Miracle-dz/UniFAR-FTUs huggingface dataset:
Three-dimensional Simultaneous Shape and Pose Estimation for Extended Objects Using Spherical Harmonics

We propose a new recursive method for simultaneous estimation of both the pose and the shape of a three-dimensional extended object. The key idea of the presented method is to represent the shape of the object using spherical harmonics, similar to the way Fourier series can be used in the two-dimensional case. This allows us to derive a measurement equation that can be used within the framework of nonlinear filters such as the UKF. We provide both simulative and experimental evaluations of the novel techniques.
Adaptive schemes for piecewise deterministic Monte Carlo algorithms

The Bouncy Particle sampler (BPS) and the Zig-Zag sampler (ZZS) are continuous time, non-reversible Monte Carlo methods based on piecewise deterministic Markov processes. Experiments show that the speed of convergence of these samplers can be affected by the shape of the target distribution, as for instance in the case of anisotropic targets. We propose an adaptive scheme that iteratively learns all or part of the covariance matrix of the target and takes advantage of the obtained information to modify the underlying process with the aim of increasing the speed of convergence. Moreover, we define an adaptive scheme that automatically tunes the refreshment rate of the BPS or ZZS. We prove ergodicity and a law of large numbers for all the proposed adaptive algorithms. Finally, we show the benefits of the adaptive samplers with several numerical simulations.
Aldol condensation reaction between formaldehyde and acetone over heat-treated synthetic hydrotalcite and hydrotalcite-like compounds.

Synthetic hydrotalcite, Mg6Al2(OH)16CO3·4H2O, and its analogues were synthesized, heat-treated at 773 K, and used as catalysts for the title reaction to form methyl vinyl ketone (MVK) at 673 K. MVK was formed over all the catalysts examined, the highest MVK yield (20% on acetone basis) being found over the heat-treated hydrotalcite.
Evaluation of the Impacts of Rain Gauge Density and Distribution on Gauge-Satellite Merged Precipitation Estimates

The capacity of combined gauge-satellite precipitation estimates largely depends on the characteristics of the input data such as the number, location and reliability of rain gauges, and satellite-derived precipitation quality. The objective of this study is to examine the influence of rain gauge network configuration including density and spatial distribution on the performance of the gauge-satellite merging estimation at monthly and ten-day temporal scales. Dense rain gauge observations and satellite-derived precipitation data (i.e., TMPA 3B42 Version 7 and Version 06 IMERG Final Run) in two provinces of China are used. A two-stage downscaling-integration approach is applied in the gauge-satellite precipitation estimation. Various scenarios of rain gauge density and combination are designed and their corresponding merged precipitation estimates are evaluated using statistical indices. The merged results using the TMPA and IMERG precipitation product, respectively, are compared. The results show that: 1) the influence of rain gauge network configuration on the gauge-satellite merged precipitation estimates gradually decreases with the increase in rain gauge density, and the gauge-satellite merged precipitation estimates are more sensitive to the rain gauge network density in wet season and ten-day temporal scale than in dry season and monthly scale, respectively and 2) the merged precipitation estimation using the IMERG precipitation data generally outperforms the estimation using TMPA precipitation data in the low gauge density scenarios, and the gap decreases with the increase in the rain gauge network density. In the areas with sparse rain gauges, improving the quality of satellite precipitation data would significantly improve the performance of the gauge-satellite merging estimation.
Curtus: An NLP Tool to Map Job Skills to Academic Courses

Many businesses are burdened with the need to train students for the job instead of finding them prepared for it. Few business leaders feel that colleges prepare students for future jobs from day one. It can be a challenge for colleges to determine if their curricula meet the industry needs. Mapping industry needs to academic courses can be advantageous to both parties as it will allow colleges to be aligned with the industry needs and accordingly satisfy those needs and will allow the industry to hire better prepared graduates. In an attempt to address this, a system prototype that uses a collection of job descriptions from various sites and syllabi of college courses as the input knowledge was developed. The primary goal of the system is to help students to find courses that would be most beneficial in providing them with the skills that match a given job description. The secondary goal is to help faculty to quickly find out information about current skills and tools covered in the existing courses, which accordingly can help them to make decisions about their future courses to satisfy the inductry needs. The system was developed using the Natural Language Toolkit (NLTK) and the Python programming language. Two sets of keywords were used to test the system; the first one is the most common keywords and the second one includes the most and least common keywords. Results from testing the system demonstrate that using the former set of keywords allowed for better results with precision equal to 55% and recall equal to 39.61%.
Freshness-Optimal Caching for Information Updating Systems with Limited Cache Storage Capacity

In this paper, we investigate a cache updating system with a server containing $N$ files, $K$ relays and $M$ users. The server keeps the freshest versions of the files which are updated with fixed rates. Each relay can download the fresh files from the server in a certain period of time. Each user can get the fresh files from any relay as long as the relay has stored the fresh versions of the requested files. Due to the limited storage capacity and updating capacity of each relay, different cache designs will lead to different average freshness of all updating files at users. In order to keep the average freshness as large as possible in the cache updating system, we formulate an average freshness-optimal cache updating problem (AFOCUP) to obtain an optimal cache scheme. However, because of the nonlinearity of the AFOCUP, it is difficult to seek out the optimal cache scheme. As a result, an linear approximate model is suggested by distributing the total update rates completely in accordance with the number of files in the relay in advance. Then we utilize the greedy algorithm to search the optimal cache scheme that is satisfied with the limited storage capacity of each relay. Finally, some numerical examples are provided to illustrate the performance of the approximate solution.
Oscillatory states, standing waves, and additional synchronized clusters in networks of second-order oscillators: uncovering the role of inertia

We discuss the appearance of oscillatory and standing wave states in second-order oscillator networks showing that it is a special case of a more general mechanism involving secondary synchronized clusters induced by inertia. Using a time-periodic mean-field ansatz, we find a bistable mechanism involving a stable fixed point and an invariant curve of an appropriate Poincaré map. The bistability and the devil’s staircase associated to the rotation number on the invariant curve provide an explanation for the appearance of the secondary synchronized clusters. The effect of inertias in the self-organization process is analyzed through a simplified model. This shows that the effect of giant synchronized clusters on the other oscillators is weakened by inertias, thus leading to secondary synchronized clusters during the transition process to synchronization.
Radiochemical analysis of rubble and trees collected from Fukushima Daiichi Nuclear Power Station

To characterize the rubble and trees contaminated by radionuclides released by the recent accident at the Fukushima Daiichi Nuclear Power Station, the radiochemical analysis protocols were modified using those developed by the Japan Atomic Energy Agency for the waste generated by research, industrial, and medical facilities. The radioactivity concentrations of gamma-ray-emitting nuclides 60Co, 94Nb, 152Eu, and 154Eu, and beta-particle-emitting nuclides 14C, 129I, 36Cl, 79Se, and 99Tc were successfully applied by the modified analytical method. In contrast, the radioactivity concentrations of 3H, 90Sr, 137Cs, and alpha-particle-emitting nuclides were applied by the conventional method. Unfortunately, 36Cl, 94Nb, 129I, 152Eu, 154Eu, and alpha-particle-emitting nuclides were below the detection limit of the conventional method. The measured radioactivity concentrations, except for that of 3H, were not uniform in the area but depended on the reactor unit. Although the radioactivity concentrations were varied widely, this analysis successfully clarified the characteristics of the radioactivity concentrations of the rubble and trees.
Some comparisons of Blanchfield pairings and cohomology pairings of knots

We study some comparison between a bilinear cohomology pairing in local coefficients and the Blanchfield pairing of a knot. We show that the former pairing is an S-equivalent invariant, and give a criterion to a relation between the two pairings. We also observe that the pairings of some knots are equivalent, and that the pairings of other knots are not equivalent.
From Quantum Source Compression to Quantum Thermodynamics

This thesis addresses problems in the field of quantum information theory, specifically, quantum Shannon theory. The first part of the thesis is opened with concrete definitions of general quantum source models and their compression, and each subsequent chapter addresses the compression of a specific source model as a special case of the initially defined general models. First, we find the optimal compression rate of a general mixed state source which includes as special cases all the previously studied models such as Schumacher’s pure and ensemble sources and other mixed state ensemble models. For an interpolation between the visible and blind Schumacher’s ensemble model, we find the optimal compression rate region for the entanglement and quantum rates. Later, we comprehensively study the classical-quantum variation of the celebrated Slepian-Wolf problem and find the optimal rates considering per-copy fidelity; with block fidelity we find single letter achievable and converse bounds which match up to continuity of a function appearing in the bounds. The first part of the thesis is closed with a chapter on the ensemble model of quantum state redistribution for which we find the optimal compression rate considering per-copy fidelity and single-letter achievable and converse bounds matching up to continuity of a function which appears in the bounds. The second part of the thesis revolves around information theoretical perspective of quantum thermodynamics. We start with a resource theory point of view of a quantum system with multiple non-commuting charges where the objects and allowed operations are thermodynamically meaningful; using tools from quantum Shannon theory we classify the objects and find explicit quantum operations which map the objects of the same class to one another. Subsequently, we apply this resource theory framework to study a traditional thermodynamics setup with multiple non-commuting conserved quantities consisting of a main system, a thermal bath and batteries to store various conserved quantities of the system. We state the laws of the thermodynamics for this system, and show that a purely quantum effect happens in some transformations of the system, that is, some transformations are feasible only if there are quantum correlations between the final state of the system and the thermal bath.
The Miyaoka–Yau inequality on smooth minimal models

In this short note, we offer an observation that the Miyaoka–Yau inequality holds for any compact Kähler manifold with nef canonical bundle, that is, a smooth minimal model. It follows directly from the existence of cscK metrics in a neighborhood of the canonical class which was confirmed both by the work of Dyrefelt and Song using different approaches.
Etched plastic searches for dark matter

Large panels of etched plastic, situated aboard the Skylab Space Station and inside the Ohya quarry near Tokyo, have been used to set limits on fluxes of cosmogenic particles. These plastic particle track detectors also provide the best sensitivity for some heavy dark matter that interacts strongly with nuclei. We revisit prior dark matter bounds from Skylab, and incorporate geometry-dependent thresholds, a halo velocity distribution, and a complete accounting of observed through-going particle fluxes. These considerations reduce the Skylab bound’s mass range by a few orders of magnitude. However, a new analysis of Ohya data covers a portion of the prior Skylab bound, and excludes dark matter masses up to the Planck mass. Prospects for future etched plastic dark matter searches are discussed.
On a Japanese Subjective Well-Being Indicator Based on Twitter data

This study presents for the first time the SWB-J index, a subjective well-being indicator for Japan based on Twitter data. The index is composed by eight dimensions of subjective well-being and is estimated relying on Twitter data by using human supervised sentiment analysis. The index is then compared with the analogous SWB-I index for Italy, in order to verify possible analogies and cultural differences. Further, through structural equation models, a causal assumption is tested to see whether the economic and health conditions of the country influence the well-being latent variable and how this latent dimension affects the SWB-J and SWB-I indicators. It turns out that, as expected, the economic and health welfare is only one aspect of the multidimensional well-being that is captured by the Twitter-based indicator. Keywords— subjective well-being, Japan, Twitter data, sentiment analysis
Variance Reduction on Adaptive Stochastic Mirror Descent

We study the idea of variance reduction applied to adaptive stochastic mirror descent algorithms in nonsmooth nonconvex ﬁnite-sum optimization problems. We propose a simple yet generalized adaptive mirror descent algorithm with variance reduction named SVRAMD and provide its convergence analysis in different settings. We prove that variance reduction reduce the gradient complexity of most adaptive mirror descent algorithms and boost their convergence. In particular, our general theory implies variance reduction can be applied to algorithms using time-varying steps sizes and self-adaptive algorithms such as AdaGrad and RM-SProp. Moreover, our convergence rates recover the best exisiting rates of non-adaptive algorithms. We check the validity of our claims using experiments in deep learning.
Detecting Medical Misinformation on Social Media Using Multimodal Deep Learning

In 2019, outbreaks of vaccine-preventable diseases reached the highest number in the US since 1992. Medical misinformation, such as antivaccine content propagating through social media, is associated with increases in vaccine delay and refusal. Our overall goal is to develop an automatic detector for antivaccine messages to counteract the negative impact that antivaccine messages have on the public health. Very few extant detection systems have considered multimodality of social media posts (images, texts, and hashtags), and instead focus on textual components, despite the rapid growth of photo-sharing applications (e.g., Instagram). As a result, existing systems are not sufficient for detecting antivaccine messages with heavy visual components (e.g., images) posted on these newer platforms. To solve this problem, we propose a deep learning network that leverages both visual and textual information. A new semantic- and task-level attention mechanism was created to help our model to focus on the essential contents of a post that signal antivaccine messages. The proposed model, which consists of three branches, can generate comprehensive fused features for predictions. Moreover, an ensemble method is proposed to further improve the final prediction accuracy. To evaluate the proposed model's performance, a real-world social media dataset that consists of more than 30,000 samples was collected from Instagram between January 2016 and October 2019. Our 30 experiment results demonstrate that the final network achieves above 97% testing accuracy and outperforms other relevant models, demonstrating that it can detect a large amount of antivaccine messages posted daily. The implementation code is available at https://github.com/wzhings/antivaccine_detection.
Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentation

Point cloud segmentation is a fundamental visual understanding task in 3D vision. A fully supervised point cloud segmentation network often requires a large amount of data with point-wise annotations, which is expensive to obtain. In this work, we present the Compositional Prototype Network that can undertake point cloud segmentation with only a few labeled training data. Inspired by the few-shot learning literature in images, our network directly transfers label information from the limited training data to unlabeled test data for prediction. The network decomposes the representations of complex point cloud data into a set of local regional representations and utilizes them to calculate the compositional prototypes of a visual concept. Our network includes a key Multi-View Comparison Component that exploits the redundant views of the support set. To evaluate the proposed method, we create a new segmentation benchmark dataset, ScanNet-$6^i$, which is built upon ScanNet dataset. Extensive experiments show that our method outperforms baselines with a significant advantage. Moreover, when we use our network to handle the long-tail problem in a fully supervised point cloud segmentation dataset, it can also effectively boost the performance of the few-shot classes.
The Born Rule as Dutch-Book Coherence (and only a little more)

The subjective Bayesian interpretation of probability asserts that the rules of the probability calculus follow from the normative principle of Dutch-book coherence: A decision-making agent should not assign probabilities such that a series of monetary transactions based on those probabilities would lead them to expect a sure loss. Similarly, the subjective Bayesian interpretation of quantum mechanics (QBism) asserts that the Born rule is a normative rule in analogy to Dutch-book coherence, but with the addition of one or more empirically based assumptions—i.e., the “only a little more” that connects quantum theory to the particular characteristics of the physical world. Here we make this link explicit for a conjectured representation of the Born rule which holds true if symmetric informationally complete POVMs (or SICs) exist for every finite dimensional Hilbert space. We prove that an agent who thinks they are gambling on the outcomes of measurements on a sufficiently quantum-like system, but refuses to use this form of the Born rule when placing their bets is vulnerable to a Dutch book. The key property for being sufficiently quantum-like is that the system admits a symmetric reference measurement, but that this measurement is not sampling any hidden variables.
Rationale and Recommendations for Particle Size-Selective Sampling in the Workplace

Abstract Because many aerosol hazards depend upon particle size, the American Conference of Governmental Industrial Hygienists established an Air Sampling Procedures Committee to “recommend size-selective aerosol sampling procedures which will permit reliable collection of aerosol fractions which can be expected to be available for deposition in the various major subregions of the human respiratory tract. ” After reviewing available data on regional deposition of inhaled particles and on the collection efficiencies of sampling instruments, the committee recommends use of three particulate mass fractions for workplace sampling: inspirable particulate mass (IPM), for materials which may be hazardous anywhere in the respiratory tract; thoracic particulate mass (TPM), for materials which may be hazardous anywhere within the lung airways and the gas exchange region; and respirable particulate mass (RPM), for materials which may be hazardous in the gas exchange region of the lung. The mass fractions are defined .
Wet etching of GaN grown by molecular beam epitaxy on Si(111)

A wet etching method for GaN and AlxGa1-xN, based on aqueous solutions of KOH, is presented. A detailed analysis of the etching rate dependence with temperature and concentration is described. This etching has been used for the fabrication of high optical quality pyramidal nanostructures in wurtzite N-face GaN grown on AlN-buffered Si(111) substrates by molecular beam epitaxy. These nanostructures have been studied by high-resolution transmission and scanning electron microscopy and their optical quality has been analysed by low-temperature photoluminescence (PL) measurements. The pyramids are parallel to the basal plane and limited by {111} planes and its presence improves the overall PL response of the sample. The relationship between the polarity of GaN and the characteristics of the AlN buffer has also been analysed.
Aplicação de técnicas multivariadas e inteligência artificial na análise de espectros de infravermelho para determinação de matéria orgânica em amostras de solos

APPLICATION OF MULTIVARIATE CALIBRATION AND ARTIFICIAL INTELLIGENCE IN THE ANALYSIS OF INFRARED SPECTRA TO QUANTIFY ORGANIC MATTER IN SOIL SAMPLES. In this paper studies based on Multilayer Perception Artificial Neural Network and Least Square Support Vector Machine (LS-SVM) techniques are applied to determine of the concentration of Soil Organic Matter (SOM). Performances of the techniques are compared. SOM concentrations and spectral data from Mid-Infrared are used as input parameters for both techniques. Multivariate regressions were performed for a set of 1117 spectra of soil samples, with concentrations ranging from 2 to 400 g kg-1. The LS-SVM resulted in a Root Mean Square Error of Prediction of 3.26 g kg-1 that is comparable to the deviation of the Walkley-Black method (2.80 g kg-1).
Thin-Layer Drying of Flax Fiber: I. Analysis of Modeling Using Fick's Second Law of Diffusion

Thin-layer drying of moist flax fiber was performed at four temperatures of 30, 50, 70, and 100°C with a constant absolute humidity of 0.0065 kg water per kg dry air. The coefficients of diffusion of the fiber at different drying conditions were estimated by modeling the drying process using the one- to five-term solutions of the second Fick's law of diffusion. The models underestimated the drying process during the initial stages of drying and overestimated this process during the final stages. The estimated coefficient of diffusions ranged from 5.11 × 10−9 to 1.92 × 10−8 m2/s and linearly increased with the drying air temperature.