Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural properties of additive binary hard-sphere mixtures.

An approach to obtain the structural properties of additive binary hard-sphere mixtures is presented. Such an approach, which is a nontrivial generalization of the one recently used for monocomponent hard-sphere fluids [S. Pieprzyk, A. C. Brańka, and D. M. Heyes, Phys. Rev. E 95, 062104 (2017)2470-004510.1103/PhysRevE.95. 062104], combines accurate molecular-dynamics simulation data, the pole structure representation of the total correlation functions, and the Ornstein-Zernike equation. A comparison of the direct correlation functions obtained with the present scheme with those derived from theoretical results stemming from the Percus-Yevick (PY) closure and the so-called rational-function approximation (RFA) is performed. The density dependence of the leading poles of the Fourier transforms of the total correlation functions and the decay of the pair correlation functions of the mixtures are also addressed and compared to the predictions of the two theoretical approximations. A very good overall agreement between the results of the present scheme and those of the RFA is found, thus suggesting that the latter (which is an improvement over the PY approximation) can safely be used to predict reasonably well the long-range behavior, including the structural crossover, of the correlation functions of additive binary hard-sphere mixtures.
On Random Matrices Arising in Deep Neural Networks. Gaussian Case

The paper deals with distribution of singular values of product of random matrices arising in the analysis of deep neural networks. The matrices resemble the product analogs of the sample covariance matrices, however, an important difference is that the population covariance matrices, which are assumed to be non-random in the standard setting of statistics and random matrix theory, are now random, moreover, are certain functions of random data matrices. The problem has been considered in recent work [21] by using the techniques of free probability theory. Since, however, free probability theory deals with population matrices which are independent of the data matrices, its applicability in this case requires an additional justification. We present this justification by using a version of the standard techniques of random matrix theory under the assumption that the entries of data matrices are independent Gaussian random variables. In the subsequent paper [18] we extend our results to the case where the entries of data matrices are just independent identically distributed random variables with several finite moments. This, in particular, extends the property of the so-called macroscopic universality on the considered random matrices.
Tethered Aerial Visual Assistance

In this paper, an autonomous tethered Unmanned Aerial Vehicle (UAV) is developed into a visual assistant in a marsupial co-robots team, collaborating with a tele-operated Unmanned Ground Vehicle (UGV) for robot operations in unstructured or confined environments. These environments pose extreme challenges to the remote tele-operator due to the lack of sufficient situational awareness, mostly caused by the unstructuredness and confinement, stationary and limited field-of-view and lack of depth perception from the robot's onboard cameras. To overcome these problems, a secondary tele-operated robot is used in current practices, who acts as a visual assistant and provides external viewpoints to overcome the perceptual limitations of the primary robot's onboard sensors. However, a second tele-operated robot requires extra manpower and teamwork demand between primary and secondary operators. The manually chosen viewpoints tend to be subjective and sub-optimal. Considering these intricacies, we develop an autonomous tethered aerial visual assistant in place of the secondary tele-operated robot and operator, to reduce human robot ratio from 2:2 to 1:2. Using a fundamental viewpoint quality theory, a formal risk reasoning framework, and a newly developed tethered motion suite, our visual assistant is able to autonomously navigate to good-quality viewpoints in a risk-aware manner through unstructured or confined spaces with a tether. The developed marsupial co-robots team could improve tele-operation efficiency in nuclear operations, bomb squad, disaster robots, and other domains with novel tasks or highly occluded environments, by reducing manpower and teamwork demand, and achieving better visual assistance quality with trustworthy risk-aware motion.
Plato Dialogue System: A Flexible Conversational AI Research Platform

As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture, from standard architectures to architectures with jointly-trained components, single- or multi-party interactions, and offline or online training of any conversational agent component. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
How fast do quantum walks mix?

The fundamental problem of sampling from the limiting distribution of quantum walks on networks, known as mixing, finds widespread applications in several areas of quantum information and computation. Of particular interest in most of these applications is the minimum time beyond which the instantaneous probability distribution of the quantum walk remains close to this limiting distribution, known as the "quantum mixing time". However, this quantity is only known for a handful of specific networks. In this Letter, we prove an upper bound on the quantum mixing time for almost all networks, i.e., the fraction of networks for which our bound holds, goes to one in the asymptotic limit. To this end, using several results in random matrix theory, we find the quantum mixing time of Erdös-Renyi random networks: networks of n nodes where each edge exists with probability p independently. For example, for dense random networks, where p is a constant, we show that the quantum mixing time is O(n^{3/2+o(1)}). In addition to opening avenues for the analytical study of quantum dynamics on random networks, our work could find applications beyond quantum information processing. Owing to the universality of Wigner random matrices, our results on the spectral properties of random graphs hold for general classes of random matrices that are ubiquitous in several areas of physics. In particular, our results could lead to novel insights into the equilibration times of isolated quantum systems defined by random Hamiltonians, a foundational problem in quantum statistical mechanics.
Hα KINEMATICS OF THE ISOLATED INTERACTING GALAXY PAIR KPG 486 (NGC 6090)

In optical images, the barely studied isolated interacting galaxy pair KPG 486 (NGC 6090) displays similar features to the Antennae (NGC 4038/39) galaxy pair. To compare the distribution of ionized hydrogen gas, morphology and kinematic and dynamic behaviour of both galaxy pairs, we present observations in the Hα emission line of NGC 6090 acquired with the scanning Fabry-Perot interferometer, PUMA. For each galaxy in NGC 6090 we obtained several kinematic parameters, its velocity field and its rotation curve. We also analysed some of the perturbations induced by their encounter. We verified the consistency of our results by comparing them with kinematic results from the literature. The comparison of our results on NGC 6090 with those obtained in a previous similar kinematic analysis of the Antennae highlighted great differences between these galaxy pairs.
The paradox of youth for ALMA planet candidates

Recent ALMA observations indicate that the majority of bright protoplanetary discs show signatures of young moderately massive planets. I show that this result is paradoxical. The planets should evolve away from their observed states by radial migration and gas accretion in about 1\% of the system age. These systems should then hatch tens of giant planets in their lifetime, and there should exist a very large population of bright planet-less discs; none of this is observationally supported. An alternative scenario, in which the population of bright ALMA discs is dominated by secondary discs recently rejuvenated by deposition of new gas, is proposed. The data are well explained if the gaseous mass of the discs is comparable to a Jovian planet mass, and they last a small fraction of a Million years. Self-disruptions of dusty gas giant protoplanets, previously predicted in the context of the Tidal Downsizing theory of planet formation, provide a suitable mechanism for such injections of new fuel, and yield disc and planet properties commensurate with ALMA observations. If this scenario is correct, then the secondary discs have gas-to-dust ratios considerably smaller than 100, and long look ALMA and NIR/optical observations of dimmer targets should uncover dusty, not yet disrupted, gas clumps with sizes of order an AU. Alternatively, secondary discs could originate from late external deposition of gas into the system, in which case we expect widespread signatures of warped outer discs that have not yet come into alignment with the planets.
Gradient descent with momentum - to accelerate or to super-accelerate?

We consider gradient descent with `momentum', a widely used method for loss function minimization in machine learning. This method is often used with `Nesterov acceleration', meaning that the gradient is evaluated not at the current position in parameter space, but at the estimated position after one step. In this work, we show that the algorithm can be improved by extending this `acceleration' --- by using the gradient at an estimated position several steps ahead rather than just one step ahead. How far one looks ahead in this `super-acceleration' algorithm is determined by a new hyperparameter. Considering a one-parameter quadratic loss function, the optimal value of the super-acceleration can be exactly calculated and analytically estimated. We show explicitly that super-accelerating the momentum algorithm is beneficial, not only for this idealized problem, but also for several synthetic loss landscapes and for the MNIST classification task with neural networks. Super-acceleration is also easy to incorporate into adaptive algorithms like RMSProp or Adam, and is shown to improve these algorithms.
Vision Meets Drones: Past, Present and Future

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide range of applications, including agriculture, aerial photography, and surveillance. Consequently, automatic understanding of visual data collected from drones becomes highly demanding, bringing computer vision and drones more and more closely. To promote and track the evelopments of object detection and tracking algorithms, we have organized two challenge workshops in conjunction with ECCV 2018, and ICCV 2019, attracting more than 100 teams around the world. We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i.e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking. In this paper, we first presents a thorough review of object detection and tracking datasets and benchmarks, and discuss the challenges of collecting large-scale drone-based object detection and tracking datasets with fully manual annotations. After that, we describe our VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South. Being the largest such dataset ever published, VisDrone enables extensive evaluation and investigation of visual analysis algorithms on the drone platform. We provide a detailed analysis of the current state of the field of large-scale object detection and tracking on drones, and conclude the challenge as well as propose future directions. We expect the benchmark largely boost the research and development in video analysis on drone platforms. All the datasets and experimental results can be downloaded from the website: this https URL
ADAMT: A Stochastic Optimization with Trend Correction Scheme

Adam-type optimizers, as a class of adaptive moment estimation methods with the exponential moving average scheme, have been successfully used in many applications of deep learning. Such methods are appealing for capability on large-scale sparse datasets with high computational efficiency. In this paper, we present a new framework for adapting Adam-type methods, namely AdamT. Instead of applying a simple exponential weighted average, AdamT also includes the trend information when updating the parameters with the adaptive step size and gradients. The additional terms promise an efficient movement on the complex cost surface, and thus the loss would converge more rapidly. We show empirically the importance of adding the trend component, where AdamT outperforms the vanilla Adam method constantly with state-of-the-art models on several classical real-world datasets.
Automated Pavement Crack Damage Detection Using Deep Multiscale Convolutional Features

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg—an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection.
Non-Exosomal and Exosomal Circulatory MicroRNAs: Which Are More Valid as Biomarkers?

MicroRNAs (miRNAs) are a group of small non-coding RNAs with approximately 19–25 nucleotides that are involved in regulating a range of developmental and physiological processes. Non-exosomal circulating and exosomal miRNAs have also been proposed to be useful in diagnostics as biomarkers for diseases and different types of cancer. In this review, the quantity of miRNAs and of reliable experimental data analyses of miRNAs that come from exosomal and non-exosomal sources are discussed from the perspective of their use as biomarkers for cancer and other diseases, including viral infections, nervous system disorders, cardiovascular disorders, and diabetes. We summarize other research findings regarding the use of miRNA from these two sources as biomarkers in diagnostics and clinical use. The challenges in using miRNA from these two sources in cancer and disease diagnostics are evaluated and discussed. Validation of specific miRNA signatures as biomarkers is a critical milestone in diagnostics.
A Community-Based Cross-Sectional Study about the Quality of Life in Postmenopausal Women in Rural Puducherry

Background: The aim of this study was to assess the quality of life (QOL) of postmenopausal women, in the rural areas of Puducherry and to find the associated factors. Materials and Methods: A cross-sectional study was done in the rural field practicing area of SLIMS, Puducherry. The sample size was 172 based on the previous study, and simple random sampling technique was used. The women who were receiving hormonal treatment, having chronic illness and those who refused to participate were excluded from the study. The sociodemographic information and menopause-specific QOL questionnaire were used, and data were analyzed using the SPSS 21. Results: In this study, mean age of the postmenopausal women was 61 ± 7.5 years, 42.4% belonged to socioeconomic status (SES) Class IV. The prevalence of one or more symptoms of vasomotor, psychological, and sexual domains were 23.8%, 87%, and 68%, respectively. We found an association between SES and both vasomotor and psychological symptoms. Furthermore, age and psychological symptoms were associated. Conclusion: The menopause-related symptoms had a negative effect on the QOL of postmenopausal women. The study can help in creating awareness and also in helping in educating women for early identification of the frequent menopausal symptoms.
Physical Violence Against Doctors: A Content Analysis from Online Indian Newspapers

Sir, Violence against doctors, which are frequently reported in newspapers, has intimidatingly increased all over the world including India. As per the World Health Organization (WHO), health workers suffer from physical violence between 8% and 38% at some point of time in their careers. [1] The Indian Medical Association report (2015) says more than 75% of doctors had faced violence at work. [2] In spite of various laws and safety measures, continuing attacks on doctors pose a great threat to the health-care system of the community and yet remains under-reported. Since media helps to shape the understanding of the general public on a given issue, a descriptive content analysis of news on physical violence against doctor was done. [3]
Investigation of cooking oil quality at fast food restaurants in Mashhad City

Aims: Deep frying of foods at high temperatures results in an increase in the unique sensorial properties of fried food including fried flavor, golden brown color, and crispy texture, which ultimately leads to changes in the physical and chemical properties of the oil. This study was conducted to evaluate the quality of oils used in 60 centers of fast food distribution in Mashhad city. Materials and Methods: A validated questionnaire based on the instruction from the Ministry of Health and Medical Sciences was used to collect the data and to investigate the health status of the places and foods. Total polar material (TPM), temperature, and acidity of the oil were measured using oil test portable device (DOM-24 model) while the peroxide value (PV) was measured by iodometry method. The data were analyzed by SPSS software V.16 via statistical tests including t-test, ANOVA, and Chi-squared tests.Results: The mean values of TPM, acidity and PV in the studied oils were 31.8 ± 7.2 %, 3.7 ± 0.8, 4.7 ± 6.7 meq/kg, respectively. There was a significant relationship between TPM value with acidity, temperature and duration of oil use based on Pearson correlation test (P > 0.005). Furthermore, there was a significant relationship between the PV and area, the type of oil, and sanitation of instruments. Conclusion: The levels of hydrogen peroxide, polarity, and acidity of oils in fast food shops were higher than the guideline values. We recommend that health inspectors systematically monitor the oils fried in these centers.
Determinants of Sugar-Sweetened Beverage Consumption among Indian Adults: Findings from the National Family Health Survey-4

Background: Sugar-sweetened beverages (SSBs) are associated with obesity, and various other noncommunicable diseases (NCDs). The aim of the study was to study the patterns of consumption of SSBs and association of SSB consumption with various socioeconomic factors and fried food consumption. Methodology: We used data of the 4th round of National Family Health Survey. We used multiple logistic regression to estimate the extent of the relationship between consumption of aerated drinks and various predictors. Furthermore, generalized structural equation modeling (GSEM) was used to derive a path diagram that showed a significant linkage between aerated drinks and observed variables. Results: Our study showed a clear association between consumption of aerated drinks with socioeconomic variables age, sex, marital status, and wealth index. The consumption of aerated drinks was also significantly associated with watching television and eating fried foods. Conclusion: Aerated drinks are a popular source of added sugar in the Indian diet. Limiting such factors can prove to be beneficial in reducing their consumption and further help in reducing the burden of NCDs.
Linguistic Analysis of Statements Concerning Paintings Viewed Under Different Instructions by Experts and Novices in the Visual Arts

Abstract This research aimed to perform linguistic analysis of the statements of experts and novices in the arts concerning figurative paintings from the 16th to 19th century of different aesthetic value under different instructions. The experts were selected based on a formal criterion of education in visual arts. Based on previous research, the paintings were divided into three groups: beautiful, not beautiful and controversial. The participants viewed them from different points of view defined by seven instructions. The Linguistic Inquiry Word Count (LIWC) was used to measure the connotation of statements in emotional and cognitive terms. Hypotheses, according to which the statements of novices are marked more with emotional, and those of experts more with cognitive processes, were only partially confirmed. It turned out that the emotional or cognitive connotation of statements concerning paintings is mostly modified by the point from which they are viewed and their aesthetic value.
Spleen Derived Immune Cells in Acute Ischemic Brain Injury: A Short Review

Abstract Spleen-derived immune cells are considered to play central role in the progression of ischemic brain damage contributing to both the local and systemic inflammatory response initiated by an ischemic insult in the brain tissue. Brain-spleen communication in acute ischemic brain injury has been studied especially in rodent models of stroke, which mimic the acute focal brain ischemia in humans. Rodent spleens decrease in size after experimentally induced stroke, due mainly by the release of spleen`s immune-cells into the circulation. Splenectomy prior to middle cerebral artery occlusion is protective to the ischemic brain resulting in decreased infarct volume and reduced neuroinflammation. Various therapeutic strategies in clinical use aiming to protect the neural tissue after stroke were found to involve the modulation of splenic activity, altogether indicating that the spleen might be a potential target for therapy in ischemic brain injury. Importantly, the most clinical studies demonstrated that the splenic response in stroke patients is similar to the changes seen in rodent models. Thus, despite the limitations to extrapolate the results of animal experiments to humans, rodent models of stroke represent an important tool for the study and understanding of brain-spleen communication in the pathogenesis of acute brain ischemia.