Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on the constituents of Ailanthus altissima Swingle. III: The alkaloidal constituents

1-Acetyl-4-methoxy-β-carboline (I), canthin-6-one (VIIIa), 1-methoxycanthin-6-one (VIIIb), and canthin-6-one-3N-oxide (IXa), and three new alkaloids, 1-(2'-hydroxyethyl)-4-methoxy-β-carboline (IIa), 1-(1', 2'-dihydroxyethyl)-4-methoxy-β-carboline (IIIa), and 1-methoxycanthin-6-one-3N-oxide (IXb), were isolated from the root bark of Ailanthus altissima SWINGLE (Simaroubaceae). The structures of IIa, IIIa, and IXb were elucidated by spectroscopic and chemical studies.
Geometric robust descriptor for 3D point cloud

—We propose rotation robust and density robust local geometric descriptor. Local geometric feature of point cloud is used in many applications, for example, to ﬁnd correspondences in 3D registration and to segment local regions. Usually, application accuracy depends on the discriminative power of the local geometric features. However, there are some problems such as point sparsity, rotated point cloud, and so on. In this paper, we present new local feature generation method to make a rotation robust and density robust descriptor. First, we place kernels aligned around each point and align them to the normal of the point. To avoid the sign problem of the normal vector, we use symmetric kernel point distribution with respect to the tangent plane. Next, from each kernel point, we estimate geometric information which is rotation robust and discriminative. Finally, we operate convolution process with consideration of kernel point structure, and aggregate all kernel features. We experiment our local descriptors on the ModelNet40 dataset [1] for registration and classiﬁcation, and the ShapeNet part dataset [2] for segmentation. Our descriptor shows discriminative power regardless of point distribution.
Hybrid Beamforming for Millimeter Wave Full-Duplex Under Limited Receive Dynamic Range

Full-duplex millimeter wave (mmWave) communication has shown increasing promise for self-interference cancellation via hybrid precoding and combining. This paper proposes a novel mmWave multiple-input multiple-output (MIMO) design for configuring the analog and digital beamformers of a full-duplex transceiver. This work is the first to holistically consider the key practical constraints of analog beamforming codebooks, a minimal number of radio frequency (RF) chains, limited channel knowledge, beam alignment, and a limited receive dynamic range. To prevent self-interference from saturating receive components, such as LNAs and ADCs, a design framework is developed that limits the degree of self-interference on a per-antenna and per-RF chain basis. We present a means for constructing analog beamforming candidates from beam alignment measurements to afford our design greater flexibility in its aim to reduce self-interference. Numerical results evaluate the design in a variety of settings and validate the need to prevent receiver-side saturation. These results and corresponding insights serve as useful design references and benchmarks for practical full-duplex mmWave transceivers.
Knowledge Graphs Evolution and Preservation - A Technical Report from ISWS 2019

One of the grand challenges discussed during the Dagstuhl Seminar"Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web"and described in its report is that of a:"Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [. ] This grand challenge extends this further by asking if we can create a knowledge graph of"everything"ranging from common sense concepts to location based entities. This knowledge graph should be"open to the public"in a FAIR manner democratizing this mass amount of knowledge. "Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution.
MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning

The obfuscated images on envelopes are believed to be secure and have been widely used to protect the information contained in a mail. In this paper, we present a new algorithm that can conduct character recognition from obfuscated images. Specifically, by using a transfer learning method, we prove that an attacker can effectively recognize the letter without unfolding the envelope. We believe that the presented method reveals the potential threat to current postal services. To defend against the proposed attack, we introduce a context-related shader to prevent such threats from occurring.
The impact of solar wind variability on pulsar timing

Context. High-precision pulsar timing requires accurate corrections for dispersive delays of radio waves, parametrized by the dispersion measure (DM), particularly if these delays are variable in time. In a previous paper, we studied the solar wind (SW) models used in pulsar timing to mitigate the excess of DM that is annually induced by the SW and found these to be insufficient for high-precision pulsar timing. Here we analyze additional pulsar datasets to further investigate which aspects of the SW models currently used in pulsar timing can be readily improved, and at what levels of timing precision SW mitigation is possible. Aims. Our goals are to verify: (a) whether the data are better described by a spherical model of the SW with a time-variable amplitude, rather than a time-invariant one as suggested in literature, and (b) whether a temporal trend of such a model’s amplitudes can be detected. Methods. We use the pulsar timing technique on low-frequency pulsar observations to estimate the DM and quantify how this value changes as the Earth moves around the Sun. Specifically, we monitor the DM in weekly to monthly observations of 14 pulsars taken with parts of the LOw-Frequency ARray (LOFAR) across time spans of up to 6 years. We develop an informed algorithm to separate the interstellar variations in the DM from those caused by the SW and demonstrate the functionality of this algorithm with extensive simulations. Assuming a spherically symmetric model for the SW density, we derive the amplitude of this model for each year of observations. Results. We show that a spherical model with a time-variable amplitude models the observations better than a spherical model with a constant amplitude, but that both approaches leave significant SW-induced delays uncorrected in a number of pulsars in the sample. The amplitude of the spherical model is found to be variable in time, as opposed to what has been previously suggested.
High temperature metamaterial terahertz quantum detector

We demonstrate a high temperature performance quantum detector of Terahertz (THz) radiation based on three-dimensional metamaterial. The metamaterial unit cell consists of an inductor-capacitor (LC) resonator laterally coupled with antenna elements. The absorbing region, consisting of semiconductor quantum wells is contained in the strongly ultra-subwavelength capacitors of the LC structure. The high radiation loss of the antenna allows strongly increased collection efficiency for the incident THz radiation, while the small effective volume of the LC resonator allows intense light-matter coupling with reduced electrical area. As a result, our detectors operates at much higher temperatures than conventional quantum well detectors demonstrated so far.
Reducing solid-state drive read latency by optimizing read-retry

3D NAND flash memory with advanced multi-level cell techniques provides high storage density, but suffers from significant performance degradation due to a large number of read-retry operations. Although the read-retry mechanism is essential to ensuring the reliability of modern NAND flash memory, it can significantly in-crease the read latency of an SSD by introducing multiple retry steps that read the target page again with adjusted read-reference voltage values. Through a detailed analysis of the read mechanism and rigorous characterization of 160 real 3D NAND flash memory chips, we find new opportunities to reduce the read-retry latency by exploiting two advanced features widely adopted in modern NAND flash-based SSDs: 1) the CACHE READ command and 2) strong ECC engine. First, we can reduce the read-retry latency using the advanced CACHE READ command that allows a NAND flash chip to perform consecutive reads in a pipelined manner. Second, there exists a large ECC-capability margin in the final retry step that can be used for reducing the chip-level read latency. Based on our new findings, we develop two new techniques that effectively reduce the read-retry latency: 1) Pipelined Read-Retry (PR²) and 2) Adaptive Read-Retry (AR²). PR² reduces the latency of a read-retry operation by pipelining consecutive retry steps using the CACHE READ command. AR² shortens the latency of each retry step by dynamically reducing the chip-level read latency depending on the current operating conditions that determine the ECC-capability margin. Our evaluation using twelve real-world workloads shows that our proposal improves SSD response time by up to 31.5% (17% on average)over a state-of-the-art baseline with only small changes to the SSD controller.
Domain Adaptation of NMT models for English-Hindi Machine Translation Task at AdapMT ICON 2020

Recent advancements in Neural Machine Translation (NMT) models have proved to produce a state of the art results on machine translation for low resource Indian languages. This paper describes the neural machine translation systems for the English-Hindi language presented in AdapMT Shared Task ICON 2020. The shared task aims to build a translation system for Indian languages in specific domains like Artificial Intelligence (AI) and Chemistry using a small in-domain parallel corpus. We evaluated the effectiveness of two popular NMT models i.e, LSTM, and Transformer architectures for the English-Hindi machine translation task based on BLEU scores. We train these models primarily using the out of domain data and employ simple domain adaptation techniques based on the characteristics of the in-domain dataset. The fine-tuning and mixed-domain data approaches are used for domain adaptation. Our team was ranked first in the chemistry and general domain En-Hi translation task and second in the AI domain En-Hi translation task.
Underwater image filtering: methods, datasets and evaluation

Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhance the appearance of objects captured in an underwater image. Restoration methods compensate for the actual degradation, whereas enhancement methods improve either the perceived image quality or the performance of computer vision algorithms. The growing interest in underwater image filtering methods--including learning-based approaches used for both restoration and enhancement--and the associated challenges call for a comprehensive review of the state of the art. In this paper, we review the design principles of filtering methods and revisit the oceanology background that is fundamental to identify the degradation causes. We discuss image formation models and the results of restoration methods in various water types. Furthermore, we present task-dependent enhancement methods and categorise datasets for training neural networks and for method evaluation. Finally, we discuss evaluation strategies, including subjective tests and quality assessment measures. We complement this survey with a platform ( https://puiqe.eecs.qmul.ac.uk/ ), which hosts state-of-the-art underwater filtering methods and facilitates comparisons.
OH in the diffuse interstellar medium: physical modelling and prospects with upcoming SKA precursor/pathfinder surveys

Hydroxyl ($\rm OH$) is known to form efficiently in cold gas ($T\sim 100$K) along with the molecule $\rm H_2$ and can be used as an efficient tracer of the diffuse molecular gas in the interstellar medium (ISM). Using a simple formalism describing the $\rm H\,I/H_2$ transition and a reduced network of major chemical reactions, we present a semi-analytical prescription to estimate the abundances of O-bearing molecules in the diffuse ISM. We show that predictions based on our prescription are in good agreement with the estimates obtained using the MEUDON PDR code which utilizes the full reaction network. We investigate the dependence of the relative abundances of $\rm OH/H\,I$ and $\rm OH/H_2$ on the variations of physical conditions i.e., the metallicity, number density ($n$), cosmic ray ionization rate ($\zeta$) and strength of UV field ($\chi$) in the medium. We find that the $\rm OH/H\,I$ abundances observed in the Galactic ISM can be reproduced by models with $n\sim 50$cm$^{-3}$, $\chi\sim 1$ (Mathis field) and $\zeta\sim3\times10^{-17}$s$^{-1}$, with a variation of about one dex allowed around these values. Using the constrained $\rm H_2$ column density distribution function at $z\sim3$, we estimate the $\rm OH$ column density distribution function and discuss future prospects with the upcoming large radio absorption line surveys.
MyGym: Modular Toolkit for Visuomotor Robotic Tasks

We introduce myGym, a toolkit suitable for fast prototyping of neural networks in the area of robotic manipulation and navigation. Our toolbox is fully modular, enabling users to train their algorithms on different robots, environments, and tasks. We also include pretrained neural network modules for the real-time vision that allows training visuomotor tasks with sim2real transfer. The visual modules can be easily retrained using the dataset generation pipeline with domain augmentation and randomization. Moreover, myGym provides automatic evaluation methods and baselines that help the user to directly compare their trained model with the state-of-the-art algorithms. We additionally present a novel metric, called learnability, to compare the general learning capability of algorithms in different settings, where the complexity of the environment, robot, and the task is systematically manipulated. The learnability score tracks differences between the performance of algorithms in increasingly challenging setup conditions, and thus allows the user to compare different models in a more systematic fashion. The code is accessible at https://github.com/incognite-lab/myGym
Ice front shaping by upward convective current

Ziqi Wang, Linfeng Jiang, Yihong Du, Chao Sun, 2, ∗ and Enrico Calzavarini † Center for Combustion Energy, Key laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Energy and Power Engineering, Tsinghua University, Beijing 100084, China Department of Engineering Mechanics, School of Aerospace Engineering, Tsinghua University, Beijing 100084, China Univ. Lille, Unité de Mécanique de Lille J. Boussinesq UML ULR 7512, F-59000 Lille, France (Dated: August 31, 2021)
Off-Policy Optimization of Portfolio Allocation Policies under Constraints

The dynamic portfolio optimization problem in finance frequently requires learning policies that adhere to various constraints, driven by investor preferences and risk. We motivate this problem of finding an allocation policy within a sequential decision making framework and study the effects of: (a) using data collected under previously employed policies, which may be sub-optimal and constraint-violating, and (b) imposing desired constraints while computing near-optimal policies with this data. Our framework relies on solving a minimax objective, where one player evaluates policies via off-policy estimators, and the opponent uses an online learning strategy to control constraint violations. We extensively investigate various choices for off-policy estimation and their corresponding optimization sub-routines, and quantify their impact on computing constraint-aware allocation policies. Our study shows promising results for constructing such policies when back-tested on historical equities data, under various regimes of operation, dimensionality and constraints.
A Couple of Transcendental Prime-Representing Constants

Abstract It is well known that the arithmetic nature of Mills’ prime-representing constant is uncertain: we do not know if Mills’ constant is a rational or irrational number. In the case of other prime-representing constants, irrationality can be proved, but it is not known whether these constants are algebraic or transcendental numbers. By using Liouville or Roth’s theorems about approximation by rationals, we find a couple of prime-representing constants that can be proved to be transcendental numbers.
The GALAH+ Survey: A New Library of Observed Stellar Spectra Improves Radial Velocities and Reveals Motions within M67

GALAH+ is a magnitude-limited survey of high resolution stellar spectra obtained by the HERMES spectrograph at the Australian Astronomical Observatory. Its third data release provides reduced spectra with new derivations of stellar parameters and abundances of 30 chemical elements for 584,015 dwarfs and giants, 88% of them in the Gaia magnitude range 11 < < 14. Here we use these improved values of stellar parameters to build a library of observed spectra which is useful to study variations of individual spectral lines with stellar parameters. This and other improvements are used to derive radial velocities with uncertainties which are generally within 0.1 km s−1 or ∼ 25% smaller than in the previous release. The median difference to radial velocities from Gaia DR2 for the 225,082 spectra of matching objects equals +0.009 km s−1 for dwarfs and −0.122 km s−1 for giants. We identify 4483 stars with intrinsically variable velocities and 225 stars for which the velocity stays constant over > 3 visits spanning more than a year. The combination of radial velocities fromGALAH+with distances and sky plane motions from Gaia enables studies of dynamics within streams and clusters. For example, we show that the open cluster M67 contracts at a rate of ∼ 0.36 km s−1. Such a breathing motion is likely a consequence of a perturbation during the cluster’s last passage through the Galactic disc ∼ 40 Myr ago.
Precision Tests of the Standard Model

This write-up of lectures given at TASI 2020 provides an introduction into precision tests of the electroweak Standard Model. The lecture notes begin with a hands-on review of the (on-shell) renormalization procedure, and subsequently highlight a few subtleties that occur in the renormalization of a theory with electroweak symmetry breaking and massive gauge bosons. After that a set of typical electroweak precision observables is introduced, as well as a range of input parameter measurements that are needed for making predictions within the Standard Model. Finally, it is discussed how comparisons of the electroweak precision observables between experiment and theory can be used to stress-test the Standard Model and probe new physics.
Multiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data

Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this article is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="koh-ieq1-3045154.gif"/></alternatives></inline-formula>-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning

The anticipated increase in the count of IoT devices in the coming years motivates the development of efficient algorithms that can help in their effective management while keeping the power consumption low. In this paper, we propose an intelligent multi-channel resource allocation algorithm for dense LoRa networks termed LoRaDRL and provide a detailed performance evaluation. Our results demonstrate that the proposed algorithm not only significantly improves LoRaWAN's packet delivery ratio (PDR) but is also able to support mobile end-devices (EDs) while ensuring lower power consumption hence increasing both the lifetime and capacity of the network. } Most previous works focus on proposing different MAC protocols for improving the network capacity, i.e., LoRaWAN, delay before transmit etc. We show that through the use of LoRaDRL, we can achieve the same efficiency with ALOHA \textcolor{black}{compared to LoRaSim, and LoRa-MAB while moving the complexity from EDs to the gateway thus making the EDs simpler and cheaper. Furthermore, we test the performance of LoRaDRL under large-scale frequency jamming attacks and show its adaptiveness to the changes in the environment. We show that LoRaDRL's output improves the performance of state-of-the-art techniques resulting in some cases an improvement of more than 500\% in terms of PDR compared to learning-based techniques.
Modelling Human Routines: Conceptualising Social Practice Theory for Agent-Based Simulation

Our routines play an important role in a wide range of social challenges such as climate change, disease outbreaks and coordinating staff and patients in a hospital. To use agent-based simulations (ABS) to understand the role of routines in social challenges we need an agent framework that integrates routines. This paper provides the domain-independent Social Practice Agent (SoPrA) framework that satisfies requirements from the literature to simulate our routines. By choosing the appropriate concepts from the literature on agent theory, social psychology and social practice theory we ensure SoPrA correctly depicts current evidence on routines. By creating a consistent, modular and parsimonious framework suitable for multiple domains we enhance the usability of SoPrA. SoPrA provides ABS researchers with a conceptual, formal and computational framework to simulate routines and gain new insights into social systems.
Analytical Solutions of the Schrodinger Equation for Hua Potential within the Framework of two Approximations Scheme

In this paper, we solve analytically the Schrodinger equation for s-wave and arbitrary angular momenta with the Hua potential is investigated respectively. The wave function as well as energy equation are obtained in an exact analytical manner via the Nikiforov Uvarov method using two approximations scheme. Some special cases of this potentials are also studied.