Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of a data-driven equation-discovery mesoscale parameterization into an ocean model

Mesoscale eddies are poorly represented in climate ocean models, and therefore their effects on the large scale circulation must be parameterized. Classical parameterizations, which represent the bulk effect of the unresolved eddies, can be improved with new subgrid models learned directly from data. Zanna and Bolton (2020) (ZB20) applied an equation-discovery algorithm to reveal an interpretable expression parameterizing the subgrid mesoscale fluxes through the components of the velocity-gradient tensor. In this work, we implement the ZB20 parameterization into the primitive-equation GFDL MOM6 ocean model and test it in two idealized configurations with significantly different stratification and topography. In addition, we propose an approach based on spatial filtering to improve the representation of large-scale energy backscatter and numerical properties of the parameterization. The ZB20 parameterization led to improved climatological mean flow and energy distributions, compared to the current state-of-the-art energy backscatter parameterizations. The ZB20 is scale-aware and can be used with a single value of the non-dimensional scaling coefficient for a range of resolutions. The successful application of the ZB20 to parameterize mesoscale eddies in two idealized configurations offers a promising opportunity to reduce long-standing biases in global ocean simulations in future studies.
TACNET: Temporal Audio Source Counting Network

In this paper, we introduce the Temporal Audio Source Counting Network (TaCNet), an innovative architecture that addresses limitations in audio source counting tasks. TaCNet operates directly on raw audio inputs, eliminating complex preprocessing steps and simplifying the workflow. Notably, it excels in real-time speaker counting, even with truncated input windows. Our extensive evaluation, conducted using the LibriCount dataset, underscores TaCNet's exceptional performance, positioning it as a state-of-the-art solution for audio source counting tasks. With an average accuracy of 74.18 percentage over 11 classes, TaCNet demonstrates its effectiveness across diverse scenarios, including applications involving Chinese and Persian languages. This cross-lingual adaptability highlights its versatility and potential impact.
Optimal Treatment Allocation for Efficient Policy Evaluation in Sequential Decision Making

A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimators.
Neural-Based Compression Scheme for Solar Image Data

Studying the solar system and especially the Sun relies on the data gathered daily from space missions. These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make. Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy, which could affect scientific analysis of the data. On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression. In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions. We chose NASA's Solar Dynamics Observatory (SDO) mission, which transmits 1.4 TB of data each day as a proof of concept for the proposed algorithm. In this work, we propose an adversarially trained neural network, equipped with local and nonlocal attention modules to capture both the local and global structure of the image resulting in a better tradeoff in rate-distortion (RD) compared with conventional hand-engineered codecs. The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective. We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image. Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs, such as JPEG and JPEG-2000, in terms of the RD performance when compressing extreme-ultraviolet (EUV) data. As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole detection using our compressed images, and generated consistent segmentations, even at a compression rate of $\sim\! 0.1$ bits per pixel (compared with 8 bits per pixel on the original data) using EUV data from SDO.
Criticality and Chaos in Auditory and Vestibular Sensing

The auditory and vestibular systems exhibit remarkable sensitivity of detection, responding to deflections on the order of Angstroms, even in the presence of biological noise. Further, these complex systems exhibit high temporal acuity and frequency selectivity, allowing us to make sense of the acoustic world around us. As this acoustic environment of interest spans several orders of magnitude in both amplitude and frequency, these systems rely heavily on nonlinearities and power-law scaling. The behavior of these sensory systems has been extensively studied in the context of dynamical systems theory, with many empirical phenomena described by critical dynamics. Other phenomena have been explained by systems in the chaotic regime, where weak perturbations drastically impact the future state of the system. We first review the conceptual framework behind these two types of detectors, as well as the detection features that they can capture. We then explore the intersection of the two types of systems and propose ideal parameter regimes for auditory and vestibular systems.
Hole spin coherence in InAs/InAlGaAs self-assembled quantum dots emitting at telecom wavelengths

We report measurements of the longitudinal and transverse spin relaxation times of holes in an ensemble of self-assembled InAs/InAlGaAs quantum dots (QDs), emitting in the telecom spectral range. The spin coherence of a single carrier is determined using spin mode-locking in the inhomogeneous ensemble of QDs. Modeling the signal allows us to extract the hole spin coherence time to be in the range of T$_2 = 0.02-0.4$ $\mu$s. The longitudinal spin relaxation time T$_1 = 0.5$ $\mu$s is measured using the spin inertia method.
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance

Navigating deceptive domains has often been a challenge in machine learning due to search algorithms getting stuck at sub-optimal local optima. Many algorithms have been proposed to navigate these domains by explicitly maintaining diversity or equivalently promoting exploration, such as Novelty Search or other so-called Quality Diversity algorithms. In this paper, we present an approach with promise to solve deceptive domains without explicit diversity maintenance by optimizing a potentially large set of defined objectives. These objectives can be extracted directly from the environment by sub-aggregating the raw performance of individuals in a variety of ways. We use lexicase selection to optimize for these objectives as it has been shown to implicitly maintain population diversity. We compare this technique with a varying number of objectives to a commonly used quality diversity algorithm, MAP-Elites, on a set of discrete optimization as well as reinforcement learning domains with varying degrees of deception. We find that decomposing objectives into many objectives and optimizing them outperforms MAP-Elites on the deceptive domains that we explore. Furthermore, we find that this technique results in competitive performance on the diversity-focused metrics of QD-Score and Coverage, without explicitly optimizing for these things. Our ablation study shows that this technique is robust to different subaggregation techniques. However, when it comes to non-deceptive, or ``illumination"domains, quality diversity techniques generally outperform our objective-based framework with respect to exploration (but not exploitation), hinting at potential directions for future work.
Complex Fermiology and Electronic Structure of Antiferromagnet EuSnP

We studied the electronic structure of a layered antiferromagnetic metal, EuSnP, in the paramagnetic and in the antiferromagnetic phase using angle resolved photoemission spectroscopy (ARPES) alongside density functional theory (DFT) based first principles calculations. The temperature dependence of the magnetic susceptibility measurements exhibits an antiferromagnetic transition at a Neel temperature of 21 K. Employing high resolution ARPES, the valence band structure was measured at several temperatures above and below the Neel temperature, which produced identical spectra independent of temperature. Through analysis of the ARPES results presented here, we attribute the temperature independent spectra to the weak coupling of the Sn, and P conduction electrons with Eu 4f states.
Quantum-inspired anomaly detection, a QUBO formulation

Anomaly detection is a crucial task in machine learning that involves identifying unusual patterns or events in data. It has numerous applications in various domains such as finance, healthcare, and cybersecurity. With the advent of quantum computing, there has been a growing interest in developing quantum approaches to anomaly detection. After reviewing traditional approaches to anomaly detection relying on statistical or distance-based methods, we will propose a Quadratic Unconstrained Binary Optimization (QUBO) model formulation of anomaly detection, compare it with classical methods, and discuss its scalability on current Quantum Processing Units (QPU).
Institutional Screening and the Sustainability of Conditional Cooperation

This paper studies a preference evolution model in which a population of agents are matched to play a sequential prisoner's dilemma in an incomplete information environment. An institution can design an incentive-compatible screening scheme, such as a special zone that requires an entry fee, or a costly label for purchase, to segregate the conditional cooperators from the non-cooperators. We show that institutional intervention of this sort can help the conditional cooperators to prevail when the psychological benefit of cooperating for them is sufficiently strong and the membership of the special zone or the label is inheritable with a sufficiently high probability.
Enhancing the Performance of Pneu-net Actuators Using a Torsion Resistant Strain Limiting Layer

Pneunets are the primary form of soft robotic grippers. A key limitation to their wider adoption is their inability to grasp larger payloads due to objects slipping out of grasps. We have overcome this limitation by introducing a torsionally rigid strain limiting layer (TRL). This reduces out-of-plane bending while maintaining the gripper's softness and in-plane flexibility. We characterize the design space of the strain limiting layer for a Pneu-net gripper using simulation and experiment and map bending angle and relative grip strength. We found that the use of our TRL reduced out-of-plane bending by up to 97.7% in testing compared to a benchmark Pneu-net gripper from the Soft Robotics Toolkit. We demonstrate a lifting capacity of 5kg when loading using the TRL. We also see a relative improvement in peak grip force of 3N and stiffness of 1200N/m compared to 1N and 150N/m for a Pneu-net gripper without our TRL at equal pressures. Finally, we test the TRL gripper on a suite of six YCB objects above the demonstrated capability of a traditional Pneu-net gripper. We show success on all but one demonstrating significant increased capabilities.
Spatial Process Approximations: Assessing Their Necessity

In spatial statistics and machine learning, the kernel matrix plays a pivotal role in prediction, classification, and maximum likelihood estimation. A thorough examination reveals that for large sample sizes, the kernel matrix becomes ill-conditioned, provided the sampling locations are fairly evenly distributed. This condition poses significant challenges to numerical algorithms used in prediction and estimation computations and necessitates an approximation to prediction and the Gaussian likelihood. A review of current methodologies for managing large spatial data indicates that some fail to address this ill-conditioning problem. Such ill-conditioning often results in low-rank approximations of the stochastic processes. This paper introduces various optimality criteria and provides solutions for each.
AI techniques for uncovering resolved Planetary Nebula candidates from Wide-field VPHAS+ survey data

AI and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved Planetary Nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important to study late-stage stellar evolution of low to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming due to the large data size and areal coverage of modern astronomical surveys. The training and validation dataset of our algorithm was built with IPHAS survey and true PNe from the HASH database. Our algorithm correctly identified 444 PNe in the validation set of 454 ones, with only 16 explicable ‘false’ positives, achieving a precision rate of 96.5% and a recall rate of 97.8%. After transfer learning, it was then applied to VPHAS+ survey, examining 979 out of 2284 survey fields, each covering 1○ × 1○. It returned ∼20,000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found 74% of them are true, likely and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques with full details to be given in paper-II.
Nepali Video Captioning using CNN-RNN Architecture

This article presents a study on Nepali video captioning using deep neural networks. Through the integration of pre-trained CNNs and RNNs, the research focuses on generating precise and contextually relevant captions for Nepali videos. The approach involves dataset collection, data preprocessing, model implementation, and evaluation. By enriching the MSVD dataset with Nepali captions via Google Translate, the study trains various CNN-RNN architectures. The research explores the effectiveness of CNNs (e.g., EfficientNetB0, ResNet101, VGG16) paired with different RNN decoders like LSTM, GRU, and BiLSTM. Evaluation involves BLEU and METEOR metrics, with the best model being EfficientNetB0 + BiLSTM with 1024 hidden dimensions, achieving a BLEU-4 score of 17 and METEOR score of 46. The article also outlines challenges and future directions for advancing Nepali video captioning, offering a crucial resource for further research in this area.
End-to-end Material Thermal Conductivity Prediction through Machine Learning

We investigated the accelerated prediction of the thermal conductivity of materials through end-to-end structure-based approaches employing machine learning methods. Due to the non-availability of high-quality thermal conductivity data, we first performed high-throughput calculations based on first principles and the Boltzmann transport equation for 225 materials, effectively more than doubling the size of the existing dataset. We assessed the performance of state-of-the-art machine learning models for thermal conductivity prediction on this expanded dataset and observed that all these models suffered from overfitting. To address this issue, we introduced a different graph-based neural network model, which demonstrated more consistent and regularized performance across all evaluated datasets. Nevertheless, the best mean absolute percentage error achieved on the test dataset remained in the range of 50–60%. This suggests that while these models are valuable for expediting material screening, their current accuracy is still limited.
Data-Driven Bayesian Nonparametric Wasserstein Distributionally Robust Optimization

In this letter, we develop a novel data-driven Bayesian nonparametric Wasserstein distributionally robust optimization (BNWDRO) framework for decision-making under uncertainty. The proposed framework unifies a Bayesian nonparametric method and the Wasserstein metric to decipher the global-local features of uncertainty data and encode these features into a novel data-driven ambiguity set. By establishing the theoretical connection between this data-driven ambiguity set and the conventional Wasserstein ambiguity set, we prove that the proposed framework enjoys the finite sample guarantee and asymptotic consistency. To efficiently solve the resulting distributionally robust optimization problem under the BNWDRO framework, we derive for this optimization problem an equivalent reformulation, which is kept tractable for many practical scenarios. Numerical experiments on a unit commitment problem verify the effectiveness of the proposed BNWDRO framework compared with existing methods.
RecAGT: Shard Testable Codes with Adaptive Group Testing for Malicious Nodes Identification in Sharding Permissioned Blockchain

Recently, permissioned blockchain has been extensively explored in various fields, such as asset management, supply chain, healthcare, and many others. Many scholars are dedicated to improving its verifiability, scalability, and performance based on sharding techniques, including grouping nodes and handling cross-shard transactions. However, they ignore the node vulnerability problem, i.e., there is no guarantee that nodes will not be maliciously controlled throughout their life cycle. Facing this challenge, we propose RecAGT, a novel identification scheme aimed at reducing communication overhead and identifying potential malicious nodes. First, shard testable codes are designed to encode the original data in case of a leak of confidential data. Second, a new identity proof protocol is presented as evidence against malicious behavior. Finally, adaptive group testing is chosen to identify malicious nodes. Notably, our work focuses on the internal operation within the committee and can thus be applied to any sharding permissioned blockchains. Simulation results show that our proposed scheme can effectively identify malicious nodes with low communication and computational costs.
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)

Efficient exploration for an agent is challenging in reinforcement learning (RL). In this paper, a novel actor-critic framework namely virtual action actor-critic (VAAC), is proposed to address the challenge of efficient exploration in RL. This work is inspired by humans' ability to imagine the potential outcomes of their actions without actually taking them. In order to emulate this ability, VAAC introduces a new actor called virtual actor (VA), alongside the conventional actor-critic framework. Unlike the conventional actor, the VA takes the virtual action to anticipate the next state without interacting with the environment. With the virtual policy following a Gaussian distribution, the VA is trained to maximize the anticipated novelty of the subsequent state resulting from a virtual action. If any next state resulting from available actions does not exhibit high anticipated novelty, training the VA leads to an increase in the virtual policy entropy. Hence, high virtual policy entropy represents that there is no room for exploration. The proposed VAAC aims to maximize a modified Q function, which combines cumulative rewards and the negative sum of virtual policy entropy. Experimental results show that the VAAC improves the exploration performance compared to existing algorithms.
Variational Autoencoders for Noise Reduction in Industrial LLRF Systems

Industrial particle accelerators inherently operate in much dirtier environments than typical research accelerators. This leads to an increase in noise both in the RF system and in other electronic systems. Combined with the fact that industrial accelerators are mass produced, there is less attention given to optimizing the performance of an individual system. As a result, industrial systems tend to under perform considering their hardware hardware capabilities. With the growing demand for accelerators for medical sterilization, food irradiation, cancer treatment, and imaging, improving the signal processing of these machines will increase the margin for the deployment of these systems. Our work is focusing on using machine learning techniques to reduce the noise of RF signals used for pulse-to-pulse feedback in industrial accelerators. We will review our algorithms, simulation results, and results working with measured data. We will then discuss next steps for deployment and testing on an industrial system.
ChaTA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs

To address the challenges of scalable and intelligent question-answering (QA), we introduce an innovative solution that leverages open-source Large Language Models (LLMs) to ensure data privacy. We use models from the LLaMA-2 family and augmentations including retrieval augmented generation (RAG), supervised fine-tuning (SFT), and an alternative to reinforcement learning with human feedback (RLHF). We perform our experiments on a Piazza dataset from an introductory CS course with 10 k QA pairs and 1 . 5 k pairs of preferences data and conduct both human evaluations and automatic LLM evaluations on a small subset. We find preliminary evidence that modeling techniques collectively enhance the quality of answers by 33%, and RAG is an impactful addition. This work paves the way for the development of C HA TA, an intelligent QA assistant customizable for courses with an online QA platform.