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Understanding many body systems is a key challenge in physics. Single atom resolved imaging techniques have unlocked access to microscopic correlations in ultracold quantum gases. However they cannot be used when the relevant length scales are obscured by the resolution of the detection technique. We present a matterwave magnification scheme, based on evolutions in optical potentials, tailored to magnify the wave function of atoms, such that all length scales can be resolved. To showcase this method, we image atoms in the strongly interacting regime, establishing a new way to characterize correlated systems., Comment: 7 pages, 4 figures

Learning from Demonstration (LfD) is a useful paradigm for training policies that solve tasks involving complex motions. In practice, the successful application of LfD requires overcoming error accumulation during policy execution, i.e. the problem of drift due to errors compounding over time and the consequent out-of-distribution behaviours. Existing works seek to address this problem through scaling data collection, correcting policy errors with a human-in-the-loop, temporally ensembling policy predictions or through learning the parameters of a dynamical system model. In this work, we propose and validate an alternative approach to overcoming this issue. Inspired by reservoir computing, we develop a novel neural network layer that includes a fixed nonlinear dynamical system with tunable dynamical properties. We validate the efficacy of our neural network layer on the task of reproducing human handwriting motions using the LASA Human Handwriting Dataset. Through empirical experiments we demonstrate that incorporating our layer into existing neural network architectures addresses the issue of compounding errors in LfD. Furthermore, we perform a comparative evaluation against existing approaches including a temporal ensemble of policy predictions and an Echo State Networks (ESNs) implementation. We find that our approach yields greater policy precision and robustness on the handwriting task while also generalising to multiple dynamics regimes and maintaining competitive latency scores., Comment: 21 pages, 9 figures

This paper addresses a challenging interactive task learning scenario we call rearrangement under unawareness: to manipulate a rigid-body environment in a context where the robot is unaware of a concept that's key to solving the instructed task. We propose SECURE, an interactive task learning framework designed to solve such problems by fixing a deficient domain model using embodied conversation. Through dialogue, the robot discovers and then learns to exploit unforeseen possibilities. Using SECURE, the robot not only learns from the user's corrective feedback when it makes a mistake, but it also learns to make strategic dialogue decisions for revealing useful evidence about novel concepts for solving the instructed task. Together, these abilities allow the robot to generalise to subsequent tasks using newly acquired knowledge. We demonstrate that a robot that is semantics-aware -- that is, it exploits the logical consequences of both sentence and discourse semantics in the learning and inference process -- learns to solve rearrangement under unawareness more effectively than a robot that lacks such capabilities., Comment: 10 pages,4 figures, 2 tables

The Small Magellanic Cloud (SMC) is an irregular dwarf galaxy that has recently undergone an interaction with the Large Magellanic Cloud. The young massive stars in the SMC formed in the disturbed low-metallicity environment are important targets in astrophysics. We present a catalog of $\sim$ 76,800 far ultraviolet (FUV) sources towards the SMC detected using the Ultra Violet Imaging Telescope (UVIT) onboard AstroSat. We created an FUV catalog with $\sim$ 62900 probable SMC members which predominantly comprise main-sequence, giant, and subgiant stars. We selected 4 young populations (Young 1, Young 2, Young 3, and Blue Loop (BL) stars) identified from the Gaia optical color-magnitude diagram to study the morphology and kinematics of the young SMC using this catalog. We detect a clumpy morphology with a broken bar, a shell-like structure, and the inner SMC Wing for the 4 stellar populations. The eastern region and the northeastern regions are mainly populated by Young 1, 2, and 3. The central region predominantly has the Young 2 and 3 populations, whereas the SW has BL stars, Young 2 and 3. The 2-D kinematic study using proper motion (PM) reveals that Young 2 and 3 populations show two kinematically distinct sub-populations with low and high PM dispersion, whereas the Young 1 and BL stars show two kinematically distinct populations with low dispersion. Our analysis points to a kinematic disturbance along the RA direction for stars younger than $\sim$ 150 Myr located in the eastern region, with no significant disturbance along the Declination., Comment: 14 figures, 6 tables, Accepted for publication in the Astronomical Journal

We study the complexity of testing properties of quantum channels. First, we show that testing identity to any channel $\mathcal N: \mathbb C^{d_{\mathrm{in}} \times d_{\mathrm{in}}} \to \mathbb C^{d_{\mathrm{out}} \times d_{\mathrm{out}}}$ in diamond norm distance requires $\Omega(\sqrt{d_{\mathrm{in}} / \varepsilon})$ queries, even in the strongest algorithmic model that admits ancillae, coherence, and adaptivity. This is due to the worst-case nature of the distance induced by the diamond norm. Motivated by this limitation and other theoretical and practical applications, we introduce an average-case analogue of the diamond norm, which we call the average-case imitation diamond (ACID) norm. In the weakest algorithmic model without ancillae, coherence, or adaptivity, we prove that testing identity to certain types of channels in ACID distance can be done with complexity independent of the dimensions of the channel, while for other types of channels the complexity depends on both the input and output dimensions. Building on previous work, we also show that identity to any fixed channel can be tested with $\tilde O(d_{\mathrm{in}} d_{\mathrm{out}}^{3/2} / \varepsilon^2)$ queries in ACID distance and $\tilde O(d_{\mathrm{in}}^2 d_{\mathrm{out}}^{3/2} / \varepsilon^2)$ queries in diamond distance in this model. Finally, we prove tight bounds on the complexity of channel tomography in ACID distance.

Security researchers are continually challenged by the need to stay current with rapidly evolving cybersecurity research, tools, and techniques. This constant cycle of learning, unlearning, and relearning, combined with the repetitive tasks of sifting through documentation and analyzing data, often hinders productivity and innovation. This has led to a disparity where only organizations with substantial resources can access top-tier security experts, while others rely on firms with less skilled researchers who focus primarily on compliance rather than actual security. We introduce "LLM Augmented Pentesting," demonstrated through a tool named "Pentest Copilot," to address this gap. This approach integrates Large Language Models into penetration testing workflows. Our research includes a "chain of thought" mechanism to streamline token usage and boost performance, as well as unique Retrieval Augmented Generation implementation to minimize hallucinations and keep models aligned with the latest techniques. Additionally, we propose a novel file analysis approach, enabling LLMs to understand files. Furthermore, we highlight a unique infrastructure system that supports if implemented, can support in-browser assisted penetration testing, offering a robust platform for cybersecurity professionals, These advancements mark a significant step toward bridging the gap between automated tools and human expertise, offering a powerful solution to the challenges faced by modern cybersecurity teams., Comment: 9 pages, 7 figures

Inverse Constrained Reinforcement Learning (ICRL) is the task of inferring the implicit constraints followed by expert agents from their demonstration data. As an emerging research topic, ICRL has received considerable attention in recent years. This article presents a categorical survey of the latest advances in ICRL. It serves as a comprehensive reference for machine learning researchers and practitioners, as well as starters seeking to comprehend the definitions, advancements, and important challenges in ICRL. We begin by formally defining the problem and outlining the algorithmic framework that facilitates constraint inference across various scenarios. These include deterministic or stochastic environments, environments with limited demonstrations, and multiple agents. For each context, we illustrate the critical challenges and introduce a series of fundamental methods to tackle these issues. This survey encompasses discrete, virtual, and realistic environments for evaluating ICRL agents. We also delve into the most pertinent applications of ICRL, such as autonomous driving, robot control, and sports analytics. To stimulate continuing research, we conclude the survey with a discussion of key unresolved questions in ICRL that can effectively foster a bridge between theoretical understanding and practical industrial applications., Comment: 29 pages

Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale `in-the-wild' dataset for identifying human perceptions about the `Most Important Person (MIP)' in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thorough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to `in-the-wild' situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The code and data is available at https://github.com/surbhimadan92/MIP-GAF., Comment: Accepted for publication at WACV 2025

Modern web services adopt cloud-native principles to leverage the advantages of microservices. To consistently guarantee high Quality of Service (QoS) according to Service Level Agreements (SLAs), ensure satisfactory user experiences, and minimize operational costs, each microservice must be provisioned with the right amount of resources. However, accurately provisioning microservices with adequate resources is complex and depends on many factors, including workload intensity and the complex interconnections between microservices. To address this challenge, we develop a model that captures the relationship between an end-to-end latency, requests at the front-end level, and resource utilization. We then use the developed model to predict the end-to-end latency. Our solution leverages the Temporal Fusion Transformer (TFT), an attention-based architecture equipped with interpretability features. When the prediction results indicate SLA non-compliance, we use the feature importance provided by the TFT as covariates in Kernel Ridge Regression (KRR), with the response variable being the desired latency, to learn the parameters associated with the feature importance. These learned parameters reflect the adjustments required to the features to ensure SLA compliance. We demonstrate the merit of our approach with a microservice-based application and provide a roadmap to deployment., Comment: 14 pages, 5 figures

The ocean ecology is badly impacted by large-scale oil spills, plastic waste, and chemical pollution, which destroy ecosystems and endanger marine life. Acknowledging the detrimental effects of oil spills on ecosystems, our research aims to establish the foundation for creative methods to lessen their impact. With an emphasis on the containment and prediction of oil spills, this research investigates the potential of acoustic levitation as a cutting-edge technique for environmental cleanup. Effectively separating and eliminating pollutants without causing additional ecological harm is a major issue for traditional oil spill cleanup techniques. Acoustic levitation provides a non-invasive, accurate, and effective alternative by using sound waves to precisely and subtly separate oil droplets from water in controlled environments. This proposed approach can reduce the negative effects on the environment and increase the efficacy of cleanup efforts. The findings have been examined and assessed by proof of concept experiments with oil droplets, identifying the relationship between the intensity of ultrasonic pressure and the proportion of oil droplets collected.

Ethanethiol (C$_2$H$_5$SH), a molecule detected in the interstellar medium (ISM), indicates the rich chemistry involving sulfur atoms. However, its behavior at low temperatures remains elusive, particularly the reported transition from an amorphous phase to a crystal. This study employs classical molecular dynamics (MD) simulations to reproduce the liquid-state properties of ethanethiol and to simulate the initial amorphous state of ethanethiol films deposited on a KBr substrate. The amorphous ethanethiol did not show spontaneous crystallization upon increasing temperature. Also, ethanethiol ice crystals exhibit melting behavior on KBr substrate at elevated temperatures. Our MD simulations of thin ice samples do not show any signature reversible phase change. It will be interesting to continue this study with a thicker sample, which is beyond our current computational means. These findings underscore the complexity of icy mantle morphology on cold ISM dust grains., Comment: Manuscript accepted for publication in "Astrophysics and Space Science Proceedings", Springer nature (Symposium: ISRA 2023)

Recent advancements in deep learning for Medical Artificial Intelligence have demonstrated that models can match the diagnostic performance of clinical experts in adult chest X-ray (CXR) interpretation. However, their application in the pediatric context remains limited due to the scarcity of large annotated pediatric image datasets. Additionally, significant challenges arise from the substantial variability in pediatric CXR images across different hospitals and the diverse age range of patients from 0 to 18 years. To address these challenges, we propose SCC, a novel approach that combines transfer learning with self-supervised contrastive learning, augmented by an unsupervised contrast enhancement technique. Transfer learning from a well-trained adult CXR model mitigates issues related to the scarcity of pediatric training data. Contrastive learning with contrast enhancement focuses on the lungs, reducing the impact of image variations and producing high-quality embeddings across diverse pediatric CXR images. We train SCC on one pediatric CXR dataset and evaluate its performance on two other pediatric datasets from different sources. Our results show that SCC's out-of-distribution (zero-shot) performance exceeds regular transfer learning in terms of AUC by 13.6% and 34.6% on the two test datasets. Moreover, with few-shot learning using 10 times fewer labeled images, SCC matches the performance of regular transfer learning trained on the entire labeled dataset. To test the generality of the framework, we verify its performance on three benchmark breast cancer datasets. Starting from a model trained on natural images and fine-tuned on one breast dataset, SCC outperforms the fully supervised learning baseline on the other two datasets in terms of AUC by 3.6% and 5.5% in zero-shot learning., Comment: 15 pages, 6 figures, 4 tables

Robust model predictive control algorithms are essential for addressing unavoidable errors due to the uncertainty in predicting real-world systems. However, the formulation of such algorithms typically results in a trade-off between conservatism and computational complexity. Monotone systems facilitate the efficient computation of reachable sets and thus the straightforward formulation of a robust model predictive control approach optimizing over open-loop predictions. We present an approach based on the division of reachable sets to incorporate feedback in the predictions, resulting in less conservative strategies. The concept of mixed-monotonicity enables an extension of our methodology to non-monotone systems. The potential of the proposed approaches is demonstrated through a nonlinear high-dimensional chemical tank reactor cascade case study., Comment: Submitted to "IEEE Transactions on Automatic Control", Code: https://github.com/MoritzHein/RobMPCExploitMon

We study classes of constant-depth circuits with gates that compute restricted polynomial threshold functions, recently introduced by [Kum23] as a family that strictly generalizes $\mathsf{AC}^0$. Denoting these circuit families $\mathsf{bPTFC}^0[k]$ for $\textit{bounded polynomial threshold circuits}$ parameterized by an integer-valued degree-bound $k$, we prove three hardness results separating these classes from constant-depth quantum circuits ($\mathsf{QNC}^0$). $\hspace{2em}$ - We prove that the parity halving problem [WKS+19], which $\mathsf{QNC}^0$ over qubits can solve with certainty, remains average-case hard for polynomial size $\mathsf{bPTFC}^0[k]$ circuits for all $k=\mathcal{O}(n^{1/(5d)})$. $\hspace{2em}$ - We construct a new family of relation problems based on computing $\mathsf{mod}\ p$ for each prime $p>2$, and prove a separation of $\mathsf{QNC}^0$ circuits over higher dimensional quantum systems (`qupits') against $\mathsf{bPTFC}^0[k]$ circuits for the same degree-bound parameter as above. $\hspace{2em}$ - We prove that both foregoing results are noise-robust under the local stochastic noise model, by introducing fault-tolerant implementations of non-Clifford $\mathsf{QNC}^0/|\overline{T^{1/p}}>$ circuits, that use logical magic states as advice. $\mathsf{bPTFC}^0[k]$ circuits can compute certain classes of Polynomial Threshold Functions (PTFs), which in turn serve as a natural model for neural networks and exhibit enhanced expressivity and computational capabilities. Furthermore, for large enough values of $k$, $\mathsf{bPTFC}^0[k]$ contains $\mathsf{TC}^0$ as a subclass. The main challenges we overcome include establishing classical average-case lower bounds, designing non-local games with quantum-classical gaps in winning probabilities and developing noise-resilient non-Clifford quantum circuits necessary to extend beyond qubits to higher dimensions.

Forward Error Correction (FEC) provides reliable data flow in wireless networks despite the presence of noise and interference. However, its processing demands significant fraction of a wireless network's resources, due to its computationally-expensive decoding process. This forces network designers to compromise between performance and implementation complexity. In this paper, we investigate a novel processing architecture for FEC decoding, one based on the quantum approximate optimization algorithm (QAOA), to evaluate the potential of this emerging quantum compute approach in resolving the decoding performance-complexity tradeoff. We present FDeQ, a QAOA-based FEC Decoder design targeting the popular NextG wireless Low Density Parity Check (LDPC) and Polar codes. To accelerate QAOA-based decoding towards practical utility, FDeQ exploits temporal similarity among the FEC decoding tasks. This similarity is enabled by the fixed structure of a particular FEC code, which is independent of any time-varying wireless channel noise, ambient interference, and even the payload data. We evaluate FDeQ at a variety of system parameter settings in both ideal (noiseless) and noisy QAOA simulations, and show that FDeQ achieves successful decoding with error performance at par with state-of-the-art classical decoders at low FEC code block lengths. Furthermore, we present a holistic resource estimation analysis, projecting quantitative targets for future quantum devices in terms of the required qubit count and gate duration, for the application of FDeQ in practical wireless networks, highlighting scenarios where FDeQ may outperform state-of-the-art classical FEC decoders.

Vision-based motion estimation methods show promise in accurately and unobtrusively estimating human body motion for healthcare purposes. However, these methods are not specifically designed for healthcare purposes and face challenges in real-world applications. Human pose estimation methods often lack the accuracy needed for detecting fine-grained, subtle body movements, while optical flow-based methods struggle with poor lighting conditions and unseen real-world data. These issues result in human body motion estimation errors, particularly during critical medical situations where the body is motionless, such as during unconsciousness. To address these challenges and improve the accuracy of human body motion estimation for healthcare purposes, we propose the OPPH operator designed to enhance current vision-based motion estimation methods. This operator, which considers human body movement and noise properties, functions as a multi-stage filter. Results tested on two real-world and one synthetic human motion dataset demonstrate that the operator effectively removes real-world noise, significantly enhances the detection of motionless states, maintains the accuracy of estimating active body movements, and maintains long-term body movement trends. This method could be beneficial for analyzing both critical medical events and chronic medical conditions.

The high incidence of irreproducible research has led to urgent appeals for transparency and equitable practices in open science. For the scientific disciplines that rely on computationally intensive analyses of large data sets, a granular understanding of the analysis methodology is an essential component of reproducibility. This paper discusses the guiding principles of a computational reproducibility framework that enables a scientist to proactively generate a complete reproducible trace as analysis unfolds, and share data, methods and executable tools as part of a scientific publication, allowing other researchers to verify results and easily re-execute the steps of the scientific investigation.

At first light, the Thirty Meter Telescope (TMT) near-infrared (NIR) instruments will be fed by a multiconjugate adaptive optics instrument known as the Narrow Field Infrared Adaptive Optics System (NFIRAOS). NFIRAOS will use six laser guide stars to sense atmospheric turbulence in a volume corresponding to a field of view of 2', but natural guide stars (NGSs) will be required to sense tip/tilt and focus. To achieve high sky coverage (50% at the north Galactic pole), the NFIRAOS client instruments use NIR on-instrument wavefront sensors that take advantage of the sharpening of the stars by NFIRAOS. A catalog of guide stars with NIR magnitudes as faint as 22 mag in the J band (Vega system), covering the TMT-observable sky, will be a critical resource for the efficient operation of NFIRAOS, and no such catalog currently exists. Hence, it is essential to develop such a catalog by computing the expected NIR magnitudes of stellar sources identified in deep optical sky surveys using their optical magnitudes. This paper discusses the generation of a partial NIR Guide Star Catalog (IRGSC), similar to the final IRGSC for TMT operations. The partial catalog is generated by applying stellar atmospheric models to the optical data of stellar sources from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) optical data and then computing their expected NIR magnitudes. We validated the computed NIR magnitudes of the sources in some fields by using the available NIR data for those fields. We identified the remaining challenges of this approach. We outlined the path for producing the final IRGSC using the Pan-STARRS data. We have named the Python code to generate the IRGSC as irgsctool, which generates a list of NGS for a field using optical data from the Pan-STARRS 3pi survey and also a list of NGSs having observed NIR data from the UKIRT Infrared Deep Sky Survey if they are available.

To enhance the variational quantum eigensolver (VQE), the CAFQA method can utilize classical computational capabilities to identify a better initial state than the Hartree-Fock method. Previous research has demonstrated that the initial state provided by CAFQA recovers more correlation energy than that of the Hartree-Fock method and results in faster convergence. In the present study, we advance the investigation of CAFQA by demonstrating its advantages on a high-fidelity trapped-ion quantum computer located at the Duke Quantum Center -- this is the first experimental demonstration of CAFQA-bootstrapped VQE on a TI device and on any academic quantum device. In our VQE experiment, we use LiH and BeH$_2$ as test cases to show that CAFQA achieves faster convergence and obtains lower energy values within the specified computational budget limits. To ensure the seamless execution of VQE on this academic device, we develop a novel hardware-software interface framework that supports independent software environments for both the circuit and hardware end. This mechanism facilitates the automation of VQE-type job executions as well as mitigates the impact of random hardware interruptions. This framework is versatile and can be applied to a variety of academic quantum devices beyond the trapped-ion quantum computer platform, with support for integration with customized packages.

This study examines the pressure exerted by a cavitation bubble collapsing near a rigid wall. A laser-generated bubble in a water basin undergoes growth, collapse, second growth, and final collapse. Shock waves and liquid jets from non-spherical collapses are influenced by the stand-off ratio ${\gamma}$, defined as the bubble centroid distance from the wall divided by the bubble radius. We detail shock mechanisms, such as tip or torus collapse, for various ${\gamma}$ values. High-speed and Schlieren imaging visualize the microjet and shock waves. The microjet's evolution is tracked for large ${\gamma}$, while shock waves are captured in composite images showing multiple shock positions. Quantitative analyses of the microjet interface, shock wave velocities, and impact times are reported. Wall-mounted sensors and a needle hydrophone measure pressure and compare with high-speed observations to assess the dominant contributions to pressure changes with ${\gamma}$, revealing implications for cavitation erosion mechanisms.

Studying low-likelihood high-impact extreme weather events in a warming world is a significant and challenging task for current ensemble forecasting systems. While these systems presently use up to 100 members, larger ensembles could enrich the sampling of internal variability. They may capture the long tails associated with climate hazards better than traditional ensemble sizes. Due to computational constraints, it is infeasible to generate huge ensembles (comprised of 1,000-10,000 members) with traditional, physics-based numerical models. In this two-part paper, we replace traditional numerical simulations with machine learning (ML) to generate hindcasts of huge ensembles. In Part I, we construct an ensemble weather forecasting system based on Spherical Fourier Neural Operators (SFNO), and we discuss important design decisions for constructing such an ensemble. The ensemble represents model uncertainty through perturbed-parameter techniques, and it represents initial condition uncertainty through bred vectors, which sample the fastest growing modes of the forecast. Using the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System (IFS) as a baseline, we develop an evaluation pipeline composed of mean, spectral, and extreme diagnostics. Using large-scale, distributed SFNOs with 1.1 billion learned parameters, we achieve calibrated probabilistic forecasts. As the trajectories of the individual members diverge, the ML ensemble mean spectra degrade with lead time, consistent with physical expectations. However, the individual ensemble members' spectra stay constant with lead time. Therefore, these members simulate realistic weather states, and the ML ensemble thus passes a crucial spectral test in the literature. The IFS and ML ensembles have similar Extreme Forecast Indices, and we show that the ML extreme weather forecasts are reliable and discriminating.

In Part I, we created an ensemble based on Spherical Fourier Neural Operators. As initial condition perturbations, we used bred vectors, and as model perturbations, we used multiple checkpoints trained independently from scratch. Based on diagnostics that assess the ensemble's physical fidelity, our ensemble has comparable performance to operational weather forecasting systems. However, it requires several orders of magnitude fewer computational resources. Here in Part II, we generate a huge ensemble (HENS), with 7,424 members initialized each day of summer 2023. We enumerate the technical requirements for running huge ensembles at this scale. HENS precisely samples the tails of the forecast distribution and presents a detailed sampling of internal variability. For extreme climate statistics, HENS samples events 4$\sigma$ away from the ensemble mean. At each grid cell, HENS improves the skill of the most accurate ensemble member and enhances coverage of possible future trajectories. As a weather forecasting model, HENS issues extreme weather forecasts with better uncertainty quantification. It also reduces the probability of outlier events, in which the verification value lies outside the ensemble forecast distribution.

The linear stability of a shear-thinning, viscoelastic fluid undergoing any of the canonical rectilinear shear flows, viz., plane Couette flow and pressure-driven flow through a channel or a tube is analyzed in the creeping-flow limit using the White--Metzner model with a power-law variation of the viscosity with shear rate. While two-dimensional disturbances are considered for plane Couette and channel flows, axisymmetric disturbances are considered for pressure-driven flow in a tube. For all these flows, when the shear-thinning exponent is less than $0.3$, there exists an identical instability at wavelengths much smaller than the relevant geometric length scale (gap between the plates or tube radius). There is also a finite-wavelength instability in these configurations governed by the details of the geometry and boundary conditions at the centerline of the channel or tube. The most unstable mode could be either of the short-wave or finite-wavelength instabilities depending on model parameters. For pressure-driven channel flow, it is possible to have sinuous or varicose unstable modes depending on the symmetry of the normal velocity eigenfunction about the channel centerline. This difference in symmetry is relevant only for the finite wavelength instability, in which case sinuous modes turn out to be more unstable, in accordance with experimental observations. In all the three configurations, the short wavelength unstable modes are localized near the walls, and are insensitive to symmetry conditions at the centerline. It is argued that this instability should be a generic feature in any wall-bounded shear flow of strongly shear-thinning viscoelastic fluids. Our predictions for the finite-wavelength instability in pressure-driven channel and pipe flows are in good agreement with experimental observations for the flow of concentrated polymer solutions in these geometries.

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

We study the flow field induced by a sphere translating in a viscous density-stratified ambient, specifically, in the limit of small Reynolds $(Re = \rho U a/\mu \ll 1)$, and viscous Richardson numbers $(Ri_v = \gamma a^3 g/\mu U\ll 1)$, and large Peclet number $(Pe = Ua/D\gg 1)$. Here, $a$ is the sphere radius, $U$ its translational velocity, $\rho$ an appropriate reference density within the Boussinesq framework, $\mu$ the ambient viscosity, $\gamma$ the absolute value of the background density gradient, and $D$ the diffusivity of the stratifying agent. For the scenario where buoyancy forces first become comparable to viscous forces at large distances, corresponding to the Stokes-stratification regime defined by $Re \ll Ri_v^{1/3} \ll 1$ for $Pe \gg 1$, important flow features such as a vertical reverse jet and a horizontal wake, on scales larger than the primary screening length of $\mathcal{O}(aRi_v^{-1/3})$, have been identified by Varanasi and Subramanian (2022). Here, we show that the reverse jet is only the central portion of a columnar structure with multiple annular cells. In the absence of diffusion this columnar structure extends to downstream infinity with the number of annular cells diverging in this limit. We provide expressions for the boundary of the structure, and the number of cells within, as a function of the downstream distance. For small but finite diffusion, two additional length scales emerge - a secondary screening length of $O(aRi_v^{-1/2}Pe^{1/2})$, where diffusion starts to smear out density variations across cells, leading to exponentially decaying flow field; and a tertiary screening length, of $O(aRi_v^{-1/2}Pe^{1/2}\ln(Ri_v^{-1}Pe^3))$, beyond which the columnar structure ceases to exist and the downstream disturbance field reverts from an exponential to eventual algebraic decay, analogous to that prevalent at large distances upstream.

Quantum computing is moving swiftly from theoretical to practical applications, making it crucial to establish a significant quantum advantage. Despite substantial investments, access to quantum devices is still limited, with users facing issues like long wait times and inefficient resource management. Unlike the mature cloud solutions for classical computing, quantum computing lacks effective infrastructure for resource optimization. We propose a Quantum Resource Infrastructure Orchestrator (QRIO), a state-of-the-art cloud resource manager built on Kubernetes that is tailored to quantum computing. QRIO seeks to democratize access to quantum devices by providing customizable, user-friendly, open-source resource management. QRIO's design aims to ensure equitable access, optimize resource utilization, and support diverse applications, thereby speeding up innovation and making quantum computing more accessible and efficient to a broader user base. In this paper, we discuss QRIO's various features and evaluate its capability in several representative usecases., Comment: To appear at the IEEE International Symposium on Workload Characterization, 2024

The Industrial Control System (ICS) environment encompasses a wide range of intricate communication protocols, posing substantial challenges for Security Operations Center (SOC) analysts tasked with monitoring, interpreting, and addressing network activities and security incidents. Conventional monitoring tools and techniques often struggle to provide a clear understanding of the nature and intent of ICS-specific communications. To enhance comprehension, we propose a software solution powered by a Large Language Model (LLM). This solution currently focused on BACnet protocol, processes a packet file data and extracts context by using a mapping database, and contemporary context retrieval methods for Retrieval Augmented Generation (RAG). The processed packet information, combined with the extracted context, serves as input to the LLM, which generates a concise packet file summary for the user. The software delivers a clear, coherent, and easily understandable summary of network activities, enabling SOC analysts to better assess the current state of the control system., Comment: 12 pages

The Indian Institute of Astrophysics (IIA) is developing a Multi-Conjugate Adaptive Optics (MCAO) system for the Kodaikanal Tower Telescope (KTT). In this context, we have measured the daytime turbulence strength profile at the Kodaikanal Observatory. The first method based on wavefront sensor (WFS) images, called S-DIMM+ (Solar-Differential Image Motion Monitor+), was used to estimate the higher altitude turbulence up to a height of 5 - 6 km. The second method used balloon-borne temperature sensors to measure the near-Earth turbulence up to 350 m. We also carried out simulations to validate the performance of our system. We report the first-ever daytime turbulence strength profile measurements at the observatory. We have identified the presence of a strong turbulence layer about 3 km above the observatory. The measured near-Earth turbulence matches the trend that is expected from the model for daytime component of turbulence and gives an integrated $r_0$ of about 4 cm at 500 nm. This is consistent with earlier seeing measurements. This shows that a low-cost setup with a small telescope and a simple array of temperature sensors can be used for estimating the turbulence strength profile at the site., Comment: Accepted for publication in JATIS

Locations of DNA replication initiation in prokaryotes, called "origins of replication", are well-characterized. However, a mechanistic understanding of the sequence-dependence of the local unzipping of double-stranded DNA, the first step towards replication initiation, is lacking. Here, utilizing a Markov chain model that was created to address the directional nature of DNA unzipping and replication, we model the sequence dependence of local melting of double-stranded linear DNA segments. We show that generalized palindromic sequences with high nucleotide skews have a low kinetic barrier for local melting near melting temperatures. This allows for such sequences to function as replication origins. We support our claim with evidence for high-skew palindromic sequences within the replication origins of mitochondrial DNA, bacteria, archaea and plasmids., Comment: 20 pages, 9 figures

One of the reasons why stochastic dynamic games with an underlying dynamic system are challenging is since strategic players have access to enormous amount of information which leads to the use of extremely complex strategies at equilibrium. One approach to resolve this challenge is to simplify players' strategies by identifying appropriate compression of information maps so that the players can make decisions solely based on the compressed version of information, called the information state. For finite dynamic games with asymmetric information, inspired by the notion of information state for single-agent control problems, we propose two notions of information states, namely mutually sufficient information (MSI) and unilaterally sufficient information (USI). Both these information states are obtained with information compression maps independent of the strategy profile. We show that Bayes-Nash Equilibria (BNE) and Sequential Equilibria (SE) exist when all players use MSI-based strategies. We prove that when all players employ USI-based strategies the resulting sets of BNE and SE payoff profiles are the same as the sets of BNE and SE payoff profiles resulting when all players use full information-based strategies. We prove that when all players use USI-based strategies the resulting set of weak Perfect Bayesian Equilibrium (wPBE) payoff profiles can be a proper subset of all wPBE payoff profiles. We identify MSI and USI in specific models of dynamic games in the literature. We end by presenting an open problem: Do there exist strategy-dependent information compression maps that guarantee the existence of at least one equilibrium or maintain all equilibria that exist under perfect recall? We show, by a counterexample, that a well-known strategy-dependent information compression map used in the literature does not possess any of the properties of MSI or USI., Comment: 54 pages, 3 figures

We present the analysis of optical photometric and spectroscopic observations of two non-magnetic cataclysmic variables, namely CRTS J080846.2+313106 and V416 Dra. CRTS J080846.2+313106 has been found to vary with a period of 4.9116$\pm$0.0003 h, which was not found in earlier studies and is provisionally suggested as the orbital period of the system. In both long-period systems, the observed dominant signal at second harmonic of the orbital frequency and the orbital modulation during quiescence are suggestive of ellipsoidal variation from changing aspects of the secondary, with an additional contribution from the accretion stream or hotspot. However, during the outburst, the hotspot itself is overwhelmed by the increased brightness, which is possibly associated with the accretion disc. The mid-eclipse phase for V416 Dra occurs earlier and the width of the eclipse is greater during outbursts compared to quiescence, suggesting an increased accretion disc radius during outbursts. Furthermore, the investigation of accretion disc eclipse in V416 Dra implies that a total disc eclipse is possible during quiescence, whereas the disc seems to be partially obscured during outbursts, which further signifies that the disc may grow in size as the outburst progresses. Optical spectra of CRTS J080846.2+313106 and V416 Dra are typical of dwarf novae during quiescence, and they both show a significant contribution from the M2-4V secondary. The light curve patterns, orbital periods, and spectra observed in both systems look remarkably similar, and seem to resemble the characteristics of U Gem-type dwarf novae., Comment: 14 pages, 11 Figures, and 3 Tables, Accepted for publication in A&A

Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions., Comment: Preprint Version, 18 Figures

Protein sequence design, determined by amino acid sequences, are essential to protein engineering problems in drug discovery. Prior approaches have resorted to evolutionary strategies or Monte-Carlo methods for protein design, but often fail to exploit the structure of the combinatorial search space, to generalize to unseen sequences. In the context of discrete black box optimization over large search spaces, learning a mutation policy to generate novel sequences with reinforcement learning is appealing. Recent advances in protein language models (PLMs) trained on large corpora of protein sequences offer a potential solution to this problem by scoring proteins according to their biological plausibility (such as the TM-score). In this work, we propose to use PLMs as a reward function to generate new sequences. Yet the PLM can be computationally expensive to query due to its large size. To this end, we propose an alternative paradigm where optimization can be performed on scores from a smaller proxy model that is periodically finetuned, jointly while learning the mutation policy. We perform extensive experiments on various sequence lengths to benchmark RL-based approaches, and provide comprehensive evaluations along biological plausibility and diversity of the protein. Our experimental results include favorable evaluations of the proposed sequences, along with high diversity scores, demonstrating that RL is a strong candidate for biological sequence design. Finally, we provide a modular open source implementation can be easily integrated in most RL training loops, with support for replacing the reward model with other PLMs, to spur further research in this domain. The code for all experiments is provided in the supplementary material., Comment: 22 pages, 7 figures, 4 tables