Abstract

Leptoquarks (LQs) can contribute to the magnetic and electric dipole moments of charged leptons, which the current experiments have measured with good accuracy. We revisit the parameter spaces of TeV-scale vector LQs that contribute to these observables and study how these models fare against the LHC bounds. We show that significant portions of the parameter space are excluded when the current LHC data is utilized effectively. We find that only U1and V2 can explain the observed positive shift in (aμexp-aμSM) through a lepton chirality-flipping contribution with O(1) LQ-quark-lepton coupling. We also see how these two LQs can fit the electron dipole moment and atomic parity violation measurements. We find that the current electric dipole moment measurements of the muon cannot restrain the LQ-quark-lepton couplings within perturbative regions.

Abstract

There is growing excitement about the potential of Language Models (LMs) to accelerate scientific discovery. Falsifying hypotheses is key to scientific progress, as it allows claims to be iteratively refined over time. This process requires significant researcher effort, reasoning, and ingenuity. Yet current benchmarks for LMs predominantly assess their ability to generate solutions rather than challenge them. We advocate for developing benchmarks that evaluate this inverse capability — creating counterexamples for subtly incorrect solutions. To demonstrate this approach, we start with the domain of algorithmic problem solving, where counterexamples can be evaluated automatically using code execution. Specifically, we introduce REFUTE, a dynamically updating benchmark that includes recent problems and incorrect submissions from programming competitions, where human experts successfully identified counterexamples. Our analysis finds that the best reasoning agents, even OpenAI o3-mini (high) with code execution feedback, can create counterexamples for only < 9% of incorrect solutions in REFUTE, even though ratings indicate its ability to solve up to 48% of these problems from scratch. We hope our work spurs progress in evaluating and enhancing LMs’ ability to falsify incorrect solutions — a capability that is crucial for both accelerating research and making models self-improve through reliable reflective reasoning.

Abstract

This study investigates the landscape of environmental litigation in India, analyzing how environmental disputes unfold within the judiciary. It aims to identify significant patterns in case subject matter, types of appellants, and regional distribution of such cases. By examining the broader socio-legal context, the research explores how various stakeholders interact with the legal system to address environmental issues. The methodology involves assembling a dataset of legal cases and applying computational techniques to extract metadata and detect trends across multiple dimensions. Statistical analysis was used to assess correlations between case topics and appellant categories, while spatial data provided insights into regional variations in environmental disputes. Additionally, a qualitative review of relevant legislation and policies cited in key rulings helped contextualize the legal reasoning behind environmental judgments. Through a computational lens, this study uncovers the institutional dynamics that shape environmental justice in India, emphasizing the value of data-driven approaches in understanding the interplay between law, governance, and civic engagement in environmental policymaking.

Abstract

In this paper, we propose an FPGA based system for non-contact monitoring of respiration rates (RR) using a mmWave frequency modulated continuous wave (FMCW) radar. The novel FPGA based hardware architecture presented in this paper classifies normal and abnormal RR with a Support Vector Machine (SVM) model using mmWave radar-based sensing. SVM with various kernels are evaluated in order to identify the most effective approach for respiratory rate (RR) classification. SVM with a quadratic polynomial kernel shows a high classification accuracy of 95%. While the accuracy was comparable to other kernels, the quadratic polynomial kernel achieved a substantial reduction in the number of support vectors and other parameters. This optimisation results in lower resource utilisation and power consumption, making it highly efficient for FPGA implementa- tion.

Abstract

This letter presents the development of a real-time object detection system using frequency modulated continuous wave millimeter-wave (mmWave) radar signals and the python productivity for zynq ultrascale + mpsocs (PYNQ-ZU) field-programmable gate array (FPGA) board, which is widely used in advanced driving assistance system and robotic applications. A hardware FPGA platform serves as a valid embedded architecture for the purpose of validating object detection and recognition applications. We used our experiment’s point cloud images to apply different machine learning models to detect these objects. Using a top-view (TV) filter to convert 3-D point cloud images into 2-D representations made object detection more accurate. Following the use of filtration techniques, we extracted features from the filtered 2-D image using the visual geometry group (VGG) 16 model. We then assessed four machine learning models for object detection and found that the support vector machine (SVM) model and logistic regression (LR) had better results, obtaining an accuracy of 97%. Our proposed work uses mmWave radar, TV filter, VGG 16 Model, and LR to highly increase object detection accuracy over existing methods.

Abstract

Time series data related to traffic and air quality are useful indicators for urban planning but they frequently have missing values. Imputation of multi-sensor time series data is thus a vital pre-processing step for forecasting, anomaly detection and other downstream tasks. We develop an efficient architecture – Sparse Attention-based Imputation Network for Time series (SAINT) – which outperforms the state-of- the-art imputation networks. Efficiency is achieved by separating the computations on the space-time product graph sequentially into channel independent temporal attention and sparse space-time transformer. This channel independent network can reduce overfitting to effectively repre- sent general temporal patterns. Sparse space-time transformer performs message passing on the spatial graph conditioned on time. We consider real-world datasets for evaluation – PEMS-BAY, METR-LA and AQI – which are gathered from sensor networks in major cities. We demonstrate the effectiveness and robustness of SAINT across complex missing data scenarios. Additionally, SAINT generalizes well to short-term forecasting and is practical for long-term forecasting with limited resources.

Abstract

Besides global attention on extreme precipitation, a limited research has been done in the Arctic due to constraints of data availability. In this backdrop, we attempt to analyze extreme precipitation events at three Arctic stations (Bjørnøya, Ny-Ålesund, and Svalbard Lufthavn) in Svalbard using extreme value theory. The analysis revealed that these high-latitudinal Arctic stations were characterized by heavy-tailed distributions for the exceedances, suggesting a higher probability of the occurrence of extreme precipitation events. Ny-Ålesund and Bjørnøya have exhibited a significant increase in return values over the last three decades. Among the three stations, Ny-Ålesund displayed the strongest return values, especially in winter post-1994 when the atmospheric temperature was characterized by an enhanced positive trend. Significant seasonal variability in return values has also been observed; the fall in Ny-Ålesund was characterized by a low-intensity regime as indicated by the shape parameter. Ny-Ålesund precipitation had shifted from heavy-tailed distribution in pre-1994 to bounded tail distribution post-1994 during spring. Bjørnøya’s extremes are driven by cyclonic circulation, while southerly winds drive extremes in Ny-Ålesund and Svalbard Lufthavn. Even though, Svalbard Lufthavn, displayed regime changes, showed low variability, likely due to its position in a rain shadow region. This research highlights the nuanced responses of Arctic hydrology to warming, emphasizing the need for localized studies and active collaboration with policymakers to translate these insights into effective climate adaptation and mitigation strategies.

Abstract

We present the emergence of topological phase transition in the minimal model of two-dimensional rockpaper- scissors cycle in the form of a doublet chain. The evolutionary dynamics of the doublet chain is obtained by solving the anti-symmetric Lotka-Volterra equation. We show that the mass decays exponentially towards edges and robust against small perturbation in the rate of change of mass transfer, a signature of a topological phase. For one of the configuration of our doublet chain, the mass is transferred towards both edges and the bulk is gaped. Further, we confirm this phase transition within the framework of topological band theory. For this, we calculate the winding number, which change from zero to one for trivial and nontrivial topological phases, respectively.

Abstract

We introduce a new information-theoretic measure to characterize non-Markovianity in a tripartite quantum state of genuine quantum origin. This measure reveals a novel tripartite quantum correlation and may be interpreted as entanglement between two systems when conditioned on the third (memory) system. We formulate a convex resource-theoretic framework to characterize genuine quantum non-Markovianity in states as a quantum resource and provide operational meaning to this resource in various information processing tasks.

Abstract

Location-based services (LBSs) in a mobile network environment that provide personalized and timely information to users entail privacy concerns due to the leakage of user locations to adversaries. In the literature, several cloaking-based privacy preservation approaches have been proposed by considering the P2P environment. However, existing spatial cloaking approaches form a large cluster of mobile users to cloak the user query location. Maintaining such structures in a highly dynamic mobile-P2P network is challenging. In addition to user’s location, the user’s intent is also an important concern and needs to be preserved from an adversary. This paper proposes a location privacy and intent privacy preservation scheme, especially for spatial range queries. The key contributions of our work are three-fold. First, we introduce the concept of ijk-anonymity to achieve improved location privacy. Second, we propose a location-based privacy-preservation approach, which we designate as ijkCloak, for spatial range queries in a mobile network environment. The proposed approach preserves user location and intent information from the LBS provider and nearby peers. Third, we conduct theoretical analysis and experiments on resistance to attacks. We show that ijkCloak effectively facilitates improved user location privacy and intent privacy by employing fewer peers w.r.t. existing approaches.