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This chartbook is the 14th version to be produced for the Appalachian Regional Commission (ARC) by the Population Reference Bureau (PRB). The Chartbook describes the diversity of the Appalachian Region on a host of demographic and economic measures and provides an important annual view of the area and its people. The data contained in the 2018-2022 Chartbook describe how residents in the Appalachian Region were faring before and during the COVID-19 pandemic that began in March 2020. Nearly half of the years during this time period were pre-pandemic and half were during the pandemic era. Thus, this Chartbook is a blend of these two eras. As future data releases reflect the post-pandemic era, data users will have additional insights on the long-term changes that the pandemic brought to Appalachia's social and economic dynamics. Most of the data shown here come from the 2018-2022 American Community Survey (ACS), a nationwide study collected continuously every year in every county in the United States by the U.S. Census Bureau. The ACS is designed to provide communities with reliable and timely demographic, social, economic, and housing data each year. To provide as much county-level data as possible, we use ACS 5-year data files which provide reliable estimates for geographic areas with fewer than 20,000 people. Since many counties in the Appalachian Region have fewer than 20,000 residents, these data permit comparable statistics for all 423 counties in the Region. The primary purpose of the ACS is to measure the changing characteristics of the U.S. population in a way that is continually updated. The estimates in this Chartbook, therefore, are data collected over the five-year (or 60-month) period from January 2018 through December 2022. These ACS estimates are not averages of monthly or annual values, but rather an aggregation of data collected continuously over that time period.

Decades of chronic underfunding and inequitable funding have been spiraling Pennsylvania's public school system into crisis--failing kids and threatening the future of the Commonwealth. The Commonwealth Court has ruled that the state's current funding system for K-12 schools is unconstitutional and the state must take action to adequately and equitably fund schools. This report provides a cautionary tale for those considering expanding existing taxpayer-funded vouchers to attend private schools in Pennsylvania or creating a new program. Pennsylvania does not need to rely only on evidence from other states in evaluating whether to establish a new taxpayer-funded school voucher program. The reason: Pennsylvania already has two existing voucher programs now funded for a combined $470 million per year, over four times more than in 2012-13, the first year of the Opportunity Scholarship Tax Credit (OSTC) program, the newer of the two new voucher programs. The legislature first provided funds for the Educational Improvement Tax Credit (EITC) program in 2001-02. This report updates a 2017 Keystone Research Center report on these two existing voucher programs, which have received about $2.6 billion in state tax credits since their inception. This report finds: (1) All students in the state do not benefit equally from Pennsylvania's voucher program--students in rural communities often don't have access and participating schools are allowed to, and do, discriminate against students; (2) By design, neither the EITC nor OSTC program requires meaningful educational or financial accountability; Scholarship organizations and schools have few reporting requirements, making it difficult to assess impact or program compliance; (3) The EITC/OSTC programs are subsidizing religious schools that have few state-required standards regarding curriculum. Some of these schools receiving public money teach creationism as science and present the Bible as factually accurate history; and (4) PA's existing voucher program subsidizes the state's most elite and expensive private schools, as well as affluent families. The inadequate accountability in Pennsylvania's $470 million OSTC and EITC programs (for 2023-24) makes clear that the state should not divert more taxpayer dollars to these programs or create a new one. At minimum, a comprehensive financial and educational audit of these programs must be done first, including to evaluate curricula schools use and make recommendations regarding basic curriculum standards. The Independent Fiscal Office (IFO) attempted to evaluate the effectiveness of these programs and reported that, due to a lack of reporting requirements, it could not do so. Pennsylvania should also prioritize fully and fairly funding public schools, which serve all of Pennsylvania's students. A public dollar spent on expanding existing or creating new voucher programs in Pennsylvania is a dollar less that can be spent to meet the court-ordered mandate to fix the unconstitutional public school funding system.

Understanding group behavior is essential for improving collaboration and productivity. While research on group behavior in virtual reality (VR) is significantly advanced, understanding group dynamics in mixed reality (MR) remains understudied. Understanding MR group dynamics will enable designing systems that optimize collaboration, enhance productivity, and improve user experiences. This work outlines how MR headsets sensory systems can effectively capture group behavior, devise algorithms to process and interpret the data and demonstrate the correlation between group behavior and task-related performance metrics. We propose a framework for group behavior analysis in MR, or GroupBeaMR for short, to capture and analyze group behavior in MR. Using the rich sensory capabilities of MR headsets, GroupBeaMR passively collects data on conversation, shared attention, and proximity. This data is processed using social network analysis techniques to identify patterns of interaction and assess group behavior. Our evaluation, involving 44 participants in 11 groups, demonstrates the effectiveness of GroupBeaMR in capturing and analyzing group behavior in collaborative MR tasks. An example of insight from GroupBeaMR is that balanced participation in different types of interaction leads to higher group cohesion. These findings enable real-time assessments of group behavior in MR that can enhance collaborative experiences., Comment: 37 pages, 18 figures, 10 tables

The advantage of quantum protocols lies in the inherent properties of the shared quantum states. These states are sometimes provided by sources that are not trusted, and therefore need to be verified. Finding secure and efficient quantum state verification protocols remains a big challenge, and recent works illustrate trade-offs between efficiency and security for different groups of states in restricted settings. However, whether a universal trade-off exists for all quantum states and all verification strategies remains unknown. In this work, we instantiate the categorical composable cryptography framework to show a fundamental limit for quantum state verification for all cut-and-choose approaches used to verify arbitrary quantum states. Our findings show that the prevailing cut-and-choose techniques cannot lead to quantum state verification protocols that are both efficient and secure.

The effect of electron irradiation on 2D materials is an important topic, both for the correct interpretation of electron microscopy experiments and for possible applications in electron lithography. After the importance of including inelastic scattering damage in theoretical models describing beam damage, and the lack of oxygen-sensitivity under electron irradiation in 2D MoS2 was recently shown, the role of temperature has remained unexplored on a quantitative level. Here we show the effect of temperature on both the creation of individual defects as well as the effect of temperature on defect dynamics. Based on the measured displacement cross section of sulphur atoms in MoS2 by atomic resolution scanning transmission electron microscopy, we find an increased probability for defect creation for temperatures up to 150{\deg}C, in accordance with theoretical predictions. However, higher temperatures lead to a decrease of the observed cross sections. Despite this apparent decrease, we find that the elevated temperature does not mitigate the creation of defects as this observation would suggest, but rather hides the created damage due to rapid thermal diffusion of the created vacancies before their detection, leading to the formation of vacancy lines and pores outside the measurements field of view. Using the experimental data in combination with previously reported theoretical models for the displacement cross section, we estimate the migration energy barrier of sulphur vacancies in MoS2 to be 0.47 +- 0.24 eV. These results mark another step towards the complete understanding of electron beam damage in MoS2 ., Comment: 7 pages, 4 figures

We set out to explore the possibility of investigating the critical behavior of systems with first-order phase transition using deep machine learning. We propose a machine learning protocol with ternary classification of instantaneous spin configurations using known values of disordered phase energy and ordered phase energy. The trained neural network is used to predict whether a given sample belong to one or the other phase of matter. This allows us to estimate the probability that configurations with a certain energy belong to the ordered phase, mixed phase and, disordered phase. From these probabilities, we obtained estimates of the values of the critical energies and the latent heat for the Potts model with 10 and 20 components, which undergoes a strong discontinuous transition. We also find that the probabilities can reflect geometric transitions in the mixed phase., Comment: 5 pages, 5 figures, 4 tables

This work introduces a novel paradigm for solving optimal control problems for hybrid dynamical systems under uncertainties. Robotic systems having contact with the environment can be modeled as hybrid systems. Controller design for hybrid systems under disturbances is complicated by the discontinuous jump dynamics, mode changes with inconsistent state dimensions, and variations in jumping timing and states caused by noise. We formulate this problem into a stochastic control problem with hybrid transition constraints and propose the Hybrid Path Integral (H-PI) framework to obtain the optimal controller. Despite random mode changes across stochastic path samples, we show that the ratio between hybrid path distributions with varying drift terms remains analogous to the smooth path distributions. We then show that the optimal controller can be obtained by evaluating a path integral with hybrid constraints. Importance sampling for path distributions with hybrid dynamics constraints is introduced to reduce the variance of the path integral evaluation, where we leverage the recently developed Hybrid iterative-Linear-Quadratic-Regulator (H-iLQR) controller to induce a hybrid path distribution proposal with low variance. The proposed method is validated through numerical experiments on various hybrid systems and extensive ablation studies. All the sampling processes are conducted in parallel on a Graphics Processing Unit (GPU)., Comment: 14 pages

We develop EigenVI, an eigenvalue-based approach for black-box variational inference (BBVI). EigenVI constructs its variational approximations from orthogonal function expansions. For distributions over $\mathbb{R}^D$, the lowest order term in these expansions provides a Gaussian variational approximation, while higher-order terms provide a systematic way to model non-Gaussianity. These approximations are flexible enough to model complex distributions (multimodal, asymmetric), but they are simple enough that one can calculate their low-order moments and draw samples from them. EigenVI can also model other types of random variables (e.g., nonnegative, bounded) by constructing variational approximations from different families of orthogonal functions. Within these families, EigenVI computes the variational approximation that best matches the score function of the target distribution by minimizing a stochastic estimate of the Fisher divergence. Notably, this optimization reduces to solving a minimum eigenvalue problem, so that EigenVI effectively sidesteps the iterative gradient-based optimizations that are required for many other BBVI algorithms. (Gradient-based methods can be sensitive to learning rates, termination criteria, and other tunable hyperparameters.) We use EigenVI to approximate a variety of target distributions, including a benchmark suite of Bayesian models from posteriordb. On these distributions, we find that EigenVI is more accurate than existing methods for Gaussian BBVI., Comment: 25 pages, 9 figures. Advances in Neural Information Processing Systems (NeurIPS), 2024

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National Science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science.

Streamlined Byzantine Fault Tolerant (BFT) protocols, such as HotStuff [PODC'19], and weighted voting represent two possible strategies to improve consensus in the distributed systems world. Several studies have been conducted on both techniques, but the research on combining the two is scarce. To cover this knowledge gap, we introduce a weighted voting approach on Hotstuff, along with two optimisations targeting weight assignment distribution and leader rotation in the underlying state replication protocol. Moreover, the weighted protocols developed rely on studies proving the effectiveness of a specific voting power assignment based on discrete values. We generalise this approach by presenting a novel continuous weighting scheme applied to the Hotstuff protocol to highlight the effectiveness of this technique in faulty scenarios. We prove the significant latency reduction impact of weighted voting on streamlined protocols and advocate for further research., Comment: This work has been done in the context of a Bachelor research project, see https://resolver.tudelft.nl/uuid:907bd47c-394a-4e26-b901-de713159dcb8

Black-box variational inference (BBVI) scales poorly to high dimensional problems when it is used to estimate a multivariate Gaussian approximation with a full covariance matrix. In this paper, we extend the batch-and-match (BaM) framework for score-based BBVI to problems where it is prohibitively expensive to store such covariance matrices, let alone to estimate them. Unlike classical algorithms for BBVI, which use gradient descent to minimize the reverse Kullback-Leibler divergence, BaM uses more specialized updates to match the scores of the target density and its Gaussian approximation. We extend the updates for BaM by integrating them with a more compact parameterization of full covariance matrices. In particular, borrowing ideas from factor analysis, we add an extra step to each iteration of BaM -- a patch -- that projects each newly updated covariance matrix into a more efficiently parameterized family of diagonal plus low rank matrices. We evaluate this approach on a variety of synthetic target distributions and real-world problems in high-dimensional inference.

Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. Recurrence risk assessment plays a crucial role in personalizing treatment. Current methods, including genomic assays, have limited accuracy and clinical utility, leading to suboptimal decisions for many patients. We developed a test for breast cancer patient stratification based on digital pathology and clinical characteristics using novel AI methods. Specifically, we utilized a vision transformer-based pan-cancer foundation model trained with self-supervised learning to extract features from digitized H&E-stained slides. These features were integrated with clinical data to form a multi-modal AI test predicting cancer recurrence and death. The test was developed and evaluated using data from a total of 8,161 breast cancer patients across 15 cohorts originating from seven countries. Of these, 3,502 patients from five cohorts were used exclusively for evaluation, while the remaining patients were used for training. Our test accurately predicted our primary endpoint, disease-free interval, in the five external cohorts (C-index: 0.71 [0.68-0.75], HR: 3.63 [3.02-4.37, p<0.01]). In a direct comparison (N=858), the AI test was more accurate than Oncotype DX, the standard-of-care 21-gene assay, with a C-index of 0.67 [0.61-0.74] versus 0.61 [0.49-0.73], respectively. Additionally, the AI test added independent information to Oncotype DX in a multivariate analysis (HR: 3.11 [1.91-5.09, p<0.01)]). The test demonstrated robust accuracy across all major breast cancer subtypes, including TNBC (C-index: 0.71 [0.62-0.81], HR: 3.81 [2.35-6.17, p=0.02]), where no diagnostic tools are currently recommended by clinical guidelines. These results suggest that our AI test can improve accuracy, extend applicability to a wider range of patients, and enhance access to treatment selection tools.

We compute the conductor exponents at odd places using the machinery of cluster pictures of curves for three infinite families of hyperelliptic curves. These are families of Frey hyperelliptic curves constructed by Kraus and Darmon in the study of the generalised Fermat equations of signatures $(r,r,p)$ and $(p,p,r)$, respectively. Here, $r$ is a fixed prime number and $p$ is a prime that is allowed to vary. In the context of the modular method, Billerey-Chen-Dieulefait-Freitas computed all conductor exponents for the signature $(r,r,p)$. We recover their computations at odd places, providing an alternative approach. In a similar setup, Chen-Koutsianas computed all conductor exponents for the signature $(p,p,5)$. We extend their work to the general case of signature $(p,p,r)$ at odd places. Our work can also be used to compute local arithmetic data for the curves in these families., Comment: 38 pages. Appendix by Martin Azon. Comments welcome

This paper presents the methods used for LegalLens-2024 shared task, which focused on detecting legal violations within unstructured textual data and associating these violations with potentially affected individuals. The shared task included two subtasks: A) Legal Named Entity Recognition (L-NER) and B) Legal Natural Language Inference (L-NLI). For subtask A, we utilized the spaCy library, while for subtask B, we employed a combined model incorporating RoBERTa and CNN. Our results were 86.3% in the L-NER subtask and 88.25% in the L-NLI subtask. Overall, our paper demonstrates the effectiveness of transformer models in addressing complex tasks in the legal domain. The source code for our implementation is publicly available at https://github.com/NimaMeghdadi/uOttawa-at-LegalLens-2024-Transformer-based-Classification, Comment: Just accepted at the the EMNLP conference

Research towards energy optimization in buildings heavily relies on building-related data such as measured indoor climate factors. While data collection is a labor- and cost-intensive task, simulations are a cheap alternative to generate datasets of arbitrary sizes, particularly useful for data-intensive deep learning methods. In this paper, we present the tool EnergyPlus Room Simulator, which enables the simulation of indoor climate in a specific room of a building using the simulation software EnergyPlus. It allows to alter room models and simulate various factors such as temperature, humidity, and CO2 concentration. In contrast to manually working with EnergyPlus, this tool enhances the simulation process by offering a convenient interface, including a user-friendly graphical user interface (GUI) as well as a REST API. The tool is intended to support scientific, building-related tasks such as occupancy detection on a room level by facilitating fast access to simulation data that may, for instance, be used for pre-training machine learning models., Comment: Presented at BuildSim Nordic 2024. The conference was held from June 9 to 11, 2024, in Espoo, Finland

Large-scale recommendation models are currently the dominant workload for many large Internet companies. These recommenders are characterized by massive embedding tables that are sparsely accessed by the index for user and item features. The size of these 1TB+ tables imposes a severe memory bottleneck for the training and inference of recommendation models. In this work, we propose a novel recommendation framework that is small, powerful, and efficient to run and train, based on the state-of-the-art Deep Learning Recommendation Model (DLRM). The proposed framework makes inference more efficient on the cloud servers, explores the possibility of deploying powerful recommenders on smaller edge devices, and optimizes the workload of the communication overhead in distributed training under the data parallelism settings. Specifically, we show that quantization-aware training (QAT) can impose a strong regularization effect to mitigate the severe overfitting issues suffered by DLRMs. Consequently, we achieved INT4 quantization of DLRM models without any accuracy drop. We further propose two techniques that improve and accelerate the conventional QAT workload specifically for the embedding tables in the recommendation models. Furthermore, to achieve efficient training, we quantize the gradients of the embedding tables into INT8 on top of the well-supported specified sparsification. We show that combining gradient sparsification and quantization together significantly reduces the amount of communication. Briefly, DQRM models with INT4 can achieve 79.07% accuracy on Kaggle with 0.27 GB model size, and 81.21% accuracy on the Terabyte dataset with 1.57 GB, which even outperform FP32 DLRMs that have much larger model sizes (2.16 GB on Kaggle and 12.58 on Terabyte).

In Byzantine Agreement (BA), there is a set of $n$ parties, from which up to $t$ can act byzantine. All honest parties must eventually decide on a common value (agreement), which must belong to a set determined by the inputs (validity). Depending on the use case, this set can grow or shrink, leading to various possible desiderata collectively known as validity conditions. Varying the validity property requirement can affect the regime under which BA is solvable. We study how the selected validity property impacts BA solvability in the network-agnostic model, where the network can either be synchronous with up to $t_s$ byzantine parties or asynchronous with up to $t_a \leq t_s$ byzantine parties. We show that for any non-trivial validity property the condition $2t_s + t_a < n$ is necessary for BA to be solvable, even with cryptographic setup. Noteworthy, specializing this claim to $t_a = 0$ gives that $t < n / 2$ is required when one expects a purely synchronous protocol to also work in asynchrony when there are no corruptions. This is especially surprising given that for some validity properties $t < n$ are known to be achievable without the last stipulation. Thereafter, we give necessary and sufficient conditions for a validity property to render BA solvable, both for the case with cryptographic setup and for the one without. This traces the precise boundary of solvability in the network-agnostic model for every validity property. Our proof of sufficiency provides a universal protocol, that achieves BA for a given validity property whenever the provided conditions are satisfied., Comment: Working paper

AI systems increasingly shape critical decisions across personal and societal domains. While empirical risk minimization (ERM) drives much of the AI success, it typically prioritizes accuracy over trustworthiness, often resulting in biases, opacity, and other adverse effects. This paper discusses how key requirements for trustworthy AI can be translated into design choices for the components of ERM. We hope to provide actionable guidance for building AI systems that meet emerging standards for trustworthiness of AI.

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$\Phi$NE to study the performance of this detector, exploring the possibility to build a dedicated setup., Comment: 10 pages, 3 figures, eQCD conference

This article presents a theoretical analysis of a one-dimensional heat transfer problem in two layers involving diffusion, advection, internal heat generation or loss linearly dependent on temperature in each layer, and heat generation due to external sources. Additionally, the thermal resistance at the interface between the materials is considered. The situation of interest is modeled mathematically, explicit analytical solutions are found using Fourier techniques, and a convergent finite difference scheme is formulated to simulate specific cases. The solution is consistent with previous results. A numerical example is included that shows coherence between the obtained results and the physics of the problem. The conclusions drawn in this work expand the theoretical understanding of two-layer heat transfer and may also contribute to improving the thermal design of multilayer engineering systems., Comment: 22 Pages, 7 Figures, 3 Tables

The common model to describe exciton-plasmon interaction phenomenologically is the coupled oscillator model. Originally developed for atomic systems rather than solid-state matter, this model treats both excitons and plasmons as single harmonic oscillators coupled via a constant which can be fitted to experiments. In this work, we present a modified coupled oscillator model specifically designed for exciton-plasmon interactions in hybrids composed of two-dimensional excitons, such as in a transition metal dichalcogenide (TMDC) monolayers and metal nanoparticles while maintaining the simplicity of the commonly applied coupled oscillator models. Our approach is based on a microscopic perspective and Maxwell's equations, allowing to analytically derive an effective exciton-plasmon coupling constant. Our findings highlight the importance of the spatial dispersion, i.e., the delocalized nature of TMDC excitons, necessitating the distinction between bright and momentum-dark excitons. Both types of excitons occur at different resonance energies and exhibit a qualitatively different coupling with localized plasmons. We find a strong coupling between the plasmon and momentum-dark excitons, while a weakly coupled bright exciton manifests as an additional, third peak in the spectrum. Consequently, we propose a realistic modeling of the primary spectral features in experiments incorporating three harmonic oscillator equations instead of the conventional two. However, we also shed light on the limitations of the three coupled oscillator model in describing the line shape of extinction and scattering cross section spectra.

Recent years have seen a growing concern about climate change and its impacts. While Earth System Models (ESMs) can be invaluable tools for studying the impacts of climate change, the complex coupling processes encoded in ESMs and the large amounts of data produced by these models, together with the high internal variability of the Earth system, can obscure important source-to-impact relationships. This paper presents a novel and efficient unsupervised data-driven approach for detecting statistically-significant impacts and tracing spatio-temporal source-impact pathways in the climate through a unique combination of ideas from anomaly detection, clustering and Natural Language Processing (NLP). Using as an exemplar the 1991 eruption of Mount Pinatubo in the Philippines, we demonstrate that the proposed approach is capable of detecting known post-eruption impacts/events. We additionally describe a methodology for extracting meaningful sequences of post-eruption impacts/events by using NLP to efficiently mine frequent multivariate cluster evolutions, which can be used to confirm or discover the chain of physical processes between a climate source and its impact(s).

The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, potentially leading to distrust in human-machine interactions. To address this issue, we introduce a novel dataset of 1,015 tissue microarray core images, annotated by an international group of 54 pathologists. The annotations provide detailed localized pattern descriptions for Gleason grading in line with international guidelines. Utilizing this dataset, we develop an inherently explainable AI system based on a U-Net architecture that provides predictions leveraging pathologists' terminology. This approach circumvents post-hoc explainability methods while maintaining or exceeding the performance of methods trained directly for Gleason pattern segmentation (Dice score: 0.713 $\pm$ 0.003 trained on explanations vs. 0.691 $\pm$ 0.010 trained on Gleason patterns). By employing soft labels during training, we capture the intrinsic uncertainty in the data, yielding strong results in Gleason pattern segmentation even in the context of high interobserver variability. With the release of this dataset, we aim to encourage further research into segmentation in medical tasks with high levels of subjectivity and to advance the understanding of pathologists' reasoning processes., Comment: 58 pages, 15 figures (incl. supplementary)

This paper deals with the long-term behaviour and incidence of a vaccine-preventable contact disease, under the assumption that both vaccine protection and immunity after recovery are not lifelong. The mathematical model is developed in a stochastic markovian framework. The evolution of the disease in a finite population is thus represented by a three-dimensional continuous-time Markov chain, which is versatile enough to be able to compensate for the loss of protection by including vaccination before the onset of the outbreak and also during the course of the epidemics.

In this paper, we applied a deep neural network to study the issue of knowledge transferability between statistical mechanics models. The following computer experiment was conducted. A convolutional neural network was trained to solve the problem of binary classification of snapshots of the Ising model's spin configuration on a two-dimensional lattice. During testing, snapshots of the Ising model spins on a lattice with diagonal ferromagnetic and antiferromagnetic connections were fed to the input of the neural network. Estimates of the probability of samples belonging to the paramagnetic phase were obtained from the outputs of the tested network. The analysis of these probabilities allowed us to estimate the critical temperature and the critical correlation length exponent. It turned out that at weak anisotropy the neural network satisfactorily predicts the transition point and the value of the correlation length exponent. Strong anisotropy leads to a noticeable deviation of the predicted values from the precisely known ones. Qualitatively, strong anisotropy is associated with the presence of oscillations of the correlation function above the Stephenson disorder temperature and further approach to the point of the fully frustrated case., Comment: 5 pages, 5 figures

State Space Models (SSMs) have emerged as an appealing alternative to Transformers for large language models, achieving state-of-the-art accuracy with constant memory complexity which allows for holding longer context lengths than attention-based networks. The superior computational efficiency of SSMs in long sequence modeling positions them favorably over Transformers in many scenarios. However, improving the efficiency of SSMs on request-intensive cloud-serving and resource-limited edge applications is still a formidable task. SSM quantization is a possible solution to this problem, making SSMs more suitable for wide deployment, while still maintaining their accuracy. Quantization is a common technique to reduce the model size and to utilize the low bit-width acceleration features on modern computing units, yet existing quantization techniques are poorly suited for SSMs. Most notably, SSMs have highly sensitive feature maps within the selective scan mechanism (i.e., linear recurrence) and massive outliers in the output activations which are not present in the output of token-mixing in the self-attention modules. To address this issue, we propose a static 8-bit per-tensor SSM quantization method which suppresses the maximum values of the input activations to the selective SSM for finer quantization precision and quantizes the output activations in an outlier-free space with Hadamard transform. Our 8-bit weight-activation quantized Mamba 2.8B SSM benefits from hardware acceleration and achieves a 1.72x lower generation latency on an Nvidia Orin Nano 8G, with only a 0.9% drop in average accuracy on zero-shot tasks. The experiments demonstrate the effectiveness and practical applicability of our approach for deploying SSM-based models of all sizes on both cloud and edge platforms.

This work addresses the problem of optimally steering the state covariance of a linear stochastic system from an initial to a target, subject to hybrid transitions. The nonlinear and discontinuous jump dynamics complicate the control design for hybrid systems. Under uncertainties, stochastic jump timing and state variations further intensify this challenge. This work aims to regulate the hybrid system's state trajectory to stay close to a nominal deterministic one, despite uncertainties and noises. We address this problem by directly controlling state covariances around a mean trajectory, and this problem is termed the Hybrid Covariance Steering (H-CS) problem. The jump dynamics are approximated to the first order by leveraging the Saltation Matrix. When the jump dynamics are nonsingular, we derive an analytical closed-form solution to the H-CS problem. For general jump dynamics with possible singularity and changes in the state dimensions, we reformulate the problem into a convex optimization over path distributions by leveraging Schrodinger's Bridge duality to the smooth covariance control problem. The covariance propagation at hybrid events is enforced as equality constraints to handle singularity issues. The proposed convex framework scales linearly with the number of jump events, ensuring efficient, optimal solutions. This work thus provides a computationally efficient solution to the general H-CS problem. Numerical experiments are conducted to validate the proposed method., Comment: 14 pages

This work reports a pulsed laser-assisted synthesis, detailed structural characterization, and study of plasmonic properties of three sets of TiN/TiNO thin films with high electron density. The first two sets of TiN films were grown at 600C and 700C under a high vacuum condition. The third set of TiN film was grown in the presence of 5 mTorr of molecular oxygen at 700C. The results have shown that TiN films deposited in a high vacuum are metallic, have large optical reflectance, and have high optical and electrical conductivity. The TiN films, grown in 5 mTorr O2, were partially oxidized and semiconducting with room temperature resistivity nearly three times larger than those of the TiN films grown under high vacuum conditions. The optical conductivity of these films was analyzed using a Kramers-Kronig transformation of reflectance and a Lorentz-Drude model; the optical conductivity determined by these two different methods agreed very well. To corroborate our experimental spectral observations, we have calculated the phonon dispersions and Raman active modes of TiNO using the virtual crystal approximation. A comparative analysis of the phonon dispersions between rutile TiO2 and rocksalt TiNO has shown that the incorporation of nitrogen atoms does not significantly alter the phonon dispersions of rutile TiO2. However, it results in the emergence of new phonon modes at approximately 7.128 THz at the Gamma point, which corresponds to the experimentally observed Multi-Photon Phase-MPP. From the collateral study of experimental results and theoretical corroboration, a suitable multi-layer optical model was proposed for the TiN/TINO epitaxial thin films to extract the individual complex dielectric function from which many other optical parameters can be calculated.

The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$\Phi$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics., Comment: 15 pages, 9 figures, 3 tables

Halide perovskites is a new class of semiconductors with exceptional optoelectronic properties. Among many advantages offered by halide perovskites, the bandgap energy can be tuned in a much broader range than what was possible in conventional semiconductors. This was commonly achieved in previous research by mixing different species of halides into solid solutions. The tuned bandgap using this method, however, often underwent an energy shift under optical or electrical stimuli due to halide segregation. In this work, we discovered an alternative approach to achieve super-bandgap electroluminescence from CsPbBr3. The peak energy of the light emission can be 0.7 eV higher than the reported bandgap energy. Evidence pointed to the radiative recombination at the perovskite-PEDOT:PSS interface being responsible for the unexpected blueshift of electroluminescence. We speculated that perovskite nanocrystals were formed therein and produced higher-energy photons due to quantum confinement. The results suggested an alternative strategy to manipulate and stabilize the color of electroluminescence and achieve particularly blue emission in perovskite-based LEDs.

This study investigates who should bear the responsibility of combating the spread of misinformation in social networks. Should that be the online platforms or their users? Should that be done by debunking the "fake news" already in circulation or by investing in preemptive efforts to prevent their diffusion altogether? We seek to answer such questions in a stylized opinion dynamics framework, where agents in a network aggregate the information they receive from peers and/or from influential external sources, with the aim of learning a ground truth among a set of competing hypotheses. In most cases, we find centralized sources to be more effective at combating misinformation than distributed ones, suggesting that online platforms should play an active role in the fight against fake news. In line with literature on the "backfire effect", we find that debunking in certain circumstances can be a counterproductive strategy, whereas some targeted strategies (akin to "deplatforming") and/or preemptive campaigns turn out to be quite effective. Despite its simplicity, our model provides useful guidelines that could inform the ongoing debate on online disinformation and the best ways to limit its damaging effects.

As the cultural heritage sector increasingly adopts technologies like Retrieval-Augmented Generation (RAG) to provide more personalised search experiences and enable conversations with collections data, the demand for specialised evaluation datasets has grown. While end-to-end system testing is essential, it's equally important to assess individual components. We target the final, answering task, which is well-suited to Machine Reading Comprehension (MRC). Although existing MRC datasets address general domains, they lack the specificity needed for cultural heritage information. Unfortunately, the manual creation of such datasets is prohibitively expensive for most heritage institutions. This paper presents a cost-effective approach for generating domain-specific MRC datasets with increased difficulty using Reinforcement Learning from Human Feedback (RLHF) from synthetic preference data. Our method leverages the performance of existing question-answering models on a subset of SQuAD to create a difficulty metric, assuming that more challenging questions are answered correctly less frequently. This research contributes: (1) A methodology for increasing question difficulty using PPO and synthetic data; (2) Empirical evidence of the method's effectiveness, including human evaluation; (3) An in-depth error analysis and study of emergent phenomena; and (4) An open-source codebase and set of three llama-2-chat adapters for reproducibility and adaptation., Comment: is to be published in NLP4DH 2024

We present a transmission spectrum of the misaligned hot Jupiter WASP-15b from 2.8--5.2 microns observed with JWST's NIRSpec/G395H grating. Our high signal to noise data, which has negligible red noise, reveals significant absorption by H$_2$O ($4.2\sigma$) and CO$_2$ ($8.9\sigma$). From independent data reduction and atmospheric retrieval approaches, we infer that WASP-15b's atmospheric metallicity is super-solar ($\gtrsim 15\times$ solar) and its C/O is consistent with solar, that together imply planetesimal accretion. Our GCM simulations for WASP-15b suggest that the C/O we measure at the limb is likely representative of the entire photosphere due to the mostly uniform spatial distribution of H$_2$O, CO$_2$ and CO. We additionally see evidence for absorption by SO$_2$ and absorption at 4.9$\mu$m, for which the current leading candidate is OCS, albeit with several caveats. If confirmed, this would be the first detection of OCS in an exoplanet atmosphere and point towards complex photochemistry of sulphur-bearing species in the upper atmosphere. These are the first observations from the BOWIE-ALIGN survey which is using JWST's NIRSpec/G395H instrument to compare the atmospheric compositions of aligned/low-obliquity and misaligned/high-obliquity hot Jupiters around F stars above the Kraft break. The goal of our survey is to determine whether the atmospheric composition differs across two populations of planets that have likely undergone different migration histories (disc versus disc-free) as evidenced by their obliquities (aligned versus misaligned)., Comment: 24 pages, 23 figures, 6 tables. Submitted to MNRAS

During the first charge-discharge cycle, silicon-based batteries show an important capacity loss because of the formation of the solid electrolyte interphase (SEI) and morphological changes due to expansion-contraction sequence upon alloying. To understand this first-cycle irreversibility, quantitative methods are needed to characterize the chemical environment of silicon and lithium in the bulk of the cycled electrodes. Here we report a methodology based on multi-edge X-ray Raman Scattering performed on model silicon electrodes prepared in fully lithiated and fully delithiated states after the first cycle. The spectra were recorded at the C, O, F and Li K edges, as well as Si L2,3 edge. They were analysed using linear combinations of both experimental and computed reference spectra. We used prototypical SEI compounds as Li2CO3, LiF and LiPF6, as well as electrode constituents as binder and conductive carbon, cristalline Si, native SiO2,LixSi phases (x being the lithiation index) to identify the main species, isolate their relative contributions, and quantitatively evaluate the proportions of organic and inorganic products. We find that 30% of the carbonates formed in the SEI during the lithiation are dissolved on delithiation, and that part of the Li15Si4 alloys remain present after delithiation. By combining electrochemical analysis and XRS results, we identify that 17% of the lithium lost in the first cycle is trapped in disconnected silicon particles, while 30% form a fluorine-rich stable SEI and 53% a carbonate-rich partially-dissolvable SEI. These results pave the way to systematic, reference data-informed, and modelling assisted studies of SEI characteristics in the bulk of electrodes prepared under controlled state-of-charge and state-of-health conditions.

This paper is concerned with an optimisation problem of Robin Laplacian eigenvalue with respect to an indefinite weight, which is formulated as a shape optimisation problem thanks to the known bang-bang distribution of the optimal weight function. The minimisation of the principal eigenvalue of the problem in a spherical shell of an arbitrary dimension is fully solved.

Recent developments in polymer microwave fiber (PMF) have opened great opportunities for robust, low-cost, and high-speed sub-terahertz (THz) communications. Noticing this great potential, this paper addresses the problem of estimation of the propagation distance of a sub-Thz signal along a radio over fiber structure. Particularly, this paper considers a novel cascaded structure that interconnects multiple radio units (RUs) via fiber for applications in indoor scenarios. Herein, we consider the cascaded effects of distortions introduced by non-linear power amplifiers at the RUs, and the propagation channel over the fiber is based on measurements obtained from transmissions of sub-THz signals on high-density polyethylene fibers. For the estimation of the propagation distance, non-linear least-squares algorithms are proposed, and our simulation results demonstrate that the proposed estimators present a good performance on the propagation distance estimation even in the presence of the cascaded effect of non-linear PAs., Comment: in proceedings of 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

In strategic classification, agents manipulate their features, at a cost, to receive a positive classification outcome from the learner's classifier. The goal of the learner in such settings is to learn a classifier that is robust to strategic manipulations. While the majority of works in this domain consider accuracy as the primary objective of the learner, in this work, we consider learning objectives that have group fairness guarantees in addition to accuracy guarantees. We work with the minimax group fairness notion that asks for minimizing the maximal group error rate across population groups. We formalize a fairness-aware Stackelberg game between a population of agents consisting of several groups, with each group having its own cost function, and a learner in the agnostic PAC setting in which the learner is working with a hypothesis class H. When the cost functions of the agents are separable, we show the existence of an efficient algorithm that finds an approximately optimal deterministic classifier for the learner when the number of groups is small. This algorithm remains efficient, both statistically and computationally, even when H is the set of all classifiers. We then consider cost functions that are not necessarily separable and show the existence of oracle-efficient algorithms that find approximately optimal randomized classifiers for the learner when H has finite strategic VC dimension. These algorithms work under the assumption that the learner is fully transparent: the learner draws a classifier from its distribution (randomized classifier) before the agents respond by manipulating their feature vectors. We highlight the effectiveness of such transparency in developing oracle-efficient algorithms. We conclude with verifying the efficacy of our algorithms on real data by conducting an experimental analysis.

We present the Merian Survey, an optical imaging survey optimized for studying the physical properties of bright star-forming dwarf galaxies. Merian is carried out with two medium-band filters ($N708$ and $N540$, centered at $708$ and $540$ nm), custom-built for the Dark Energy Camera (DECam) on the Blanco telescope. Merian covers $\sim 750\,\mathrm{deg}^2$ of equatorial fields, overlapping with the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) wide, deep, and ultra-deep fields. When combined with the HSC-SSP imaging data ($grizy$), the new Merian DECam medium-band imaging allows for photometric redshift measurements via the detection of H$\rm\alpha$ and [OIII] line emission flux excess in the $N708$ and $N540$ filters, respectively, at $0.06

A strong edge-coloring of a graph is a proper edge-coloring, in which the edges of every path of length 3 receive distinct colors; in other words, every pair of edges at distance at most 2 must be colored differently. The least number of colors needed for a strong edge-coloring of a graph is the strong chromatic index. We consider the list version of the coloring and prove that the list strong chromatic index of graphs with maximum degree 3 is at most 10. This bound is tight and improves the previous bound of 11 colors. We also consider the question whether the strong chromatic index and the list strong chromatic index always coincide. We answer it in negative by presenting an infinite family of graphs for which the two invariants differ. For the special case of the Petersen graph, we show that its list strong chromatic index equals 7, while its strong chromatic index is 5. Up to our best knowledge, this is the first known edge-coloring for which there are graphs with distinct values of the chromatic index and its list version. In relation to the above, we also initiate the study of the list version of the normal edge-coloring. A normal edge-coloring of a cubic graph is a proper edge-coloring, in which every edge is adjacent to edges colored with 4 colors or to edges colored with 2 colors. It is conjectured that 5 colors suffice for a normal edge-coloring of any bridgeless cubic graph which is equivalent to the Petersen Coloring Conjecture. Similarly to the strong edge-coloring, the list normal edge-coloring is much more restrictive and consequently for many graphs the list normal chromatic index is greater than the normal chromatic index. In particular, we show that there are cubic graphs with the list normal chromatic index at least 9, there are bridgeless cubic graphs with its value at least 8, and there are cyclically 4-edge-connected cubic graphs with value at least 7.

AI in Medical Imaging project aims to enhance the National Cancer Institute's (NCI) Image Data Commons (IDC) by developing nnU-Net models and providing AI-assisted segmentations for cancer radiology images. We created high-quality, AI-annotated imaging datasets for 11 IDC collections. These datasets include images from various modalities, such as computed tomography (CT) and magnetic resonance imaging (MRI), covering the lungs, breast, brain, kidneys, prostate, and liver. The nnU-Net models were trained using open-source datasets. A portion of the AI-generated annotations was reviewed and corrected by radiologists. Both the AI and radiologist annotations were encoded in compliance with the the Digital Imaging and Communications in Medicine (DICOM) standard, ensuring seamless integration into the IDC collections. All models, images, and annotations are publicly accessible, facilitating further research and development in cancer imaging. This work supports the advancement of imaging tools and algorithms by providing comprehensive and accurate annotated datasets.

Functional Ordinary Kriging is the most widely used method to predict a curve at a given spatial point. However, uncertainty remains an open issue. In this article a distribution-free prediction method based on two different modulation functions and two conformity scores is proposed. Through simulations and benchmark data analyses, we demonstrate the advantages of our approach when compared to standard methods.

Audio separation in real-world scenarios, where mixtures contain a variable number of sources, presents significant challenges due to limitations of existing models, such as over-separation, under-separation, and dependence on predefined training sources. We propose OpenSep, a novel framework that leverages large language models (LLMs) for automated audio separation, eliminating the need for manual intervention and overcoming source limitations. OpenSep uses textual inversion to generate captions from audio mixtures with off-the-shelf audio captioning models, effectively parsing the sound sources present. It then employs few-shot LLM prompting to extract detailed audio properties of each parsed source, facilitating separation in unseen mixtures. Additionally, we introduce a multi-level extension of the mix-and-separate training framework to enhance modality alignment by separating single source sounds and mixtures simultaneously. Extensive experiments demonstrate OpenSep's superiority in precisely separating new, unseen, and variable sources in challenging mixtures, outperforming SOTA baseline methods. Code is released at https://github.com/tanvir-utexas/OpenSep.git, Comment: Accepted in EMNLP 2024 Main

This paper tackles the challenge of running multiple ML inference jobs (models) under time-varying workloads, on a constrained on-premises production cluster. Our system Faro takes in latency Service Level Objectives (SLOs) for each job, auto-distills them into utility functions, "sloppifies" these utility functions to make them amenable to mathematical optimization, automatically predicts workload via probabilistic prediction, and dynamically makes implicit cross-job resource allocations, in order to satisfy cluster-wide objectives, e.g., total utility, fairness, and other hybrid variants. A major challenge Faro tackles is that using precise utilities and high-fidelity predictors, can be too slow (and in a sense too precise!) for the fast adaptation we require. Faro's solution is to "sloppify" (relax) its multiple design components to achieve fast adaptation without overly degrading solution quality. Faro is implemented in a stack consisting of Ray Serve running atop a Kubernetes cluster. Trace-driven cluster deployments show that Faro achieves 2.3$\times$-23$\times$ lower SLO violations compared to state-of-the-art systems., Comment: 13 pages, 16 figures, To appear in Eurosys 2025

We compare the concepts of protomodular and weakly protomodular objects within the context of unital categories. Our analysis demonstrates that these two notions are generally distinct. To establish this, we introduce left pseudocancellative unital magmas and characterise weakly protomodular objects within the variety of algebras they constitute. Subsequently, we present an example of a weakly protomodular object that is not protomodular in this category.

In this article we obtain certain inequalities involving the entropy of a positive integer and divergence of two positive integers, respectively the entropy of an ideal and divergence of two ideals of a ring of algebraic integers., Comment: 16 pages, 1 figure. Key words and phrases. entropy, divergence, numbers, ideals, ramification theory in algebraic number fields

This paper presents the development of a new entropy-based feature selection method for identifying and quantifying impacts. Here, impacts are defined as statistically significant differences in spatio-temporal fields when comparing datasets with and without an external forcing in Earth system models. Temporal feature selection is performed by first computing the cross-fuzzy entropy to quantify similarity of patterns between two datasets and then applying changepoint detection to identify regions of statistically constant entropy. The method is used to capture temperate north surface cooling from a 9-member simulation ensemble of the Mt. Pinatubo volcanic eruption, which injected 10 Tg of SO2 into the stratosphere. The results estimate a mean difference decrease in near surface air temperature of -0.560 K with a 99% confidence interval between -0.864 K and -0.257 K between April and November of 1992, one year following the eruption. A sensitivity analysis with decreasing SO2 injection revealed that the impact is statistically significant at 5 Tg but not at 3 Tg. Using identified features, a dependency graph model composed of 68 nodes and 229 edges directly connecting initial aerosol optical depth changes in the tropics to solar flux and temperature changes before the temperate north surface cooling is presented.

We revisit the fundamentals of two different methods for calculating classical observables: the eikonal method, which is a scattering amplitude-based method, and the worldline quantum field theory (WQFT) method. The latter has been considered an extension of the worldline effective field theory. We show that the eikonal and WQFT methods are equivalent and that calculations can be translated freely between them. Concretely, we focus on 2-into-2 scattering processes mediated by massless force carriers. On the one hand, taking the classical limit of the QFT scattering amplitude leads to the eikonal method. On the other hand, since in the classical limit the scattering particles are almost on-shell throughout the scattering process, the worldline, a first quantized formalism, is the most efficient framework to study the scattering amplitude. This is an alternate but equivalent formalism to the quantum field theoretic (QFT) framework. By taking the classical limit of the scattering amplitude computed in the worldline, we can derive the WQFT rules of Mogull, Plefka and Steinhoff. In WQFT, the Feynman diagrams are reorganized into a new set of diagrams that facilitate the $\hbar$ expansion. Unlike the QFT eikonal method, which works recursively in identifying the eikonal phase, the worldline-based computation allows to target and systematically extract the classical contributions directly through a specific set of WQFT diagrams. In worldline formalism the perturbative expansion of the scattering amplitude is naturally organized in diagrams which factorize (reducible) and diagrams which are new to that order (irreducible), in a one-to-one map with the structure of the amplitude in the eikonal method. This opened up the possibility to investigate and prove the conjectured exponentiation of the eikonal phase in arXiv: 2409.12895., Comment: 75 pages

Superconductors exhibit remarkable properties such as zero resistivity and diamagnetism at the boiling temperature of liquid hydrogen (20 K) and even aboven the boiling temperature of liquid nitrogen (77 K), making them promising candidates for various applications including electrical engines, energy generation, storage, and high-tech devices like single photon detectors. In this overview, we explore the correlation between ceramic superconductors and the United Nations (UN) Development Goals, emphasizing their potential impact on sustainable development. Through bibliometric analysis, we underscore the significance of ceramic superconductors in addressing global challenges outlined by the UN. Additionally, we discuss the application of supermagnets and second-generation tapes in healthcare systems, particularly in magnetic resonance imaging (MRI) devices for diagnostic imaging. Electric superconducting motors offer a clean alternative to highly polluting diesel engines in maritime transportation and superconducting wires/cables enable effective transport of energy on large scale as well as in industrial structures. Magnetic levitation technology holds promise for developing zero-emission public transportation systems, and magnetic separation with strong magnets will contribute to solve the microplastic pollution. The combination of superconductivity with the planned hydrogen economy further offers new possibilities to bring superconductivity to common applications. At the nanoscale, superconducting nanowire single photon detectors (SNSPDs) enable real-time monitoring of environmental health, exemplified by applications in plant physiology, and superconducting qubits provide the best-engineered structures for quantum computers. However it is still crordwork to works with superconductors UN develepoment goals., Comment: 51 pages, 17 figures, 7 tables, Review paper based on bibliometry