Backward Correlation Research Articles

Differential-Linear Cryptanalysis of Reduced Round ChaCha

ChaCha is a well-known stream cipher that has been used in many network protocols and software. In this paper, we study the security of reduced round ChaCha. First, by considering the differential-linear hull effect, we improve the correlation of a four-round differential-linear distinguisher proposed at FSE 2023 by providing other intermediate linear masks. Then, based on the four-round differential-linear distinguisher and the PNB method, by using the assignment 100 ··· 00 for consecutive PNBs, higher backward correlation is obtained and improved key recovery attacks of 7-round and 7.25-round ChaCha are obtained with time complexity 2189.7 and 2223.9, which improve the previously best-known attacks by 217.1 and 214.44, respectively. Finally, we consider the equivalence of the security between (R + 0.25)-round and (R + 0.5)⊕-round ChaCha, and show that (R + 0.25)-round and (R + 0.5)⊕-round ChaCha provide the same security against chosen(known) plaintext attacks. As a result, improved differential-linear cryptanalysis of 7.5⊕-round ChaCha can also be obtained similarly to that of 7.25-round ChaCha, which improves the previously best-known attack by 219.

A model dependent study of forward–backward multiplicity correlation in pp collisions at LHC energies

A model dependent study of forward–backward multiplicity correlation in pp collisions at LHC energies

Study of forward–backward correlation and multiplicity fluctuations of pions produced in p–p collisions at recent LHC energies

The two-particle rapidity correlation among the pions produced in p–p collisions at recent LHC energies 2.76, 5.02, 7 and 13 TeV are studied in this paper by using the analysis of events generated by UrQMD-3.4, AMPT-2.26 and PYTHIA-8.3. Our investigation for bin-to-bin fluctuation using the variance of forward–backward asymmetry parameter in the forward–backward pseudo-rapidity windows is done through the simulated data. In addition to that, energy-wise variation of correlation strength (β), the probability distribution of roughness parameter () and number density fluctuation parameter (d ik ) are also incorporated in this investigation. Also, we have tried to study the same for the data of different impact parameters generated using the UrQMD model, and the comparison is made with its minimum biased events data-set. The analysis signifies, that the signature of dynamical fluctuation is not so prominent but increases a bit with the increase in energy. An increase in the correlation strength with the increase in energy signifies the contribution of long-range correlation is more in comparison to the short-range one. However, there is no such accountable effect of various impact parameters seen in the present study.

A Modification of the Backward Correlation Method for the Brillouin Frequency Shift Accurate Extraction

A Modification of the Backward Correlation Method for the Brillouin Frequency Shift Accurate Extraction

Interacting Colour Strings Approach in Modelling of Rapidity Correlations

In this paper, using the concept of multi-pomeron exchange, we develope a Monte Carlo model of interacting quark–gluon strings acting as particle-emitting sources aimed at describing inelastic proton–proton interactions at high energies. The implemented 3D (three-dimensional) dynamics of colour string formation resulted in their finite length in the rapidity space and in the fluctuating event-by-event spatial density. Thus, this results in string cluster formation because of the fusion mechanism and the appearance of long-range multiplicity and mean transverse momentum (mean-pT) correlations in rapidity. We study, via the pseudorapidity dependence, the sensitivity to the details of the 3D dynamical formation of strings for several observables such as the forward–backward correlation coefficient value, strongly intensive quantity, Σ, and the “almost” strongly intensive observable, the variance, σC2, of the distribution of the asymmetry coefficient, C. The strongly intensive quantity Σ is used in this study to suppress trivial statistical fluctuations in the number of particles emitting similar types of sources and to reveal the intrinsic fluctuations of a single source. We demonstrate the connection between Σ and such often used observables as cumulants, factorial cumulants, and σC2. We stress the importance of the contribution of “short” strings and the event asymmetry of the initial conditions on the long-range correlation measures. We argue that string cluster formation because of the fusion mechanism explains the collective effects seen in multiplicity and transverse momentum–multiplicity, ⟨pT⟩–N, long-range correlation functions.

A Refinement of Backward Correlation Technique for Precise Brillouin Frequency Shift Extraction

A new method for extracting the Brillouin frequency shift (BFS) from the Brillouin gain spectrum (BGS), the modified backward correlation method (MBWC), is presented. The possibilities of using MBWC, and MBWC in combination with the Lorentzian curve fitting (LCF) based on Levenberg–Marquardt (LM) method, are studied. The effectiveness of the new method, and its combination with LM, has been demonstrated for processing spectra with a low signal-to-noise ratio (SNR). The experiments, which were in good agreement with the performed simulation, showed that at SNR = 0 dB, the combined (MBWC + LM) method provided the BFS extraction error of less than 4 MHz, while the state-of-the-art LM algorithm extracted it with the error greater than 4.5 MHz. The advantage of correlation methods becomes more significant with the decreasing SNR: at SNR = −2 dB, the LM’s error is 14.3 MHz, and that of the combined one is 8.1 MHz.

Value Chain and the Integrated Development of Manufacturing and Modernized Services: A Case Study of Shandong Province, China

The integrated development of manufacturing and modernized service (IDMMS) is an important direction for the transformation and upgrading of manufacturing. Building upon value co-creation, industry chain theory and the industrial boundary literature, this paper explores an analytical framework of IDMMS, combining theoretical and empirical evidence. Then, this study constructs several indicators for measurement of industrial integration. The results indicate that: (1) manufacturing has more self-circulation, and the degree of backward correlation between the modernized services and the manufacturing is weak, while the support and driving effect of the manufacturing is insufficient; (2) most industrial sectors play a relatively stable role in influencing production demand. The findings reveal that there exist several problems referring to industry and enterprises. Finally, this paper discusses the mechanism of IDMMS and reaches its conclusions. The conclusions provide a comprehensive and detailed industrial understanding of the formation of comprehensive and systematic regional economic strategies and suggest targeted industrial policies to achieve sustainable development.

Spatial-Temporal Feature with Dual-Attention Mechanism for Encrypted Malicious Traffic Detection

While encryption ensures the confidentiality and integrity of user data, more and more attackers try to hide attack behaviours through encryption, which brings new challenges to malicious traffic identification. How to effectively detect encrypted malicious traffic without decrypting traffic and protecting user privacy has become an urgent problem to be solved. Most of the current research only uses a single CNN, RNN, and SAE network to detect encrypted malicious traffic, which does not consider the forward and backward correlation between data packets, so it is difficult to effectively identify malicious features in encrypted traffic. This study proposes an approach that combines spatial-temporal feature with dual-attention mechanism, which is called TLARNN. Specifically, first we use 1D-CNN and BiGRU to extract spatial features in encrypted traffic packets and temporal features between encrypted streams, respectively, which enriches the features of different dimensions, and then, the soft attention mechanism is focused on the encrypted data packets to extract features. Ultimately, the second layer of the soft attention mechanism is used for aggregating malicious features. Several comparative experiments are designed to prove the effectiveness of the proposed scheme. The experimental results demonstrate that the proposed scheme has a significant performance improvement compared to existing ones.

A new method for heart rate prediction based on LSTM-BiLSTM-Att

This paper proposes a new method for heart rate prediction based on LSTM-BiLSTM-Att model (Long Short Term Memory, Bidirectional LSTM, Attention Mechanism). In this LSTM-BiLSTM-Att model, LSTM is used to construct the long-term relationship of the heart rate data, and then abstract the high-dimensional features of the heart rate. BiLSTM has the ability to capture the forward and backward correlation information of these heart rate data, and then effectively learn the features of the heart rate data extracted by LSTM. The attention mechanism is added to this proposed model, which can further improve the performance of this heart rate prediction method. The number of neurons, the length of sliding window, and the depth of the LSTM-BiLSTM-Att model are optimized. Two volunteers were randomly selected from 30 volunteers to test the resting heart rate prediction by using random forest (RF) method, ARIMA, the feed forward neural network (FNN), LSTM model, BiLSTM model, LSTM-BiLSTM model, and LSTM-BiLSTM-Att model. The experimental results show that the root mean square error (RMSE) of the resting heart rate of the male volunteer by using this proposed LSTM-BiLSTM-Att model is 2.520, which is 43.9% of RF, 45.9% of ARIMA, 92.1% of FNN and 98.2% of LSTM, respectively; the RMSE of the male volunteer by using the LSTM-BiLSTM-Att model is 1.729, which is 45.5% of RF, 49.5% of ARIMA, 93.6% of FNN and 96.2% of LSTM, respectively. This proposed LSTM-BiLSTM-Att method effectively improves the accuracy of heart rate prediction, and the experimental results prove the effectiveness of this method.

Isotopes-based characterization of precipitation compositions and atmospheric water vapor sources over typical Eurasian steppes in south mongolian Plateau

An inland river grassland basin is a fragile ecosystem, and precipitation is of great significance to its resilience. However, the knowledge of where precipitation-forming water vapor originated is not well understood, limiting the predictability of future precipitation provided climate change. The objective of this study was to characterize the hydrogen (δD) and oxygen (δ18O) isotopes and hydro-chemical compositions of precipitation as well as decipher atmospheric water vapor sources in a typical Eurasian steppe river basin. In this regard, this study collected 59 water samples from 44 storm events in 2019 and 2020. The samples were analyzed in our laboratory for ions, δD, and δ18O, which in turn were used to examine the stable isotopic features, hydro-chemical characteristics, and water vapor transport at different temporal scales by a backward trajectory model and correlation and redundancy analyses. The results showed negative δD and δ18O during the wet season with a trend of positive deuterium excess, indicating that precipitation effect was significant. In comparison with a linear model of global meteoric water levels, the local meteoric water lines had smaller slopes and intercepts, with a maximum slope in August and a minimum slope in July. In addition, HCO3− and Ca2+ were the primary hydro-chemical ions in precipitation. Ca2+ and Mg2+ were significantly correlated with HCO3− and Na+, indicating that they shared terrestrial and marine sources. NH4+ originated mainly from human activities and chemical reactions, whereas K+ originated primarily from continental and marine processes. Further, the atmospheric water vapor was mainly transported from the northeast, northwest, and southeast directions into the study basin, as affected by the westerly wind circulation, the East Asian summer monsoon, northeast anticyclone circulation, and local evapotranspiration. Moreover, the water vapor was from both onsite and offsite sources: over 40% from Eurasia, 10 to 25% from the Arctic Ocean, and 35 to 45% from the Pacific Ocean.

Video Snapshot Compressive Imaging Using Residual Ensemble Network

Video snapshot compressive imaging (SCI) system enables high-frame-rate imaging by projecting multiple frames into a 2D snapshot measurement during a single exposure, and the original video frames can be reconstructed by solving an optimization problem. However, existing methods usually cannot achieve a good balance between reconstruction time and reconstruction quality, which has become a major obstacle for practical application of video SCI. In order to cope with this issue, we propose a residual ensemble network to learn the explicit inverse mapping from the 2D snapshot measurement to the original video. Specifically, the proposed network aims to exploit the spatiotemporal correlations between video frames for improving reconstruction quality. The spatiotemporal correlations of video frames demonstrate multiple types, including intra-frame spatial correlation, inter-frame forward and backward temporal correlation. With the purpose of fully capturing these differentiated correlations, we design four sub-networks, namely, a pseudo-3D U-shape sub-network, two residual sub-networks, and a serial forward and backward recurrent sub-network, and further assemble these four sub-networks into an ensemble network through alternate residual links. This ensemble network can effectively fuse the predictions of each sub-network and maintain spatiotemporal consistency between video frames. We further design a compound loss function to guide the network learning, and the new video can be fast reconstructed by simply feeding its 2D snapshot measurement into the learned network. The experimental results demonstrate that our network can significantly improve the reconstruction quality while maintaining low computational cost.

A Study on Changes in the Dependency Structure between Korea and Japan

Purpose: This paper focuses on empirically analyzing the changes and characteristics of the division of labor between the two countries after measuring the dependency relationship between Korea and Japan for the past 20 years. The reason for this is due to the increasing protectionism barriers such as export restrictions between Korea and Japan and trade frictions between the US and China. Research design, data, and methodology: The analysis The analysis industries in this study are 9 manufacturing industries in Korea and Japan, and the analysis period was set from 2000 to 2020. The analysis method introduces the backward and forward correlation effects of the international industry model, and ADB-MRIO is used for statistics. Results: As a result of the analysis, it was observed that over the past 20 years, the structure of division of labor between Korea and Japan is changing from Korea’s unilateral dependence on Japan in the past to a two-way structure of dependence between Korea and Japan. In particular, both Korea and Japan are highly dependent on each other in the same industries, so-called electric·electronic and precision machinery, metal products, chemical products, general machinery, and transportation machinery, confirming that a close division of labor has been established. Implications: The analysis results of this study indicate that the level of Korea’s production technology for Japan has been improving over the past 20 years, and at the same time suggest that the structure of division of labor between the two countries is deepening due to the increase in Japan’s dependence on Korea’s production technology.

Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring.

In this paper, we studied the possibility of increasing the Brillouin frequency shift (BFS) detection accuracy in distributed fibre-optic sensors by the separate and joint use of different algorithms for finding the spectral maximum: Lorentzian curve fitting (LCF, including the Levenberg–Marquardt (LM) method), the backward correlation technique (BWC) and a machine learning algorithm, the generalized linear model (GLM). The study was carried out on real spectra subjected to the subsequent addition of extreme digital noise. The precision and accuracy of the LM and BWC methods were studied by varying the signal-to-noise ratios (SNRs) and by incorporating the GLM method into the processing steps. It was found that the use of methods in sequence gives a gain in the accuracy of determining the sensor temperature from tenths to several degrees Celsius (or MHz in BFS scale), which is manifested for signal-to-noise ratios within 0 to 20 dB. We have found out that the double processing (BWC + GLM) is more effective for positive SNR values (in dB): it gives a gain in BFS measurement precision near 0.4 °C (428 kHz or 9.3 ); for BWC + GLM, the difference of precisions between single and double processing for SNRs below 2.6 dB is about 1.5 °C (1.6 MHz or 35 ). In this case, double processing is more effective for all SNRs. The described technique’s potential application in structural health monitoring (SHM) of concrete objects and different areas in metrology and sensing were also discussed.

System scan of the multiplicity correlation between forward and backward rapidities in relativistic heavy-ion collisions using a multi-phase transport model * *Supported in part the National Natural Science Foundation of China (11890710, 11890714, 11875066, 11925502, 11961141003), the Strategic Priority Research Program of CAS (XDB34000000), National Key R&D Program of China (2018YFE0104600, 2016YFE0100900), and Guangdong Major Project of Basic and Applied

A systematic study on forward–backward (FB) multiplicity correlations from large systems to small ones through a multi-phase transport model (AMPT) has been performed and the phenomenon that correlation strength increases with centrality can be explained by taking the distribution of events as the superposition of a series of Gaussian distributions. It is also found that correlations in the plane can imply the shape of the event. Furthermore, long-range correlations originate from the fluctuations associated with the source information. FB correlations allow us to decouple long-range correlations from short-range correlations, and may provide a chance to investigate the α-clustering structure in initial colliding light nuclei as well. It seems the tetrahedron 16O + 16O collision gives a more uniform and symmetrical fireball, that emits the final particles more isotropically or independently in the longitudinal direction, indicating that the forward–backward multiplicity correlation could be used to identify the pattern of α-clustered 16O in future experiments.

Evidence of forward–backward correlation of pions in ultra-relativistic ring- and jet-like events in $$^{16}O-Ag/Br$$ interactions at $$E_{lab}=60$$ A GeV

Evidence of forward–backward correlation of pions in ultra-relativistic ring- and jet-like events in $$^{16}O-Ag/Br$$ interactions at $$E_{lab}=60$$ A GeV

Forward–backward multiplicity correlation and event-by-event multiplicity fluctuation in nucleus–nucleus collisions at 200A GeV

Correlation among singly charged particles emitted in the forward and backward pseudo-rapidity cones is measured in [Formula: see text]O-Ag/Br and [Formula: see text]S-Ag/Br interactions at an incident energy of [Formula: see text] GeV/nucleon. Event-by-event fluctuations in the charged particle multiplicities and their pseudo-rapidity values are also investigated in terms of some known statistical measures. Evidences of short-ranged particle correlation and cluster formation in the pseudo-rapidity space are found from our analysis. A microscopic transport model based on the Ultra-relativistic Quantum Molecular Dynamics could not match the experimental results. The differences between experimental observation and corresponding simulation could neither be accounted for even when a Bose–Einstein type of correlation effect is implemented into the simulated data.

Dynamic speckle deflectometry based on backward digital image correlation

Traditional phase measuring deflectometry locates the screen source point (SSP) using the sinusoidal fringe based phase extraction and unwrapping methods, which cannot realize dynamic measurement. And the digital image correlation based deflectometry needs at least 2 shots for surface reconstruction. We propose a single-shot dynamic speckle deflectometry (DSD) that locates the SSP by directly matching the captured single-shot speckle pattern with a computer synthesized speckle pattern. The theoretical basis is detailed. Numerical simulation is conducted to evaluate the validity and robustness of DSD with comparison to the fringe based approaches. The accuracy performance of DSD is quantified by measuring a glass piece, the surface result differs from that of interferometer 46.0 nm in root mean square. The dynamic motion of a wafer deformed by squeezing force is recorded by DSD, the squeezing force brings a shape deformation mainly in the form of astigmatism to the wafer surface.

Dynamic crossover in [VIO2+][Tf2N−]2 ionic liquid* *Project supported by the Science Foundation of Civil Aviation Flight University of China (Grant Nos. J2019-059 and JG2019-19).

Ionic liquids usually behave as fragile liquids, and the temperature dependence of their dynamic properties obeys supper-Arrhenius law. In this work, a dynamic crossover is observed in ([VIO2+][Tf2N−]2) ionic liquid at the temperature of 240–800 K. The diffusion coefficient does not obey a single Arrhenius law or a Vogel–Fulcher–Tammann (VFT) relation, but can be well fitted by three Arrhenius laws or a combination of a VFT relation and an Arrhenius law. The origin of the dynamic crossover is analyzed from correlation, structure, and thermodynamics. Ion gets a stronger backward correlation at a lower temperature, as shown by the fractal dimension of the random walk. The temperature dependence function of fractal dimension, heterogeneity order parameter, and thermodynamic data can be separated into three regions similar to that observed in the diffusion coefficient. The two crossover temperatures observed in the three types of data are almost the same as that in diffusion coefficient fitted by three Arrhenius laws. The results indicate that the dynamic crossover of [VIO2+][Tf2N−]2 is attributed to the heterogeneous structure when it undergoes cooling.

Deep Canonical Correlation Analysis For Decoding The Auditory Brain.

The process of decoding the auditory brain for an acoustic stimulus involves finding the relationship between the audio input and the brain activity measured in terms of Electroencephalography (EEG) recordings. Prior methods focus on linear analysis methods like Canonical Correlation Analysis (CCA) to establish a relationship. In this paper, we present a deep learning framework that is learned to maximize correlation. For dealing with high levels of noise in EEG data, we employ regularization techniques and experiment with various model architectures. With a paired dataset of audio envelope and EEG, we perform several experiments with deep correlation analysis using forward and backward correlation models. In these experiments, we show that regularized deep CCA is consistently able to outperform the linear models in terms of providing improved correlation (up to 9% absolute improvement in Pearson correlation which is statistically significant). We present an analysis that highlights the benefits of using dropouts for neural network regularization in the deep CCA model.Clinical relevance - The proposed method helps to decode human auditory attention. In the case of overlapping speech from two speakers, decoding the auditory attention provides information about how well the sources are separated in the brain and which of the sources is attended. This can impact cochlear implants that use EEG for decoding attention as well as in development of BCI applications. The correlation method proposed in this work can also be extended to other modalities like visual stimuli.

Дименсии на възникващата зрелост и дистрес на идентичността

Възникващата зрелост се характеризира с психосоциален мораториум до 30 годишна възраст, а психосоциалният мораториум предполага криза или дистрес на идентичността. Интерес за нас представляват възможните взаимовръзки между характеристиките на развитието и дистреса на идентичността и тяхното опосредстване от процеси на себерефлексия. В изследването участват 202 изследвани лица на възраст 17-30 години. Използвани са Въпросник за измерване на дименсиите на възникващата зрелост (IDEA), Скала за измерване на дистреса, свързан с идентичността (IDM) и Въпросник за проактивно-ригидно осмисляне (RRQ). Резултатите разкриват, че съществува права корелация между общото ниво на дискомфорт и негативността/ нестабилността и обратна корелация при кариерния избор и чувството за „помежду“ от друга страна. Негативността/ нестабилността е предиктор както за по-силно преживяване на общ дискомфорт, така и за по-продължително преживяване на дистрес на идентичността. Отделянето от родителите е също предиктор за преживяване на по-високо общо ниво на дискомфорт, а работата (паралелно с образованието) – предиктор за по-продължителен преживяван дистрес. Ригидното осмисляне е медиатор във връзката между дименсията негативност/ нестабилност и продължителността на преживяван дистрес.