Miracle-dz/UniFAR-FTUs huggingface dataset:
Force-Sensor-Less Bilateral Teleoperation Control of Dissimilar Master–Slave System With Arbitrary Scaling

This study designs a high-precision bilateral teleoperation control for a dissimilar master–slave system. The proposed nonlinear control design takes advantage of a novel subsystem-dynamics-based control method that allows designing of individual (decentralized) model-based controllers for the manipulators locally at the subsystem level. Very importantly, a dynamic model of the human operator is incorporated into the control of the master manipulator. The individual controllers for the dissimilar master and slave manipulators are connected in a specific communication channel for the bilateral teleoperation to function. Stability of the overall control design is rigorously guaranteed with arbitrary time delays. Novel features of this study include the completely force-sensor-less design for the teleoperation system with a solution for a uniquely introduced computational algebraic loop, a method of estimating the exogenous operating force of an operator and the use of a commercial haptic manipulator. Most importantly, we conduct experiments on a dissimilar system in two degrees of freedom (DOFs). As an illustration of the performance of the proposed system, a force scaling factor of up to 800 and position scaling factor of up to 4 was used in the experiments. The experimental results show an exceptional tracking performance, verifying the real-world performance of the proposed concept.
Continuous Silent Speech Recognition using EEG

In this paper we explore continuous silent speech recognition using electroencephalography (EEG) signals. We implemented a connectionist temporal classification (CTC) automatic speech recognition (ASR) model to translate EEG signals recorded in parallel while subjects were reading English sentences in their mind without producing any voice to text. Our results demonstrate the feasibility of using EEG signals for performing continuous silent speech recognition. We demonstrate our results for a limited English vocabulary consisting of 30 unique sentences.
A simple certifying algorithm for 3-edge-connectivity

A linear-time certifying algorithm for 3-edge-connectivity is presented. Given an undirected graph G, if G is 3-edge-connected, the algorithm generates a construction sequence as a positive certificate for G. Otherwise, the algorithm decomposes G into its 3-edge-connected components and at the same time generates a construction sequence for each connected component as well as the bridges and a cactus representation of the cut-pairs in G. All of these are done by making only one pass over G using an innovative graph contraction technique. Moreover, the graph need not be 2-edge-connected.
MFFW: A new dataset for multi-focus image fusion

Multi-focus image fusion (MFF) is a fundamental task in the field of computational photography. Current methods have achieved significant performance improvement. It is found that current methods are evaluated on simulated image sets or Lytro dataset. Recently, a growing number of researchers pay attention to defocus spread effect, a phenomenon of real-world multi-focus images. Nonetheless, defocus spread effect is not obvious in simulated or Lytro datasets, where popular methods perform very similar. To compare their performance on images with defocus spread effect, this paper constructs a new dataset called MFF in the wild (MFFW). It contains 19 pairs of multi-focus images collected on the Internet. We register all pairs of source images, and provide focus maps and reference images for part of pairs. Compared with Lytro dataset, images in MFFW significantly suffer from defocus spread effect. In addition, the scenes of MFFW are more complex. The experiments demonstrate that most state-of-the-art methods on MFFW dataset cannot robustly generate satisfactory fusion images. MFFW can be a new baseline dataset to test whether an MMF algorithm is able to deal with defocus spread effect.
VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Generative models that can model and predict sequences of future events can, in principle, learn to capture complex real-world phenomena, such as physical interactions. However, a central challenge in video prediction is that the future is highly uncertain: a sequence of past observations of events can imply many possible futures. Although a number of recent works have studied probabilistic models that can represent uncertain futures, such models are either extremely expensive computationally as in the case of pixel-level autoregressive models, or do not directly optimize the likelihood of the data. To our knowledge, our work is the first to propose multi-frame video prediction with normalizing flows, which allows for direct optimization of the data likelihood, and produces high-quality stochastic predictions. We describe an approach for modeling the latent space dynamics, and demonstrate that flow-based generative models offer a viable and competitive approach to generative modelling of video.
Rembrandts and Robots: Using Neural Networks to Explore Authorship in Painting

We use convolutional neural networks (CNNs) to analyze authorship questions surrounding works of representational art. Trained on the works of an artist under study and visually comparable works of other artists, our system can identify forgeries and provide attributions. Our system can also assign classification probabilities within a painting, revealing mixed authorship and identifying regions painted by different hands. We describe and illustrate performance in connection with Rembrandt portraits and van Gogh landscapes. With one interesting exception, our system’s attributions match those of authoritative experts for works whose authorship has been the subject of longstanding controversy. In less than a decade, neural networks have achieved widespread deployment in applications ranging from self-driving cars to real-time language processing to medical diagnostics. The rate and volume level at which new potential uses are announced obscures how few human workers have actually been displaced. For applications whose value is high enough to justify the cost of research and development, the risks of taking human expertise out of the loop may be too great. For applications involving creativity and subjective judgment, the uncertainties may be irreducible. Analysis of artwork represents one such creative domain. Neural networks have been used to categorize art images by movement and style (Lecoutre et al., 2017; Balakrishnan et al., 2017; Tan et al., 2016; Saleh et al., 2015; Bar et al., 2014), to classify paintings by artist rather than style (van Noord et al., 2015), and to extract and analyze artists’ brushstrokes (Li et al., 2012). These techniques have achieved varying degrees of success but few efforts have addressed the more difficult question of attribution and forgery detection. The reason for this stems from some fundamental limitations of neural networks. Organized in a highly interconnected brain-like fashion, they can analyze and recognize patterns in a wide range of complex input  an image, spoken words, stock prices, or weather data, for example. But a neural network must be trained for a given task. Convolutional neural networks (CNNs) have
Constructing a Highlight Classifier with an Attention Based LSTM Neural Network

Data is being produced in larger quantities than ever before in human history. It's only natural to expect a rise in demand for technology that aids humans in sifting through and analyzing this inexhaustible supply of information. This need exists in the market research industry, where large amounts of consumer research data is collected through video recordings. At present, the standard method for analyzing video data is human labor. Market researchers manually review the vast majority of consumer research video in order to identify relevant portions - highlights. The industry state of the art turnaround ratio is 2.2 - for every hour of video content 2.2 hours of manpower are required. In this study we present a novel approach for NLP-based highlight identification and extraction based on a supervised learning model that aides market researchers in sifting through their data. Our approach hinges on a manually curated user-generated highlight clips constructed from long and short-form video data. The problem is best suited for an NLP approach due to the availability of video transcription. We evaluate multiple classes of models, from gradient boosting to recurrent neural networks, comparing their performance in extraction and identification of highlights. The best performing models are then evaluated using four sampling methods designed to analyze documents much larger than the maximum input length of the classifiers. We report very high performances for the standalone classifiers, ROC AUC scores in the range 0.93-0.94, but observe a significant drop in effectiveness when evaluated on large documents. Based on our results we suggest combinations of models/sampling algorithms for various use cases.
Notes on index of quantum integrability

A quantum integrability index was proposed in Komatsu et al (2019 SciPost Phys. 7 065). It systematizes the Goldschmidt and Witten’s operator counting argument (Goldschmidt and Witten 1980 Phys. Lett. B 91 392) by using the conformal symmetry. In this work we compute the quantum integrability indexes for the symmetric coset models SU(N)/SO(N) and SO(2N)/SO(N)×SO(N) . The indexes of these theories are all non-positive except for the case of SO(4)/SO(2)×SO(2) . Moreover we extend the analysis to the theories with fermions and consider a concrete theory: the CPN model coupled with a massless Dirac fermion. We find that the indexes for this class of models are non-positive as well.
Intrinsic Anomalous Hall Effect in Ni-Substituted Magnetic Weyl Semimetal Co3Sn2S2

Topological Weyl semimetals have recently attracted considerable attention among materials scientists as their properties are predicted to be protected against perturbations such as lattice distortion and chemical substitution. However, any experimental proof of such robustness is still lacking. In this study, we experimentally demonstrate that the topological properties of the ferromagnetic kagomé compound Co3Sn2S2 are preserved upon Ni substitution. We systematically vary the Ni content in Co3Sn2S2 single crystals and study their magnetic and anomalous transport properties. For the intermediate Ni substitution, we observe a remarkable increase in the coercive field while still maintaining significant anomalous Hall conductivity. The large anomalous Hall conductivity of these compounds is intrinsic, consistent with first-principles calculations, which proves its topological origin. Our results can guide further studies on the chemical tuning of topological materials for better understanding.
A Simple Modification of HLLEM Approximate Riemann Solver Applied to the Compressible Euler System at Low Mach Number

Due to its attractive properties in computing compressible flows, such as sharp capturing of discontinuities, satisfying entropy condition and positivity preservation, the HLLEM approximate Riemann solver has been widely applied for simulations of many compressible flow problems. However, when it comes to weakly compressible or incompressible flows, the HLLEM scheme cannot give physically correct solutions. In the current study, a simple low Mach number fix is applied to improve the accuracy and stability of HLLEM approximate Riemann solver in the low Mach limit. As a result, a modified HLLEM scheme called LM-HLLEM scheme is proposed. Numerical results demonstrate that the proposed LM-HLLEM scheme is able to compute various flow problems accurately and robustly ranging from compressible to low Mach incompressible flows.
Carbon dioxide activation of the plane tree seeds derived bio-char: Kinetic properties and application

Goal of this work is to establish technical feasibility and fundamentals of producing activated carbon from plane tree seeds (PTS) biomass for porous materials derivation. Bio-chars produced via carbonization from PTS precursor were activated in CO2 at 750 and 850°C, during various residence times. Their surface area and porosity were characterized by N2 adsorption at 77 K. Surface areas of activated carbons can be correlated with kinetics mechanism and activation energy magnitudes of oxidation reaction by CO2, which are closely related to applied activation temperature. Result showed that high temperature activated carbon had higher gas adsorption as compared to activated carbon obtained from lower temperature during two hour residence time. Breakthrough behavior was detected at 850°C where surface reactions dominate, and it is characterized by autocatalytic kinetic model under designed conditions. Both, temperature and CO2 concentration in vicinity of solid surface affect on breakthrough time of adsorbent. Derived bio-chars are converted into high quality activated carbons, with surface area of 776.55 m2 g-1, where micropores with pore diameters less than 2 nm prevail. Produced activated carbons have properties comparable with commercially available activated carbons, which can be successfully used for removal of harmful gaseous pollutants toward air purification. [Projects of the Serbian Ministry of Education, Science and Technological Development, Grant no. 172015, Grant no. III45005, Grant no. TR34011, Grant no. TR34013, Grant no. 172018 and Grant no. III45012]
The acetyltransferase Eco1 elicits cohesin dimerization during S phase

Cohesin is a DNA-associated protein complex that forms a tripartite ring controlling sister chromatid cohesion, chromosome segregation and organization, DNA replication, and gene expression. Sister chromatid cohesion is established by the protein acetyltransferase Eco1, which acetylates two conserved lysine residues on the cohesin subunit Smc3 and thereby ensures correct chromatid separation in yeast (Saccharomyces cerevisiae) and other eukaryotes. However, the consequence of Eco1-catalyzed cohesin acetylation is unknown, and the exact nature of the cohesive state of chromatids remains controversial. Here, we show that self-interactions of the cohesin subunits Scc1/Rad21 and Scc3 occur in a DNA replication–coupled manner in both yeast and human cells. Using cross-linking MS-based and in vivo disulfide cross-linking analyses of purified cohesin, we show that a subpopulation of cohesin may exist as dimers. Importantly, upon temperature-sensitive and auxin-induced degron-mediated Eco1 depletion, the cohesin-cohesin interactions became significantly compromised, whereas deleting either the deacetylase Hos1 or the Eco1 antagonist Wpl1/Rad61 increased cohesin dimer levels by ∼20%. These results indicate that cohesin dimerizes in the S phase and monomerizes in mitosis, processes that are controlled by Eco1, Wpl1, and Hos1 in the sister chromatid cohesion-dissolution cycle. These findings suggest that cohesin dimerization is controlled by the cohesion cycle and support the notion that a double-ring cohesin model operates in sister chromatid cohesion.
Long-Term Homogeneity, Trend, and Change-Point Analysis of Rainfall in the Arid District of Ananthapuramu, Andhra Pradesh State, India

The objective of this study is to evaluate the homogeneity, trend, and trend change points in the rainfall data. Daily rainfall data was collected for the arid district of Ananthapuramu, Andhra Pradesh state, India from 1981 to 2016 at the subdistrict level and aggregated to monthly, annual, seasonal rainfall totals, and the number of rainy days. After quality checks and homogeneity analysis, a total of 27 rain gauge locations were considered for trend analysis. A serial correlation test was applied to all the time series to identify serially independent series. NonParametric Mann–Kendall test and Spearman’s rank correlation tests were applied to serially independent series. The magnitude of the trend was calculated using Sen’s slope method. For the data influenced by serial correlation, various modified versions of Mann–Kendall tests (pre-whitening, trend-free pre-whitening, bias-corrected pre-whitening, and two variants of variance correction approaches) were applied. A significant increasing summer rainfall trend is observed in six out of 27 stations. Significant decreasing trends are observed at two stations during the southwest monsoon season and at two stations during the northeast monsoon season. To identify the trend change points in the time series, distribution−free cumulative sum test, and sequential Mann–Kendall tests were applied. Two open−source library packages were developed in R language namely, ”modifiedmk” and ”trendchange” to implement the statistical tests mentioned in this paper. The study results benefit water resource management, drought mitigation, socio−economic development, and sustainable agricultural planning in the region.
A Mutant of Listeria monocytogenes Shows Decreased Virulence and Confers Protection Against Listeriosis in Mice

This research was performed to obtain a safe and highly immunogenic Listeria strain and evaluate the biological characteristics of the deletion mutant. Based on homologous recombination technology, we constructed a deletion mutant Lm90-ΔinlB of L. monocytogenes. Meanwhile, we characterized its safety and protective efficacy against listeriosis infection in mice. The results showed that the virulence of Lm90-ΔinlB could significantly decrease compared with the parental strain (Lm90). The deletion strain retained hemolytic activity and induced CD8+ T cell response comparable to that of Lm90. Mice immunized with Lm90-ΔinlB were capable of stimulating specific CD8+ T cells to the listerial epitopes LLO91-99 and P60217-225 at levels equivalent to Lm90. Importantly, immunization of mice with Lm90-ΔinlB displayed good protection against listeriosis. In conclusion, strain Lm90-ΔinlB is a vaccine candidate with the potential to be more immunogenic yet considerably less toxic than the parental strain.
The relationship of volume and headway on heterogen traffic conditions in Makassar City

The purpose of this study was to examine the relationship between vehicle distance (gap) formed by the vehicle volume. For this reason, in this study, there were 16 (sixteen) roads that were used as objects of research. To get the values from the gap variable, the VISSIM software is used in the data processing. In the use of this software calibration and validation are done by trial and error to produce simulation results that can replicate the actual traffic conditions. The results of this study indicate that statistically from the coefficient of determination and F test conducted concluded that there is a significant relationship between vehicle volume and gap.
British and disengaged: national identification and political engagement before and after naturalisation

ABSTRACT Empirical work has documented the socio-economic characteristics of immigrants who naturalise and the effects of naturalisation on labour market outcomes. Political engagement and national identity are, however, salient but understudied dimensions of citizenship. Using two waves of the U.K. Household Longitudinal Study, I investigate immigrants’ national identification and political engagement before and after naturalisation. I find that before naturalisation those who acquire citizenship are more likely to identify as British, be familiar with the British political system and are less interested in politics compared to those who do not. I also find that after naturalisation, the importance of new citizens give to their British identity is higher than before, but their interest in politics is lower. I argue that citizenship retains its role as a marker of national identity for immigrants and that the negative association between naturalisation and interest in politics for immigrants is compatible with the low political engagement of the British-born population. I suggest that the further decline in interest in politics following naturalisation may be explained by immigrants’ disillusionment with a political narrative that fails to include them. I reflect on the implications of my findings for the conceptualisation of citizenship, for policy, and for future research.
Facilitating responsive interaction between occupants and building systems through dynamic post-occupancy evaluation

Post-occupancy evaluation (POE) is a process that can reveal the interrelations between key building performance factors and successfully integrate indoor environmental quality, thermal comfort, functionality, environmental strategy and occupants’ satisfaction. POE has become a prerequisite for several building certification systems and it is often presented as a method to improve the commissioning of buildings and as a user experience feedback mechanism. This paper is based on a POE undertaken through stages at the University of Southampton Mayflower Halls of Residence complex. The first stage included the evaluation of occupant satisfaction, indoor environment quality and energy use. Results from temperature and relative humidity monitoring and an online POE questionnaire were analysed in the context of energy use, thermal comfort and building controls’ functionality. The second part of this study monitored the air temperature in a sub-sample of 30 rooms where the residents participated in a thermal comfort survey with a “right-here-right-now” questionnaire and a portable instrument that monitored air temperature, relative humidity, globe temperature and air velocity in the rooms. This paper presents the results of the POE and discusses approaches for the improvement in the buildings’ energy performance and the environmental conditions in the living spaces of the students. Results suggest that current use of controls is not always effective, with implications for the buildings’ energy use. Large variability was found in occupants’ thermal perception and preferences, which points to a need for occupant-centric solutions. In this study, POE is approached as a dynamic process that could be used to facilitate the responsive interaction of occupants with building systems and deliver through their engagement high energy performance and comfort.
On involutes of order k of a null Cartan curve in Minkowski spaces

In this paper, we define an involute and an evolving involute of order k of a null Cartan curve in Minkowski space En1 for n ? 3 and 1 ? k ? n-1. In relation to that, we prove that if a null Cartan helix has a null Cartan involute of order 1 or 2, then it is Bertrand null Cartan curve and its involute is its Bertrand mate curve. In particular, we show that Bertrand mate curve of Bertrand null Cartan curve can also be a non-null curve and find the relationship between the Cartan frame of a null Cartan curve and the Frenet or the Cartan frame of its non-null or null Cartan involute of order 1 ? k ? 2. We show that among all null Cartan curves in E31 , only the null Cartan cubic has two families of involutes of order 1, one of which lies on B-scroll. We also give some relations between involutes of orders 1 and 2 of a null Cartan curve in Minkowski 3-space. As an application, we show that involutes of order 1 of a null Cartan curve in E31 , evolving according to null Betchov-Da Rios vortex filament equation, generate timelike Hasimoto surfaces.
Research on Hull Assembly Process Planning Based on Rule Reasoning

Currently, hull assembly process is mainly dependent on experience, and lack of normative standard. In this paper, hull structure for assembly features is systematically studied, and an assembly sequence planning method based on Rule Reasoning is proposed. Firstly, assembly feature and sequence expression of hull structure is analyzed and the constraint relation of connection information is described. Secondly, the relationship between established structures and the constraint function is designed. Setting the double bottom of the ship as an example, hull assembly sequence is obtained based on geometric constraints and optimized by the fuzzy evaluation method and finally realized the computer aided evaluation of the hull assembly sequence.