Infinite Horizon Mean-Field Linear Quadratic Optimal Control Problems with Jumps and the related Hamiltonian Systems

In this work, we focus on an infinite horizon mean-field linear-quadratic stochastic control problem with jumps. Firstly, the infinite horizon linear mean-field stochastic differential equations and backward stochastic differential equations with jumps are studied to support the research of the control problem. The global integrability properties of their solution processes are studied by introducing a kind of so-called dissipation conditions suitable for the systems involving the mean-field terms and jumps. For the control problem, we conclude a sufficient and necessary condition of open-loop optimal control by the variational approach. Besides, a kind of infinite horizon fully coupled linear mean-field forward-backward stochastic differential equations with jumps is studied by using the method of continuation. Such a research makes the characterization of the open-loop optimal controls more straightforward and complete.Comment: 27page

New separation protocol reveals spray painting as a neglected source of microplastics in soils

Microplastics are recently discovered contaminants, yet knowledge on their sources and analysis is limited. For instance, paint microplastics are poorly known because soil separation protocols using flotation solutions cannot separate paint microplastics due to the higher density of paint microplastic versus common microplastics. Here, we designed a new two-step density separation protocol for paint microplastics, allowing paint microplastics to be separated from the soil without digestion. Paint particles were separated from soil samples collected around the graffiti wall at the Mauerpark, Berlin, then quantified according to their shape and color characteristic. The presence of polymers as binders in the paint particles was verified by Fourier transform infrared spectroscopy. Results show concentrations from 1.1 × 105 to 2.9 × 105 microplastics per Kg of dry soil, representing the highest microplastic concentration ever reported in the literature. Particle concentrations decreased and the median size increased with soil depth. Our results provide first evidence that spray painting, a technique with a wide range of applications from industry to art, leaves a legacy of environmental microplastic in soils that has so far gone unnoticed

Rotational-Linear Attack: A New Framework of Cryptanalysis on ARX ciphers with Applications to Chaskey

In this paper, we formulate a new framework of cryptanalysis called rotational-linear attack on ARX ciphers. We firstly build an efficient distinguisher for the cipher $E$ consisted of the rotational attack and the linear attack together with some intermediate variables. Then a key recovery technique is introduced with which we can recover some bits of the last whitening key in the related-key scenario. To decrease data complexity of our attack, we also apply a new method, called bit flipping, in the rotational cryptanalysis for the first time and the effective partitioning technique to the key-recovery part. Applying the new framework of attack to the MAC algorithm Chaskey, we build a full-round distinguisher over it. Besides, we have recovered $21$ bits of information of the key in the related-key scenario, for keys belonging to a large weak-key class based on 6-round distinguisher. The data complexity is $2^{38.8}$ and the time complexity is $2^{46.8}$. Before our work, the rotational distinguisher can only be used to reveal key information by checking weak-key conditions. This is the first time it is applied in a last-rounds key-recovery attack. We build a 17-round rotational-linear distinguisher for ChaCha permutation as an improvement compared to single rotational cryptanalysis over it

Soil Storage Conditions Alter the Effects of Tire Wear Particles on Microbial Activities in Laboratory Tests

In this study, we focused on the fact that soil storage conditions in the laboratory have never been considered as a key factor potentially leading to high variation when measuring effects of microplastics on soil microbial activity. We stored field-collected soils under four different conditions [room-temperature storage, low-temperature storage (LS), air drying (AD), and heat drying] prior to the experiment. Each soil was treated with tire wear particles (TWPs), and soil microbial activities and water aggregate stability were investigated after soil incubation. As a result, microbial activities, including soil respiration and three enzyme activities (β-glucosidase, N-acetyl-β-glucosaminidase, and phosphatase), were shown to depend on soil storage conditions. Soil respiration rates increased with the addition of TWPs, and the differences from the control group (no TWPs added) were more pronounced in the AD TWP treatment than in soils stored under other conditions. In contrast, phosphatase activity followed an opposing trend after the addition of TWPs. The AD soil had higher phosphatase activity after the addition of TWPs, while the LS soil had a lower level than the control group. We suggest that microplastic effects in laboratory experiments can strongly depend on soil storage conditions

A Decoding Scheme for Incomplete Motor Imagery EEG With Deep Belief Network

High accuracy decoding of electroencephalogram (EEG) signal is still a major challenge that can hardly be solved in the design of an effective motor imagery-based brain-computer interface (BCI), especially when the signal contains various extreme artifacts and outliers arose from data loss. The conventional process to avoid such cases is to directly reject the entire severely contaminated EEG segments, which leads to a drawback that the BCI has no decoding results during that certain period. In this study, a novel decoding scheme based on the combination of Lomb-Scargle periodogram (LSP) and deep belief network (DBN) was proposed to recognize the incomplete motor imagery EEG. Particularly, instead of discarding the entire segment, two forms of data removal were adopted to eliminate the EEG portions with extreme artifacts and data loss. The LSP was utilized to steadily extract the power spectral density (PSD) features from the incomplete EEG constructed by the remaining portions. A DBN structure based on the restricted Boltzmann machine (RBM) was exploited and optimized to perform the classification task. Various comparative experiments were conducted and evaluated on simulated signal and real incomplete motor imagery EEG, including the comparison of three PSD extraction methods (fast Fourier transform, Welch and LSP) and two classifiers (DBN and support vector machine, SVM). The results demonstrate that the LSP can estimate relative robust PSD features and the proposed scheme can significantly improve the decoding performance for the incomplete motor imagery EEG. This scheme can provide an alternative decoding solution for the motor imagery EEG contaminated by extreme artifacts and data loss. It can be beneficial to promote the stability, smoothness and maintain consecutive outputs without interruption for a BCI system that is suitable for the online and long-term application