Drug-target interactions are critical for understanding biological processes and advancing drug discovery. However, traditional methods such as ComplEx-SE, TransE, and DistMult struggle with unseen relationships and negative triplets, which limits their effectiveness in drug-target prediction. To address these challenges, we propose Multi-Context-Aware Sampling (MuCoS), an efficient and positively accurate method for drug-target prediction. MuCoS reduces computational complexity by prioritizing neighbors of higher density to capture informative structural patterns. These optimized neighborhood representations are integrated with BERT, enabling contextualized embeddings for accurate prediction of missing relationships or tail entities. MuCoS avoids the need for negative triplet sampling, reducing computation while improving performance over unseen entities and relations. Experiments on the KEGG50k biomedical dataset show that MuCoS improved over existing models by 13% on MRR, 7% on Hits@1, 4% on Hits@3, and 18% on Hits@10 for the general relationship, and by 6% on MRR, 1% on Hits@1, 3% on Hits@3, and 12% on Hits@10 for prediction of drug-target relationship.

As with many other problems, real-world regression is plagued by the presence of noisy labels, an inevitable issue that demands our attention. Fortunately, much real-world data often exhibits an intrinsic property of continuously ordered correlations between labels and features, where data points with similar labels are also represented with closely related features. In response, we propose a novel approach named ConFrag, where we collectively model the regression data by transforming them into disjoint yet contrasting fragmentation pairs. This enables the training of more distinctive representations, enhancing the ability to select clean samples. Our ConFrag framework leverages a mixture of neighboring fragments to discern noisy labels through neighborhood agreement among expert feature extractors. We extensively perform experiments on six newly curated benchmark datasets of diverse domains, including age prediction, price prediction, and music production year estimation. We also introduce a metric called Error Residual Ratio (ERR) to better account for varying degrees of label noise. Our approach consistently outperforms fourteen state-of-the-art baselines, being robust against symmetric and random Gaussian label noise.

The Musical Instrument Digital Interface (MIDI), introduced in 1983, revolutionized music production by allowing computers and instruments to communicate efficiently. MIDI files encode musical instructions compactly, facilitating convenient music sharing. They benefit Music Information Retrieval (MIR), aiding in research on music understanding, computational musicology, and generative music. The GigaMIDI dataset contains over 1.4 million unique MIDI files, encompassing 1.8 billion MIDI note events and over 5.3 million MIDI tracks. GigaMIDI is currently the largest collection of symbolic music in MIDI format available for research purposes under fair dealing. Distinguishing between non-expressive and expressive MIDI tracks is challenging, as MIDI files do not inherently make this distinction. To address this issue, we introduce a set of innovative heuristics for detecting expressive music performance. These include the Distinctive Note Velocity Ratio (DNVR) heuristic, which analyzes MIDI note velocity; the Distinctive Note Onset Deviation Ratio (DNODR) heuristic, which examines deviations in note onset times; and the Note Onset Median Metric Level (NOMML) heuristic, which evaluates onset positions relative to metric levels. Our evaluation demonstrates these heuristics effectively differentiate between non-expressive and expressive MIDI tracks. Furthermore, after evaluation, we create the most substantial expressive MIDI dataset, employing our heuristic, NOMML. This curated iteration of GigaMIDI encompasses expressively-performed instrument tracks detected by NOMML, containing all General MIDI instruments, constituting 31% of the GigaMIDI dataset, totalling 1,655,649 tracks.

We study the application of emerging photonic and quantum computing architectures to solving the Traveling Salesman Problem (TSP), a well-known NP-hard optimization problem. We investigate several approaches: Simulated Annealing (SA), Quadratic Unconstrained Binary Optimization (QUBO-Ising) methods implemented on quantum annealers and Optical Coherent Ising Machines, as well as the Quantum Approximate Optimization Algorithm (QAOA) and the Quantum Phase Estimation (QPE) algorithm on gate-based quantum computers. QAOA and QPE were tested on the IBM Quantum platform. The QUBO-Ising method was explored using the D-Wave quantum annealer, which operates on superconducting Josephson junctions, and the QCI Dirac machine, a nonlinear optoelectronic Ising machine. Gate-based quantum computers demonstrated accurate results for small TSP instances in simulation. However, real quantum devices are hindered by noise and limited scalability. Circuit complexity grows with problem size, restricting performance to TSP instances with a maximum of 6 nodes. In contrast, Ising-based architectures show improved scalability for larger problem sizes. SQUID-based Ising machines can handle TSP instances with up to 12 nodes, while nonlinear optoelectronic Ising machines extend this capability to 18 nodes. Nevertheless, the solutions tend to be suboptimal due to hardware limitations and challenges in achieving ground state convergence as the problem size increases. Despite these limitations, Ising machines demonstrate significant time advantages over classical methods, making them a promising candidate for solving larger-scale TSPs efficiently.

Graph data has become a pivotal modality due to its unique ability to model relational datasets. However, real-world graph data continues to grow exponentially, resulting in a quadratic increase in the complexity of most graph algorithms as graph sizes expand. Although graph condensation (GC) methods have been proposed to address these scalability issues, existing approaches often treat the training set as static, overlooking the evolving nature of real-world graph data. This limitation leads to inefficiencies when condensing growing training sets. In this paper, we introduce GECC (Graph Evolving Clustering Condensation), a scalable graph condensation method designed to handle large-scale and evolving graph data. GECC employs a traceable and efficient approach by performing class-wise clustering on aggregated features. Furthermore, it can inherits previous condensation results as clustering centroids when the condensed graph expands, thereby attaining an evolving capability. This methodology is supported by robust theoretical foundations and demonstrates superior empirical performance. Comprehensive experiments show that GECC achieves better performance than most state-of-the-art graph condensation methods while delivering an around 1,000x speedup on large datasets.

Stars and their associated planets originate from the same cloud of gas and dust, making a star's elemental composition a valuable indicator for indirectly studying planetary compositions. While the connection between a star's iron (Fe) abundance and the presence of giant exoplanets is established (e.g. Gonzalez 1997; Fischer & Valenti 2005), the relationship with small planets remains unclear. The elements Mg, Si, and Fe are important in forming small planets. Employing machine learning algorithms like XGBoost, trained on the abundances (e.g., the Hypatia Catalog, Hinkel et al. 2014) of known exoplanet-hosting stars (NASA Exoplanet Archive), allows us to determine significant "features" (abundances or molar ratios) that may indicate the presence of small planets. We test on three groups of exoplanets: (a) all small, R$_{P}$ $<$ 3.5 $R_{\oplus}$, (b) sub-Neptunes, 2.0 $R_{\oplus}$ $<$ R$_{P}$ $<$ 3.5 $R_{\oplus}$, and (c) super-Earths, 1.0 $R_{\oplus}$ $<$ R$_{P}$ $<$ 2.0 $R_{\oplus}$ -- each subdivided into 7 ensembles to test different combinations of features. We created a list of stars with $\geq90\%$ probability of hosting small planets across all ensembles and experiments ("overlap stars"). We found abundance trends for stars hosting small planets, possibly indicating star-planet chemical interplay during formation. We also found that Na and V are key features regardless of planetary radii. We expect our results to underscore the importance of elements in exoplanet formation and machine learning's role in target selection for future NASA missions: e.g., the James Webb Space Telescope (JWST), Nancy Grace Roman Space Telescope (NGRST), Habitable Worlds Observatory (HWO) -- all of which are aimed at small planet detection.

Interpreting data is central to modern research. Large language models (LLMs) show promise in providing such natural language interpretations of data, yet simple feature extraction methods such as prompting often fail to produce accurate and versatile descriptions for diverse datasets and lack control over granularity and scale. To address these limitations, we propose a domain-agnostic method for dataset featurization that provides precise control over the number of features extracted while maintaining compact and descriptive representations comparable to human expert labeling. Our method optimizes the selection of informative binary features by evaluating the ability of an LLM to reconstruct the original data using those features. We demonstrate its effectiveness in dataset modeling tasks and through two case studies: (1) Constructing a feature representation of jailbreak tactics that compactly captures both the effectiveness and diversity of a larger set of human-crafted attacks; and (2) automating the discovery of features that align with human preferences, achieving accuracy and robustness comparable to expert-crafted features. Moreover, we show that the pipeline scales effectively, improving as additional features are sampled, making it suitable for large and diverse datasets.

The proliferation of text-to-image diffusion models has raised significant privacy and security concerns, particularly regarding the generation of copyrighted or harmful images. To address these issues, research on machine unlearning has developed various concept erasure methods, which aim to remove the effect of unwanted data through post-hoc training. However, we show these erasure techniques are vulnerable, where images of supposedly erased concepts can still be generated using adversarially crafted prompts. We introduce RECORD, a coordinate-descent-based algorithm that discovers prompts capable of eliciting the generation of erased content. We demonstrate that RECORD significantly beats the attack success rate of current state-of-the-art attack methods. Furthermore, our findings reveal that models subjected to concept erasure are more susceptible to adversarial attacks than previously anticipated, highlighting the urgency for more robust unlearning approaches. We open source all our code at https://github.com/LucasBeerens/RECORD

The paper presents an innovative methodology for designing frequency selective surface (FSS) based radar absorbing materials using machine learning (ML) technique. In conventional electromagnetic design, unit cell dimensions of FSS are used as input and absorption coefficient is then predicted for a given design. In this paper, absorption coefficient is considered as input to ML model and image of FSS unit cell is predicted. Later, this image is used for generating the FSS unit cell parameters. Eleven different ML models are studied over a wide frequency band of 1GHz to 30GHz. Out of which six ML models (i.e. (a) Random Forest classification, (b) K- Neighbors Classification, (c) Grid search regression, (d) Random Forest regression, (e) Decision tree classification, and (f) Decision tree regression) show training accuracy more than 90%. The absorption coefficients with varying frequencies of these predicted images are subsequently evaluated using commercial electromagnetic solver. The performance of these ML models is encouraging, and it can be used for accelerating design and optimization of high performance FSS based radar absorbing material for advanced electromagnetic applications in future.

In Federated Learning (FL), several clients jointly learn a machine learning model: each client maintains a local model for its local learning dataset, while a master server maintains a global model by aggregating the local models of the client devices. However, the repetitive communication between server and clients leaves room for attacks aimed at compromising the integrity of the global model, causing errors in its targeted predictions. In response to such threats on FL, various defense measures have been proposed in the literature. In this paper, we present a powerful defense against malicious clients in FL, called FedSV, using the Shapley Value (SV), which has been proposed recently to measure user contribution in FL by computing the marginal increase of average accuracy of the model due to the addition of local data of a user. Our approach makes the identification of malicious clients more robust, since during the learning phase, it estimates the contribution of each client according to the different groups to which the target client belongs. FedSV's effectiveness is demonstrated by extensive experiments on MNIST datasets in a cross-silo context under various attacks.