A Set of Efficient Methods to Generate High-Dimensional Binary Data With Specified Correlation Structures by Wei Jiang (14986)

“…In this article, we introduce several computationally efficient algorithms that generate high-dimensional binary data with specified correlation structures and unequal probabilities. …”

The proposed genetic structure of programs. by Pavel Kodytek (10140509)

Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

“…<p>The DullRazor algorithm systematically removes hair artifacts through a multi-step process: (1) converting the original hairy input image to grayscale, (2) applying a black hat filter to detect and isolate hair structures, (3) creating a binary mask of detected hair regions, and (4) removing hair regions and inpainting the underlying skin texture. …”

Generating SIM illumination patterns with the DMD chip. by Musa Aydın (13767400)

Data_Sheet_1_Posiform planting: generating QUBO instances for benchmarking.pdf by Georg Hahn (12530469)

“…While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting, for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. …”

CNN structure for feature extraction. by Md. Sabbir Hossain (9958939)

BCN-M: A Free Computational Tool for Generating Wulff-like Nanoparticle Models with Controlled Stoichiometry by Danilo González (6320369)

“…BCN-M has been applied to 15 different materials, leading to a variety of models that cover the most relevant binary ionic structures and symmetries (cubic, tetragonal, hexagonal, and monoclinic). …”

Analysis and design of algorithms for the manufacturing process of integrated circuits by Sonia Fleytas (16856403)

“…The (approximate) solution proposals of state-of-the-art methods include rule-based approaches, genetic algorithms, and reinforcement learning. There is a binary integer programming model for this problem in the literature, from which its authors proposed a genetic algorithm to obtain approximate solutions. …”

Secure MANET routing with blockchain-enhanced latent encoder coupled GANs and BEPO optimization by Sandeep Jagonda Patil (22048337)

“…The performance of the proposed LEGAN-BEPO-BCMANET technique attains 29.786%, 19.25%, 22.93%, 27.21%, 31.02%, 26.91%, and 25.61% greater throughput, compared to existing methods like Blockchain-based BATMAN protocol utilizing MANET with an ensemble algorithm (BATMAN-MANET), Block chain-based trusted distributed routing scheme with optimized dropout ensemble extreme learning neural network in MANET (DEELNN-MANET), A secured trusted routing utilizing structure of a new directed acyclic graph-blockchain in MANET internet of things environment (DAG-MANET), An Optimized Link State Routing Protocol with Blockchain Framework for Efficient Video-Packet Transmission and Security over MANET (OLSRP-MANET), Auto-metric Graph Neural Network based Blockchain Technology for Protected Dynamic Optimum Routing in MANET (AGNN-MANET) and Data security-based routing in MANETs under key management process (DSR-MANET) respectively.…”

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles by Peter S. Rice (11805875)

Algoritmo de detección de odio en español (Algorithm for detection of hate speech in Spanish) by Elias Said-Hung (10790310)

Algoritmo de clasificación de expresiones de odio por tipos en español (Algorithm for classifying hate expressions by type in Spanish) by Daniel Pérez Palau (11097348)

“…</li></ul><p dir="ltr"><b>File Structure</b></p><p dir="ltr">The code generates and saves:</p><ul><li>Weights of the trained model (.h5)</li><li>Configured tokenizer</li><li>Training history in CSV</li><li>Requirements file</li></ul><p dir="ltr"><b>Important Notes</b></p><ul><li>The model excludes category 2 during training</li><li>Implements transfer learning from a pre-trained model for binary hate detection</li><li>Includes early stopping callbacks to prevent overfitting</li><li>Uses class weighting to handle category imbalances</li></ul><p dir="ltr">The process of creating this algorithm is explained in the technical report located at: Blanco-Valencia, X., De Gregorio-Vicente, O., Ruiz Iniesta, A., & Said-Hung, E. (2025). …”

Probability graphical model of scMSI. by Yuqian Liu (1413763)

Data_Sheet_1_DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads.docx by Jinxin Dong (9229511)

“…A 2D binary search tree is used to search the neighbor points effectively. …”

Effect of different randomization constraints on pattern detection. by Zachary P. Neal (9946331)

Solubility Prediction of Different Forms of Pharmaceuticals in Single and Mixed Solvents Using Symmetric Electrolyte Nonrandom Two-Liquid Segment Activity Coefficient Model by Getachew S. Molla (6416744)

“…The methodology incorporates key features of the symmetric eNRTL-SAC model structure to reduce the number of parameters and uses a hybrid of global search algorithms for parameter estimation. …”

Structure-based antibody paratope prediction with 3D Zernike descriptors and SVM by Sebastian Daberdaku (4391767)

“…<br><br><b>seq_descriptors_4.5.tar.gz -</b> Contains the sequence descriptors generated from the antibody structures in the development/validation set which were used for feature selection with the Randomized Logistic Regression algorithm.…”

Schematic overview of SINATRA Pro: A novel framework for discovering biophysical signatures that differentiate classes of proteins. by Wai Shing Tang (9541179)

“…<p><b>(A)</b> The SINATRA Pro algorithm requires the following inputs: <i>(i)</i> (<i>x</i>, <i>y</i>, <i>z</i>)-coordinates corresponding to the structural position of each atom in every protein; <i>(ii)</i> <b>y</b>, a binary vector denoting protein class or phenotype (e.g., mutant versus wild-type); <i>(iii)</i> <i>r</i>, the cutoff distance for simplicial construction (i.e., constructing the mesh representation for every protein); <i>(iv)</i> <i>c</i>, the number of cones of directions; <i>(v)</i> <i>d</i>, the number of directions within each cone; <i>(vi)</i> <i>θ</i>, the cap radius used to generate directions in a cone; and <i>(vii)</i> <i>l</i>, the number of sublevel sets (i.e., filtration steps) used to compute the differential Euler characteristic (DEC) curve along a given direction. …”

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction by Raul A. Flores (2910539)

“…We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.…”

G-clusteron classification of handwritten digits (MNIST) and the resultant distribution of synaptic locations and activations. by Toviah Moldwin (10866708)