Miracle-dz/UniFAR-FTUs huggingface dataset:
Possible quantum kinematics

Space-time is a fundamental conception that underlines the most significant physical theories. Therefore the analysis of a possible space-time model or kinematics has the fundamental meaning for physics. Space and time in nonrelativistic physics were regarded as independent what mathematically is connected with the fiber property of Galilei kinematics. In special relativity it was determined that space and time depend on each other and must be regarded as an integrated object, namely flat Minkowski space-time with a pseudo-Euclidean metric. The notion of curvature was introduced in physics by general relativity. Anti-de Sitter and de Sitter kinematics with constant positive and negative curvature, respectively, are the simplest relativistic space-time models with curvature. Possible kinematics, which satisfy the natural physical postulates: space is isotropic and rotations in space-time planes form a noncompact subgroup, were described in Ref. 1 on the level of Lie algebras. From the point of view of geometry these kinematics are realized as constant curvature spaces, which can be obtained from the spherical space by contractions and analytical continuations known as a Cayley–Klein CK scheme. 2 The Snyder quantized space-time coordinates 3 or, respectively, the curved momentum space, are the oldest example of using the noncommutative geometry in physics. The simplest curved de Sitter geometry with constant curvature was used instead of flat Minkowski space in different generalizations of quantum field theory 4–7 as a momentum space model. The universal constant, the fundamental length l, or fundamental mass M, related to l by l = / Mc, where is the Plank constant and c is the velocity of light, enters necessarily into the theory. 4,6,7 A new possibility for construction of the noncommutative space-time models is provided by quantum groups and quantum vector spaces. 8 According to Dirac 9 from the early 30s, “The most powerful method of advance that can be suggested at present is to employ all the resources of pure mathematics in attempts to perfect and generalize the mathematical formalism that forms the existing basis of theoretical physics, and after each success in this direction, to try to interpret the new mathematical features in terms of physical entities. ” Similar views were expressed by Faddeev, 10 where we found this cite. The quantum Poincare group related to the -Poincare algebra as well as the -Minkowski kinematics were suggested. 11–13 A general formalism that allows the construction of field theory in -Minkowski space-time was developed. 14
Singularities of electron kernel functions in an external electromagnetic field

The electron kernel functions are derived from solutions of the second-order wave equation, using the proper-time parametrization. Iterated kernel functions are introduced and a gauge-independent perturbation theory is developed. The separation of singular parts proceeds in terms of the iterated kernel functions valid in the absence of an electromagnetic field, and the singular expressions which have to be compensated in order to determine the physically significant part of the vacuum polarization are obtained in a more transparent form than those given originally by Heisenberg.
Confli-T5: An AutoPrompt Pipeline for Conflict Related Text Augmentation

Recent advances in natural language processing (NLP) and Big Data technologies have been crucial for scientists to analyze political unrest and violence, prevent harm, and promote global conflict management. Government agencies and public security organizations have invested heavily in deep learning-based applications to study global conflicts and political violence. However, such applications involving text classification, information extraction, and other NLP-related tasks require extensive human efforts in annotating/labeling texts. While limited labeled data may drastically hurt the models’ performance (over-fitting), large demands on annotation tasks may turn real-world applications impracticable. To address this problem, we propose Confli-T5, a prompt-based method that leverages the domain knowledge from existing political science ontology to generate synthetic but realistic labeled text samples in the conflict and mediation domain. Our model allows generating textual data from the ground up and employs our novel Double Random Sampling mechanism to improve the quality (coherency and consistency) of the generated samples. We conduct experiments over six standard datasets relevant to political science studies to show the superiority of Confli-T5. Our codes are publicly available 1.
New block properties for flowshop scheduling with blocking and their application in an iterated greedy algorithm

This paper proposes new block properties for the flowshop scheduling problem with blocking to minimise makespan. A pruning procedure based on these proposed properties is used in the construction phase of an iterated greedy algorithm to decrease the total number of solutions to be examined to find an optimal schedule. Computational results using Taillard’s benchmark problem instances show that the new block properties help to eliminate more ‘unpromising’ solutions than the classic properties. In addition, the effectiveness of the proposed algorithm is verified by comparison with some high-performing algorithms for the considered problem.
A Novel Rigorous Measurement Model for Big Data Quality Characteristics

Satisfiable data quality is the basic guarantee for data-based research, decision-making, and service. Today, new trends in the creation, collection, and utilization of data are constantly emerging. With the usage of massive data, the problem of data quality is highlighted. Several studies on the measurement, evaluation, and management of big data quality have been proposed, and the data quality problem in the big data environment has received attention. The big data characteristics Vs model describes the dimensions and attributes information of data sources in detail, which can be implemented in big data quality measurement. In this paper, a novel rigorous big data quality measurement architecture is proposed for automatically and parallelly quantifying the value of six big data Vs, which are Volume, Variety, Velocity, Veracity, Validity, and Vincularity according to the developed algorithms in every big data process step and time phase of the big data pipeline. Thresholds for the six big data Vs are provided correspondingly for analyzing the result values. The hierarchical measurement model is constructed with multiple-based measures, derived measures, and indicators. The model is verified by comparative experiments and experiments results indicate that the designed architecture can improve the outcomes of data source implementation.
Increasing Flood Frequencies Under Warming in the West‐Central Himalayas

The west‐central Himalayan River basins face significant damage caused by extreme floods annually. We selected the upper Ganga basin (UGB), the west‐central Himalayan basin, to demonstrate how maximum cumulative storm and total rainfall received during monsoon control the extreme flow variability. In addition, the downscaled CMIP6 data sets were used for projections of extremes. The results suggest that historically Alaknanda basin receives a relatively higher magnitude of monsoon and storm rainfall than the other regions in the UGB, which manifested into significant extreme flow variability at the Alaknanda outlet. The extreme flows at the UGB outlet are projected to be more frequent under warming scenarios. The nonstationary flood frequency magnitudes are higher than those obtained from the stationary frequency method, implying a need for a review of return periods used in the designs of flood control structures in the region.
Electric vehicle energy trading framework in V2G network based on blockchain

The meaning of the V2G (Vehicle-to-Grid) is the network that provides energy from the electric vehicle to the grid to solve the problem of unstable energy generation. Most of the V2G network transaction models of existing research depends on the central server and there is no suitable pricing strategy. In order to realize the distributed transaction of electric vehicles in the V2G network, this paper first proposes a distributed power transaction framework based on the blockchain to realize the point-to-point transaction of electric vehicles. Then, in order to maximize the income of the charging vehicle and the discharge vehicle, this paper uses a reverse auction mechanism based on dynamic pricing of bidding times to reduce the cost of charging vehicles and increase the revenue of discharge vehicles. Finally, in order to verify the proposed pricing strategic performance, we have simulated experiments on the Ethereum platform to verify the feasibility of the program and compare the traditional divided pricing model.
On the physical contributions to the third-order nonlinear optical response in plasmonic nanocomposites

Au and Ag isotropic and anisotropic nanocomposites were prepared using the ion implantation technique. Their optical properties were studied at several wavelengths in the optical range 300–800 nm, across their plasmon resonances. The linear regime was characterized by measuring the absorption spectrum and the third-order nonlinear regime by means of the Z-scan technique using a tunable picosecond pulsed laser system (26 ps). Open-aperture Z-scan traces show a superposition of different optical nonlinear absorption (NLA) processes in the whole range studied. We associate these phenomena with the excitation of inter- and intra-band electronic transitions, which contribute with a positive sign to NLA, and to the formation of hot-electrons, which contribute with opposite sign to NLA. Closed-aperture traces for measuring nonlinear refraction (NLR) show different signs for Au and Ag samples, and a change of sign in Au is found when purely inter-band transitions are excited. In this work, for the appropriate wavelength, it is worth remarking on the free-electron response to the exciting light and its strong contribution to the nonlinear optical properties for low (intra-band) and high (hot-electrons) irradiances.
Trip Destination Prediction with Candidate Retrieval and Negative Sampling : IEEE BigData Cup 2022: Trip Destination Prediction

Destination prediction is crucial for many location-based applications, such as sightseeing recommendations, advertising, and governance. The accurate prediction can help to solve travel demand estimation, traffic congestion, and transit planning. Accordingly, this topic was related to one of the BigData Cup competitions at the 2022 IEEE International Conference on Big Data.This paper presents a pipeline with two steps: candidate retrieval to generate the training and testing set and model training by using the extreme gradient boosting model (LightGBM [1]) with features engineering from trip data and parameter tuning. The solution achieved first place with a categorization accuracy score of 0.43967 in the final result.
RECENT DEVELOPMENTS IN BIAXIAL LIQUID CRYSTALS: AN NMR PERSPECTIVE

A survey of recent studies of biaxial liquid crystals (LCs), whose nematic and/or smectic-A phases do not possess optical uniaxiality (viz., more than one optical axis exists), is given in this review. In particular, we emphasize on how Nuclear Magnetic Resonance (NMR) spectroscopy can help to advance the understanding of phase biaxiality in general, and to examine recent debates on the existence of biaxial nematic phase reported in low molecular mass bent-core or V-shaped mesogens. A general discussion of orientational order parameters is detailed, particularly in smectic-C (SmC) and biaxial nematic phases. How these orientational order parameters can be determined by various techniques such as NMR, IR absorbance and Raman scattering studies, will be mentioned. Recent X-ray observations of SmC clusters in the nematic phase of V-shaped mesogens are highlighted and contrasted with probable theory. Moreover, deuterium and carbon-13 NMR techniques are briefly reviewed, and their possible utilization to ident.
Observation of a significant influence of Earth's motion on the velocity of photons in our terrestrial laboratory

The paper reports the positive results obtained with a stationary Michelson-Morley interferometer operating during two consecutive years (2003-2005) in Bogota, Colombia. After subtracting the environmental periodical effects, there is still a periodic residual that is no longer correlated to the environmental variables. There is, however, a significant correlation between the daily fringe-shift residuals for each month and the velocity of motion of the earth relative to the center of our galaxy at that particular time. This hints to a possible dependence of the velocity of light in our terrestrial laboratory and the velocity of the earth. This result is contrary to the current model for the photon that postulates a constant speed of light. From our data we have calculated the solar velocity consistent with our observations: 500 km/s, right ascension 16h-40 min, declination -75°.
Butylparaben Exposure Induced Darker Skin Pigmentation in Nile Tilapia (Oreochromis niloticus)

Butylparaben (BuP), as an emerging contaminant with endocrine-disrupting effects, may exert effects on skin pigmentation in fish by interfering with the neuroendocrine system. Therefore, models of BuP exposure in Nile tilapia (Oreochromis niloticus) were established by adding different doses of BuP (0, 5, 50, 500, and 5000 ng/L) for 56 days. The obtained results showed that BuP exposure induced darker skin pigmentation, manifested as increased melanin content of skin, while genes related to melanin synthesis, including α-MSH and Asip2, significantly changed. In addition, BuP exposure reduced dopamine and γ-aminobutyric acid content in the brain, which is related to the synthesis of α-MSH. Furthermore, the release of neurotransmitters from the brain is affected by light. Thus, the relative gene expression levels in the phototransduction pathway were evaluated to explore the molecular mechanism of BuP-induced darker skin pigmentation, and the obtained results showed that Arr3a and Arr3b expression was significantly upregulated, whereas Opsin expression was significantly downregulated in a BuP dose-dependent manner, indicating that BuP inhibited phototransduction from the retina to the brain. Importantly, correlation analysis results showed that all melanin indexes were significantly positively correlated with Arr3b expression and negatively correlated with Opsin expression. This study indicated that BuP induced darker skin pigmentation in Nile tilapia via the neuroendocrine circuit, which reveals the underlying molecular mechanism for the effects of contaminants in aquatic environments on skin pigmentation in fish.
VIMTS: Variational-based Imputation for Multi-modal Time Series

Multi-modal time series data in real applications often contain data of different dimensionalities, e.g., high-dimensional modality such as image data series, and low-dimensional univariate time series. Multi-modal time series data with missing high-dimensional modal values are ubiquitous in real-world classification and regression applications. To accurately predict the target labels, it is important to appropriately impute the high-dimensional modal missing values. However, most existing imputation methods focus on multivariate time series, fail to simultaneously consider temporal dependencies within each series and the correlations across the series, and also lack a probabilistic interpretation. In this paper, we propose a novel method, which uses a new structured variational approximation technique for the imputation of missing values in multi-modal time series. Instead of directly imputing high-dimensional modal missing values, we use the variational approximation technique to impute intermediate lower-dimensional feature representations of high-dimensional modal missing values from simple modalities related to high-dimensional modality and then feed them into a dynamical model. The dynamical model captures the temporal dependencies of the feature representations and finally predicts the target labels. In order to address the optimization difficulties caused by the lack of ground truth values of lower-dimensional feature representations, we also propose a two-stage isolated optimization strategy for better convergence. We evaluate our method on a real-world stream monitoring dataset. Our extensive experiments demonstrate that the proposed method outperforms several state-of-the-art methods in both data imputation and prediction performance.
Traffic Congestion Prediction Using Toll and Route Search Log Data

Predicting future people’s behavior can significantly impact various industries. Intelligent transportation system (ITS) advancement, in particular, depends on the ability to predict traffic congestion. If we can do so, we can encourage people to alter their behavior, which reduces traffic congestion, traffic accidents, travel times, and CO2 emissions while also promoting the development of applications like dynamic pricing. However, predicting traffic congestion a few days ahead is challenging owing to its spatial and temporal dependence and its nature of being susceptible to external factors, such as weather, local events, and the pandemic of infectious diseases. For these reasons, previous studies have been limited to predicting the next few minutes to a few hours. To address this limitation, we propose using search log data of the toll route search service owned by East Nippon Expressway Co., Ltd. (NEXCO East), which operates expressway services in Japan, as these data are available several days before the prediction and comprehensively explain multiple external factors. We show that search log data can contribute to predicting people’s behavior by verifying the improvement in the accuracy of traffic congestion prediction.
A time series illustration of approximate conditional likelihood

SUMMARY We consider the calculation of confidence intervals for the correlation parameter, based on observations of a large number of short Gaussian first-order autoregressive processes with different means but common correlation and variance. Ordinary profile likelihood gives very misleading answers. The approximate conditional likelihood of Cox & Reid (1987) is calculated and shown to lead to consistent estimation of the correlation. Simulations verify that the usual asymptotic properties of the statistics derived from the conditional likelihood are quite satisfactory. In this example the approximate conditional likelihood is the same as the marginal likelihood for the standardized residuals, although this is not true in general.
Maximum Fillet Stress Analysis Based on Load Sharing in Normal Contact Ratio Spur Gear Drives#

An optimum design of high-quality gears requires an accurate estimation of the tooth load. Since only a fraction of the total load is shared by each pair, when there is contact in more than one pair during transmission, the same has been estimated with a reasonable accuracy through the load-sharing ratio calculated using the single-point loaded model. The reliability of this method has also been justified by comparison with multipair contact models and also by checking with the values available in the literature. The influence of finite-element modeling, boundary conditions, gear ratio, teeth number, module, pressure angle at pitch circle, backup ratio, generating rack cutter tip radius, and addendum modification factor on the load-sharing ratio and in turn in the maximum fillet stress has been studied and discussed using single-point loaded model of normal contact ratio gear pairs in this study.
On the Marchenko equation for multicomponent single-sided reflection data

Recent work on the Marchenko equation has shown that the scalar 3-D Green’s function for a virtual source in the subsurface can be retrieved from the single-sided reflection response at the surface and an estimate of the direct arrival. Here, we discuss the first steps towards extending this result to multicomponent data. After introducing a unified multicomponent 3-D Green’s function representation, we analyse its 1-D version for elastodynamic waves in more detail. It follows that the main additional requirement is that the multicomponent direct arrival, needed to initiate the iterative solution of the Marchenko equation, includes the forward-scattered field. Under this and other conditions, the multicomponent Green’s function can be retrieved from single-sided reflection data, and this is demonstrated with a 1-D numerical example.
A novel self-adaptive method for improving patient monitoring with composite early-warning scores

Wearable sensors utilize small, low-cost, noninvasive, and wireless components. These sensors capture vital signs, allowing the monitoring of patients remotely. In this manner, they are efficient tools to enhance patient care and can be used to monitor vulnerable populations, and keep track of the development of chronic diseases, and the transmission of infectious illnesses – such as during pandemics. However, there are many challenges to monitoring patients using wearables, with massive data generation and battery power consumption being significant constraints. Strategies to reduce data generation should be applied taking into account the patient’s clinical status and health risks. Previous studies took advantage of single early-warning scores (EWS) utilized in infirmaries to detect emergencies, reduce transmissions, and be a reference for self-adaptive features embedded in the devices. Our work proposes the use of composite EWS to infer health deterioration risk, minimize data transmissions and power consumption, and reduce excessive alarms through self-adaptive features based on these scores. We also compare our method with previous studies using real patient data. Further, we propose applying self-adaptive features to sampling, processing, and transmission rates. Our method demonstrated enhanced data reduction, 81% fewer readings than the baseline, significant pruning of the number of alarms, and dynamic and automatic inference of patient risk.
Identification of binary file compilation information

Cross-architecture binary code similarity detection technology has been widely used in vulnerability discovery, reverse engineering and patch detection. The identification of binary file compilation information is conducive to the improvement of the accuracy in binary code similarity detection. The compilation information of binary files includes compilation architecture, compiler, optimization option and obfuscation strategy. For the compilation architecture, we build a compiling architecture feature library based on the ELF header information of the binary file for identification; for the compiler, we use Linux system commands to identify; for the optimization option and obfuscation strategy, we extract 70 static features of binary file function-level assembly language and establish a genetic neural network model for identification. In addition, we set up five experimental tasks to learn more about the compilation architecture and compiler impact on model identification optimization options and obfuscation strategies. The final experimental results show that the accuracy of the binary file compilation information identification model designed by us is 100% for both compilation architectures and compilers identification, and the F-Score for optimization options identification can reach 89.46%. The F-Score for obfuscation strategies identification can reach 88.74%, and the F-Score for simultaneous identification of optimization options and obfuscation strategies can reach 84.28%, which is significantly better than previous works.