Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks

Split learning of deep neural networks (SplitNN) has provided a promising solution to learning jointly for the mutual interest of a guest and a host, which may come from different backgrounds, holding features partitioned vertically. However, SplitNN creates a new attack surface for the adversarial participant, holding back its practical use in the real world. By investigating the adversarial effects of highly threatening attacks, including property inference, data reconstruction, and feature hijacking attacks, we identify the underlying vulnerability of SplitNN and propose a countermeasure. To prevent potential threats and ensure the learning guarantees of SplitNN, we design a privacy-preserving tunnel for information exchange between the guest and the host. The intuition is to perturb the propagation of knowledge in each direction with a controllable unified solution. To this end, we propose a new activation function named R3eLU, transferring private smashed data and partial loss into randomized responses in forward and backward propagations, respectively. We give the first attempt to secure split learning against three threatening attacks and present a fine-grained privacy budget allocation scheme. The analysis proves that our privacy-preserving SplitNN solution provides a tight privacy budget, while the experimental results show that our solution performs better than existing solutions in most cases and achieves a good tradeoff between defense and model usability.Comment: 23 page

A Novel Tunable Triple-Band Left-Handed Metamaterial

A novel tunable triple-band left-handed metamaterial (LHM) composed of a single-loop resonator (SLR) and a variable capacitor-loaded short wire pair (CL-SWP) printed on both sides of a substrate is presented in this paper. The CL-SWP-based metamaterial (MTM) is a novel single-sided LHM. It is theoretically analyzed capable of extracting tunable negative permeability and a wide-band negative permittivity. We ran simulations for the CL-SWP-based MTM, the SLR-based MTM, and the proposed LHM. Together with the measured results, it is identified that this novel LHM exhibits a tunable triple-band left-handed (LH) property. With the increase of the loaded capacitance, one LH band is relatively stable, while the other two are moving towards lower frequencies with their bandwidth getting wider and narrower, respectively. The surface current density distributions indicate that the first LH band is mainly decided by the SLR, one of the rest 2 LH bands is mainly decided by the CL-SWP, and the other one is decided by the SLR and CL-SWP together

Ampere-hour-scale soft-package potassium-ion hybrid capacitors enabling 6-minute fast-charging

Extreme fast charging of Ampere-hour (Ah)-scale electrochemical energy storage devices targeting charging times of less than 10 minutes are desired to increase widespread adoption. However, this metric is difficult to achieve in conventional Li-ion batteries due to their inherent reaction mechanism and safety hazards at high current densities. In this work, we report 1 Ah soft-package potassium-ion hybrid supercapacitors (PIHCs), which combine the merits of high-energy density of battery-type negative electrodes and high-power density of capacitor-type positive electrodes. The PIHC consists of a defect-rich, high specific surface area N-doped carbon nanotube-based positive electrode, MnO quantum dots inlaid spacing-expanded carbon nanotube-based negative electrode, carbonate-based non-aqueous electrolyte, and a binder- and current collector-free cell design. Through the optimization of the cell configuration, electrodes, and electrolyte, the full cells (1 Ah) exhibit a cell voltage up to 4.8 V, high full-cell level specific energy of 140 Wh kg-1 (based on the whole mass of device) with a full charge of 6 minutes. An 88% capacity retention after 200 cycles at 10 C (10 A) and a voltage retention of 99% at 25 ± 1 °C are also demonstrated

A proposed disease classification system for duck viral hepatitis

The nomenclature of duck viral hepatitis (DVH) was historically not a problem. However, 14 hepatotropic viruses among 10 different genera are associated with the same disease name, DVH. Therefore, the disease name increasingly lacks clarity and may no longer fit the scientific description of the disease. Because one disease should not be attributed to 10 genera of viruses, this almost certainly causes misunderstanding regarding the disease-virus relationship. Herein, we revisited the problem and proposed an update to DVH disease classification. This classification is based on the nomenclature of human viral hepatitis and the key principle of Koch's postulates (“one microbe and one disease”). In total, 10 types of disease names have been proposed. These names were literately matched with hepatitis-related viruses. We envision that this intuitive nomenclature system will facilitate scientific communication and consistent interpretation in this field, especially in the Asian veterinary community, where these diseases are most commonly reported

Ethylene is involved in the regulation of iron homeostasis by regulating the expression of iron-acquisition-related genes in Oryza sativa

Plants employ two distinct strategies to obtain iron (Fe) from the soil. In Strategy I but not Strategy II plants, Fe limitation invokes ethylene production which regulates Fe deficiency responses. Oryza sativa (rice) is the only graminaceous plant described that possesses a Strategy I-like system for iron uptake as well as the classic Strategy II system. Ethylene production of rice roots was significantly increased when grown under Fe-depleted conditions. Moreover, 1-aminocyclopropane-1-carboxylic acid (ACC) treatment, a precursor of ethylene, conferred tolerance to Fe deficiency in rice by increasing internal Fe availability. Gene expression analysis of rice iron-regulated bHLH transcription factor OsIRO2, nicotianamine synthases 1 and 2 (NAS1 and NAS2), yellow-stripe like transporter 15 (YSL15) and iron-regulated transporter (IRT1) indicated that ethylene caused an increase in transcript abundance of both Fe (II) and Fe (III)-phytosiderophore uptake systems. RNA interference of OsIRO2 in transgenic rice showed that ethylene acted via this transcription factor to induce the expression of OsNAS1, OsNAS2, OsYSL15, and OsIRT1. By contrast, in Hordeum vulgare L. (barley), no ethylene production or ethylene-mediated effects of Fe response could be detected. In conclusion, Fe-limiting conditions increased ethylene production and signalling in rice, which is novel in Strategy II plant species

Three pillars of sustainability: in search of conceptual origins

The three-pillar conception of (social, economic and environmental) sustainability, commonly represented by three intersecting circles with overall sustainability at the centre, has become ubiquitous. With a view of identifying the genesis and theoretical foundations of this conception, this paper reviews and discusses relevant historical sustainability literature. From this we find that there is no single point of origin of this three-pillar conception, but rather a gradual emergence from various critiques in the early academic literature of the economic status quo from both social and ecological perspectives on the one hand, and the quest to reconcile economic growth as a solution to social and ecological problems on the part of the United Nations on the other. The popular three circles diagram appears to have been first presented by Barbier (Environ Conserv 14:101, doi: 10.1017/s0376892900011449, 1987), albeit purposed towards developing nations with foci which differ from modern interpretations. The conceptualisation of three pillars seems to predate this, however. Nowhere have we found a theoretically rigorous description of the three pillars. This is thought to be in part due to the nature of the sustainability discourse arising from broadly different schools of thought historically. The absence of such a theoretically solid conception frustrates approaches towards a theoretically rigorous operationalisation of ‘sustainability’

Space-polarization Collaborative Suppression Method for Ionospheric Clutter in HFSWR

High Frequency Surface Wave Radar (HFSWR) is able to receive surface target and low-flying aircraft echoes at a long-distance, but it suffers severely from ionospheric clutter. In this paper, a spacepolarization collaborative-based filter is introduced to mitigate ionospheric clutter. For parameter estimation on ionospheric clutter used for filters, a spatial parameter estimation algorithm based on compressive sensing is introduced to the DOA estimation of ionospheric clutter. In addition, a polarized parameter estimation algorithm based on statistical characteristics is proposed for ionospheric clutter in the range-Doppler spectrum. Higher estimation accuracy is achieved as a result of the range-Doppler spectrum; therefore, these two estimation algorithms enhance the performance of the space-polarization collaborative suppression method for ionospheric clutter. Experimental results of practical dual-polarized HFSWR data show the effectiveness of the two algorithms and the above mentioned filter for ionospheric clutter suppression

OTFS Channel Estimation based on OGCE-BEM

【Objective】With the development of the sixth generation mobile communication technology, the inter-carrier interference in the traditional Orthogonal Frequency Division Multiplexing (OFDM) system makes the channel estimation performance insufficient to provide highly reliable communication, and Orthogonal Time-Frequency Space (OTFS) system can effectively solve the problem of communication system reliability degradation caused by fast time variability and Doppler effect, which has received wide attention in recent years.【Methods】In order to effectively meet the channel estimation performance requirements of OTFS systems, this paper uses an Optimized Generalized Complex Exponential (OGCE) Basis Expansion Model (BEM) to calculate the channel impulse response as a time-invariant basis function with basis function coefficients, which can effectively fit fast time-varying channels in high-speed mobile communication scenarios. The OGCE-BEM improves the spectral leakage by more intensive sampling and reduces the error of the high-frequency basis model by adding correction coefficients to reduce the error of the HF-based model.【Results】The simulation results show that the proposed algorithm is suitable for high-speed mobile communication scenarios with more reasonable design of the basis function. The estimation method has lower mean square error than the fixed forgetting factor, and the channel estimation results are more accurate. Compared with Least Square (LS), BEM-LS and BEM-Linear Minimum Mean Square Error (LMMSE) channel estimation methods, the performance of mean square error is significantly improved.【Conclusion】It can be seen that the channel estimation algorithm based on OGCE-BEM can effectively reduce the number of unknown parameters to be estimated and improve the accuracy of channel estimation

time series matrix factorization prediction of internet traffic matrices

Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks. © 2012 IEEE.IEEE Computer Society; IEEE Comput. Soc. Tech. Comm. Comput. Commun. (TCCC)Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks. © 2012 IEEE