We propose an operational measure of information leakage in a non-stochastic setting to formalize privacy against a brute-force guessing adversary. We use uncertain variables, non-probabilistic counterparts of random variables, to construct a guessing framework in which an adversary is interested in determining private information based on uncertain reports. We consider brute-force trial-and-error guessing in which an adversary can potentially check all the possibilities of the private information that are compatible with the available outputs to find the actual private realization. The ratio of the worst-case number of guesses for the adversary in the presence of the output and in the absence of it captures the reduction in the adversary’s guessing complexity and is thus used as a measure of private information leakage. We investigate the relationship between the newly-developed measure of information leakage with maximin information and stochastic maximal leakage that are shown to arise in one-shot guessing.

HLA-B*15:02 screening before administering carbamazepine is recommended to prevent life-threatening hypersensitivity. However, the unavailability of a point-of-care device impedes this screening process. Our research group previously developed a two-step HLA-B*15:02 detection technique utilizing loop-mediated isothermal amplification (LAMP) on the tube, which requires two-stage device development to translate into a portable platform. Here, we report a heater-integrated lab-on-a-chip device for the LAMP amplification, which can rapidly detect HLA-B alleles colorimetrically. A gold-patterned micro-sized heater was integrated into a 3D-printed chip, allowing microfluidic pumping, valving, and incubation. The performance of the chip was tested with color dye. Then LAMP assay was conducted with human genomic DNA samples of known HLA-B genotypes in the LAMP-chip parallel with the tube assay. The LAMP-on-chip results showed a complete match with the LAMP-on-tube assay, demonstrating the detection system's concurrence.

In a distributed cognitive radio (CR) sensor network, transmission and reception on vacant channels require cognitive radio nodes to achieve rendezvous. Because of the lack of adequate assistance from the network environment, such as the central controller and other nodes, assisted rendezvous for distributed CR is inefficient in a dynamic network. As a result, non-assisted blind rendezvous, which is unaware of its counterpart node, has recently led to a lot of interest in the research arena. In this paper, we study a channel rendezvous method based on prime number theory and propose a new multi-radio-based technique for non-assisted rendezvous with the blind and heterogeneous condition. The required time and the optimal number of radios for the guaranteed rendezvous are calculated using probability-based measurement. Analytical expressions for probabilistic guaranteed rendezvous conditions are derived and verified by Monte Carlo simulation. In addition, the maximum time to rendezvous (MTTR) is derived in closed form using statistical and probabilistic analysis. Under different channel conditions, our proposed solution leads to a substantial time reduction for guaranteed rendezvous. For the sake of over-performance of our proposed system, the simulation outcome is compared to a recently proposed heterogeneous and blind rendezvous method. The Matlab simulation results show that our proposed system's MTTR gains range from 11% to over 95% for various parametric values of the system model.

Rare-earth elements (which include all lanthanides, scandium, and yttrium) play a key role in many fields including oil refining, metallurgy, electronics manufacturing, and other high-technology applications. Although the available lanthanide resources are enough for current levels of manufacturing, increased future demand for lanthanides will require new, efficient recovery methods to provide a sustainable supply. Membrane adsorbers are promising separation materials to recover lanthanides from high volumes of wastewater due to their tailorable surface chemistry, high binding capacity and high throughput. In this work, membrane adsorbers were synthesized by first using ultraviolet-initiated free radical polymerization to graft a poly(glycidyl methacrylate) (p-GMA) layer to the surface of polyethersulfone membranes. Then, the reactive epoxy groups of the grafted p-GMA were used for the covalent attachment of lysine molecules via a zinc perchlorate-catalyzed, epoxide ring-opening reaction at 35°C. Changes in membrane surface chemistry throughout the functionalization process were monitored with Fourier Transform Infrared Spectroscopy. The degree of grafting for the p-GMA film was quantified gravimetrically and increased with increasing polymerization time. Equilibrium adsorption experiments were performed for single specie solutions of La3+, Ce3+, Nd3+, Na+, Ca2+, and Mg2+ at pH 5.25 ± 0.25. Lysine-modified membranes showed negligible uptake of Na+, Ca2+, and Mg2+. The maximum capacities modeled by the Langmuir isotherm for La3+ and Ce3+ were 6.11 ± 0.58 and 6.45 ± 1.29 mg/g adsorbent, respectively. Nd3+ adsorbed to the membrane; however, the fit of the Langmuir model was not significant and it adsorbed to a lower extent than La3+ and Ce3+. Lower adsorption of the higher charge density species indicates that the primary binding mode is through the amine moieties of lysine and not the carboxylic acid. Dynamic adsorption experiments were conducted with 1 ppm La3+ feed solutions at different flow rates using either a single membrane or three membranes in series. The dynamic binding capacity at 50% breakthrough was independent of flowrate within the tested range. The low-temperature membrane functionalization methodology presented in this work can be used to immobilize biomolecules with even higher specificity, like engineered peptides or proteins, on membrane surfaces.

The prize‐collecting Euclidean Steiner tree (PCEST) problem is a generalization of the well‐known Euclidean Steiner tree (EST) problem. All points given in an EST problem instance are connected by the shortest possible network in a solution. A solution can include additional points called Steiner points. A PCEST problem instance differs from an EST problem instance by the addition of weights for each given point. A PCEST solution connects a subset of the given points in order to maximize the net value of the network (the sum of the selected point weights, less than the length of the network). We present an algorithmic framework for solving the PCEST problem. Included in the framework are efficient methods to determine subsets of points that must be in every solution, and subsets of points that cannot be in any solution. Also included are methods to generate and concatenate full Steiner trees.

AIM: Voluntary dehydration, or lack of fluid intake despite water availability, is common in otherwise healthy children, and can lead to adverse effects. Most dehydration biomarkers are impractical for routine assessment in paediatric populations. This study aimed to assess two non-invasive hydration assessment tools, urine specific gravity (USG ) and a novel point-of-care (POC) salivary osmolarity (SOSM) sensor, in healthy children. METHODS: Volunteers were tested by colorimetric USG and a handheld SOSM system. Observed values were compared against previous studies to determine hydration status, as was the concordance between parameters. RESULTS: At the common USG threshold of 1.020, 42.4% of the 139 healthy children were dehydrated. The same prevalence was found using the 70-mOSM cut-off value. Comparative analysis of SOSM at varying USG thresholds demonstrated significantly higher SOSM in dehydrated children with a USG  ≥ 1.030 (P = 0.002). CONCLUSION: At the USG threshold of 1.020 and SOSM threshold of 70 mOSM, 42.4% of healthy children were found to be voluntarily dehydrated. Significantly higher SOSM was observed in dehydrated children (USG  ≥ 1.030). As the first study on the utility of POC SOSM measurements for detecting dehydration, these results provide a foundation for future POC characterisation of SOSM in other populations and clinical contexts.

Although many nanoscale materials such as quantum dots and metallic nanocrystals exhibit size dependent optical properties, it has been difficult to incorporate them into optical or electronic devices because there are currently no methods for precise, large‐scale deposition of single nanocrystals. Of particular interest is the need to control the orientation of single nanocrystals since the optical properties are usually strongly anisotropic. Here a method based on electrophoretic deposition (EPD) is reported to precisely assemble vertically oriented, single gold nanorods. It is demonstrated that the orientation of gold nanorods during deposition is controlled by the electric dipole moment induced along the rod by the electric field. Dissipative particle dynamics simulations indicate that the magnitude of this dipole moment is dominated by the polarizability of the solution phase electric double layer around the nanorod. The resulting vertical gold nanorod arrays exhibit reflected colors due to selective excitation of the transverse surface plasmon mode. The EPD method allows assembly of arrays with a density of over one million, visually resolvable, vertical nanorods per square millimeter.

We consider machine learning, particularly regression, using locally-differentially private datasets. The Wasserstein distance is used to define an ambiguity set centered at the empirical distribution of the dataset corrupted by local differential privacy noise. The radius of the ambiguity set is selected based on privacy budget, spread of data, and size of the problem. Machine learning with private dataset is rewritten as a distributionally-robust optimization. For general distributions, the distributionally-robust optimization problem can be relaxed as a regularized machine learning problem with the Lipschitz constant of the machine learning model as a regularizer. For Gaussian data, the distributionally-robust optimization problem can be solved exactly to find an optimal regularizer. Training with this regularizer can be posed as a semi-definite program.

A secure and private nonlinear networked control systems (NCSs) design using semi-homomorphic encryption is studied. Static feedback controllers are used and network architectures are provided to enable control signal computation using encrypted signals directly. As a result, the security of the NCSs is further enhanced by preserving the privacy of information flowing through the whole network. Whereas in traditional encryption techniques, encrypted signals are decrypted before control computation and are encrypted again after computation for transmission. While this is highly desirable from privacy point of view, additional technical difficulties in the design and analysis of NCSs are induced compared to standard NCSs. In this chapter, we provide sufficient conditions on the encryption parameters that guarantee robust stability of the NCS in the presence of disturbances in a semi-global practical sense and discuss the trade-offs between the required computational resources, security guarantees, and the closed-loop performance. The proof technique is based on Lyapunov methods.

We employ a game-theoretic model to analyze the interaction between an adversary and a classifier. There are two (i.e., positive and negative) classes to which data points can belong. The adversary wants to maximize the probability of miss-detection for the positive class (i.e., false negative probability) while it does not want to significantly modify the data point so that it still maintains favourable traits of the original class. The classifier, on the other hand, wants maximize the probability of correct detection for the positive class (i.e., true positive probability) subject to a lower-bound on the probability of correct detection for the negative class (i.e., true negative probability). For conditionally Gaussian data points (conditioned on the class) and linear support vector machine classifiers, we rewrite the optimization problems of the adversary and the classifier as convex problems and use best response dynamics to learn an equilibrium of the game. This results in computing a linear support vector machine classifier that is robust against adversarial input manipulations.

Valley polarization is among the most critical attributes of atomically thin materials. However, increasing contrast from monolayer transition metal dichalcogenides (TMDs) has so far been challenging. In this work, a large degree of circular polarization up to 45% from a monolayer WS2 is achieved at room temperature by using a single chiral plasmonic nanoparticle. The increased contrast is attributed to the selective enhancement of both the excitation and the emission rate having one particular handedness of the circular polarization, together with accelerated radiative recombination of valley excitons due to the Purcell effect. The experimental results are corroborated by the optical simulation using the finite-difference time-domain (FDTD) method. Additionally, the single chiral nanoparticle enables the observation of valley-polarized luminescence with a linear excitation. The results provide a promising pathway to enhance valley contrast from monolayer TMDs and utilize them for nanophotonic devices.