Miracle-dz/UniFAR-FTUs huggingface dataset:
The CMT Model of Free Will 08 . 01 . 2020

Here we propose a compatibilist theory of free will, in the tradition of naturalized philosophy, that attempts: 1) to provide a synthesis of a variety of well-known theories, capable of addressing problems of the latter; 2) to account for the fact that free will comes in degrees; 3) to interface with natural sciences, especially neurobiology. We argue that free will comes in degrees, as suggested by neuroscience. We suggest that a concept that can precisely ‘measure’ the variability of free will is that of ‘theory’, or rather, the capacity to make assumptions and use theories. Our model, then, highlights that free-willed actions are consciously monitored by the agent, through beliefs, assumptions and ultimately theories – hence the CMT model (for Conscious-through-Monitoring-through-Theories). The ultimate goal of this attempt at synthesis is to make the comparison with a variety of well-known accounts, and to show that our model can address several of their problems.
Determining the influence of cavitation treatment on the octane number of gas-condensate gasoline modified with isopropanol

S . K u d r y a v t s e v PhD, Associate Professor* E-mail: sergeykudryavtsevsnu@gmail.com O . T s e l i s h c h e v Doctor of Technical Sciences, Associate Professor* E-mail: atp00@ukr.net S . L e o n e n k o Postgraduate Student Department of Public Administration, Management and Marketing** E-mail: leonenko.2307@gmail.com S . B o i c h e n k o Doctor of Technical Sciences, Professor Department of Environmental Safety National Aviation University Lubomyr Husar ave., 1, Kyiv, Ukraine, 03058 M . L o r i a Doctor of Technical Sciences, Associate Professor Department of Computer-Integrated Control Systems** E-mail: atp01@ukr.net *Department of Chemical Engineering and Ecology** **Volodymyr Dahl East Ukrainian National University Tsentralny ave, 59-a, Severodonetsk, Ukraine, 93400 This paper reports a study into the effect of cavitation on the octane number of gas-condensate gasoline with the addition of isopropanol in the amount of 0‒12 % by volume. The papers that confirm the impact of cavitation on the intensification of oil cracking reactions have been analyzed. Cavitation also initiates reactions of interaction between free radicals and alcohols. A laboratory installation scheme has been proposed to investigate the cavitation treatment process on the characteristics of gasoline modified with alcohols. A methodology has been devised for studying the effect of cavitation treatment intensity on the octane number of gasoline. A 0.3‒0.9-point increase in the octane number of gas-condensate gasoline modified with isopropanol was experimentally proven following its cavitation treatment. The effect of the number of cavitation treatment cycles on the octane number indicator has been studied; it is shown that the stable value of an increase in the octane number is achieved over 7‒8 ycles of cavitation treatment at a pressure at the outlet from the nozzle of 9.0 MPa. A reduction in the isopropanol additive, required to produce gasoline brands A-95 and A-98, when using a cavitation treatment technology was substantiated. It has been experimentally confirmed that compared to simple mechanical mixing of alcohol and hydrocarbon gasoline, the application of cavitation reduces the consumption of isopropanol by 17 % (from 3.0 % to 2.5 % by volume) in the production of gasoline brand A-95; and by 14 % (from 8.1 % to 7.0 % by volume) in the production of gasoline brand A-98. The effect of isopropanol concentration on the increase in the octane number of gasoline, measured by research method, under conditions of cavitation treatment is nonlinear in nature: with highs at concentrations of 1.0 % by volume, 3.5 % by volume, and 6.5 % by volume. Varying the initial concentration of isopropanol and the octane number of a hydrocarbon gasoline fraction can optimize the technological mode of production of gasoline brands A-95 and A-98 in terms of raw materials and energy consumption
A Structural Analysis of Capital Determinants of growth in LivestockproductionOutput in Nigeria

-The study examined the structural analysis of capital determinants of growth in Livestock production output in Nigeria. The objectives of the study were to examine savings, human capital formation and gross capital formation as a major capital determinants of livestock production output in Nigeria. Data for the study were sourced from CBN statistical bulletin and National bureau of statistics statistical bulletin. The econometric method of OLS, Co-integration and error correction methods and Granger causality tests were used as the analytical tools for the study. The study employed the E-view 7.1 econometric software in its analysis. The long run results depicted by the parsimonious error correction mechanism revealedthat the explanatory variables are rightly signed. Also, the coefficient of the parsimonious ECM has the appropriate sign, which is negative and statistically significant. Meaning that the short run dynamics adjust to long run equilibrium relationship. Evidence was drawn from the parsimonious ECM result. Therefore, the study recommends for strong policy reforms to boost livestock production output in Nigeria.
Hydrography and phytoplankton biomass in the Campeche Canyon and Bank, southern Gulf of Mexico, during February of 2011

The present study discusses the relationship between the hydrography and the phytoplankton biomass in the Campeche Canyon and Bank in the southern Gulf of Mexico (GM) during February of 2011 (a “Nortes” season). High-resolution observations were gathered during a research cruise covering 48 hydrographic stations, where a CTD probe equipped with a fluorescence sensor was used to collect data. The results indicated the presence of five water masses in different proportions. The phytoplankton biomass distribution showed dependence on the water column hydrographic conditions, particularly the thermal structure, which showed that the highest horizontal values were closely related with the presence of a warm temperature core. Two vertical distribution patterns were observed: 1) a maximum peak related with the thermocline in the stations over the Campeche Canyon and 2) a maximum peak closely related with the bottom at the stations over the Campeche Bank. The results presented here contribute to the knowledge of the phytoplankton ecology of the southern GM during a “Nortes” season, a time of year when in situ measurements from ships are scarce, and highlight the need to obtain more observations in order to determine patterns of phytoplankton biomass distribution and explore their seasonal variations.
In-plane mesoporous graphene oxide nanosheet assembled membranes for molecular separation

In-plane mesoporous graphene oxide (GO) sheets were prepared by a re-oxidation process and subsequently assembled into lamellar membranes. The in-plane pores significantly shortened the mass transport paths, resulting in 2–3 times higher water permeance than that of the pristine GO membrane, but continued to reject 3 nm molecules.
A Multi-Agent Approach to Develop Courses in terms of Learning Objects with Videogames

Videogames have been identified as potential learning tools, then the videogames are considered here inside the main components of a learning object. It is possible the learning objects can be produced and saved in the repositories form different educative institutions, then some challenges are required to solve such as the research and use of this kind of educational contents. Current work proposes a multi-agent approach in order to help a tutor to develop online course in terms of learning objects with videogames coming from several distributed repositories located in different educative institutions. Finally, the performance of current proposal is presented throughout a case study.
Planning Using Combinatory Categorial Grammars

This paper presents a new model of planning based on representing domain knowledge using Combinatorial Categorial Grammars taken from natural language processing. This enables the capturing of plans with context-free expressiveness. It uses the same representation that has previously been used for plan recognition and has been shown to be learnable. Thus it represents a solid link between planning, plan recognition, and natural language processing. The paper also compares our open source implementation to two other well known hierarchical planners.
University of Birmingham 3D-Printed 96 GHz Bull’s-Eye Antenna with Off-Axis Beaming

Reducing the profile, footprint and weight of antennas embarked on aircrafts, drones or satellites has been a long pursued objective. Here we tackle this issue by developing a millimeter-wave 96 GHz elliptical Bull’s-Eye antenna with offaxis radiation at 16.5o that has been fabricated by low cost 3D printing stereolithography, followed by metal coating. The theoretical basis for optimum off-axis operations is explained. Measurement results show an overall good agreement with simulations, displaying a gain of 17 dB and a 3.5o beamwidth (Eplane) at the operational frequency. The off-axis beaming enlarges the potential applicability of this technology with respect to the broadside beam solution.
Biochemical Detection of Cytidine Protonation within RNA

Perturbation of active site functional group pKas is an important strategy employed by protein enzymes to achieve catalysis. There is increasing evidence to indicate that RNAs also utilize functional group pKa perturbation for folding and reactivity. One of the best candidates for a functionally relevant pKa perturbation is the N3 of C (pKa 4.2), which could be sufficiently raised to allow protonation near physiological pH. Here we report the synthesis and use of a series of α-phosphorothioate tagged cytidine analogues whose altered N3 pKas make it possible to efficiently detect functionally relevant protonation events by nucleotide analogue interference mapping. 6-Azacytidine (n6CαS) and 5-fluorocytidine (f 5CαS) both have enhanced acidity at the N3 position (pKa 2.6 and 2.3, respectively) but leave the hydrogen bonding face of C otherwise unaffected. In contrast, pseudoisocytidine (ΨiCαS) is a charge neutral analogue that mimics the hydrogen bonding character of protonated C. To test the utility of thes.
Water defluoridation using nanofiltration vs. reverse osmosis: the first world unit, Thiadiaye (Senegal)

Abstract The possibility of producing drinking water from brackish groundwater polluted with F− using nanofiltration (NF) process was previously studied at a pilot scale. Brackish groundwaters were taken from the south of Morocco (Tan Tan city) and from the center west of Senegal (Thiadiaye City). The performances of few commercialized NF spiral wound elements, low-pressure reverse osmosis (LPRO), and reverse osmosis (RO) were compared to a large-scale pilot experiments (membrane area, 7.6 m2). The following parameters were determined: hydraulic permeability, total salinity; and Cl−, F−, and SO4 2− removals were compared under the influence of various experimental parameters such as flow yield ratio, transmembrane pressure, energy consumption and water compositions before and after treatment. This study shows that NF provides specific advantages for fluoride removal as compared to LPRO and RO. Indeed, NF permits to reduce partially the total salinity and to reject selectively fluorides with a high flow yi.
International Journal of Instruction

In this paper the challenge of promoting computational thinking for all by contextualized computing education is addressed. The two phases learning session we designed was implemented in the AlgoRythmics environment which includes ten algorithmic dance choreographies (and attached interactive computer animations). In addition to previous studies that had focused only on supporting students in assimilating the strategy of the studied algorithms, this study examined whether they are able to build on this knowledge by extracting from visualizations some algorithm efficiency related concepts too. To this end, the learning environment was complemented/supplemented with targeted questioning (with and without teacher guidance). Participants (first year undergraduate students) were grouped based on the number of years they had learned programming in high school (0, 1/2 or 4 years). We performed two analyses: (1) group-0 vs. group-1/2 and group-4 (N=181; self-paced setting: algorithm visualization complemented with teacher prepared questions); (2) group-0 (one-group pretest-posttest design, N=46; supplementary Socratic questioning). Research results revealed that the AlgoRythmics environment, if complemented/supplemented with tutorial questionasking, could be an effective instrument in introducing students with no prior knowledge in computing (group-0), even with deeper Computer Science concepts such as algorithm efficiency.
Hybrid Edge Partitioner: Partitioning Large Power-Law Graphs under Memory Constraints

Distributed systems that manage and process graph-structured data internally solve a graph partitioning problem to minimize their communication overhead and query run-time. Besides computational complexity---optimal graph partitioning is NP-hard---another important consideration is the memory overhead. Real-world graphs often have an immense size, such that loading the complete graph into memory for partitioning is not economical or feasible. Currently, the common approach to reduce memory overhead is to rely on streaming partitioning algorithms. While the latest streaming algorithms lead to reasonable partitioning quality on some graphs, they are still not completely competitive to in-memory partitioners. In this paper, we propose a new system, Hybrid Edge Partitioner (HEP), that can partition graphs that fit partly into memory while yielding a high partitioning quality. HEP can flexibly adapt its memory overhead by separating the edge set of the graph into two sub-sets. One sub-set is partitioned by NE++, a novel, efficient in-memory algorithm, while the other sub-set is partitioned by a streaming approach. Our evaluations on large real-world graphs show that in many cases, HEP outperforms both in-memory partitioning and streaming partitioning at the same time. Hence, HEP is an attractive alternative to existing solutions that cannot fine-tune their memory overheads. Finally, we show that using HEP, we achieve a significant speedup of distributed graph processing jobs on Spark/GraphX compared to state-of-the-art partitioning algorithms.
Considerations and Challenges of Measuring Operator Performance in Telepresence and Teleoperation Entailing Mixed Reality Technologies

Assessing human performance in robotic scenarios such as those seen in telepresence and teleoperation has always been a challenging task. With the recent spike in mixed reality technologies and the subsequent focus by researchers, new pathways have opened in elucidating human perception and maximising overall immersion. Yet with the multitude of different assessment methods in evaluating operator performance in virtual environments within the field of HCI and HRI, inter-study comparability and transferability are limited. In this short paper, we present a brief overview of existing methods in assessing operator performance including subjective and objective approaches while also attempting to capture future technical challenges and frontiers. The ultimate goal is to assist and pinpoint readers towards potentially important directions with the future hope of providing a unified immersion framework for teleoperation and telepresence by standardizing a set of guidelines and evaluation methods.
EmgAuth: Unlocking Smartphones With EMG Signals

Screen lock is a critical security feature for smartphones to prevent unauthorized access. Although various screen unlocking technologies, including fingerprint and facial recognition, have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3D-printed head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth enables users to unlock their smartphones by leveraging the EMG data of the smartphone users collected from Myo armbands. When training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband, which makes EmgAuth free of calibration. We conduct extensive experiments including 80 participants and the evaluation results verify that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different screen sizes and for different scenarios when users are placing smartphones at different locations and with different orientations. EmgAuth shows great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.
The SIBELIUS Project: E Pluribus Unum

We introduce "Simulations Beyond The Local Universe" (SIBELIUS) that connect the Local Group to its cosmic environment. We show that introducing hierarchical small-scale perturbations to a density field constrained on large scales by observations provides an efficient way to explore the sample space of Local Group analogues. Frommore than 60 000 simulations, we identify a hierarchy of Local Group characteristics emanating from different scales: the total mass, orientation, orbital energy and the angular momentum are largely determined by modes above λ = 1.6 comoving Mpc (cMpc) in the primordial density field. Smaller scale variations are mostly manifest as perturbations to the MW-M31 orbit, and we find that the observables commonly used to describe the Local Group –the MW-M31 separation and radial velocity– are transient and depend on specifying scales down to 0.2 cMpc in the primordial density field. We further find that the presence of M33/LMC analogues significantly affects the MW-M31 orbit and its sensitivity to small-scale perturbations. We construct initial conditions that lead to the formation of a Local Group whose primary observables precisely match the current observations.
Group-aware Label Transfer for Domain Adaptive Person Re-identification

Unsupervised Domain Adaptive (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations. Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion. However, offline interaction between these two steps may allow noisy pseudo labels to substantially hinder the capability of the model. In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning. Specifically, a label transfer algorithm simultaneously uses pseudo labels to train the data while refining the pseudo labels as an online clustering algorithm. It treats the online label refinery problem as an optimal transport problem, which explores the minimum cost for assigning M samples to N pseudo labels. More importantly, we introduce a group-aware strategy to assign implicit attribute group IDs to samples. The combination of the online label refining algorithm and the group-aware strategy can better correct the noisy pseudo label in an online fashion and narrow down the search space of the target identity. The effectiveness of the proposed GLT is demonstrated by the experimental results (Rank-1 accuracy) for Market1501→DukeMTMC (82.0%) and DukeMTMC→Market1501 (92.2%), remarkably closing the gap between unsupervised and supervised performance on person re-identification. 1
Simulations of state-of-the-art fermionic neural network wave functions with diffusion Monte Carlo

Recently developed neural network-based \emph{ab-initio} solutions (Pfau et. al arxiv:1909.02487v2) for finding ground states of fermionic systems can generate state-of-the-art results on a broad class of systems. In this work, we improve the results for this Ansatz with Diffusion Monte Carlo. Additionally, we introduce several modifications to the network (Fermi Net) and optimization method (Kronecker Factored Approximate Curvature) that reduce the number of required resources while maintaining or improving the modelling performance. In terms of the model, we remove redundant computations and alter the way data is handled in the permutation equivariant function. The Diffusion Monte Carlo results exceed or match state-of-the-art performance for all systems investigated: atomic systems Be-Ne, and the carbon cation C$^+$.
Biologically Inspired Collision Avoidance Without Distance Information

Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.
A New Approach To Text Rating Classification Using Sentiment Analysis

Typical use cases of sentiment analysis usually revolve around assessing the probability of a text belonging to a certain sentiment and deriving insight concerning it; little work has been done to explore further use cases derived using those probabilities in the context of rating. In this paper, we redefine the sentiment proportion values as building blocks for a triangle structure, allowing us to derive variables for a new formula for classifying text given in the form of product reviews into a group of higher and a group of lower ratings and prove a dependence exists between the sentiments and the ratings. Keywords— natural language processing, sentiment analysis, statistical inference, machine learning
Diagnosing transient magnetic fields of ultrarelativistic laser plasma via ejected electron polarization

Interaction of an ultrastrong short laser pulse with non-prepolarized near critical density plasma is investigated in an ultrarelativistic regime, with an emphasis on the radiative spin polarization of ejected electrons. Our two dimensional particle-in-cell simulations show explicit correlations between the angle resolved electron polarization and the structure and properties of the transient quasistatic plasma magnetic field. While the magnitude of the spin signal is the indicator of the magnetic field strength created by the longitudinal electron current, the asymmetry of electron polarization is found to gauge the island-like magnetic distribution which emerges due to the transverse current induced by the laser wave front. Our studies demonstrate that the spin degree of freedom of ejected electrons could potentially serve as an efficient diagnostic tool to identify strong plasma fields.