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We present a study the initial stages of ice growth on pristine and oxygen-functionalized highly oriented pyrolytic graphite (O-HOPG), combining low-temperature scanning tunneling microscopy (LT-STM) and machine-learning structural searches. LT-STM images show that oxygen atoms act as nucleation sites for ice growth, and that the size, structure and porosity of the nanometer-sized ice clusters depend strongly on the growth temperature. Machine learning-assisted structural searches and first-principles energy calculations confirm that clusters of water molecules are likely to bind to chemisorbed oxygen atoms through hydrogen bonding. During the early stages of the cluster growth clusters of water molecules are likely to be immobilized by binding to more than one chemisorbed oxygen atom through hydrogen bonding. However, the energy gain by hydrogen bond formation of a molecule, upon incorporation into smaller clusters only bound to a single oxygen atom, is large enough to induce cluster diffusion and favor the growth of larger ice clusters. Our results demonstrate that the mobility of water molecules is significantly lowered in the presence of defects on the surface. The observed lower mobility on defected carbon presented here offers an enhanced understanding of macroscopic anti-icing properties observed for functionalized HOPG under ambient conditions and provides insight into the early stages of ice growth on dust grain surfaces in interstellar space.

We propose new constructions for a two-dimensional ($2$D) perfect array, complete complementary code (CCC), and multiple CCCs as an optimal symmetrical $Z$-complementary code set (ZCCS). We propose a method to generate a two-dimensional perfect array and CCC. By utilising mutually orthogonal sequences, we developed a method to extend the length of a CCC without affecting the set or code size. Additionally, this concept is extended to include the development of multiple CCCs, and the correlation characteristics of these multiple CCCs are identical with the characteristics of optimal symmetrical ZCCS., Comment: This paper has been accepted in 'IEEE International Symposium on Information Theory (ISIT 2024)'

Complete complementary codes (CCCs) are highly valuable in the fields of information security, radar and communication. The spectrally null constrained (SNC) problem arises in radar and modern communication systems due to the reservation or prohibition of specific spectrums from transmission. The literature on SNC-CCCs is somewhat limited in comparison to the literature on traditional CCCs. The main objective of this paper is to discover several configurations of SNC-CCCs that possess more flexibility in their parameters. The proposed construction utilised the existing CCCs and mutually orthogonal sequences. The proposed construction can cover almost all lengths with the smallest alphabets $\{-1,0,1\}$. Further, the idea of SNC-CCC is extended to multiple SNC-CCC with an inter-set zero cross-correlation zone (ZCCZ). Based on our construction, we can also control the correlation value outside the ZCCZ. The beauty of the obtained codes have aperiodic and periodic inter-set ZCCZ and low cross-correlation side-lobs., Comment: This Paper has been presented in ''SEquences and Their Applications (SETA) 2024''

For a type-I $(K,M,Z,N)$-ZCCS, it follows $K \leq M \left\lfloor \frac{N}{Z}\right\rfloor$. In this paper, we propose a construction of type-II $(p^{k+n},p^k,p^{n+r}-p^r+1,p^{n+r})$-$Z$ complementary code set (ZCCS) using an extended Boolean function, its properties of Hamiltonian paths and the concept of isolated vertices, where $p\ge 2$. However, the proposed type-II ZCCS provides $K = M(N-Z+1)$ codes, where as for type-I $(K,M,N,Z)$-ZCCS, it is $K \leq M \left\lfloor \frac{N}{Z}\right\rfloor$. Therefore, the proposed type-II ZCCS provides a larger number of codes compared to type-I ZCCS. Further, as a special case of the proposed construction, $(p^k,p^k,p^n)$-CCC can be generated, for any integral value of $p\ge2$ and $k\le n$.

In this letter, we propose a novel construction of type-II $Z$-complementary code set (ZCCS) having arbitrary sequence length using the Kronecker product between a complete complementary code (CCC) and mutually orthogonal uni-modular sequences. In this construction, Barker sequences are used to reduce row sequence peak-to-mean envelope power ratio (PMEPR) for some specific lengths sequence and column sequence PMEPR for some specific sizes of codes. The column sequence PMEPR of the proposed type-II ZCCS is upper bounded by a number smaller than $2$. The proposed construction also contributes new lengths of type-II $Z$-complementary pair (ZCP) and type-II $Z$-complementary set (ZCS). Furthermore, the PMEPR of these new type-II ZCPs is also lower than existing type-II ZCPs.

Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to class imbalance. Synthetic data generation, i.e., oversampling, is a common remedy used to improve classifier performance. State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can be used to generate synthetic samples from the convex space of the minority class to improve classifier performance in such cases. Deep generative networks are common deep learning approaches for synthetic sample generation, widely used for synthetic image generation. However, their scope on synthetic tabular data generation in the context of imbalanced classification is not adequately explored. In this article, we show that existing deep generative models perform poorly compared to linear interpolation based approaches for imbalanced classification problems on smaller tabular datasets. To overcome this, we propose a deep generative model, ConvGeN that combines the idea of convex space learning with deep generative models. ConvGeN learns the coefficients for the convex combinations of the minority class samples, such that the synthetic data is distinct enough from the majority class. Our benchmarking experiments demonstrate that our proposed model ConvGeN improves imbalanced classification on such small datasets, as compared to existing deep generative models, while being at-par with the existing linear interpolation approaches. Moreover, we discuss how our model can be used for synthetic tabular data generation in general, even outside the scope of data imbalance and thus, improves the overall applicability of convex space learning.