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Quantum computing has the potential to offer significant advantages over classical computing, making it a promising avenue for exploring alternative methods in High Energy Physics (HEP) simulations. This work presents the implementation of a Quantum Generative Adversarial Network (qGAN) to simultaneously generate gluon-initiated jet images for both ECAL and HCAL detector channels, a task crucial for high-energy physics simulations at the Large Hadron Collider (LHC). The results demonstrate high fidelity in replicating energy deposit patterns and preserving the implicit training data features. This study marks the first step toward generating multi-channel pictures and quark-initiated jet images using quantum computing., Comment: This paper was presented at the Quantum Computing and Artificial Intelligence Conference 2025 (QCAI 2025) [https://sites.google.com/view/qcai2025]

For a high-performance blockchain like Supra's Layer 1, minimizing latencies across key components is crucial-such as data dissemination, consensus (or ordering), and transaction execution. While through significant innovations we have improved the first two, transaction execution remains an area for further optimization. Software Transactional Memory (STM) is a widely used technique for parallel execution, with Aptos' BlockSTM pioneering its application of efficient blockchain transaction processing on multi-core validator nodes. Subsequently, PEVM [13] adapted BlockSTM for EVM transaction execution. However, we identified a gap in existing STM techniques-while access specifications have been used in industry (e.g., Solana's user-provided read-write sets), they have not been leveraged to enhance STM efficiency. Our experimental analysis demonstrates that specification-aware STMs outperform their plain counterparts on both EVM and MoveVM. To maximize these benefits, we have designed specification-aware SupraSTM (saSupraSTM), a novel algorithm that fully utilizes access specifications. Through extensive testing, saSupraSTM outperforms both our specification-aware adaptation of Aptos' BlockSTM and specification-aware PEVM, setting a new benchmark for transaction execution efficiency in the context of blockchain networks., Comment: 13 pages and 7 figures

With cooperative perception, autonomous vehicles can wirelessly share sensor data and representations to overcome sensor occlusions, improving situational awareness. Securing such data exchanges is crucial for connected autonomous vehicles. Existing, automated reputation-based approaches often suffer from a delay between detection and exclusion of misbehaving vehicles, while majority-based approaches have communication overheads that limits scalability. In this paper, we introduce CATS, a novel automated system that blends together the best traits of reputation-based and majority-based detection mechanisms to secure vehicle-to-everything (V2X) communications for cooperative perception, while preserving the privacy of cooperating vehicles. Our evaluation with city-scale simulations on realistic traffic data shows CATS's effectiveness in rapidly identifying and isolating misbehaving vehicles, with a low false negative rate and overheads, proving its suitability for real world deployments.

Based on their cosmological origin, the stars of a galaxy can be divided into two categories: those that enter through merger events (ex situ) and those born in the main progenitor (in situ). We used the TNG50 cosmological magnetohydrodynamical simulation and its Lagrangian tracer particles to explore and quantify the origin of gas that ultimately forms the in situ stars of galaxies. We tracked back the baryonic mass contributing to the $z=0$ in situ stellar populations of galaxies, studying trends in mass from dwarfs to group-scale halos. We find that more massive halos acquire this matter earlier than lower-mass halos, reflecting an overall earlier assembly of their in situ stellar mass. Defining the Lagrangian half-mass radius R$_{\rm L, 1/2}$ of a galaxy as the distance containing half of the mass that will form its in situ stars by $z=0$, we find that R$_{\rm L, 1/2}$ is larger for more massive halos at early times, reflecting larger "in situ Lagrangian regions." However, the dependence of this radius on halo mass becomes flat at $z \simeq 3$ and then inverts toward $z=0$. In addition, R$_{\rm L, 1/2}$ increases rapidly with redshift, surpassing the virial radii of halos at $z \sim 2$. This marks the cosmic epoch at which most of the gas that eventually forms the in situ stars of galaxies leaves the intergalactic medium (IGM) and enters halos, a transition that occurs earlier for more massive halos. The formation redshift of the in situ stellar component increases with halo mass, while the formation redshift of the dark matter halo decreases, indicative of a differential assembly history between these two components. Finally, we decomposed the $z=0$ in situ stellar mass into its distinct modes of accretion. Smooth accretion from the IGM is the most important for low-mass galaxies, while mergers and satellite-stripped gas become relevant and even dominant only for high-mass galaxies., Comment: 16 pages, 16 figures, 1 table, submitted the revised version to A&A

Magnetic skyrmions are topologically protected spin states enabling high-density, low-power spin electronics. Despite growing efforts to find new skyrmion host systems, the microscopic mechanisms leading to skyrmion phase transitions at specific temperatures and magnetic fields remain elusive. Here, we systematically study the isostructural centrosymmetric magnets- GdRu2X2 (X = Si and Ge), and the role of X-p orbitals in modifying magnetic exchange interactions. GdRu2Ge2 single crystals, synthesized by arc melting, exhibit two high-entropy pockets associated with skyrmion phases at 0.9 T < H < 1.2 T and 1.3 T < H < 1.7 T, 2 K < T < 30 K-more accessible condition at lower fields and higher temperatures than that in the Si counterpart. Entropy estimations from heat capacity measurements align with magnetization data, and transport studies confirm a topological Hall effect, highlighting the system's nontrivial spin textures and Berry curvature. Compared to GdRu2Si2, electronic structure and exchange interaction evaluations reveal the more extended Ge-4p orbitals enhance competing exchange interactions in GdRu2Ge2, thereby manifesting the rich skyrmion behavior. This work demonstrates how modifying exchange interactions at the atomic level enables the tunability of topologically nontrivial electronic states while advancing our understanding of skyrmion formation mechanisms for future spintronics.

Cyanothiazoles, small and quite overlooked molecules, possess remarkable optical properties that can be fine-tuned through coordination with transition metals. In this study, we investigate a promising application of cyanothiazoles, where their combination with copper(I) iodide forms a new class of complexes exhibiting outstanding optical properties. X-ray crystallography of copper(I) iodide complexes with isomeric cyanothiazoles revealed key structural features, such as {\pi}-{\pi} stacking, hydrogen bonding, and rare halogen-chalcogen I-S interactions, enhancing stability and reactivity. Advanced spectroscopy and computational modeling allowed precise identification of spectral signatures in FTIR, NMR, and UV-Vis spectra. Fluorescence studies, along with XANES synchrotron analyses, highlighted their unique thermal and electronic properties, providing a solid foundation for further research in the field.

Thiazolothiazoles (TzTz) represent a class of compounds with distinctive structural motifs and exceptional optical properties, positioning them as promising candidates for breakthroughs in optoelectronic and sensing technologies. X-ray crystallographic analyses of TzTz units symmetrically substituted with functional groups such as imidazole, o-vanillin, p-vanillin, phenyl, thiazole, cinnamate, and bistrifluoromethylphenyl have revealed complex structural features, including {\pi}-{\pi} stacking interactions, hydrogen-bond networks, and specific chalcogen and halogen interactions. These interactions collectively enhance the stability and define the unique spectroscopic profiles of these compounds. Beyond classical spectral fingerprints (FTIR, NMR, and UV-Vis spectra), fluorescence studies at various temperatures, complemented by XANES synchrotron analyses, have underscored their remarkable thermal and electronic properties. The findings presented here offer a comprehensive framework for the characterization and analysis of TzTz compounds, emphasizing their potential as components in smart electronic and optoelectronic devices.

Though the common metal electrode-based perovskite solar cells have achieved a power conversion efficiency of >25%, they also play a crucial role in accelerating the degradation of the cells. In this study, we investigated phase transition engineering in Ag electrodes via Cu and Zn alloying, transforming from a cubic to a tetragonal phase. These alloyed electrodes are then thermally deposited as back electrodes in perovskite solar cells. We conducted a comprehensive analysis of the pure Ag and Ag-Cu-Zn alloys deposited atop a hole-transport layer for use in Cs0.05(FA0.83MA0.17)0.95Pb(I0.83Br0.17)3-based solar cells. Our findings reveal that solar cells developed with pure Ag electrodes demonstrate a power conversion efficiency (PCE) of 18.71%, characterized by a fill factor (FF) of 74.8%, an open-circuit voltage (VOC) of 1.08 V, and a short-circuit current density (JSC) of 23.17 mA/cm2. Conversely, solar cells fabricated with optimized Ag0.875Cu0.120Zn0.005 electrodes exhibit enhanced performance metrics, with an FF of 72.5%, VOC of 1.12 V, and JSC of 23.39 mA/cm2, culminating in an elevated PCE of 19.02%. Moreover, this electrode demonstrates remarkable durability, sustaining operational integrity for 460 hours for the PSCs stored in the N2 glove box, in contrast to the 320 hours for cells with Ag electrodes. The Ag-Cu-Zn alloys exhibited high resistance to corrosion and good adhesion on the hole-transport material layer compared to a layer of Ag. These advancements may lead to the realization of cost-effective, durable, and efficient solar energy conversion systems., Comment: 25 pages, 22 figures

Understanding the classifications of deep neural networks, e.g. used in safety-critical situations, is becoming increasingly important. While recent models can locally explain a single decision, to provide a faithful global explanation about an accurate model's general behavior is a more challenging open task. Towards that goal, we introduce the Quadratic Programming Enhanced Model (QPM), which learns globally interpretable class representations. QPM represents every class with a binary assignment of very few, typically 5, features, that are also assigned to other classes, ensuring easily comparable contrastive class representations. This compact binary assignment is found using discrete optimization based on predefined similarity measures and interpretability constraints. The resulting optimal assignment is used to fine-tune the diverse features, so that each of them becomes the shared general concept between the assigned classes. Extensive evaluations show that QPM delivers unprecedented global interpretability across small and large-scale datasets while setting the state of the art for the accuracy of interpretable models.

We report the (111) facet-engineered cubic phase SnO2 (C-SnO2) as a novel electron transport layer (ETL) for triple-cation mixed-halide Cs0.05(FA0.83MA0.17)0.95Pb(I0.83Br0.17)3 perovskite solar cells (PSCs). The C-SnO2 layer was prepared via a normal sol-gel process followed by the spin-coating technique. The (111) facet C-SnO2 layer provides a larger surface contact area with an adjacent perovskite layer, enhancing charge transfer dynamics at the interface. In addition, the well-matched overlapping band structures improve the charge extraction efficiency between the two layers. Using (111) facet C-SnO2 as ETLs, we obtain PSCs with a higher power conversion efficiency of 20.34% (0.09 cm2) than those employing tetragonal phase SnO2 ETL. The PSCs with C-SnO2 ETL retain over 81% of their initial efficiency even after 480 h. This work concludes with a brief discussion on recombination and charge transport mechanisms, providing ways to optimize C-SnO2 ETL to improve the PSCs' performance and stability., Comment: 18 pages, 9 figures

Eating monitoring has remained an open challenge in medical research for years due to the lack of non-invasive sensors for continuous monitoring and the reliable methods for automatic behavior detection. In this paper, we present a pilot study using the wearable 24-hour ECG for sensing and tailoring the sophisticated deep learning for ad-hoc and interpretable detection. This is accomplished using a collocative learning framework in which 1) we construct collocative tensors as pseudo-images from 1D ECG signals to improve the feasibility of 2D image-based deep models; 2) we formulate the cardiac logic of analyzing the ECG data in a comparative way as periodic attention regulators so as to guide the deep inference to collect evidence in a human comprehensible manner; and 3) we improve the interpretability of the framework by enabling the backtracking of evidence with a set of methods designed for Class Activation Mapping (CAM) decoding and decision tree/forest generation. The effectiveness of the proposed framework has been validated on the largest ECG dataset of eating behavior with superior performance over conventional models, and its capacity of cardiac evidence mining has also been verified through the consistency of the evidence it backtracked and that of the previous medical studies.

Deployment of robots into hazardous environments typically involves a ``Human-Robot Teaming'' (HRT) paradigm, in which a human supervisor interacts with a remotely operating robot inside the hazardous zone. Situational Awareness (SA) is vital for enabling HRT, to support navigation, planning, and decision-making. This paper explores issues of higher-level ``semantic'' information and understanding in SA. In semi-autonomous, or variable-autonomy paradigms, different types of semantic information may be important, in different ways, for both the human operator and an autonomous agent controlling the robot. We propose a generalizable framework for acquiring and combining multiple modalities of semantic-level SA during remote deployments of mobile robots. We demonstrate the framework with an example application of search and rescue (SAR) in disaster response robotics. We propose a set of ``environment semantic indicators" that can reflect a variety of different types of semantic information, e.g. indicators of risk, or signs of human activity, as the robot encounters different scenes. Based on these indicators, we propose a metric to describe the overall situation of the environment called ``Situational Semantic Richness (SSR)". This metric combines multiple semantic indicators to summarise the overall situation. The SSR indicates if an information-rich and complex situation has been encountered, which may require advanced reasoning for robots and humans and hence the attention of the expert human operator. The framework is tested on a Jackal robot in a mock-up disaster response environment. Experimental results demonstrate that the proposed semantic indicators are sensitive to changes in different modalities of semantic information in different scenes, and the SSR metric reflects overall semantic changes in the situations encountered.

Large Language Models (LLMs) have been used in cybersecurity in many ways, including their recent use as intelligent agent systems for autonomous security analysis. Capture the Flag (CTF) challenges serve as benchmarks for assessing the automated task-planning abilities of LLM agents across various cybersecurity skill sets. Early attempts to apply LLMs for solving CTF challenges relied on single-agent systems, where feedback was restricted to a single reasoning-action loop. This approach proved inadequate for handling complex CTF tasks. Drawing inspiration from real-world CTF competitions, where teams of experts collaborate, we introduce the D-CIPHER multi-agent LLM framework for collaborative CTF challenge solving. D-CIPHER integrates agents with distinct roles, enabling dynamic feedback loops to enhance reasoning on CTF challenges. It introduces the Planner-Executor agent system, consisting of a Planner agent for overall problem-solving along with multiple heterogeneous Executor agents for individual tasks, facilitating efficient allocation of responsibilities among the LLMs. Additionally, D-CIPHER incorporates an Auto-prompter agent, which improves problem-solving by exploring the challenge environment and generating a highly relevant initial prompt. We evaluate D-CIPHER on CTF benchmarks using multiple LLM models and conduct comprehensive studies to highlight the impact of our enhancements. Our results demonstrate that the multi-agent D-CIPHER system achieves a significant improvement in challenges solved, setting a state-of-the-art performance on three benchmarks: 22.0% on NYU CTF Bench, 22.5% on Cybench, and 44.0% on HackTheBox. D-CIPHER is available at https://github.com/NYU-LLM-CTF/nyuctf_agents as the nyuctf_multiagent package.

Reducing the environmental impact of cloud computing requires efficient workload distribution across geographically dispersed Data Center Clusters (DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time shift of workloads within individual data centers (DC). This paper introduces Green-DCC, which proposes a Reinforcement Learning (RL) based hierarchical controller to optimize both workload and liquid cooling dynamically in a DCC. By incorporating factors such as weather, carbon intensity, and resource availability, Green-DCC addresses realistic constraints and interdependencies. We demonstrate how the system optimizes multiple data centers synchronously, enabling the scope of digital twins, and compare the performance of various RL approaches based on carbon emissions and sustainability metrics while also offering a framework and benchmark simulation for broader ML research in sustainability.

Self-assembled InGaAs/GaAs quantum dots (QDs) are of particular importance for the deterministic generation of spin-photon entanglement. One promising scheme relies on the Larmor precession of a spin in a transverse magnetic field, which is governed by the in-plane $g$-factors of the electron and valence band heavy-hole. We probe the origin of heavy-hole $g$-factor anisotropy with respect to the in-plane magnetic field direction and uncover how it impacts the entanglement generated between the spin and the photon polarization. First, using polarization-resolved photoluminescence measurements on a single QD, we determine that the impact of valence-band mixing dominates over effects due to a confinement-renormalized cubic Luttinger $q$ parameter. From this, we construct a comprehensive hole $g$-tensor model. We then use this model to simulate the concurrence and fidelity of spin-photon entanglement generation with anisotropic hole $g$-factors, which can be tuned via magnetic field angle and excitation polarization. The results demonstrate that post-growth control of the hole $g$-factor can be used to improve spin-photon cluster state generation.

After a successful kick-off meeting in 2021. two workshops in 2022 and 2023 on the future Global Cosmic-Ray Observatory (GCOS) focused mainly on a straw man design of the detector and science possibilities for astro- and particle physics. About 100 participants gathered for in-person and hybrid panel discussions. In this report, we summarize these discussions, present a preliminary straw-man design for GCOS and collect short write-ups of the flash talks given during the focus sessions., Comment: 48 pages, 27 figures

Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semantically guided autonomous underwater cave exploration. We present the hardware and edge-AI design considerations to deploy this framework on a novel AUV (Autonomous Underwater Vehicle) named CavePI. The guided navigation is driven by a computationally light yet robust deep visual perception module, delivering a rich semantic understanding of the environment. Subsequently, a robust control mechanism enables CavePI to track the semantic guides and navigate within complex cave structures. We evaluate the system through field experiments in natural underwater caves and spring-water sites and further validate its ROS (Robot Operating System)-based digital twin in a simulation environment. Our results highlight how these integrated design choices facilitate reliable navigation under feature-deprived, GPS-denied, and low-visibility conditions., Comment: V1, 15 pages

Gross and Zagier defined certain `higher Green's functions' on products of modular curves and conjectured that the value of these functions at complex multiplication points should be logarithms of algebraic numbers. This is now a theorem of Li. We relate this question to the existence of motivic cycles in the universal family of products of elliptic curves along the lines of Mellit and Zhang. We then construct infinitely many such cycles. In the appendix we work out an example of algebraicity. The work of Li, Bruinier-Ehlen-Yang, Viazovska and others relate this conjecture to Borcherds' lifts of weakly holomorphic modular forms. This suggests that there should be a link between motivic cycles in the universal family on the one hand and Borcherds' lifts on the other. We formulate a precise conjecture relating the two objects., Comment: 35 pages

Device sizing is crucial for meeting performance specifications in operational transconductance amplifiers (OTAs), and this work proposes an automated sizing framework based on a transformer model. The approach first leverages the driving-point signal flow graph (DP-SFG) to map an OTA circuit and its specifications into transformer-friendly sequential data. A specialized tokenization approach is applied to the sequential data to expedite the training of the transformer on a diverse range of OTA topologies, under multiple specifications. Under specific performance constraints, the trained transformer model is used to accurately predict DP-SFG parameters in the inference phase. The predicted DP-SFG parameters are then translated to transistor sizes using a precomputed look-up table-based approach inspired by the gm/Id methodology. In contrast to previous conventional or machine-learning-based methods, the proposed framework achieves significant improvements in both speed and computational efficiency by reducing the need for expensive SPICE simulations within the optimization loop; instead, almost all SPICE simulations are confined to the one-time training phase. The method is validated on a variety of unseen specifications, and the sizing solution demonstrates over 90% success in meeting specifications with just one SPICE simulation for validation, and 100% success with 3-5 additional SPICE simulations., Comment: Title: Accelerating OTA Circuit Design: Transistor Sizing Based on a Transformer Model and Precomputed Lookup Tables Authors: Subhadip Ghosh, Endalk Y. Gebru, Chandramouli V. Kashyap, Ramesh Harjani, Sachin S. Sapatnekar Accepted in conference: Proceedings of Design, Automation and Test in Europe, 2025 No. of Pages: 7 No. of figures: 7 No. of tables: 9

Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is a medical imaging technique that plays a crucial role in the detailed visualization and identification of tissue perfusion in abnormal lesions and radiological suggestions for biopsy. However, DCE-MRI involves the administration of a Gadolinium based (Gad) contrast agent, which is associated with a risk of toxicity in the body. Previous deep learning approaches that synthesize DCE-MR images employ unimodal non-contrast or low-dose contrast MRI images lacking focus on the local perfusion information within the anatomy of interest. We propose AAD-DCE, a generative adversarial network (GAN) with an aggregated attention discriminator module consisting of global and local discriminators. The discriminators provide a spatial embedded attention map to drive the generator to synthesize early and late response DCE-MRI images. Our method employs multimodal inputs - T2 weighted (T2W), Apparent Diffusion Coefficient (ADC), and T1 pre-contrast for image synthesis. Extensive comparative and ablation studies on the ProstateX dataset show that our model (i) is agnostic to various generator benchmarks and (ii) outperforms other DCE-MRI synthesis approaches with improvement margins of +0.64 dB PSNR, +0.0518 SSIM, -0.015 MAE for early response and +0.1 dB PSNR, +0.0424 SSIM, -0.021 MAE for late response, and (ii) emphasize the importance of attention ensembling. Our code is available at https://github.com/bhartidivya/AAD-DCE., Comment: Accepted at ICASSP 2025

Logic locking secures hardware designs in untrusted foundries by incorporating key-driven gates to obscure the original blueprint. While this method safeguards the integrated circuit from malicious alterations during fabrication, its influence on data confidentiality during runtime has been ignored. In this study, we employ path sensitization to formally examine the impact of logic locking on confidentiality. By applying three representative logic locking mechanisms on open-source cryptographic benchmarks, we utilize an automatic test pattern generation framework to evaluate the effect of locking on cryptographic encryption keys and sensitive data signals. Our analysis reveals that logic locking can inadvertently cause sensitive data leakage when incorrect logic locking keys are used. We show that a single malicious logic locking key can expose over 70% of an encryption key. If an adversary gains control over other inputs, the entire encryption key can be compromised. This research uncovers a significant security vulnerability in logic locking and emphasizes the need for comprehensive security assessments that extend beyond key-recovery attacks., Comment: 8 pages, accepted at 26th International Symposium on Quality Electronic Design (ISQED'25)

Even highly capable large language models (LLMs) can produce biased or unsafe responses, and alignment techniques, such as RLHF, aimed at mitigating this issue, are expensive and prone to overfitting as they retrain the LLM. This paper introduces a novel inference-time alignment approach that ensures LLMs generate safe responses almost surely, i.e., with a probability approaching one. We achieve this by framing the safe generation of inference-time responses as a constrained Markov decision process within the LLM's latent space. Crucially, we augment a safety state that tracks the evolution of safety constraints and enables us to demonstrate formal safety guarantees upon solving the MDP in the latent space. Building on this foundation, we propose InferenceGuard, a practical implementation that safely aligns LLMs without modifying the model weights. Empirically, we demonstrate InferenceGuard effectively balances safety and task performance, outperforming existing inference-time alignment methods in generating safe and aligned responses.

Question answering systems for knowledge graph (KGQA), answer factoid questions based on the data in the knowledge graph. KGQA systems are complex because the system has to understand the relations and entities in the knowledge-seeking natural language queries and map them to structured queries against the KG to answer them. In this paper, we introduce Chronos, a comprehensive evaluation framework for KGQA at industry scale. It is designed to evaluate such a multi-component system comprehensively, focusing on (1) end-to-end and component-level metrics, (2) scalable to diverse datasets and (3) a scalable approach to measure the performance of the system prior to release. In this paper, we discuss the unique challenges associated with evaluating KGQA systems at industry scale, review the design of Chronos, and how it addresses these challenges. We will demonstrate how it provides a base for data-driven decisions and discuss the challenges of using it to measure and improve a real-world KGQA system.

Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes.

As malware such as ransomware becomes sophisticated, the ability to find and neutralize it requires more robust and tamper-resistant solutions. Current methods rely on data from compromised hosts, lack hardware isolation, and cannot detect emerging threats. To address these limitations, we introduce SHIELD - a detection architecture leveraging FPGA-based open-source SATA and Network Block Device (NBD) technology to provide off-host, tamper-proof measurements for continuous observation of disk activity for software executing on a target device. SHIELD provides three distinct contributions: It (1) develops a framework to obtain and analyze multi-level hardware metrics at NBD, FPGA, and SATA storage levels, and shows their ability to differentiate between harmless and malicious software; (2) Broadens the functionality of an open-source FPGA-driven SATA Host Bus Adapter (HBA) to offer complete data storage capabilities through NBD without relying on the host system; (3) Provides a foundation for using the methodology and metrics in automated machine learning-assisted detection and ASIC integration for advanced mitigation capabilities in data storage devices. SHIELD analyzes 10 benign programs and 10 modern ransomware families to illustrate its capacity for real-time monitoring and use in distinguishing between ransomware and benign software. Experimental evidence shows SHIELD's robust host-independent and hardware-assisted metrics are a basis for detection, allowing to observe program execution and detect malicious activities at the storage level., Comment: 7 pages, 4 figures

Atomically flat surfaces of van der Waals (vdW) materials pave an avenue for addressing a long-standing fundamental issue of how a perfectly compensated antiferromagnet (AFM) surface frustrates a ferromagnetic (FM) overlayer in FM/AFM heterostructures. By revealing the AFM and FM spin structures separately in vdW Fe5GeTe2/NiPS3 heterostructures, we find that C-type in-plane AFM NiPS3 develops three equivalent AFM domains which are robust against external magnetic field and magnetic coupling with Fe5GeTe2. Consequently, spin frustration at the Fe5GeTe2/NiPS3 interface was shown to develop a perpendicular Fe5GeTe2 magnetization in the interfacial region that switches separately from the bulk of the Fe5GeTe2 magnetizations. In particular, we discover an unconventional spin twisting state that the Fe5GeTe2 spins twist from perpendicular direction near the interface to in-plane direction away from the interface in Fe5GeTe2/NiPS3. Our finding of the twisting spin texture is a unique property of spin frustration in van der Waals magnetic heterostructures., Comment: 28 pages, 8 figures

Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless mutations over millions of years, they represent just one set of realized evolutionary paths. Testing hypotheses about how environmental pressures shaped eye evolution remains challenging since we cannot experimentally isolate individual factors. Computational evolution offers a way to systematically explore alternative trajectories. Here we show how environmental demands drive three fundamental aspects of visual evolution through an artificial evolution framework that co-evolves both physical eye structure and neural processing in embodied agents. First, we demonstrate computational evidence that task specific selection drives bifurcation in eye evolution - orientation tasks like navigation in a maze leads to distributed compound-type eyes while an object discrimination task leads to the emergence of high-acuity camera-type eyes. Second, we reveal how optical innovations like lenses naturally emerge to resolve fundamental tradeoffs between light collection and spatial precision. Third, we uncover systematic scaling laws between visual acuity and neural processing, showing how task complexity drives coordinated evolution of sensory and computational capabilities. Our work introduces a novel paradigm that illuminates evolutionary principles shaping vision by creating targeted single-player games where embodied agents must simultaneously evolve visual systems and learn complex behaviors. Through our unified genetic encoding framework, these embodied agents serve as next-generation hypothesis testing machines while providing a foundation for designing manufacturable bio-inspired vision systems. Website: http://eyes.mit.edu/, Comment: Website: http://eyes.mit.edu/

Very long baseline interferometry observations reveal that relativistic jets like the one in M87 have a limb-brightened, double-edged structure. Analytic and numerical models struggle to reproduce this limb-brightening. We propose a model in which we invoke anisotropy in the distribution function of synchrotron-emitting nonthermal electrons such that electron velocities are preferentially directed parallel to magnetic field lines, as suggested by recent particle-in-cell simulations of electron acceleration and the effects of synchrotron cooling. We assume that the energy injected into nonthermal electrons is proportional to the jet Poynting flux, and we account for synchrotron cooling via a broken power-law energy distribution. We implement our emission model in both general relativistic magnetohydrodynamic (GRMHD) simulations and axisymmetric force-free electrodynamic (GRFFE) jet models and produce simulated jet images at multiple scales and frequencies using polarized general relativistic radiative transfer. We find that the synchrotron emission is concentrated parallel to the local helical magnetic field and that this feature produces limb-brightened jet images on scales ranging from tens of microarcseconds to hundreds of milliarcseconds in M87. We present theoretical predictions for horizon-scale M87 jet images at 230 and 345 GHz that can be tested with next generation instruments. Due to the scale-invariance of the GRMHD and GRFFE models, our emission prescription can be applied to other targets and serve as a foundation for a unified description of limb-brightened synchrotron images of extragalactic jets., Comment: 21 pages, 6 figures. Submitted to ApJ

We present a Reinforcement Learning Platform for Adversarial Black-box untargeted and targeted attacks, RLAB, that allows users to select from various distortion filters to create adversarial examples. The platform uses a Reinforcement Learning agent to add minimum distortion to input images while still causing misclassification by the target model. The agent uses a novel dual-action method to explore the input image at each step to identify sensitive regions for adding distortions while removing noises that have less impact on the target model. This dual action leads to faster and more efficient convergence of the attack. The platform can also be used to measure the robustness of image classification models against specific distortion types. Also, retraining the model with adversarial samples significantly improved robustness when evaluated on benchmark datasets. The proposed platform outperforms state-of-the-art methods in terms of the average number of queries required to cause misclassification. This advances trustworthiness with a positive social impact., Comment: Under Review for 2025 AAAI Conference on Artificial Intelligence Proceedings

Communication between multiple language model (LM) agents has been shown to scale up the reasoning ability of LMs. While natural language has been the dominant medium for inter-LM communication, it is not obvious this should be the standard: not only does natural language communication incur high inference costs that scale quickly with the number of both agents and messages, but also the decoding process abstracts away too much rich information that could be otherwise accessed from the internal activations. In this work, we propose a simple technique whereby LMs communicate via activations; concretely, we pause an LM $\textit{B}$'s computation at an intermediate layer, combine its current activation with another LM $\textit{A}$'s intermediate activation via some function $\textit{f}$, then pass $\textit{f}$'s output into the next layer of $\textit{B}$ and continue the forward pass till decoding is complete. This approach scales up LMs on new tasks with zero additional parameters and data, and saves a substantial amount of compute over natural language communication. We test our method with various functional forms $\textit{f}$ on two experimental setups--multi-player coordination games and reasoning benchmarks--and find that it achieves up to $27.0\%$ improvement over natural language communication across datasets with $<$$1/4$ the compute, illustrating the superiority and robustness of activations as an alternative "language" for communication between LMs.

The role of grain size in determining fracture toughness in metals is incompletely understood with apparently contradictory experimental observations. We study this grain-size dependence computationally by building a model that combines the phase-field formulation of fracture mechanics with dislocation density-based crystal plasticity. We apply the model to cleavage fracture of body-centered cubic materials in plane strain conditions, and find non-monotonic grain-size dependence of plastic-brittle transgranular fracture. We find two mechanisms at play. The first is the nucleation of failure due to cross-slip in critically located grains within transgranular band of localized deformation, and this follows the classical Hall-Petch law that predicts a higher failure stress for smaller grains. The second is the resistance to the propagation of a mode I crack, where grain boundaries can potentially pin a crack, and this follows an inverse Hall-Petch law with higher toughness for larger grains. The result of the competition between the two mechanisms gives rise to non-monotonic behavior and reconciles the apparently contradictory experimental observations.

With increasingly sophisticated cyber-adversaries able to access a wider repertoire of mechanisms to implant malware such as ransomware, CPU/GPU keyloggers, and stealthy kernel rootkits, there is an urgent need for techniques to detect and mitigate such attacks. While state of the art relies on digital and analog side channel measurements assuming trustworthiness of measurements obtained on the main processor, such an approach has limitations since processor-based side channel measurements are potentially untrustworthy. Sophisticated adversaries (especially in late stage cyber attacks when they have breached the computer and network security systems such as firewalls and antivirus and penetrated the computer's OS) can compromise user-space and kernel-space measurements. To address this key limitation of state of the art, we propose a "subcomponent-level" approach to collect side channel measurements so as to enable robust anomaly detection in a modern computer even when the main processor is compromised. Our proposed approach leverages the fact that modern computers are complex systems with multiple interacting subcomponents and measurements from subcomponents can be used to detect anomalies even when the main processor is no longer trustworthy. We develop mechanisms to obtain time series measurements of activity of several subcomponents and methodologies to process and fuse these measurements for anomaly detection. The subcomponents include network interface controller, GPU, CPU Hardware Performance Counters, CPU power, and keyboard. Our main hypothesis is that subcomponent measurements can enable detection of security threats without requiring a trustworthy main processor. By enabling real-time measurements from multiple subcomponents, the goal is to provide a deeper visibility into system operation, thereby yielding a powerful tool to track system operation and detect anomalies., Comment: 12 pages, 29 figures

Let $\Gamma$ be an undirected and simple graph. A set $ S $ of vertices in $\Gamma$ is called a {cyclic vertex cutset} of $\Gamma$ if $\Gamma - S$ is disconnected and has at least two components containing cycles. If $\Gamma$ has a cyclic vertex cutset, then it is said to be {cyclically separable}. The {cyclic vertex connectivity} of $\Gamma$ is the minimum of cardinalities of the cyclic vertex cutsets of $\Gamma$. For any finite group $G$, the order supergraph $\mathcal{S}(G)$ is the simple and undirected graph whose vertices are elements of $G$, and two vertices are adjacent if the order of one divides that of the other. In this paper, we characterize the finite nilpotent groups and various non-nilpotent groups whose order super graphs are cyclically separable.

The amplitude, polarization, frequency spectrum and energy fluence carried by the electric field at a given measurement position are the key parameters for retrieving information from radio signals generated by extensive air showers. Accurate reconstruction of the electric field from the signals recorded by the antennas is therefore essential for the radio detection technique. Conventional reconstruction methods primarily focus on electric field reconstruction for antennas with two horizontal polarizations. In this paper, we introduce an analytical least-squares ($\chi^2$) reconstruction method that operates with both two and three polarizations, providing the reconstructed electric field at each antenna. This solution has been verified for simple and realistic antenna responses, with a particular focus on inclined air showers. Our method achieves an accuracy better than 4\% in determining the Hilbert peak amplitude of the electric field and better than 6\% accuracy on the estimation of the energy fluence, with minimal bias. Additionally, this was found to be reliable for almost any arrival directions, and the direction dependence has been investigated. This work also demonstrates that incorporating vertically polarized antennas enhances the precision of reconstruction, leading to a more accurate and reliable electric field estimation for inclined air showers. Consequently, the method enhances our ability to extract information about cosmic rays from the detected signals in current and future experiments.