Search

Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using Computational Fluid Dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.

GPT-4o is an autoregressive omni model that accepts as input any combination of text, audio, image, and video, and generates any combination of text, audio, and image outputs. It's trained end-to-end across text, vision, and audio, meaning all inputs and outputs are processed by the same neural network. GPT-4o can respond to audio inputs in as little as 232 milliseconds, with an average of 320 milliseconds, which is similar to human response time in conversation. It matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster and 50\% cheaper in the API. GPT-4o is especially better at vision and audio understanding compared to existing models. In line with our commitment to building AI safely and consistent with our voluntary commitments to the White House, we are sharing the GPT-4o System Card, which includes our Preparedness Framework evaluations. In this System Card, we provide a detailed look at GPT-4o's capabilities, limitations, and safety evaluations across multiple categories, focusing on speech-to-speech while also evaluating text and image capabilities, and measures we've implemented to ensure the model is safe and aligned. We also include third-party assessments on dangerous capabilities, as well as discussion of potential societal impacts of GPT-4o's text and vision capabilities.

Ballots are the core records of elections. Electronic records of actual ballots cast (cast vote records) are available to the public in some jurisdictions. However, they have been released in a variety of formats and have not been independently evaluated. Here we introduce a database of cast vote records from the 2020 U.S. general election. We downloaded publicly available unstandardized cast vote records, standardized them into a multi-state database, and extensively compared their totals to certified election results. Our release includes vote records for President, Governor, U.S. Senate and House, and state upper and lower chambers -- covering 42.7 million voters in 20 states who voted for more than 2,204 candidates. This database serves as a uniquely granular administrative dataset for studying voting behavior and election administration. Using this data, we show that in battleground states, 1.9 percent of solid Republicans (as defined by their congressional and state legislative voting) in our database split their ticket for Joe Biden, while 1.2 percent of solid Democrats split their ticket for Donald Trump., Comment: 26 pages and appendix

Drones which can swarm and loiter in a certain area cost hundreds of dollars, but mosquitos can do the same and are essentially worthless. To control swarms of low-cost robots, researchers may end up spending countless hours brainstorming robot configurations and policies to ``organically" create behaviors which do not need expensive sensors and perception. Existing research explores the possible emergent behaviors in swarms of robots with only a binary sensor and a simple but hand-picked controller structure. Even agents in this highly limited sensing, actuation, and computational capability class can exhibit relatively complex global behaviors such as aggregation, milling, and dispersal, but finding the local interaction rules that enable more collective behaviors remains a significant challenge. This paper investigates the feasibility of training spiking neural networks to find those local interaction rules that result in particular emergent behaviors. In this paper, we focus on simulating a specific milling behavior already known to be producible using very simple binary sensing and acting agents. To do this, we use evolutionary algorithms to evolve not only the parameters (the weights, biases, and delays) of a spiking neural network, but also its structure. To create a baseline, we also show an evolutionary search strategy over the parameters for the incumbent hand-picked binary controller structure. Our simulations show that spiking neural networks can be evolved in binary sensing agents to form a mill., Comment: 8 pages, 7 figures, presented at the 2024 International Conference on Neuromorphic Systems

Quantum error correction (QEC) will be essential to achieve the accuracy needed for quantum computers to realise their full potential. The field has seen promising progress with demonstrations of early QEC and real-time decoded experiments. As quantum computers advance towards demonstrating a universal fault-tolerant logical gate set, implementing scalable and low-latency real-time decoding will be crucial to prevent the backlog problem, avoiding an exponential slowdown and maintaining a fast logical clock rate. Here, we demonstrate low-latency feedback with a scalable FPGA decoder integrated into the control system of a superconducting quantum processor. We perform an 8-qubit stability experiment with up to $25$ decoding rounds and a mean decoding time per round below $1$ ${\mu}s$, showing that we avoid the backlog problem even on superconducting hardware with the strictest speed requirements. We observe logical error suppression as the number of decoding rounds is increased. We also implement and time a fast-feedback experiment demonstrating a decoding response time of $9.6$ ${\mu}s$ for a total of $9$ measurement rounds. The decoder throughput and latency developed in this work, combined with continued device improvements, unlock the next generation of experiments that go beyond purely keeping logical qubits alive and into demonstrating building blocks of fault-tolerant computation, such as lattice surgery and magic state teleportation., Comment: 11 pages, 4 figures, Supplementary Information

Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges. We develop a method that leverages foundation models to refine predictions from existing driving perception models -- such as enhancing object classification accuracy -- while minimizing the frequency of using these resource-intensive models. The method quantitatively characterizes uncertainties in the perception model's predictions and engages the foundation model only when these uncertainties exceed a pre-specified threshold. Specifically, it characterizes uncertainty by calibrating the perception model's confidence scores into theoretical lower bounds on the probability of correct predictions using conformal prediction. Then, it sends images to the foundation model and queries for refining the predictions only if the theoretical bound of the perception model's outcome is below the threshold. Additionally, we propose a temporal inference mechanism that enhances prediction accuracy by integrating historical predictions, leading to tighter theoretical bounds. The method demonstrates a 10 to 15 percent improvement in prediction accuracy and reduces the number of queries to the foundation model by 50 percent, based on quantitative evaluations from driving datasets.

The NIFFTE fission Time Projection Chamber (fissionTPC) has been used to measure the $^{239}$Pu(n,f)/$^{235}$U(n,f) cross-section ratio for neutron-induced fission in the range of 0.1 - 100 MeV, with high precision. A white neutron source was provided by the Los Alamos Neutron Science Center, where the experiment was conducted as a remeasurement to evaluate a roughly 2% discrepancy of the previous fissionTPC results with ENDF/B-VIII.0. A detailed accounting of measurement uncertainties was performed, based on the fissionTPC's novel ability to provide three-dimensional reconstruction of fission-fragment ionization profiles. Current results obtained using a vapor-deposited, highly uniform $^{239}$Pu target, in comparison to the measurement published in 2021, where a $^{239}$Pu electroplated target was used, are presented and discussed. The remeasurement presented here is in agreement with the previous fissionTPC result within measurement uncertainties.

We present Nebula, a system for differential private histogram estimation of data distributed among clients. Nebula enables clients to locally subsample and encode their data such that an untrusted server learns only data values that meet an aggregation threshold to satisfy differential privacy guarantees. Compared with other private histogram estimation systems, Nebula uniquely achieves all of the following: \textit{i)} a strict upper bound on privacy leakage; \textit{ii)} client privacy under realistic trust assumptions; \textit{iii)} significantly better utility compared to standard local differential privacy systems; and \textit{iv)} avoiding trusted third-parties, multi-party computation, or trusted hardware. We provide both a formal evaluation of Nebula's privacy, utility and efficiency guarantees, along with an empirical evaluation on three real-world datasets. We demonstrate that clients can encode and upload their data efficiently (only 0.0058 seconds running time and 0.0027 MB data communication) and privately (strong differential privacy guarantees $\varepsilon=1$). On the United States Census dataset, the Nebula's untrusted aggregation server estimates histograms with above 88\% better utility than the existing local deployment of differential privacy. Additionally, we describe a variant that allows clients to submit multi-dimensional data, with similar privacy, utility, and performance. Finally, we provide an open source implementation of Nebula.

The use of an intense ultrashort laser pulse to induce electron polarization has been proposed in existing literature. The Python programming language is used to recreate the local constant crossed-field approximation (LCFA) with the aim of determining values for transverse polarization given a nonzero initial polarization. It has been shown that over multiple laser shots, lower values of the quantum efficiency parameter are associated with higher transverse polarization output, yet require a greater number of shots to attain maximal polarization. Moreover, the quantum efficiency parameter has been redefined as a function of intensity for a Ti:sapphire laser necessary to induce polarization in the Electron-Ion Collide

Dynamic Vision Sensors (DVS) have emerged as a revolutionary technology with a high temporal resolution that far surpasses RGB cameras. DVS technology draws biological inspiration from photoreceptors and the initial retinal synapse. Our research showcases the potential of additional retinal functionalities to extract visual features. We provide a domain-agnostic and efficient algorithm for ego-motion compensation based on Object Motion Sensitivity (OMS), one of the multiple robust features computed within the mammalian retina. We develop a framework based on experimental neuroscience that translates OMS' biological circuitry to a low-overhead algorithm. OMS processes DVS data from dynamic scenes to perform pixel-wise object motion segmentation. Using a real and a synthetic dataset, we highlight OMS' ability to differentiate object motion from ego-motion, bypassing the need for deep networks. This paper introduces a bio-inspired computer vision method that dramatically reduces the number of parameters by a factor of 1000 compared to prior works. Our work paves the way for robust, high-speed, and low-bandwidth decision-making for in-sensor computations.

Real-time robotic systems require advanced perception, computation, and action capability. However, the main bottleneck in current autonomous systems is the trade-off between computational capability, energy efficiency and model determinism. World modeling, a key objective of many robotic systems, commonly uses occupancy grid mapping (OGM) as the first step towards building an end-to-end robotic system with perception, planning, autonomous maneuvering, and decision making capabilities. OGM divides the environment into discrete cells and assigns probability values to attributes such as occupancy and traversability. Existing methods fall into two categories: traditional methods and neural methods. Traditional methods rely on dense statistical calculations, while neural methods employ deep learning for probabilistic information processing. Recent works formulate a deterministic theory of neural computation at the intersection of cognitive science and vector symbolic architectures. In this study, we propose a Fourier-based hyperdimensional OGM system, VSA-OGM, combined with a novel application of Shannon entropy that retains the interpretability and stability of traditional methods along with the improved computational efficiency of neural methods. Our approach, validated across multiple datasets, achieves similar accuracy to covariant traditional methods while approximately reducing latency by 200x and memory by 1000x. Compared to invariant traditional methods, we see similar accuracy values while reducing latency by 3.7x. Moreover, we achieve 1.5x latency reductions compared to neural methods while eliminating the need for domain-specific model training.

We present the first measurement of the user-effect and privacy impact of "Related Website Sets," a recent proposal to reduce browser privacy protections between two sites if those sites are related to each other. An assumption (both explicitly and implicitly) underpinning the Related Website Sets proposal is that users can accurately determine if two sites are related via the same entity. In this work, we probe this assumption via measurements and a user study of 30 participants, to assess the ability of Web users to determine if two sites are (according to the Related Website Sets feature) related to each other. We find that this is largely not the case. Our findings indicate that 42 (36.8%) of the user determinations in our study are incorrect in privacy-harming ways, where users think that sites are not related, but would be treated as related (and so due less privacy protections) by the Related Website Sets feature. Additionally, 22 (73.3%) of participants made at least one incorrect evaluation during the study. We also characterise the Related Website Sets list, its composition over time, and its governance.

Recent advances in retinal neuroscience have fueled various hardware and algorithmic efforts to develop retina-inspired solutions for computer vision tasks. In this work, we focus on a fundamental visual feature within the mammalian retina, Object Motion Sensitivity (OMS). Using DVS data from EV-IMO dataset, we analyze the performance of an algorithmic implementation of OMS circuitry for motion segmentation in presence of ego-motion. This holistic analysis considers the underlying constraints arising from the hardware circuit implementation. We present novel CMOS circuits that implement OMS functionality inside image sensors, while providing run-time re-configurability for key algorithmic parameters. In-sensor technologies for dynamical environment adaptation are crucial for ensuring high system performance. Finally, we verify the functionality and re-configurability of the proposed CMOS circuit designs through Cadence simulations in 180nm technology. In summary, the presented work lays foundation for hardware-algorithm re-engineering of known biological circuits to suit application needs.

A crucial yet challenging task in galaxy evolution studies is the identification of distant merging galaxies, a task which suffers from a variety of issues ranging from telescope sensitivities and limitations to the inherently chaotic morphologies of young galaxies. In this paper, we use random forests and convolutional neural networks to identify high-redshift JWST CEERS galaxy mergers. We train these algorithms on simulated $3

A tensor category $\mathcal{C}$ over a field $\mathbb{K}$ is said to be invertible if there's a tensor category $\mathcal{D}$ such that $\mathcal{C}\boxtimes\mathcal{D}$ is Morita equivalent to $\mathrm{Vec}_{\mathbb{K}}$. When $\mathbb{K}$ is algebraically closed, it is well-known that the only invertible fusion category is $\mathrm{Vec}_{\mathbb{K}}$, and any invertible multi-fusion category is Morita equivalent to $\mathrm{Vec}_{\mathbb{K}}$. By contrast, we show that for general $\mathbb{K}$ the invertible multi-fusion categories over a field $\mathbb{K}$ are classified (up to Morita equivalence) by $H^3(\mathbb{K};\mathbb{G}_m)$, the third Galois cohomology of the absolute Galois group of $\mathbb{K}$. We explicitly construct a representative of each class that is fusion (but not split fusion) in the sense that the unit object is simple (but not split simple). One consequence of our results is that fusion categories with braided equivalent Drinfeld centers need not be Morita equivalent when this cohomology group is nontrivial., Comment: 35 pages, 2 figures

Dopants play an important role in improving electrical and thermal transport. In the traditional perspective, a dopant suppresses lattice thermal conductivity kL by adding point defect (PD) scattering term to the phonon relaxation time, which has been adopted for decades. In this study, we propose an innovative perspective to solve the kL of defective systems-the holistic approach, i.e., treating dopant and matrix as a holism. This approach allows us to handle the influences from defects explicitly by the calculations of defective systems, about their changed phonon dispersion, phonon-phonon and electron-phonon interaction, etc, due to the existence of dopants. The kL reduction between defective MxNb1-xFeSb (M=V, Ti) and NbFeSb is used as an example for the holistic approach, and comparable results with experiments are obtained. It is notable that light elemental dopants also induced the avoided-crossing behavior. It can be further rationalized by a one-dimensional atomic chain model. The mass and force constant imbalance generally generates the avoided-crossing phonons, mathematically in a similar way as the coefficients in traditional PD scattering, but along a different direction in kL reduction. Our work provides another perspective for understanding the mechanism of dopants influence in material's thermal transport.

This paper introduces a deep transformer network for estimating the relative 6D pose of a Unmanned Aerial Vehicle (UAV) with respect to a ship using monocular images. A synthetic dataset of ship images is created and annotated with 2D keypoints of multiple ship parts. A Transformer Neural Network model is trained to detect these keypoints and estimate the 6D pose of each part. The estimates are integrated using Bayesian fusion. The model is tested on synthetic data and in-situ flight experiments, demonstrating robustness and accuracy in various lighting conditions. The position estimation error is approximately 0.8\% and 1.0\% of the distance to the ship for the synthetic data and the flight experiments, respectively. The method has potential applications for ship-based autonomous UAV landing and navigation., Comment: 23 pages, 25 figures, 3 tables

Literature is the primary expression of scientific knowledge and an important source of research data. However, scientific knowledge expressed in narrative text documents is not inherently machine reusable. To facilitate knowledge reuse, e.g. for synthesis research, scientific knowledge must be extracted from articles and organized into databases post-publication. The high time costs and inaccuracies associated with completing these activities manually has driven the development of techniques that automate knowledge extraction. Tackling the problem with a different mindset, we propose a pre-publication approach, known as reborn, that ensures scientific knowledge is born reusable, i.e. produced in a machine-reusable format during knowledge production. We implement the approach using the Open Research Knowledge Graph infrastructure for FAIR scientific knowledge organization. We test the approach with three use cases, and discuss the role of publishers and editors in scaling the approach. Our results suggest that the proposed approach is superior compared to classical manual and semi-automated post-publication extraction techniques in terms of knowledge richness and accuracy as well as technological simplicity.

We use the Ultraviolet Imaging of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey fields (UVCANDELS) to measure half-light radii in the rest-frame far-UV for $\sim$16,000 disk-like galaxies over $0.5\leq z \leq 3$. We compare these results to rest-frame optical sizes that we measure in a self-consistent way and find that the stellar mass-size relation of disk galaxies is steeper in the rest-frame UV than in the optical across our entire redshift range. We show that this is mainly driven by massive galaxies ($\gtrsim10^{10}$M$_\odot$), which we find to also be among the most dusty. Our results are consistent with the literature and have commonly been interpreted as evidence of inside-out growth wherein galaxies form their central structures first. However, they could also suggest that the centers of massive galaxies are more heavily attenuated than their outskirts. We distinguish between these scenarios by modeling and selecting galaxies at $z=2$ from the VELA simulation suite in a way that is consistent with UVCANDELS. We show that the effects of dust alone can account for the size differences we measure at $z=2$. This indicates that, at different wavelengths, size differences and the different slopes of the stellar mass-size relation do not constitute evidence for inside-out growth., Comment: Accepted for publication in ApJ. 22 pages, 12 figures, and 4 tables

Many of today's most interesting questions involve understanding and interpreting complex relationships within graph-based structures. For instance, in materials science, predicting material properties often relies on analyzing the intricate network of atomic interactions. Graph neural networks (GNNs) have emerged as a popular approach for these tasks; however, they suffer from limitations such as inefficient hardware utilization and over-smoothing. Recent advancements in neuromorphic computing offer promising solutions to these challenges. In this work, we evaluate two such neuromorphic strategies known as reservoir computing and hyperdimensional computing. We compare the performance of both approaches for bandgap classification and regression using a subset of the Materials Project dataset. Our results indicate recent advances in hyperdimensional computing can be applied effectively to better represent molecular graphs., Comment: 5 pages, 2 figures, 1 table

In parallel with the continuously increasing parameter space dimensionality, search and optimization algorithms should support distributed parameter evaluations to reduce cumulative runtime. Intel's neuromorphic optimization library, Lava-Optimization, was introduced as an abstract optimization system compatible with neuromorphic systems developed in the broader Lava software framework. In this work, we introduce Lava Multi-Agent Optimization (LMAO) with native support for distributed parameter evaluations communicating with a central Bayesian optimization system. LMAO provides an abstract framework for deploying distributed optimization and search algorithms within the Lava software framework. Moreover, LMAO introduces support for random and grid search along with process connections across multiple levels of mathematical precision. We evaluate the algorithmic performance of LMAO with a traditional non-convex optimization problem, a fixed-precision transductive spiking graph neural network for citation graph classification, and a neuromorphic satellite scheduling problem. Our results highlight LMAO's efficient scaling to multiple processes, reducing cumulative runtime and minimizing the likelihood of converging to local optima., Comment: 4 pages, 2 figures, 2 algorithms, 2 tables

LLMs have demonstrated proficiency in contextualizing their outputs using human input, often matching or beating human-level performance on a variety of tasks. However, LLMs have not yet been used to characterize synergistic learning in students' collaborative discourse. In this exploratory work, we take a first step towards adopting a human-in-the-loop prompt engineering approach with GPT-4-Turbo to summarize and categorize students' synergistic learning during collaborative discourse. Our preliminary findings suggest GPT-4-Turbo may be able to characterize students' synergistic learning in a manner comparable to humans and that our approach warrants further investigation., Comment: In press at the 25th international conference on Artificial Intelligence in Education (AIED) Late-Breaking Results (LBR) track

This work presents Unbundle-Rewrite-Rebundle (URR), a system for detecting privacy-harming portions of bundled JavaScript code and rewriting that code at runtime to remove the privacy-harming behavior without breaking the surrounding code or overall application. URR is a novel solution to the problem of JavaScript bundles, where websites pre-compile multiple code units into a single file, making it impossible for content filters and ad-blockers to differentiate between desired and unwanted resources. Where traditional content filtering tools rely on URLs, URR analyzes the code at the AST level, and replaces harmful AST sub-trees with privacy-and-functionality maintaining alternatives. We present an open-sourced implementation of URR as a Firefox extension and evaluate it against JavaScript bundles generated by the most popular bundling system (Webpack) deployed on the Tranco 10k. We evaluate URR by precision (1.00), recall (0.95), and speed (0.43s per script) when detecting and rewriting three representative privacy-harming libraries often included in JavaScript bundles, and find URR to be an effective approach to a large-and-growing blind spot unaddressed by current privacy tools., Comment: In Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS '24)

Let $\rho$ be a representation of a knot group (or more generally, the fundamental group of a tangle complement) into $\operatorname{SL}_2(\mathbb{C})$ expressed in terms of the Wirtinger generators of a diagram $D$. In this note we give a direct algebraic formula for the geometric parameters of the octahedral decomposition of the knot complement associated to $D$. Our formula gives a new, explicit criterion for whether $\rho$ occurs as a critical point of the diagram's Neumann-Zagier--Yokota potential function., Comment: 14 pages + references

Performing optimization with event-based asynchronous neuromorphic systems presents significant challenges. Intel's neuromorphic computing framework, Lava, offers an abstract application programming interface designed for constructing event-based computational graphs. In this study, we introduce a novel framework tailored for asynchronous Bayesian optimization that is also compatible with Loihi 2. We showcase the capability of our asynchronous optimization framework by connecting it with a graph-based satellite scheduling problem running on physical Loihi 2 hardware., Comment: 2 figures

Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, these approaches are limited to traditional Von Neumann-based computing systems, which still face hardware inefficiencies. In this study, we present a fully neuromorphic implementation of spiking graph neural networks designed for Loihi 2. We optimize network parameters using Lava Bayesian Optimization, a novel hyperparameter optimization system compatible with neuromorphic computing architectures. We showcase the performance benefits of combining neuromorphic Bayesian optimization with our approach for citation graph classification using fixed-precision spiking neurons. Our results demonstrate the capability of integer-precision, Loihi 2 compatible spiking neural networks in performing citation graph classification with comparable accuracy to existing floating point implementations., Comment: 6 pages, 4 figures, 3 tables

Mariah is an augmented reality (AR) mobile application that exposes power structures (e.g., capitalism, patriarchy, white supremacy) through storytelling and celebrates acts of resistance against them. People can use Mariah to "legally trespass" the metaverse as a form of protest. Mariah provides historical context to the user's physical surroundings by superimposing images and playing stories about people who have experienced, and resisted, injustice. We share two implementations of Mariah that raise questions about free speech and property rights in the metaverse: (1) a protest against museums accepting "dirty money" from the opioid epidemic; and (2) a commemoration of sites where people have resisted power structures. Mariah is a case study for how experimenting with a technology in non-sanctioned ways (i.e., "hacking") can expose ways that it might interact with, and potentially amplify, existing power structures., Comment: Presented at CHI 2024 (arXiv:2404.05889)

The ubiquity and on-the-go availability of mobile devices makes them central to many tasks such as interpersonal communication and media consumption. However, despite the potential of mobile devices for on-demand exploratory data visualization, existing mobile interactions are difficult, often using highly custom interactions, complex gestures, or multi-modal input. We synthesize limitations from the literature and outline four motivating principles for improved mobile interaction: leverage ubiquitous modalities, prioritize discoverability, enable rapid in-context data exploration, and promote graceful recovery. We then contribute thirteen interaction candidates and conduct a formative study with twelve participants who experienced our interactions in a testbed prototype. Based on these interviews, we discuss design considerations and tradeoffs from four main themes: precise and rapid inspection, focused navigation, single-touch and fixed orientation interaction, and judicious use of motion., Comment: 4 pages, 1 figure, 1 table, EuroVis 2024 Short Papers

Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deployed to provide marketing insights, reducing the time and effort required to make critical decisions. In practice, there are substantial challenges that need to be overcome to reliably use such models. We focus on domain-specific question-answering, SQL generation needed for data retrieval, and tabular analysis and show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods. These models are tested on sample use cases specific to marketing mix modeling and attribution., Comment: 16 pages, 5 figures, presented at the 2nd International Conference on NLP & AI (NLPAI 2024)

Density functional theory (DFT) is a powerful tool for predicting the electronic band structures of functional materials. However, it is often difficult to intuit how major band structure features$-$such as band gap magnitude, location of band extrema, effective masses, etc.$-$arise from the underlying crystal chemistry of a material. Band structure is represented in reciprocal space, but arises from the orbital and bonding interactions between atoms in real space. Here, we present a conceptual and computable framework to extract chemical bonding origins of DFT-calculated band structure features. As a key example, we explain here the indirect band gap of silicon, which profoundly impacts its properties for use in photovoltaics and electronics. Even in this basic semiconductor material, our approach leads to new insights to understand and engineer its conduction band minimum position. These calculation techniques can be broadly applied to reveal the crystal chemistry origins of electronic structure features in other optical, electronic, and magnetic materials.

We examine the simple model put forth in a recent note by Loeb regarding the brightness of space debris in the size range of 1-10 cm and their impact on the Rubin Observatory Legacy Survey of Space and Time (LSST) transient object searches. Their main conclusion was that "image contamination by untracked space debris might pose a bigger challenge [than large commercial satellite constellations in LEO]". Following corrections and improvements to this model, we calculate the apparent brightness of tumbling low-Earth orbit (LEO) debris of various sizes, and we briefly discuss the likely impact and potential mitigations of glints from space debris in LSST. We find the majority of the difference in predicted signal-to-noise ratio (S/N), about a factor of 6, arises from the defocus of LEO objects due to the large Simonyi Survey Telescope primary mirror and finite range of the debris. The largest change from the Loeb estimates is that 1-10 cm debris in LEO pose no threat to LSST transient object alert generation because their S/N for detection will be much lower than estimated by Loeb due to defocus. We find that only tumbling LEO debris larger than 10 cm or with significantly greater reflectivity, which give 1 ms glints, might be detected with high confidence (S/N > 5). We estimate that only one in five LSST exposures low on the sky during twilight might be affected. More slowly tumbling objects of larger size can give flares in brightness that are easily detected; however, these will not be cataloged by the LSST Science Pipelines because of the resulting long streak., Comment: 6 pages, 1 figure, accepted for publication in ApJL