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In observational studies, the identification of causal estimands depends on the no unmeasured confounding (NUC) assumption. As this assumption is not testable from observed data, sensitivity analysis plays an important role in observational studies to investigate the impact of unmeasured confounding on the causal conclusions. In this paper, we proposed a risk-ratio-based sensitivity analysis framework by introducing a modified marginal sensitivity model for observational studies with binary treatments. We further extended the proposed framework to the multivalued treatment setting.We then showed how the point estimate intervals and the corresponding percentile bootstrap confidence intervals can be constructed efficiently under the proposed framework. Simulation results suggested that the proposed framework of sensitivity analysis performs well in the presence of adequate overlap among the treatment groups. Lastly, we demonstrated our proposed sensitivity analysis framework by estimating the causal effect of maternal education on female fertility in Bangladesh.

This contribution addresses the problem of image reconstruction of radioactivity distribution for which the available information arises from several classes of data, each associated with a specific combination of detections. We introduce a theoretical framework to measure the amount of information brought by each class and we develop an iterative algorithm dedicated to multi-class reconstruction based on maximum likelihood.We apply our approach to the XEMIS2 camera, a preclinical prototype of a Compton telescope dedicated to 3-photon PET imaging for which four distinct classes of partial detections coexist with the full detection class.Based on Monte Carlo simulations, we present the first elements of our model., Comment: in French language, GRETSI'23, Groupe de Recherche et d'Etudes de Traitement du Signal et des Images, Aug 2023, Grenoble, France

The CORSIKA 8 project aims to develop a versatile and modern framework for particle shower simulations that meets the new needs of experiments and addresses the caveats of existing codes. Of particular relevance is the ability to compute particle showers that pass through two or more different media, of varying density, in a single run within a single code. CORSIKA 8 achieves this flexibility by using a volume tree that specifies volume containment, allowing one to quickly query to which medium a point belongs. Thanks to this design we are able to construct very specific environments with different geometries and media. As an example, we demonstrate this new functionality by running particle showers penetrating from air into Antarctic ice and validating them with a combination of the well-established CORSIKA 7 and GEANT4 codes., Comment: Proceedings of the 38th International Cosmic Ray Conference (ICRC) in Nagoya, Japan

Over the past two decades high energy-resolution inelastic proton scattering studies were used to gain an understanding of the origin of fine structure observed in the isoscalar giant quadrupole resonance (ISGQR) and the isovector giant dipole resonance (IVGDR). Recently, the isoscalar giant monopole resonance (ISGMR) in $^{58}$Ni, $^{90}$Zr, $^{120}$Sn and $^{208}$Pb was studied at the iThemba Laboratory for Accelerator Based Sciences (iThemba LABS) by means of inelastic $\alpha$-particle scattering at very forward scattering angles (including $0\circ$). The good energy resolution of the measurement revealed significant fine structure of the ISGMR.~To extract scales by means of wavelet analysis characterizing the observed fine structure of the ISGMR in order to investigate the role of different mechanisms contributing to its decay width. Characteristic energy scales are extracted from the fine structure using continuous wavelet transforms. The experimental energy scales are compared to different theoretical approaches performed in the framework of quasiparticle random phase approximation (QRPA) and beyond-QRPA including complex configurations using both non-relativistic and relativistic density functional theory. All models highlight the role of Landau fragmentation for the damping of the ISGMR especially in the medium-mass region. Models which include the coupling between one particle-one hole (1p-1h) and two particle-two hole (2p-2h) configurations modify the strength distributions and wavelet scales indicating the importance of the spreading width. The effect becomes more pronounced with increasing mass number. Wavelet scales remain a sensitive measure of the interplay between Landau fragmentation and the spreading width in the description of the fine structure of giant resonances., Comment: 13 pages,7 figures, regular article

As satellites become smaller, the ability to maintain stable pointing decreases as external forces acting on the satellite come into play. At the same time, reaction wheels used in the attitude determination and control system (ADCS) introduce high frequency jitter which can disrupt pointing stability. For space domain awareness (SDA) tasks that track objects tens of thousands of kilometres away, the pointing accuracy offered by current nanosats, typically in the range of 10 to 100 arcseconds, is not sufficient. In this work, we develop a novel payload that utilises a neuromorphic event sensor (for high frequency and highly accurate relative attitude estimation) paired in a closed loop with a piezoelectric stage (for active attitude corrections) to provide highly stable sensor-specific pointing. Event sensors are especially suited for space applications due to their desirable characteristics of low power consumption, asynchronous operation, and high dynamic range. We use the event sensor to first estimate a reference background star field from which instantaneous relative attitude is estimated at high frequency. The piezoelectric stage works in a closed control loop with the event sensor to perform attitude corrections based on the discrepancy between the current and desired attitude. Results in a controlled setting show that we can achieve a pointing accuracy in the range of 1-5 arcseconds using our novel payload at an operating frequency of up to 50Hz using a prototype built from commercial-off-the-shelf components. Further details can be found at https://ylatif.github.io/ultrafinestabilisation

One of the fundamental challenges in drawing causal inferences from observational studies is that the assumption of no unmeasured confounding is not testable from observed data. Therefore, assessing sensitivity to this assumption's violation is important to obtain valid causal conclusions in observational studies. Although several sensitivity analysis frameworks are available in the casual inference literature, very few of them are applicable to observational studies with multivalued treatments. To address this issue, we propose a sensitivity analysis framework for performing sensitivity analysis in multivalued treatment settings. Within this framework, a general class of additive causal estimands has been proposed. We demonstrate that the estimation of the causal estimands under the proposed sensitivity model can be performed very efficiently. Simulation results show that the proposed framework performs well in terms of bias of the point estimates and coverage of the confidence intervals when there is sufficient overlap in the covariate distributions. We illustrate the application of our proposed method by conducting an observational study that estimates the causal effect of fish consumption on blood mercury levels.

This survey paper provides a comprehensive overview of the recent advancements and challenges in applying large language models to the field of audio signal processing. Audio processing, with its diverse signal representations and a wide range of sources--from human voices to musical instruments and environmental sounds--poses challenges distinct from those found in traditional Natural Language Processing scenarios. Nevertheless, \textit{Large Audio Models}, epitomized by transformer-based architectures, have shown marked efficacy in this sphere. By leveraging massive amount of data, these models have demonstrated prowess in a variety of audio tasks, spanning from Automatic Speech Recognition and Text-To-Speech to Music Generation, among others. Notably, recently these Foundational Audio Models, like SeamlessM4T, have started showing abilities to act as universal translators, supporting multiple speech tasks for up to 100 languages without any reliance on separate task-specific systems. This paper presents an in-depth analysis of state-of-the-art methodologies regarding \textit{Foundational Large Audio Models}, their performance benchmarks, and their applicability to real-world scenarios. We also highlight current limitations and provide insights into potential future research directions in the realm of \textit{Large Audio Models} with the intent to spark further discussion, thereby fostering innovation in the next generation of audio-processing systems. Furthermore, to cope with the rapid development in this area, we will consistently update the relevant repository with relevant recent articles and their open-source implementations at https://github.com/EmulationAI/awesome-large-audio-models., Comment: Under review, Repo URL: https://github.com/EmulationAI/awesome-large-audio-models

In this paper, we price European Call three different option pricing models, where the volatility is dynamically changing i.e. non constant. In stochastic volatility (SV) models for option pricing a closed form approximation technique is used, indicating that these models are computationally efficient and have the same level of performance as existing ones. We show that the calibration of SV models, such as Heston model and the High Order Moment based Stochastic Volatility (MSV) is often faster and easier. On 15 different datasets of index options, we show that models which incorporates stochastic volatility achieves accuracy comparable with the existing models. Further, we compare the In Sample and Out Sample pricing errors of each model on each date. Lastly, the pricing of models is compared among three different market to check model performance in different markets. Keywords: Option Pricing Model, Simulations, Index Options, Stochastic Volatility Models, Loss Function http://www.sci-int.com/pdf/638279543859822650.pdf

The Askaryan Radio Array Station 1 (A1), the first among five autonomous stations deployed for the ARA experiment at the South Pole, is a unique ultra-high energy neutrino (UHEN) detector based on the Askaryan effect that uses Antarctic ice as the detector medium. Its 16 radio antennas (distributed across 4 strings, each with 2 Vertically Polarized (VPol), 2 Horizontally Polarized (HPol) receivers), and 2 strings of transmitting antennas (calibration pulsers, CPs), each with 1 VPol and 1 HPol channel, are deployed at depths less than 100 m within the shallow firn zone of the 2.8 km thick South Pole (SP) ice. We apply different methods to calibrate its Ice Ray Sampler second generation (IRS2) chip for timing offset and ADC-to-Voltage conversion factors using a known continuous wave input signal to the digitizer, and achieve a precision of sub-nanoseconds. We achieve better calibration for odd, compared to even samples, and also find that the HPols under-perform relative to the VPol channels. Our timing calibrated data is subsequently used to calibrate the ADC-to-Voltage conversion as well as precise antenna locations, as a precursor to vertex reconstruction. The calibrated data will then be analyzed for UHEN signals in the final step of data compression. The ability of A1 to scan the firn region of SP ice sheet will contribute greatly towards a 5-station analysis and will inform the design of the planned IceCube Gen-2 radio array., Comment: 10 pages

The recent discovery of a 4 $\times$ 10$^7$ M$_{\odot}$ black hole (BH) in UHZ1 at $z =$ 10.3, just 450 Myr after the big bang, suggests that the seeds of the first quasars may have been direct-collapse black holes (DCBHs) from the collapse of supermassive primordial stars at $z \sim$ 20. This object was identified in James Webb Space Telescope (JWST) NIRcam and Chandra X-ray data, but recent studies suggest that radio emission from such a BH should also be visible to the Square Kilometer Array (SKA) and the next-generation Very Large Array (ngVLA). Here, we present estimates of radio flux densities for UHZ1 from 0.1 - 10 GHz, and find that SKA and ngVLA could detect it with integration times of 10 - 100 hr and just 1 - 10 hr, respectively. It may be possible to see this object with VLA now with longer integration times. The detection of radio emission from UHZ1 would be a first test of exciting new synergies between near infrared (NIR) and radio observatories that could open the era of $z \sim$ 5 - 15 quasar astronomy in the coming decade., Comment: 5 pages, 2 figures, accepted by ApJ

Relative localization is crucial for multi-robot systems to perform cooperative tasks, especially in GPS-denied environments. Current techniques for multi-robot relative localization rely on expensive or short-range sensors such as cameras and LIDARs. As a result, these algorithms face challenges such as high computational complexity (e.g., map merging), dependencies on well-structured environments, etc. To remedy this gap, we propose a new distributed approach to perform relative localization (RL) using a common Access Point (AP). To achieve this efficiently, we propose a novel Hierarchical Gaussian Processes (HGP) mapping of the Radio Signal Strength Indicator (RSSI) values from a Wi-Fi AP to which the robots are connected. Each robot performs hierarchical inference using the HGP map to locate the AP in its reference frame, and the robots obtain relative locations of the neighboring robots leveraging AP-oriented algebraic transformations. The approach readily applies to resource-constrained devices and relies only on the ubiquitously-available WiFi RSSI measurement. We extensively validate the performance of the proposed HGR-PL in Robotarium simulations against several state-of-the-art methods. The results indicate superior performance of HGP-RL regarding localization accuracy, computation, and communication overheads. Finally, we showcase the utility of HGP-RL through a multi-robot cooperative experiment to achieve a rendezvous task in a team of three mobile robots., Comment: Accepted in IROS 2024

In recent years, the use of automated source code generation utilizing transformer-based generative models has expanded, and these models can generate functional code according to the requirements of the developers. However, recent research revealed that these automatically generated source codes can contain vulnerabilities and other quality issues. Despite researchers' and practitioners' attempts to enhance code generation models, retraining and fine-tuning large language models is time-consuming and resource-intensive. Thus, we describe FRANC, a lightweight framework for recommending more secure and high-quality source code derived from transformer-based code generation models. FRANC includes a static filter to make the generated code compilable with heuristics and a quality-aware ranker to sort the code snippets based on a quality score. Moreover, the framework uses prompt engineering to fix persistent quality issues. We evaluated the framework with five Python and Java code generation models and six prompt datasets, including a newly created one in this work (SOEval). The static filter improves 9% to 46% Java suggestions and 10% to 43% Python suggestions regarding compilability. The average improvement over the NDCG@10 score for the ranking system is 0.0763, and the repairing techniques repair the highest 80% of prompts. FRANC takes, on average, 1.98 seconds for Java; for Python, it takes 0.08 seconds., Comment: Accepted at the 24th IEEE International Conference on Source Code Analysis and Manipulation (SCAM 2024)

Despite recent advancements in speech emotion recognition (SER) models, state-of-the-art deep learning (DL) approaches face the challenge of the limited availability of annotated data. Large language models (LLMs) have revolutionised our understanding of natural language, introducing emergent properties that broaden comprehension in language, speech, and vision. This paper examines the potential of LLMs to annotate abundant speech data, aiming to enhance the state-of-the-art in SER. We evaluate this capability across various settings using publicly available speech emotion classification datasets. Leveraging ChatGPT, we experimentally demonstrate the promising role of LLMs in speech emotion data annotation. Our evaluation encompasses single-shot and few-shots scenarios, revealing performance variability in SER. Notably, we achieve improved results through data augmentation, incorporating ChatGPT-annotated samples into existing datasets. Our work uncovers new frontiers in speech emotion classification, highlighting the increasing significance of LLMs in this field moving forward., Comment: Accepted in IEEE Computational Intelligence Magazine

Localizing mobile robotic nodes in indoor and GPS-denied environments is a complex problem, particularly in dynamic, unstructured scenarios where traditional cameras and LIDAR-based sensing and localization modalities may fail. Alternatively, wireless signal-based localization has been extensively studied in the literature yet primarily focuses on fingerprinting and feature-matching paradigms, requiring dedicated environment-specific offline data collection. We propose an online robot localization algorithm enabled by collaborative wireless sensor nodes to remedy these limitations. Our approach's core novelty lies in obtaining the Collaborative Direction of Arrival (CDOA) of wireless signals by exploiting the geometric features and collaboration between wireless nodes. The CDOA is combined with the Expectation Maximization (EM) and Particle Filter (PF) algorithms to calculate the Gaussian probability of the node's location with high efficiency and accuracy. The algorithm relies on RSSI-only data, making it ubiquitous to resource-constrained devices. We theoretically analyze the approach and extensively validate the proposed method's consistency, accuracy, and computational efficiency in simulations, real-world public datasets, as well as real robot demonstrations. The results validate the method's real-time computational capability and demonstrate considerably-high centimeter-level localization accuracy, outperforming relevant state-of-the-art localization approaches., Comment: Accepted to the IEEE Internet of Things Journal. arXiv admin note: text overlap with arXiv:2201.05105

Frontier exploration and reinforcement learning have historically been used to solve the problem of enabling many mobile robots to autonomously and cooperatively explore complex surroundings. These methods need to keep an internal global map for navigation, but they do not take into consideration the high costs of communication and information sharing between robots. This study offers CQLite, a novel distributed Q-learning technique designed to minimize data communication overhead between robots while achieving rapid convergence and thorough coverage in multi-robot exploration. The proposed CQLite method uses ad hoc map merging, and selectively shares updated Q-values at recently identified frontiers to significantly reduce communication costs. The theoretical analysis of CQLite's convergence and efficiency, together with extensive numerical verification on simulated indoor maps utilizing several robots, demonstrates the method's novelty. With over 2x reductions in computation and communication alongside improved mapping performance, CQLite outperformed cutting-edge multi-robot exploration techniques like Rapidly Exploring Random Trees and Deep Reinforcement Learning. Related codes are open-sourced at \url{https://github.com/herolab-uga/cqlite}., Comment: Accepted by RA-L

Magnetic fields are expected to be efficiently amplified during the formation of the first massive black holes via the small-scale dynamo and in the presence of strong accretion shocks occurring during gravitational collapse. Here, we analyze high-resolution cosmological magneto-hydrodynamical simulations of gravitational collapse in atomic cooling halos, exploring the dynamical role of magnetic fields, particularly concerning the effect of magnetic braking and angular momentum transport. We find that after the initial amplification, magnetic fields contribute to the transport of angular momentum and reduce it compared to pure hydrodynamical simulations. However, the magnetic and Reynolds torques do not fully compensate for the inward advection of angular momentum, which still accumulates over timescales of $\sim1$~Myr. A Jeans analysis further shows that magnetic pressure strongly contributes to suppressing fragmentation on scales of $0.1-10$~pc. Overall, the presence of magnetic fields thus aids in the transport of angular momentum and favors the formation of massive objects., Comment: Accepted for Publication in ApJL, comments are welcome