Abstract

Quantum information processing and computing tasks can be understood as quantum networks, comprising quantum states and channels and possible physical transformations on them. It is hence pertinent to estimate the change in informational content of quantum processes due to physical transformations they undergo. The physical transformations of quantum states are described by quantum channels, while the transformations of quantum channels are described by quantum superchannels. In this work, we determine fundamental limitations on how well the physical transformation on quantum channels can be undone or reversed, which are of crucial interest to design and benchmark quantum information and computation devices. In particular, we refine (strengthen) the quantum data processing inequality for quantum channels under the action of quantum superchannels. We identify a class of quantum superchannels, which appears to be the superchannel analog of subunital quantum channels, under the action of which the entropy of an arbitrary quantum channel is nondecreasing. We also provide a refined inequality for the entropy change of quantum channels under the action of an arbitrary quantum superchannel.

Abstract

Existing sustainability assessment tools for carbon emissions produced by cloud infrastructure predominantly focus on postdeployment monitoring or require access to application code. To address this gap, we present a framework that enables early-stage estimation of carbon footprint from terraform configurations.

Abstract

Machine learning enabled systems (MLS) often operate in settings where they regularly encounter uncertainties arising from changes in their surrounding environment. Without structured oversight, such changes can degrade model behavior, increase operational cost, and reduce the usefulness of deployed systems. Although Machine Learning Operations (MLOps) streamlines the lifecycle of ML models, it provides limited support for addressing runtime uncertainties that influence the longer term sustainability of MLS. To support continued viability, these systems need a mechanism that detects when execution drifts outside acceptable bounds and adjusts system behavior in response. Despite the growing interest in sustainable and self-adaptive MLS, there has been limited work towards exemplars that allow researchers to study these challenges in MLOps pipelines. This paper presents Harmonica, a self-adaptation exemplar built on the Harmonica approach, designed to enable the sustainable operation of such pipelines. Harmonica introduces structured adaptive control through MAPE-K loop, separating high-level adaptation policy from low-level tactic execution. It continuously monitors sustainability metrics, evaluates them against dynamic adaptation boundaries, and automatically triggers architectural tactics when thresholds are violated. We demonstrate the tool through case studies in time series regression and computer vision, examining its ability to improve system stability and reduce manual intervention. The results show that Harmonica offers a practical and reusable foundation for enabling adaptive behavior in MLS that rely on MLOps pipelines for sustained operation.

Abstract

The emergence of Agentic AI systems has outpaced the architectural thinking required to operate them effectively. These agents differ fundamentally from traditional software: their behavior is not fixed at deployment but continuously shaped by experience, feedback, and context. Applying operational principles inherited from DevOps or MLOps, built for deterministic software and traditional ML systems, assumes that system behavior can be managed through versioning, monitoring, and rollback. This assumption breaks down for Agentic AI systems whose learning trajectories diverge over time. This introduces non-determinism making system reliability a challenge at runtime. We argue that architecting such systems requires a shift from managing control loops to enabling dynamic co-evolution among agents, infrastructure, and human oversight. To guide this shift, we introduce CHANGE, a conceptual framework comprising six capabilities for operationalizing Agentic AI systems: Contextualize, Harmonize, Anticipate, Negotiate, Generate, and Evolve. CHANGE provides a foundation for architecting an AgentOps platform to manage the lifecycle of evolving Agentic AI systems, illustrated through a customer-support system scenario. In doing so, CHANGE redefines software architecture for an era where adaptation to uncertainty and continuous evolution are inherent properties of the system.

Abstract

Rapid detection of creatinine level is significant for the diagnosis of renal dysfunction. Conventional methods used for creatinine detection are not portable and cannot provide real-time results. This paper describes the design and validation of a microwave biosensor to overcome these limitations. The resonator is designed using a complementary split-ring resonator (CSRR) and an interdigitated capacitor (IDC) fabricated on a FR-4 substrate of dimensions 50×25mm2. Simulations showed a resonance frequency of 2.013 GHz, while measurement results yielded a resonance of 1.941 GHz, with a deviation of 72 MHz. The fabricated device was tested with 10μL creatinine samples of 0.8 to 2.0mg/dL, which are in the physiological range. A sensitivity of 0.77% is achieved for detecting changes in concentration in chemical solutions. These results show the capability of the biosensor for real-time biomedical diagnosis.

Abstract

Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the absence of physical contact and traditional liveness cues. Most existing methods rely on single-image acquisition and appearance-based features, which often generalize poorly across devices, capture conditions, and spoof materials. In this work, we study paired flash-non-flash contactless fingerprint acquisition as a lightweight active sensing mechanism for spoof detection. Through a preliminary empirical analysis, we show that flash illumination accentuates material- and structure-dependent properties, including ridge visibility, subsurface scattering, micro-geometry, and surface oils, while non-flash images provide a baseline appearance context. We analyze lighting-induced differences using interpretable metrics such as inter-channel correlation, specular reflection characteristics, texture realism, and differential imaging. These complementary features help discriminate genuine fingerprints from printed, digital, and molded presentation attacks. We further examine the limitations of paired acquisition, including sensitivity to imaging settings, dataset scale, and emerging high-fidelity spoofs. Our findings demonstrate the potential of illumination-aware analysis to improve robustness and interpretability in contactless fingerprint presentation attack detection, motivating future work on paired acquisition and physics-informed feature design. Code is available in the repository.

Abstract

Vector leptoquarks (vLQs) are popular candidates for searching for physics beyond the Standard Model (SM). In this paper, we present updated exclusion limits on various vLQ species, accounting for all the relevant production mechanisms at the LHC. In particular, we highlight the critical role of indirect production and its interference with the SM Drell-Yan process. This interference can be constructive or destructive, depending on the specific quantum numbers of the vLQ, and significantly impacts the sensitivity of current searches. Furthermore, we demonstrate that including QCD+⁢QED mixed pair production channels leads to a noticeable shift in model-independent mass limits. Additionally, we examine the validity of the full theory with vLQs and corresponding effective operators in the high mass regime. Overall, our analysis yields a substantial improvement in the exclusion limits on vLQs compared to the existing results in the literature.

Abstract

Leptoquarks (LQs) offer a promising framework for exploring physics beyond the Standard Model (BSM). These particles are currently probed at the LHC, primarily through pair production (PP). Our study improves the LHC exclusion limits by including additional production modes like single productions, t-channel LQ exchange, and its interference with the Standard Model background, sometimes providing more substantial constraints than PP alone. We also account for model-independent production through mixed QCD-QED processes, which can significantly shift the exclusion limits based on the electric charge of the LQs. Furthermore, since LQs often arise alongside other BSM particles, such as vector-like fermions and right-handed neutrinos, we investigate the potential for strong production of these particles along with LQs at the High-Luminosity LHC (HL-LHC), offering new perspectives on collider search strategies.

Abstract

In this study, we propose an interesting production mechanism for Vector-Like Leptons (VLLs) through Leptoquarks (LQs). We explore the prospect of producing them at the High-Luminosity LHC around 3000 fb^-1 through various channels involving both scalar and vector LQs. Here, we have performed an analysis of third-generation VLL. Further, we combine various production channels to enhance the detection sensitivity in SM-heavy backgrounds.

Abstract

This study investigates TeV-scale vector leptoquarks (vLQs) and their contributions to the magnetic dipole moments of charged leptons. Significant parameter space for these LQs is excluded using current LHC data. Among vLQs only U1 and V2 can account for the observed positive shift in (a^mu_exp-a^mu_SM) through a lepton chirality-flipping contribution with
LQ-quark-lepton coupling. These LQs can also fits for the electron dipole moment and atomic-parity violation measurements.