Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative analysis of SERS-active colloidal silver solutions of various type and prospects of their applications

Raman spectroscopy is a promising method for optical vibrational spectroscopy. Nowadays, Raman spectroscopy finds many applications, in particular, in biological and medical diagnostics. However, the Raman scattering can be enhanced using the Surface-enhanced Raman scattering method. Colloidal solutions of noble metals are used as SERS-active systems. In this work, the enhancing factors were estimated for colloidal silver solutions of three different types (citrate, borohydride, chloride) with two substances (phenylalanine, cytochrome C). Phenylalanine is a widely used model substance for Raman and Surface-enhanced Raman spectroscopy. Cytochrome C is one of the most researched proteins. It involves in the electron transport chain of the mitochondrial inner membrane and provides cellular respiration. Borohydride, citrate and chloride sols with phenylalanine gave an enhancement about 50, 200 and 30 times, respectively, and with cytochrome C about 30, 160 and 20, respectively. A comparative analysis of active and inactive sols by SERS and absorption spectroscopy was also performed. The absorption spectra of active sols have characteristic maxima in the region of 400 nm. Both the SERS method of model substances and absorption spectroscopy can be used to assess the enhancing properties.
Motional Quantum Ground State of a Levitated Nanoparticle from Room Temperature

We report quantum ground state cooling of a levitated nanoparticle in a room temperature environment. Using coherent scattering into an optical cavity we cool the center of mass motion of a $143$ nm diameter silica particle by more than $7$ orders of magnitude to $n_x=0.43\pm0.03$ phonons along the cavity axis, corresponding to a temperature of $12~\mu$K. We infer a heating rate of $\Gamma_x/2\pi = 21\pm 3$ kHz, which results in a coherence time of $7.6~\mu$s -- or $15$ coherent oscillations -- while the particle is optically trapped at a pressure of $10^{-6}$ mbar. The inferred optomechanical coupling rate of $g_x/2\pi = 71$ kHz places the system well into the regime of strong cooperativity ($C \approx 5$). We expect that a combination of ultra-high vacuum with free-fall dynamics will allow to further expand the spatio-temporal coherence of such nanoparticles by several orders of magnitude, thereby opening up new opportunities for macrosopic quantum experiments.
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles

Text style transfer is usually performed using attributes that can take a handful of discrete values (e.g., positive to negative reviews). In this work, we introduce an architecture that can leverage pre-trained consistent continuous distributed style representations and use them to transfer to an attribute unseen during training, without requiring any re-tuning of the style transfer model. We demonstrate the method by training an architecture to transfer text conveying one sentiment to another sentiment, using a fine-grained set of over 20 sentiment labels rather than the binary positive/negative often used in style transfer. Our experiments show that this model can then rewrite text to match a target sentiment that was unseen during training.
Beyond Statistical Relations: Integrating Knowledge Relations into Style Correlations for Multi-Label Music Style Classification

Automatically labeling multiple styles for every song is a comprehensive application in all kinds of music websites. Recently, some researches explore review-driven multi-label music style classification and exploit style correlations for this task. However, their methods focus on mining the statistical relations between different music styles and only consider shallow style relations. Moreover, these statistical relations suffer from the underfitting problem because some music styles have little training data. To tackle these problems, we propose a novel knowledge relations integrated framework (KRF) to capture the complete style correlations, which jointly exploits the inherent relations between music styles according to external knowledge and their statistical relations. Based on the two types of relations, we use graph convolutional network to learn the deep correlations between styles automatically. Experimental results show that our framework significantly outperforms the state-of-the-art methods. Further studies demonstrate that our framework can effectively alleviate the underfitting problem and learn meaningful style correlations.
Preparation and Comparative Study of Aerogels Based on Cellulose Nanocrystals and Nanofibers from Eucalyptus Pulp

Nanocellulose-based materials have attracted significant attention because of their attractive advantages. Particularly, aerogel, a porous nanocellulose material, have been used in diverse applications owing to their unique properties. In this study, short rod-like cellulose nanocrystals (CNCs) and long filament-like cellulose nanofibers (CNFs) were isolated from a eucalyptus pulp source using acidolysis and oxidation/mechanical methods, respectively. Subsequently, two different aerogels were prepared from the CNCs and CNFs using the sol-gel method and their properties were compared. The morphology, chemical structure, chemical composition, shrinkage rate, internal structure, thermal degradation, biophysical properties, and mechanical properties of the as-prepared aerogels were compared. Furthermore, the shrinkage of the CNC and CNF aerogels was effectively controlled using a supercritical CO2 drying process. Additionally, three decomposition regions were observed in the thermogravimetric analysis curves of the aerogels; however, the CNF aerogels exhibited enhanced thermal stability than the CNC aerogels. Further, the CNC and CNF aerogels exhibited a mesoporous structure, and the compressive strength of the CNC and CNF aerogels under 85% strain was 269.5 and 299.5 KPa, respectively. This study provides fundamental knowledge on the fabrication of CNCs, CNFs, and corresponding aerogels from lignocellulosic biomass, and their characteristics.
New evidence of illicit cigarette consumption and government revenue loss in Indonesia

INTRODUCTION Illicit cigarettes because of their affordability could increase smoking prevalence, especially among young people. They also cause a large revenue loss for the government. This study aims to estimate illicit cigarette consumption and government revenue loss in Indonesia, a country with a very high smoking prevalence, especially among males. METHODS We estimated illicit cigarette trade in terms of volume and revenue loss. Illicit trade was estimated as the discrepancy between legal cigarette sales and domestic consumption recorded by national representative surveys. Data sources included Basic Health Research Survey, Global Adult Tobacco Survey, National Socioeconomic Survey, and data from Ministry of Finance. RESULTS We found that illicit cigarette consumption fluctuated from 19 billion sticks in 2007 to 14 billion sticks in 2013, and sharply increased to 59 billion sticks in 2018. Relative to cigarette consumption, illicit cigarettes were the lowest at 5% in 2013 and highest at 19% of consumption in 2018 (assuming 0% underreporting). The estimated government revenue loss ranged from IDR 24.2 to 42.0 trillion (US$ 1668 to 2897 million), which corresponds to 15.8% to 27.5% of cigarette excise revenue in 2018. CONCLUSIONS In Indonesia, illicit cigarette consumption was found to be high and increasing, which contributed to a large government revenue loss (almost onethird of tobacco excise tax revenue). To reduce illegal cigarette production and smuggling, the government should increase resources to enforce the regulation on the excise tax system including stronger penalties, especially related to illicit cigarette production.
Q2DW‐Tide and ‐Ionosphere Interactions as Observed From ICON and Ground‐Based Radars

A quasi‐2‐day wave (Q2DW) event during January‐February, 2020, is investigated in terms of its propagation from 96 to 250 km as a function of latitude (10°S to 30°N), its nonlinear interactions with migrating tides to produce 16 and 9.6‐h secondary waves (SWs), and the plasma drift and density perturbations that it produces in the topside F‐region (590–607 km) between magnetic latitudes 18°S and 18°N. This is accomplished through analysis of coincident Ionospheric Connections Explorer (ICON) measurements of neutral winds, plasma drifts and ion densities, and wind measurements from four low‐latitude (±15°) specular meteor radars (SMRs). The Q2DW westward‐propagating components that existed during this period consist of zonal wavenumbers s = 2 and s = 3, that is, Q2DW+2 and Q2DW+3 (e.g., He, Chau et al., 2021, https://doi.org/10.1029/93jd00380). SWs in the ICON measurements are inferred from Q2DW+2 and Q2DW+3 characteristics derived from traditional longitude‐UT fits that potentially contain aliasing contributions from SWs (“apparent” Q2DWs), from fits to space‐based zonal wavenumbers that each reflect the aggregate signature of either Q2DW+2 or Q2DW+3 and its SWs combined (“effective” Q2DWs), and based on information contained in published numerical simulations. The total Q2DW ionospheric responses consists of F‐region field‐aligned and meridional drifts of order ±25 ms−1 and ±5–7 ms−1, respectively, and total ion density perturbations of order (±10%–25%). It is shown that the SWs can sometimes make substantial contributions to the Q2DW winds, drifts, and plasma densities.
Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development

Biomedical data, particularly in the field of genomics, has characteristics which make it challenging for machine learning applications - it can be sparse, high dimensional and noisy. Biomedical applications also present challenges to model selection - whilst powerful, accurate predictions are necessary, they alone are not sufficient for a model to be deemed useful. Due to the nature of the predictions, a model must also be trustworthy and transparent, empowering a practitioner with confidence that its use is appropriate and reliable. In this paper, we propose that this can be achieved through the use of judiciously built feature sets coupled with Bayesian models, specifically Gaussian processes. We apply Gaussian processes to drug discovery, using inexpensive transcriptomic profiles from human cell lines to predict animal kidney and liver toxicity after treatment with specific chemical compounds. This approach has the potential to reduce invasive and expensive animal testing during clinical trials if in vitro human cell line analysis can accurately predict model animal phenotypes. We compare results across a range of feature sets and models, to highlight model importance for medical applications.
Central charges of aperiodic holographic tensor-network models

Central to the AdS/CFT correspondence is a precise relationship between the curvature of an anti-de Sitter (AdS) spacetime and the central charge of the dual conformal field theory (CFT) on its boundary. Our work shows that such a relationship can also be established for tensor network models of AdS/CFT based on regular bulk geometries, leading to an analytical form of the maximal central charges exhibited by the boundary states. We identify a class of tensors based on Majorana dimer states that saturate these bounds in the large curvature limit, while also realizing perfect and block-perfect holographic quantum error correcting codes. Furthermore, the renormalization group description of the resulting model is shown to be analogous to the strong disorder renormalization group, thus giving the first example of an exact quantum error correcting code that gives rise to a well-understood critical system. These systems exhibit a large range of fractional central charges, tunable by the choice of bulk tiling. Our approach thus provides a precise physical interpretation of tensor network models on regular hyperbolic geometries and establishes quantitative connections to a wide range of existing models.
Human Driver Behavior Prediction based on UrbanFlow*

How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers’ behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehicle is surrounded by human drivers who have various driving behaviors and patterns of interaction with other vehicles. In this paper, we propose an LSTM-based trajectory prediction method for human drivers which can help the autonomous vehicle make better decisions, especially in urban intersection scenarios. Meanwhile, in order to collect human drivers’ driving behavior data in the urban scenario, we describe a system called UrbanFlow which includes the whole procedure from raw bird’s-eye view data collection via drone to the final processed trajectories. The system is mainly intended for urban scenarios but can be extended to be used for any traffic scenarios.
De novo QTL-seq Identifies Loci Linked to Blanchability in Peanut (Arachis hypogaea) and Refines Previously Identified QTL with Low Coverage Sequence

Blanchability is an often overlooked, but important trait for peanut breeding. The process of blanching, removing the skin, is an important step in the processing of raw nuts for manufacturing. Under strong genetic control and requiring considerable effort to phenotype, blanchability is conducive for marker-assisted selection. We used QTL sequencing (QTL-seq) to identify two QTLs related to blanchability using previously phenotyped breeding populations. To validate the QTLs, we show that two markers can select for significantly increased blanchability in an independent recombinant inbred line (RIL) population. Two wild introgressions from Arachis cardenasii conferring strong disease resistance were segregated in the population and were found to negatively impact blanchability. Finally, we show that by utilizing highly accurate sequence analysis pipelines, low coverage sequencing can be used to genotype whole populations with increased power and precision. This study highlights the potential to mine breeding data to identify and develop useful markers for genetic improvement programs, and provide powerful tools for breeding for processing and quality traits.
The Impact of Stimulation Intensity on Spatial Discrimination with Multi-Pad Finger Electrode

Multi-pad electrotactile stimulation can be used to provide tactile feedback in different applications. The electrotactile interface needs to be calibrated before each use, which entails adjusting the intensity to obtain clear sensations while allowing the subjects to differentiate between active pads. The present study investigated how the stimulation intensity affects the localization of sensations using a multi-pad electrode placed on a fingertip and proximal phalange. First, the sensation, localization, smearing and discomfort thresholds were determined in 11 subjects. Then, the same subjects performed a spatial discrimination test across a range of stimulation intensities. The results have shown that all thresholds were significantly different, while there was no difference in the threshold values between the pads and phalanges. Despite the subjective feeling of spreading of sensations, the success rates in spatial discrimination were not significantly different across the tested stimulation intensities. However, the performance was better for distal compared to proximal phalange. Presented results indicate that spatial discrimination is robust to changes in the stimulation intensity. Considering the lack of significant difference in the thresholds between the pads, these results imply that more coarse adjustment of stimulation amplitude (faster calibration) might be enough for practical applications of a multi-pad electrotactile interface.
Hybrid Localization: A Low Cost, Low Complexity Approach Based on Wi-Fi and Odometry

Localization in indoor environments is essential to further support automation in many scenarios such as warehouses and factories. Moreover, direction-of-arrival knowledge is essential to supporting high speed millimeter-wave (mmWave) links in indoor environments, since most mmWave links are of a line-of-sight nature to combat the high pathloss in this band. Accurate localization in indoor environments, however, has proved a challenging task due to multi- path fading methods such as trilateration alone do not result in accurate localization. As such, in this paper we propose to combine the knowledge of wireless localization methods with other sensors used for odometry to track the location of a mobile robot. This paper presents significant real world localization measurement results for both Wi-Fi and odometry in diverse environments at the Boise State University campus. Using these results, we devise an algorithm to combine data from both odometry and wireless localization. This algorithm is shown in hardware testing to enhance the localization accuracy by reducing the localization error for a mobile robot.
Perturbation theories for symmetry-protected bound states in the continuum on two-dimensional periodic structures

On dielectric periodic structures with a reflection symmetry in a periodic direction, there can be antisymmetric standing waves (ASWs) that are symmetry-protected bound states in the continuum (BICs). The BICs have found many applications, mainly because they give rise to resonant modes of extremely large quality-factors ($Q$-factors). The ASWs are robust to symmetric perturbations of the structure, but they become resonant modes if the perturbation is non-symmetric. The $Q$-factor of a resonant mode on a perturbed structure is typically $O(1/\delta^2)$ where $\delta$ is the amplitude of the perturbation, but special perturbations can produce resonant modes with larger $Q$-factors. For two-dimensional (2D) periodic structures with a 1D periodicity, we derive conditions on the perturbation profile such that the $Q$-factors are $O(1/\delta^4)$ or $O(1/\delta^6)$. For the unperturbed structure, an ASW is surrounded by resonant modes with a nonzero Bloch wave vector. For 2D periodic structures, the $Q$-factors of nearby resonant modes are typically $O(1/\beta^2)$, where $\beta$ is the Bloch wavenumber. We show that the $Q$-factors can be $O(1/\beta^6)$ if the ASW satisfies a simple condition.
PVN3D: A Deep Point-Wise 3D Keypoints Voting Network for 6DoF Pose Estimation

In this work, we present a novel data-driven method for robust 6DoF object pose estimation from a single RGBD image. Unlike previous methods that directly regressing pose parameters, we tackle this challenging task with a keypoint-based approach. Specifically, we propose a deep Hough voting network to detect 3D keypoints of objects and then estimate the 6D pose parameters within a least-squares fitting manner. Our method is a natural extension of 2D-keypoint approaches that successfully work on RGB based 6DoF estimation. It allows us to fully utilize the geometric constraint of rigid objects with the extra depth information and is easy for a network to learn and optimize. Extensive experiments were conducted to demonstrate the effectiveness of 3D-keypoint detection in the 6D pose estimation task. Experimental results also show our method outperforms the state-of-the-art methods by large margins on several benchmarks. Code and video are available at https://github.com/ethnhe/PVN3D.git.
Higher-order Weighted Graph Convolutional Networks

Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure. Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power. In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors. Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations. Our model, called higher-order weighted GCN(HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.
Text classification with pixel embedding

We propose a novel framework to understand the text by converting sentences or articles into video-like 3-dimensional tensors. Each frame, corresponding to a slice of the tensor, is a word image that is rendered by the word's shape. The length of the tensor equals to the number of words in the sentence or article. The proposed transformation from the text to a 3-dimensional tensor makes it very convenient to implement an $n$-gram model with convolutional neural networks for text analysis. Concretely, we impose a 3-dimensional convolutional kernel on the 3-dimensional text tensor. The first two dimensions of the convolutional kernel size equal the size of the word image and the last dimension of the kernel size is $n$. That is, every time when we slide the 3-dimensional kernel over a word sequence, the convolution covers $n$ word images and outputs a scalar. By iterating this process continuously for each $n$-gram along with the sentence or article with multiple kernels, we obtain a 2-dimensional feature map. A subsequent 1-dimensional max-over-time pooling is applied to this feature map, and three fully-connected layers are used for conducting text classification finally. Experiments of several text classification datasets demonstrate surprisingly superior performances using the proposed model in comparison with existing methods.
Simulating the galactic system in interaction AM 2229-735 and the formation of its polar structure

We study the formation of polar ring galaxies via minor mergers. We used N-body+hydrodynamics simulations to reproduce the dynamics of the observed system AM 2229-735 that is a minor merger whose interaction signals are those of a progenitor for a polar ring galaxy. We used the observational information of the system to get initial conditions for the orbit and numerical realisations of the galaxies to run the simulations. Our simulations reproduce the global characteristics of interaction observed in the system such as arms and a material bridge connecting the galaxies. As a merger remnant, we found a quasi-stable and self gravitating planar tidal stream with dark matter, stars and gas orbiting in a plane approximately perpendicular to the main galaxy disk leading in the future to a polar ring galaxy. We studied the dynamical conditions of the polar structure and found evidence suggesting that this kind of merger remnant can settle down in a disk-like structure with isothermal support, providing inspiring evidence about the process of formation of galactic disks and providing a potentially independent scenario to study the presence of dark matter in this kind of galaxies.