Researcher profile

Suman Saha

Suman Saha contributes to research discovery and scholarly infrastructure.

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Computer Vision astro-ph.EP Computation and Language Cryptography and Security Machine Learning Artificial Intelligence astro-ph.SR nlin.AO nlin.CD Robotics

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preprint2026arXiv

A Pilot Benchmark for NL-to-FOL Translation in Planetary Exploration

Future planetary exploration envisions autonomous robotic agents operating under severe communication constraints, without global positioning, and with minimal human intervention. In such environments, agents must not only perceive and act, but also reason over mission objectives, operational constraints, and evolving environmental conditions. While prior work has largely focused on perception and control, the translation of high-level mission knowledge into structured, machine-interpretable representations remains underexplored. We introduce a pilot benchmark for translating natural language (NL) into First-Order Logic (FOL) within the domain of planetary exploration. The dataset is constructed from real mission documentation sourced from NASA's Planetary Data System (PDS), spanning missions from 2003 to 2013. These documents describe mission phases such as launch, boost, coast, cruise, and orbital operations in rich natural language. We manually annotate these documents with corresponding FOL representations that capture temporal structure, agent roles, and operational dependencies. In addition, we provide structured predicate vocabularies and typed constants to enable controlled experimentation with varying levels of prior knowledge. This pilot benchmark provides a foundation for research at the intersection of language understanding and formal reasoning, grounded in real-world, safety-critical mission data. The dataset is provided at: https://github.com/HaydenMM/planetary-logic-benchmark/blob/main/pilot_benchmark.json

preprint2026arXiv

Improving LLM-Assisted Secure Code Generation through Retrieval-Augmented-Generation and Multi-Tool Feedback

Large Language Models (LLMs) can generate code but often introduce security vulnerabilities, logical inconsistencies, and compilation errors. Prior work demonstrates that LLMs benefit substantially from structured feedback, static analysis, retrieval augmentation, and execution-based refinement. We propose a retrieval-augmented, multi-tool repair workflow in which a single code-generating LLM iteratively refines its outputs using compiler diagnostics, CodeQL security scanning, and KLEE symbolic execution. A lightweight embedding model is used for semantic retrieval of previously successful repairs, providing security-focused examples that guide generation. Evaluated on a combined dataset of 3,242 programs generated by DeepSeek-Coder-1.3B and CodeLlama-7B, the system demonstrates significant improvements in robustness. For DeepSeek, security vulnerabilities were reduced by 96%. For the larger CodeLlama model, the critical security defect rate was decreased from 58.55% to 22.19%, highlighting the efficacy of tool-assisted self-repair even on "stubborn" models.

preprint2026arXiv

Mass estimates of the young TOI-451 transiting planets: Multidimensional Gaussian Process on stellar spectroscopic and photometric signals

The young TOI-451 planetary system, aged 125 Myr, provides a unique opportunity to test theories of planetary internal structures and atmospheric mass loss through examination of its three transiting planets. We present an exhaustive photometric and spectroscopic follow-up to determine the orbital and physical properties of the system. We perform multidimensional Gaussian Process regression with the code pyaneti on spectroscopic time-series and NGTS/LCO light curves to disentangle the stellar and planetary signal in ESPRESSO radial velocities. We show how contemporaneous photometry serves as an activity indicator to inform RV modelling within a multidimensional Gaussian Processes framework. We argue that this can be exploited when spectroscopic observations are adversely affected by low signal-to-noise and/or poor sampling. We estimate the Doppler semi-amplitudes of Kb = 2.6(+1.1,-1.2) m/s, Kc = 1.2(+1.0,-0.8) m/s and Kd = 2.7 +/- 1.2 m/s. This translates into 2-sigma mass estimates for TOI-451 b and d of Mb = 4.7(+2.1,-2.2) Earth masses and Md = 10.2(+4.6,-4.5) Earth masses, as well as a mass upper limit for TOI-451 c of Mc < 11.5 Earth masses. The derived planetary properties suggest that planets c and d contain significant hydrogen-rich envelopes. The inferred parameters of TOI-451 b are consistent with either a rocky world that still retains a small hydrogen envelope or a water world. These insights make the TOI-451 system an ideal laboratory for future follow-up studies aimed at measuring atmospheric compositions, detecting atmospheric mass-loss signatures, and further exploring planetary formation and evolution processes.

preprint2026arXiv

Rule-Based Approaches to Atomic Sentence Extraction

Natural language often combines multiple ideas into complex sentences. Atomic sentence extraction, the task of decomposing complex sentences into simpler sentences that each express a single idea, improves performance in information retrieval, question answering, and automated reasoning systems. Previous work has formalized the "split-and-rephrase" task and established evaluation metrics, and machine learning approaches using large language models have improved extraction accuracy. However, these methods lack interpretability and provide limited insight into which linguistic structures cause extraction failures. Although some studies have explored dependency-based extraction of subject-verb-object triples and clauses, no principled analysis has examined which specific clause structures and dependencies lead to extraction difficulties. This study addresses this gap by analyzing how complex sentence structures, including relative clauses, adverbial clauses, coordination patterns, and passive constructions, affect the performance of rule-based atomic sentence extraction. Using the WikiSplit dataset, we implemented dependency-based extraction rules in spaCy, generated 100 gold=standard atomic sentence sets, and evaluated performance using ROUGE and BERTScore. The system achieved ROUGE-1 F1 = 0.6714, ROUGE-2 F1 = 0.478, ROUGE-L F1 = 0.650, and BERTScore F1 = 0.5898, indicating moderate-to-high lexical, structural, and semantic alignment. Challenging structures included relative clauses, appositions, coordinated predicates, adverbial clauses, and passive constructions. Overall, rule-based extraction is reasonably accurate but sensitive to syntactic complexity.

preprint2026arXiv

SecureCodeRL: Security-Aware Reinforcement Learning for Code Generation with Partial-Credit Rewards

Large Language Models (LLMs) can generate plausible code, but in settings that require exact stdin/stdout behavior they frequently produce programs that compile yet fail tests, and in some cases they introduce security-sensitive patterns. This paper presents SecureCodeRL, a reinforcement learning (RL) pipeline for security-aware code generation that optimizes a combined reward R = αRfunc + \b{eta}Rsec. The key idea is a partial-credit functional reward that assigns intermediate scores for syntactic validity, successful execution, and producing output, reducing reward sparsity that otherwise stalls learning on competitive programming style tasks. I evaluate supervised fine-tuning (SFT) and PPO variants on a small held-out prompt set from APPS+ and observe that PPO with partial credit (using a continued-training variant) improves syntax validity from 45% (SFT) to 60% and achieves the only non-zero test success signal in this pilot evaluation (5% at-least-one-test-pass), while remaining 100% clean under Bandit static analysis. Although Bandit findings were absent in this small evaluation, the security term is integrated into training to discourage insecure shortcuts when they appear.

preprint2022arXiv

Resilience in multiplex networks by addition of cross-repulsive links

A multiplex network of identical dynamical units becomes resilient against parameter perturbation by adding selective linear diffusive cross-coupling links. A parameter drift at any instant in one or multiple network nodes can destroy synchrony, causing failure and even collapse in the network performance. We introduced [PRE 95, 062204(2017)] a recovery strategy by selective addition of cross-coupling links to save synchrony in the network from the edge of failure due to parameter mismatch (small or large) in any nodes. This concept is extended to 2-layered multiplex networks when the emergent synchrony becomes resilient against a small or large parameter drifting. In addition, the stability of the synchronous state is enhanced from local stability to global stability of synchrony. By the addition of cross-coupling, the network revives complete synchrony in all the nodes except the perturbed nodes, which emerges into a type of generalized synchrony with all the unperturbed nodes. The generalized synchrony is manifested simply by a linear amplitude response in the state variable(s) of the perturbed node(s) by a scaling factor proportional to the mismatch. A set of systematic rules has been derived from the linear flow matrix of the dynamical system representing the nodes dynamics that helps find the connectivity matrix of the cross-coupling links. Lyapunov function stability condition is used to determine the cross-coupling link strength that, in turn, establishes global stability of synchrony of the multiplex network. We verify the efficacy of our proposed coupling scheme with analytical results and numerical simulations of two examples of multiplex networks. In the first example, we use non-local connectivity in each layer, with nodal dynamics of the FitzHugh-Nagumo neuron model.

preprint2022arXiv

ROAD: The ROad event Awareness Dataset for Autonomous Driving

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road events and their evolution. Injecting these capabilities in autonomous vehicles can thus take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset annotated with bounding boxes showing the location in the image plane of each road event. We benchmark various detection tasks, proposing as a baseline a new incremental algorithm for online road event awareness termed 3D-RetinaNet. We also report the performance on the ROAD tasks of Slowfast and YOLOv5 detectors, as well as that of the winners of the ICCV2021 ROAD challenge, which highlight the challenges faced by situation awareness in autonomous driving. ROAD is designed to allow scholars to investigate exciting tasks such as complex (road) activity detection, future event anticipation and continual learning. The dataset is available at https://github.com/gurkirt/road-dataset; the baseline can be found at https://github.com/gurkirt/3D-RetinaNet.

preprint2022arXiv

Transit Light-curves for Exomoons: Analytical Formalism

The photometric transit method has been the most effective method to detect and characterize exoplanets as several ground-based as well as space-based survey missions have discovered thousands of exoplanets using this method. With the advent of the upcoming next generation large telescopes, the detection of exomoons in a few of these exoplanetary systems is very plausible. In this paper, we present a comprehensive analytical formalism in order to model the transit light curves for such moon hosting exoplanets. In order to achieve analytical formalism, we have considered circular orbit of the exomoon around the host planet, which is indeed the case for tidally locked moons. The formalism uses the radius and orbital properties of both the host planet and its moon as model parameters. The co-alignment or non-coalignment of the orbits of the planet and the moon is parameterized using two angular parameters and thus can be used to model all the possible orbital alignments for a star-planet-moon system. This formalism also provides unique and direct solutions to every possible star-planet-moon three circular body alignments. Using the formula derived, a few representative light curves are also presented.

preprint2020arXiv

Domain Agnostic Feature Learning for Image and Video Based Face Anti-spoofing

Nowadays, the increasingly growing number of mobile and computing devices has led to a demand for safer user authentication systems. Face anti-spoofing is a measure towards this direction for bio-metric user authentication, and in particular face recognition, that tries to prevent spoof attacks. The state-of-the-art anti-spoofing techniques leverage the ability of deep neural networks to learn discriminative features, based on cues from the training set images or video samples, in an effort to detect spoof attacks. However, due to the particular nature of the problem, i.e. large variability due to factors like different backgrounds, lighting conditions, camera resolutions, spoof materials, etc., these techniques typically fail to generalize to new samples. In this paper, we explicitly tackle this problem and propose a class-conditional domain discriminator module, that, coupled with a gradient reversal layer, tries to generate live and spoof features that are discriminative, but at the same time robust against the aforementioned variability factors. Extensive experimental analysis shows the effectiveness of the proposed method over existing image- and video-based anti-spoofing techniques, both in terms of numerical improvement as well as when visualizing the learned features.

preprint2020arXiv

Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference

Multi-task networks are commonly utilized to alleviate the need for a large number of highly specialized single-task networks. However, two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning). Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference). In this paper, we show that both can be achieved simply by reparameterizing the convolutions of standard neural network architectures into a non-trainable shared part (filter bank) and task-specific parts (modulators), where each modulator has a fraction of the filter bank parameters. Thus, our reparameterization enables the model to learn new tasks without adversely affecting the performance of existing ones. The results of our ablation study attest the efficacy of the proposed reparameterization. Moreover, our method achieves state-of-the-art on two challenging multi-task learning benchmarks, PASCAL-Context and NYUD, and also demonstrates superior incremental learning capability as compared to its close competitors.

preprint2020arXiv

Two-Stream AMTnet for Action Detection

In this paper, we propose Two-Stream AMTnet, which leverages recent advances in video-based action representation[1] and incremental action tube generation[2]. Majority of the present action detectors follow a frame-based representation, a late-fusion followed by an offline action tube building steps. These are sub-optimal as: frame-based features barely encode the temporal relations; late-fusion restricts the network to learn robust spatiotemporal features; and finally, an offline action tube generation is not suitable for many real-world problems such as autonomous driving, human-robot interaction to name a few. The key contributions of this work are: (1) combining AMTnet's 3D proposal architecture with an online action tube generation technique which allows the model to learn stronger temporal features needed for accurate action detection and facilitates running inference online; (2) an efficient fusion technique allowing the deep network to learn strong spatiotemporal action representations. This is achieved by augmenting the previous Action Micro-Tube (AMTnet) action detection framework in three distinct ways: by adding a parallel motion stIn this paper, we propose a new deep neural network architecture for online action detection, termed ream to the original appearance one in AMTnet; (2) in opposition to state-of-the-art action detectors which train appearance and motion streams separately, and use a test time late fusion scheme to fuse RGB and flow cues, by jointly training both streams in an end-to-end fashion and merging RGB and optical flow features at training time; (3) by introducing an online action tube generation algorithm which works at video-level, and in real-time (when exploiting only appearance features). Two-Stream AMTnet exhibits superior action detection performance over state-of-the-art approaches on the standard action detection benchmarks.

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