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Combining high peak and high average power has long been a key challenge of ultrafast laser technology, crucial for applications such as laser-plasma acceleration and strong-field physics. A promising solution lies in post-compressed ytterbium lasers, but scaling these to high pulse energies presents a major bottleneck. Post-compression techniques, particularly Herriott-type multi-pass cells (MPCs), have enabled large peak power boosts at high average powers but their pulse energy acceptance reaches practical limits defined by setup size and coating damage threshold. In this work, we address this challenge and demonstrate a novel type of compact, energy-scalable MPC (CMPC). By employing a novel MPC configuration and folding the beam path, the CMPC introduces a new degree of freedom for downsizing the setup length, enabling compact setups even for large pulse energies. We experimentally and numerically verify the CMPC approach, demonstrating post-compression of 8 mJ pulses from 1 ps down to 51 fs in atmospheric air using a cell roughly 45 cm in length at low fluence values. Additionally, we discuss the potential for energy scaling up to 200 mJ with a setup size reaching 2.5 m. Our work presents a new approach to high-energy post-compression, with up-scaling potential far beyond the demonstrated parameters. This opens new routes for achieving the high peak and average powers necessary for demanding applications of ultrafast lasers., Comment: 18 pages, 13 figures

Recent studies have been increasingly demonstrating that high-quality data is crucial for effective pretraining of language models. However, the precise definition of "high-quality" remains underexplored. Focusing on the code domain, we introduce Arctic-SnowCoder-1.3B, a data-efficient base code model pretrained on 555B tokens through three phases of progressively refined data: (1) general pretraining with 500B standard-quality code tokens, preprocessed through basic filtering, deduplication, and decontamination, (2) continued pretraining with 50B high-quality tokens, selected from phase one by a BERT-style quality annotator trained to distinguish good code from random data, using positive examples drawn from high-quality code files, along with instruction data from Magicoder and StarCoder2-Instruct, and (3) enhanced pretraining with 5B synthetic data created by Llama-3.1-70B using phase two data as seeds, adapting the Magicoder approach for pretraining. Despite being trained on a limited dataset, Arctic-SnowCoder achieves state-of-the-art performance on BigCodeBench, a coding benchmark focusing on practical and challenging programming tasks, compared to similarly sized models trained on no more than 1T tokens, outperforming Phi-1.5-1.3B by 36%. Across all evaluated benchmarks, Arctic-SnowCoder-1.3B beats StarCoderBase-3B pretrained on 1T tokens. Additionally, it matches the performance of leading small base code models trained on trillions of tokens. For example, Arctic-SnowCoder-1.3B surpasses StarCoder2-3B, pretrained on over 3.3T tokens, on HumanEval+, a benchmark that evaluates function-level code generation, and remains competitive on BigCodeBench. Our evaluation presents a comprehensive analysis justifying various design choices for Arctic-SnowCoder. Most importantly, we find that the key to high-quality data is its alignment with the distribution of downstream applications.

Computer vision in agriculture is game-changing with its ability to transform farming into a data-driven, precise, and sustainable industry. Deep learning has empowered agriculture vision to analyze vast, complex visual data, but heavily rely on the availability of large annotated datasets. This remains a bottleneck as manual labeling is error-prone, time-consuming, and expensive. The lack of efficient labeling approaches inspired us to consider self-supervised learning as a paradigm shift, learning meaningful feature representations from raw agricultural image data. In this work, we explore how self-supervised representation learning unlocks the potential applicability to diverse agriculture vision tasks by eliminating the need for large-scale annotated datasets. We propose a lightweight framework utilizing SimCLR, a contrastive learning approach, to pre-train a ResNet-50 backbone on a large, unannotated dataset of real-world agriculture field images. Our experimental analysis and results indicate that the model learns robust features applicable to a broad range of downstream agriculture tasks discussed in the paper. Additionally, the reduced reliance on annotated data makes our approach more cost-effective and accessible, paving the way for broader adoption of computer vision in agriculture.

This paper presents a real-time trajectory planning framework for Urban Air Mobility (UAM) that is both safe and scalable. The proposed framework employs a decentralized, free-flight concept of operation in which each aircraft independently performs separation assurance and conflict resolution, generating safe trajectories by accounting for the future states of nearby aircraft. The framework consists of two main components: a data-driven reachability analysis tool and an efficient Markov Decision Process (MDP) based decision maker. The reachability analysis over-approximates the reachable set of each aircraft through a discrepancy function learned online from simulated trajectories. The decision maker, on the other hand, uses a 6-degrees-of-freedom guidance model of fixed-wing aircraft to ensure collision-free trajectory planning. Additionally, the proposed framework incorporates reward shaping and action shielding techniques to enhance safety performance. The proposed framework is evaluated through simulation experiments involving up to 32 aircraft in a UAM setting, with performance measured by the number of Near Mid Air Collisions (NMAC) and computational time. The results demonstrate the safety and scalability of the proposed framework.

Advancing ultrafast high-repetition-rate lasers to shortest pulse durations comprising only a few optical cycles while pushing their energy into the multi-millijoule regime opens a route towards terawatt-class peak powers at unprecedented average power. We explore this route via efficient post-compression of high-energy 1.2 ps pulses from an Ytterbium InnoSlab laser to 9.6 fs duration using gas-filled multi-pass cells (MPCs) at a repetition rate of 1 kHz. Employing dual-stage compression with a second MPC stage supporting a close-to-octave-spanning bandwidth enabled by dispersion-matched dielectric mirrors, a record compression factor of 125 is reached at 70% overall efficiency, delivering 6.7 mJ pulses with a peak power of about 0.3 TW. Moreover, we show that post-compression can improve the temporal contrast at picosecond delay by at least one order of magnitude. Our results demonstrate efficient conversion of multi-millijoule picosecond lasers to high-peak-power few-cycle sources, opening up new parameter regimes for laser plasma physics, high energy physics, biomedicine and attosecond science.

Implicit neural representations (INR) have gained significant popularity for signal and image representation for many end-tasks, such as superresolution, 3D modeling, and more. Most INR architectures rely on sinusoidal positional encoding, which accounts for high-frequency information in data. However, the finite encoding size restricts the model's representational power. Higher representational power is needed to go from representing a single given image to representing large and diverse datasets. Our approach addresses this gap by representing an image with a polynomial function and eliminates the need for positional encodings. Therefore, to achieve a progressively higher degree of polynomial representation, we use element-wise multiplications between features and affine-transformed coordinate locations after every ReLU layer. The proposed method is evaluated qualitatively and quantitatively on large datasets like ImageNet. The proposed Poly-INR model performs comparably to state-of-the-art generative models without any convolution, normalization, or self-attention layers, and with far fewer trainable parameters. With much fewer training parameters and higher representative power, our approach paves the way for broader adoption of INR models for generative modeling tasks in complex domains. The code is available at \url{https://github.com/Rajhans0/Poly_INR}, Comment: Accepted at CVPR 2023

The excess temperature of the solar corona over the photosphere poses a challenge. Multiple energetic events contribute to maintaining the corona at such high temperatures. The energy released in different events can vary across several orders of magnitude. Large energy events of geomagnetic importance like flares and coronal mass ejections (CMEs) contribute little to the global energetics of the solar corona. Therefore, events with several (9-10) orders of magnitudes of lower energy, with much higher frequency of occurrence, need to be studied in great detail. Observations suggest that these impulsive events with different energies follow a power-law distribution, indicating a common underlying mechanism. We perform observation-motivated modeling of coronal loops (magnetic flux tubes) to understand the energetics of these small transient events and their similarity with impulsive events like flares. This thesis uses the EBTEL code based on the 0D hydrodynamical description of coronal loops. This approach is appropriate for getting quick estimates of the energetics of the system over a wide range of parameters. We then discuss the improvement of EBTEL to make it suitable over a broader range of parameters. This is followed by using improved EBTEL to explore the possibility of simulating impulsive events of different energy generated using a single power-law distribution. Comparison between observed emissions from various components of multi-thermal plasma and hydrodynamical models suggest the heating to be impulsive. Since field-aligned flows induced due to impulsive events are a crucial part of our modeling of coronal loops, we discuss the implications of such flows in the context of transition region heating., Comment: Ph.D. Thesis

Studying Doppler shifts provides deeper insights into the flow of mass and energy in the solar atmosphere. We perform a comprehensive measurement of Doppler shifts in the transition region and its center-to-limb variation (CLV) in the strong field regions ($|\textbf{B}| \geq$ 50 G) of 50 active regions (ARs), using the \ion{Si}{4} 1394~{\AA} line recorded by the Interface Region Imaging Spectrometer(IRIS). To locate the ARs and identify strong field regions, we have used the magnetograms obtained by the Helioseismic and Magnetic Imager (HMI). We find that in strong field regions, on average, all the ARs show mean redshifts ranging between 4{--}11~ km/s, which varies with ARs. These flows show a mild CLV, with sizable magnitudes at the limb and substantial scatter at the mid-longitude range. Our observations do not support the idea that redshifts in the lower transition region (T $<\sim$ 0.1 MK) are produced by field-aligned downflows as a result of impulsive heating and warrant alternative interpretation, such as downflow of type-\rm{II} spicules in the presence of a chromospheric wall created by cooler type-\rm{I} spicules., Comment: Accepted for publication in the Astrophysical Journal on 16th January 2023

Simulation-based software testing supports engineers in finding faults in Simulink models. It typically relies on search algorithms that iteratively generate test inputs used to exercise models in simulation to detect design errors. While simulation-based software testing techniques are effective in many practical scenarios, they are typically not fully integrated within the Simulink environment and require additional manual effort. Many techniques require engineers to specify requirements using logical languages that are neither intuitive nor fully supported by Simulink, thereby limiting their adoption in industry. This work presents HECATE, a testing approach for Simulink models using Test Sequence and Test Assessment blocks from Simulink Test. Unlike existing testing techniques, HECATE uses information from Simulink models to guide the search-based exploration. Specifically, HECATE relies on information provided by the Test Sequence and Test Assessment blocks to guide the search procedure. Across a benchmark of 16 Simulink models from different domains and industries, our comparison of HECATE with the state-of-the-art testing tool S-TALIRO indicates that HECATE is both more effective (more failure-revealing test cases) and efficient (less iterations and computational time) than S-TALIRO for ~94% and ~81% of benchmark models respectively. Furthermore, HECATE successfully generated a failure-revealing test case for a representative case study from the automotive domain demonstrating its practical usefulness.

Post-compression methods for ultrafast laser pulses typically face challenging limitations including saturation effects and temporal pulse break-up when large compression factors and broad bandwidths are targeted. To overcome these limitations, we exploit direct dispersion control in a gas-filled multi-pass cell, enabling for the first time single-stage post-compression of 150 fs pulses and up to 250 uJ pulse energy from an Ytterbium (Yb) fiber laser down to sub-20 fs. Dispersion-engineered dielectric cavity mirrors are used to achieve nonlinear spectral broadening dominated by self-phase-modulation over large compression factors and bandwidths at 98% throughput. Our method opens a route towards single-stage post-compression of Yb lasers into the few-cycle regime.

Deep networks for image classification often rely more on texture information than object shape. While efforts have been made to make deep-models shape-aware, it is often difficult to make such models simple, interpretable, or rooted in known mathematical definitions of shape. This paper presents a deep-learning model inspired by geometric moments, a classically well understood approach to measure shape-related properties. The proposed method consists of a trainable network for generating coordinate bases and affine parameters for making the features geometrically invariant yet in a task-specific manner. The proposed model improves the final feature's interpretation. We demonstrate the effectiveness of our method on standard image classification datasets. The proposed model achieves higher classification performance compared to the baseline and standard ResNet models while substantially improving interpretability., Comment: Accepted at CVPR 2023 Workshop: Deep Learning for Geometric Computing

Plasma-filled loop structures are common in the solar corona. Because detailed modeling of the dynamical evolution of these structures is computationally costly, an efficient method for computing approximate but quick physics-based solutions is to rely on space integrated 0D simulations. The enthalpy-based thermal evolution of loops EBTEL framework is a commonly used method to study the exchange of mass and energy between the corona and transition region. EBTEL solves for density, temperature, and pressure averaged over the coronal part of the loop, the velocity at the coronal base, and the instantaneous differential emission measure distribution in the transition region. The current single-fluid version of the code, EBTEL2, assumes that at all stages the flows are subsonic. However, sometimes the solutions show the presence of supersonic flows during the impulsive phase of heat input. It is thus necessary to account for this effect. Here, we upgrade EBTEL2 to EBTEL3 by including the kinetic energy term in the Navier-Stokes equation. We compare the solutions from EBTEL3 with those obtained using EBTEL2 as well as the state-of-the-art field-aligned hydrodynamics code HYDRAD. We find that the match in pressure between EBTEL3 and HYDRAD is better than that between EBTEL2 and HYDRAD. Additionally, the velocities predicted by EBTEL3 are in close agreement with those obtained with HYDRAD when the flows are subsonic. However, EBTEL3 solutions deviate substantially from HYDRAD's when the latter predicts supersonic flows. Using the mismatches in the solution, we propose a criterion to determine the conditions under which EBTEL can be used to study flows in the system., Comment: Accepted for publication in ApJ

Ensuring nominal asymptotic stability of the Nonlinear Model Predictive Control controller is not trivial. Stabilizing ingredients such as terminal penalty term and terminal region are crucial in establishing the asymptotic stability. Current work presents alternate approaches namely arbitrary controller based approach and linear quadratic regulator based approach, which provide larger degrees of freedom for enlarging the terminal region as against conservative approaches from the literature. Efficacy of the proposed approaches is demonstrated using benchmark two state continuous stirrer tank reactor system around an unstable operating point. Terminal regions obtained using the arbitrary controller based approach and linear quadratic regulator based approach are approximately 45 and 412 times larger by area measure when compared to the largest terminal region obtained using the approach from the literature. As a result, there is significant reduction in the prediction and control horizon time., Comment: 19 pages, 6 figures, 4 tables. arXiv admin note: substantial text overlap with arXiv:2107.09267

Ensuring nominal asymptotic stability of the Non-linear Model Predictive Control (NMPC) controller is not trivial. Stabilizing ingredients such as terminal penalty term and terminal region are crucial in establishing the asymptotic stability. Approaches available in the literature provide limited degrees of freedom for the characterization of the terminal region for the discrete time Quasi Infinite Horizon NMPC (QIH-NMPC) formulation. Current work presents alternate approaches namely arbitrary controller based approach and LQR based approach, which provide large degrees of freedom for enlarging the terminal region. Both the approaches are scalable to system of any state and input dimension. Approach from the literature provides a scalar whereas proposed approaches provide a linear controller and two additive matrices as tuning parameters for shaping of the terminal region. Proposed approaches involve solving modified Lyapunov equations to compute terminal penalty term, followed by explicit characterization of the terminal region. Efficacy of the proposed approaches is demonstrated using benchmark two state system. Terminal region obtained using the arbitrary controller based approach and LQR based approach are approximately 10.4723 and 9.5055 times larger by area measure when compared to the largest terminal region obtained using the approach from the literature., Comment: 6 pages, 1 figure, 6 tables

Small scale transients occur in the Solar corona at much higher frequencies than flares and play a significant role in coronal dynamics. Here we study three well-identified transients discovered by Hi-C and also detected by the EUV channels of Atmospheric Imaging Assembly (AIA) on board Solar Dynamics Observatory (SDO). We use 0-D enthalpy-based hydrodynamical simulations and produce synthetic light curves to compare with AIA observations. We have modeled these transients as loops of ~ 1.0~Mm length depositing energies ~ 10^23 ergs in ~ 50 seconds. The simulated synthetic light curves show reasonable agreement with the observed light curves. During the initial phase, conduction flux from the corona dominates over the radiation, like impulsive flaring events. Our results further show that the time-integrated net enthalpy flux is positive, hence into the corona. The fact that we can model the observed light curves of these transients reasonably well by using the same physics as those for nanoflares, microflares, and large flares, suggests that these transients may have a common origin., Comment: Published in ApJ, 16 pages, 13 figures, and 3 tables

Each year, over 2.5 million people, most of them in developed countries, die from pneumonia [1]. Since many studies have proved pneumonia is successfully treatable when timely and correctly diagnosed, many of diagnosis aids have been developed, with AI-based methods achieving high accuracies [2]. However, currently, the usage of AI in pneumonia detection is limited, in particular, due to challenges in generalizing a locally achieved result. In this report, we propose a roadmap for creating and integrating a system that attempts to solve this challenge. We also address various technical, legal, ethical, and logistical issues, with a blueprint of possible solutions.

Recently, there has been substantial progress in image synthesis from semantic labelmaps. However, methods used for this task assume the availability of complete and unambiguous labelmaps, with instance boundaries of objects, and class labels for each pixel. This reliance on heavily annotated inputs restricts the application of image synthesis techniques to real-world applications, especially under uncertainty due to weather, occlusion, or noise. On the other hand, algorithms that can synthesize images from sparse labelmaps or sketches are highly desirable as tools that can guide content creators and artists to quickly generate scenes by simply specifying locations of a few objects. In this paper, we address the problem of complex scene completion from sparse labelmaps. Under this setting, very few details about the scene (30\% of object instances) are available as input for image synthesis. We propose a two-stage deep network based method, called `Halluci-Net', that learns co-occurence relationships between objects in scenes, and then exploits these relationships to produce a dense and complete labelmap. The generated dense labelmap can then be used as input by state-of-the-art image synthesis techniques like pix2pixHD to obtain the final image. The proposed method is evaluated on the Cityscapes dataset and it outperforms two baselines methods on performance metrics like Fr\'echet Inception Distance (FID), semantic segmentation accuracy, and similarity in object co-occurrences. We also show qualitative results on a subset of ADE20K dataset that contains bedroom images., Comment: Accepted to AI for Content Creation Workshop @CVPR 2021

We present a system, Spoke, for creating and searching internal knowledge base (KB) articles for organizations. Spoke is available as a SaaS (Software-as-a-Service) product deployed across hundreds of organizations with a diverse set of domains. Spoke continually improves search quality using conversational user feedback which allows it to provide better search experience than standard information retrieval systems without encoding any explicit domain knowledge. We achieve this by using a real-time online learning-to-rank (L2R) algorithm that automatically customizes relevance scoring for each organization deploying Spoke by using a query similarity kernel. The focus of this paper is on incorporating practical considerations into our relevance scoring function and algorithm that make Spoke easy to deploy and suitable for handling events that naturally happen over the life-cycle of any KB deployment. We show that Spoke outperforms competitive baselines by up to 41% in offline F1 comparisons., Comment: Proceedings of the 2019 IJCAI Workshop SCAI: The 4th International Workshop on Search-Oriented Conversational AI

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However, one of the common assumptions in most GAN architectures is the assumption of simple parametric latent-space distributions. While easy to implement, a simple latent-space distribution can be problematic for uses such as interpolation. This is due to distributional mismatches when samples are interpolated in the latent space. We present a straightforward formalization of this problem; using basic results from probability theory and off-the-shelf-optimization tools, we develop ways to arrive at appropriate non-parametric priors. The obtained prior exhibits unusual qualitative properties in terms of its shape, and quantitative benefits in terms of lower divergence with its mid-point distribution. We demonstrate that our designed prior helps improve image generation along any Euclidean straight line during interpolation, both qualitatively and quantitatively, without any additional training or architectural modifications. The proposed formulation is quite flexible, paving the way to impose newer constraints on the latent-space statistics.

In resource-constrained environments, one can employ spatial multiplexing cameras to acquire a small number of measurements of a scene, and perform effective reconstruction or high-level inference using purely data-driven neural networks. However, once trained, the measurement matrix and the network are valid only for a single measurement rate (MR) chosen at training time. To overcome this drawback, we answer the following question: How can we jointly design the measurement operator and the reconstruction/inference network so that the system can operate over a \textit{range} of MRs? To this end, we present a novel training algorithm, for learning \textbf{\textit{rate-adaptive}} networks. Using standard datasets, we demonstrate that, when tested over a range of MRs, a rate-adaptive network can provide high quality reconstruction over a the entire range, resulting in up to about 15 dB improvement over previous methods, where the network is valid for only one MR. We demonstrate the effectiveness of our approach for sample-efficient object tracking where video frames are acquired at dynamically varying MRs. We also extend this algorithm to learn the measurement operator in conjunction with image recognition networks. Experiments on MNIST and CIFAR-10 confirm the applicability of our algorithm to different tasks.

The non-relativistic hydrogen atom and the Zwanziger problem have the same dynamical symmetry for bound and scattering states.We show that this is also true for a Hilbert space which is non-commutative in co-ordinates. The group structure is described using the redefined velocity operator and Laplace Runge-Lenz operator in terms of left and right handed representations of the non-commutative Hilbert space $ R_{\lambda}^{3}$.The bound state algebra is SO(4) and the scattering state algebra is SO(3,1)., Comment: 9 pages, work in review at (journal) Physics Education- Indian Association of Physics Teachers Manuscript number-PE14-12-276

Coherence resonance can be explained using differential entropy and mutual information. This theory explores the role of external noise in stabilising chaotic circuits such as the uni-junction transistor relaxation oscillator.The phenomenon of coherence resonance maximizes differential entropy and mutual information.Thus most natural chaotic oscillators show coherence resonance in the presence of an external driving noise., Comment: 6 pages, 2nd International Conference on Chaos, Complexity and Leadership, Ankara, 17-19th December,2013, Turkey