Electric Double Layer from Phase Demixing Reinforced by Strong Coupling Electrostatics

Ionic liquids (ILs) are appealing electrolytes for their favorable physicochemical properties. However, despite their longstanding use, understanding the capacitive behavior of ILs remains challenging. This is largely due to the formation of a non-conventional electric double layer (EDL) at the electrode-electrolyte interface. This study shows that the short-range Yukawa interactions, representing the large anisotropically charged ILs, demix IL to create a spontaneous surface charge separation, which is reinforced by the strongly coupled charge interaction. The properties of the condensed layer, the onset of charge separation, and the rise of overscreening and crowding critically depend on the asymmetry of Yukawa interactions

Renormalization Group-Motivated Learning

We introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the two closest data in a regular real-space RG in a lattice while considers the overall information loss in momentum-space RG. Here we compromise the two measures for the non-spatial data set. For weakly correlated data close to Gaussian, we use the correlation of data as a metric for the proximity of data points, but minimize an overall projection error for optimal coarse-graining steps. It compresses the data to maximize the correlation between the two data points to be compressed while minimizing the correlation between the paired data and other data points. We show that this approach can effectively reduce the dimensionality of the data while preserving the essential features. We extend our method to incorporate non-linear features by replacing correlation measures with mutual information. This results in an information-bottleneck-like trade-off: maximally compress the data while preserving the information among the compressed data and the rest. Indeed, our approach can be interpreted as an exact form of information-bottleneck-like trade off near linear data. We examine our method with random Gaussian data and the Ising model to demonstrate its validity and apply glass systems. Our approach has potential applications in various fields, including machine learning and statistical physics

On the Linear Transformation in White-box Cryptography

Linear transformations are applied to the white-box cryptographic implementation for the diffusion effect to prevent key-dependent intermediate values from being analyzed. However, it has been shown that there still exists a correlation before and after the linear transformation, and thus this is not enough to protect the key against statistical analysis. So far, the Hamming weight of rows in the invertible matrix has been considered the main cause of the key leakage from the linear transformation. In this study, we present an in-depth analysis of the distribution of intermediate values and the characteristics of block invertible binary matrices. Our mathematical analysis and experimental results show that the balanced distribution of the key-dependent intermediate value is the main cause of the key leakage

Table Redundancy Method for Protecting against Fault Attacks

Fault attacks (FA) intentionally inject some fault into the encryption process for analyzing a secret key based on faulty intermediate values or faulty ciphertexts. One of the easy ways for software-based countermeasures is to use time redundancy. However, existing methods can be broken by skipping comparison operations or by using non-uniform distributions of faulty intermediate values. In this paper, we propose a secure software-based redundancy, aptly named table redundancy, applying different linear and nonlinear transformations to redundant computations of table-based block cipher structures. To reduce the table size and the number of lookups, some outer tables that are not subjected to FA are shared, while the inner tables are protected by table redundancy. The basic idea is that different transformations protecting redundant computations are correctly decoded if the redundant outcomes are combined without faulty values. In addition, this recombination provides infective computations because a faulty byte is likely to propagate its error to adjacent bytes due to the use of 32-bit linear transformations. Our method also presents a stateful feature in the connection with detected faults and subsequent plaintexts for preventing iterative fault injection. We demonstrate the protection of AES-128 against FA and show a negligible advantage of FA

Nexus between directionality of THz waves and structural parameters in groove-patterned InAs

We have performed terahertz (THz)-time domain spectroscopy in various geometries, for characterizing the directivity of THz waves emitted from groove-patterned InAs structures. First, we have distinguished the THz emission mechanisms as a function of epilayer thickness. The carrier drift was predominant in thin sample group (10-70 nm) which the electronic diffusion motion was overriding the oppositely aligned drifting dipoles in thick sample group (370-900 nm) as revealed via amplitude and phase variations. By combined use of the electron-beam lithography and the inductively coupled plasma etching in 1 {\mu}m-thick InAs epilayers, we have further fabricated either asymmetric V-groove patterns or symmetric parabolic patterns. The THz amplitude was enhanced, particularly along line-of-sight transmissive direction when the groove patterns act as microscale reflective mirrors periodically separated by a scale of diffusion length.Comment: 5 pages, 4 figure