functionalities of Retrogenes

We defined retrogene as an “intact” retrocopy that has transcriptional evidence (see Results section). It is possible that some retrocopies are mis-annotated as retrogenes and included in our dataset. So, at first, we checked that all DNA sequences of candidate retrogenes have no frameshift mutations and no premature stop codons as compared to its parental gene, which ensures that all retrocopies in our study are “intact”. Next, we chose those “intact” retrocopies with experimental evidence as retrogenes. However, not all species have sufficient functional evidence for the putative retrogenes we identified. To deal with this difficulty, we further refined our data under three criteria with different stringency of functional evidence. During this process, we should ensure both high data quality (i.e. to minimize the influence of pseudogenes) and sufficient number of genes for analyses.
Absorbing Boundary Condition for Nonlinear Hyperbolic Partial Diferential Equations with Unknown Riemann Invariants

A general methodology for developing absorbing boundary conditions for general non-linear hyperbolic advective-diffusive equations with unknown Riemann invariants is presented. In problems where the Riemann invariants (RI) are known (e.g. the ﬂow in a shallow rectangular channel, the gas ﬂow equations), the imposition of non-reﬂective boundary conditions is straightforward. In problems where Riemann invariants are unknown (e.g. the ﬂow in a non-rectangular channels, the stratiﬁed 2D shallow water ﬂows) it is possible to impose that kind of conditions analyzing the projection of the Jacobians of advective ﬂux functions onto normal directions to ﬁctitious surfaces or boundaries. The advantage of the method is that it is very easy to implement in a ﬁnite element code and is only based on computing the advective ﬂux functions (and the their Jacobian projections), then, imposing non-linear constraints via Lagrange Multipliers or Penalty Methods. The application of the dynamic absorbing boundary conditions to typical wave propagation problems with unknown Riemann invariants, like nonlinear Saint-Venant system of conservation laws for non-rectangular and non-prismatic 1D channels and stratiﬁed 1D/2D shallow water equations, is presented. Also, the new absorbent/dynamic condition can handle automatically the change of Jacobians structure when the ﬂow regime changes from subcritical to supercritical and viceversa, or when recirculating zones are present in regions near ﬁctitious walls.
Impact of Dielectric and Copper Via Design on Wafer-to-Wafer Hybrid Bonding

Hybrid bonding is key to achieving high-quality interconnect interfaces for fine pitch integration. It has an advantage over other types of interconnects as it allows a high I/O count for high-density memory, increased power and improved signal speed. To achieve high-quality hybrid bonding, Cu interconnects are embedded in the dielectric. The surface is planarized using chemical mechanical polishing. The final CMP process is usually a two-step process involving copper bulk CMP and then barrier CMP. Barrier CMP leads to the final surface finish which is used for hybrid bonding. The final surface has four key surface properties such as copper recess in the vias known as dishing, erosion and roughness of the dielectric layer, and profile change from high-density copper vias towards low-density copper vias. Most of these parameters are tuned using the CMP process. In this work, we present the impact of dielectric on the metal CMP process and look into the atomic force microscopy (AFM) dishing and roll-off behaviour of the interconnect vias, scaling down from 5 $\mu \mathrm{m}$ pitch to 1 $\mu \mathrm{m}$ pitch using silicon oxynitride dielectric as the bonding dielectric.
The H.E.S.S. II GRB observation scheme

Gamma-ray bursts (GRBs) are some of the Universe’s most enigmatic and exotic events. However, at energies above 10 GeV their behaviour remains largely unknown. Although space based telescopes such as the Fermi-LAT have been able to detect GRBs in this energy range, their photon statistics are limited by the small detector size. Such limitations are not present in ground based gamma-ray telescopes such as the H.E. S.S. experiment, which has now entered its second phase with the addition of a large 600 m2 telescope to the centre of the array. Such a large telescope allows H.E. S.S. to access the sub 100-GeV energy range while still maintaining a large effective collection area, helping to potentially probe the short timescale emission of these events.We present a description of the H.E. S.S. GRB observation programme, summarising the performance of the rapid GRB repointing system and the conditions under which GRB observations are initiated. Additionally we will report on the GRB follow-ups made during the 201.
Metamaterial Based Compact Patch Antenna Array for Antenna-in- Package Solutions in Frequency Handover Applications

A dual band compact antenna array is designed on an ultra-low loss and smooth surface roughness dual-layered fused silica substrate to simultaneously operate in both a 5G new radio (5G-NR) of 28 GHz and an loT band of 24GHz, for a frequency handover application. The proposed antenna array operates based on coupled resonance theory. The resonant frequencies are the result of resonant coupling between closely located meanderline complementary split ring resonators (CSRRs). A 2 × 2 array is fabricated on a 350μm/180μm double layer fused silica substrate and the measurement results agree well with simulation results.
Application of Hall Position Sensor in Control and Position Estimation of PMSM - A Review

This paper reviews the use of permanent magnet synchronous motors (PMSMs) across industries and the significance of precise rotor position and motor speed data. It presents two methods of obtaining rotor position information: using mechanical position sensors, which are expensive and less reliable, and the rotor position observer method, which is sensitive to changes in motor parameters. This paper reviews the advantages, drawbacks, challenges, and applications of low-resolution Hall position sensors for PMSM position estimation and control, in order to balance performance and cost. It also discusses recent solutions to address issues related to misplaced Hall sensors and fault-tolerant control, while looking forward to future developments in this area.
A Novel Polymer-Based Ultra-High Density Bonding Interconnection

3D heterogeneous integration with advanced packaging and high-density chip stacking technologies are critical design breakthroughs to support market demand and enable success. Increasingly, hybrid bonding interconnection has been recognized as the unique bonding method that can satisfy the demand for high-density chip stacking. In this study, we demonstrate chip-to-wafer bonding with high density micro-bumping that utilizes an organic dielectric material to replace the inorganic dielectric material. This new hybrid bonding interface can exhibit improved particle sensitivity compared to oxide, along with better dielectric properties. This work demonstrates that such a bonding material has potential compatibility with both high-precision alignment at low temperatures and high-temperature copper annealing. A two-stage assembly process including pre-bond and gang bond is used to increase copper-to-copper metal solid-state diffusion to improve bond strength to nearly match that of oxide hybrid bonding. Ultimately, scratch-free copper polishing pads can provide good copper height uniformity after Chemical Mechanical Planarization (CMP) processing, allowing void free formation of polymer and copper bonds. Together, a chip bonding process was developed with a three step bonding profile to simulate NCF (Non-Conductive Film) bonding mechanism. The polymer is slightly fluid during thermal-compression bonding allowing it to fill completely into all bonding interfaces.