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We introduce a quantum spin Hall semimetal or Fermi liquid characterized with a Z2 topological invariant, measurable through circularly polarized light. We propose its engineering through two topological metallic band structures in crystals on the honeycomb lattice, with spin-orbit interaction, realizable through the interplay of a charge or spin density wave substrate and Zeeman effects, in between a quantum spin Hall and a quantum anomalous Hall insulator. These systems show topologically protected helical edge or photo-induced currents.

Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal compositions. To this end, we train a video generation model conditioned on spatio-temporally sparse or dense motion trajectories. In contrast to prior motion conditioning work, this flexible representation can encode any number of trajectories, object-specific or global scene motion, and temporally sparse motion; due to its flexibility we refer to this conditioning as motion prompts. While users may directly specify sparse trajectories, we also show how to translate high-level user requests into detailed, semi-dense motion prompts, a process we term motion prompt expansion. We demonstrate the versatility of our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing. Our results showcase emergent behaviors, such as realistic physics, suggesting the potential of motion prompts for probing video models and interacting with future generative world models. Finally, we evaluate quantitatively, conduct a human study, and demonstrate strong performance. Video results are available on our webpage: https://motion-prompting.github.io/, Comment: Project page: https://motion-prompting.github.io/

We investigate Roche lobe overflow mass transfer (MT) in eccentric binary systems between stars and compact objects (COs), modeling the coupled evolution of both the star and the orbit due to eccentric MT (eMT) in a self-consistent framework. We implement the analytic expressions for secular rates of change of the orbital semi-major axis and eccentricity, assuming a delta function MT at periapse, into the binary stellar evolution code MESA. Two scenarios are examined: (1) a simplified model isolating the effects of eMT on stellar and orbital evolution, and (2) realistic binary configurations that include angular momentum exchange (e.g., tides, mass loss, spin-orbit coupling, and gravitational wave radiation). Unlike the ad hoc approach of instant circularization that is often employed, explicit modeling of eMT reveals a large fraction of binaries can remain eccentric post-MT. Even binaries which naturally circularize during eMT have different properties (donor mass and orbital size) compared to predictions from instant circularization, with some showing fundamentally different evolutionary outcomes (e.g., stable versus unstable MT). We demonstrate that a binary's initial mass ratio and eccentricity are predictive of whether it will remain eccentric or circularize after eMT. These findings underscore the importance of eMT in understanding CO-hosting binary populations, including X-ray binaries, gravitational wave sources, and other high-energy transients., Comment: Submitted to ApJ, 21 pages, 8 figures

Confidential container is becoming increasingly popular as it meets both needs for efficient resource management by cloud providers, and data protection by cloud users. Specifically, confidential containers integrate the container and the enclave, aiming to inherit the design-wise advantages of both (i.e., resource management and data protection). However, current confidential containers suffer from large performance overheads caused by i) a larger startup latency due to the enclave creation, and ii) a larger memory footprint due to the non-shareable characteristics of enclave memory. This paper explores a design conundrum of confidential container, examining why the confidential containers impose such large performance overheads. Surprisingly, we found there is a universal misconception that an enclave can only be used by a single (containerized) process that created it. However, an enclave can be shared across multiple processes, because an enclave is merely a set of physical resources while the process is an abstraction constructed by the host kernel. To this end, we introduce TeeMate, a new approach to utilize the enclaves on the host system. Especially, TeeMate designs the primitives to i) share the enclave memory between processes, thus preserving memory abstraction, and ii) assign the threads in enclave between processes, thus preserving thread abstraction. We concretized TeeMate on Intel SGX, and implemented confidential serverless computing and confidential database on top of TeeMate based confidential containers. The evaluation clearly demonstrated the strong practical impact of TeeMate by achieving at least 4.5 times lower latency and 2.8 times lower memory usage compared to the applications built on the conventional confidential containers.

Understanding and improving generalization capabilities is crucial for both classical and quantum machine learning (QML). Recent studies have revealed shortcomings in current generalization theories, particularly those relying on uniform bounds, across both classical and quantum settings. In this work, we present a margin-based generalization bound for QML models, providing a more reliable framework for evaluating generalization. Our experimental studies on the quantum phase recognition (QPR) dataset demonstrate that margin-based metrics are strong predictors of generalization performance, outperforming traditional metrics like parameter count. By connecting this margin-based metric to quantum information theory, we demonstrate how to enhance the generalization performance of QML through a classical-quantum hybrid approach when applied to classical data., Comment: 18 pages, 6 figures

Genome analysis has revolutionized fields such as personalized medicine and forensics. Modern sequencing machines generate vast amounts of fragmented strings of genome data called reads. The alignment of these reads into a complete DNA sequence of an organism (the read mapping process) requires extensive data transfer between processing units and memory, leading to execution bottlenecks. Prior studies have primarily focused on accelerating specific stages of the read-mapping task. Conversely, this paper introduces a holistic framework called DART-PIM that accelerates the entire read-mapping process. DART-PIM facilitates digital processing-in-memory (PIM) for an end-to-end acceleration of the entire read-mapping process, from indexing using a unique data organization schema to filtering and read alignment with an optimized Wagner Fischer algorithm. A comprehensive performance evaluation with real genomic data shows that DART-PIM achieves a 5.7x and 257x improvement in throughput and a 92x and 27x energy efficiency enhancement compared to state-of-the-art GPU and PIM implementations, respectively.

Sodium-ion batteries are emerging as promising alternatives to lithium-ion batteries due to the abundance of sodium resources. Na3V2(PO4)2F3 (NVPF), a cathode material for sodium ion batteries, is attracting attention from its rate capability and high working voltage, but its low discharge capacity is one of the challenges. In this work, we aim to design a high-capacity NASICON-type cathode of sodium-ion battery by discovering element combinations that can stabilize the sodium excess phase in NVPFs. For the efficient discovery of element combinations, we propose a Bayesian optimization-based algorithm for chemical composition discovery. Specifically, we propose an element mapping technique to solve the limitation of Bayesian optimization in discovering chemical composition. By constructing a chemical space applicable to Bayesian optimization through element mapping and optimizing the constructed chemical space, we found optimal binary element combinations. This work not only offers insights into designing high-capacity cathodes, but also demonstrates the efficacy of the proposed algorithm in data-driven materials design., Comment: 20 pages, 5 figures

We study quasi-periodic eigenvalue problems that arise in the stability analysis of periodic traveling wave solutions to Hamiltonian PDEs. We establish bounds on regions in the complex plane when the eigenvalues may deviate from the imaginary axis, and estimates for the number of such off-axis eigenvalues. These relations hold when the dispersion relation grows sufficiently rapidly in the wavenumber. The proofs involve a Gershgorin disk argument together with the Hamiltonian symmetry of the spectrum. The results are applicable to a broad class of nonlinear dispersive equations including the generalized Korteweg--de Vries, Benjamin--Bona--Mahoney, and Kawanhara equations., Comment: 20 pages; 3 figures

Masked generative models (MGMs) have shown impressive generative ability while providing an order of magnitude efficient sampling steps compared to continuous diffusion models. However, MGMs still underperform in image synthesis compared to recent well-developed continuous diffusion models with similar size in terms of quality and diversity of generated samples. A key factor in the performance of continuous diffusion models stems from the guidance methods, which enhance the sample quality at the expense of diversity. In this paper, we extend these guidance methods to generalized guidance formulation for MGMs and propose a self-guidance sampling method, which leads to better generation quality. The proposed approach leverages an auxiliary task for semantic smoothing in vector-quantized token space, analogous to the Gaussian blur in continuous pixel space. Equipped with the parameter-efficient fine-tuning method and high-temperature sampling, MGMs with the proposed self-guidance achieve a superior quality-diversity trade-off, outperforming existing sampling methods in MGMs with more efficient training and sampling costs. Extensive experiments with the various sampling hyperparameters confirm the effectiveness of the proposed self-guidance., Comment: NeurIPS 2024. Code is available at: https://github.com/JiwanHur/UnlockMGM

Despite the recent progress, existing frame interpolation methods still struggle with processing extremely high resolution input and handling challenging cases such as repetitive textures, thin objects, and large motion. To address these issues, we introduce a patch-based cascaded pixel diffusion model for frame interpolation, HiFI, that excels in these scenarios while achieving competitive performance on standard benchmarks. Cascades, which generate a series of images from low- to high-resolution, can help significantly with large or complex motion that require both global context for a coarse solution and detailed context for high resolution output. However, contrary to prior work on cascaded diffusion models which perform diffusion on increasingly large resolutions, we use a single model that always performs diffusion at the same resolution and upsamples by processing patches of the inputs and the prior solution. We show that this technique drastically reduces memory usage at inference time and also allows us to use a single model at test time, solving both frame interpolation and spatial up-sampling, saving training cost. We show that HiFI helps significantly with high resolution and complex repeated textures that require global context. HiFI demonstrates comparable or beyond state-of-the-art performance on multiple benchmarks (Vimeo, Xiph, X-Test, SEPE-8K). On our newly introduced dataset that focuses on particularly challenging cases, HiFI also significantly outperforms other baselines on these cases. Please visit our project page for video results: https://hifi-diffusion.github.io, Comment: Project page: https://hifi-diffusion.github.io/

Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this paper, we present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes. Our key insight is that by simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes. However, this approach presents a significant challenge: the scarcity of suitable training data, namely dynamic, posed videos with depth labels. Despite this, we show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics, even without an explicit motion representation. Based on this, we introduce new optimizations for several downstream video-specific tasks and demonstrate strong performance on video depth and camera pose estimation, outperforming prior work in terms of robustness and efficiency. Moreover, MonST3R shows promising results for primarily feed-forward 4D reconstruction., Comment: Project page: https://monst3r-project.github.io/

Blindly estimating the direction of arrival (DoA) of early room reflections without prior knowledge of the room impulse response or source signal is highly valuable in audio signal processing applications. The FF-PHALCOR (Frequency Focusing PHase ALigned CORrelation) method was recently developed for this purpose, extending the original PHALCOR method to work with arbitrary arrays rather than just spherical ones. Previous studies have provided only initial insights into its performance. This study offers a comprehensive analysis of the method's performance and limitations, examining how reflection characteristics such as delay, amplitude, and spatial density affect its effectiveness. The research also proposes improvements to overcome these limitations, enhancing detection quality and reducing false alarms. Additionally, the study examined how spatial perception is affected by generating room impulse responses using estimated reflection information. The findings suggest a perceptual advantage of the proposed approach over the baseline, with particularly high perceptual quality when using the spherical array with 32 microphones. However, the quality is somewhat reduced when using a semi-circular array with only 6 microphones.

The increasing popularity of spatial audio in applications such as teleconferencing, entertainment, and virtual reality has led to the recent developments of binaural reproduction methods. However, only a few of these methods are well-suited for wearable and mobile arrays, which typically consist of a small number of microphones. One such method is binaural signal matching (BSM), which has been shown to produce high-quality binaural signals for wearable arrays. However, BSM may be suboptimal in cases of high direct-to-reverberant ratio (DRR) as it is based on the diffuse sound field assumption. To overcome this limitation, previous studies incorporated sound-field models other than diffuse. However, this approach was not studied comprehensively. This paper extensively investigates two BSM-based methods designed for high DRR scenarios. The methods incorporate a sound field model composed of direct and reverberant components.The methods are investigated both mathematically and using simulations, finally validated by a listening test. The results show that the proposed methods can significantly improve the performance of BSM , in particular in the direction of the source, while presenting only a negligible degradation in other directions. Furthermore, when source direction estimation is inaccurate, performance of these methods degrade to equal that of the BSM, presenting a desired robustness quality.

In this paper, we present a new method for designing wavelet filter banks for any dilation matrices and in any dimension. Our approach utilizes extended Laplacian pyramid matrices to achieve this flexibility. By generalizing recent tight wavelet frame construction methods based on the sum of squares representation, we introduce the sum of vanishing products (SVP) condition, which is significantly easier to satisfy. These flexible design methods rely on our main results, which establish the equivalence between the SVP and mixed unitary extension principle conditions. Additionally, we provide illustrative examples to showcase our main findings., Comment: This paper was submitted to wspc/ws-ijwmip

Model execution allows us to prototype and analyse software engineering models by stepping through their possible behaviours, using techniques like animation and simulation. On the other hand, deductive verification allows us to construct formal proofs demonstrating satisfaction of certain critical properties in support of high-assurance software engineering. To ensure coherent results between execution and proof, we need unifying semantics and automation. In this paper, we mechanise Interaction Trees (ITrees) in Isabelle/HOL to produce an execution and verification framework. ITrees are coinductive structures that allow us to encode infinite labelled transition systems, yet they are inherently executable. We use ITrees to create verification tools for stateful imperative programs, concurrent programs with message passing in the form of the CSP and \Circus languages, and abstract system models in the style of the Z and B methods. We demonstrate how ITrees can account for diverse semantic presentations, such as structural operational semantics, a relational program model, and CSP's failures-divergences trace model. Finally, we demonstrate how ITrees can be executed using the Isabelle code generator to support the animation of models., Comment: 41 pages, accepted for publication in ACM TOSEM. Journal extension of arXiv:2105.05133

Topology profoundly influences diverse fields of science, providing a powerful framework for classifying phases of matter and predicting nontrivial excitations, such as solitons, vortices, and skyrmions. These topological defects are typically characterized by integer numbers, called topological charges, representing the winding number in their order parameter field. The classification and prediction of topological defects, however, become challenging when singularities are included within the integration domain for calculating the topological charge. While such exotic nonlinear excitations have been proposed in the superfluid $^3$He-A phase and spinor Bose-Einstein condensate of atomic gases, experimental observation of these structures and studies of their stability have long been elusive. Here we report the observation of a singular skyrmion that goes beyond the framework of topology in a ferromagnetic superfluid. The exotic skyrmions are sustained by undergoing anomalous symmetry breaking associated with the eccentric spin singularity and carry half of the elementary charge, distinctive from conventional skyrmions or merons. By successfully realizing the universal regime of the quantum Kelvin-Helmholtz instability, we identified the eccentric fractional skyrmions, produced by emission from a magnetic domain wall and a spontaneous splitting of an integer skyrmion with spin singularities. The singular skyrmions are stable and can be observed after 2~s of hold time. Our results confirm the universality between classical and quantum Kelvin-Helmholtz instabilities and broaden our understanding on complex nonlinear dynamics of nontrivial texture beyond skyrmion in topological quantum systems., Comment: 13 pages, 5 main figures and 7 supplemental figures

As machine-learning models grow in size, their implementation requirements cannot be met by a single computer system. This observation motivates distributed settings, in which intermediate computations are performed across a network of processing units, while the central node only aggregates their outputs. However, distributing inference tasks across low-precision or faulty edge devices, operating over a network of noisy communication channels, gives rise to serious reliability challenges. We study the problem of an ensemble of devices, implementing regression algorithms, that communicate through additive noisy channels in order to collaboratively perform a joint regression task. We define the problem formally, and develop methods for optimizing the aggregation coefficients for the parameters of the noise in the channels, which can potentially be correlated. Our results apply to the leading state-of-the-art ensemble regression methods: bagging and gradient boosting. We demonstrate the effectiveness of our algorithms on both synthetic and real-world datasets.

This study investigates the approach of direction-dependent selection of Head-Related Transfer Functions (HRTFs) and its impact on sound localization accuracy. For applications such as virtual reality (VR) and teleconferencing, obtaining individualized HRTFs can be beneficial yet challenging, the objective of this work is therefore to assess whether incorporating HRTFs in a direction-dependent manner could improve localization precision without the need to obtain individualized HRTFs. A localization experiment conducted with a VR headset assessed localization errors, comparing an overall best HRTF from a set, against selecting the best HRTF based on average performance in each direction. The results demonstrate a substantial improvement in elevation localization error with the method motivated by direction-dependent HRTF selection, while revealing insignificant differences in azimuth errors., Comment: Accepted for publication in the 2024 AES International Conference on Audio for Virtual and Augmented Reality, 5 pages, 4 figures

Binaural reproduction for headphone-centric listening has become a focal point in ongoing research, particularly within the realm of advancing technologies such as augmented and virtual reality (AR and VR). The demand for high-quality spatial audio in these applications is essential to uphold a seamless sense of immersion. However, challenges arise from wearable recording devices equipped with only a limited number of microphones and irregular microphone placements due to design constraints. These factors contribute to limited reproduction quality compared to reference signals captured by high-order microphone arrays. This paper introduces a novel optimization loss tailored for a beamforming-based, signal-independent binaural reproduction scheme. This method, named iMagLS-BSM incorporates an interaural level difference (ILD) error term into the previously proposed binaural signal matching (BSM) magnitude least squares (MagLS) rendering loss for lateral plane angles. The method leverages nonlinear programming to minimize the introduced loss. Preliminary results show a substantial reduction in ILD error, while maintaining a binaural magnitude error comparable to that achieved with a MagLS BSM solution. These findings hold promise for enhancing the overall spatial quality of resultant binaural signals., Comment: Paper accepted for publication in IWAENC 2024, 4 pages, 2 figures

Binaural reproduction is rapidly becoming a topic of great interest in the research community, especially with the surge of new and popular devices, such as virtual reality headsets, smart glasses, and head-tracked headphones. In order to immerse the listener in a virtual or remote environment with such devices, it is essential to generate realistic and accurate binaural signals. This is challenging, especially since the microphone arrays mounted on these devices are typically composed of an arbitrarily-arranged small number of microphones, which impedes the use of standard audio formats like Ambisonics, and provides limited spatial resolution. The binaural signal matching (BSM) method was developed recently to overcome these challenges. While it produced binaural signals with low error using relatively simple arrays, its performance degraded significantly when head rotation was introduced. This paper aims to develop the BSM method further and overcome its limitations. For this purpose, the method is first analyzed in detail, and a design framework that guarantees accurate binaural reproduction for relatively complex acoustic environments is presented. Next, it is shown that the BSM accuracy may significantly degrade at high frequencies, and thus, a perceptually motivated extension to the method is proposed, based on a magnitude least-squares (MagLS) formulation. These insights and developments are then analyzed with the help of an extensive simulation study of a simple six-microphone semi-circular array. It is further shown that the BSM-MagLS method can be very useful in compensating for head rotations with this array. Finally, a listening experiment is conducted with a four-microphone array on a pair of glasses in a reverberant speech environment and including head rotations, where it is shown that BSM-MagLS can indeed produce binaural signals with a high perceived quality., Comment: Submitted to EURASIP Journal on audio speech and music processing

We present a comprehensive theoretical study of interacting and disordered topological phases of coupled Kitaev wires, which may support further realistic applications of Majorana fermions. We develop a variety of analytical, mathematical and numerical methods for one and two-coupled wires, associated with a topological marker accessible from real-space correlation functions on the wire(s). We verify the stability of the topological superconducting phase and quantify disorder effects close to the quantum phase transitions, e.g. through two-point correlation functions or using a renormalization group (RG) analysis of disorder. We show for the first time that the double critical Ising (DCI) phase -- a fractional Majorana liquid characterized by a pair of half central charges and topological numbers -- is stabilized by strong interactions against disorder which respects the inversion symmetry between the wires (ie. parity conservation on each wire). In the presence of an inter-wire hopping term, the DCI phase turns into a protected topological phase with a bulk gap. We study the localization physics developing along the critical line for weaker interactions., Comment: 13 pages, 11 figures (+ Appendices)

In this study, we investigated Rydberg atom-based microwave electrometry using polarization spectroscopy in a room-temperature vapor cell. By measuring Autler-Townes splitting in the electromagnetically induced transparency (EIT) spectrum, we determined that the minimum measurable microwave electric field is approximately five times lower than conventional EIT techniques. The results are well reproduced by a full optical Bloch equation model, which takes into account all the hyperfine levels involved. Subsequently, the EIT setup was used to characterize a custom microwave cylindrical lens, which increases the field at the focus by a factor of three, decreasing the minimum measurable microwave electric field by the same amount. Our results indicate that the combination of polarization spectroscopy and a microwave lens may enhance microwave electrometry.

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

We introduce a new convexified matching method for missing value imputation and individualized inference inspired by computational optimal transport. Our method integrates favorable features from mainstream imputation approaches: optimal matching, regression imputation, and synthetic control. We impute counterfactual outcomes based on convex combinations of observed outcomes, defined based on an optimal coupling between the treated and control data sets. The optimal coupling problem is considered a convex relaxation to the combinatorial optimal matching problem. We estimate granular-level individual treatment effects while maintaining a desirable aggregate-level summary by properly constraining the coupling. We construct transparent, individual confidence intervals for the estimated counterfactual outcomes. We devise fast iterative entropic-regularized algorithms to solve the optimal coupling problem that scales favorably when the number of units to match is large. Entropic regularization plays a crucial role in both inference and computation; it helps control the width of the individual confidence intervals and design fast optimization algorithms.

Practitioners often deploy a learned prediction model in a new environment where the joint distribution of covariate and response has shifted. In observational data, the distribution shift is often driven by unobserved confounding factors lurking in the environment, with the underlying mechanism unknown. Confounding can obfuscate the definition of the best prediction model (concept shift) and shift covariates to domains yet unseen (covariate shift). Therefore, a model maximizing prediction accuracy in the source environment could suffer a significant accuracy drop in the target environment. This motivates us to study the domain adaptation problem with observational data: given labeled covariate and response pairs from a source environment, and unlabeled covariates from a target environment, how can one predict the missing target response reliably? We root the adaptation problem in a linear structural causal model to address endogeneity and unobserved confounding. We study the necessity and benefit of leveraging exogenous, invariant covariate representations to cure concept shifts and improve target prediction. This further motivates a new representation learning method for adaptation that optimizes for a lower-dimensional linear subspace and, subsequently, a prediction model confined to that subspace. The procedure operates on a non-convex objective-that naturally interpolates between predictability and stability/invariance-constrained on the Stiefel manifold. We study the optimization landscape and prove that, when the regularization is sufficient, nearly all local optima align with an invariant linear subspace resilient to both concept and covariate shift. In terms of predictability, we show a model that uses the learned lower-dimensional subspace can incur a nearly ideal gap between target and source risk. Three real-world data sets are investigated to validate our method and theory.

Despite the striking advances in recent language generation performance, model-generated responses have suffered from the chronic problem of hallucinations that are either untrue or unfaithful to a given source. Especially in the task of knowledge grounded conversation, the models are required to generate informative responses, but hallucinated utterances lead to miscommunication. In particular, entity-level hallucination that causes critical misinformation and undesirable conversation is one of the major concerns. To address this issue, we propose a post-hoc refinement method called REM. It aims to enhance the quality and faithfulness of hallucinated utterances by refining them based on the source knowledge. If the generated utterance has a low source-faithfulness score with the given knowledge, REM mines the key entities in the knowledge and implicitly uses them for refining the utterances. We verify that our method reduces entity hallucination in the utterance. Also, we show the adaptability and efficacy of REM with extensive experiments and generative results. Our code is available at https://github.com/YOONNAJANG/REM., Comment: Accepted at EMNLP 2023

We present the first on-sky results from an ultra-low-readout-noise Skipper CCD focal plane prototype for the SOAR Integral Field Spectrograph (SIFS). The Skipper CCD focal plane consists of four 6k x 1k, 15 $\mu$m pixel, fully-depleted, p-channel devices that have been thinned to ~250 $\mu$m, backside processed, and treated with an anti-reflective coating. These Skipper CCDs were configured for astronomical spectroscopy, i.e., single-sample readout noise < 4.3 e- rms/pixel, the ability to achieve multi-sample readout noise $\ll$ 1 e- rms/pixel, full-well capacities ~40,000-65,000 e-, low dark current and charge transfer inefficiency (~2 x 10$^{-4}$ e-/pixel/s and 3.44 x 10$^{-7}$, respectively), and an absolute quantum efficiency of $\gtrsim$ 80% between 450 nm and 980 nm ($\gtrsim$ 90% between 600 nm and 900 nm). We optimized the readout sequence timing to achieve sub-electron noise (~0.5 e- rms/pixel) in a region of 2k x 4k pixels and photon-counting noise (~0.22 e- rms/pixel) in a region of 220 x 4k pixels, each with a readout time of $\lesssim$ 17 min. We observed two quasars (HB89 1159+123 and QSO J1621-0042) at redshift z ~ 3.5, two high-redshift galaxy clusters (CL J1001+0220 and SPT-CL J2040-4451), an emission line galaxy at z = 0.3239, a candidate member star of the Bo\"{o}tes II ultra-faint dwarf galaxy, and five CALSPEC spectrophotometric standard stars (HD074000, HD60753, HD106252, HD101452, HD200654). We present charge-quantized, photon-counting observations of the quasar HB89 1159+123 and show the detector sensitivity increase for faint spectral features. We demonstrate signal-to-noise performance improvements for SIFS observations in the low-background, readout-noise-dominated regime. We outline scientific studies that will leverage the SIFS-Skipper CCD data and new detector architectures that utilize the Skipper floating gate amplifier with faster readout times., Comment: 20 pages, 13 figures, 1 table; Proc. SPIE

In this article, we characterize the Beurling and Model subspaces of the Hardy-Hilbert space $H^2(\mathbb{D})$ invariant under the composition operator $C_{\phi_a}f=f\circ\phi_a$, where $\phi_a(z) = az + 1 - a$ for $a \in (0,1)$ is an affine self-map of the open unit disk $\mathbb{D}$. These operators have universal translates (in the sense of Rota) and have attracted attention recently due to their connection with the Invariant Subspace Problem (ISP) and the classical Ces\`aro operator., Comment: 9 pages, updated references

Tight wavelet frames (TWFs) in $L^2(\mathbb{R}^n)$ are versatile and practical structures that provide the perfect reconstruction property. Nevertheless, existing TWF construction methods exhibit limitations, including a lack of specific methods for generating mother wavelets in extension-based construction, and the necessity to address the sum of squares (SOS) problem even when specific methods for generating mother wavelets are provided in SOS-based construction. It is a common practice for current TWF constructions to begin with a given refinable function. However, this approach places the entire burden on finding suitable mother wavelets. In this paper, we introduce TWF construction methods that spread the burden between both types of functions: refinable functions and mother wavelets. These construction methods offer an alternative approach to circumvent the SOS problem while providing specific techniques for generating mother wavelets. We present examples to illustrate our construction methods.

The increasing volume of electronic health records (EHRs) across healthcare institutions presents the opportunity to enhance model accuracy and robustness in clinical prediction tasks. Federated learning enables training on data from multiple institutions while preserving patient privacy and complying to regulatory constraints. However, most federated learning research focuses on constructing a global model for multiple clients, overlooking the practical need for institution-specific models. In this work, we introduce EHRFL, a federated learning framework using EHRs designed to develop a model tailored to a single healthcare institution. Our framework addresses two key challenges: (1) enabling federated learning across institutions with heterogeneous EHR systems using text-based EHR modeling, and (2) reducing the costs associated with federated learning by selecting suitable participating clients using averaged patient embeddings, which enables optimizing the number of participants without compromising model performance for the institution. Our experiment results on multiple open-source EHR datasets demonstrate the effectiveness of EHRFL in addressing the two challenges, establishing it as a practical solution for institution-specific model development in federated learning.

The economic success of individuals is often determined by a combination of talent, luck, and assistance from others. We introduce a new agent-based model that simultaneously considers talent, luck, and social interaction. This model allows us to explore how network structure (how agents interact) and talent distribution among agents affect the dynamics of capital accumulation through analytical and numerical methods. We identify a phenomenon as ``talent configuration effect", which refers to the influence of how talent is allocated to individuals (nodes) in the network. We analyze this effect through two key properties: talent assortativity (TA) and talent-degree correlation (TD). In particular, we focus on three economic indicators: growth rate ($n_{\rm rate}$), Gini coefficient (inequality: $n_{\rm Gini}$), and meritocratic fairness ($n_{LT}$). This investigation helps us understand the interplay between talent configuration and network structure on capital dynamics. We find that, in the short term, positive correlations exist between TA and TD for all three economic indicators. Furthermore, the dominant factor influencing capital dynamics depends on the network topology. In scale-free networks, TD has a stronger influence on the economic indices than TA. Conversely, in lattice-like networks, TA plays a more significant role. Our findings address that high socioeconomic homophily can create a dilemma between growth and equality, and that hub monopolization by few highly talented agents makes economic growth strongly dependent on their performances., Comment: 20 pages = (12 pages, 6 figures, and 1 table in main text and Appendix A-D) + (8 pages, 8 figures and 1 table as Supplemental Material); Published version with extra typo correction

Despite the progress of learning-based methods for 6D object pose estimation, the trade-off between accuracy and scalability for novel objects still exists. Specifically, previous methods for novel objects do not make good use of the target object's 3D shape information since they focus on generalization by processing the shape indirectly, making them less effective. We present GenFlow, an approach that enables both accuracy and generalization to novel objects with the guidance of the target object's shape. Our method predicts optical flow between the rendered image and the observed image and refines the 6D pose iteratively. It boosts the performance by a constraint of the 3D shape and the generalizable geometric knowledge learned from an end-to-end differentiable system. We further improve our model by designing a cascade network architecture to exploit the multi-scale correlations and coarse-to-fine refinement. GenFlow ranked first on the unseen object pose estimation benchmarks in both the RGB and RGB-D cases. It also achieves performance competitive with existing state-of-the-art methods for the seen object pose estimation without any fine-tuning.