Robust Probing Research Articles

Fast Pure Shift NMR Spectroscopy Using Attention-Assisted Deep Neural Network.

Pure shift NMR spectroscopy enables the robust probing on molecular structure and dynamics, benefiting from great resolution enhancements. Despite extensive application landscapes in various branches of chemistry, the long experimental times induced by the additional time dimension generally hinder its further developments and practical deployments, especially for multi-dimensional pure shift NMR. Herein, this study proposes and implements the fast, reliable, and robust reconstruction for accelerated pure shift NMR spectroscopy with lightweight attention-assisted deep neural network. This deep learning protocol allows one to regain high-resolution signals and suppress undersampling artifacts, as well as furnish high-fidelity signal intensities along with the accelerated pure shift acquisition, benefitting from the introduction of the attention mechanism to highlight the spectral feature and information of interest. Extensive results of simulated and experimental NMR data demonstrate that this attention-assisted deep learning protocol enables the effective recovery of weak signals that are almost drown in the serious undersampling artifacts, and the distinction and recognition of close chemical shifts even though using merely 5.4% data, highlighting its huge potentials on fast pure shift NMR spectroscopy. As a result, this study affords a promising paradigm for the AI-assisted NMR protocols toward broader applications in chemistry, biology, materials, and life sciences, and among others.

Investigation of directional discrimination in the nociceptive system using temperature-controlled laser stimuli.

Cutaneous laser stimulation has commonly been employed to investigate the thermal properties of the nociceptive system. The aim of this study was to investigate how a temperature-controlled laser system improves the assessment of directional discrimination in the nociceptive system. In total, twenty healthy volunteers participated in this study. To determine the directional discrimination threshold (stimulation length 50% correct, expressed in mm), thermal stimuli were delivered using a diode laser and the laser beam was perpendicularly displaced across the skin to give a linear stimulation in four different directions (distal, proximal, lateral and medial) and displacement lengths (3 for lateral-medial and 5 for distal-proximal). Two temperature control modes were used in the stimulation system, open-loop and closed-loop control. The subjects had to report the perceived stimulus direction, the degree of certainty regarding the perceived direction and the intensity of the perceived stimulus (0-10 numerical rating scale, 3: pain threshold). During closed-loop control, the orientation of stimuli was discriminated significantly more accurately than during open-loop control. During closed-loop control, the directional discrimination threshold was 31.9 and 26.1 mm for distal-proximal and lateral-medial directed stimuli, respectively. A numerical rating scale was significantly higher for the lateral/medial directions. Moreover, the variability of the discrimination threshold is reduced in the closed-loop control system. The findings show that discrimination ability is better in the lateral-medial directions compared to the distal-proximal directions. This study indicates that using a system enabling closed-loop temperature control, allows more robust probing of the temporo-spatial mechanisms in the nociceptive system. This study shows that a newly developed temperature-controlled laser stimulation system enhances the possibilities to investigate the nociceptive temporo-spatial integration, as shown by a less variable directional discrimination threshold. The results also show that different orthogonal directions are discriminated differently. This new method allows a better investigation of the combined temporal and spatial mechanisms in the nociceptive system.

Lighting up Lipidic Nanoflares with Self-Powered and Multivalent 3D DNA Rolling Motors for High-Efficiency MicroRNA Sensing in Serum and Living Cells.

Intelligent DNA nanomachines are powerful and versatile molecular tools for bioimaging and biodiagnostic applications; however, they are generally constrained by complicated synthetic processes and poor reaction efficiencies. In this study, we developed a simple and efficient molecular machine by coupling a self-powered rolling motor with a lipidic nanoflare (termed RMNF), enabling high-contrast, robust, and rapid probing of cancer-associated microRNA (miRNA) in serum and living cells. The lipidic nanoflare is a cholesterol-based lipidic micelle decorated with hairpin-shaped tracks that can be facilely synthesized by stirring in buffered solution, whereas the 3D rolling motor (3D RM) is a rigidified tetrahedral DNA scaffold equipped with four single-stranded "legs" each silenced by a locking strand. Once exposed to the target miRNA, the 3D RM can be activated, followed by self-powered precession based on catalyzed hairpin assembly (CHA) and lighting up of the lipidic nanoflare. Notably, the multivalent 3D RM that moves using four DNA legs, which allows the motor to continuously and acceleratedly interreact with DNA tracks rather than dissociate from the surface of the nanoflare, yielded a limit of detection (LOD) of 500 fM at 37 °C within 1.5 h. Through the nick-hidden and rigidified structure design, RMNF exhibits high biostability and a low false-positive signal under complex physiological settings. The final application of RMNF for miRNA detection in clinical samples and living cells demonstrates its considerable potential for biomedical imaging and clinical diagnosis.

Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.

Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both in silico and in experimental phantom studies using spectral micro-CT priors. Overall, this is the first successful demonstration of micro-CT and DOT fusion using deep learning, greatly enhancing the prospect of rapid data-integration strategies, often demanded in challenging pre-clinical scenarios.

PROLEAD

Even today, Side-Channel Analysis attacks pose a serious threat to the security of cryptographic implementations fabricated with low-power and nanoscale feature technologies. Fortunately, the masking countermeasures offer reliable protection against such attacks based on simple security assumptions. However, the practical application of masking to a cryptographic algorithm is not trivial, and the designer may overlook possible security flaws, especially when masking a complex circuit. Moreover, abstract models like probing security allow formal verification tools to evaluate masked implementations. However, this is computationally too expensive when dealing with circuits that are not based on composable gadgets. Unfortunately, using composable gadgets comes at some area overhead. As a result, such tools can only evaluate subcircuits, not their compositions, which can become the Achilles’ heel of such masked implementations.In this work, we apply logic simulations to evaluate the security of masked implementations which are not necessarily based on composable gadgets. We developed PROLEAD, an automated tool analyzing the statistical independence of simulated intermediates probed by a robust probing adversary. Compared to the state of the art, our approach (1) does not require any power model as only the state of a gate-level netlist is simulated, (2) can handle masked full cipher implementations, and (3) can detect flaws related to the combined occurrence of glitches and transitions as well as higher-order multivariate leakages. With PROLEAD, we can evaluate masked mplementations that are too complex for existing formal verification tools while being in line with the robust probing model. Through PROLEAD, we have detected security flaws in several publicly-available masked implementations, which have been claimed to be robust probing secure.

Composable Gadgets with Reused Fresh Masks

Albeit its many benefits, masking cryptographic hardware designs has proven to be a non-trivial and error-prone task, even for experienced engineers. Masked variants of atomic logic gates, like AND or XOR – commonly referred to as gadgets – aim to facilitate the process of masking large circuits by offering free composition while sustaining the overall design’s security in the d-probing adversary model. A wide variety of research has already been conducted to (i) find formal properties a gadget must fulfill to guarantee composability and (ii) construct gadgets that fulfill these properties, while minimizing overhead requirements. In all existing composition frameworks like NI/SNI/PINI and all corresponding gadget realizations, the security argument relies on the fact that each gadget requires individual fresh randomness. Naturally, this approach leads to very high randomness requirements of the resulting composed circuit. In this work, we present composable gadgets with reused fresh masks (COMAR), allowing the composition of any first-order secure hardware circuit utilizing only 6 fresh masks in total. By construction, our newly presented gadgets render individual fresh randomness unnecessary, while retaining free composition and first-order security in the robust probing model. More precisely, we give an instantiation of gadgets realizing arbitrary XOR and AND gates with an arbitrary number of inputs which can be trivially extended to all basic logic gates. With these, we break the linear dependency between the number of (non-linear) gates in a circuit and the randomness requirements, hence offering the designers the possibility to highly optimize a masked circuit’s randomness requirements while keeping error susceptibility to a minimum.

Robust and high-sensitivity thermal probing at the nanoscale based on resonance Raman ratio (R3)

Raman spectroscopy-based temperature sensing usually tracks the change of Raman wavenumber, linewidth and intensity, and has found very broad applications in characterizing the energy and charge transport in nanomaterials over the last decade. The temperature coefficients of these Raman properties are highly material-dependent, and are subjected to local optical scattering influence. As a result, Raman-based temperature sensing usually suffers quite large uncertainties and has low sensitivity. Here, a novel method based on dual resonance Raman phenomenon is developed to precisely measure the absolute temperature rise of nanomaterial (nm WS2 film in this work) from 170 to 470 K. A 532 nm laser (2.33 eV photon energy) is used to conduct the Raman experiment. Its photon energy is very close to the excitonic transition energy of WS2 at temperatures close to room temperature. A parameter, termed resonance Raman ratio (R3) is introduced to combine the temperature effects on resonance Raman scattering for the A1g and E2g modes. Ω has a change of more than two orders of magnitude from 177 to 477 K, and such change is independent of film thickness and local optical scattering. It is shown that when Ω is varied by 1%, the temperature probing sensitivity is 0.42 K and 1.16 K at low and high temperatures, respectively. Based on Ω, the in-plane thermal conductivity (k) of a ∼25 nm-thick suspended WS2 film is measured using our energy transport state-resolved Raman (ET-Raman). k is found decreasing from 50.0 to 20.0 Wm−1 K−1 when temperature increases from 170 to 470 K. This agrees with previous experimental and theoretical results and the measurement data using our FET-Raman. The R3 technique provides a very robust and high-sensitivity method for temperature probing of nanomaterials and will have broad applications in nanoscale thermal transport characterization, non-destructive evaluation, and manufacturing monitoring.

On the spectral features of robust probing security

In this work we provide a spectral formalization of non-interference in the presence of glitches. Our goal is to present new theoretical and practical tools to reason about robust-d-probing security. We show that the current understanding of extended probes lends itself to probes that participate, during gadget composition, to the creation of additional extended probes. In turn, this enables a natural extension of non-interference definitions into robust ones to build a new reasoning framework that can formally explain some semi-formal results already appeared in the past and be used to synthesize new robust-d-SNI gadgets.

Glitch-Resistant Masking Revisited

Implementing the masking countermeasure in hardware is a delicate task. Various solutions have been proposed for this purpose over the last years: we focus on Threshold Implementations (TIs), Domain-Oriented Masking (DOM), the Unified Masking Approach (UMA) and Generic Low Latency Masking (GLM). The latter generally come with innovative ideas to cope with physical defaults such as glitches. Yet, and in contrast to the situation in software-oriented masking, these schemes have not been formally proven at arbitrary security orders and their composability properties were left unclear. So far, only a 2-cycle implementation of the seminal masking scheme by Ishai, Sahai and Wagner has been shown secure and composable in the robust probing model – a variation of the probing model aimed to capture physical defaults such as glitches – for any number of shares.In this paper, we argue that this lack of proofs for TIs, DOM, UMA and GLM makes the interpretation of their security guarantees difficult as the number of shares increases. For this purpose, we first put forward that the higher-order variants of all these schemes are affected by (local or composability) security flaws in the (robust) probing model, due to insufficient refreshing. We then show that composability and robustness against glitches cannot be analyzed independently. We finally detail how these abstract flaws translate into concrete (experimental) attacks, and discuss the additional constraints robust probing security implies on the need of registers. Despite not systematically leading to improved complexities at low security orders, e.g., with respect to the required number of measurements for a successful attack, we argue that these weaknesses provide a case for the need of security proofs in the robust probing model (or a similar abstraction) at higher security orders.

Composable Masking Schemes in the Presence of Physical Defaults & the Robust Probing Model

Composability and robustness against physical defaults (e.g., glitches) are two highly desirable properties for secure implementations of masking schemes. While tools exist to guarantee them separately, no current formalism enables their joint investigation. In this paper, we solve this issue by introducing a new model, the robust probing model, that is naturally suited to capture the combination of these properties. We first motivate this formalism by analyzing the excellent robustness and low randomness requirements of first-order threshold implementations, and highlighting the difficulty to extend them to higher orders. Next, and most importantly, we use our theory to design and prove the first higher-order secure, robust and composable multiplication gadgets. While admittedly inspired by existing approaches to masking (e.g., Ishai-Sahai-Wagner-like, threshold, domain-oriented), these gadgets exhibit subtle implementation differences with these state-of-the-art solutions (none of which being provably composable and robust). Hence, our results illustrate how sound theoretical models can guide practically-relevant implementations.

Scanning magnetoresistive microscopy: An advanced characterization tool for magnetic nanosystems.

An advanced scanning magnetoresistive microscopy (SMRM) - a robust magnetic imaging and probing technique - will be presented, which utilizes state-of-the-art recording heads of a hard disk drive as sensors. The spatial resolution of modern tunneling magnetoresistive sensors is nowadays comparable to the more commonly used magnetic force microscopes. Important advantages of SMRM are the ability to detect pure magnetic signals directly proportional to the out-of-plane magnetic stray field, negligible sensor stray fields, and the ability to apply local bipolar magnetic field pulses up to 10 kOe with bandwidths from DC up to 1 GHz. Moreover, the SMRM can be further equipped with a heating stage and external magnetic field units. The performance of this method and corresponding best practices are demonstrated by presenting various examples, including a temperature dependent recording study on hard magnetic L1(0) FeCuPt thin films, imaging of magnetic vortex states in an in-plane magnetic field, and their controlled manipulation by applying local field pulses.

Variable stiffness probing systems for micro-coordinate measuring machines

Micro-scale probing systems are used on specialist micro-coordinate measuring machines to measure small, intricate and fragile components. Probe stiffness is a critical property of micro-scale probing systems; it influences contact force, robustness, ease of manufacture, accuracy and dynamic response. Selecting the optimum stiffness, therefore, represents a significant design challenge, and often leads to undesirable compromises. For example, when contacting fragile surfaces the probe stiffness should be low to prevent damage; however, for a more robust probing system the stiffness should be increased. This paper presents a novel concept for micro-scale probing systems with the ability to quickly and easily change and control probe stiffness during use. The intended strategy for using the proposed probe is first explained. Then the new concept is fully defined and explored through a combination of finite element analysis and experimental results. Two possible configurations of probe are described, and models for predicted performance for each are presented and compared. The models demonstrate significant stiffness reduction is possible with the proposed concept, and show it is theoretically possible to achieve a probing system with perfectly isotopic stiffness.

Barnacle larvae exploring surfaces with variable hydrophilicity: influence of morphology and adhesion of "footprint" proteins by AFM.

Interaction forces of adhesive proteins employed by cyprid larvae of Amphibalanus amphitrite for temporary attachment during surface exploration in marine fouling were studied by AFM force spectroscopy using chemically modified, reactive colloidal probes. The proteins were covalently attached to the surfaces of the probes by incubation in the protein deposits (footprints) left behind at the surface by the cyprids. This covalent coupling enabled robust and reproducible probing of adhesion of the attachment proteins to model surfaces with variable hydrophilicity. Three model monolayer surfaces were designed and prepared that exhibited different wettabilities derived from variations in the monolayer chemical composition. The morphology and size of cyprid protein deposits was imaged by AFM. The deposits showed larger area of spreading on more hydrophobic surfaces, whereas the overall volume of the secreted proteins exhibited no significant variation. Notable difference in adhesion forces was found among the surfaces by force spectroscopy, with substantially higher values measured on the hydrophobic surface (21 ± 2 nN) than that measured on the more hydrophilic surface (7.2 ± 1 nN). The same surfaces were also tested in laboratory essays. Rather surprisingly, no significant differences were found in values of fractional cyprid settlement among the surfaces studied, indicating that variations of surface wettability and adhesion strength of settlement proteins may be insufficient to explain settlement trends.

Probing of Polymer Surfaces in the Viscoelastic Regime

In this Feature Article, we discussed the experimental and modeling methods and analyzed the limitations of the surface probing of nanomechanical properties of polymeric and biological materials in static and dynamic regimes with atomic force microscopy (AFM), which are widely utilized currently. To facilitate such measurements with minimized ambiguities, in this study we present a combined method to evaluate the viscoelastic properties of compliant polymeric materials. We collected force-distance data in the static regime for a benchmark polymer material (poly(n-butyl methacrylate)) with an easily accessible glass-transition temperature (about 25 °C) at different loading rates and different temperatures across the glassy state, glass-transition region, and rubbery state. For this analysis, we exploited a Johnson-modified Sneddon's approach in a combination with the standard linear solid model. Critical experimental steps suggested for robust probing are (i) the use of a tip with a well-characterized parabolic shape, (ii) probing in a liquid environment in order to reduce jump-in phenomenon, and (iii) minute indentations to ensure the absence of plastic deformation. Whereas the standard Sneddon's model generates quantitatively adequate elastic modulus values below and above the glass transition, this traditional approach can significantly underestimate actual modulus values in the vicinity of the glass-transition region (15 °C above or below Tg), with major deviations occurring at the loss tangent peak. The analysis of the experimental data with Sneddon's model for the elastic region (glassy and rubbery states) and Johnson's model for the viscoelastic region allowed for the restoration of the universal master curve and the evaluation of the storage modulus, loss modulus, loss tangent, relaxation times, and activation energies of the polymer surface across the glass-transition region and at relevant loading rates.

How Random is Your Tomographic Noise? A Number Theoretic Transform (NTT) Approach

Discrete Tomography (DT), differently from GT and CT, focuses on the case where only few specimen projections are known and the images contain a small number of different colours (e.g. black-and-white). A concise review on main contemporary physical and mathematical CT system problems is offered. Stochastic vs. Combinatorially Optimized Noise generation is compared and presented by two visual examples to emphasise a major double-bind problem at the core of contemporary most advanced instrumentation systems. Automatic tailoring denoising procedures to real dynamic system characteristics and performance can get closer to ideal self-registering and self-linearizing system to generate virtual uniform and robust probing field during its whole designed service life-cycle. The first attempt to develop basic principles for system background low-level noise source automatic characterization, profiling and identification by CICT, from discrete system parameter, is presented. As a matter of fact, CICT can supply us with cyclic numeric sequences perfectly tuned to their low-level multiplicative source generators, related to experimental high-level overall perturbation (according to high-level classic perturbation computational model under either additive or multiplicative perturbation hypothesis). Numeric examples are presented. Furthermore, a practical NTT example is given. Specifically, advanced CT system, HRO and Mission Critical Project (MCP) for very low Technological Risk (TR) and Crisis Management (CM) system will be highly benefitted mostly by CICT infocentric worldview. The presented framework, concepts and techniques can be used to boost the development of next generation algorithms and advanced applications quite conveniently.

An integrated robust probing motion planning and control scheme: A tube-based MPC approach

‘This paper introduces the integration of a probing scheme into a robust MPC-based robot motion planning and control algorithm. The proposed solution tackles the output-feedback tube-based MPC problem using the partially-closed loop strategy to incorporate future measurements in a computationally efficient manner. This combination will provide not only a robust controller but also avoids overly conservative planning which is a drawback of the original implementation of the output-feedback tube-based MPC. The proposed solution is composed of two controllers: (i) a nominal MPC controller with probing feature to plan a globally convergent trajectory in conjunction with active localization, and (ii) an ancillary MPC controller to stabilize the robot motion around the planned trajectory. The performance and real-time implementation of the proposed planning and control algorithms have been verified through both extensive numerical simulations and experiments with a mobile robot.