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Predicting time-dependent dynamics of complex systems governed by non-linear partial differential equations (PDEs) with varying parameters and domains is a challenging task motivated by applications across various fields. We introduce a novel family of neural operators based on our Graph Fourier Neural Kernels, designed to learn solution generators for nonlinear PDEs in which the highest-order term is diffusive, across multiple domains and parameters. G-FuNK combines components that are parameter- and domain-adapted with others that are not. The domain-adapted components are constructed using a weighted graph on the discretized domain, where the graph Laplacian approximates the highest-order diffusive term, ensuring boundary condition compliance and capturing the parameter and domain-specific behavior. Meanwhile, the learned components transfer across domains and parameters using our variant Fourier Neural Operators. This approach naturally embeds geometric and directional information, improving generalization to new test domains without need for retraining the network. To handle temporal dynamics, our method incorporates an integrated ODE solver to predict the evolution of the system. Experiments show G-FuNK's capability to accurately approximate heat, reaction diffusion, and cardiac electrophysiology equations across various geometries and anisotropic diffusivity fields. G-FuNK achieves low relative errors on unseen domains and fiber fields, significantly accelerating predictions compared to traditional finite-element solvers.

This contribution reports on a software framework that uses physically-based rendering to simulate camera operation in lunar conditions. The focus is on generating synthetic images qualitatively similar to those produced by an actual camera operating on a vehicle traversing and/or actively interacting with lunar terrain, e.g., for construction operations. The highlights of this simulator are its ability to capture (i) light transport in lunar conditions and (ii) artifacts related to the vehicle-terrain interaction, which might include dust formation and transport. The simulation infrastructure is built within an in-house developed physics engine called Chrono, which simulates the dynamics of the deformable terrain-vehicle interaction, as well as fallout of this interaction. The Chrono::Sensor camera model draws on ray tracing and Hapke Photometric Functions. We analyze the performance of the simulator using two virtual experiments featuring digital twins of NASA's VIPER rover navigating a lunar environment, and of the NASA's RASSOR excavator engaged into a digging operation. The sensor simulation solution presented can be used for the design and testing of perception algorithms, or as a component of in-silico experiments that pertain to large lunar operations, e.g., traversability, construction tasks., Comment: 19 pages, 20 figures, 3 tables. This work has been submitted to the 2025 IEEE Aerospace Conference for possible publication

Reducing the likelihood of generating harmful and toxic output is an essential task when aligning large language models (LLMs). Existing methods mainly rely on training an external reward model (i.e., another language model) or fine-tuning the LLM using self-generated data to influence the outcome. In this paper, we show that LLMs have the capability of self-detoxification without the use of an additional reward model or re-training. We propose \textit{Self-disciplined Autoregressive Sampling (SASA)}, a lightweight controlled decoding algorithm for toxicity reduction of LLMs. SASA leverages the contextual representations from an LLM to learn linear subspaces characterizing toxic v.s. non-toxic output in analytical forms. When auto-completing a response token-by-token, SASA dynamically tracks the margin of the current output to steer the generation away from the toxic subspace, by adjusting the autoregressive sampling strategy. Evaluated on LLMs of different scale and nature, namely Llama-3.1-Instruct (8B), Llama-2 (7B), and GPT2-L models with the RealToxicityPrompts, BOLD, and AttaQ benchmarks, SASA markedly enhances the quality of the generated sentences relative to the original models and attains comparable performance to state-of-the-art detoxification techniques, significantly reducing the toxicity level by only using the LLM's internal representations., Comment: 20 pages

Robust autonomous navigation in environments with limited visibility remains a critical challenge in robotics. We present a novel approach that leverages Non-Line-of-Sight (NLOS) sensing using single-photon LiDAR to improve visibility and enhance autonomous navigation. Our method enables mobile robots to "see around corners" by utilizing multi-bounce light information, effectively expanding their perceptual range without additional infrastructure. We propose a three-module pipeline: (1) Sensing, which captures multi-bounce histograms using SPAD-based LiDAR; (2) Perception, which estimates occupancy maps of hidden regions from these histograms using a convolutional neural network; and (3) Control, which allows a robot to follow safe paths based on the estimated occupancy. We evaluate our approach through simulations and real-world experiments on a mobile robot navigating an L-shaped corridor with hidden obstacles. Our work represents the first experimental demonstration of NLOS imaging for autonomous navigation, paving the way for safer and more efficient robotic systems operating in complex environments. We also contribute a novel dynamics-integrated transient rendering framework for simulating NLOS scenarios, facilitating future research in this domain., Comment: Project webpage: https://github.com/camera-culture/nlos-aided-autonomous-navigation

We produce beams of neutral titanium (Ti) atoms in their metastable $3d^{3}(^4F){4}s$ $a^5F_5$ state by laser ablation into He, N$_2$, and Ar buffer gases. The high temperatures realized in the ablation process populate the $a^5F_5$ level without the need for optical pumping. Remarkably, we observe that Ti atoms in the $a^5F_5$ state survive $1000$'s of collisions with He and Ar buffer gas atoms without being quenched to lower-energy states. We study the yield of Ti atoms when ablated into buffer gases of varying species and pressure, quantify quenching rates and diffusion cross sections based on simple models, and provide insight into optimal design parameters for an ablation cell. Using a $3.3\,$cm ablation cell with interchangeable exit apertures, we produce metastable atom beams and quantify their brilliance and velocity distributions as functions of buffer gas pressure.

The effectiveness of automatic evaluation of generative models is typically measured by comparing it to human evaluation using correlation metrics. However, metrics like Krippendorff's $\alpha$ and Randolph's $\kappa$, originally designed to measure the reliability of human labeling, make assumptions about human behavior and the labeling process. In this paper, we show how *relying on a single aggregate correlation score* can obscure fundamental differences between human behavior and automatic evaluation methods, including LLM-as-a-Judge. Specifically, we demonstrate that when the proportion of samples with variation or uncertainty in human labels (gathered during human evaluation) is relatively high, machine labels (generated by automatic evaluation methods) may superficially appear to have similar or better correlation with the human majority label compared to human-to-human (HH) correlation. This can create the misleading impression that automatic evaluation is accurate enough to approximate the human majority label. However, as the proportion of samples with consistent human labels increases, the correlation between machine labels and human majority labels declines, falling below HH correlation. Based on these findings, we first propose stratifying results by human label uncertainty to provide a more robust analysis of automatic evaluation performance. Second, recognizing that uncertainty and variation are inherent in perception-based human evaluations, such as those involving attitudes or preferences, we introduce a new metric - *binned Jensen-Shannon Divergence for perception* for such scenarios to better measure the effectiveness of automatic evaluations. Third, we present visualization techniques -- *perception charts*, to compare the strengths and limitations of automatic evaluation and to contextualize correlation measures appropriately

Neural Radiance Fields (NeRF) are widely used for novel-view synthesis and have been adapted for 3D Object Detection (3DOD), offering a promising approach to 3DOD through view-synthesis representation. However, NeRF faces inherent limitations: (i) limited representational capacity for 3DOD due to its implicit nature, and (ii) slow rendering speeds. Recently, 3D Gaussian Splatting (3DGS) has emerged as an explicit 3D representation that addresses these limitations. Inspired by these advantages, this paper introduces 3DGS into 3DOD for the first time, identifying two main challenges: (i) Ambiguous spatial distribution of Gaussian blobs: 3DGS primarily relies on 2D pixel-level supervision, resulting in unclear 3D spatial distribution of Gaussian blobs and poor differentiation between objects and background, which hinders 3DOD; (ii) Excessive background blobs: 2D images often include numerous background pixels, leading to densely reconstructed 3DGS with many noisy Gaussian blobs representing the background, negatively affecting detection. To tackle the challenge (i), we leverage the fact that 3DGS reconstruction is derived from 2D images, and propose an elegant and efficient solution by incorporating 2D Boundary Guidance to significantly enhance the spatial distribution of Gaussian blobs, resulting in clearer differentiation between objects and their background. To address the challenge (ii), we propose a Box-Focused Sampling strategy using 2D boxes to generate object probability distribution in 3D spaces, allowing effective probabilistic sampling in 3D to retain more object blobs and reduce noisy background blobs. Benefiting from our designs, our 3DGS-DET significantly outperforms the SOTA NeRF-based method, NeRF-Det, achieving improvements of +6.6 on mAP@0.25 and +8.1 on mAP@0.5 for the ScanNet dataset, and impressive +31.5 on mAP@0.25 for the ARKITScenes dataset., Comment: Code Page: https://github.com/yangcaoai/3DGS-DET

Quantum gates are essential elements for processing quantum information. However, realizing them in a photonic platform is challenging due to the unique way photons propagate and interfere. In this study, we introduce new design of quantum photonic gates that operate based on continuous time two-dimensional random walking photons. These gates can be implemented using the inverse design method, where photons randomly walk in a two-dimensional silicon host medium embedded with silicon dioxide scatterers. We propose a C-NOT gate as a multiqubit gate and an X-gate as a single qubit gate. We will also provide numerical demonstrations of the gate operations using quantum formalism. Additionally, our investigation involves studying the non-trivial spatial correlations of random walking photons by utilizing the quantum correlation function. The results demonstrate high-fidelity probabilistic quantum gates. Further work is required to address error-correction. This work advances the practical implementation of integrated photonics in linear quantum optics.

The Gemini Planet Imager (GPI) is a high contrast imaging instrument that aims to detect and characterize extrasolar planets. GPI is being upgraded to GPI 2.0, with several subsystems receiving a re-design to improve its contrast. To enable observations on fainter targets and increase performance on brighter ones, one of the upgrades is to the adaptive optics system. The current Shack-Hartmann wavefront sensor (WFS) is being replaced by a pyramid WFS with an low-noise electron multiplying CCD (EMCCD). EMCCDs are detectors capable of counting single photon events at high speed and high sensitivity. In this work, we characterize the performance of the HN\"u 240 EMCCD from N\"uv\"u Cameras, which was custom-built for GPI 2.0. Through our performance evaluation we found that the operating mode of the camera had to be changed from inverted-mode (IMO) to non-inverted mode (NIMO) in order to improve charge diffusion features found in the detector's images. Here, we characterize the EMCCD's noise contributors (readout noise, clock-induced charges, dark current) and linearity tests (EM gain, exposure time) before and after the switch to NIMO., Comment: Proceeding of the SPIE Astronomical Telescopes+Instrumentation. 14 pages, 15 figures

In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in that it is optimized for reasoning. We find that o1 substantially outperforms previous LLMs in many cases, with particularly large improvements on rare variants of common tasks (e.g., forming acronyms from the second letter of each word in a list, rather than the first letter). Despite these quantitative improvements, however, o1 still displays the same qualitative trends that we observed in previous systems. Specifically, o1 -- like previous LLMs -- is sensitive to the probability of examples and tasks, performing better and requiring fewer "thinking tokens" in high-probability settings than in low-probability ones. These results show that optimizing a language model for reasoning can mitigate but might not fully overcome the language model's probability sensitivity., Comment: 6 pages; updated to fix typo in Fig 4 caption

This paper systematically investigates the quasinormal modes (QNMs) of a scalar field around a charged loop quantum black hole (BH). The BH is characterized by two key parameters: the quantum parameter $b_0$ and the charge parameter $Q$. We explore two scenarios: one where the multipole quantum number $l=0$, in which only the gravitational potential is relevant, and another where $l > 0$, in which both $Q$ and $b_0$ jointly influence the quasinormal frequencies (QNFs). For $l=0$, quantum gravity effects lead to a pronounced overtone outburst in the QNFs, with the outburst and accompanying oscillatory patterns becoming more pronounced as the overtone number increases. For $l > 0$, the overtone outburst disappears due to the centrifugal potential's suppressive the quantum gravity effect. However, a non-monotonic behavior in the first two overtones with respect to $b_0$ is observed, indicating that subtle quantum gravity effects persist. This behavior vanishes as $Q$ increases, further confirming the suppressive influence of the centrifugal potential on the quantum gravity effect.

Radiology reports often remain incomprehensible to patients, undermining patient-centered care. We present ReXplain (Radiology eXplanation), an innovative AI-driven system that generates patient-friendly video reports for radiology findings. ReXplain uniquely integrates a large language model for text simplification, an image segmentation model for anatomical region identification, and an avatar generation tool, producing comprehensive explanations with plain language, highlighted imagery, and 3D organ renderings. Our proof-of-concept study with five board-certified radiologists indicates that ReXplain could accurately deliver radiological information and effectively simulate one-on-one consultations. This work demonstrates a new paradigm in AI-assisted medical communication, potentially improving patient engagement and satisfaction in radiology care, and opens new avenues for research in multimodal medical communication., Comment: 13 pages

Centipedes exhibit great maneuverability in diverse environments due to their many legs and body-driven control. By leveraging similar morphologies, their robotic counterparts also demonstrate effective terrestrial locomotion. However, the success of these multi-legged robots is largely limited to forward locomotion; steering is substantially less studied, in part due to the challenges in coordinating their many body joints. Furthermore, steering behavior is complex and can include different combinations of desired rotational/translational displacement. In this paper, we explore steering strategies in multi-legged robots based on tools derived from geometric mechanics (GM). We characterize the steering motion in the plane by the rotation angle, the steering radius, and the heading direction angle. We identify an effective turning strategy by superimposing two traveling waves in the lateral body undulation and further explore variations of the "turning wave" to enable a broad spectrum of steering behaviors. By combining an amplitude modulation and a phase modulation, we develop a control strategy for steering behaviors that enables steering with a range of rotation angles (from 0{\deg} to 20{\deg}) and steering radius (from 0.28 to 0.38 body length) while keeping the heading direction angle close to 0. Lastly, we test our control framework on an elongate multi-legged robot model to verify the effectiveness of our proposed strategy. Our work demonstrates the generality of the two-wave template for effective steering of multi-legged elongate robots.

Naturally situated conversations capture the underlying social norms appropriate for the topic of conversation, the relationship between interlocutors and their communicative intent. This paper proposes a framework for controlled generation of dialogues, spanning a wide range of interlocutors attributes (such as age group, profession and personality types), relationship types, conversation topics and conversational trajectories. We use this framework to generate NormHint, a collection of dialogues consistent with these rich settings and analyzed for norm violation leading to conflicts, and potential steps for avoiding these conflicts by adhering to social norms and preferring respectful utterances maintaining the communicative intents of the original utterance. We present the results of human validation and automated analysis of NormHint and show it captures a wide range of conversational topics and scored highly by humans for the naturalness of the conversations based on the prompted context., Comment: Pre-Print

The wide deployment of Large Language Models (LLMs) has given rise to strong demands for optimizing their inference performance. Today's techniques serving this purpose primarily focus on reducing latency and improving throughput through algorithmic and hardware enhancements, while largely overlooking their privacy side effects, particularly in a multi-user environment. In our research, for the first time, we discovered a set of new timing side channels in LLM systems, arising from shared caches and GPU memory allocations, which can be exploited to infer both confidential system prompts and those issued by other users. These vulnerabilities echo security challenges observed in traditional computing systems, highlighting an urgent need to address potential information leakage in LLM serving infrastructures. In this paper, we report novel attack strategies designed to exploit such timing side channels inherent in LLM deployments, specifically targeting the Key-Value (KV) cache and semantic cache widely used to enhance LLM inference performance. Our approach leverages timing measurements and classification models to detect cache hits, allowing an adversary to infer private prompts with high accuracy. We also propose a token-by-token search algorithm to efficiently recover shared prompt prefixes in the caches, showing the feasibility of stealing system prompts and those produced by peer users. Our experimental studies on black-box testing of popular online LLM services demonstrate that such privacy risks are completely realistic, with significant consequences. Our findings underscore the need for robust mitigation to protect LLM systems against such emerging threats., Comment: This work was submitted for review on Sept. 5, 2024, and the initial version was uploaded to Arxiv on Sept. 30, 2024

Rest-frame far-ultraviolet (FUV) observations from JWST are revolutionizing our understanding of the high-z galaxies that drove reionization and the mechanisms by which they accomplished it. To fully interpret these observations, we must be able to diagnose how properties of the interstellar medium (ISM; e.g., column density, covering fraction, outflow velocity) directly relate to the absorption features produced. Using the high-S/N and high-resolution FUV spectra of 45 nearby star-forming galaxies from CLASSY, we present the largest uniform, simultaneous characterization of neutral and low-ionization state (LIS) interstellar UV absorption lines (OI, SiII, SII, CII, AlII) across a wide range of galaxy properties. We also present 21-cm HI observations for 35 galaxies, multiple of which are gas-poor or non-detected, possibly indicating the onset of a post-starburst phase. We find that our simultaneous 1-component Voigt profile fits are capable of accurately modeling the LIS absorption for ~75% of galaxies, mitigating challenges associated with saturation, infilling, and degeneracies. While the most massive galaxies require additional components, our 1-component fits return average properties of the absorbing gas and follow the scaling relations described by a single gas cloud. We explore connections between LIS absorption and direct tracers of the neutral ISM (OI, Ly-alpha, HI 21-cm), finding that CII most closely traces the neutral gas trends while other ions exhibit weaker correlations. Given the challenges with directly observing HI at higher-z, we demonstrate that LIS absorption can be a powerful means to study the neutral ISM and present empirical relationships for predicting neutral gas properties., Comment: 25 pages with 16 figures and 2 tables. Long appendix with figure sets and tables. Accepted to ApJ

Motivated by the rapid experimental progress in twisted van der Waals materials, we study the triangular trimer model as a representative framework for extended Wannier orbitals in twisted bilayer graphene at 1/3-filling. This deceptively simple model exhibits a rich suite of complex phases, including unusual excitations exhibiting the physics of fractionalization and fractons. For our investigations, we carry out extensive Monte Carlo simulations using an efficient cluster algorithm. The so-obtained finite-temperature phase diagram reveals a novel polar fluid and an ordered brick-wall phase characterized by fractionally charged $e/3$ excitations with subdimensional lineonic dynamics. Notably, we identify a critical trimer liquid phase for the particularly simple model of hard trimers. For this, we derive a new field theory which takes the form of a U(1)$\times$U(1) gauge theory. Its $e/3$ monomers are fractionalized bionic excitations: they carry a {\it pair} of emergent gauge charges, as evidenced by algebraic correlations with two distinct exponents. These field theoretical predictions offer theoretical grounds for numerical observations of critical exponents. Our study highlights the triangular trimer model as a new key platform for investigating fractionalization and fractons, where trimer liquid bionic monomers can transform into lineons or fractons in proximate phases, and calls for experimental investigations of this physics in twisted van der Waals materials and a broader class of systems with intermediate-range interactions., Comment: 8+13 pages

We tour several harmonious Euclidean properties of Poncelet triangles inscribed in an ellipse and circumscribing the incircle. We also show that a number of degenerate behaviors are triggered by the presence of an equilateral triangle in the family., Comment: 40 pages, 33 figures, 2 tables

We propose a novel framework that leverages Visual Question Answering (VQA) models to automate the evaluation of LLM-generated data visualizations. Traditional evaluation methods often rely on human judgment, which is costly and unscalable, or focus solely on data accuracy, neglecting the effectiveness of visual communication. By employing VQA models, we assess data representation quality and the general communicative clarity of charts. Experiments were conducted using two leading VQA benchmark datasets, ChartQA and PlotQA, with visualizations generated by OpenAI's GPT-3.5 Turbo and Meta's Llama 3.1 70B-Instruct models. Our results indicate that LLM-generated charts do not match the accuracy of the original non-LLM-generated charts based on VQA performance measures. Moreover, while our results demonstrate that few-shot prompting significantly boosts the accuracy of chart generation, considerable progress remains to be made before LLMs can fully match the precision of human-generated graphs. This underscores the importance of our work, which expedites the research process by enabling rapid iteration without the need for human annotation, thus accelerating advancements in this field.

Let $\emph{spex}_{\mathcal{OP}}(n,F)$ and $\emph{spex}_{\mathcal{P}}(n,F)$ be the maximum spectral radius over all $n$-vertex $F$-free outerplanar graphs and planar graphs, respectively. Define $tC_l$ as $t$ vertex-disjoint $l$-cycles, $B_{tl}$ as the graph obtained by sharing a common vertex among $t$ edge-disjoint $l$-cycles %$B_{tl}$ as the graph obtained by connecting all cycles in $tC_l$ at a single vertex, and $(t+1)K_{2}$ as the disjoint union of $t+1$ copies of $K_2$. In the 1990s, Cvetkovi\'c and Rowlinson conjectured $K_1 \vee P_{n-1}$ maximizes spectral radius in outerplanar graphs on $n$ vertices, while Boots and Royle (independently, Cao and Vince) conjectured $K_2 \vee P_{n-2} $ does so in planar graphs. Tait and Tobin [J. Combin. Theory Ser. B, 2017] determined the fundamental structure as the key to confirming these two conjectures for sufficiently large $n.$ Recently, Fang et al. [J. Graph Theory, 2024] characterized the extremal graph with $\emph{spex}_{\mathcal{P}}(n,tC_l)$ in planar graphs by using this key. In this paper, we first focus on outerplanar graphs and adopt a similar approach to describe the key structure of the connected extremal graph with $\emph{spex}_{\mathcal{OP}}(n,F)$, where $F$ is contained in $K_1 \vee P_{n-1}$ but not in $K_{1} \vee ((t-1)K_2\cup(n-2t+1)K_1)$. Based on this structure, we determine $\emph{spex}_{\mathcal{OP}}(n,B_{tl})$ and $\emph{spex}_{\mathcal{OP}}(n,(t+1)K_{2})$ along with their unique extremal graphs for all $t\geq1$, $l\geq3$ and large $n$. Moreover, we further extend the results to planar graphs, characterizing the unique extremal graph with $\emph{spex}_{\mathcal{P}}(n,B_{tl})$ for all $t\geq3$, $l\geq3$ and large $n$., Comment: arXiv admin note: text overlap with arXiv:2304.06942 by other authors

This paper explores the potential of AI-powered tools to reshape data analysis, focusing on design considerations and challenges. We explore how the emergence of large language and multimodal models offers new opportunities to enhance various stages of data analysis workflow by translating high-level user intentions into executable code, charts, and insights. We then examine human-centered design principles that facilitate intuitive interactions, build user trust, and streamline the AI-assisted analysis workflow across multiple apps. Finally, we discuss the research challenges that impede the development of these AI-based systems such as enhancing model capabilities, evaluating and benchmarking, and understanding end-user needs.

Controlling magnetic order via external fields or heterostructures enables precise manipulation and tracking of spin and exciton information, facilitating the development of high-performance optical spin valves. However, the weak magneto-optical signals and instability of two dimensional (2D) antiferromagnetic (AFM) materials have hindered comprehensive studies on the complex coupling between magnetic order and excitons in bulk-like systems. Here, we leverage magneto-optical spectroscopy to reveal the impact of magnetic order on exciton-phonon coupling and exciton-magnetic order coupling which remains robust even under non-extreme temperature conditions (80 K) in thick layered CrSBr. A 0.425T in-plane magnetic field is sufficient to induce spin flipping and transition from AFM to ferromagnetic (FM) magnetic order in CrSBr, while magnetic circular dichroism (MCD) spectroscopy under an out-of-plane magnetic field provides direct insight into the complex spin canting behavior in thicker layers. Theoretical calculations reveal that the strong coupling between excitons and magnetic order, especially the 32 meV exciton energy shift during magnetic transitions, stems from the hybridization of Cr and S orbitals and the larger exciton wavefunction radius of higher-energy B excitons. These findings offer new opportunities and a solid foundation for future exploration of 2D AFM materials in magneto-optical sensors and quantum communication using excitons as spin carriers.

We study the implementation of fault-tolerant logical Clifford gates on stabilizer quantum error correcting codes based on their symmetries. Our approach is to map the stabilizer code to a binary linear code, compute its automorphism group, and impose constraints based on the Clifford operators permitted. We provide a rigorous formulation of the method for finding automorphisms of stabilizer codes and generalize ZX-dualities to non-CSS codes. We provide a Python package implementing our algorithms which uses the computational algebra system MAGMA. Our algorithms map automorphism group generators to physical circuits, calculate Pauli corrections based on the destabilizers of the code, and determine their logical action. We discuss the fault tolerance of the circuits and include examples of gates through automorphisms for the [[4,2,2]] and perfect [[5,1,3]] codes, bivariate bicycle codes, and the best known distance codes., Comment: 24 pages, 6 figures

With the boom of renewable energy sources (RES), the number of power inverters proliferates. Power inverters are the key electronic devices that transform the direct current (DC) power from RES to the alternating current (AC) power on the grids, and their security can affect the stable operation of RES and even power grids. This paper analyzes the security of photovoltaic (PV) inverters from the aspects of internal sensors since they serve as the foundation for safe power conversion. We discover that both the embedded current sensors and voltage sensors are vulnerable to electromagnetic interference (EMI) of 1 GHz or higher, despite electromagnetic compatibility (EMC) countermeasures. Such vulnerabilities can lead to incorrect measurements and deceiving the control algorithms, and we design ReThink that could produce three types of consequences on PV inverters by emitting carefully crafted EMI, i.e., Denial of Service (DoS), damaging inverters physically or damping the power output. We successfully validate these consequences on 5 off-the-shelf PV inverters, and even in a real-world microgrid, by transmitting EMI signals at a distance of 100-150cm and a total power within 20W. Our work aims to raise awareness of the security of power electronic devices of RES, as they represent an emerging Cyber-Physical attack surface to the future RES-dominated grid. Finally, to cope with such threats, we provide hardware and software-based countermeasures., Comment: Accepted by NDSS Symposium 2025. Please cite this paper as "Fengchen Yang, Zihao Dan, Kaikai Pan, Chen Yan, Xiaoyu Ji, Wenyuan Xu. ReThink: Reveal the Threat of Electromagnetic Interference on Power Inverters. In the Network and Distributed System Security Symposium 2025 (NDSS 2025)."

Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how anatomical structure supports brain function and how spontaneous functional fluctuations emerge remarkable cognition is still elusive. Meanwhile, tremendous efforts have been made in the realm of machine learning to establish the nonlinear mapping between neuroimaging data and phenotypic traits. However, the absence of neuroscience insight in the current approaches poses significant challenges in understanding cognitive behavior from transient neural activities. To address this challenge, we put the spotlight on the coupling mechanism of structural connectivity (SC) and functional connectivity (FC) by formulating such network neuroscience question into an expressive graph representation learning problem for high-order topology. Specifically, we introduce the concept of topological detour to characterize how a ubiquitous instance of FC (direct link) is supported by neural pathways (detour) physically wired by SC, which forms a cyclic loop interacted by brain structure and function. In the clich\'e of machine learning, the multi-hop detour pathway underlying SC-FC coupling allows us to devise a novel multi-head self-attention mechanism within Transformer to capture multi-modal feature representation from paired graphs of SC and FC. Taken together, we propose a biological-inspired deep model, coined as NeuroPath, to find putative connectomic feature representations from the unprecedented amount of neuroimages, which can be plugged into various downstream applications such as task recognition and disease diagnosis. We have evaluated NeuroPath on large-scale public datasets including HCP and UK Biobank under supervised and zero-shot learning, where the state-of-the-art performance by our NeuroPath indicates great potential in network neuroscience., Comment: Accepted by NeurIPS 2024

The Arrhenius equation was used to describe the dynamics of two-state switching in mesoscale, ferromagnetic particles. Using square, permalloy dots as an idealized two-state switching system, measurements of the prefactor of the Arrhenius law changed by 26 decades over barrier heights from 30 meV to 700 meV. Measurements of the prefactor ratios for a two well system revealed significant deviations from the common interpretation of the Arrhenius law. The anomalous Arrhenius prefactors and the prefactor ratios can be fitted to a modified model that includes entropic contributions to two-state transitions. Similar considerations are likely for the application of the Arrhenius law to other mesoscale systems., Comment: 19 pages, 6 figures, to be published in Journal of Applied Physics

Picking diverse objects in the real world is a fundamental robotics skill. However, many objects in such settings are bulky, heavy, or irregularly shaped, making them ungraspable by conventional end effectors like suction grippers and parallel jaw grippers (PJGs). In this paper, we expand the range of pickable items without hardware modifications using bimanual nonprehensile manipulation. We focus on a grocery shopping scenario, where a bimanual mobile manipulator equipped with a suction gripper and a PJG is tasked with retrieving ungraspable items from tightly packed grocery shelves. From visual observations, our method first identifies optimal grasp points based on force closure and friction constraints. If the grasp points are occluded, a series of nonprehensile nudging motions are performed to clear the obstruction. A bimanual grasp utilizing contacts on the side of the end effectors is then executed to grasp the target item. In our replica grocery store, we achieved a 90% success rate over 102 trials in uncluttered scenes, and a 67% success rate over 45 trials in cluttered scenes. We also deployed our system to a real-world grocery store and successfully picked previously unseen items. Our results highlight the potential of bimanual nonprehensile manipulation for in-the-wild robotic picking tasks. A video summarizing this work can be found at youtu.be/g0hOrDuK8jM

Coherent control of single photon sources is a key requirement for the advancement of photonic quantum technologies. Among them, nanowire-based quantum dot sources are popular due to their potential for on-chip hybrid integration. Here we demonstrate on-demand single-photon generation ($g^{(2)}(0)(X^{*}) =0.078$ and $g^{(2)}(0)(X)= 0.03$) from resonantly excited InAsP/InP nanowire quantum dots and observe Rabi oscillations in the dot emission, indicating successful coherent manipulation of the excitonic states in the nanowire. We also measure a low emission time jitter for resonant excitation as compared to above-band excitation. This work addresses the long-standing challenge of resonantly exciting nanowire-quantum dots. It paves the way for hybrid quantum photonic integration, enabling spin-photon entanglement and matter memories on-chip.

Since the release of ChatGPT in November 2022, large language models (LLMs) have seen considerable success, including in the open-source community, with many open-weight models available. However, the requirements to deploy such a service are often unknown and difficult to evaluate in advance. To facilitate this process, we conducted numerous tests at the Centre Inria de l'Universit\'e de Bordeaux. In this article, we propose a comparison of the performance of several models of different sizes (mainly Mistral and LLaMa) depending on the available GPUs, using vLLM, a Python library designed to optimize the inference of these models. Our results provide valuable information for private and public groups wishing to deploy LLMs, allowing them to evaluate the performance of different models based on their available hardware. This study thus contributes to facilitating the adoption and use of these large language models in various application domains.

As artificial intelligence continues to evolve, it is increasingly capable of handling a wide range of video analytics tasks with merely one large model. One of the key foundation technologies is the Segment Anything Model (SAM), which allows the video analytics tasks to be determined on the fly according to the input prompts from the user. However, achieving real-time response in video analytics applications is crucial for user experiences due to the limited communication and computation resources on the edge, especially with SAM, where users may continuously interact by adding or adjusting prompts. In this paper, we propose SAMEdge, a novel edge-cloud computing architecture designed to support SAM computations for edge users. SAMEdge integrates new modules on the edge and the cloud to maximize analytics accuracy under visual prompts and image prompts input with latency constraints. It addresses resource challenges associated with prompt encoding and image encoding by offering a visual prompt transformation algorithm for visual prompts and efficient workload partitioning for image encoding. SAMEdge is implemented by extending the open-source SAM project from Meta AI. We demonstrate the practical application of SAMEdge through a case study on a Visual Tour Guide application. Our evaluation indicates that SAMEdge significantly enhances the accuracy of the video analytics application under distinct network bandwidths across various prompts.

LLMs can now act as autonomous agents that interact with digital environments and complete specific objectives (e.g., arranging an online meeting). However, accuracy is still far from satisfactory, partly due to a lack of large-scale, direct demonstrations for digital tasks. Obtaining supervised data from humans is costly, and automatic data collection through exploration or reinforcement learning relies on complex environmental and content setup, resulting in datasets that lack comprehensive coverage of various scenarios. On the other hand, there is abundant knowledge that may indirectly assist task completion, such as online tutorials that were created for human consumption. In this work, we present Synatra, an approach that effectively transforms this indirect knowledge into direct supervision at scale. We define different types of indirect knowledge, and carefully study the available sources to obtain it, methods to encode the structure of direct demonstrations, and finally methods to transform indirect knowledge into direct demonstrations. We use 100k such synthetically-created demonstrations to finetune a 7B CodeLlama, and demonstrate that the resulting agent surpasses all comparably sized models on three web-based task benchmarks Mind2Web, MiniWoB++ and WebArena, as well as surpassing GPT-3.5 on WebArena and Mind2Web. In addition, while synthetic demonstrations prove to be only 3% the cost of human demonstrations (at $0.031 each), we show that the synthetic demonstrations can be more effective than an identical number of human demonstrations collected from limited domains.

In practical scenarios, federated learning frequently necessitates training personalized models for each client using heterogeneous data. This paper proposes a backbone self-distillation approach to facilitate personalized federated learning. In this approach, each client trains its local model and only sends the backbone weights to the server. These weights are then aggregated to create a global backbone, which is returned to each client for updating. However, the client's local backbone lacks personalization because of the common representation. To solve this problem, each client further performs backbone self-distillation by using the global backbone as a teacher and transferring knowledge to update the local backbone. This process involves learning two components: the shared backbone for common representation and the private head for local personalization, which enables effective global knowledge transfer. Extensive experiments and comparisons with 12 state-of-the-art approaches demonstrate the effectiveness of our approach., Comment: Pubished in ACM MMAsia 2023

Existing efforts in text-based video question answering (TextVideoQA) are criticized for their opaque decisionmaking and heavy reliance on scene-text recognition. In this paper, we propose to study Grounded TextVideoQA by forcing models to answer questions and spatio-temporally localize the relevant scene-text regions, thus decoupling QA from scenetext recognition and promoting research towards interpretable QA. The task has three-fold significance. First, it encourages scene-text evidence versus other short-cuts for answer predictions. Second, it directly accepts scene-text regions as visual answers, thus circumventing the problem of ineffective answer evaluation by stringent string matching. Third, it isolates the challenges inherited in VideoQA and scene-text recognition. This enables the diagnosis of the root causes for failure predictions, e.g., wrong QA or wrong scene-text recognition? To achieve Grounded TextVideoQA, we propose the T2S-QA model that highlights a disentangled temporal-to-spatial contrastive learning strategy for weakly-supervised scene-text grounding and grounded TextVideoQA. To facilitate evaluation, we construct a new dataset ViTXT-GQA which features 52K scene-text bounding boxes within 2.2K temporal segments related to 2K questions and 729 videos. With ViTXT-GQA, we perform extensive experiments and demonstrate the severe limitations of existing techniques in Grounded TextVideoQA. While T2S-QA achieves superior results, the large performance gap with human leaves ample space for improvement. Our further analysis of oracle scene-text inputs posits that the major challenge is scene-text recognition. To advance the research of Grounded TextVideoQA, our dataset and code are at \url{https://github.com/zhousheng97/ViTXT-GQA.git}

Animal sounds can be recognised automatically by machine learning, and this has an important role to play in biodiversity monitoring. Yet despite increasingly impressive capabilities, bioacoustic species classifiers still exhibit imbalanced performance across species and habitats, especially in complex soundscapes. In this study, we explore the effectiveness of transfer learning in large-scale bird sound classification across various conditions, including single- and multi-label scenarios, and across different model architectures such as CNNs and Transformers. Our experiments demonstrate that both fine-tuning and knowledge distillation yield strong performance, with cross-distillation proving particularly effective in improving in-domain performance on Xeno-canto data. However, when generalizing to soundscapes, shallow fine-tuning exhibits superior performance compared to knowledge distillation, highlighting its robustness and constrained nature. Our study further investigates how to use multi-species labels, in cases where these are present but incomplete. We advocate for more comprehensive labeling practices within the animal sound community, including annotating background species and providing temporal details, to enhance the training of robust bird sound classifiers. These findings provide insights into the optimal reuse of pretrained models for advancing automatic bioacoustic recognition., Comment: 25 pages

Transient surveys routinely detect supernovae (SNe) without obvious host galaxies. To understand the demographics of these "hostless" SNe and to constrain the possible host properties, we identify 161 SNe reported to the Transient Name Server since 2016 that do not have hosts cataloged from pre-explosion wide-field galaxy surveys. Using forced aperture photometry, we detect excess flux around only 56 of these SNe. Both thermonuclear and core-collapse (CC) SNe are present in our sample. Compared to flux-limited SNe samples with known hosts, superluminous supernovae (SLSNe), particularly hydrogen-deficient SLSNe, are over-represented here relative to all other SNe types; among CC SNe, there is also a higher fraction of interacting SNe than non-interacting. On the low-luminosity side, seven SNe have host absolute magnitude upper limits fainter than M_g=-12, about 1 per cent of the Small Magellanic Cloud's luminosity; the faintest limits are close to the luminosity of globular clusters or ultra-faint dwarf galaxies (M_g~-8). Fitting multi-band forced photometry, 11 SNe have host stellar masses <10^6 Msun assuming quiescent hosts, and 13 SNe have host stellar masses <10^5 Msun assuming star-forming hosts. The spatial distribution of hostless SNe indicates that the majority are not associated with known galaxy groups and clusters, ruling out intracluster stellar light as the primary contributor of such SNe. Hostless Type Ia SNe tend to be more luminous and slow-fading than SNe Ia with known host galaxies, implying a hidden population of low-mass and star-forming hosts. We conclude that any undetected host galaxies are likely star-forming dwarfs in the field., Comment: Accepted for publication in MNRAS

Analogous to the gravitational spin Hall effect (SHE), light possessing the intrinsic orbital angular momentum (IOAM) is expected to exhibit ray trajectories deviating from null geodesics when propagating through a gravitational field and we term this phenomenon the gravitational orbital Hall effect (OHE). In our text, we utilize the Wentzel-Kramers-Brillouin approximation to study the ray dynamics of the vortex light in arbitrary curved spacetimes. We start by taking the WKB method to the field action of the optical system and finally obtain the effective ray equations. At large but finite frequencies, the corrections beyond the geodesic equation matters. We also find that these effective equations are the same in form as those for the polarized light, highlighting the consistency between the gravitational OHE and the gravitational SHE. Particularly, we obtain physical quantities within extended geometrical optics, including the Berry connection and the Berry phase, demonstrating their identity as the source of the gravitational OHE. Finally, we derive the equivalent ray equations of vortex light in an inhomogeneous medium and successfully simulate the gravitational OHE in microstructured optical waveguides numerically., Comment: 22 pages,10 figures

The stochastic formation of defects during Laser Powder Bed Fusion (L-PBF) negatively impacts its adoption for high-precision use cases. Optical monitoring techniques can be used to identify defects based on layer-wise imaging, but these methods are difficult to scale to high resolutions due to cost and memory constraints. Therefore, we implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate, enabling cost-efficient process monitoring. To do so, a conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images, recovering the distribution of small-scale features and surface roughness. We first evaluate the performance of the model by analyzing the reconstruction quality of the generated images using peak-signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) and wavelet covariance metrics that describe the preservation of high-frequency information. Additionally, we design a framework based upon the Segment Anything foundation model to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples. Finally, we explore the zero-shot generalization capabilities of the implemented framework to other part geometries by creating synthetic low-resolution data.

We present recent laboratory results demonstrating high-contrast coronagraphy for the future space-based large IR/Optical/Ultraviolet telescope recommended by the Decadal Survey. The High-contrast Imager for Complex Aperture Telescopes (HiCAT) testbed aims to implement a system-level hardware demonstration for segmented aperture coronagraphs with wavefront control. The telescope hardware simulator employs a segmented deformable mirror with 37 hexagonal segments that can be controlled in piston, tip, and tilt. In addition, two continuous deformable mirrors are used for high-order wavefront sensing and control. The low-order sensing subsystem includes a dedicated tip-tilt stage, a coronagraphic target acquisition camera, and a Zernike wavefront sensor that is used to measure and correct low-order aberration drifts. We explore the performance of a segmented aperture coronagraph both in static operations (limited by natural drifts and instabilities) and in dynamic operations (in the presence of artificial wavefront drifts added to the deformable mirrors), and discuss the estimation and control strategies used to reach and maintain the dark-zone contrast using our low-order wavefront sensing and control. We summarize experimental results that quantify the performance of the testbed in terms of contrast, inner/outer working angle and bandpass, and analyze limiting factors., Comment: 17 pages, 14 figures, SPIE Astronomical Telescopes + Instrumentation, 2022, Montr\'eal, Qu\'ebec, Canada

Quantum error correction and the use of quantum error correction codes is likely to be essential for the realisation of practical quantum computing. Because the error models of quantum devices vary widely, quantum codes which are tailored for a particular error model may have much better performance. In this work, we present a novel evolutionary algorithm which searches for an optimal stabiliser code for a given error model, number of physical qubits and number of encoded qubits. We demonstrate an efficient representation of stabiliser codes as binary strings -- this allows for random generation of valid stabiliser codes, as well as mutation and crossing of codes. Our algorithm finds stabiliser codes whose distance closely matches the best-known-distance codes of codetables.de for n <= 20 physical qubits. We perform a search for optimal distance CSS codes, and compare their distance to the best-known-codes. Finally, we show that the algorithm can be used to optimise stabiliser codes for biased error models, demonstrating a significant improvement in the undetectable error rate for [[12, 1]] codes versus the best-known-distance code with the same parameters. As part of this work, we also introduce an evolutionary algorithm QDistEvol for finding the distance of quantum error correction codes., Comment: 14 pages, 11 figures

Recently, 3D Gaussian Splatting (3D-GS) has achieved impressive results in novel view synthesis, demonstrating high fidelity and efficiency. However, it easily exhibits needle-like artifacts, especially when increasing the sampling rate. Mip-Splatting tries to remove these artifacts with a 3D smoothing filter for frequency constraints and a 2D Mip filter for approximated supersampling. Unfortunately, it tends to produce over-blurred results, and sometimes needle-like Gaussians still persist. Our spectral analysis of the covariance matrix during optimization and densification reveals that current 3D-GS lacks shape awareness, relying instead on spectral radius and view positional gradients to determine splitting. As a result, needle-like Gaussians with small positional gradients and low spectral entropy fail to split and overfit high-frequency details. Furthermore, both the filters used in 3D-GS and Mip-Splatting reduce the spectral entropy and increase the condition number during zooming in to synthesize novel view, causing view inconsistencies and more pronounced artifacts. Our Spectral-GS, based on spectral analysis, introduces 3D shape-aware splitting and 2D view-consistent filtering strategies, effectively addressing these issues, enhancing 3D-GS's capability to represent high-frequency details without noticeable artifacts, and achieving high-quality photorealistic rendering.

In a recent study, Wei et al. [arXiv:2409.09333] proposed a universal classification scheme that interprets black hole solutions as topological defects within the thermodynamic parameter space, and then divides black hole solutions into four distinct classes, denoted as $W^{1-}$, $W^{0+}$, $W^{0-}$, and $W^{1+}$, offering insights into deeper aspects of black hole thermodynamics and gravity. In this paper, we investigate the universal thermodynamic topological classification of the singly rotating Kerr black holes in all dimensions, as well as the four-dimensional Kerr-Newman black hole. We show that the innermost small black hole states of the $d \geq 6$ singly rotating Kerr black holes are thermodynamically unstable, while those of the four-dimensional Kerr-Newman black hole and the $d = 4, 5$ singly rotating Kerr black holes are thermodynamically stable. On the other hand, the outermost large black holes exhibit unstable behavior in all these cases. At the low-temperature limit, the $d \geq 6$ singly rotating Kerr black holes have one large thermodynamically unstable black hole, while the four-dimensional Kerr-Newman black hole and the $d = 4, 5$ singly rotating Kerr black holes feature one large unstable branch and one small stable branch. Conversely, at the high-temperature limit, the $d \geq 6$ singly rotating Kerr black holes exhibit a small unstable black hole state, while the four-dimensional Kerr-Newman black hole and the $d = 4, 5$ singly rotating Kerr black holes have no black hole states at all. Consequently, we demonstrate that the $d \geq 6$ singly rotating Kerr black holes belong to the class $W^{1-}$, whereas the four-dimensional Kerr-Newman and $d = 4, 5$ singly rotating Kerr black holes belong to the class $W^{0+}$, thereby further support the conjecture proposed in [arXiv:2409.09333]., Comment: 11 pages, 8 figures, 4 tables, revtex4-1.cls. Submitted to PRD on September 19th

Virtual screening plays a pivotal role in early drug discovery, traditionally dominated by physics-based methods. While these approaches offer detailed insights, they are often hindered by high computational costs, limited sampling, and forcefield inaccuracies. Advances in Machine Learning (Ml)and Deep Learning (DL) present resource-efficient alternatives, with approaches like predictive geometric ML (EQUIBIND) and generative geometric ML (DIFFDOCK)showing promise in enhancing both efficiency and predictive capability. Here, we compare these two strategies, retrospectively and prospectively, for identifying novel agonists targeting the dopamine D2 receptor. To complement DIFFDOCK's dual functionality in protein-ligand conformer generation and confidence estimation, we adopted a complementary atom-type-based confidence model for EQUIBIND. This pipeline, termed the discriminative model, integrates a featurization step and an XGBoost classifier to differentiate between active and inactive ligands. The top-ranked compounds from both models were evaluated using an ultrafast dopaminergic biosensor assay, dLight. Our results demonstrate that the generative model achieved a higher hit rate, notably leading to the discovery of Compound 1, a nanomolar dopamine D2 receptor agonist with a novel scaffold.

The MMT Adaptive optics exoPlanet characterization System (MAPS) is currently in its engineering phase, operating on-sky at the MMT Telescope on Mt. Hopkins in southern Arizona. The MAPS Adaptive Secondary Mirror's actuators are controlled by a closed loop modified PID control law and an open loop feed-forward law, which in combination allows for faster actuator response time. An essential element of achieving the secondary's performance goals involves the process of PID gain tuning. To start, we briefly discuss the design of the MAPS ASM and its actuators. We then describe the actuator positional control system and control law. Next, we discuss a few of the issues that make ASM tuning difficult. We then outline our initial attempts at tuning the actuator controllers and discuss the use of actuator positional power spectra for both tuning and determining the health and failure states of individual actuators. We conclude by presenting the results of our latest round of tuning configuration trials, which have been successful at decreasing mirror latency, increasing operational mirror modes and improving image PSF., Comment: To be published in Proceedings of SPIE, Optics and Photonics 2024. 24 pages, 16 figures, 5 tables. Lead Author, J. Johnson. Second Lead Author, A. Vaz. Project P.I., K. Morzinski. Project Second P.I.s, J. Patience and S. Sivanandam, Project Manager, M. Montoya

Generative AI models provide a wide range of tools capable of performing complex tasks in a fraction of the time it would take a human. Among these, Large Language Models (LLMs) stand out for their ability to generate diverse texts, from literary narratives to specialized responses in different fields of knowledge. This paper explores the use of fine-tuned LLMs to identify physical descriptions of people, and subsequently create accurate representations of avatars using the SMPL-X model by inferring shape parameters. We demonstrate that LLMs can be trained to understand and manipulate the shape space of SMPL, allowing the control of 3D human shapes through natural language. This approach promises to improve human-machine interaction and opens new avenues for customization and simulation in virtual environments., Comment: Accepted to ECCV 2024 Workshop on Foundation Models for 3D Humans. Code repository: https://github.com/baldoarbol/BodyShapeGPT

Deep learning models can achieve high inference accuracy by extracting rich knowledge from massive well-annotated data, but may pose the risk of data privacy leakage in practical deployment. In this paper, we present an effective teacher-student learning approach to train privacy-preserving deep learning models via differentially private data-free distillation. The main idea is generating synthetic data to learn a student that can mimic the ability of a teacher well-trained on private data. In the approach, a generator is first pretrained in a data-free manner by incorporating the teacher as a fixed discriminator. With the generator, massive synthetic data can be generated for model training without exposing data privacy. Then, the synthetic data is fed into the teacher to generate private labels. Towards this end, we propose a label differential privacy algorithm termed selective randomized response to protect the label information. Finally, a student is trained on the synthetic data with the supervision of private labels. In this way, both data privacy and label privacy are well protected in a unified framework, leading to privacy-preserving models. Extensive experiments and analysis clearly demonstrate the effectiveness of our approach., Comment: Published by IEEE MMSP 2022

The task of infectious disease contact tracing is crucial yet challenging, especially when meeting strict privacy requirements. Previous attempts in this area have had limitations in terms of applicable scenarios and efficiency. Our paper proposes a highly scalable, practical contact tracing system called PREVENT that can work with a variety of location collection methods to gain a comprehensive overview of a person's trajectory while ensuring the privacy of individuals being tracked, without revealing their plain text locations to any party, including servers. Our system is very efficient and can provide real-time query services for large-scale datasets with millions of locations. This is made possible by a newly designed secret-sharing based architecture that is tightly integrated into unique private space partitioning trees. Notably, our experimental results on both real and synthetic datasets demonstrate that our system introduces negligible performance overhead compared to traditional contact tracing methods. PREVENT could be a game-changer in the fight against infectious diseases and set a new standard for privacy-preserving location tracking.

Hypersparse traffic matrices constructed from network packet source and destination addresses is a powerful tool for gaining insights into network traffic. SuiteSparse: GraphBLAS, an open source package or building, manipulating, and analyzing large hypersparse matrices, is one approach to constructing these traffic matrices. Suricata is a widely used open source network intrusion detection software package. This work demonstrates how Suricata network flow records can be used to efficiently construct hypersparse matrices using GraphBLAS.

Twenty-one-centimetre signals from the Epoch of Reionization (EoR) are expected to be detected in the low-frequency radio window by the next-generation interferometers, particularly the Square Kilometre Array (SKA). However, precision data analysis pipelines are required to minimize the systematics within an infinitesimal error budget. Consequently, there is a growing need to characterize the sources of errors in EoR analysis. In this study, we identify one such error origin, namely source blending, which is introduced by the overlap of objects in the densely populated observing sky under SKA1-Low's unprecedented sensitivity and resolution, and evaluate its two-fold impact in both the spatial and frequency domains using a novel hybrid evaluation (HEVAL) pipeline combining end-to-end simulation with an analytic method to mimic EoR analysis pipelines. Sky models corrupted by source blending induce small but severe frequency-dependent calibration errors when coupled with astronomical foregrounds, impeding EoR parameter inference with strong additive residuals in the two-dimensional power spectrum space. We report that additive residuals from poor calibration against sky models with blending ratios of 5 and 0.5 per cent significantly contaminate the EoR window. In contrast, the sky model with a 0.05 per cent blending ratio leaves little residual imprint within the EoR window, therefore identifying a blending tolerance at approximately 0.05 per cent. Given that the SKA observing sky is estimated to suffer from an extended level of blending, strategies involving de-blending, frequency-dependent error mitigation, or a combination of both, are required to effectively attenuate the calibration impact of source-blending defects., Comment: 25 pages, 15 figures, 5 tables, accepted for publication in Monthly Notices of the Royal Astronomical Society (MNRAS), Fg21Sim+ github repository: https://github.com/Fg21Sim/Fg21SimPlus

A symmetry preserving treatment of a vector $\otimes$ vector contact interaction (SCI) is used as the basis for calculations of the two pion transverse momentum dependent parton distribution functions (TMDs); namely, that for unpolarised valence degrees-of-freedom and the analogous Boer-Mulders (BM) function. Amongst other things, the analysis enables the following themes to be addressed: the quark current mass dependence of pion TMDs; the impact of the gauge link model on the positivity constraint that bounds the BM function relative to the unpolarised TMD; the equivalence of direct diagrammatic and light-front wave function TMD calculations; and the size of the BM shift. Interpreted astutely, these SCI results enable one to draw insightful pictures of pion TMDs., Comment: 14 pages, 9 figures, 1 table

The rise of time-domain astronomy including electromagnetic counterparts to gravitational waves, gravitational microlensing, explosive phenomena, and even astrometry with Gaia, are showing the power and need for surveys with high-cadence, large area, and long time baselines to study the transient universe. A constellation of SmallSats or CubeSats providing wide, instantaneous sky coverage down to 21 Vega mag at optical wavelengths would be ideal for addressing this need. We are assembling CuRIOS-ED (CubeSats for Rapid Infrared and Optical Survey--Exploration Demo), an optical telescope payload which will act as a technology demonstrator for a larger constellation of several hundred 16U CubeSats known as CuRIOS. In preparation for CuRIOS, CuRIOS-ED will launch in late 2025 as part of the 12U Starspec InspireSat MVP payload. CuRIOS-ED will be used to demonstrate the StarSpec ADCS pointing capabilities to <1" and to space-qualify a commercial camera package for use on the full CuRIOS payload. The CuRIOS-ED camera system will utilize a Sony IMX455 CMOS detector delivered in an off-the-shelf Atik apx60 package which we modified to be compatible with operations in vacuum as well as the CubeSat form factor, power, and thermal constraints. By qualifying this commercial camera solution, the cost of each CuRIOS satellite will be greatly decreased (~100x) when compared with current space-qualified cameras with IMX455 detectors. We discuss the CuRIOS-ED mission design with an emphasis on the disassembly, repackaging, and testing of the Atik apx60 for space-based missions. Characterization of the apx60's read noise, dark current, patterned noise, and thermal behavior are reported for a range of temperatures (-35 C to 40 C) and exposure times (0.001s to 30 s). Additionally, we comment on preliminary environmental testing results from a successful thermal vacuum test., Comment: 17 pages, 10 figures, 1 table