DuaDeep-SeqAffinity: Dual-Stream Deep Learning Framework for Sequence-Only Antigen-Antibody Affinity Prediction

Predicting the binding affinity between antigens and antibodies is fundamental to drug discovery and vaccine development. Traditional computational approaches often rely on experimentally determined 3D structures, which are scarce and computationally expensive to obtain. This paper introduces DuaDeep-SeqAffinity, a novel sequence-only deep learning framework that predicts affinity scores solely from their amino acid sequences using a dual-stream hybrid architecture. Our approach leverages pre-trained ESM-2 protein language model embeddings, combining 1D Convolutional Neural Networks (CNNs) for local motif detection with Transformer encoders for global contextual representation. A subsequent fusion module integrates these multi-faceted features, which are then passed to a fully connected network for final score regression. Experimental results demonstrate that DuaDeep-SeqAffinity significantly outperforms individual architectural components and existing state-of-the-art (SOTA) methods. DuaDeep achieved a superior Pearson correlation of 0.688, an R^2 of 0.460, and a Root Mean Square Error (RMSE) of 0.737, surpassing single-branch variants ESM-CNN and ESM-Transformer. Notably, the model achieved an Area Under the Curve (AUC) of 0.890, outperforming sequence-only benchmarks and even surpassing structure-sequence hybrid models. These findings prove that high-fidelity sequence embeddings can capture essential binding patterns typically reserved for structural modeling. By eliminating the reliance on 3D structures, DuaDeep-SeqAffinity provides a highly scalable and efficient solution for high-throughput screening of vast sequence libraries, significantly accelerating the therapeutic discovery pipeline.

Hybrid Deep Reinforcement Learning for Joint Resource Allocation in Multi-Active RIS-Aided Uplink Communications

Active Reconfigurable Intelligent Surfaces (RIS) are a promising technology for 6G wireless networks. This paper investigates a novel hybrid deep reinforcement learning (DRL) framework for resource allocation in a multi-user uplink system assisted by multiple active RISs. The objective is to maximize the minimum user rate by jointly optimizing user transmit powers, active RIS configurations, and base station (BS) beamforming. We derive a closed-form solution for optimal beamforming and employ DRL algorithms: Soft actor-critic (SAC), deep deterministic policy gradient (DDPG), and twin delayed DDPG (TD3) to solve the high-dimensional, non-convex power and RIS optimization problem. Simulation results demonstrate that SAC achieves superior performance with high learning rate leading to faster convergence and lower computational cost compared to DDPG and TD3. Furthermore, the closed-form of optimally beamforming enhances the minimum rate effectively.

A Light Weight Neural Network for Automatic Modulation Classification in OFDM Systems

Automatic Modulation Classification (AMC) is a vital component in the development of intelligent and adaptive transceivers for future wireless communication systems. Existing statistically-based blind modulation classification methods for Orthogonal Frequency Division Multiplexing (OFDM) often fail to achieve the required accuracy and performance. Consequently, the modulation classification research community has shifted its focus toward deep learning techniques, which demonstrate promising performance, but come with increased computational complexity. In this paper, we propose a lightweight subcarrier-based modulation classification method for OFDM systems. In the proposed approach, a selected set of subcarriers in an OFDM frame is classified first, followed by the prediction of the modulation types for the remaining subcarriers based on the initial results. A Lightweight Neural Network (LWNN) is employed to identify the initially selected set of subcarriers, and its output is fed into a Recurrent Neural Network (RNN) as an embedded vector to predict the modulation schemes of the remaining subcarriers in the OFDM frame.

HWL-HIN: A Hypergraph-Level Hypergraph Isomorphism Network as Powerful as the Hypergraph Weisfeiler-Lehman Test with Application to Higher-Order Network Robustness

Robustness in complex systems is of significant engineering and economic importance. However, conventional attack-based a posteriori robustness assessments incur prohibitive computational overhead. Recently, deep learning methods, such as Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs), have been widely employed as surrogates for rapid robustness prediction. Nevertheless, these methods neglect the complex higher-order correlations prevalent in real-world systems, which are naturally modeled as hypergraphs. Although Hypergraph Neural Networks (HGNNs) have been widely adopted for hypergraph learning, their topological expressive power has not yet reached the theoretical upper bound. To address this limitation, inspired by Graph Isomorphism Networks, this paper proposes a hypergraph-level Hypergraph Isomorphism Network framework. Theoretically, this approach is proven to possess an expressive power strictly equivalent to the Hypergraph Weisfeiler-Lehman test and is applied to predict hypergraph robustness. Experimental results demonstrate that while maintaining superior efficiency in training and prediction, the proposed method not only outperforms existing graph-based models but also significantly surpasses conventional HGNNs in tasks that prioritize topological structure representation.

From In Silico to In Vitro: Evaluating Molecule Generative Models for Hit Generation

Hit identification is a critical yet resource-intensive step in the drug discovery pipeline, traditionally relying on high-throughput screening of large compound libraries. Despite advancements in virtual screening, these methods remain time-consuming and costly. Recent progress in deep learning has enabled the development of generative models capable of learning complex molecular representations and generating novel compounds de novo. However, using ML to replace the entire drug-discovery pipeline is highly challenging. In this work, we rather investigate whether generative models can replace one step of the pipeline: hit-like molecule generation. To the best of our knowledge, this is the first study to explicitly frame hit-like molecule generation as a standalone task and empirically test whether generative models can directly support this stage of the drug discovery pipeline. Specifically, we investigate if such models can be trained to generate hit-like molecules, enabling direct incorporation into, or even substitution of, traditional hit identification workflows. We propose an evaluation framework tailored to this task, integrating physicochemical, structural, and bioactivity-related criteria within a multi-stage filtering pipeline that defines the hit-like chemical space. Two autoregressive and one diffusion-based generative models were benchmarked across various datasets and training settings, with outputs assessed using standard metrics and target-specific docking scores. Our results show that these models can generate valid, diverse, and biologically relevant compounds across multiple targets, with a few selected GSK-3$β$ hits synthesized and confirmed active in vitro. We also identify key limitations in current evaluation metrics and available training data.

Statistical vs. Deep Learning Models for Estimating Substance Overdose Excess Mortality in the US

Substance overdose mortality in the United States claimed over 80,000 lives in 2023, with the COVID-19 pandemic exacerbating existing trends through healthcare disruptions and behavioral changes. Estimating excess mortality, defined as deaths beyond expected levels based on pre-pandemic patterns, is essential for understanding pandemic impacts and informing intervention strategies. However, traditional statistical methods like SARIMA assume linearity, stationarity, and fixed seasonality, which may not hold under structural disruptions. We present a systematic comparison of SARIMA against three deep learning (DL) architectures (LSTM, Seq2Seq, and Transformer) for counterfactual mortality estimation using national CDC data (2015-2019 for training/validation, 2020-2023 for projection). We contribute empirical evidence that LSTM achieves superior point estimation (17.08% MAPE vs. 23.88% for SARIMA) and better-calibrated uncertainty (68.8% vs. 47.9% prediction interval coverage) when projecting under regime change. We also demonstrate that attention-based models (Seq2Seq, Transformer) underperform due to overfitting to historical means rather than capturing emergent trends. Ourreproducible pipeline incorporates conformal prediction intervals and convergence analysis across 60+ trials per configuration, and we provide an open-source framework deployable with 15 state health departments. Our findings establish that carefully validated DL models can provide more reliable counterfactual estimates than traditional methods for public health planning, while highlighting the need for calibration techniques when deploying neural forecasting in high-stakes domains.

S&P 500 Stock's Movement Prediction using CNN

This paper is about predicting the movement of stock consist of S&P 500 index. Historically there are many approaches have been tried using various methods to predict the stock movement and being used in the market currently for algorithm trading and alpha generating systems using traditional mathematical approaches [1, 2]. The success of artificial neural network recently created a lot of interest and paved the way to enable prediction using cutting-edge research in the machine learning and deep learning. Some of these papers have done a great job in implementing and explaining benefits of these new technologies. Although most these papers do not go into the complexity of the financial data and mostly utilize single dimension data, still most of these papers were successful in creating the ground for future research in this comparatively new phenomenon. In this paper, I am trying to use multivariate raw data including stock split/dividend events (as-is) present in real-world market data instead of engineered financial data. Convolution Neural Network (CNN), the best-known tool so far for image classification, is used on the multi-dimensional stock numbers taken from the market mimicking them as a vector of historical data matrices (read images) and the model achieves promising results. The predictions can be made stock by stock, i.e., a single stock, sector-wise or for the portfolio of stocks.

First Provable Guarantees for Practical Private FL: Beyond Restrictive Assumptions

Federated Learning (FL) enables collaborative training on decentralized data. Differential privacy (DP) is crucial for FL, but current private methods often rely on unrealistic assumptions (e.g., bounded gradients or heterogeneity), hindering practical application. Existing works that relax these assumptions typically neglect practical FL features, including multiple local updates and partial client participation. We introduce Fed-$α$-NormEC, the first differentially private FL framework providing provable convergence and DP guarantees under standard assumptions while fully supporting these practical features. Fed-$α$-NormE integrates local updates (full and incremental gradient steps), separate server and client stepsizes, and, crucially, partial client participation, which is essential for real-world deployment and vital for privacy amplification. Our theoretical guarantees are corroborated by experiments on private deep learning tasks.

Inference-based GAN Video Generation

Video generation has seen remarkable progresses thanks to advancements in generative deep learning. Generated videos should not only display coherent and continuous movement but also meaningful movement in successions of scenes. Generating models such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs) and more recently Diffusion Networks have been used for generating short video sequences, usually of up to 16 frames. In this paper, we first propose a new type of video generator by enabling adversarial-based unconditional video generators with a variational encoder, akin to a VAE-GAN hybrid structure, in order to enable the generation process with inference capabilities. The proposed model, as in other video deep learning-based processing frameworks, incorporates two processing branches, one for content and another for movement. However, existing models struggle with the temporal scaling of the generated videos. In classical approaches when aiming to increase the generated video length, the resulting video quality degrades, particularly when considering generating significantly long sequences. To overcome this limitation, our research study extends the initially proposed VAE-GAN video generation model by employing a novel, memory-efficient approach to generate long videos composed of hundreds or thousands of frames ensuring their temporal continuity, consistency and dynamics. Our approach leverages a Markov chain framework with a recall mechanism, with each state representing a VAE-GAN short-length video generator. This setup allows for the sequential connection of generated video sub-sequences, enabling temporal dependencies, resulting in meaningful long video sequences.

AI for Mycetoma Diagnosis in Histopathological Images: The MICCAI 2024 Challenge

Mycetoma is a neglected tropical disease caused by fungi or bacteria leading to severe tissue damage and disabilities. It affects poor and rural communities and presents medical challenges and socioeconomic burdens on patients and healthcare systems in endemic regions worldwide. Mycetoma diagnosis is a major challenge in mycetoma management, particularly in low-resource settings where expert pathologists are limited. To address this challenge, this paper presents an overview of the Mycetoma MicroImage: Detect and Classify Challenge (mAIcetoma) which was organized to advance mycetoma diagnosis through AI solutions. mAIcetoma focused on developing automated models for segmenting mycetoma grains and classifying mycetoma types from histopathological images. The challenge attracted the attention of several teams worldwide to participate and five finalist teams fulfilled the challenge objectives. The teams proposed various deep learning architectures for the ultimate goal of this challenge. Mycetoma database (MyData) was provided to participants as a standardized dataset to run the proposed models. Those models were evaluated using evaluation metrics. Results showed that all the models achieved high segmentation accuracy, emphasizing the necessitate of grain detection as a critical step in mycetoma diagnosis. In addition, the top-performing models show a significant performance in classifying mycetoma types.