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Influence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employed. This paper addresses scalability challenges in scale-out shared memory system by focusing on the state-of-the-art Influence Maximization via Martingales (IMM) benchmark. To enhance the work efficiency of the current IMM implementation, we propose EFFICIENTIMM with key strategies, including new parallelization scheme, NUMA-aware memory usage, dynamic load balancing and fine-grained adaptive data structures. Benchmarking on a 128-core CPU system with 8 NUMA nodes, EFFICIENTIMM demonstrated significant performance improvements, achieving an average 5.9x speedup over Ripples across 8 diverse SNAP datasets, when compared to the best execution times of the original Ripples framework. Additionally, on the Youtube graph, EFFICIENTIMM demonstrates a better memory access pattern with 357.4x reduction in L1+L2 cache misses as compared to Ripples.

Current trends in Machine Learning prefer explainability even when it comes at the cost of performance. Therefore, explainable AI methods are particularly important in the field of Fraud Detection. This work investigates the applicability of Differentiable Inductive Logic Programming (DILP) as an explainable AI approach to Fraud Detection. Although the scalability of DILP is a well-known issue, we show that with some data curation such as cleaning and adjusting the tabular and numerical data to the expected format of background facts statements, it becomes much more applicable. While in processing it does not provide any significant advantage on rather more traditional methods such as Decision Trees, or more recent ones like Deep Symbolic Classification, it still gives comparable results. We showcase its limitations and points to improve, as well as potential use cases where it can be much more useful compared to traditional methods, such as recursive rule learning.

To determine the causal effect of a treatment using observational data, it is important to balance the covariate distributions between treated and control groups. However, achieving balance can be difficult when treated and control groups lack overlap. In the presence of limited overlap, researchers typically choose between two types of methods: 1) methods (e.g., inverse propensity score weighting) that imply traditional estimands (e.g., ATE) but whose estimators are at risk of variance inflation and considerable statistical bias; and 2) methods (e.g., overlap weighting) which imply a different estimand, thereby changing the target population to reduce variance. In this work, we introduce a framework for characterizing estimands by their target populations and the statistical performance of their estimators. We introduce a bias decomposition that encapsulates bias due to 1) the statistical bias of the estimator; and 2) estimand mismatch, i.e., deviation from the population of interest. We propose a design-based estimand selection procedure that helps navigate the tradeoff between these two sources of bias and variance of the resulting estimators. Our procedure allows the analyst to incorporate their domain-specific preference for preservation of the original population versus reduction of statistical bias. We demonstrate how to select an estimand based on these preferences by applying our framework to a right heart catheterization study., Comment: 34 pages, 7 main text figures, 9 supplementary figures

We present a neural network emulator to constrain the thermal parameters of the intergalactic medium (IGM) at $\displaystyle{5.4}\le{z}\le{6.0}$ using the Lyman-$\displaystyle\alpha$ (Ly$\displaystyle\alpha$) forest flux auto-correlation function. Our auto-differentiable JAX-based framework accelerates the surrogate model generation process using approximately 100 sparsely sampled Nyx hydrodynamical simulations with varying combinations of thermal parameters, i.e., the temperature at mean density $\displaystyle{T}_{{0}}$, the slope of the temperature$\displaystyle-$density relation $\displaystyle\gamma$, and the mean transmission flux $\displaystyle{\langle}{F}{\rangle}$. We show that this emulator has a typical accuracy of 1.0% across the specified redshift range. Bayesian inference of the IGM thermal parameters, incorporating emulator uncertainty propagation, is further expedited using NumPyro Hamiltonian Monte Carlo. We compare both the inference results and computational cost of our framework with the traditional nearest-neighbor interpolation approach applied to the same set of mock Ly$\alpha$ flux. By examining the credibility contours of the marginalized posteriors for $\displaystyle{T}_{{0}},\gamma,\text{and}{\langle}{F}{\rangle}$ obtained using the emulator, the statistical reliability of measurements is established through inference on 100 realistic mock data sets of the auto-correlation function.

We prove Lie's Third Theorem for Lie $\infty$-algebras: Every finite-type, homologically and non-negatively graded $L_\infty$-algebra over $\mathbb{R}$ integrates to a finite-dimensional Lie $\infty$-group., Comment: 88 pages. Comments welcome

We investigate fractal structures in African and African diasporic music, building on hypotheses of choreographer Reggie Wilson and research on fractals in African material culture by Ron Eglash., Comment: 8 pages

Choreographer Reggie Wilson and mathematician Jesse Wolfson describe interactions of math and dance emerging from their 12+ year engagement with Black movement and music traditions as part of Wilson's research-to-performance/performance-to-research choreographic practice, with examples including fractals, braids and choreographic and mathematical notions of space, time and movement.

We explore generating factual and accurate tables from the parametric knowledge of large language models (LLMs). While LLMs have demonstrated impressive capabilities in recreating knowledge bases and generating free-form text, we focus on generating structured tabular data, which is crucial in domains like finance and healthcare. We examine the table generation abilities of four state-of-the-art LLMs: GPT-3.5, GPT-4, Llama2-13B, and Llama2-70B, using three prompting methods for table generation: (a) full-table, (b) row-by-row; (c) cell-by-cell. For evaluation, we introduce a novel benchmark, WikiTabGen which contains 100 curated Wikipedia tables. Tables are further processed to ensure their factual correctness and manually annotated with short natural language descriptions. Our findings reveal that table generation remains a challenge, with GPT-4 reaching the highest accuracy at 19.6%. Our detailed analysis sheds light on how various table properties, such as size, table popularity, and numerical content, influence generation performance. This work highlights the unique challenges in LLM-based table generation and provides a solid evaluation framework for future research. Our code, prompts and data are all publicly available: https://github.com/analysis-bots/WikiTabGen

Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to become familiar with it, more often, the exploration process is conducted with a specific analysis goal or question in mind. To assist users in exploring a new dataset, Automated Data Exploration (ADE) systems have been devised in previous work. These systems aim to auto-generate a full exploration session, containing a sequence of queries that showcase interesting elements of the data. However, existing ADE systems are often constrained by a predefined objective function, thus always generating the same session for a given dataset. Therefore, their effectiveness in goal-oriented exploration, in which users need to answer specific questions about the data, are extremely limited. To this end, this paper presents LINX, a generative system augmented with a natural language interface for goal-oriented ADE. Given an input dataset and an analytical goal described in natural language, LINX generates a personalized exploratory session that is relevant to the user's goal. LINX utilizes a Large Language Model (LLM) to interpret the input analysis goal, and then derive a set of specifications for the desired output exploration session. These specifications are then transferred to a novel, modular ADE engine based on Constrained Deep Reinforcement Learning (CDRL), which can adapt its output according to the specified instructions. To validate LINX's effectiveness, we introduce a new benchmark dataset for goal-oriented exploration and conduct an extensive user study. Our analysis underscores LINX's superior capability in producing exploratory notebooks that are significantly more relevant and beneficial than those generated by existing solutions, including ChatGPT, goal-agnostic ADE, and commercial systems.

Recent quasar absorption line observations suggest that reionization may end as late as $z \approx 5.3$. As a means to search for large neutral hydrogen islands at $z<6$, we revisit long dark gaps in the Ly$\beta$ forest in VLT/X-Shooter and Keck/ESI quasar spectra. We stack the Ly$\alpha$ forest corresponding to both edges of these Ly$\beta$ dark gaps and identify a damping wing-like extended absorption profile. The average redshift of the stacked forest is $z=5.8$. By comparing these observations with reionization simulations, we infer that such a damping wing-like feature can be naturally explained if these gaps are at least partially created by neutral islands. Conversely, simulated dark gaps lacking neutral hydrogen struggle to replicate the observed damping wing features. Furthermore, this damping wing-like profile implies that the volume-averaged neutral hydrogen fraction must be $\langle x_{\rm HI} \rangle \geq 6.1 \pm 3.9\%$ at $z = 5.8$. Our results offer robust evidence that reionization extends below $z=6$., Comment: 8 pages, 5 figures, 1 table; accepted for publication in MNRAS Letters

This appendix volume supplements "Evaluating the Federal Innovative Assessment Demonstration Authority: Early Implementation and Progress of State Efforts to Develop New Statewide Academic Assessments. Evaluation Report. NCEE 2023-004" analyzing the early progress of the first five assessment systems participating in the Innovative Assessment Demonstration Authority (IADA) created by the 2015 reauthorization of the Elementary and Secondary Education Act. These systems include: (1) the New Hampshire Performance Assessment of Competency Education (NH PACE); (2) the Louisiana Educational Assessment Program 2025 Humanities (LEAP 2025 Humanities); (3) the North Carolina Personalized Assessment Tool (NC PAT); (4) the Georgia MAP Assessment Partnership (GMAP) through-year assessment and (5) the Georgia Navvy assessment system. The volume documents the features of the IADA program and the five IADA systems, the study team's research activities, and includes more detailed findings than were summarized in the report. [For the full report, see ED627872. For the Study Highlights, see ED627880.]

Education officials have long hoped that the statewide academic assessments most students take each year could be used not only for accountability but also to guide instruction. Congress established the Innovative Assessment Demonstration Authority (IADA) program in 2015 to help address this goal, offering up to seven states temporary flexibility from federal testing requirements so that they may more easily make progress toward replacing their current assessments with more innovative ones. The key incentive to participate in IADA is that students trying out the innovative assessment are not required to also take the state's current assessment. However, states approved for IADA must still show that their innovative assessments meet most requirements for federal accountability, and they are expected to implement the new assessments statewide within 5 years. This report describes the progress of the first five assessment systems approved under IADA in order to help policymakers consider expanding the program to more states. The report is primarily based on an analysis of states' IADA applications and performance reports to the U.S. Department of Education through the 2020-2021 school year and is part of a broader evaluation of IADA required by Congress. [For the Appendix, see ED627873. For the Study Highlights, see ED627880.]

Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this paper, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix representation to cluster the data without the calculation of a distance matrix. Our technique is substantially faster than conventional methods based on computing pairwise distances via sequence alignment algorithms and also enhances interpretability of results. We compare our method to distance-based hierarchical clustering and nTreeClus through simulation studies and an application to data collected by Daynamica, an app that turns sensor data into a daily summary of a user's activities. Among days that contain a large portion of time spent at home, our method distinguishes days that also contain multiple hours of travel or other activities, while both comparison methods fail to identify these patterns. We further identify which day patterns classified by our method are associated with higher concern for contracting COVID-19 with implications for public health messaging.

The ability to synthesize realistic data in a parametrizable way is valuable for a number of reasons, including privacy, missing data imputation, and evaluating the performance of statistical and computational methods. When the underlying data generating process is complex, data synthesis requires approaches that balance realism and simplicity. In this paper, we address the problem of synthesizing sequential categorical data of the type that is increasingly available from mobile applications and sensors that record participant status continuously over the course of multiple days and weeks. We propose the paired Markov Chain (paired-MC) method, a flexible framework that produces sequences that closely mimic real data while providing a straightforward mechanism for modifying characteristics of the synthesized sequences. We demonstrate the paired-MC method on two datasets, one reflecting daily human activity patterns collected via a smartphone application, and one encoding the intensities of physical activity measured by wearable accelerometers. In both settings, sequences synthesized by paired-MC better capture key characteristics of the real data than alternative approaches.

While methods for measuring and correcting differential performance in risk prediction models have proliferated in recent years, most existing techniques can only be used to assess fairness across relatively large subgroups. The purpose of algorithmic fairness efforts is often to redress discrimination against groups that are both marginalized and small, so this sample size limitation often prevents existing techniques from accomplishing their main aim. We take a three-pronged approach to address the problem of quantifying fairness with small subgroups. First, we propose new estimands built on the "counterfactual fairness" framework that leverage information across groups. Second, we estimate these quantities using a larger volume of data than existing techniques. Finally, we propose a novel data borrowing approach to incorporate "external data" that lacks outcomes and predictions but contains covariate and group membership information. This less stringent requirement on the external data allows for more possibilities for external data sources. We demonstrate practical application of our estimators to a risk prediction model used by a major Midwestern health system during the COVID-19 pandemic.

Resolvent degree is an invariant measuring the complexity of algebraic and geometric phenomena, including the complexity of finite groups. To date, the resolvent degree of a finite simple group $G$ has only been investigated when $G$ is a cylic group; an alternating group; a simple factor of a Weyl group of type $E_6$, $E_7$, or $E_8$; or $\operatorname{PSL}\left(2, \mathbb{F}_7\right)$. In this paper, we establish upper bounds on the resolvent degrees of the sporadic groups by using the invariant theory of their projective representations. To do so, we introduce the notion of (weak) $\operatorname{RD}_k^{\leq d}$-versality, which we connect to the existence of "special points" on varieties., Comment: 26 pages, 7 figures. To appear in the Journal of Algebra

Retrieval-augmented language models (RALMs) hold promise to produce language understanding systems that are are factual, efficient, and up-to-date. An important desideratum of RALMs, is that retrieved information helps model performance when it is relevant, and does not harm performance when it is not. This is particularly important in multi-hop reasoning scenarios, where misuse of irrelevant evidence can lead to cascading errors. However, recent work has shown that retrieval augmentation can sometimes have a negative effect on performance. In this work, we present a thorough analysis on five open-domain question answering benchmarks, characterizing cases when retrieval reduces accuracy. We then propose two methods to mitigate this issue. First, a simple baseline that filters out retrieved passages that do not entail question-answer pairs according to a natural language inference (NLI) model. This is effective in preventing performance reduction, but at a cost of also discarding relevant passages. Thus, we propose a method for automatically generating data to fine-tune the language model to properly leverage retrieved passages, using a mix of relevant and irrelevant contexts at training time. We empirically show that even 1,000 examples suffice to train the model to be robust to irrelevant contexts while maintaining high performance on examples with relevant ones.

The auto-correlation function of the Lyman-$\alpha$ (Ly$\alpha$) forest flux from high-z quasars can statistically probe all scales of the intergalactic medium (IGM) just after the epoch of reionization. The thermal state of the IGM, which is determined by the physics of reionization, sets the amount of small-scale power seen in the \lya forest. To study the sensitivity of the auto-correlation function to the thermal state of the IGM, we compute the auto-correlation function from cosmological hydrodynamical simulations with semi-numerical models of the thermal state of the IGM. We create mock data sets of 20 quasars to forecast constraints on $T_0$ and $\gamma$, which characterize a tight temperature-density relation in the IGM, at $5.4 \leq z \leq 6$. At $z = 5.4$ we find that an ideal data set constrains $T_0$ to 29\% and $\gamma$ to 9\%. In addition, we investigate four realistic reionization scenarios that combine temperature and ultra-violet background (UVB) fluctuations at $z = 5.8$. We find that, when using mock data generated from a model that includes temperature and UVB fluctuations, we can rule out a model with no temperature or UVB fluctuations at $>1\sigma$ level 50.5\% of the time., Comment: 18 pages, 13 figures, comments welcome. arXiv admin note: text overlap with arXiv:2208.09013

Recently, the Lyman-$\alpha$ (Ly$\alpha$) forest flux auto-correlation function has been shown to be sensitive to the mean free path of hydrogen-ionizing photons, $\lambda_{\text{mfp}}$, for simulations at $z \geq 5.4$. Measuring $\lambda_{\text{mfp}}$ at these redshifts will give vital information on the ending of reionization. Here we present the first observational measurements of the Ly$\alpha$ forest flux auto-correlation functions in ten redshift bins from $5.1 \leq z \leq 6.0$. We use a sample of 35 quasar sightlines at $z > 5.7$ from the extended XQR-30 data set, this data has signal-to-noise ratios of $> 20$ per spectral pixel. We carefully account for systematic errors in continuum reconstruction, instrumentation, and contamination by damped Ly$\alpha$ systems. With these measurements, we introduce software tools to generate auto-correlation function measurements from any simulation. For an initial comparison, we show our auto-correlation measurements with simulation models for recently measured $\lambda_{\text{mfp}}$ values and find good agreements. Further work in modeling and understanding the covariance matrices of the data is necessary to get robust measurements of $\lambda_{\text{mfp}}$ from this data., Comment: 17 pages, 13 figures, submitted to MNRAS

A widely accepted definition of intelligence in the context of Artificial Intelligence (AI) still eludes us. Due to our exceedingly rapid development of AI paradigms, architectures, and tools, the prospect of naturally arising AI consciousness seems more likely than ever. In this paper, we claim that all current intelligence tests are insufficient to point to the existence or lack of intelligence \textbf{as humans intuitively perceive it}. We draw from ideas in the philosophy of science, psychology, and other areas of research to provide a clearer definition of the problems of artificial intelligence, self-awareness, and agency. We furthermore propose a new heuristic approach to test for artificial self-awareness and outline a possible implementation. Finally, we discuss some of the questions that arise from this new heuristic, be they philosophical or implementation-oriented., Comment: 4 double-column pages, 2 figures

Increasingly during the past decade, researchers have sought to leverage auxiliary data for enhancing individualized inference. Many existing methods, such as multisource exchangeability models (MEM), have been developed to borrow information from multiple supplemental sources to support parameter inference in a primary source. MEM and its alternatives decide how much information to borrow based on the exchangeability of the primary and supplemental sources, where exchangeability is defined as equality of the target parameter. Other information that may also help determine the exchangeability of sources is ignored. In this article, we propose a generalized Reinforced Borrowing Framework (RBF) leveraging auxiliary data for enhancing individualized inference using a distance-embedded prior which utilizes data not only about the target parameter, but also uses different types of auxiliary information sources to "reinforce" inference on the target parameter. RBF improves inference with minimal additional computational burden. We demonstrate the application of RBF to a study investigating the impact of the COVID-19 pandemic on individual activity and transportation behaviors, where RBF achieves 20-40% lower MSE compared with existing methods., Comment: Presented during ENAR 2023 Spring Meeting

The final phase of the reionization process can be probed by rest-frame UV absorption spectra of quasars at z>6, shedding light on the properties of the diffuse intergalactic medium within the first Gyr of the Universe. The ESO Large Programme "XQR-30: the ultimate XSHOOTER legacy survey of quasars at z~5.8-6.6" dedicated ~250 hours of observations at the VLT to create a homogeneous and high-quality sample of spectra of 30 luminous quasars at z~6, covering the rest wavelength range from the Lyman limit to beyond the MgII emission. Twelve quasar spectra of similar quality from the XSHOOTER archive were added to form the enlarged XQR-30 sample, corresponding to a total of ~350 hours of on-source exposure time. The median effective resolving power of the 42 spectra is R~11400 and 9800 in the VIS and NIR arm, respectively. The signal-to-noise ratio per 10 km/s pixel ranges from ~11 to 114 at $\lambda \simeq 1285$ \AA rest frame, with a median value of ~29. We describe the observations, data reduction and analysis of the spectra, together with some first results based on the E-XQR-30 sample. New photometry in the H and K bands are provided for the XQR-30 quasars, together with composite spectra whose characteristics reflect the large absolute magnitudes of the sample. The composite and the reduced spectra are released to the community through a public repository, and will enable a range of studies addressing outstanding questions regarding the first Gyr of the Universe., Comment: 21 pages, 10 figures. Final version accepted by MNRAS

Simplicial objects $\mathsf{sC}$ in descent categories $\mathsf{C}$, as introduced by Behrend and Getzler, provide a context in which to study higher stacks. In this note, we extend the construction of the canonical cocycle of a smooth principal $G$-bundle to the context of principal $G$-bundles in $\mathsf{sC}_{/X}$. As an application, we show how this specializes to $\mathsf{C}=\mathsf{Sets}$ to give a streamlined construction of $k$-invariants of reduced Kan complexes. We adapt this to give a similarly streamlined construction of minimal Kan complexes, with the goal of clarifying the role of the axiom of choice; Postnikov towers of minimal Kan complexes provide examples of towers of simplicial principal bundles of the type we consider., Comment: For Ezra Getzler, on the occasion of his 60th birthday

Modern systems for multi-hop question answering (QA) typically break questions into a sequence of reasoning steps, termed chain-of-thought (CoT), before arriving at a final answer. Often, multiple chains are sampled and aggregated through a voting mechanism over the final answers, but the intermediate steps themselves are discarded. While such approaches improve performance, they do not consider the relations between intermediate steps across chains and do not provide a unified explanation for the predicted answer. We introduce Multi-Chain Reasoning (MCR), an approach which prompts large language models to meta-reason over multiple chains of thought, rather than aggregating their answers. MCR examines different reasoning chains, mixes information between them and selects the most relevant facts in generating an explanation and predicting the answer. MCR outperforms strong baselines on 7 multi-hop QA datasets. Moreover, our analysis reveals that MCR explanations exhibit high quality, enabling humans to verify its answers., Comment: Accepted for publication in The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023). Author's final version