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Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse unsupervised representation learning with cascaded VoxelHop units, 2) decoder: coarse to fine segmentation prediction with voxel-wise classification and local refinement. Experiments are conducted on the publicly available ISBI-2013 dataset, as well as on a larger private one. Experimental analysis shows that our proposed PSHop is effective, robust and lightweight in the tasks of prostate gland and zonal segmentation, achieving a Dice Similarity Coefficient (DSC) of 0.873 for the gland segmentation task. PSHop achieves a competitive performance comparatively to other deep learning methods, while keeping the model size and inference complexity an order of magnitude smaller., Comment: 11 pages, 5 figures, 5 tables

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as ``black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity., Comment: 13 pages, 7 figures, 5 tables

Large Language Models (LLMs) like ChatGPT and Llama have revolutionized natural language processing and search engine dynamics. However, these models incur exceptionally high computational costs. For instance, GPT-3 consists of 175 billion parameters, where inference demands billions of floating-point operations. Caching is a natural solution to reduce LLM inference costs on repeated queries, which constitute about 31% of the total queries. However, existing caching methods are incapable of finding semantic similarities among LLM queries nor do they operate on contextual queries, leading to unacceptable false hit-and-miss rates. This paper introduces MeanCache, a user-centric semantic cache for LLM-based services that identifies semantically similar queries to determine cache hit or miss. Using MeanCache, the response to a user's semantically similar query can be retrieved from a local cache rather than re-querying the LLM, thus reducing costs, service provider load, and environmental impact. MeanCache leverages Federated Learning (FL) to collaboratively train a query similarity model without violating user privacy. By placing a local cache in each user's device and using FL, MeanCache reduces the latency and costs and enhances model performance, resulting in lower false hit rates. MeanCache also encodes context chains for every cached query, offering a simple yet highly effective mechanism to discern contextual query responses from standalone. Our experiments benchmarked against the state-of-the-art caching method, reveal that MeanCache attains an approximately 17% higher F-score and a 20% increase in precision during semantic cache hit-and-miss decisions while performing even better on contextual queries. It also reduces the storage requirement by 83% and accelerates semantic cache hit-and-miss decisions by 11%., Comment: This study presents the first privacy aware semantic cache for LLMs based on Federated Learning. MeanCache is the first cache that can handle contextual queries efficiently. Total pages 14

Understanding the dynamics of dissipative quantum systems, particularly beyond the weak coupling approximation, is central to various quantum applications. While numerically exact methods provide accurate solutions, they often lack the analytical insight provided by theoretical approaches. In this study, we employ the recently-developed method dubbed the effective Hamiltonian theory to understand the dynamics of system-bath configurations without resorting to a perturbative description of the system-bath coupling energy. Through a combination of mapping steps and truncation, the effective Hamiltonian theory offers both analytical insights into signatures of strong couplings in open quantum systems and a straightforward path for numerical simulations. To validate the accuracy of the method, we apply it to two canonical models: a single spin immersed in a bosonic bath and two noninteracting spins in a common bath. In both cases, we study the transient regime and the steady state limit at nonzero temperature, and spanning system-bath interactions from the weak to the strong regime. By comparing the results of the effective Hamiltonian theory with numerically exact simulations, we show that although the former overlooks non-Markovian features in the transient equilibration dynamics, it correctly captures non-perturbative bath-generated couplings between otherwise non-interacting spins as observed in their synchronization dynamics and correlations. Altogether, the effective Hamiltonian theory offers a powerful approach to understanding strong coupling dynamics and thermodynamics, capturing the signatures of such interactions in both relaxation dynamics and in the steady state limit., Comment: Journal version

The recent development of quantum computing, which uses entanglement, superposition, and other quantum fundamental concepts, can provide substantial processing advantages over traditional computing. These quantum features help solve many complex problems that cannot be solved with conventional computing methods. These problems include modeling quantum mechanics, logistics, chemical-based advances, drug design, statistical science, sustainable energy, banking, reliable communication, and quantum chemical engineering. The last few years have witnessed remarkable advancements in quantum software and algorithm creation and quantum hardware research, which has significantly advanced the prospect of realizing quantum computers. It would be helpful to have comprehensive literature research on this area to grasp the current status and find outstanding problems that require considerable attention from the research community working in the quantum computing industry. To better understand quantum computing, this paper examines the foundations and vision based on current research in this area. We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate., Comment: Technical Report: 11 pages, 2 figures

Shifts in the visibility and recognition of LGBTQ+ identity have been accompanied by an evolution in understanding how educational policies, curricula, and environments impact well-being, health, and academic success. Since 2015, landmark decisions by the U.S. Supreme Court recognizing same-sex marriage and expansively defining sex under employment law have been joined by a retrenchment in public opinion as well as federal and state policy. This paradox of both increasing LGBTQ+ visibility and resistance toward LGBTQ+ acceptance has centered LGBTQ+ youth in political debates, with a particular focus on issues related to transgender and nonbinary youth. Historically, the literature on LGBTQ+ students in schools has focused on discrimination and poor social relationships, such as bullying, harassment, and victimization. While situated in a deficit-based framing, students' reports of negative school environments and their connection to poor academic and health outcomes provide the motivation for policymakers, educators, parents, and other educational stakeholders to invest in structural and social reform efforts. The law has played a prominent role in both the expansion and retrenchment of students' civil rights in schools, and this has been true for LGBTQ+ students. LGBTQ+ students have experienced many favorable but fluctuating rulings in many courts, so school officials would be wise to keep apprised of the evolving decisions in their jurisdictions. Educational stakeholders should familiarize themselves with the legal landscape, advocate for inclusive and protective state and local policies, ensure that local district practices protect LGBTQ+ students from discrimination and harassment in schools, leverage LGBTQ+-inclusive community organizations and resources, participate in trainings to improve inclusive school practices, and build LGBTQ+-inclusive facilities, teaching practices, and social supports for youth. [This paper was published.]

We introduce a new complexity measure for finite strings using probabilistic finite-state automata (PFAs), in the same spirit as existing notions employing DFAs and NFAs, and explore its properties. The PFA complexity $A_P(x)$ is the least number of states of a PFA for which $x$ is the most likely string of its length to be accepted. The variant $A_{P,\delta}(x)$ adds a real-valued parameter $\delta$ specifying a required lower bound on the gap in acceptance probabilities between $x$ and other strings. We prove $A_{P,\delta}$ is $\delta$-computable for all $\delta$, relate $A_P$ to the DFA and NFA complexities, and obtain a complete classification of binary strings with $A_P=2$. Finally, we discuss several other variations on $A_P$ with a view to obtaining additional desirable properties., Comment: 46 pages, 5 figures. This work extends Chapter 2 of the author's PhD dissertation at Penn State. Version 4: Add new computability result (Theorems 5.1 and 5.3), fix error in Proposition 3.4, various minor improvements

We report the discovery of a brown dwarf orbiting a M1 host star. We first identified the brown dwarf within the Next Generation Transit Survey data, with supporting observations found in TESS sectors 11 and 38. We confirmed the discovery with follow-up photometry from the South African Astronomical Observatory, SPECULOOS-S, and TRAPPIST-S, and radial velocity measurements from HARPS, which allowed us to characterise the system. We find an orbital period of ~1.25 d, a mass of 69.0+5.3-4.8 MJ, close to the Hydrogen burning limit, and a radius of 0.95 +- 0.05 RJ. We determine the age to be >0.5 Gyr, using model isochrones, which is found to be in agreement with SED fitting within errors. NGTS-28Ab is one of the shortest period systems found within the brown dwarf desert, as well as one of the highest mass brown dwarfs that transits an M dwarf. This makes NGTS-28Ab another important discovery within this scarcely populated region., Comment: 20 pages (inc. appendices), 16 figures, accepted for publication in MNRAS

Let $\mathcal{C}$ be a conjugacy class of involutions in a group $G$. We study the graph $\Gamma(\mathcal{C})$ whose vertices are elements of $\mathcal{C}$ with $g,h\in\mathcal{C}$ connected by an edge if and only if $gh\in\mathcal{C}$. For $t\in \mathcal{C}$, we define the component group of $t$ to be the subgroup of $G$ generated by all vertices in $\Gamma(\mathcal{C})$ that lie in the connected component of the graph that contains $t$. We classify the component groups of all involutions in simple groups of Lie type over a field of characteristic $2$. We use this classification to partially classify the transitive binary actions of the simple groups of Lie type over a field of characteristic $2$ for which a point stabilizer has even order. The classification is complete unless the simple group in question is a symplectic or unitary group.

We present a study of the detection efficiency for the TESS mission, focusing on the yield of longer-period transiting exoplanets ($P > 25$ days). We created the Transit Investigation and Recoverability Application (TIaRA) pipeline to use real TESS data with injected transits to create sensitivity maps which we combine with occurrence rates derived from Kepler. This allows us to predict longer-period exoplanet yields, which will help design follow-up photometric and spectroscopic programs, such as the NGTS Monotransit Program. For the TESS Year 1 and Year 3 SPOC FFI lightucurves, we find $2271^{+241}_{-138}$ exoplanets should be detectable around AFGKM dwarf host stars. We find $215^{+37}_{-23}$ exoplanets should be detected from single-transit events or "monotransits". An additional $113^{+22}_{-13}$ detections should result from "biennial duotransit" events with one transit in Year 1 and a second in Year 3. We also find that K dwarf stars yield the most detections by TESS per star observed. When comparing our results to the TOI catalogue we find our predictions agree within $1\sigma$ of the number of discovered systems with periods between 0.78 and 6.25 days and agree to $2\sigma$ for periods between 6.25 and 2 days. Beyond periods of 25 days we predict $403^{+64}_{-38}$ detections, which is 3 times as many detections as there are in the TOI catalogue with $>3\sigma$ confidence. This indicates a significant number of long-period planets yet to be discovered from TESS data as monotransits or biennial duotransits., Comment: 17 pages, 16 figures. Accepted for Publication in Monthly Notices of the Royal Astronomical Society. Machine readable versions of sensitivity and yield grids available from: https://github.com/TobyRodel/TIaRA-TESS-Yields

A Latin square of order $n$ is an $n\times n$ matrix in which each row and column contains each of $n$ symbols exactly once. For $\epsilon>0$, we show that with high probability a uniformly random Latin square of order $n$ has no proper subsquare of order larger than $n^{1/2}\log^{1/2+\epsilon}n$. Using this fact we present a canonical labelling algorithm for Latin squares of order $n$ that runs in average time bounded by a polynomial in $n$. The algorithm can be used to solve isomorphism problems for many combinatorial objects that can be encoded using Latin squares, including quasigroups, Steiner triple systems, Mendelsohn triple systems, $1$-factorisations, nets, affine planes and projective planes., Comment: New reference added, minor typos fixed

Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. We therefore propose two deep learning models for correction and unmixing, which can account for the nonlinear effects and produce more accurate estimates of abundances. Both models use an autoencoder-like architecture to process the captured spectra. One is trained with protoporphyrin IX (PpIX) concentration labels. The other undergoes semi-supervised training, first learning hyperspectral unmixing self-supervised and then learning to correct fluorescence emission spectra for heterogeneous optical and geometric properties using a reference white-light reflectance spectrum in a few-shot manner. The models were evaluated against phantom and pig brain data with known PpIX concentration; the supervised model achieved Pearson correlation coefficients (R values) between the known and computed PpIX concentrations of 0.997 and 0.990, respectively, whereas the classical approach achieved only 0.93 and 0.82. The semi-supervised approach's R values were 0.98 and 0.91, respectively. On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better removing bright spots of specular reflectance and reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery., Comment: 20 pages, 8 figures, 3 tables - Under Review

Background: Acute kidney injury (AKI), the decline of kidney excretory function, occurs in up to 18% of hospitalized admissions. Progression of AKI may lead to irreversible kidney damage. Methods: This retrospective cohort study includes adult patients admitted to a non-intensive care unit at the University of Pittsburgh Medical Center (UPMC) (n = 46,815) and University of Florida Health (UFH) (n = 127,202). We developed and compared deep learning and conventional machine learning models to predict progression to Stage 2 or higher AKI within the next 48 hours. We trained local models for each site (UFH Model trained on UFH, UPMC Model trained on UPMC) and a separate model with a development cohort of patients from both sites (UFH-UPMC Model). We internally and externally validated the models on each site and performed subgroup analyses across sex and race. Results: Stage 2 or higher AKI occurred in 3% (n=3,257) and 8% (n=2,296) of UFH and UPMC patients, respectively. Area under the receiver operating curve values (AUROC) for the UFH test cohort ranged between 0.77 (UPMC Model) and 0.81 (UFH Model), while AUROC values ranged between 0.79 (UFH Model) and 0.83 (UPMC Model) for the UPMC test cohort. UFH-UPMC Model achieved an AUROC of 0.81 (95% confidence interval [CI] [0.80, 0.83]) for UFH and 0.82 (95% CI [0.81,0.84]) for UPMC test cohorts; an area under the precision recall curve values (AUPRC) of 0.6 (95% CI, [0.05, 0.06]) for UFH and 0.13 (95% CI, [0.11,0.15]) for UPMC test cohorts. Kinetic estimated glomerular filtration rate, nephrotoxic drug burden and blood urea nitrogen remained the top three features with the highest influence across the models and health centers. Conclusion: Locally developed models displayed marginally reduced discrimination when tested on another institution, while the top set of influencing features remained the same across the models and sites.

We continue our investigation of binary actions of simple groups. In this paper, we demonstrate a connection between the graph $\Gamma(\mathcal{C})$ based on the conjugacy class $\mathcal{C}$ of the group $G$, which was introduced in our previous work, and the notion of a strongly embedded subgroup of $G$. We exploit this connection to prove a result concerning the binary actions of finite simple groups that contain a single conjugacy class of involutions., Comment: arXiv admin note: substantial text overlap with arXiv:2303.06003

We present the design, fabrication, and characterization of a 100 mm diameter, flat, gradient-index (GRIN) lens fabricated with high-resistivity silicon, combined with a three-layer anti-reflection (AR) structure optimized for 160-355 GHz. Multi-depth, deep reactive-ion etching (DRIE) enables patterning of silicon wafers with sub-wavelength structures (posts or holes) to locally change the effective refractive index and thus create anti-reflection layers and a radial index gradient. The structures are non-resonant and, for sufficiently long wavelengths, achromatic. Hexagonal holes varying in size with distance from the optical axis create a parabolic index profile decreasing from 3.15 at the center of the lens to 1.87 at the edge. The AR structure consists of square holes and cross-shaped posts. We have fabricated a lens consisting of a stack of five 525 $\mu$m thick GRIN wafers and one AR wafer on each face. We have characterized the lens over the frequency range 220-330 GHz, obtaining behavior consistent with Gaussian optics down to -14 dB and transmittance between 75% and 100%.

A long standing problem in time dependent density functional theory (TD-DFT) has been the absence of a functional able to capture excitonic physics under laser pump conditions. Here we introduce a Kohn-Sham-Proca scheme that we demonstrate reproduces both linear response excitonic effects in the absorption in excellent agreement with experiment for a wide range of materials, but also captures excitonic physics in the highly non-linear regime of ultrafast laser pumping, in particular laser fluence dependent "bleaching" (i.e. reduction) of the excitonic weight and the appearance of excitonic side bands. The approach is a procedural functional -- the Kohn-Sham and Proca equations are simultaneously time propagated -- allowing the straight-forward inclusion of, for example, lattice and spin degrees of freedom into excitonic physics., Comment: 4 page 3 figures

We have measured the spherically averaged bispectrum of the SDSS DR17 main galaxy sample, considering a volume-limited $[273\, \rm Mpc]^3$ data cube with mean galaxy number density $1.76 \times 10^{-3} \, {\rm Mpc}^{-3}$ and median redshift $0.093$. Our analysis considers $\sim 1.37 \times 10^{8}$ triangles, for which we have measured the binned bispectrum and analyzed its dependence on the size and shape of the triangle. It spans wavenumbers $k_1=(0.082-0.472)\,{\rm Mpc}^{-1}$ for equilateral triangles, and a smaller range of $k_1$ (the largest side) for triangles of other shapes. For all shapes, we find that the measured bispectrum is well modelled by a power law $A\big(k_1/1 Mpc^{-1}\big)^{n}$, where the best-fit values of $A$ and $n$ vary with the shape. The parameter $A$ is the minimum for equilateral triangles and increases as the shape is deformed to linear triangles where the two largest sides are nearly aligned, reaching its maximum value for squeezed triangles. The values of $n$ are all negative, $|n|$ is minimum $(3.31 \pm 0.17)$ for squeezed triangles, and $4.12 \pm 0.16$ for equilateral. We have also analyzed mock galaxy samples constructed from $\Lambda$CDM N-body simulations by applying a simple Eulerian bias prescription where the galaxies reside in regions where the smoothed density field exceeds a threshold. We find that the bispectrum from the mock samples with bias $b_1=1.2$ is in good agreement with the SDSS results., Comment: 27 pages, 12 figures, 2 tables, significantly revised, analysis for red and blue galaxies using SDSS data are included, accepted for publication in New Astronomy

Optical atomic clocks demonstrate a better stability and lower systematic uncertainty than the highest performance microwave atomic clocks. However, the best performing optical clocks have a large footprint in a laboratory environment and require specialist skills to maintain continuous operation. Growing and evolving needs across several sectors are increasing the demand for compact robust and portable devices at this capability level. In this paper we discuss the design of a physics package for a compact laser-cooled 88Sr+ optical clock that would, with further development, be suitable for space deployment. We review the design parameters to target a relative frequency uncertainty at the low parts in 10^18 with this system. We then explain the results of finite element modelling to simulate the response of the ion trap and vacuum chamber to vibration, shock and thermal conditions expected during launch and space deployment. Additionally, an electrostatic model has been developed to investigate the relationship between the ion trap geometrical tolerances and the trapping efficiency. We present the results from these analyses that have led to the design of a more robust prototype ready for experimental testing., Comment: 21 Pages, 20 Figures, Approved for publication in "Proceedings of the Royal Society A"

Axions and axion-like particles (ALPs) are leading candidates for dark matter. They are well motivated in many extensions of the Standard Model and supported by astronomical observations. We propose an iterative transformation of the existing facilities of the gravitational-wave detector and technology testbed GEO600, located near Ruthe in Germany, into a kilometre-scale upgrade of the laser-interferometric axion detector LIDA. The final DarkGEO detector could search for coincident signatures of axions and ALPs and significantly surpass the current constraints of both direct searches and astrophysical observations in the measurement band from $10^{-16}$ to $10^{-8}$ $\text{eV}$. We discuss realistic parameters and design sensitivities for the configurations of the different iteration steps as well as technical challenges known from the first LIDA results. The proposed DarkGEO detector will be well suited to probe the parameter space associated with predictions from theoretical models, like grand-unified theories, as well as from astrophysical evidence, like the cosmic infrared background.

Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading detection. Coded language evolves quickly and its use varies over time. This paper proposes a methodology for detecting emerging coded hate-laden terminology. The methodology is tested in the context of online antisemitic discourse. The approach considers posts scraped from social media platforms, often used by extremist users. The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews. The method begins by identifying the expressions most representative of each post and calculating their frequency in the whole corpus. It filters out grammatically incoherent expressions as well as previously encountered ones so as to focus on emergent well-formed terminology. This is followed by an assessment of semantic similarity to known antisemitic terminology using a fine-tuned large language model, and subsequent filtering out of the expressions that are too distant from known expressions of hatred. Emergent antisemitic expressions containing terms clearly relating to Jewish topics are then removed to return only coded expressions of hatred., Comment: 9 pages, 4 figures, 2 algorithms, 3 tables

Complex quantum systems and their various applications are susceptible to noise of coherent and incoherent nature. Characterization of noise and its sources is an open, key challenge in quantum technology applications, especially in terms of distinguishing between inherent incoherent noise and systematic coherent errors. In this paper, we study a scheme of repeated sequential measurements that enables the characterization of incoherent errors by reducing the effects of coherent errors. We demonstrate this approach using a coherently controlled Nitrogen Vacancy in diamond, coupled to both a natural nuclear spin bath (non-Markovian) and to experimentally controlled relaxation through an optical pumping process (nearly Markovian). Our results show mitigation of coherent errors both for Markovian and Non-Markovian incoherent noise profiles. We apply this scheme to the estimation of the dephasing time ($T_2^*$) due to incoherent noise. We observe an improved robustness against coherent errors in the estimation of dephasing time ($T_2^*$) compared to the standard (Ramsey) measurement.

Hot Jupiters were many of the first exoplanets discovered in the 1990s, but in the decades since their discovery, the mysteries surrounding their origins remain. Here, we present nine new hot Jupiters (TOI-1855 b, TOI-2107 b, TOI-2368 b, TOI-3321 b, TOI-3894 b, TOI-3919 b, TOI-4153 b, TOI-5232 b, and TOI-5301 b) discovered by NASA's TESS mission and confirmed using ground-based imaging and spectroscopy. These discoveries are the first in a series of papers named the Migration and Evolution of giant ExoPlanets (MEEP) survey and are part of an ongoing effort to build a complete sample of hot Jupiters orbiting FGK stars, with a limiting Gaia $G$-band magnitude of 12.5. This effort aims to use homogeneous detection and analysis techniques to generate a set of precisely measured stellar and planetary properties that is ripe for statistical analysis. The nine planets presented in this work occupy a range of masses (0.55 Jupiter masses (M$_{\rm{J}}$) $<$ M$_{\rm{P}}$ $<$ 3.88 M$_{\rm{J}}$) and sizes (0.967 Jupiter radii (R$_{\rm{J}}$) $<$ R$_{\rm{P}}$ $<$ 1.438 R$_{\rm{J}}$) and orbit stars that range in temperature from 5360 K $<$ Teff $<$ 6860 K with Gaia $G$-band magnitudes ranging from 11.1 to 12.7. Two of the planets in our sample have detectable orbital eccentricity: TOI-3919 b ($e = 0.259^{+0.033}_{-0.036}$) and TOI-5301 b ($e = 0.33^{+0.11}_{-0.10}$). These eccentric planets join a growing sample of eccentric hot Jupiters that are consistent with high-eccentricity tidal migration, one of the three most prominent theories explaining hot Jupiter formation and evolution., Comment: 35 pages, 7 tables, and 14 figures. Submitted to AAS Journals on 2023 Dec 28

Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress., Comment: Preprint submitted to Telematics and Informatics Reports, Elsevier (2024)

In Federated Learning, clients train models on local data and send updates to a central server, which aggregates them into a global model using a fusion algorithm. This collaborative yet privacy-preserving training comes at a cost--FL developers face significant challenges in attributing global model predictions to specific clients. Localizing responsible clients is a crucial step towards (a) excluding clients primarily responsible for incorrect predictions and (b) encouraging clients who contributed high-quality models to continue participating in the future. Existing ML explainability approaches are inherently inapplicable as they are designed for single-model, centralized training. We introduce TraceFL, a fine-grained neuron provenance capturing mechanism that identifies clients responsible for the global model's prediction by tracking the flow of information from individual clients to the global model. Since inference on different inputs activates a different set of neurons of the global model, TraceFL dynamically quantifies the significance of the global model's neurons in a given prediction. It then selectively picks a slice of the most crucial neurons in the global model and maps them to the corresponding neurons in every participating client to determine each client's contribution, ultimately localizing the responsible client. We evaluate TraceFL on six datasets, including two real-world medical imaging datasets and four neural networks, including advanced models such as GPT. TraceFL achieves 99% accuracy in localizing the responsible client in FL tasks spanning both image and text classification tasks. At a time when state-of-the-art ML debugging approaches are mostly domain-specific (e.g., image classification only), TraceFL is the first technique to enable highly accurate automated reasoning across a wide range of FL applications., Comment: 13 pages. TraceFL is the first interpretability technique in FL that can work on both image and text classification tasks. For source code please contact at waris@vt.edu

Observations of low-mass stars have frequently shown a disagreement between observed stellar radii and radii predicted by theoretical stellar structure models. This ``radius inflation'' problem could have an impact on both stellar and exoplanetary science. We present the final results of our observation programme with the CHEOPS satellite to obtain high-precision light curves of eclipsing binaries with low mass stellar companions (EBLMs). Combined with the spectroscopic orbits of the solar-type companion, we can derive the masses, radii and effective temperatures of 23 M-dwarf stars. We use the PYCHEOPS data analysis software to analyse their primary and secondary occultations. For all but one target, we also perform analyses with TESS light curves for comparison. We have assessed the impact of starspot-induced variation on our derived parameters and account for this in our radius and effective temperature uncertainties using simulated light curves. We observe trends for inflation with both metallicity and orbital separation. We also observe a strong trend in the difference between theoretical and observational effective temperatures with metallicity. There is no such trend with orbital separation. These results are not consistent with the idea that observed inflation in stellar radius combines with lower effective temperature to preserve the luminosity predicted by low-mass stellar models. Our EBLM systems are high-quality and homogeneous measurements that can be used in further studies into radius inflation., Comment: 21 pages, 10 figures, accepted for publication in MNRAS, Supplementary material provided as ancillary files

Building on work by Hang and He, we show how the residual five-dimensional diffeomorphism symmetries of compactified gravitational theories with a warped extra dimension imply Equivalence theorems which ensure that the scattering amplitudes of helicity-0 and helicity-1 spin-2 Kaluza-Klein states equal (to leading order in scattering energy) those of the corresponding Goldstone bosons present in the `t-Hooft-Feynman gauge. We derive a set of Ward identities that lead to a transparent power-counting of the scattering amplitudes involving spin-2 Kaluza-Klein states. We explicitly calculate these amplitudes in terms of the Goldstone bosons in the Randall-Sundrum model, check the correspondence to previous unitary-gauge computations, and demonstrate the efficacy of `t-Hooft-Feynman gauge for accurately computing amplitudes for scattering of the spin-2 states both among themselves and with matter. Power-counting for the Goldstone boson interactions establishes that the scattering amplitudes grow no faster than $O(s)$, explaining the origin of the behavior previously shown to arise from intricate cancellations between different contributions to these scattering amplitudes in unitary gauge. We describe how our results apply to more general warped geometries, including models with a stabilized extra dimension. In an appendix we explicitly identify the symmetry algebra of the residual 5D diffeomorphisms of a Randall-Sundrum extra-dimensional theory., Comment: 19 pages, 7 figures

For quasi-Newton methods in unconstrained minimization, it is valuable to develop methods that are robust, i.e., methods that converge on a large number of problems. Trust-region algorithms are often regarded to be more robust than line-search methods, however, because trust-region methods are computationally more expensive, the most popular quasi-Newton implementations use line-search methods. To fill this gap, we develop a trust-region method that updates an $LDL^T$ factorization, scales quadratically with the size of the problem, and is competitive with a conventional line-search method.

A countable structure is indivisible if for every coloring with finite range there is a monochromatic isomorphic subcopy of the structure. Each indivisible structure naturally corresponds to an indivisibility problem which outputs such a subcopy given a presentation and coloring. We investigate the Weihrauch complexity of the indivisibility problems for two structures: the rational numbers $\mathbb{Q}$ as a linear order, and the equivalence relation $\mathscr{E}$ with countably many equivalence classes each having countably many members. We separate the Weihrauch degrees of both corresponding indivisibility problems from several benchmarks, showing in particular that the indivisibility problem for $\mathbb{Q}$ cannot solve the problem of finding a monochromatic rational interval given a coloring for which there is one; and that the Weihrauch degree of the indivisibility problem for $\mathscr{E}$ is strictly between those of $\mathsf{RT}^2$ and $\mathsf{SRT}^2$, two widely studied variants of Ramsey's theorem for pairs whose reverse-mathematical separation was open until recently., Comment: 22 pages, 4 figures. This work extends the results of Sections 1.2 and 1.3 of the author's Ph.D. thesis at Penn State University. Version 5: various corrections and improvements

Purpose: This study assesses the effectiveness of Deep Learning (DL) for creating synthetic CT (sCT) images in MR-guided adaptive radiation therapy (MRgART). Methods: A Cycle-GAN model was trained with MRI and CT scan slices from MR-LINAC treatments, generating sCT volumes. The analysis involved retrospective treatment plan data from patients with various tumors. sCT images were compared with standard CT scans using mean absolute error in Hounsfield Units (HU) and image similarity metrics (SSIM, PSNR, NCC). sCT volumes were integrated into a clinical treatment system for dosimetric re-evaluation. Results: The model, trained on 8405 frames from 57 patients and tested on 357 sCT frames from 17 patients, showed sCTs comparable to dCTs in electron density and structural similarity with MRI scans. The MAE between sCT and dCT was 49.2 +/- 13.2 HU, with sCT NCC exceeding dCT by 0.06, and SSIM and PSNR at 0.97 +/- 0.01 and 19.9 +/- 1.6 respectively. Dosimetric evaluations indicated minimal differences between sCTs and dCTs, with sCTs showing better air-bubble reconstruction. Conclusions: DL-based sCT generation on MR-Linacs is accurate for dose calculation and optimization in MRgART. This could facilitate MR-only treatment planning, enhancing simulation and adaptive planning efficiency on MR-Linacs.