Miracle-dz/UniFAR-FTUs huggingface dataset:
Rotational spectroscopy of methyl mercaptan CH332SH at millimeter and submillimeter wavelengths

We present a new global study of the millimeter (mm) wave, submillimeter (sub-mm) wave, and terahertz (THz) spectra of the lowest three torsional states of methyl mercaptan (CH3SH). New measurements have been carried out between 50 and 510 GHz using the Kharkiv mm wave and the Cologne sub-mm wave spectrometers whereas THz spectra records were used from our previous study. The new data, involving torsion–rotation transitions with J up to 61 and Ka up to 18, were combined with previously published measurements and fit using the rho-axis-method torsion–rotation Hamiltonian. The final fit used 124 parameters to give an overall weighted root-mean-square deviation of 0.72 for the dataset consisting of 6965 microwave (MW) and 16 345 far-infrared line frequencies sampling transitions within and between the ground, first, and second excited torsional states. This investigation presents a two-fold expansion in the J quantum numbers and a significant improvement in the fit quality, especially for the MW part of the data, thus allowing us to provide more reliable predictions to support astronomical observations.
Semi-supervised Sequence Modeling for Elastic Impedance Inversion

Recent applications of machine learning algorithms in the seismic domain have shown great potential in different areas such as seismic inversion and interpretation. However, such algorithms rarely enforce geophysical constraints — the lack of which might lead to undesirable results. To overcome this issue, we have developed a semisupervised sequence modeling framework based on recurrent neural networks for elastic impedance inversion from multiangle seismic data. Specifically, seismic traces and elastic impedance (EI) traces are modeled as a time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for EI. The proposed workflow uses well-log data to guide the inversion. In addition, it uses seismic forward modeling to regularize the training and to serve as a geophysical constraint for the inversion. The proposed workflow achieves an average correlation of 98% between the estimated and target EI using 10 well logs for training on a synthetic data set.
Multi-Antenna Relaying and Reconfigurable Intelligent Surfaces: End-to-End SNR and Achievable Rate

In this report, we summarize the end-to-end signal-to-noise ratio and the rate of half-duplex, full-duplex, amplify-and-forward, and decode-and-forward relay-aided communications, and well as the signal-to-noise ratio and the rate of the emerging technology known as reconfigurable intelligent surfaces.
Some physical implications of regularization ambiguities in SU(2) gauge-invariant loop quantum cosmology

The way physics of loop quantum gravity is affected by the underlying quantization ambiguities is an open question. We address this issue in the context of loop quantum cosmology using gauge-covariant fluxes. Consequences are explored for two choices of regularization parameters: $\mu_0$ and $\bar \mu$ in presence of a positive cosmological constant, and two choices of regularizations of the Hamiltonian constraint in loop quantum cosmology: the standard and the Thiemann regularization. We show that novel features of singularity resolution and bounce, occurring due to gauge-covariant fluxes, exist also for Thiemann-regularized dynamics. The $\mu_0$-scheme is found to be unviable as in standard loop quantum cosmology when a positive cosmological constant is included. Our investigation brings out a surprising result that the nature of emergent matter in the pre-bounce regime is determined by the choice of regulator in the Thiemann regularization of the scalar constraint whether or not one uses gauge-covaraint fluxes. Unlike $\bar \mu$-scheme where the emergent matter is a cosmological constant, the emergent matter in $\mu_0$-scheme behaves as a string gas.
PIMBALL

Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for performance, energy efficiency, or hardware/software complexity. Here, we introduce a spintronic, re-configurable in-memory BNN accelerator, PIMBALL: Processing In Memory BNN AcceL(L)erator, which allows for massively parallel and energy efficient computation. PIMBALL is capable of being used as a standard spintronic memory (STT-MRAM) array and a computational substrate simultaneously. We evaluate PIMBALL using multiple image classifiers and a genomics kernel. Our simulation results show that PIMBALL is more energy efficient than alternative CPU-, GPU-, and FPGA-based implementations while delivering higher throughput.
Bayesian hierarchical factor regression models to infer cause of death from verbal autopsy data

In low‐resource settings where vital registration of death is not routine it is often of critical interest to determine and study the cause of death (COD) for individuals and the cause‐specific mortality fraction (CSMF) for populations. Post‐mortem autopsies, considered the gold standard for COD assignment, are often difficult or impossible to implement due to deaths occurring outside the hospital, expense and/or cultural norms. For this reason, verbal autopsies (VAs) are commonly conducted, consisting of a questionnaire administered to next of kin recording demographic information, known medical conditions, symptoms and other factors for the decedent. This article proposes a novel class of hierarchical factor regression models that avoid restrictive assumptions of standard methods, allow both the mean and covariance to vary with COD category, and can include covariate information on the decedent, region or events surrounding death. Taking a Bayesian approach to inference, this work develops an MCMC algorithm and validates the FActor Regression for Verbal Autopsy (FARVA) model in simulation experiments. An application of FARVA to real VA data shows improved goodness‐of‐fit and better predictive performance in inferring COD and CSMF over competing methods. Code and a user manual are made available at https://github.com/kelrenmor/farva.
An Optimized Quantum Maximum or Minimum Searching Algorithm and its Circuits.

Finding a maximum or minimum is a fundamental building block in many mathematical models. Compared with classical algorithms, Durr, Hoyer's quantum algorithm (DHA) achieves quadratic speed. However, its key step, the quantum exponential searching algorithm (QESA), which is based on Grover algorithm, is not a sure-success algorithm. Meanwhile, quantum circuits encounter the gate decomposition problem due to variation of the scale of data. In this paper, we propose an optimized quantum algorithm for searching maximum and minimum, based on DHA and the optimal quantum exact search algorithm. Furthermore, we provide the corresponding quantum circuits, together with three equivalent simplifications. In circumstances when we can exactly estimate the ratio of the number of solutions M and the searched space N, our method can improve the successful probability close to 100%. Furthermore, compared with DHA, our algorithm shows an advantage in complexity with large databases and in the gate complexity of constructing oracles. Experiments have been executed on an IBM superconducting processor with two qubits, and a practical problem of finding the minimum from Titanic passengers' age was numerically simulated. Both showed that our optimized maximum or minimum performs more efficiently compared with DHA. Our algorithm can serve as an important subroutine in various quantum algorithms which involves searching maximum or minimum.
Chaotic wave-packet spreading in two-dimensional disordered nonlinear lattices.

We reveal the generic characteristics of wave-packet delocalization in two-dimensional nonlinear disordered lattices by performing extensive numerical simulations in two basic disordered models: the Klein-Gordon system and the discrete nonlinear Schrödinger equation. We find that in both models (a) the wave packet's second moment asymptotically evolves as t^{a_{m}} with a_{m}≈1/5 (1/3) for the weak (strong) chaos dynamical regime, in agreement with previous theoretical predictions [S. Flach, Chem. Phys. 375, 548 (2010)CMPHC20301-010410.1016/j.chemphys. 2010.02. 022]; (b) chaos persists, but its strength decreases in time t since the finite-time maximum Lyapunov exponent Λ decays as Λ∝t^{α_{Λ}}, with α_{Λ}≈-0.37 (-0.46) for the weak (strong) chaos case; and (c) the deviation vector distributions show the wandering of localized chaotic seeds in the lattice's excited part, which induces the wave packet's thermalization. We also propose a dimension-independent scaling between the wave packet's spreading and chaoticity, which allows the prediction of the obtained α_{Λ} values.
Distributed Hypothesis Testing over a Noisy Channel: Error-Exponents Trade-Off

A two-terminal distributed binary hypothesis testing problem over a noisy channel is studied. The two terminals, called the observer and the decision maker, each has access to n independent and identically distributed samples, denoted by U and V, respectively. The observer communicates to the decision maker over a discrete memoryless channel, and the decision maker performs a binary hypothesis test on the joint probability distribution of (U,V) based on V and the noisy information received from the observer. The trade-off between the exponents of the type I and type II error probabilities is investigated. Two inner bounds are obtained, one using a separation-based scheme that involves type-based compression and unequal error-protection channel coding, and the other using a joint scheme that incorporates type-based hybrid coding. The separation-based scheme is shown to recover the inner bound obtained by Han and Kobayashi for the special case of a rate-limited noiseless channel, and also the one obtained by the authors previously for a corner point of the trade-off. Finally, we show via an example that the joint scheme achieves a strictly tighter bound than the separation-based scheme for some points of the error-exponents trade-off.
Towards Better Understanding of Spontaneous Conversations: Overcoming Automatic Speech Recognition Errors With Intent Recognition

In this paper, we present a method for correcting automatic speech recognition (ASR) errors using a finite state transducer (FST) intent recognition framework. Intent recognition is a powerful technique for dialog flow management in turn-oriented, human-machine dialogs. This technique can also be very useful in the context of human-human dialogs, though it serves a different purpose of key insight extraction from conversations. We argue that currently available intent recognition techniques are not applicable to human-human dialogs due to the complex structure of turn-taking and various disfluencies encountered in spontaneous conversations, exacerbated by speech recognition errors and scarcity of domain-specific labeled data. Without efficient key insight extraction techniques, raw human-human dialog transcripts remain significantly unexploited. Our contribution consists of a novel FST for intent indexing and an algorithm for fuzzy intent search over the lattice - a compact graph encoding of ASR's hypotheses. We also develop a pruning strategy to constrain the fuzziness of the FST index search. Extracted intents represent linguistic domain knowledge and help us improve (rescore) the original transcript. We compare our method with a baseline, which uses only the most likely transcript hypothesis (best path), and find an increase in the total number of recognized intents by 25%.
Entropy in Thermodynamics: from Foliation to Categorization

Abstract We overview the notion of entropy in thermodynamics. We start from the smooth case using differential forms on the manifold, which is the natural language for thermodynamics. Then the axiomatic definition of entropy as ordering on a set that is induced by adiabatic processes will be outlined. Finally, the viewpoint of category theory is provided, which reinterprets the ordering structure as a category of pre-ordered sets.
First 20 Years of Green Radios

Green radios jointly consider the spectrum efficiency and the energy efficiency in wireless networks in order to provide sustainable development. In the past two decades, various green radio solutions for different types of terminals and radio access networks have been developed, some of which have been used in the designs of wireless products. In this paper, we provide a historical view on some fundamental works and practical issues. In addition to providing a comprehensive overview on theoretical achievements, we also discuss several important power saving solutions with notable engineering impacts, such as Doherty power amplifier and separated baseband unit - remote radio unit architecture, which might be overlooked in previous publications. Moreover, with the huge growth of wireless traffic in the near future, green radio design for future wireless networks shall involve an end-to-end energy efficiency investigation of mobile terminals, radio access and core networks, and different applications. By introducing green radio schemes for advanced terminals and future wireless networks, this article will not only be beneficial for readers with only preliminary background in communications and signal processing but also have reference and historical values for researchers and practical engineers in the area.
Differentiated context-aware hook placement for different owners' smartphones

A hook is a piece of code. It checks user privacy policy before some sensitive operations happen. We propose an automated solution named Prihook for hook placement in the Android Framework. Addressing specific context-aware user privacy concerns, the hook placement in Prihook is personalized. Specifically, we design User Privacy Preference Table (UPPT) to help a user express his privacy concerns. And we leverage machine learning to discover a Potential Method Set (consisting of Sensor Data Access Methods and Sensor Control Methods) from which we can select a particular subset to put hooks. We propose a mapping from words in the UPPT lexicon to methods in the Potential Method Set. With this mapping, Prihook is able to (a) select a specific set of methods; and (b) generate and place hooks automatically. We test Prihook separately on 6 typical UPPTs representing 6 kinds of resource-sensitive UPPTs, and no user privacy violation is found. The experimental results show that the hooks placed by PriHook have small runtime overhead.
Capturing Word Semantics From Co-occurrences Using Dynamic Mutual Information

Semantic relations between words are crucial for information retrieval and natural language processing tasks. Distributional representations are based on word co-occurrence, and have been proven successful. Recent neural network approaches such as Word2vec and Glove are all derived from co-occurrence information. In particular, they are based on Shifted Positive Pointwise Mutual Information (SPPMI). In SPPMI, PMI values are shifted uniformly by a constant, which is typically five. Although SPPMI is effective in practice, it lacks theoretical explanation, and has space for improvement. Intuitively, shifting is to remove co-occurrence pairs that could have co-occurred due to randomness, i.e., the pairs whose expected co-occurrence count is close to its observed appearances. We propose a new shifting scheme, called Dynamic Mutual Information (DMI), where the shifting is based on the variance of co-occurrences and Chebyshev’s Inequality. Intuitively, DMI shifts more aggressively for rare word pairs. We demonstrate that DMI outperforms the state-of-the-art SPPMI in a variety of word similarity evaluation tasks.
Higher plant growth modelling for life support systems : global model design and simulation of mass and energy transfers at the plant level

For long-term manned space missions, it is necessary to develop efficient life support systems recycling air, water and food with a minimum supply of matter and energy. Air and water can be recycled from purely physico-chemical systems; however food requires se presence of living organisms. The Micro-Ecological Life Support System Alternative (MELiSSA) project of the European Space Agency includes higher plants grown in a closed and controlled chamber associated with other microbial compartments. The long-term control of the growth chamber and entire life support system requires efficient predictive models. The mass balance closure and the prediction in uncommon extraterrestrial environments highlight the importance of mechanistic models based on the mass and energy balances principles.An extensive bibliographic study has been performed in order to list and analyse the existing models of higher plant growth. Many models already exist, simulating most of the plant processes. However none of the global, structured models is sufficiently mechanistic and balanced in terms of matter exchange for an application in closed life support systems. Then a new structure is proposed in order to simulate all the terms of the mass balance at the plant level, including the different scales of study: general processes, organ scale and molecular scale. The results of the first approach using simple mechanistic physical laws for mass and energy exchange, a unique stoichiometry for biomass production and few empirical laws for the prediction of architectural parameters are illustrated and compared with experimental results obtained in a controlled environment. A mathematical analysis of the model is performed and all these results are discussed in order to propose further developments. This is described in detail for the implementation of more complex models of processes in the future model versions; the experiments that should be performed including the main measurements are proposed. This leads to the description of a new design of experimental growth chamber.
Automatic visual defects inspection of wind turbine blades via YOLO-based small object detection approach

Abstract. Regular inspection of wind turbine blades (WTBs), especially the detection of tiny defects, is necessary to maintain safe operation of wind turbine systems. However, current detections are inefficient and subjective because they are conducted merely by human inspectors. An autonomous visual inspection system is proposed in this paper for WTBs, in which a deep learning framework is developed by combining the convolutional neural network (CNN) and the you only look once (YOLO) model. To achieve practically acceptable detection accuracy for small-sized defects on the WTBs, a YOLO-based small object detection approach (YSODA) using a multiscale feature pyramid is proposed by amalgamating features of more layers. To evaluate the proposed YSODA, a database including 23,807 images labeled for three types of defect—crack, oil pollution, and sand inclusion, is developed. Then, the YSODA is with its architecture modified, and is trained, validated, and tested using the images from the database to provide autonomous and accurate visual inspection. After training and testing, resulting detection accuracy reaches 92.7%, 90.7%, and 90.3% for the three types of defect with the average accuracy being 91.3%. The robustness of the trained YSODA is demonstrated and verified in detecting small-sized defects. It is also compared with that of the traditional CNN-based and machine learning methods by applying to a real WTB system, which proved that the proposed YSODA is superior to existing approaches in terms of detection accuracy and reliability.
Optimization of cleaning strategies based on ANN algorithms assessing the benefit of soiling rate forecasts

Soiling puts operators of solar power plants before the challenge of finding the right strategy for the cleaning of their solar fields. The trade-off between a low cleanliness and thus low revenues on the one hand and elevated cleaning costs and high field efficiency on the other hand has to be met. In this study we address this problem using a reinforced learning algorithm. Reinforced learning is a trial and error based learning process based on a scalar reward. The algorithms improve with an increasing number of training runs, each performed on a different one-year data set. The reward being the profit of the CSP project. In order to prevent overfitting to a special case, the training data has to be sufficiently large. To increase our 5 year soiling-rate and 25 year meteorological measurement data set from CIEMAT’s Plataforma Solar de Almeria (PSA). We first present a method to create artificial long term data sets based on these measurements that are representative of the sites’ weather conditions. With the extended datasets we are able to train the algorithm sufficiently before testing it on the validation dataset. The algorithm is given the daily choice to deploy up to two cleaning units in day and/or night shifts. In a second step, it is given soiling rate forecasts with different forecast horizons. At PSA our trained algorithm can increase a project’s profit by 1.28 % compared to a reference constant cleaning frequency (RPI) if only the current cleanliness of the solar field is known. If it is given a one day soiling-rate forecast the profit can be increased by 1.33 %. A three day soiling-rate-forecast can increase the profit by 1.37 %. An extended forecast horizon does not seem to increase the RPI further. For sites with higher dust loads than PSA the RPI is expected to be significantly higher than at PSA. Reinforced learning in combination with the data extension algorithm can be a useful method to increase a CSP project’s profit over its lifetime.
Argumentation techniques for existential rules

In this thesis, we investigate reasoning techniques with argumentation graphs generated from inconsistent knowledge bases expressed in the existential rules language. The existential rules language is a subset of first-order logic in which a knowledge base is composed of two layers: a fact layer and an ontology layer. The fact layer consists of factual knowledge (usually stored in relational databases) whereas the ontology layer consists of reasoning rules of deduction and negative constraints. Since the classical query answering approaches fail in the presence of inconsistencies, we chose to work with an conflict-tolerant reasoning approach that is based on building graphs with structured arguments and attacks from the deductions of the underlying logical knowledge base. The three main results are the following. First, we study how argumentation graphs are obtained from knowledge bases expressed in existential rules, the structural properties of such graphs and show several insights as to how their generation can be improved. Second, we propose a framework that generates an argumentation graph with a special feature called sets of attacking arguments instead of the regular binary attack relation and show how it improves upon the current state-of-the-art using an empirical analysis. Third, we interest ourselves to ranking-based approaches in both the context of query answering and argumentation reasoning. In the former, we introduce a framework that is based on ranking maximal consistent subsets of facts (repairs) in order to have a more productive query answering. In the latter, we set up the foundation theory for semantics that rank arguments in argumentation graphs with sets of attacking arguments.