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A holomorphic pre-foliation $\mathscr{F}=\mathcal{C}\boxtimes\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$ is the data of a reduced complex projective curve $\mathcal{C}$ of $\mathbb{P}^{2}_{\mathbb{C}}$ and a holomorphic foliation $\mathcal{F}$ on $\mathbb{P}^{2}_{\mathbb{C}}$. When the foliation $\mathcal{F}$ is convex (resp. reduced convex) and the curve $\mathcal{C}$ is invariant by $\mathcal{F}$, we say that the pre-foliation $\mathscr{F}=\mathcal{C}\boxtimes\mathcal{F}$ is convex (resp. reduced convex). We prove that the dual web of a reduced convex pre-foliation on $\mathbb{P}^{2}_{\mathbb{C}}$ is flat. This generalizes our previous result obtained in the case where the associated curve consists only of invariant lines., Comment: 14 pages, in French language

Dataset distillation aims to distill the knowledge of a large-scale real dataset into small yet informative synthetic data such that a model trained on it performs as well as a model trained on the full dataset. Despite recent progress, existing dataset distillation methods often struggle with computational efficiency, scalability to complex high-resolution datasets, and generalizability to deep architectures. These approaches typically require retraining when the distillation ratio changes, as knowledge is embedded in raw pixels. In this paper, we propose a novel framework called Data-to-Model Distillation (D2M) to distill the real dataset's knowledge into the learnable parameters of a pre-trained generative model by aligning rich representations extracted from real and generated images. The learned generative model can then produce informative training images for different distillation ratios and deep architectures. Extensive experiments on 15 datasets of varying resolutions show D2M's superior performance, re-distillation efficiency, and cross-architecture generalizability. Our method effectively scales up to high-resolution 128x128 ImageNet-1K. Furthermore, we verify D2M's practical benefits for downstream applications in neural architecture search., Comment: Accepted in the 18th European Conference on Computer Vision (ECCV 2024), Milan, Italy, September 29 October 4, 2024

We study the appearances of $\mathsf{LS}_{12}$ and $\mathsf{S}_{16}$ in the weight-graded compactly supported cohomology of moduli spaces of curves. As applications, we prove new nonvanishing results for the middle cohomology groups of $\mathcal{M}_9$ and $\mathcal{M}_{11}$ and give evidence to support the conjecture that the dimension fo $H^{2g + k}_c(\mathcal{M}_g)$ grows at least exponentially with $g$ for almost all $k$., Comment: 28 pages

In this paper, the method of gaps, a technique for deriving closed-form expressions in terms of information measures for the generalization error of machine learning algorithms is introduced. The method relies on two central observations: $(a)$~The generalization error is an average of the variation of the expected empirical risk with respect to changes on the probability measure (used for expectation); and~$(b)$~these variations, also referred to as gaps, exhibit closed-form expressions in terms of information measures. The expectation of the empirical risk can be either with respect to a measure on the models (with a fixed dataset) or with respect to a measure on the datasets (with a fixed model), which results in two variants of the method of gaps. The first variant, which focuses on the gaps of the expected empirical risk with respect to a measure on the models, appears to be the most general, as no assumptions are made on the distribution of the datasets. The second variant develops under the assumption that datasets are made of independent and identically distributed data points. All existing exact expressions for the generalization error of machine learning algorithms can be obtained with the proposed method. Also, this method allows obtaining numerous new exact expressions, which improves the understanding of the generalization error; establish connections with other areas in statistics, e.g., hypothesis testing; and potentially, might guide algorithm designs., Comment: Submitted to the IEEE Transaction on Information Theory. November 18, 2024

The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures, submitted to ApJ

In this paper, we derive first and second-order optimality conditions of KKT type for locally optimal solutions to a class of multiobjective optimal control problems with endpoint constraint and mixed pointwise constraints. We give some sufficient conditions for normality of multipliers. Namely, we show that if the linearized system is controllable or some constraint qualifications are satisfied, then the multiplier corresponding to the objective function is different from zero. To demonstrate the practical relevance of our theoretical results, we apply these conditions to a multiobjective optimal control problem for sustainable energy management in smart grids, providing insights into the trade-offs between cost, renewable energy utilization, environmental impact, and grid stability., Comment: 27 pages

User-schedulable languages (USLs) help programmers productively optimize programs by providing safe means of transforming them. Current USLs are designed to give programmers exactly the control they want, while automating all other concerns. However, there is no universal answer for what performance-conscious programmers want to control, how they want to control it, and what they want to automate, even in relatively narrow domains. We claim that USLs should, instead, be designed to grow. We present Exo 2, a scheduling language that enables users to define new scheduling operations externally to the compiler. By composing a set of trusted, fine-grained primitives, users can safely write their own scheduling library to build up desired automation. We identify actions (ways of modifying code), inspection (ways of interrogating code), and references (ways of pointing to code) as essential for any user-extensible USL. We fuse these ideas into a new mechanism called Cursors that enables the creation of scheduling libraries in user code. We demonstrate libraries that amortize scheduling effort across more than 80 high-performance kernels, reducing total scheduling code by an order of magnitude and delivering performance competitive with state-of-the-art implementations on three different platforms., Comment: To appear in ASPLOS 2025. The arXiv version contains full appendices

We prove that the number of curves of a fixed genus g over finite fields is a polynomial function of the size of the field if and only if g is at most 8. Furthermore, we determine for each positive genus g the smallest n such that the moduli space of curves of genus g with n marked points does not have polynomial point count. A key ingredient in the proofs, which is also a new result of independent interest, is the computation of the thirteenth cohomology group of the moduli spaces of stable curves of genus g with n marked points, for all g and n., Comment: 61 pages

Deep learning (DL) methods have emerged as a powerful tool for the inversion of geophysical data. When applied to field data, these models often struggle without additional fine-tuning of the network. This is because they are built on the assumption that the statistical patterns in the training and test datasets are the same. To address this, we propose a DL-based inversion scheme for Radio Magnetotelluric data where the subsurface resistivity models are generated using Gaussian Random Fields (GRF). The network's generalization ability was tested with an out-of-distribution (OOD) dataset comprising a homogeneous background and various rectangular-shaped anomalous bodies. After end-to-end training with the GRF dataset, the pre-trained network successfully identified anomalies in the OOD dataset. Synthetic experiments confirmed that the GRF dataset enhances generalization compared to a homogeneous background OOD dataset. The network accurately recovered structures in a checkerboard resistivity model, and demonstrated robustness to noise, outperforming traditional gradient-based methods. Finally, the developed scheme is tested using exemplary field data from a waste site near Roorkee, India. The proposed scheme enhances generalization in a data-driven supervised learning framework, suggesting a promising direction for OOD generalization in DL methods.

Optical metasurfaces provide novel solutions to label-free biochemical sensing by localizing light resonantly beyond the diffraction limit, thereby selectively enhancing light-matter interactions for improved analytical performance. However, high-Q resonances in metasurfaces are usually achieved in the reflection mode, which impedes metasurface integration into compact imaging systems. Here, we demonstrate a novel metasurface platform for advanced biochemical sensing based on the physics of the bound states in the continuum (BIC) and electromagnetically induced transparency (EIT) modes, which arise when two interfering resonances from a periodic pattern of tilted elliptic holes overlap both spectrally and spatially, creating a narrow transparency window in the mid-infrared spectrum. We experimentally measure these resonant peaks observed in transmission mode (Q~734 at ~8.8 um) in free-standing silicon membranes and confirm their tunability through geometric scaling. We also demonstrate the strong coupling of the BIC-EIT modes with a thinly coated PMMA film on the metasurface, characterized by a large Rabi splitting (32 cm-1) and biosensing of protein monolayers in transmission mode. Our new photonic platform can facilitate the integration of metasurface biochemical sensors into compact and monolithic optical systems while being compatible with scalable manufacturing, thereby clearing the way for on-site biochemical sensing in everyday applications.

Dataset distillation has demonstrated strong performance on simple datasets like CIFAR, MNIST, and TinyImageNet but struggles to achieve similar results in more complex scenarios. In this paper, we propose EDF (emphasizes the discriminative features), a dataset distillation method that enhances key discriminative regions in synthetic images using Grad-CAM activation maps. Our approach is inspired by a key observation: in simple datasets, high-activation areas typically occupy most of the image, whereas in complex scenarios, the size of these areas is much smaller. Unlike previous methods that treat all pixels equally when synthesizing images, EDF uses Grad-CAM activation maps to enhance high-activation areas. From a supervision perspective, we downplay supervision signals that have lower losses, as they contain common patterns. Additionally, to help the DD community better explore complex scenarios, we build the Complex Dataset Distillation (Comp-DD) benchmark by meticulously selecting sixteen subsets, eight easy and eight hard, from ImageNet-1K. In particular, EDF consistently outperforms SOTA results in complex scenarios, such as ImageNet-1K subsets. Hopefully, more researchers will be inspired and encouraged to improve the practicality and efficacy of DD. Our code and benchmark will be made public at https://github.com/NUS-HPC-AI-Lab/EDF., Comment: 24 pages, 13 figures

The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope that the ideas and perspectives are a source of inspiration. Structure: Black Hole Evaporation - 50 Years by William Unruh The Stability Problem for Extremal Black Holes by Mihalis Dafermos The Entropy of Black Holes by Robert M. Wald The Non-linear Regime of Gravity by Luis Lehner Black Holes Galore in D > 4 by Roberto Emparan Same as Ever: Looking for (In)variants in the Black Holes Landscape by Carlos A. R. Herdeiro Black Holes, Cauchy Horizons, and Mass Inflation by Matt Visser The Backreaction Problem for Black Holes in Semiclassical Gravity by Adrian del Rio Black Holes Beyond General Relativity by Enrico Barausse and Jutta Kunz Black Holes as Laboratories: Searching for Ultralight Fields by Richard Brito Primordial Black Holes from Inflation by Misao Sasaki Tests of General Relativity with Future Detectors by Emanuele Berti Black Holes as Laboratories: Tests of General Relativity by Ruth Gregory and Samaya Nissanke Simulating Black Hole Imposters by Frans Pretorius Black Hole Spectroscopy: Status Report by Gregorio Carullo VLBI as a Precision Strong Gravity Instrument by Paul Tiede Testing the nature of compact objects and the black hole paradigm by Mariafelicia De Laurentis and Paolo Pani Some Thoughts about Black Holes in Asymptotic Safety by Alessia Platania Black Hole Evaporation in Loop Quantum Gravity by Abhay Ashtekar How the Black Hole Puzzles are Resolved in String Theory by Samir D. Mathur Quantum Black Holes: From Regularization to Information Paradoxes by Niayesh Afshordi and Stefano Liberati, Comment: 221 pages, 21 contributions

Technologies based on the use of hydrogen are promising for future energy requirements in a more sustainable world. Consequently, modelling fuel cells is crucial, for instance, to optimize their control to achieve excellent performance, to test new materials and configurations on a limited budget, or to consider their degradation for improved lifespan. To develop such models, a comprehensive study is required, encompassing both well-established and the latest governing laws on matter transport and voltage polarisation for Proton Exchange Membrane Fuel Cells (PEMFCs). Recent articles often rely on outdated or inappropriate equations, lacking clear explanations regarding their background. Indeed, inconsistent understanding of theoretical and experimental choices or model requirements hinders comprehension and contributes to the misuse of these equations. Additionally, specific researches are needed to construct more accurate models. This study aims to offer a comprehensive understanding of the current state-of-the-art in PEMFC modeling. It clarifies the corresponding governing equations, their usage conditions, and assumptions, thus serving as a foundation for future developments. The presented laws and equations are applicable in most multi-dimensional, dynamic, and two-phase PEMFC models., Comment: Journal of The Electrochemical Society, 2024

Missing frequency channels pose a problem for estimating $P(k_\perp,k_\parallel)$ the redshifted 21-cm power spectrum (PS) from radio-interferometric visibility data. This is particularly severe for the Murchison Widefield Array (MWA), which has a periodic pattern of missing channels that introduce spikes along $k_\parallel$. The Tracking Tapered Gridded Estimator (TTGE) overcomes this by first correlating the visibilities in the frequency domain to estimate the multi-frequency angular power spectrum (MAPS) $C_\ell(\Delta\nu)$ that has no missing frequency separation $\Delta\nu$. We perform a Fourier transform along $\Delta\nu$ to estimate $P(k_\perp,k_\parallel)$. Considering our earlier work, simulations demonstrate that the TTGE can estimate $P(k_\perp,k_\parallel)$ without any artifacts due to the missing channels. However, the spikes were still found to persist for the actual data, which is foreground-dominated. The current work presents a detailed investigation considering both simulations and actual data. We find that the spikes arise due to a combination of the missing channels and the strong spectral dependence of the foregrounds. Based on this, we propose and demonstrate a technique to mitigate the spikes. Applying this, we find the values of $P(k_\perp,k_\parallel)$ in the region $0.004 \leq k_\perp \leq 0.048\,{\rm Mpc^{-1}}$ and $k_\parallel > 0.35 \,{\rm Mpc^{-1}}$ to be consistent with zero within the expected statistical fluctuations. We obtain the $2\sigma$ upper limit of $\Delta_{\rm UL}^2(k)=(918.17)^2\,{\rm mK^2}$ at $k=0.404\,{\rm Mpc^{-1}}$ for the mean squared brightness temperature fluctuations of the $z=8.2$ epoch of reionization (EoR) 21-cm signal. This upper limit is from just $\sim 17$ minutes of observation for a single pointing direction. We expect tighter constraints when we combine all $162$ different pointing directions of the drift scan observation., Comment: 11 pages, 16 figures, comments are welcome

This paper investigates, without any regularization or penalization procedure, a shape optimization problem involving a simplified friction phenomena modeled by a scalar Tresca friction law. Precisely, using tools from convex and variational analysis such as proximal operators and the notion of twice epi-differentiability, we prove that the solution to a scalar Tresca friction problem admits a directional derivative with respect to the shape which moreover coincides with the solution to a boundary value problem involving Signorini-type unilateral conditions. Then we explicitly characterize the shape gradient of the corresponding energy functional and we exhibit a descent direction. Finally numerical simulations are performed to solve the corresponding energy minimization problem under a volume constraint which shows the applicability., Comment: 30 pages

Edge computing has emerged as an alternative to reduce transmission and processing delay and preserve privacy of the video streams. However, the ever-increasing complexity of Deep Neural Networks (DNNs) used in video-based applications (e.g. object detection) exerts pressure on memory-constrained edge devices. Model merging is proposed to reduce the DNNs' memory footprint by keeping only one copy of merged layers' weights in memory. In existing model merging techniques, (i) only architecturally identical layers can be shared; (ii) requires computationally expensive retraining in the cloud; (iii) assumes the availability of ground truth for retraining. The re-evaluation of a merged model's performance, however, requires a validation dataset with ground truth, typically runs at the cloud. Common metrics to guide the selection of shared layers include the size or computational cost of shared layers or representation size. We propose a new model merging scheme by sharing representations (i.e., outputs of layers) at the edge, guided by representation similarity S. We show that S is extremely highly correlated with merged model's accuracy with Pearson Correlation Coefficient |r| > 0.94 than other metrics, demonstrating that representation similarity can serve as a strong validation accuracy indicator without ground truth. We present our preliminary results of the newly proposed model merging scheme with identified challenges, demonstrating a promising research future direction., Comment: 3 pages, 4 figures, ACM MobiCom '24, November 18-22, 2024, Washington D.C., DC, USA

We generalize some of the well-known results of Karlin and James to Banach spaces with besselian Schauder frames (then in particular with unconditional Schauder frames). It is well-known that each Banach space with an unconditional Schauder basis has the Pe\l czy\'{n}ski's property (u). Also, that all of the classical Banach spaces ($C(\omega^{\omega})$, $C([0,1])$, $L_{1}([0,1])$, the James's space $\mathcal{J}$, and for each $p\geq q>1$: $K(l_{p},l_{q})$, $l_{p}\otimes_{\pi}l_{q^{*}}$, and $l_{p^{*}}\otimes_{\varepsilon}l_{q}$) have no unconditional Schauder bases. In this paper, we extend these results to Banach spaces with besselian Schauder frames (then in particular with unconditional Schauder frames). Finally, for each Banach space $E$ with a finite dimensional decomposition, we give an explicit method to construct a Schauder frame for $E$. Therefore, the Szarek's famous example becomes an example of a Banach space with a Schauder frame which fails to have a Schauder basis.

This paper introduces a novel Graph Neural Network (GNN) architecture for time series classification, based on visibility graph representations. Traditional time series classification methods often struggle with high computational complexity and inadequate capture of spatio-temporal dynamics. By representing time series as visibility graphs, it is possible to encode both spatial and temporal dependencies inherent to time series data, while being computationally efficient. Our architecture is fully modular, enabling flexible experimentation with different models and representations. We employ directed visibility graphs encoded with in-degree and PageRank features to improve the representation of time series, ensuring efficient computation while enhancing the model's ability to capture long-range dependencies in the data. We show the robustness and generalization capability of the proposed architecture across a diverse set of classification tasks and against a traditional model. Our work represents a significant advancement in the application of GNNs for time series analysis, offering a powerful and flexible framework for future research and practical implementations.

Within a perception framework for autonomous mobile and robotic systems, semantic analysis of 3D point clouds typically generated by LiDARs is key to numerous applications, such as object detection and recognition, and scene reconstruction. Scene semantic segmentation can be achieved by directly integrating 3D spatial data with specialized deep neural networks. Although this type of data provides rich geometric information regarding the surrounding environment, it also presents numerous challenges: its unstructured and sparse nature, its unpredictable size, and its demanding computational requirements. These characteristics hinder the real-time semantic analysis, particularly on resource-constrained hardware architectures that constitute the main computational components of numerous robotic applications. Therefore, in this paper, we investigate various 3D semantic segmentation methodologies and analyze their performance and capabilities for resource-constrained inference on embedded NVIDIA Jetson platforms. We evaluate them for a fair comparison through a standardized training protocol and data augmentations, providing benchmark results on the Jetson AGX Orin and AGX Xavier series for two large-scale outdoor datasets: SemanticKITTI and nuScenes., Comment: Accepted to IROS 2024 PPNIV Workshop

Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously.

Curating datasets that span multiple languages is challenging. To make the collection more scalable, researchers often incorporate one or more imperfect classifiers in the process, like language identification models. These models, however, are prone to failure, resulting in some language subsets being unreliable for downstream tasks. We introduce a statistical test, the Preference Proportion Test, for identifying such unreliable subsets. By annotating only 20 samples for a language subset, we're able to identify systematic transcription errors for 10 language subsets in a recent large multilingual transcribed audio dataset, X-IPAPack (Zhu et al., 2024). We find that filtering this low-quality data out when training models for the downstream task of phonetic transcription brings substantial benefits, most notably a 25.7% relative improvement on transcribing recordings in out-of-distribution languages. Our method lays a path forward for systematic and reliable multilingual dataset auditing., Comment: 16 pages, 6 figures

To explain social phenomena and identify systematic biases, much research in computational social science focuses on comparative text analyses. These studies often rely on coarse corpus-level statistics or local word-level analyses, mainly in English. We introduce the InfoGap method -- an efficient and reliable approach to locating information gaps and inconsistencies in articles at the fact level, across languages. We evaluate InfoGap by analyzing LGBT people's portrayals, across 2.7K biography pages on English, Russian, and French Wikipedias. We find large discrepancies in factual coverage across the languages. Moreover, our analysis reveals that biographical facts carrying negative connotations are more likely to be highlighted in Russian Wikipedia. Crucially, InfoGap both facilitates large scale analyses, and pinpoints local document- and fact-level information gaps, laying a new foundation for targeted and nuanced comparative language analysis at scale., Comment: 15 pages, 3 figures. To appear at EMNLP'24

The effect of relative entropy asymmetry is analyzed in the context of empirical risk minimization (ERM) with relative entropy regularization (ERM-RER). Two regularizations are considered: $(a)$ the relative entropy of the measure to be optimized with respect to a reference measure (Type-I ERM-RER); or $(b)$ the relative entropy of the reference measure with respect to the measure to be optimized (Type-II ERM-RER). The main result is the characterization of the solution to the Type-II ERM-RER problem and its key properties. By comparing the well-understood Type-I ERM-RER with Type-II ERM-RER, the effects of entropy asymmetry are highlighted. The analysis shows that in both cases, regularization by relative entropy forces the solution's support to collapse into the support of the reference measure, introducing a strong inductive bias that can overshadow the evidence provided by the training data. Finally, it is shown that Type-II regularization is equivalent to Type-I regularization with an appropriate transformation of the empirical risk function.

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a patient's care journey. However, responding to these patients' inquiries has become a significant burden on healthcare workflows, consuming considerable time for clinical care teams. To address this, we introduce RadOnc-GPT, a specialized Large Language Model (LLM) powered by GPT-4 that has been designed with a focus on radiotherapeutic treatment of prostate cancer with advanced prompt engineering, and specifically designed to assist in generating responses. We integrated RadOnc-GPT with patient electronic health records (EHR) from both the hospital-wide EHR database and an internal, radiation-oncology-specific database. RadOnc-GPT was evaluated on 158 previously recorded in-basket message interactions. Quantitative natural language processing (NLP) analysis and two grading studies with clinicians and nurses were used to assess RadOnc-GPT's responses. Our findings indicate that RadOnc-GPT slightly outperformed the clinical care team in "Clarity" and "Empathy," while achieving comparable scores in "Completeness" and "Correctness." RadOnc-GPT is estimated to save 5.2 minutes per message for nurses and 2.4 minutes for clinicians, from reading the inquiry to sending the response. Employing RadOnc-GPT for in-basket message draft generation has the potential to alleviate the workload of clinical care teams and reduce healthcare costs by producing high-quality, timely responses.

We prove large time asymptotics for solutions of the KP I equation with small initial data. Our assumptions on the initial data rule out lump solutions but give a precise description of the radiation field at large times. Our analysis uses the inverse scattering method and involves large-time asymptotics for solutions to a non-local Riemann-Hilbert problem., Comment: 71 pages, 2 figures

We present a comprehensive analysis of the X-ray spectral properties of 198 newly identified active galactic nuclei (AGNs), leveraging archival data from the {\it Chandra} X-ray Observatory. All these AGNs exhibit a powerlaw spectral signature spanning a broad energy range of $0.5-7.0$ keV, characterized by the photon index ($\Gamma$) values ranging from $0.3^{+0.16}_{-0.14}$ to $2.54^{+0.14}_{-0.13}$. Particularly, 76 of these AGNs display discernible levels of intrinsic absorption, after considering the Galactic absorption. The column densities associated with this local absorption ($n_{\rm H}^{\rm local}$) are within a range of $\sim 10^{19} - 10^{22}\ {\rm cm^{-2}}$. We study the cosmological evolution of AGNs using the variation of $n_{\rm H}^{\rm local}$ and $\Gamma$ with their estimated redshift. The intrinsic spectral signature did not reveal any significant cosmological evolution; however, a deficit of hard sources at high redshift is possibly intrinsic. Our sample covers several decades of broadband intrinsic luminosity ($L_{\rm B}^{\rm intr}$) ranging from $4.59^{+0.41}_{-0.41} \times 10^{42}$ to $2.4^{+0.12}_{-0.12} \times 10^{46}\, {\rm erg~s}^{-1}$ with peak at 1.84 redshift. We also investigate the hardness-luminosity diagram (HLD) to further probe the AGNs. We conduct a sanity check by applying our findings to known AGNs, and the results are consistent with our observations., Comment: 15 pages, 12 figures, 1 table, accepted for publication in Monthly Notices of the Royal Astronomical Society

Vortex-based single-beam tweezers have the ability to precisely and selectively move a wide range of objects, including particles, bubbles, droplets, and cells with sizes ranging from the millimeter to micrometer scale. In 2017, Karlsen and Bruus [Phys. Rev. Appl. 7, 034017 (2017)] theoretically suggested that these tweezers could also address one of the most challenging issues: the patterning and manipulation of miscible fluids. In this paper, we experimentally demonstrate this ability using acoustic vortex beams generated by interdigital transducer-based active holograms. The experimental results are supported by a numerical model based on acoustic body force simulations. This work paves the way for the precise shaping of chemical concentration fields, a crucial factor in numerous chemical and biological processes.

Real-time object detection in indoor settings is a challenging area of computer vision, faced with unique obstacles such as variable lighting and complex backgrounds. This field holds significant potential to revolutionize applications like augmented and mixed realities by enabling more seamless interactions between digital content and the physical world. However, the scarcity of research specifically fitted to the intricacies of indoor environments has highlighted a clear gap in the literature. To address this, our study delves into the evaluation of existing datasets and computational models, leading to the creation of a refined dataset. This new dataset is derived from OpenImages v7, focusing exclusively on 32 indoor categories selected for their relevance to real-world applications. Alongside this, we present an adaptation of a CNN detection model, incorporating an attention mechanism to enhance the model's ability to discern and prioritize critical features within cluttered indoor scenes. Our findings demonstrate that this approach is not just competitive with existing state-of-the-art models in accuracy and speed but also opens new avenues for research and application in the field of real-time indoor object detection.

The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, we first propose the Compressed Binary Matrix (CBM) storage format to succinctly represent the binary adjacency matrix of an unweighted graph. Then, we show how to generalize this representation to normalized adjacency matrices of unweighted graphs which arise in the context of GNNs. Finally, we develop efficient matrix multiplication kernels based on this compressed representation. The matrix multiplication kernels proposed in this work never require more scalar operations than classic sparse matrix multiplication algorithms. Experimental evaluation shows that the matrix multiplication strategies proposed outperform the current state-of-the-art implementations provided by Intel MKL, achieving speedups close to 5$\times$. Furthermore, our optimized matrix-multiplication strategies accelerated the inference time of a GNN by up to $3\times$.

While AI has been frequently applied in the context of immigration, most of these applications focus on selection and screening, which primarily serve to empower states and authorities, raising concerns due to their understudied reliability and high impact on immigrants' lives. In contrast, this paper emphasizes the potential of AI in Canada's immigration settlement phase, a stage where access to information is crucial and service providers are overburdened. By highlighting the settlement sector as a prime candidate for reliable AI applications, we demonstrate its unique capacity to empower immigrants directly, yet it remains under-explored in AI research. We outline a vision for human-centred and responsible AI solutions that facilitate the integration of newcomers. We call on AI researchers to build upon our work and engage in multidisciplinary research and active collaboration with service providers and government organizations to develop tailored AI tools that are empowering, inclusive and safe., Comment: Accepted at the 7th AAAI Conference on AI, Ethics, and Society (AIES2024)

Two mechanisms of changes from 2D to 3D (D = dimensionality) involving 2D C(sp2) trigonal paving to C(sp3) tetrahedral stacking are proposed through puckering of the 2D layers on one hand and interlayer insertion of extra C on the other hand. Such transformations, led to original 3D hexagonal C12 and C18 allotropes respectively characterized by lon and bac topologies. Using density functional theory DFT calculations, the two allotropes were found cohesive and stable both mechanically (elastic properties) and dynamically (phonons). Comparisons of the physical properties with known uni C6 were established letting identify ranges of large Vickers hardness: HV (uni C6) = 89 GPa, HV (lon C12) = 97 GPa, and HV (bac C18) = 70 GPa. Whilst C6 was identified with acoustic phonons instability, C12 and C18 were found stable dynamically throughout the acoustic and optic frequency ranges. Furthering on the thermal properties the allotropes were characterized with a temperature dependence curve of the specific heat CV close to experimental data of diamond with best fit for novel C18. The electronic band structures reveal a small band gap of 1 eV for uni C6 and larger direct band gap of 3 eV for the two other 3D allotropes. Such modulations of the electronic and physical properties should open scopes of carbon research., Comment: 20 page, 2 tables, 5 figures; original research and findings by the author, submitted to Elsevier

Gliomas, a common type of malignant brain tumor, present significant surgical challenges due to their similarity to healthy tissue. Preoperative Magnetic Resonance Imaging (MRI) images are often ineffective during surgery due to factors such as brain shift, which alters the position of brain structures and tumors. This makes real-time intraoperative MRI (ioMRI) crucial, as it provides updated imaging that accounts for these shifts, ensuring more accurate tumor localization and safer resections. This paper presents a deep learning pipeline combining You Only Look Once Version 8 (YOLOv8) and Segment Anything Model Vision Transformer-base (SAM ViT-b) to enhance glioma detection and segmentation during ioMRI. Our model was trained using the Brain Tumor Segmentation 2021 (BraTS 2021) dataset, which includes standard magnetic resonance imaging (MRI) images, and noise-augmented MRI images that simulate ioMRI images. Noised MRI images are harder for a deep learning pipeline to segment, but they are more representative of surgical conditions. Achieving a Dice Similarity Coefficient (DICE) score of 0.79, our model performs comparably to state-of-the-art segmentation models tested on noiseless data. This performance demonstrates the model's potential to assist surgeons in maximizing tumor resection and improving surgical outcomes.

This research explores the development of multimodal vision-language models for image retrieval in low-resource languages, specifically Azerbaijani. Existing vision-language models primarily support high-resource languages, and fine-tuning them remains computationally demanding. To address challenges in vision-language retrieval for low-resource languages, we integrated the CLIP model architecture and employed several techniques to balance computational efficiency with performance. These techniques include synthetic data generation through machine translation, image augmentation, and further training the attention mechanisms of transformer-based models with domain-specific data. We integrated Multilingual BERT as a text encoder with image encoders like ResNet50, EfficientNet0, Vision Transformer (ViT), and Tiny Swin Transformer. Our study found that models like EfficientNet0 and Tiny Swin Transformer perform best on the datasets they were trained on, such as COCO, Flickr30k, and Flickr8k. Augmentation techniques boosted EfficientNet0 MAP on Flickr30k from 0.84 to 0.87 and ResNet50 MAP on MSCOCO from 0.70 to 0.80, contributing to a new state of the art in vision-language retrieval. We share our configurations and results to support further research. Code and pre-trained models are available at https://github.com/aliasgerovs/azclip.

We prove a Hankel-variant commutant lifting theorem. This also uncovers the complete structure of the Beurling-type reducing and invariant subspaces of Hankel operators. Kernel spaces of Hankel operators play a key role in the analysis., Comment: 18 pages. Revised

This paper presents an experimental study on mmWave beam profiling on a mmWave testbed, and develops a machine learning model for beamforming based on the experiment data. The datasets we have obtained from the beam profiling and the machine learning model for beamforming are valuable for a broad set of network design problems, such as network topology optimization, user equipment association, power allocation, and beam scheduling, in complex and dynamic mmWave networks. We have used two commercial-grade mmWave testbeds with operational frequencies on the 27 Ghz and 71 GHz, respectively, for beam profiling. The obtained datasets were used to train the machine learning model to estimate the received downlink signal power, and data rate at the receivers (user equipment with different geographical locations in the range of a transmitter (base station). The results have shown high prediction accuracy with low mean square error (loss), indicating the model's ability to estimate the received signal power or data rate at each individual receiver covered by a beam. The dataset and the machine learning-based beamforming model can assist researchers in optimizing various network design problems for mmWave networks., Comment: In Proceedings of IEEE International Conference on Computer Communications and Networks (ICCCN), 2023

Graph Isomorphism (GI) is a fundamental algorithmic problem. Amongst graph classes for which the computational complexity of GI has been resolved, trees are arguably the most fundamental. Tree Isomorphism is complete for deterministic logspace, a tiny subclass of polynomial time, by Lindell's result. Over three decades ago, he devised a deterministic logspace algorithm that computes a string which is a canon for the input tree -- two trees are isomorphic precisely when their canons are identical. Inspired by Miller-Reif's reduction of Tree Isomorphism to Polynomial Identity Testing, we present a new logspace algorithm for tree canonization fundamentally different from Lindell's algorithm. Our algorithm computes a univariate polynomial as canon for an input tree, based on the classical Eisenstein's criterion for the irreducibility of univariate polynomials. This can be implemented in logspace by invoking the well known Buss et al. algorithm for arithmetic formula evaluation. However, we have included in the appendix a simpler self-contained proof showing that arithmetic formula evaluation is in logspace. This algorithm is conceptually very simple, avoiding the delicate case analysis and complex recursion that constitute the core of Lindell's algorithm. We illustrate the adaptability of our algorithm by extending it to a couple of other classes of graphs., Comment: Added an appendix to include a simpler self-contained proof showing that arithmetic formula evaluation is in logspace

Over the last decade, the Atacama Large Millimeter Array (ALMA) has revealed massive, extremely dusty star-forming galaxies at $z\gtrsim5$, and the James Webb Space Telescope (JWST) is primed to uncover even more information about them. These extreme observations both need dust evolution theory to provide context and are excellent benchmarks to test this theory. Here, we investigate the evolution of galactic dust populations at cosmic dawn using a suite of cosmological zoom-in simulations of moderately massive, high-redshift ($M_*\gtrsim10^9 M_{\odot}$; $z\gtrsim5$) galaxies from the Feedback in Realistic Environments (FIRE) project, the highest resolution of such simulations to date. Our simulations incorporate a dust evolution model that accounts for the dominant sources of dust production, growth, and destruction and follows the evolution of specific dust species, allowing it to replicate a wide range of present-day observations. We find, similar to other theoretical works, that dust growth via gas-dust accretion is the dominant producer of dust mass for these galaxies. However, our fiducial model produces $M_{\rm dust}$ that fall ${\gtrsim}1$ dex below observations at any given $M_*$, which we attribute to reduced accretion efficiencies caused by a combination of low metallicities and extremely bursty star formation in these galaxies. Modest enhancements (i.e., within observational/theoretical uncertainties) to accretion and SNe II dust creation raise $M_{\rm dust}$ by ${\lesssim}1$ dex, but this still falls below observations which assume $T_{\rm dust}\sim25$ K. One possibility is that inferred dust masses for $z\gtrsim4$ galaxies are overestimated, and recent observational works that find $T_{\rm dust}\sim50$ K along with metallicity constraints tentatively support this., Comment: 22 pages, 12 figures, submitted to MNRAS. Comments welcome

The tautological Chow ring of the moduli space $\mathcal{A}_g$ of principally polarized abelian varieties of dimension $g$ was defined and calculated by van der Geer in 1999. By studying the Torelli pullback of algebraic cycles classes from $\mathcal{A}_g$ to the moduli space $\mathcal{M}_g^{\mathrm{ct}}$ of genus $g$ of curves of compact type, we prove that the product class $[\mathcal{A}_1\times \mathcal{A}_5]\in \mathsf{CH}^{5}(\mathcal{A}_6)$ is non-tautological, the first construction of an interesting non-tautological algebraic class on the moduli spaces of abelian varieties. For our proof, we use the complete description of the the tautological ring $\mathsf{R}^*(\mathcal{M}_6^{\mathrm{ct}})$ in genus 6 conjectured by Pixton and recently proven by Canning-Larson-Schmitt. The tautological ring $\mathsf{R}^*(\mathcal{M}_6^{\mathrm{ct}})$ has a 1-dimensional Gorenstein kernel, which is geometrically explained by the Torelli pullback of $[\mathcal{A}_1\times \mathcal{A}_5]$. More generally, the Torelli pullback of the difference between $[\mathcal{A}_1\times \mathcal{A}_{g-1}]$ and its tautological projection always lies in the Gorenstein kernel of $\mathsf{R}^*(\mathcal{M}_g^{\mathrm{ct}})$. The product map $\mathcal{A}_1\times \mathcal{A}_{g-1}\rightarrow \mathcal{A}_g$ is a Noether-Lefschetz locus with general Neron-Severi rank 2. A natural extension of van der Geer's tautological ring is obtained by including more general Noether-Lefschetz loci. Results and conjectures related to cycle classes of Noether-Lefschetz loci for all $g$ are presented., Comment: v2: 57 pages, updated bibliography, comments welcome!

This paper characterizes the trade-offs between information and energy transmission over an additive white Gaussian noise channel in the finite block-length regime with finite channel input symbols. These trade-offs are characterized in the form of inequalities involving the information transmission rate, energy transmission rate, decoding error probability (DEP) and energy outage probability (EOP) for a given finite block-length code. The first set of results identify the set of necessary conditions that a given code must satisfy for simultaneous information and energy transmission. Following this, a novel method for constructing a family of codes that can satisfy a target information rate, energy rate, DEP and EOP is proposed. Finally, the achievability results identify the set of tuples of information rate, energy rate, DEP and EOP that can be simultaneously achieved by the constructed family of codes., Comment: arXiv admin note: substantial text overlap with arXiv:2211.05873

Dataset Distillation aims to compress a large dataset into a significantly more compact, synthetic one without compromising the performance of the trained models. To achieve this, existing methods use the agent model to extract information from the target dataset and embed it into the distilled dataset. Consequently, the quality of extracted and embedded information determines the quality of the distilled dataset. In this work, we find that existing methods introduce misaligned information in both information extraction and embedding stages. To alleviate this, we propose Prioritize Alignment in Dataset Distillation (PAD), which aligns information from the following two perspectives. 1) We prune the target dataset according to the compressing ratio to filter the information that can be extracted by the agent model. 2) We use only deep layers of the agent model to perform the distillation to avoid excessively introducing low-level information. This simple strategy effectively filters out misaligned information and brings non-trivial improvement for mainstream matching-based distillation algorithms. Furthermore, built on trajectory matching, \textbf{PAD} achieves remarkable improvements on various benchmarks, achieving state-of-the-art performance., Comment: 19 pages, 9 figures

Measuring the properties of the intergalactic medium (IGM) and sources during the Epoch of Reionization (EoR) is of immense importance. We explore the prospects of probing the IGM and sources through redshifted 21-cm observations of individual ionized bubbles surrounding known luminous sources during the EoR. Accordingly, we simulate HI 21-cm maps, foreground contaminants and system noise which are specific to the uGMRT and SKA1-Low observations. Following the subtraction of the foreground from the total visibility, we employ a visibility-based matched filter technique to optimally combine the desired HI 21-cm signal while minimizing the system noise. Our analysis suggests that these ionized bubbles can be detected with more than $5 \sigma$ significance using approximately $\sim 2000$ and $\sim 3000$ hours of observation time with the uGMRT at redshift $7.1$ and $8.3$ respectively. The SKA1-Low should be able to detect these with more than $8 \sigma$ significance using just $\sim 100$ hrs of observations. Further, we investigate the impact of the foreground subtraction on the detectability and find the signal-to-noise ratio decreases when smaller bandwidth is used. More importantly, we show that the matched filtering method can measure ionized bubble radius and constrain HI-neutral fraction reasonably well, helping deeper insights into source properties and the intergalactic medium., Comment: 26 pages, 14 figures, 1 table

Integrating modern machine learning and clinical decision-making has great promise for mitigating healthcare's increasing cost and complexity. We introduce the Enhanced Transformer for Health Outcome Simulation (ETHOS), a novel application of the transformer deep-learning architecture for analyzing high-dimensional, heterogeneous, and episodic health data. ETHOS is trained using Patient Health Timelines (PHTs)-detailed, tokenized records of health events-to predict future health trajectories, leveraging a zero-shot learning approach. ETHOS represents a significant advancement in foundation model development for healthcare analytics, eliminating the need for labeled data and model fine-tuning. Its ability to simulate various treatment pathways and consider patient-specific factors positions ETHOS as a tool for care optimization and addressing biases in healthcare delivery. Future developments will expand ETHOS' capabilities to incorporate a wider range of data types and data sources. Our work demonstrates a pathway toward accelerated AI development and deployment in healthcare.