Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Safe Limits for Significant Task Parameters during Manual Lifting

This experimental study investigated the effect of lifting task parameters (i.e., lifting weight, frequency, coupling, asymmetric angle, and vertical, horizontal, and travel distances) for various dynamic human lifting activities on the ground reaction forces of workers. Ten male workers loaded containers from different levels asymmetrically during experimental trials. The experimental design evolved using Taguchi's Fractional Factorial Experiments. Three factors (lifting weight, frequency, and vertical distance) were observed to be significant. The results showed that vertical reaction forces increase when workers lift weight from floor to shoulder height frequently. It was also observed that instantaneous loading rate increases with more weight, vertical distance, and frequency; a significant extra loading rate is required to change the lower level of load, frequency, and vertical distance to higher levels. Safe limits for significant factors were determined to result in optimal performance of the manual lifting task.
Stabilizing non-trivial solutions of the generalized Kuramoto–Sivashinsky equation using feedback and optimal control This paper is a contribution by a finalist of the Lighthill-Thwaites 2015 Competition

The problem of controlling and stabilizing solutions to the Kuramoto–Sivashinsky (KS) equation is studied in this paper. We consider a generalized form of the equation in which the effects of an electric field and dispersion are included. Both the feedback and optimal control problems are studied. We prove that we can control arbitrary non-trivial steady states of the KS equation, including travelling wave solutions, using a finite number of point actuators. The number of point actuators needed is related to the number of unstable modes of the equation. Furthermore, the proposed control methodology is shown to be robust with respect to changing the parameters in the equation, e.g. the viscosity coefficient or the intensity of the electric field. We also study the problem of controlling solutions of coupled systems of KS equations. Possible applications to controlling thin film flows are discussed. Our rigorous results are supported by extensive numerical simulations.
Quantum anomalous Hall insulator of composite fermions in twisted bilayer graphene

Abstract We theoretically study the realization of quantum anomalous Hall insulator (QAHI) of composite fermions (CFs) in the twisted bilayer graphene (TBG) system. We show that the moir\'e pattern in TBG is not only able to provide a commensurate moir\'e superlattice, but also a tunable effective periodic potential necessary for the realization, without the need of imposing an additional superstructure as in the conventional GaAs system. These make the TBG an ideal platform for realizing the QAHI of CFs. We establish the phase diagram with respect to tunable experimental parameters based on the Dirac CF theory. We find that the topological property of the system depends critically on the orbital magnetic susceptibility of CFs, which is not specified in the pristine Dirac CF theory. The experimental realization of QAHI of CFs would be helpful for unveiling the magnetic property of CFs and clarifying the issue.
Multiplication operators onL.Lp/ and 'p-strictly singular operators

A classification of weakly compact multiplication operators onL.Lp/, 1 < p <1, is given. This answers a question raised by Saksman and Tylli in 1992. The classification involves the concept of'p-strictly singular operators, and we also investigate the structure of general'p-strictly singular operators onLp. The main result is that if an operatorT onLp, 1 < p < 2, is'p-strictly singular and TjX is an isomorphism for some subspace X of Lp, then X embeds into Lr for all r < 2, butX need not be isomorphic to a Hilbert space. It is also shown that ifT is convolution by a biased coin onLp of the Cantor group, 1 p < 2, andTjX is an isomorphism for some reflexive subspaceX ofLp, thenX is isomorphic to a Hilbert space. The casepD 1 answers a question asked by Rosenthal in 1976.
DistinguishingnHamiltonians onCnby a single measurement

If an experimentalist wants to decide which one of n possible Hamiltonians acting on an n dimensional Hilbert space is present, he can conjugate the time evolution by an appropriate sequence of known unitary transformations in such a way that the different Hamiltonians result in mutual orthogonal final states. We present a general scheme providing such a sequence.
Slow-blue nuclear hypervariables in PanSTARRS-1.

We discuss 76 large amplitude transients (Δm > 1.5) occurring in the nuclei of galaxies, nearly all with no previously known active galactic nucleus (AGN). They have been discovered as part of the Pan-STARRS1 (PS1) 3π survey, by comparison with Sloan Digital Sky Survey (SDSS) photometry a decade earlier, and then monitored with the Liverpool Telescope, and studied spectroscopically with the William Herschel Telescope (WHT). Based on colours, light-curve shape, and spectra, these transients fall into four groups. A few are misclassified stars or objects of unknown type. Some are red/fast transients and are known or likely nuclear supernovae. A few are either radio sources or erratic variables and so likely blazars. However the majority (∼66 per cent) are blue and evolve slowly, on a time-scale of years. Spectroscopy shows them to be AGN at z ∼ 0.3 − 1.4, which must have brightened since the SDSS photometry by around an order of magnitude. It is likely that these objects were in fact AGN a decade ago, but too weak to be recognized by SDSS; they could then be classed as ‘hypervariable’ AGN. By searching the SDSS Stripe 82 quasar database, we find 15 similar objects. We discuss several possible explanations for these slow-blue hypervariables – (i) unusually luminous tidal disruption events; (ii) extinction events; (iii) changes in accretion state; and (iv) large amplitude microlensing by stars in foreground galaxies. A mixture of explanations (iii) and (iv) seems most likely. Both hold promise of considerable new insight into the AGN phenomenon.
Rauvolfia serpentina L. Benth. ex Kurz.: Phytochemical, Pharmacological and Therapeutic Aspects

Rauvolfia serpentina is an important medicinal plant in the pharmaceutical world due to the presence of its immense therapeutic properties. The plant is known for curing various disorders because of the presence of alkaloids, carbohydrates, flavonoids, glycosides, phlobatannins, phenols, resins, saponins sterols, tannins and terpenes. The plant parts, root and rhizome have been used since centuries in Ayurvedic medicines for curing a large number of diseases such as high blood pressure, mental agitation, epilepsy, traumas, anxiety, excitement, schizophrenia, sedative insomnia and insanity. The plant contains more than 50 different alkaloids which belong to the monoterpenoid indole alkaloid family. The major alkaloids are ajmaline, ajmalicine, ajmalimine, deserpidine, indobine, indobinine, reserpine, reserpiline, rescinnamine, rescinnamidine, serpentine, serpentinine and yohimbine. R. serpentina is also known for its antimicrobial, antifungal, anti-inflammatory, antiproliferative, antidiuretic and anticholinergic activities. The herbal medicine is still the basis of primary health care for 75–80% of the world population because of its cultural acceptability, better compatibility with the human body and lesser side effects. Therefore, there is a need for us to search alternative, naturally available remedies for curing million’s of people worldwide. Due to all these properties, the present review aims to evaluate the various pharmacological, phytochemical and therapeutic properties of R. serpentina.
Reprint of 1977A&A....57..383Z. Tidal friction in close binary stars.

We examine various physical mechanisms which may produce tidal friction in close binary stars. We find that the most efficient in stars with convective envelopes is turbulent viscosity retarding the equilibrium tide, and in stars with radiative envelopes the action of radiative damping on the dynamical tide. Theoretical predictions based on these dissipative processes are in good agreement with the rotational velocities and orbital eccentricities observed in close binaries. The results are applied to the X-ray binaries Her X-1 and Cen X-3.
Prokaryote communities associated with different types of tissue formed and substrates inhabited by Serpula lacrymans

The basidiomycete Serpula lacrymans is responsible for timber destruction in houses. Basidiomycetes are known to harbor a diverse but poorly understood microbial community of bacteria, archaea, yeasts, and filamentous fungi in their fruiting bodies. In this study, we used amplicon-sequencing to analyze the abundance and composition of prokaryotic communities associated with fruiting bodies of S. lacrymans and compared them to communities of surrounding material to access the ‘background’ community structure. Our findings indicate that bacterial genera cluster depended on sample type, and that the main driver for microbial diversity is specimen, followed by sample origin. The most abundant bacterial phylum identified in the fruiting bodies was Pseudomonadota, followed by Actinomycetota and Bacteroidota. The prokaryote community of the mycelium was dominated by Actinomycetota, Halobacterota, and Pseudomonadota. Actinomycetota was the most abundant phylum in both environment samples (infested timber and underground scree), followed by Bacillota in wood and Pseudomonadota in underground scree. Nocardioides, Pseudomonas, Pseudonochardia, Streptomyces and Rubrobacter spp. were among others found to comprise the core microbiome of S. lacrymans basidiocarps. This research contributes to the understanding of the holobiont S. lacrymans and gives hints to potential bacterial phyla important for its development and life style. Highlights - The prokaryote communities associated with S. lacrymans mycelia and fruiting bodies as well as wood and non-woody substrate form distinct clusters. - Across all samples 30% of OTU’s were shared (core microbiome) while the number of unique OTUs was small. - Fruiting bodies (n= 8) of S. lacrymans shared a core set of 365 OTU’s, dominated by Actinobacteriodota (44%), Pseudomonadota (28%), and Acidobacteriodota (9%). - Tissue/sample type is the main factor influencing diversity, followed by sample origin.
Coherent J/ψ photoproduction in ultra-peripheral PbPb collisions at √s_NN = 2.76 TeV with the CMS experiment

The cross section for coherent J/ψ photoproduction accompanied by at least one neutron on one side of the interaction point and no neutron activity on the other side, X_(n)0_(n), is measured with the CMS experiment in ultra-peripheral PbPb collisions at √s_NN = 2.76 TeV. The analysis is based on a data sample corresponding to an integrated luminosity of 159 μb^(−1), collected during the 2011 PbPb run. The J/ψ mesons are reconstructed in the dimuon decay channel, while neutrons are detected using zero degree calorimeters. The measured cross section is dσ^(coh)_(X_(n)0_(n))/dy(J/ψ) = 0.36 ± 0.04 (stat) ± 0.04 (syst) mb in the rapidity interval 1.8 < |y| < 2.3. Using a model for the relative rate of coherent photoproduction processes, this X_(n)0_(n) measurement gives a total coherent photoproduction cross section of dσ^(coh)/dy(J/ψ) = 1.82 ± 0.22 (stat) ± 0.20 (syst) ± 0.19 (theo) mb. The data strongly disfavor the impulse approximation model prediction, indicating that nuclear effects are needed to describe coherent J/ψ photoproduction in γ+Pb interactions. The data are found to be consistent with the leading twist approximation, which includes nuclear gluon shadowing.
Erratum: Secondary-electron radiation accompanying hadronic GeV-TeV gamma-rays from supernova remnants

The synchrotron radiation from secondary electrons and positrons (SEPs) generated by hadronic interactions in the shock of supernova remnant (SNR) could be a distinct evidence of cosmic ray (CR) production in SNR shocks. Here, we provide a method where the observed gamma-ray flux from SNRs, created by pion decays, is directly used to derive the SEP distribution and hence the synchrotron spectrum. We apply the method to three gamma-ray bright SNRs. In the young SNR RX J1713.7−3946, if the observed GeV−TeV gamma-rays are of hadronic origin and the magnetic field in the SNR shock is B ≳ 0.5 mG, the SEPs may produce a spectral bump at 10−5–10−2 eV, exceeding the predicted synchrotron component of the leptonic model, and a soft spectral tail at ≳100 keV, distinct from the hard spectral slope in the leptonic model. In the middle-aged SNRs IC443 and W44, if the observed gamma-rays are of hadronic origin, the SEP synchrotron radiation with B ∼ 400–500 μG can well account for the observed radio flux and spectral slopes, supporting the hadronic origin of gamma-rays. Future microwave to far-infrared and hard X-ray (>100keV) observations are encouraged to constraining the SEP radiation and the gamma-ray origin in SNRs.
Mel-spectrogram augmentation for sequence to sequence voice conversion

For training the sequence-to-sequence voice conversion model, we need to handle an issue of insufficient data about the number of speech pairs which consist of the same utterance. This study experimentally investigated the effects of Mel-spectrogram augmentation on training the sequence-to-sequence voice conversion (VC) model from scratch. For Mel-spectrogram augmentation, we adopted the policies proposed in SpecAugment. In addition, we proposed new policies (i.e., frequency warping, loudness and time length control) for more data variations. Moreover, to find the appropriate hyperparameters of augmentation policies without training the VC model, we proposed hyperparameter search strategy and the new metric for reducing experimental cost, namely deformation per deteriorating ratio. We compared the effect of these Mel-spectrogram augmentation methods based on various sizes of training set and augmentation policies. In the experimental results, the time axis warping based policies (i.e., time length control and time warping.) showed better performance than other policies. These results indicate that the use of the Mel-spectrogram augmentation is more beneficial for training the VC model.
DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks

Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) networks horizontally to capture instantaneous, periodic and seasonal patterns in KPI time-series. It further merge with feed-forward networks to learn the impact of network configurations and other external factors. We validate the approach by predicting two important KPIs, including cell load and radio channel quality, using large-scale real network streaming measurement data from the operator. For cell load prediction, DeepAuto model showed up to 15% improvement in Root Mean Square Error (RMSE) compared to naive method of using recent measurements for short-term horizon and up to 32% improvement for longer-term prediction.
Causal Impact of Web Browsing and Other Factors on Research Publications

In this paper, we study the causal impact of the web-search activity on the research publication. We considered observational prospective study design, where research activity of 267 scientists is being studied. We considered the Poisson and negative binomial regression model for our analysis. Based on the Akaike's Model selection criterion, we found the negative binomial regression performs better than the Poisson regression. Detailed analysis indicates that the higher web-search activity of 2016 related to the sci-indexed website has a positive significant impact on the research publication of 2017. We observed that unique collaborations of 2016 and web-search activity of 2016 have a non-linear but significant positive impact on the research publication of 2017. What-if analysis indicates the high web browsing activity leads to more number of the publication. However, interestingly we see a scientist with low web activity can be as productive as others if her/his maximum hits are the sci-indexed journal. That is if the scientist uses web browsing only for research-related activity, then she/he can be equally productive even if her/his web activity is lower than fellow scientists.
Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis

This paper considers the problem of estimating the principal eigenvector of a covariance matrix from independent and identically distributed data samples in streaming settings. The streaming rate of data in many contemporary applications can be high enough that a single processor cannot finish an iteration of existing methods for eigenvector estimation before a new sample arrives. This paper formulates and analyzes a distributed variant of the classical Krasulina's method (D-Krasulina) that can keep up with the high streaming rate of data by distributing the computational load across multiple processing nodes. The analysis shows that---under appropriate conditions---D-Krasulina converges to the principal eigenvector in an order-wise optimal manner; i.e., after receiving $M$ samples across all nodes, its estimation error can be $O(1/M)$. In order to reduce the network communication overhead, the paper also develops and analyzes a mini-batch extension of D-Krasulina, which is termed DM-Krasulina. The analysis of DM-Krasulina shows that it can also achieve order-optimal estimation error rates under appropriate conditions, even when some samples have to be discarded within the network due to communication latency. Finally, experiments are performed over synthetic and real-world data to validate the convergence behaviors of D-Krasulina and DM-Krasulina in high-rate streaming settings.
Decay of cosmic global string loops

We numerically study the decay of cosmic global string loops due to radiation of Goldstone bosons and massive scalar ($\chi$) particles. The length of loops we study range from 200-1000 times the width of the string core. We find that the lifetime of a loop is $\approx 1.4 L$. The energy spectrum of Goldstone boson radiation has a $k^{-1}$ fall off, where $k$ is the wavenumber, and a sharp peak at $k \approx m_\chi/2$, where $m_\chi$ is the mass of $\chi$. The latter is a new feature and implies a peak at high energies (MeV-GeV) in the cosmological distribution of QCD axions.
Learned Image Compression With Discretized Gaussian Mixture Likelihoods and Attention Modules

Image compression is a fundamental research field and many well-known compression standards have been developed for many decades. Recently, learned compression methods exhibit a fast development trend with promising results. However, there is still a performance gap between learned compression algorithms and reigning compression standards, especially in terms of widely used PSNR metric. In this paper, we explore the remaining redundancy of recent learned compression algorithms. We have found accurate entropy models for rate estimation largely affect the optimization of network parameters and thus affect the rate-distortion performance. Therefore, in this paper, we propose to use discretized Gaussian Mixture Likelihoods to parameterize the distributions of latent codes, which can achieve a more accurate and flexible entropy model. Besides, we take advantage of recent attention modules and incorporate them into network architecture to enhance the performance. Experimental results demonstrate our proposed method achieves a state-of-the-art performance compared to existing learned compression methods on both Kodak and high-resolution datasets. To our knowledge our approach is the first work to achieve comparable performance with latest compression standard Versatile Video Coding (VVC) regarding PSNR. More importantly, our approach generates more visually pleasant results when optimized by MS-SSIM.
Evolution Strategies Converges to Finite Differences

Since the debut of Evolution Strategies (ES) as a tool for Reinforcement Learning by Salimans et al. 2017, there has been interest in determining the exact relationship between the Evolution Strategies gradient and the gradient of a similar class of algorithms, Finite Differences (FD). (Zhang et al. 2017, Lehman et al. 2018) Several investigations into the subject have been performed, investigating the formal motivational differences(Lehman et al. 2018) between ES and FD, as well as the differences in a standard benchmark problem in Machine Learning, the MNIST classification problem(Zhang et al. 2017). This paper proves that while the gradients are different, they converge as the dimension of the vector under optimization increases.
TableNet: Deep Learning Model for End-to-end Table Detection and Tabular Data Extraction from Scanned Document Images

With the widespread use of mobile phones and scanners to photograph and upload documents, the need for extracting the information trapped in unstructured document images such as retail receipts, insurance claim forms and financial invoices is becoming more acute. A major hurdle to this objective is that these images often contain information in the form of tables and extracting data from tabular sub-images presents a unique set of challenges. This includes accurate detection of the tabular region within an image, and subsequently detecting and extracting information from the rows and columns of the detected table. While some progress has been made in table detection, extracting the table contents is still a challenge since this involves more fine grained table structure(rows & columns) recognition. Prior approaches have attempted to solve the table detection and structure recognition problems independently using two separate models. In this paper, we propose TableNet: a novel end-to-end deep learning model for both table detection and structure recognition. The model exploits the interdependence between the twin tasks of table detection and table structure recognition to segment out the table and column regions. This is followed by semantic rule-based row extraction from the identified tabular sub-regions. The proposed model and extraction approach was evaluated on the publicly available ICDAR 2013 and Marmot Table datasets obtaining state of the art results. Additionally, we demonstrate that feeding additional semantic features further improves model performance and that the model exhibits transfer learning across datasets. Another contribution of this paper is to provide additional table structure annotations for the Marmot data, which currently only has annotations for table detection.