Construction of multi-mineral digital rocks for upscaling the numerical simulation of tight rock physical properties

Tight sandstone reservoirs are characterized by multi-scale pore space and high clay content, resulting in intricate rock physical responses. In this work, multi-scale imaging techniques, including computed tomography and stitched scanning electron microscopy, are applied to identify the large intergranular pores and micropores within major minerals. The pore structure of tight sandstones is quantitatively investigated using multi-scale images. Besides, multi-mineral digital rocks are constructed by performing registration and segmentation processing on the images obtained from microcomputed tomography and energy-dispersive scanning electron microscopy. These digital rocks are treated as composite materials consisting of different mineral types and micro-porosities, which enables the upscaling of the numerical simulation of rock physics properties. The results reveal that residual intergranular pores are interconnected through micropores within clay minerals, which significantly influences the electrical conductivities and permeabilities of tight sandstones. The proposed upscaling method can effectively couple the contribution of formation brine in multi-scale pores and clay minerals to bulk rock physics properties. This approach is suitable for the numerical simulation of diverse rock physical properties and can be applied to various tight reservoirs.Document Type: PerspectiveCited as: Hu, J., Xiao, Z., Ni, H., Liu, X. Construction of multi-mineral digital rocks for upscaling the numerical simulation of tight rock physical properties. Advances in Geo-Energy Research, 2023, 9(1): 68-70. https://doi.org/10.46690/ager.2023.07.0

Identification of Ghost Targets for Automotive Radar in the Presence of Multipath

Colocated multiple-input multiple-output (MIMO) technology has been widely used in automotive radars as it provides accurate angular estimation of the objects with relatively small number of transmitting and receiving antennas. Since the Direction Of Departure (DOD) and the Direction Of Arrival (DOA) of line-of-sight targets coincide, MIMO signal processing allows forming a larger virtual array for angle finding. However, multiple paths impinging the receiver is a major limiting factor, in that radar signals may bounce off obstacles, creating echoes for which the DOD does not equal the DOA. Thus, in complex scenarios with multiple scatterers, the direct paths of the intended targets may be corrupted by indirect paths from other objects, which leads to inaccurate angle estimation or ghost targets. In this paper, we focus on detecting the presence of ghosts due to multipath by regarding it as the problem of deciding between a composite hypothesis, ${\cal H}_0$ say, that the observations only contain an unknown number of direct paths sharing the same (unknown) DOD's and DOA's, and a composite alternative, ${\cal H}_1$ say, that the observations also contain an unknown number of indirect paths, for which DOD's and DOA's do not coincide. We exploit the Generalized Likelihood Ratio Test (GLRT) philosophy to determine the detector structure, wherein the unknown parameters are replaced by carefully designed estimators. The angles of both the active direct paths and of the multi-paths are indeed estimated through a sparsity-enforced Compressed Sensing (CS) approach with Levenberg-Marquardt (LM) optimization to estimate the angular parameters in the continuous domain. An extensive performance analysis is finally offered in order to validate the proposed solution.Comment: 13 pages, 10 figure

Context-I2W: Mapping Images to Context-dependent Words for Accurate Zero-Shot Composed Image Retrieval

Different from Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://github.com/Pter61/context_i2w

A Duty to Forget, a Right to be Assured? Exposing Vulnerabilities in Machine Unlearning Services

The right to be forgotten requires the removal or "unlearning" of a user's data from machine learning models. However, in the context of Machine Learning as a Service (MLaaS), retraining a model from scratch to fulfill the unlearning request is impractical due to the lack of training data on the service provider's side (the server). Furthermore, approximate unlearning further embraces a complex trade-off between utility (model performance) and privacy (unlearning performance). In this paper, we try to explore the potential threats posed by unlearning services in MLaaS, specifically over-unlearning, where more information is unlearned than expected. We propose two strategies that leverage over-unlearning to measure the impact on the trade-off balancing, under black-box access settings, in which the existing machine unlearning attacks are not applicable. The effectiveness of these strategies is evaluated through extensive experiments on benchmark datasets, across various model architectures and representative unlearning approaches. Results indicate significant potential for both strategies to undermine model efficacy in unlearning scenarios. This study uncovers an underexplored gap between unlearning and contemporary MLaaS, highlighting the need for careful considerations in balancing data unlearning, model utility, and security.Comment: To Appear in the Network and Distributed System Security Symposium (NDSS) 2024, San Diego, CA, US

Integral Attack on the Full FUTURE Block Cipher

FUTURE is a recently proposed lightweight block cipher that achieved a remarkable hardware performance due to careful design decisions. FUTURE is an Advanced Encryption Standard (AES)-like Substitution-Permutation Network (SPN) with 10 rounds, whose round function consists of four components, i.e., SubCell, MixColumn, ShiftRow and AddRoundKey. Unlike AES, it is a 64-bit-size block cipher with a 128-bit secret key, and the state can be arranged into 16 cells. Therefore, the operations of FUTURE including its S-box is defined over $\mathbb{F}_2^4$. The previous studies have shown that the integral properties of 4-bit S-boxes are usually weaker than larger-size S-boxes, thus the number of rounds of FUTURE, i.e., 10 rounds only, might be too aggressive to provide enough resistance against integral cryptanalysis. In this paper, we mount the integral cryptanalysis on FUTURE. With state-of-the-art detection techniques, we identify several integral distinguishers of 7 rounds of FUTURE. By extending this 7-round distinguisher by 3 forward rounds, we manage to recover all the 128 bits secret keys from the full FUTURE cipher without the full codebook for the first time. To further achieve better time complexity, we also present a key recovery attack on full FUTURE with full codebook. Both attacks have better time complexity than existing results

On the Field-Based Division Property: Applications to MiMC, Feistel MiMC and GMiMC (Full Version)

Recent practical applications using advanced cryptographic protocols such as multi-party computations (MPC) and zero-knowledge proofs (ZKP) have prompted a range of novel symmetric primitives described over large finite fields, characterized as arithmetization-oriented AO ciphers. Such designs, aiming to minimize the number of multiplications over fields, have a high risk of being vulnerable to algebraic attacks, especially to the higher-order differential attack. Thus, it is significant to carefully evaluate the growth of their algebraic degree. However, the degree estimation for AO ciphers has been a challenge for cryptanalysts due to the lack of general and accurate methods. In this paper, we extend the division property, a state-of-the-art framework for finding the upper bound of the algebraic degree over binary fields, to the scope of $\mathbb{F}_{2^n}$. It is a generic method to detect the algebraic degree for AO ciphers, even applicable to Feistel ciphers which have no better bounds than the trivial exponential one. In this general division property, our idea is to evaluate whether the polynomial representation of a block cipher contains some specific monomials. With a deep investigation of the arithmetical feature, we introduce the propagation rules of monomials for field-based operations, which can be efficiently modeled using the bit-vector theory of SMT. Then the new searching tool for degree estimation can be constructed due to the relationship between the algebraic degree and the exponents of monomials. We apply our new framework to some important AO ciphers, including Feistel MiMC, GMiMC, and MiMC. For Feistel MiMC, we show that the algebraic degree grows significantly slower than the native exponential bound. For the first time, we present a secret-key higher-order differential distinguisher for up to 124 rounds, much better than the 83-round distinguisher for Feistel MiMC permutation proposed at CRYPTO 2020. We also exhibit a full-round zero-sum distinguisher with a data complexity of $2^{251}$. Our method can be further extended for the general Feistel structure with more branches and exhibit higher-order differential distinguishers against the practical instance of GMiMC for up to 50 rounds. For MiMC in SP-networks, our results correspond to the exact algebraic degree proved by Bouvier et al. We also point out that the number of rounds in MiMC\u27s specification is not sufficient to guarantee the security against the higher-order differential attack for MiMC-like schemes with different exponents. The investigation of different exponents provides some guidance on the cipher design

The effects of cognitive behavioural therapy on depression and quality of life in patients with maintenance haemodialysis: a systematic review

Depression is highly prevalent among Haemodialysis (HD) patients and is known to results in a series of adverse outcomes and poor quality of life (QoL). Although cognitive behavioural therapy (CBT) has been shown to improve depressive symptoms and QoL in other chronic illness, there is uncertainty in terms of the effectiveness of CBT in HD patients with depression or depressive symptoms. All randomised controlled trials relevant to the topic were retrieved from the following databases: CINHAL, MEDLINE, PubMed, PsycINFO and CENTRAL. The grey literature, specific journals, reference lists of included studies and trials registers website were also searched. Data was extracted or calculated from included studies that had measured depression and quality of life using valid and reliable tools -this included mean differences or standardised mean differences and 95% confidence intervals. The Cochrane risk of bias tool was used to identify the methodological quality of the included studies. Six RCTs were included with varying methodological quality. Meta-analysis was undertaken for 3 studies that employed the CBT versus usual care. All studies showed that the depressive symptoms significantly improved after the CBT. Furthermore, CBT was more effective than usual care (MD = - 5.28, 95%CI - 7.9 to - 2.65, P = 0.37) and counselling (MD = - 2.39, 95%CI - 3.49 to - 1.29), while less effective than sertraline (MD = 2.2, 95%CI 0.43 to 3.97) in alleviating depressive symptoms. Additionally, the CBT seems to have a beneficial effect in improving QoL when compared with usual care, while no significant difference was found in QoL score when compared CBT with sertraline. CBT may improve depressive symptoms and QoL in HD patients with comorbid depressive symptoms. However, more rigorous studies are needed in this field due to the small quantity and varied methodological quality in the identified studies

Integrated micro/nano drug delivery system based on magnetically responsive phase-change droplets for ultrasound theranostics

Phase-change droplets (PCDs) are intelligent responsive micro and nanomaterials developed based on micro/nano bubbles. Subject to external energy inputs such as temperature and ultrasound, the core substance, perfluorocarbon (PFC), undergoes a phase transition from liquid to gas. This transformation precipitates alterations in the PCDs’ structure, size, ultrasound imaging capabilities, drug delivery efficiency, and other pertinent characteristics. This gives them the ability to exhibit “intelligent responses”. This study utilized lipids as the membrane shell material and perfluorohexane (PFH) as the core to prepare lipid phase-change droplets. Superparamagnetic nanoparticles (PEG-functionalized Fe3O4 nanoparticles) and the anti-tumor drug curcumin (Cur) were loaded into the membrane shell, forming magnetic drug-loaded phase-change droplets (Fe-Cur-NDs). These nanoscale phase-change droplets exhibited excellent magnetic resonance/ultrasound imaging capabilities and thermal/ultrasound-mediated drug release. The Fe-Cur-NDs showed excellent anti-tumor efficacy for the MCF-7 cells under low-intensity focused ultrasound (LIFU) guidance in vitro. Therefore, Fe-Cur-NDs represent a promising smart responsive theranostic integrated micro/nano drug delivery system