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Source camera identification has emerged as a vital solution to unlock incidents involving critical cases like terrorism, violence, and other criminal activities. The ability to trace the origin of an image/video can aid law enforcement agencies in gathering evidence and constructing the timeline of events. Moreover, identifying the owner of a certain device narrows down the area of search in a criminal investigation where smartphone devices are involved. This paper proposes a new pixel-based method for source camera identification, integrating Pixel Difference Convolution (PDC) with a Vision Transformer network (ViT), and named PDC-ViT. While the PDC acts as the backbone for feature extraction by exploiting Angular PDC (APDC) and Radial PDC (RPDC). These techniques enhance the capability to capture subtle variations in pixel information, which are crucial for distinguishing between different source cameras. The second part of the methodology focuses on classification, which is based on a Vision Transformer network. Unlike traditional methods that utilize image patches directly for training the classification network, the proposed approach uniquely inputs PDC features into the Vision Transformer network. To demonstrate the effectiveness of the PDC-ViT approach, it has been assessed on five different datasets, which include various image contents and video scenes. The method has also been compared with state-of-the-art source camera identification methods. Experimental results demonstrate the effectiveness and superiority of the proposed system in terms of accuracy and robustness when compared to its competitors. For example, our proposed PDC-ViT has achieved an accuracy of 94.30%, 84%, 94.22% and 92.29% using the Vision dataset, Daxing dataset, Socrates dataset and QUFVD dataset, respectively.

Recent approaches in hierarchical text classification (HTC) rely on the capabilities of a pre-trained transformer model and exploit the label semantics and a graph encoder for the label hierarchy. In this paper, we introduce an effective hierarchical text classifier RADAr (Transformer-based Autoregressive Decoder Architecture) that is based only on an off-the-shelf RoBERTa transformer to process the input and a custom autoregressive decoder with two decoder layers for generating the classification output. Thus, unlike existing approaches for HTC, the encoder of RADAr has no explicit encoding of the label hierarchy and the decoder solely relies on the label sequences of the samples observed during training. We demonstrate on three benchmark datasets that RADAr achieves results competitive to the state of the art with less training and inference time. Our model consistently performs better when organizing the label sequences from children to parents versus the inverse, as done in existing HTC approaches. Our experiments show that neither the label semantics nor an explicit graph encoder for the hierarchy is needed. This has strong practical implications for HTC as the architecture has fewer requirements and provides a speed-up by a factor of 2 at inference time. Moreover, training a separate decoder from scratch in conjunction with fine-tuning the encoder allows future researchers and practitioners to exchange the encoder part as new models arise. The source code is available at https://github.com/yousef-younes/RADAr., Comment: 7 pages + 1 for references. 2 Figure. ECAI conference

The magnetar SGR J1935+2154 entered a new active episode on October 10, 2022, with X-ray bursts and enhanced persistent emission. At the tail of high burst rate interval, lasting several hours, radio bursts were detected, revealing the connection between the X-ray activities and radio emissions. We analyzed observations of SGR J1935+2154 for nearly three months, using data from Neutron Star Interior Composition Explorer (NICER). We report the timing and spectral results following the onset of this outburst. In general, the X-ray flux of the persistent emission decays exponentially. While a flare is evident on the light curve, a fast radio burst (FRB) was detected immediately following the peak of this flare. We found a phase jump of pulse profile, with a deviation of $0.16\pm0.03$ phase, which is related to the glitch. The spectra are well fit with the combination of a blackbody and a power law model. The decay of the outburst is dominated by the drop of the non-thermal component, which also leads to the increase of thermal proportion. The photon index of the power law is inversely correlated with both the unabsorbed flux and the burst rate. We find that unlike the large variety of the persistent emission around FRB 221014, the X-ray properties are very stable when FRBs 221021 and 221201 happened. These results manifest the connection between glitch, phase jump, X-ray burst, and radio burst, crucial for studying the mutation in twisted magnetic fields and constraining the trigger mechanism of radio bursts., Comment: 16 pages, 7 figures, accepted by ApJ

In this paper, we define and study a nested family of reproducing kernel Hilbert spaces of vector fields that is indexed by a range of scales, from which we construct a reproducing kernel Hilbert space of scale-dependent vector fields. We provide a characterization of the reproducing kernel of that space, with numerical approximations ensuring quick evaluations when this kernel does not have a closed form. We then introduce a multiscale version of the large deformation diffeomorphic metric mapping (LDDMM) problem and prove the existence of solutions. Finally, we provide numerical experiments performing landmark matching using multiscale LDDMM.

We present a simple PDE construction of the sine-Gordon measure below the first threshold ($\be^2 < 4\pi$), in both the finite and infinite volume settings, by studying the corresponding parabolic sine-Gordon model. We also establish pathwise global well-posedness of the hyperbolic sine-Gordon model in finite volume for $\be^2 < 2\pi$.

We report on IXPE and NuSTAR observations that began forty days following the onset of the 2024 outburst of the magnetar 1E 1841-045, marking the first ever IXPE observation of a magnetar in an enhanced state. Our spectropolarimetric analysis indicates that a non-thermal double power-law (PL) spectral model can fit the phase-averaged intensity data well, with the soft and hard components dominating below and above around 5 keV, respectively. We find that the soft PL exhibits a polarization degree (PD) of about 20% while the hard X-ray PL displays a PD of about 50%; both components have a polarization angle (PA) compatible with 0 degree. These results are supported through model-independent polarization analysis which shows an increasing PD from about 15% to 70% in the 2-3 keV and 6-8 keV ranges, respectively, while the PA remains consistent with 0 degree. We find marginal evidence for variability in the polarization properties with pulse phase, namely a higher PD at spin phases coinciding with the peak in the hard X-ray pulse. We compare the hard X-ray PL to the expectation from direct resonant inverse Compton scattering (RICS) and secondary pair cascade synchrotron radiation from primary high-energy RICS photons, finding that both can provide reasonable spectropolarimetric agreement with the data, yet, the latter more naturally. Finally, we suggest that the soft power law X-ray component may be emission emanating from a Comptonized corona in the inner magnetosphere., Comment: Submitted to ApJL together with a companion paper by Rigoselli et al. Comments are welcome

Precision medicine has become a central focus in breast cancer management, advancing beyond conventional methods to deliver more precise and individualized therapies. Traditionally, histopathology images have been used primarily for diagnostic purposes; however, they are now recognized for their potential in molecular profiling, which provides deeper insights into cancer prognosis and treatment response. Recent advancements in artificial intelligence (AI) have enabled digital pathology to analyze histopathologic images for both targeted molecular and broader omic biomarkers, marking a pivotal step in personalized cancer care. These technologies offer the capability to extract various biomarkers such as genomic, transcriptomic, proteomic, and metabolomic markers directly from the routine hematoxylin and eosin (H&E) stained images, which can support treatment decisions without the need for costly molecular assays. In this work, we provide a comprehensive review of AI-driven techniques for biomarker detection, with a focus on diverse omic biomarkers that allow novel biomarker discovery. Additionally, we analyze the major challenges faced in this field for robust algorithm development. These challenges highlight areas where further research is essential to bridge the gap between AI research and clinical application.

Magnetar Giant Flares (MGFs) are the most energetic non-catastrophic transients known to originate from stellar objects. The first discovered events were nearby. In recent years, several extragalactic events have been identified, implying an extremely high volumetric rate. We show that future instruments with a sensitivity $\lesssim 5\times 10^{-9}$ erg cm$^{-2}$ at $\sim 1$ MeV will be dominated by extragalactic MGFs over short gamma-ray bursts (sGRBs). Clear discrimination of MGFs requires intrinsic GRB localization capability to identify host galaxies. As MGFs involve a release of a sizable fraction of the neutron star's magnetic free energy reservoir in a single event, they provide us with invaluable tools for better understanding magnetar birth properties and the evolution of their magnetic fields. A major obstacle is to identify a (currently) small sub-population of MGFs in a larger sample of more energetic and distant sGRBs. We develop the tools to analyze the properties of detected events and their occurrence rate relative to sGRBs. Even with the current (limited) number of events, we can constrain the initial internal magnetic field of a typical magnetar at formation to be $B_0\approx 4\times 10^{14}-2\times 10^{15}$\,G. Larger samples will constrain the distribution of birth fields. We also estimate the contribution of MGFs to the gravitational wave (GW) stochastic background. Depending on the acceleration time of baryon-loaded ejecta involved in MGFs, their GW emission may reach beyond 10~kHz and, if so, will likely dominate over other conventional astrophysical sources in that frequency range., Comment: Accepted for publication in ApJ

Recently, a class of long-period radio transients (LPTs) has been discovered, exhibiting emission on timescales thousands of times longer than radio pulsars. Several models had been proposed implicating either a strong magnetic field neutron star, isolated white dwarf pulsar, or a white dwarf binary system with a low-mass companion. While several models for LPTs also predict X-ray emission, no LPTs have been detected in X-rays despite extensive searches. Here we report the discovery of an extremely bright LPT (10-20 Jy in radio), ASKAP J1832-0911, which has coincident radio and X-ray emission, both with a 44.2-minute period. The X-ray and radio luminosities are correlated and vary by several orders of magnitude. These properties are unique amongst known Galactic objects and require a new explanation. We consider a $\gtrsim0.5$ Myr old magnetar with a $\gtrsim 10^{13}$ G crustal field, or an extremely magnetised white dwarf in a binary system with a dwarf companion, to be plausible explanations for ASKAP J1832-0911, although both explanations pose significant challenges to formation and emission theories. The X-ray detection also establishes a new class of hour-scale periodic X-ray transients of luminosity $\sim10^{33}$ erg/s associated with exceptionally bright coherent radio emission., Comment: 52 pages, 17 figures, 3 tables

Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The multivariate Two One-Sided Tests (TOST) procedure is typically used in this context by checking if, outcome by outcome, the marginal $100(1-2\alpha$)\% confidence intervals for the difference in means between the two conditions of interest lie within pre-defined lower and upper equivalence limits. This procedure, known to be conservative in the univariate case, leads to a rapid power loss when the number of outcomes increases, especially when one or more outcome variances are relatively large. In this work, we propose a finite-sample adjustment for this procedure, the multivariate $\alpha$-TOST, that consists in a correction of $\alpha$, the significance level, taking the (arbitrary) dependence between the outcomes of interest into account and making it uniformly more powerful than the conventional multivariate TOST. We present an iterative algorithm allowing to efficiently define $\alpha^{\star}$, the corrected significance level, a task that proves challenging in the multivariate setting due to the inter-relationship between $\alpha^{\star}$ and the sets of values belonging to the null hypothesis space and defining the test size. We study the operating characteristics of the multivariate $\alpha$-TOST both theoretically and via an extensive simulation study considering cases relevant for real-world analyses -- i.e.,~relatively small sample sizes, unknown and heterogeneous variances, and different correlation structures -- and show the superior finite-sample properties of the multivariate $\alpha$-TOST compared to its conventional counterpart. We finally re-visit a case study on ticlopidine hydrochloride and compare both methods when simultaneously assessing bioequivalence for multiple pharmacokinetic parameters., Comment: 34 pages, 13 figures. To be published in: Statistics in Medicine

Large Language Models (LLMs) have demonstrated significant reasoning capabilities in robotic systems. However, their deployment in multi-robot systems remains fragmented and struggles to handle complex task dependencies and parallel execution. This study introduces the DART-LLM (Dependency-Aware Multi-Robot Task Decomposition and Execution using Large Language Models) system, designed to address these challenges. DART-LLM utilizes LLMs to parse natural language instructions, decomposing them into multiple subtasks with dependencies to establish complex task sequences, thereby enhancing efficient coordination and parallel execution in multi-robot systems. The system includes the QA LLM module, Breakdown Function modules, Actuation module, and a Vision-Language Model (VLM)-based object detection module, enabling task decomposition and execution from natural language instructions to robotic actions. Experimental results demonstrate that DART-LLM excels in handling long-horizon tasks and collaborative tasks with complex dependencies. Even when using smaller models like Llama 3.1 8B, the system achieves good performance, highlighting DART-LLM's robustness in terms of model size. Please refer to the project website \url{https://wyd0817.github.io/project-dart-llm/} for videos and code., Comment: Submitted to the 2025 IEEE International Conference on Robotics & Automation on September 15, 2024

The intense magnetic fields of neutron stars naturally lead to strong anisotropy and polarization of radiation emanating from their surfaces, both being sensitive to the hot spot position on the surface. Accordingly, pulse phase-resolved intensities and polarizations depend on the angle between the magnetic and spin axes and the observer's viewing direction. In this paper, results are presented from a Monte Carlo simulation of neutron star atmospheres that uses a complex electric field vector formalism to treat polarized radiative transfer due to magnetic Thomson scattering. General relativistic influences on the propagation of light from the stellar surface to a distant observer are taken into account. The paper outlines a range of theoretical predictions for pulse profiles at different X-ray energies, focusing on magnetars and also neutron stars of lower magnetization. By comparing these models with observed intensity and polarization pulse profiles for the magnetar 1RXS J1708-40, and the light curve for the pulsar PSR J0821-4300, constraints on the stellar geometry angles and the size of putative polar cap hot spots are obtained., Comment: 6 pages, 3 figures, accepted for publication in Astronomische Nachrichten in the special issue for the XMM-Newton 2024 Science Workshop: The X-Ray Mysteries of Neutron Stars and White Dwarfs

A near-minimal instance of optical cooling is experimentally presented wherein the internal-state entropy of a single atom is reduced more than twofold by illuminating it with broadband, incoherent light. Since the rate of optical pumping by a thermal state increases monotonically with its temperature, the cooling power in this scenario increases with higher thermal occupation, an example of a phenomenon known as cooling by heating. In contrast to optical pumping by coherent, narrow-band laser light, here we perform the same task with fiber-coupled, broadband sunlight, the brightest laboratory-accessible source of continuous blackbody radiation., Comment: 4 pages

Identifying dynamical systems characterized by nonlinear parameters presents significant challenges in deriving mathematical models that enhance understanding of physics. Traditional methods, such as Sparse Identification of Nonlinear Dynamics (SINDy) and symbolic regression, can extract governing equations from observational data; however, they also come with distinct advantages and disadvantages. This paper introduces a novel method within the SINDy framework, termed ADAM-SINDy, which synthesizes the strengths of established approaches by employing the ADAM optimization algorithm. This facilitates the simultaneous optimization of nonlinear parameters and coefficients associated with nonlinear candidate functions, enabling precise parameter estimation without requiring prior knowledge of nonlinear characteristics such as trigonometric frequencies, exponential bandwidths, or polynomial exponents, thereby addressing a key limitation of SINDy. Through an integrated global optimization, ADAM-SINDy dynamically adjusts all unknown variables in response to data, resulting in an adaptive identification procedure that reduces the sensitivity to the library of candidate functions. The performance of the ADAM-SINDy methodology is demonstrated across a spectrum of dynamical systems, including benchmark coupled nonlinear ordinary differential equations such as oscillators, chaotic fluid flows, reaction kinetics, pharmacokinetics, as well as nonlinear partial differential equations (wildfire transport). The results demonstrate significant improvements in identifying parameterized dynamical systems and underscore the importance of concurrently optimizing all parameters, particularly those characterized by nonlinear parameters. These findings highlight the potential of ADAM-SINDy to extend the applicability of the SINDy framework in addressing more complex challenges in dynamical system identification.

Rydberg states of alkali atoms are highly sensitive to electromagnetic radiation in the GHz-to-THz regime because their transitions have large electric dipole moments. Consequently, environmental blackbody radiation (BBR) can couple Rydberg states together at $\mu$s timescales. Here, we track the BBR-induced transfer of a prepared Rydberg state to its neighbors and use the evolution of these state populations to characterize the BBR field at the relevant wavelengths, primarily at 130 GHz. We use selective field ionization readout of Rydberg states with principal quantum number $n\sim30$ in $^{85}$Rb and substantiate our ionization signal with a theoretical model. With this detection method, we measure the associated blackbody-radiation-induced time dynamics of these states, reproduce the results with a simple semi-classical population transfer model, and demonstrate that this measurement is temperature sensitive with a statistical sensitivity to the fractional temperature uncertainty of 0.09 Hz$^{-1/2}$, corresponding to 26 K$\cdot$Hz$^{-1/2}$ at room temperature. This represents a calibration-free SI-traceable temperature measurement, for which we calculate a systematic fractional temperature uncertainty of 0.006, corresponding to 2 K at room temperature when used as a primary temperature standard.

The ability to predict future events or patterns based on previous experience is crucial for many applications such as traffic control, weather forecasting, or supply chain management. While modern supervised Machine Learning approaches excel at such sequential tasks, they are computationally expensive and require large training data. A previous work presented a biologically plausible sequence learning model, developed through a bottom-up approach, consisting of a spiking neural network and unsupervised local learning rules. The model in its original formulation identifies only a specific type of sequence elements composed of synchronous spikes by activating a subset of neurons with identical stimulus preference. In this work, we extend the model to detect and learn sequences of various spatio-temporal patterns (STPs) by incorporating plastic connections in the input synapses. We showcase that the model is able to learn and predict high-order sequences. We further study the robustness of the model against different input settings and parameters., Comment: Published at the Proceedings of the International Conference on Neuromorphic Systems 2024

The capabilities of Large Language Models (LLMs) have significantly evolved, extending from natural language processing to complex tasks like code understanding and generation. We expand the scope of LLMs' capabilities to a broader context, using LLMs to execute code snippets to obtain the output. This paper pioneers the exploration of LLMs as code executors, where code snippets are directly fed to the models for execution, and outputs are returned. We are the first to comprehensively examine this feasibility across various LLMs, including OpenAI's o1, GPT-4o, GPT-3.5, DeepSeek, and Qwen-Coder. Notably, the o1 model achieved over 90% accuracy in code execution, while others demonstrated lower accuracy levels. Furthermore, we introduce an Iterative Instruction Prompting (IIP) technique that processes code snippets line by line, enhancing the accuracy of weaker models by an average of 7.22% (with the highest improvement of 18.96%) and an absolute average improvement of 3.86% against CoT prompting (with the highest improvement of 19.46%). Our study not only highlights the transformative potential of LLMs in coding but also lays the groundwork for future advancements in automated programming and the completion of complex tasks.

In this technical report, we present Falcon Mamba 7B, a new base large language model based on the novel Mamba architecture. Falcon Mamba 7B is trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based on Transformers, such as Mistral 7B, Llama3.1 8B, and Falcon2 11B. It is on par with Gemma 7B and outperforms models with different architecture designs, such as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B. Currently, Falcon Mamba 7B is the best-performing Mamba model in the literature at this scale, surpassing both existing Mamba and hybrid Mamba-Transformer models, according to the Open LLM Leaderboard. Due to its architecture, Falcon Mamba 7B is significantly faster at inference and requires substantially less memory for long sequence generation. Despite recent studies suggesting that hybrid Mamba-Transformer models outperform pure architecture designs, we demonstrate that even the pure Mamba design can achieve similar, or even superior results compared to the Transformer and hybrid designs. We make the weights of our implementation of Falcon Mamba 7B publicly available on https://huggingface.co/tiiuae/falcon-mamba-7b, under a permissive license.

We present the first examples of topological phases of matter with uniform power for measurement-based quantum computation. This is possible thanks to a new framework for analyzing the computational properties of phases of matter that is more general than previous constructions, which were limited to short-range entangled phases in one dimension. We show that ground states of the toric code in an anisotropic magnetic field yield a natural, albeit non-computationally-universal, application of our framework. We then present a new model with topological order whose ground states are universal resources for MBQC. Both topological models are enriched by subsystem symmetries, and these symmetries protect their computational power. Our framework greatly expands the range of physical models that can be analyzed from the computational perspective., Comment: 31 pages, 11 figures

Domain walls are a type of topological defects that can arise in the early universe after the spontaneous breaking of a discrete symmetry. This occurs in several beyond Standard Model theories with an extended Higgs sector such as the Next-to-Two-Higgs-Doublet model (N2HDM). In this talk, I will discuss the domain wall solution related to the singlet scalar of the N2HDM and demonstrate the possibility of electroweak symmetry restoration (EWSR) in the vicinity of the domain wall. Such symmetry restoration can have profound implications on the early universe cosmology as the sphaleron rate inside the domain wall would, in principle, be unsuppressed compared with the rate outside the wall., Comment: 4 pages, Contribution to the Proceedings of the ICHEP 2024 Conference

This contribution reports on characterisation results of a large-area (2$~\mathrm{cm}^2$) small pitch (55$~\mu$m) inverse Low-Gain Avalanche Detector (iLGAD), bonded to a Timepix3 readout chip. The ilGAD sensors were produced by Micron Semiconductor Ltd with the goal to obtain good gain uniformity and maximise the fill-factor -- an issue present with standard small-pitch LGAD designs. We have conducted detailed performance evaluations using both X-ray calibrations and beam tests. An X-ray fluorescence setup has been used to obtain energy calibration and to identify the optimal operating settings of the new devices, whereas the extensive beam tests allowed for a detailed evaluation of the detector performance. The beam-tests were performed at the CERN SPS North Area H6 beamline, using a 120 GeV/c pion beam. The reference tracking and time-stamping is achieved by a Timepix3-based beam telescope setup. The results show a gain of around 5 with very good uniformity, measured across the whole gain area, as well as a hit time resolution down to 1.3 ns without correcting for the time-walk effects. Furthermore, it is shown that the gain opens the possibility of a good X-ray energy resolution down to 4.5 keV.

The way pulsars spin down is not understood in detail, but a number of possible physical mechanisms produce a spin-down rate that scales as a power of the rotation rate ($\dot\nu\propto-\nu^n$), with the power-law index $n$ called the braking index. PSR B0540-69 is a pulsar that in 2011, after 16 years of spinning down with a constant braking index of 2.1, experienced a giant spin-down change and a reduction of its braking index to nearly zero. Here, we show that following this episode the braking index monotonically increased during a period of at least four years and stabilised at ~1.1. We also present an alternative interpretation of a more modest rotational irregularity that occurred in 2023, which was modelled as an anomalous negative step of the rotation rate. Our analysis shows that the 2023 observations can be equally well described as a transient swing of the spin-down rate (lasting ~65 days), and the Bayesian evidence indicates that this model is strongly preferred., Comment: 8 pages, 3 figures, accepted for publication by ApJL

This paper introduces Kolmogorov-Arnold Networks (KAN) as an enhancement to the traditional linear probing method in transfer learning. Linear probing, often applied to the final layer of pre-trained models, is limited by its inability to model complex relationships in data. To address this, we propose substituting the linear probing layer with KAN, which leverages spline-based representations to approximate intricate functions. In this study, we integrate KAN with a ResNet-50 model pre-trained on ImageNet and evaluate its performance on the CIFAR-10 dataset. We perform a systematic hyperparameter search, focusing on grid size and spline degree (k), to optimize KAN's flexibility and accuracy. Our results demonstrate that KAN consistently outperforms traditional linear probing, achieving significant improvements in accuracy and generalization across a range of configurations. These findings indicate that KAN offers a more powerful and adaptable alternative to conventional linear probing techniques in transfer learning., Comment: 10 pages, 5 figure

We present the detection and analysis of GRB 231115A, a candidate extragalactic magnetar giant flare (MGF) observed by Fermi/GBM and localized by INTEGRAL to the starburst galaxy M82. This burst exhibits distinctive temporal and spectral characteristics that align with known MGFs, including a short duration and a high peak energy. Gamma-ray analyses reveal significant insights into this burst, supporting conclusions already established in the literature: our time-resolved spectral studies provide further evidence that GRB 231115A is indeed a MGF. Significance calculations also suggest a robust association with M82, further supported by a high Bayes factor that minimizes the probability of chance alignment with a neutron star merger. Despite extensive follow-up efforts, no contemporaneous gravitational wave or radio emissions were detected. The lack of radio emission sets stringent upper limits on possible radio luminosity. Constraints from our analysis show no fast radio bursts (FRBs) associated with two MGFs. X-ray observations conducted post-burst by Swift/XRT and XMM/Newton provided additional data, though no persistent counterparts were identified. Our study underscores the importance of coordinated multi-wavelength follow-up and highlights the potential of MGFs to enhance our understanding of short GRBs and magnetar activities in the cosmos. Current MGF identification and follow-up implementation are insufficient for detecting expected counterparts; however, improvements in these areas may allow for the recovery of follow-up signals with existing instruments. Future advancements in observational technologies and methodologies will be crucial in furthering these studies.

Recently, Younes et al. proposed an efficient multi-party semi-quantum secret sharing (SQSS) scheme that generalizes Tian et al.'s three-party protocol \cite{Tian2021} to accommodate multiple participants. This scheme retains the original advantages, such as high qubit efficiency and allowing the secret dealer, Alice, to control the message content. However, He et al. \cite{He2024} identified a vulnerability in Tian et al.'s protocol to the double CNOT attack (DCNA), which also affects the generalized scheme. In response, He et al. proposed an improved protocol to address this issue. Despite these improvements, their protocol is limited to two participants and remains a primarily two-way communication scheme, which does not fully prevent the Trojan horse attack without expensive quantum devices such as photon number splitters (PNS) and wavelength filters (WF). To address these issues, this paper develops a novel multi-party SQSS scheme using the quantum property between Bell states and the Hadamard operation to detect eavesdroppers. This new scheme is secure against the DCNA, intercept-resend attack, and collective attack. It employs a fully one-way communication scheme, entirely preventing the Trojan horse attack without costly quantum devices, aligning with the semi-quantum environment's original intent. This new protocol also offers better qubit efficiency and allows Alice to share specific secrets., Comment: 18 pages, 1 table, research paper

This book introduces the mathematical foundations and techniques that lead to the development and analysis of many of the algorithms that are used in machine learning. It starts with an introductory chapter that describes notation used throughout the book and serve at a reminder of basic concepts in calculus, linear algebra and probability and also introduces some measure theoretic terminology, which can be used as a reading guide for the sections that use these tools. The introductory chapters also provide background material on matrix analysis and optimization. The latter chapter provides theoretical support to many algorithms that are used in the book, including stochastic gradient descent, proximal methods, etc. After discussing basic concepts for statistical prediction, the book includes an introduction to reproducing kernel theory and Hilbert space techniques, which are used in many places, before addressing the description of various algorithms for supervised statistical learning, including linear methods, support vector machines, decision trees, boosting, or neural networks. The subject then switches to generative methods, starting with a chapter that presents sampling methods and an introduction to the theory of Markov chains. The following chapter describe the theory of graphical models, an introduction to variational methods for models with latent variables, and to deep-learning based generative models. The next chapters focus on unsupervised learning methods, for clustering, factor analysis and manifold learning. The final chapter of the book is theory-oriented and discusses concentration inequalities and generalization bounds., Comment: textbook

Developmental dysgraphia is a neurological disorder that hinders children's writing skills. In recent years, researchers have increasingly explored machine learning methods to support the diagnosis of dysgraphia based on offline and online handwriting. In most previous studies, the two types of handwriting have been analysed separately, which does not necessarily lead to promising results. In this way, the relationship between online and offline data cannot be explored. To address this limitation, we propose a novel multimodal machine learning approach utilizing both online and offline handwriting data. We created a new dataset by transforming an existing online handwritten dataset, generating corresponding offline handwriting images. We considered only different types of word data (simple word, pseudoword & difficult word) in our multimodal analysis. We trained SVM and XGBoost classifiers separately on online and offline features as well as implemented multimodal feature fusion and soft-voted ensemble. Furthermore, we proposed a novel ensemble with conditional feature fusion method which intelligently combines predictions from online and offline classifiers, selectively incorporating feature fusion when confidence scores fall below a threshold. Our novel approach achieves an accuracy of 88.8%, outperforming SVMs for single modalities by 12-14%, existing methods by 8-9%, and traditional multimodal approaches (soft-vote ensemble and feature fusion) by 3% and 5%, respectively. Our methodology contributes to the development of accurate and efficient dysgraphia diagnosis tools, requiring only a single instance of multimodal word/pseudoword data to determine the handwriting impairment. This work highlights the potential of multimodal learning in enhancing dysgraphia diagnosis, paving the way for accessible and practical diagnostic tools.

Additive manufacture (AM) involves creating a part layer by layer and is a rapidly evolving manufacturing process. It has multiple strengths that apply to space-based optics, such as the ability to consolidate multiple parts into one, reducing the number of interfaces. The process also allows for greater mass reduction, making parts more cost-effective to launch, achieved by optimising the shape for intended use or creating intricate geometries like lattices. However, previous studies have highlighted issues associated with the AM process. For example, when trying to achieve high-precision optical surfaces on AM parts, the latticing on the underside of mirrors can provide insufficient support during machining, resulting in the quilting effect. This paper builds on previous work and explores such challenges further. This will be implemented by investigating ways to apply AM to a deployable mirror from a CubeSat project called A-DOT. The reflective surface has a spherical radius of curvature of 682 mm and approximate external dimensions of 106 mm x 83 mm. The aim is to produce two mirrors that will take full advantage of AM design benefits and account for the challenges in printing and machining a near-net shape. The designs will have reduced mass by using selected internal lattice designs and topology-optimised connection points, resulting in two mirrors with mass reduction targets of 50% and 70%. Once printed in aluminium using laser powder bed fusion, the reflective surface will be created using single point diamond turning. Finally, an evaluation of the dimensional accuracy will be conducted, using interferometry, to quantify the performance of the reflective surface., Comment: 30 pages, 38 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 140)

Despite the established role of additive manufacturing (AM) in aerospace and medical fields, its adoption in astronomy remains low. Encouraging AM integration in a risk-averse community necessitates documentation and dissemination of previous case studies. The objective of this study is to create the first review of AM in astronomy hardware, answering: where is AM currently being used in astronomy, what is the status of its adoption, and what challenges are preventing its widespread use? The review starts with an introduction to astronomical instruments size/cost challenges, alongside the role of manufacturing innovation. This is followed by highlighting the benefits/challenges of AM and used materials/processes in both space-based and ground-based applications. The review case studies include mirrors, optomechanical structures, compliant mechanisms, brackets and tooling applications that are either in research phase or are implemented., Comment: 27 pages, 17 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 57)

We present a quantum algorithm for simulating complex many-body systems and finding their ground states, combining the use of tensor networks and density matrix renormalization group (DMRG) techniques. The algorithm is based on von Neumann's measurement prescription, which serves as a conceptual building block for quantum phase estimation. We describe the implementation and simulation of the algorithm, including the estimation of resources required and the use of matrix product operators (MPOs) to represent the Hamiltonian. We highlight the potential applications of the algorithm in simulating quantum spin systems and electronic structure problems.

Spaced-based mirrors are a developing use-case for Additive Manufacturing (AM), the process that builds a part layer-by-layer. The increased geometric freedom results in novel and advantageous designs previously unachievable. Conventionally, mirror fabrication uses subtractive (milling & turning), formative (casting) and fabricative (bonding) manufacturing methods; however, an additive method can simplify an assembly by consolidating individual components into one, and incorporating lattice structures and function optimised geometries to reduce the mass of components, which are beneficial to space-based instrumentation as mass and volume are constrained. Attention must be given to the printability of the design - build orientation and powder/resin removal from lattices and internal cavities are challenges when designing for AM. This paper will describe the design, manufacture and metrology of mirror prototypes from the Active Deployable Optical Telescope (ADOT) 6U CubeSat project. The AM mirror is 52mm in diameter, 10mm deep, with a convex 100mm radius of curvature reflective surface and deploys telescopically on three booms. The objectives of the designs were to combine the boom mounting features into the mirror and to lightweight both prototypes by 50% and 70% using internal, thin-walled lattices. Four final lattice designs were downselected through simulation and prototype validation. Prototypes were printed in the aluminium alloy AlSi10Mg using powder bed fusion and fused silica using stereolithography. Aluminium mirrors were single point diamond turned and had surface roughness measurements taken. Fused silica designs were adapted from the aluminium designs and have completed printing., Comment: 36 pages, 38 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 6)

Domain walls are a type of topological defects that can arise in the early universe after the spontaneous breaking of a discrete symmetry. They can form in several beyond the Standard Model theories with an extended Higgs sector such as the Next to-Two-Higgs-Doublet model (N2HDM). In this work, we discuss the domain wall solution related to the singlet scalar of the N2HDM and demonstrate the possibility of restoring the electroweak symmetry inside and in the vicinity of the domain wall. Such symmetry restoration can have profound implications on early universe cosmology as the weak sphaleron rate inside the domain wall would, in principle, be unsuppressed compared to the rate outside the wall. We also discuss the possibility of generating CP-violating vacua localized in the vicinity of the domain wall. Our work is a first step towards the realization of electroweak baryogenesis mediated by domain walls in the N2HDM., Comment: 44 pages, 26 figures, DESY-24-109

We present a novel approach for recovering 3D shape and view dependent appearance from a few colored images, enabling efficient 3D reconstruction and novel view synthesis. Our method learns an implicit neural representation in the form of a Signed Distance Function (SDF) and a radiance field. The model is trained progressively through ray marching enabled volumetric rendering, and regularized with learning-free multi-view stereo (MVS) cues. Key to our contribution is a novel implicit neural shape function learning strategy that encourages our SDF field to be as linear as possible near the level-set, hence robustifying the training against noise emanating from the supervision and regularization signals. Without using any pretrained priors, our method, called SparseCraft, achieves state-of-the-art performances both in novel-view synthesis and reconstruction from sparse views in standard benchmarks, while requiring less than 10 minutes for training., Comment: ECCV 2024. Project page: https://sparsecraft.github.io

Additive manufacturing (AM; 3D printing) in aluminium using laser powder bed fusion provides a new design space for lightweight mirror production. Printing layer-by-layer enables the use of intricate lattices for mass reduction, as well as organic shapes generated by topology optimisation, resulting in mirrors optimised for function as opposed to subtractive machining. However, porosity, a common AM defect, is present in printed aluminium and it is a result of the printing environment being either too hot or too cold, or gas entrapped bubbles within the aluminium powder. When present in an AM mirror substrates, porosity manifests as pits on the reflective surface, which increases micro-roughness and therefore scattered light. There are different strategies to reduce the impact of porosity: elimination during printing, coating the aluminium print in nickel phosphorous, or to apply a heat and pressure treatment to close the pores, commonly known as a hot isostatic press (HIP). This paper explores the application of HIP on printed aluminium substrates intended for mirror production using single point diamond turning (SPDT). The objective of the HIP is to reduce porosity whilst targeting a small grain growth within the aluminium, which is important in allowing the SPDT to generate surfaces with low micro-roughness. For this study, three disks, 50 mm diameter by 5 mm, were printed in AlSi10Mg at 0 deg, 45 deg, and 90 deg with respect to the build plate. X-ray computed tomography (XCT) was conducted before and after the HIP cycle to confirm the effectiveness of HIP to close porosity. The disks were SPDT and the micro-roughness evaluated. Mechanical testing and electron backscatter diffraction (EBSD) was used to quantify the mechanical strength and the grain size after HIP., Comment: 14 pages, 8 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 141)

Additive manufacturing (AM; 3D printing) has clear benefits in the production of lightweight mirrors for astronomy: it can create optimised lightweight structures and combine multiple components into one. New capabilities in AM ceramics, silicon carbide infiltrated with silicon and fused silica, offer the possibility to combine the design benefits of AM with a material suitable for visible, ultraviolet and X-ray applications. This paper will introduce the printing methods and post-processing steps to convert AM ceramic samples into reflective mirrors. Surface roughness measurements after abrasive polishing of the AM ceramics will be presented., Comment: 15 pages, 15 figures, submitted to SPIE Astronomical Telescopes & Instrumentation, Advances in Optical and Mechanical Technologies for Telescopes and Instrumentation VI (Conference 13100, Paper 123)

The construction of quantum computers is based on the synthesis of low-cost quantum circuits. The quantum circuit of any Boolean function expressed in a Positive Polarity Reed-Muller $PPRM$ expansion can be synthesized using Multiple-Control Toffoli ($MCT$) gates. This paper proposes two algorithms to construct a quantum circuit for any Boolean function expressed in a Positive Polarity Reed-Muller $PPRM$ expansion. The Boolean function can be expressed with various algebraic forms, so there are different quantum circuits can be synthesized for the Boolean function based on its algebraic form. The proposed algorithms aim to map the $MCT$ gates into the $NCV$ gates for any quantum circuit by generating a simple algebraic form for the Boolean function. The first algorithm generates a special algebraic form for any Boolean function by rearrangement of terms of the Boolean function according to a predefined degree of term $d_{term}$, then synthesizes the corresponding quantum circuit. The second algorithm applies the decomposition methods to decompose $MCT$ circuit into its elementary gates followed by applying a set of simplification rules to simplify and optimize the synthesized quantum circuit. The proposed algorithms achieve a reduction in the quantum cost of synthesized quantum circuits when compared with relevant work in literature. The proposed algorithms synthesize quantum circuits that can applied on IBM quantum computer., Comment: Boolean Function, Algebraic Form, Quantum Circuits, Quantum Cost, Decomposition, Reorder Algorithm

We present the Manuscripts of Handwritten Arabic~(Muharaf) dataset, which is a machine learning dataset consisting of more than 1,600 historic handwritten page images transcribed by experts in archival Arabic. Each document image is accompanied by spatial polygonal coordinates of its text lines as well as basic page elements. This dataset was compiled to advance the state of the art in handwritten text recognition (HTR), not only for Arabic manuscripts but also for cursive text in general. The Muharaf dataset includes diverse handwriting styles and a wide range of document types, including personal letters, diaries, notes, poems, church records, and legal correspondences. In this paper, we describe the data acquisition pipeline, notable dataset features, and statistics. We also provide a preliminary baseline result achieved by training convolutional neural networks using this data.

We show that for any finitely presented group $G$, there is a simply connected closed 4-manifold containing an infinite family of topologically isotopic but smoothly inequivalent 2-links whose 2-link group is $G$. We also show that, if $G$ satisfies the necessary topological conditions, these 2-links have nullhomotopic components., Comment: 10 pages, 3 figures

It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field., Comment: 31 pages including references

Time-domain and multimessenger astronomy (TDAMM) involves the study of transient and time-variable phenomena across various wavelengths and messengers. The Astro2020 Decadal Survey has identified TDAMM as the top priority for NASA in this decade, emphasizing its crucial role in advancing our understanding of the universe and driving new discoveries in astrophysics. The TDAMM community has come together to provide further guidance to funding agencies, aiming to define a clear path toward optimizing scientific returns in this research domain. This encompasses not only astronomy but also fundamental physics, offering insights into gravity properties, the formation of heavy elements, the equation of state of dense matter, and quantum effects associated with extreme magnetic fields. Magnetars, neutron stars with the strongest magnetic fields known in the universe, play a critical role in this context. In this manuscript, we aim to underscore the significance of magnetars in TDAMM, highlighting the necessity of ensuring observational continuity, addressing current limitations, and outlining essential requirements to expand our knowledge in this field., Comment: Accepted by Frontiers