cssi/SciSciGPT-SciSciNet huggingface dataset:
Resource allocation in multi-project environments: Planning vs. execution strategies

One of the challenges in multi-project environments is the question how to allocate resources to assure a timely completion of all planned projects. In order to increase resource utilization, managers try to allocate all available resources at the beginning of a new cycle to all planned projects. This strategy would not allow for acting on potential risks and uncertainties. Thus, current literature on multi-project planning considers buffer concepts in various forms. The problem with these strategies is their complexities and consequently their general rejection by practitioners. In this study we address the problem of resource allocation under uncertainty by developing a counter-intuitive heuristic that is simple and effective. We developed a simulation tool allowing us to test resource allocation strategies in realistic environments. Surprisingly, the results demonstrate that resource allocation strategies with less than 100% resource allocation in the planning stage enable an overall value for a project portfolio that is close to the ideal maximum. This means that managers should keep a certain percentage of resources in the planning stage idle to encounter variations in the execution phase, enabling a maximal project portfolio value.
The effect of expanding Medicaid eligibility on Supplemental Security Income program participation

Abstract Low-income adults without dependent children have historically had few paths to obtain public health insurance unless they qualified for Supplemental Security Income (SSI) cash benefits because of a disability. However, in states that expand their Medicaid programs, childless adults may obtain Medicaid without undergoing an intensive SSI disability review process and with substantially higher income and assets than the SSI program allows. This expanded availability of Medicaid coverage, independent of SSI participation, creates an opportunity to increase earnings and savings without jeopardizing health insurance coverage. In this paper, we use the natural experiments created by state decisions to expand Medicaid to nondisabled, nonelderly adults without dependent children to study the effect of decoupling Medicaid eligibility and cash assistance using a difference-in-differences study design. We collected data on the income eligibility limits, enrollment caps, and coverage characteristics of state Medicaid expansions to childless adults from 2001 to 2013. We combine these data with the nationally representative American Community Survey to estimate the effects of state expansion on SSI participation. We find relative declines in SSI participation of 0.17 percentage points on average after the introduction of Medicaid coverage for childless adults, a 7% relative decrease. This finding suggests the potential for small but important efficiency gains from separating SSI and Medicaid eligibility.
The Spline-GARCH Model for Low Frequency Volatility and its Global Macroeconomic Causes

Twenty-five years of volatility research has left the macroeconomic environment playing a minor role. This paper proposes modeling equity volatilities as a combination of macro- economic effects and time series dynamics. High-frequency return volatility is specified to be the product of a slow-moving component, represented by an exponential spline, and a unit GARCH. This slow-moving component is the low-frequency volatility, which in this model coincides with the unconditional volatility. This component is estimated for nearly 50 countries over various sample periods of daily data. Low-frequency volatility is then modeled as a function of macroeconomic and financial variables in an unbalanced panel with a variety of dependence structures. It is found to vary over time and across countries. The low-frequency component of volatility is greater when the macroeconomic factors of GDP, inflation, and short-term interest rates are more volatile or when inflation is high and output growth is low. Volatility is higher not only for emerging markets and markets with small numbers of listed companies and market capitalization relative to GDP, but also for large economies. The model allows long horizon forecasts of volatility to depend on macroeconomic developments, and delivers estimates of the volatility to be anticipated in a newly opened market.
Regulatory Consolidation and Cross-Border Coordination Challenging the Conventional Wisdom

The conventional wisdom among scholars and policymakers holds that international regulatory coordination is more likely to arise when regulatory authority at the domestic level is consolidated within a single body rather than dispersed across an array of agencies. Many prominent participants in the ongoing debate over U.S. regulatory reform - including former Treasury Secretary Hank Paulson and former Federal Reserve Chairman Paul Volcker - have cited this supposed connection between domestic regulatory consolidation and cross-border coordination as a reason to reduce the number of U.S. agencies that share supervisory authority over the financial sector. Scholars, however, have not rigorously tested this hypothesis. This Paper is a first step toward filling this gap. It examines how changes in U.S. domestic regulatory structures across the commercial banking, securities, and insurance sectors have shaped cross-border coordination over the last twenty-five years. These case studies suggest a surprising conclusion: contrary to the conventional wisdom, regulatory consolidation at the domestic level appears to be negatively correlated with cross-border coordination. When regulatory authority is fragmented among several agencies at the domestic level, U.S. financial regulators turn to their cross-border counterparts in order to circumvent roadblocks erected by domestic rivals. By contrast, in areas where a single regulatory agency enjoys consolidated control over a particular policy matter at the domestic level, that agency is less willing to restrict its policy-making discretion through an international agreement.
ARONSZAJN TREE PRESERVATION AND BOUNDED FORCING AXIOMS

I investigate the relationships between three hierarchies of reflection principles for a forcing class : the hierarchy of bounded forcing axioms, of -absoluteness, and of Aronszajn tree preservation principles. The latter principle at level says that whenever T is a tree of height and width that does not have a branch of order type , and whenever is a forcing notion in , then it is not the case that forces that T has such a branch. -absoluteness serves as an intermediary between these principles and the bounded forcing axioms. A special case of the main result is that for forcing classes that don’t add reals, the three principles at level are equivalent. Special attention is paid to certain subclasses of subcomplete forcing, since these are natural forcing classes that don’t add reals.
Poly-ADP-ribosylation drives loss of protein homeostasis in ATM and Mre11 deficiency

Loss of the ataxia-telangiectasia mutated (ATM) kinase causes cerebellum-specific neurodegeneration in humans. We previously demonstrated that deficiency in ATM activation via oxidative stress generates insoluble protein aggregates in human cells, reminiscent of protein dysfunction in common neurodegenerative disorders. Here, we show that this process is driven by poly-ADP-ribose polymerases (PARPs) and that the insoluble protein species arise from intrinsically disordered proteins associating with PAR-associated genomic sites in ATM-deficient cells. The lesions implicated in this process are single-strand DNA breaks dependent on reactive oxygen species, transcription, and R-loops. Human cells expressing Mre11 A-T-like disorder mutants also show PARP-dependent aggregation identical to ATM deficiency. Lastly, analysis of A-T patient cerebellum samples shows widespread protein aggregation as well as loss of proteins known to be critical in human spinocerebellar ataxias that is not observed in neocortex tissues. These results provide a hypothesis accounting for loss of protein integrity and cerebellum function in A-T.
Weakly Supervised Minirhizotron Image Segmentation with MIL-CAM

We present a multiple instance learning class activation map (MIL-CAM) approach for pixel-level minirhizotron image segmentation given weak image-level labels. Minirhizotrons are used to image plant roots in situ. Minirhizotron imagery is often composed of soil containing a few long and thin root objects of small diameter. The roots prove to be challenging for existing semantic image segmentation methods to discriminate. In addition to learning from weak labels, our proposed MIL-CAM approach re-weights the root versus soil pixels during analysis for improved performance due to the heavy imbalance between soil and root pixels. The proposed approach outperforms other attention map and multiple instance learning methods for localization of root objects in minirhizotron imagery.
Equivariant stable categories for incomplete systems of transfers

In this paper, we construct incomplete versions of the equivariant stable category; i.e., equivariant stabilization of the category of $G$-spaces with respect to incomplete systems of transfers encoded by an $N_\infty$ operad $\mathcal{O}$. These categories are built from the categories of $\mathcal{O}$-algebras in $G$-spaces. Using this operadic formulation, we establish incomplete versions of the usual structural properties of the equivariant stable category, notably the tom Dieck splitting. Our work is motivated in part by the examples arising from the equivariant units and Picard space functors.
The ties that bind: Knowledge-seeking networks and auditor job performance

Abstract The dissemination of knowledge in audit firms is a critical process that has gone relatively unexamined by researchers. Using social network analysis to quantify the knowledge-seeking networks in a Big 4 audit firm in the U.S., we examine the association between the types and patterns of knowledge-seeking ties and individual auditor performance. Our initial finding is that auditor job performance is negatively associated with the number of knowledge-seeking ties. Further, our analyses demonstrate that this negative association is being driven by explicit knowledge-seeking rather than tacit knowledge-seeking activities and is stronger for higher-ranked auditors. Thus, knowledge-seeking by auditors may come at a cost, particularly when that knowledge is codifiable and when the seeking is done by those at higher levels of the firm. In a post-hoc analysis, we find that tacit knowledge-seeking ties to managers can be beneficial for auditor performance, and tacit knowledge-seeking ties to senior managers and partners is sometimes detrimental. In sum, this suggests that who is seeking knowledge and who is being sought for knowledge are both important for performance. Our findings may assist researchers to better understand how auditors leverage their social connections to learn, which in turn may affect audit efficiency and effectiveness. Further, audit firms might benefit from better understanding the consequences of knowledge-seeking from different sources and use this understanding to make decisions that maximize desirable information flows.
The Impact of COVID-19 on the U.S. Child Care Market: Evidence from Stay-at-Home Orders

Abstract Stay-at-home orders (SAHOs) were implemented in most U.S. states to mitigate the spread of COVID-19. This paper quantifies the impact of these containment policies on a measure of the supply of child care. The supply of such services may be particularly vulnerable to a SAHO-type policy shock, given that many providers are liquidity-constrained. Using plausibly exogenous variation from the staggered adoption of SAHOs across states, we find that online job postings for early care and education teachers declined by 16% after enactment. This effect is driven exclusively by private-sector services. Indeed, hiring by public programs like Head Start and pre-kindergarten has not been influenced by SAHOs. We also find that ECE job postings increased dramatically after SAHOs were lifted, although the number of such postings remains 4% lower than that during the pre-pandemic period. There is little evidence that child care search behavior among households was altered by SAHOs. Because forced supply-side changes appear to be at play, our results suggest that households may not be well-equipped to insure against the rapid transition to the production of child care. We discuss the implications of these results for child development and parental employment decisions.
Some Dynamic Properties of Rubber

Abstract The following conclusions apply to rubber of the stock tested when bonded to steel: 1. For cylinders in compression and specimens in shear, the damping can be expressed in terms of a velocity coefficient. The damping decreases with increase of frequency; it increases with increase of static strain. 2. The dynamic modulus of elasticity is slightly larger than the static modulus and is not affected by change of frequency. It increases with increase of static strain for the cylinders and decreases for the shear specimens. 3. For cylinders in compression, both damping and modulus of elasticity are dependent upon the ratio of diameter to length.
Simple heuristics yield provable algorithms for masked low-rank approximation

In $masked\ low-rank\ approximation$, one is given $A \in \mathbb{R}^{n \times n}$ and binary mask matrix $W \in \{0,1\}^{n \times n}$. The goal is to find a rank-$k$ matrix $L$ for which: $$cost(L) = \sum_{i=1}^{n} \sum_{j = 1}^{n} W_{i,j} \cdot (A_{i,j} - L_{i,j} )^2 \leq OPT + \epsilon \|A\|_F^2 ,$$ where $OPT = \min_{rank-k\ \hat{L}} cost(\hat L)$ and $\epsilon$ is a given error parameter. Depending on the choice of $W$, this problem captures factor analysis, low-rank plus diagonal decomposition, robust PCA, low-rank matrix completion, low-rank plus block matrix approximation, and many problems. Many of these problems are NP-hard, and while some algorithms with provable guarantees are known, they either 1) run in time $n^{\Omega(k^2/\epsilon)}$ or 2) make strong assumptions, e.g., that $A$ is incoherent or that $W$ is random. In this work, we show that a common polynomial time heuristic, which simply sets $A$ to $0$ where $W$ is $0$, and then finds a standard low-rank approximation, yields bicriteria approximation guarantees for this problem. In particular, for rank $k' > k$ depending on the $public\ coin\ partition\ number$ of $W$, the heuristic outputs rank-$k'$ $L$ with cost$(L) \leq OPT + \epsilon \|A\|_F^2$. This partition number is in turn bounded by the $randomized\ communication\ complexity$ of $W$, when interpreted as a two-player communication matrix. For many important examples of masked low-rank approximation, including all those listed above, this result yields bicriteria approximation guarantees with $k' = k \cdot poly(\log n/\epsilon)$. Further, we show that different models of communication yield algorithms for natural variants of masked low-rank approximation. For example, multi-player number-in-hand communication complexity connects to masked tensor decomposition and non-deterministic communication complexity to masked Boolean low-rank factorization.
COVID-19 and Mental Health/Substance Use Disorders on Reddit: A Longitudinal Study

COVID-19 pandemic has adversely and disproportionately impacted people suffering from mental health issues and substance use problems. This has been exacerbated by social isolation during the pandemic and the social stigma associated with mental health and substance use disorders, making people reluctant to share their struggles and seek help. Due to the anonymity and privacy they provide, social media emerged as a convenient medium for people to share their experiences about their day to day struggles. Reddit is a well-recognized social media platform that provides focused and structured forums called subreddits, that users subscribe to and discuss their experiences with others. Temporal assessment of the topical correlation between social media postings about mental health/substance use and postings about Coronavirus is crucial to better understand public sentiment on the pandemic and its evolving impact, especially related to vulnerable populations. In this study, we conduct a longitudinal topical analysis of postings between subreddits r/depression, r/Anxiety, r/SuicideWatch, and r/Coronavirus, and postings between subreddits r/opiates, r/OpiatesRecovery, r/addiction, and r/Coronavirus from January 2020 - October 2020. Our results show a high topical correlation between postings in r/depression and r/Coronavirus in September 2020. Further, the topical correlation between postings on substance use disorders and Coronavirus fluctuates, showing the highest correlation in August 2020. By monitoring these trends from platforms such as Reddit, epidemiologists, and mental health professionals can gain insights into the challenges faced by communities for targeted interventions.
An analytical approach to the equivariant index and Witten genus on spin manifolds

Abstract This work is divided in two cases. In the first case, we consider a spin manifold M as the set of fixed points of an S 1 -action on a spin manifold X , and in the second case we consider the spin manifold M as the set of fixed points of an S 1 -action on the loop space of M . For each case, we build on M a vector bundle, a connection and a set of bundle endomorphisms. These objects are used to build global operators on M which define an analytical index in each case. In the first case, the analytical index is equal to the topological equivariant Atiyah–Singer index, and in the second case the analytical index is equal to a topological expression where the Witten genus appears.
Partition functions of the tensionless string

We consider string theory on AdS3 × S3 × 𝕋4 in the tensionless limit, with one unit of NS-NS flux. This theory is conjectured to describe the symmetric product orbifold CFT. We consider the string on different Euclidean backgrounds such as thermal AdS3, the BTZ black hole, conical defects and wormhole geometries. In simple examples we compute the full string partition function. We find it to be independent of the precise bulk geometry, but only dependent on the geometry of the conformal boundary. For example, the string partition function on thermal AdS3 and the conical defect with a torus boundary is shown to agree, thus giving evidence for the equivalence of the tensionless string on these different background geometries. We also find that thermal AdS3 and the BTZ black hole are dual descriptions and the vacuum of the BTZ black hole is mapped to a single long string winding many times asymptotically around thermal AdS3. Thus the system yields a concrete example of the string-black hole transition. Consequently, reproducing the boundary partition function does not require a sum over bulk geometries, but rather agrees with the string partition function on any bulk geometry with the appropriate boundary. We argue that the same mechanism can lead to a resolution of the factorization problem when geometries with disconnected boundaries are considered, since the connected and disconnected geometries give the same contribution and we do not have to include them separately.
Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.
Interpretable Visualization and Higher-Order Dimension Reduction for ECoG Data

ElectroCOrticoGraphy (ECoG) technology measures electrical activity in the human brain via electrodes placed directly on the cortical surface during neurosurgery. Through its capability to record activity at an extremely fast temporal resolution, ECoG experiments have allowed scientists to better understand how the human brain processes speech. By its nature, ECoG data is extremely difficult for neuroscientists to directly interpret for two major reasons. Firstly, ECoG data tends to be extremely large in size, as each individual experiment yields data up to several GB. Secondly, ECoG data has a complex, higher-order nature; after signal processing, this type of data is typically organized as a 4-way tensor consisting of trials by electrodes by frequency by time. In this paper, we develop an interpretable dimension reduction approach called Regularized Higher Order Principal Components Analysis, as well as an extension to Regularized Higher Order Partial Least Squares, that allows neuroscientists to explore and visualize ECoG data. Our approach employs a sparse and functional Candecomp-Parafac (CP) decomposition that incorporates sparsity to select relevant electrodes and frequency bands, as well as smoothness over time and frequency, yielding directly interpretable factors. We demonstrate both the performance and interpretability of our method with an ECoG case study on audio and visual processing of human speech.
Test Set Optimization by Machine Learning Algorithms

Diagnosis results are highly dependent on the volume of test set. To derive the most efficient test set, we propose several machine learning based methods to predict the minimum amount of test data that produces relatively accurate diagnosis. By collecting outputs from failing circuits, the feature matrix and label vector are generated, which involves the inference information of the test termination point. Thus we develop a prediction model to fit the data and determine when to terminate testing. The considered methods include LASSO and Support Vector Machine(SVM) where the relationship between goals(label) and predictors(feature matrix) are considered to be linear in LASSO and nonlinear in SVM. Numerical results show that SVM reaches a diagnosis accuracy of 90.4% while deducting the volume of test set by 35.24%.
Commitment on Volunteer Crowdsourcing Platforms: Implications for Growth and Engagement

Volunteer crowdsourcing platforms, such as food recovery organizations, match volunteers with tasks which are often recurring. To ensure completion of such tasks, platforms frequently use a commitment lever known as “adoption.” Despite being effective in reducing match uncertainty, high levels of adoption reduce match availability for volunteers which in turn can suppress future engagement. We study how platforms should balance these two opposing factors. Our research is motivated by a collaboration with Food Rescue U.S.(FRUS), a volunteer-based food recovery organization active in over 33 locations across the U.S. For platforms such as FRUS, success crucially depends on efficient volunteer utilization and engagement. Consequently, effectively utilizing non-monetary levers, such as adoption, is critical. Based on our analysis of fine-grained FRUS data, we develop a model for a repeated two-sided matching market consisting of tasks (prearranged donations) and volunteers. Our model incorporates the uncertainty in match compatibility as well as the negative impact of failing to match on future engagement. We study the platform’s optimal policy for setting the adoption level to maximize the total discounted number of matches. Our analysis reveals that the optimal myopic policy is either full or no adoption. For sufficiently thick markets, we show that such a myopic policy is also optimal in the long run. In thinner markets, even though a static policy of full or no adoption can be suboptimal, we show it achieves a constant-factor approximation where the factor improves with market thickness. Using our analytical and empirical results, we revisit the current design of the FRUS platform and make location-specific policy recommendations. More broadly, our work sheds light on how other two-sided platforms can control the double-edged impacts that commitment levers have on growth and engagement.
Thermal Interface Materials

Cooling is critically needed for reliability, power and further miniaturization of microelectronics. Heat sinks are obviously important for heat dissipation. However, thermal interface materials (TIMs) are needed to improve thermal contacts, such as the thermal contact between a heat source (e.g., a microprocessor) and a heat sink. This commentary is directed at clarifying some misconceptions related to the design and testing of TIMs.
Short-run and long-run effects of peers from disrupted families

I study the short-run and long-run effects of exposure to peers from disrupted families in adolescence. Using the National Longitudinal Study of Adolescent to Adult Health (Add Health) data, I find that girls are mostly unaffected by peers from disrupted families, while boys exposed to more peers from disrupted families exhibit more school problems in adolescence and higher arrest probabilities, less stable jobs, and higher probabilities of suffering from financial stress as young adults. These results suggest negative effects on non-cognitive skills but no effect on cognitive skills, as measured by academic performance. The dramatic increase in family disruption in the USA should thus receive more attention, as the intergenerational mobility and inequality consequences could be larger than anticipated as a result of classroom spillovers.
Managing Commodity Stock-outs in Public Health Supply Chains in Developing Countries: An Empirical Analysis

Public health supply chains are channels through which health commodities are distributed among end clients. In developing countries, significant resource constraints hamper the effective and efficient delivery of health commodities, leading to supply chain failures such as “stock‐outs.” While the prevalence of commodity stock‐outs is well‐acknowledged, there is little by way of systematic and rigorous empirical research that sheds light on the factors that drive such stock‐outs in developing countries. The study documented in this article is anchored in the “logistics cycle” framework that is well‐accepted and widely adopted by organizations involved in managing public health supply chains. Using this framework, we empirically investigate how commodity range and a health facility's logistics management information system (LMIS) practices impact the likelihood of stock‐outs. We estimate our models using a novel field dataset spanning 4000 health facilities across five developing countries. Our results indicate that the likelihood of stock‐outs increases with an expansion in the range of health commodities offered through the public health supply chain. However, the detrimental impact of offering a wider range of health commodities is more severe in resource‐constrained rural facilities relative to their urban counterparts. Further, we find that urban facilities can significantly reduce the likelihood of stock‐outs by updating their LMIS records on a daily basis. However, in rural facilities, daily LMIS updating is beneficial only when used in conjunction with an electronic LMIS. Our findings have implications for resource allocation to reduce the risk of health commodity stock‐outs in developing countries.
"Second Chance" Mechanisms as a First Step to Ending the War on Drugs.

In their article “Racial Justice Requires Ending the War on Drugs,” Earp, Lewis, and Hart (2021) argue for the end of our War on Drugs in order to alleviate our reliance on mass incarceration, harm...
RECEIPT: REfine Coarse-grained IndePendent Tasks for Parallel Tip decomposition of Bipartite Graphs

Tip decomposition is a crucial kernel for mining dense subgraphs in bipartite networks, with applications in spam detection, analysis of affiliation networks etc. It creates a hierarchy of vertex-induced subgraphs with varying densities determined by the participation of vertices in butterflies (2,2-bicliques). To build the hierarchy, existing algorithms iteratively follow a delete-update(peeling) process: deleting vertices with the minimum number of butterflies and correspondingly updating the butterfly count of their 2-hop neighbors. The need to explore 2-hop neighborhood renders tip-decomposition computationally very expensive. Furthermore, the inherent sequentiality in peeling only minimum butterfly vertices makes derived parallel algorithms prone to heavy synchronization. In this paper, we propose a novel parallel tip-decomposition algorithm -- REfine CoarsE-grained Independent Tasks (RECEIPT) that relaxes the peeling order restrictions by partitioning the vertices into multiple independent subsets that can be concurrently peeled. This enables RECEIPT to simultaneously achieve a high degree of parallelism and dramatic reduction in synchronizations. Further, RECEIPT employs a hybrid peeling strategy along with other optimizations that drastically reduce the amount of wedge exploration and execution time. We perform detailed experimental evaluation of RECEIPT on a shared-memory multicore server. It can process some of the largest publicly available bipartite datasets orders of magnitude faster than the state-of-the-art algorithms -- achieving up to 1100x and 64x reduction in the number of thread synchronizations and traversed wedges, respectively. Using 36 threads, RECEIPT can provide up to 17.1x self-relative speedup. Our implementation of RECEIPT is available at this https URL.
Green’s Function Monte Carlo Calculations with Two- and Three-Nucleon Interactions from Chiral Effective Field Theory

I discuss our recent work on Green’s function Monte Carlo (GFMC) calculations of light nuclei using local nucleon-nucleon interactions derived from chiral effective field theory (EFT) up to next-to-next-to-leading order (N2 LO). I present the natural extension of this work to include the consistent three-nucleon (3N) forces at the same order in the chiral expansion. I discuss our choice of observables to fit the two low-energy constants which enter in the 3N sector at N2 LO and present some results for light nuclei.
User Preference-aware Fake News Detection

Disinformation and fake news have posed detrimental effects on individuals and society in recent years, attracting broad attention to fake news detection. The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored. The confirmation bias theory has indicated that a user is more likely to spread a piece of fake news when it confirms his/her existing beliefs/preferences. Users' historical, social engagements such as posts provide rich information about users' preferences toward news and have great potentials to advance fake news detection. However, the work on exploring user preference for fake news detection is somewhat limited. Therefore, in this paper, we study the novel problem of exploiting user preference for fake news detection. We propose a new framework, UPFD, which simultaneously captures various signals from user preferences by joint content and graph modeling. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. We release our code and data as a benchmark for GNN-based fake news detection: https://github.com/safe-graph/GNN-FakeNews.
Visual Question Rewriting for Increasing Response Rate

When a human asks questions online, or when a conversational virtual agent asks a human questions, questions triggering emotions or with details might more likely to get responses or answers. we explore how to automatically rewrite natural language questions to improve the response rate form people. In particular, a new task of Visual Question Rewriting (VQR) task is introduced to explore how visual information can be used to improve the new question(s). A data set containing -4K bland&attractive question-images triples is collected. We developed some baseline sequence to sequence models and more advanced transformer-based models, which take a bland question and a related image as input, and output a rewritten question that's expected to be more attractive. Offline experiments and mechanical Turk based evaluations show that it's possible to rewrite bland questions in a more detailed and attractive way to increase response rate, and images can be helpful.
Prior-free Dynamic Mechanism Design With Limited Liability

We study the problem of repeatedly auctioning off an item to one of k bidders where: a) bidders have a per-round individual rationality constraint, b) bidders may leave the mechanism at any point, and c) the bidders' valuations are adversarially chosen (the prior-free setting). Without these constraints, the auctioneer can run a second-price auction to "sell the business" and receive the second highest total value for the entire stream of items. We show that under these constraints, the auctioneer can attain a constant fraction of the "sell the business" benchmark, but no more than $2/e$ of this benchmark. In the course of doing so, we design mechanisms for a single bidder problem of independent interest: how should you repeatedly sell an item to a (per-round IR) buyer with adversarial valuations if you know their total value over all rounds is V but not how their value changes over time? We demonstrate a mechanism that achieves revenue $V/e$ and show that this is tight.
Periodic event-triggered control for incrementally quadratic nonlinear systems

Periodic event-triggered control (PETC) evaluates the triggering rule periodically and is well-suited for implementation on digital platforms. This paper investigates PETC design for nonlinear systems affected by external disturbances under the impulsive system formulation. Sufficient conditions are provided to ensure the input-to-state stability of the resulting closed-loop system for the state feedback and the observer-based output feedback configurations separately. For each configuration, the sampling period and the triggering functions are provided explicitly. Sufficient conditions in the form of linear matrix inequalities are provided for the PETC design of incrementally quadratic nonlinear systems. Two examples are given to illustrate the effectiveness of the proposed method.
Problems in arithmetic topology

We present a list of problems in arithmetic topology posed at the June 2019 PIMS/NSF workshop on “Arithmetic Topology.” Three problem sessions were hosted during the workshop in which participants proposed open questions to the audience and engaged in shared discussions from their own perspectives as working mathematicians across various fields of study. Participants were explicitly asked to provide problems of various levels of difficulty, with the goal of capturing a cross section of exciting challenges in the field that could help guide future activity. The problems, together with references and brief discussions when appropriate, are collected below into three categories: (1) topological analogues of arithmetic phenomena, (2) point counts, stability phenomena and the Grothendieck ring, and (3) tools, methods and examples.
Demazure crystals for specialized nonsymmetric Macdonald polynomials

Abstract We give an explicit, nonnegative formula for the expansion of nonsymmetric Macdonald polynomials specialized at t = 0 in terms of Demazure characters. Our formula results from constructing Demazure crystals whose characters are the nonsymmetric Macdonald polynomials, which also gives a new proof that these specialized nonsymmetric Macdonald polynomials are positive graded sums of Demazure characters. Demazure crystals are certain truncations of classical crystals that give a combinatorial skeleton for Demazure modules. To prove our construction, we develop further properties of Demazure crystals, including an efficient algorithm for computing their characters from highest weight elements. As a corollary, we obtain a new formula for the Schur expansion of Hall–Littlewood polynomials in terms of a simple statistic on highest weight elements of our crystals.
Controlling False Discovery Rate Using Gaussian Mirrors

Simultaneously, finding multiple influential variables and controlling the false discovery rate (FDR) for linear regression models is a fundamental problem. We here propose the Gaussian Mirror (GM)...
Universality of approximate message passing algorithms

We consider a broad class of Approximate Message Passing (AMP) algorithms defined as a Lipschitzian functional iteration in terms of an n×n random symmetric matrix A. We establish universality in noise for this AMP in the n-limit and validate this behavior in a number of AMPs popularly adapted in compressed sensing, statistical inferences, and optimizations in spin glasses.
Predictors of Success in the Bundled Payments for Care Improvement Program.

Hospitals participating in Medicare’s Bundled Payments for Care Improvement (BPCI) program were incented to reduce Medicare payments for episodes of care. To identify factors that influenced whether or not hospitals were able to save in the BPCI program, how the cost of different services changed to produce those savings, and if “savers” had lower or decreased quality of care. Retrospective cohort study. BPCI-participating hospitals. We designated hospitals that met the program goal of decreasing costs by at least 2% from baseline in average Medicare payments per 90-day episode as “savers.” We used regression models to determine condition-level, patient-level, hospital-level, and market-level characteristics associated with savings. In total, 421 hospitals participated in BPCI, resulting in 2974 hospital-condition combinations. Major joint replacement of the lower extremity had the highest proportion of savers (77.6%, average change in payments −$2235) and complex non-cervical spinal fusion had the lowest (22.2%, average change +$8106). Medical conditions had a higher proportion of savers than surgical conditions (11% more likely to save, P=0.001). Conditions that were mostly urgent/emergent had a higher proportion of savers than conditions that were mostly elective (6% more likely to save, P=0.007). Having higher than median costs at baseline was associated with saving (OR: 3.02, P<0.001). Hospitals with more complex patients were less likely to save (OR: 0.77, P=0.003). Savings occurred across both inpatient and post-acute care, and there were no decrements in clinical care associated with being a saver. Certain conditions may be more amenable than others to saving under bundled payments, and hospitals with high costs at baseline may perform well under programs which use hospitals’ own baseline costs to set targets. Findings may have implications for the BPCI-Advanced program and for policymakers seeking to use payment models to drive improvements in care.
Asset Prices and Pandemics: The Effects of Lockdowns

We examine the impact of pandemics on equilibrium in an integrated epidemic-economy model with production. Two types of technologies are considered: a neo-classical technology and one capturing the notion of time-to-produce. The impact of a shelter-in-place policy with and without layoffs is studied. The paper documents adjustments in interest rate, market price of risk, stock market and real wage as the epidemic propagates. It shows the qualitative effects of a shelter-in-place policy in the model are consistent with the patterns displayed by the stock market and real wage during the COVID-19 outbreak. Puzzles emerging from the analysis are outlined. © 2022 World Scientific Publishing Company. Midwest Finance Association.
Sound Gradual Typing Is Nominally Alive and Well

Recent research has identified significant performance hurdles that sound gradual typing needs to overcome (Takikawa et al. 2016). These performance hurdles stem from the fact that the run-time checks gradual type systems insert into code can cause a lot of overhead. We propose a small sacrifice in flexibility that enables us to implement efficient checks. We formalize the core of a nominal object-oriented language that fulfills a variety of desirable properties for gradually typed languages, and present evidence that an implementation of this language suffers minimal overhead even in adversarial benchmarks identified in earlier work, while achieving good overall performance.
Unraveling the effect of defects, domain size, and chemical doping on photophysics and charge transport in covalent organic frameworks

Understanding the underlying physical mechanisms that govern charge transport in two-dimensional (2D) covalent organic frameworks (COFs) will facilitate the development of novel COF-based devices for optoelectronic and thermoelectric applications. In this context, the low-energy mid-infrared absorption contains valuable information about the structure–property relationships and the extent of intra- and inter-framework “hole” polaron delocalization in doped and undoped polymeric materials. In this study, we provide a quantitative characterization of the intricate interplay between electronic defects, domain sizes, pore volumes, chemical dopants, and three dimensional anisotropic charge migration in 2D COFs. We compare our simulations with recent experiments on doped COF films and establish the correlations between polaron coherence, conductivity, and transport signatures. By obtaining the first quantitative agreement with the measured absorption spectra of iodine doped (aza)triangulene-based COF, we highlight the fundamental differences between the underlying microstructure, spectral signatures, and transport physics of polymers and COFs. Our findings provide conclusive evidence of why iodine doped COFs exhibit lower conductivity compared to doped polythiophenes. Finally, we propose new research directions to address existing limitations and improve charge transport in COFs for applications in functional molecular electronic devices.
Assessing the Potential of Deep Learning for Emulating Cloud Superparameterization in Climate Models With Real-Geography Boundary Conditions

We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ~250 trials. Our DNN explains over 70 percent of the temporal variance at the 15-minute sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A close look at the diurnal cycle reveals correct emulation of land-sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints vs. hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real-geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.
Quantifying Observed Prior Impact

When summarizing a Bayesian analysis, it is important to quantify the contribution of the prior distribution to the final posterior inference because this informs other researchers whether the prior information needs to be carefully scrutinized, and whether alternative priors are likely to substantially alter the conclusions drawn. One appealing and interpretable way to do this is to report an effective prior sample size (EPSS), which captures how many observations the information in the prior distribution corresponds to. However, typically the most important aspect of the prior distribution is its location relative to the data, and therefore traditional information measures are somewhat deficit for the purpose of quantifying EPSS, because they concentrate on the variance or spread of the prior distribution (in isolation from the data). To partially address this difficulty, Reimherr et al. (2014) introduced a class of EPSS measures based on prior-likelihood discordance. In this paper, we take this idea further by proposing a new measure of EPSS that not only incorporates the general mathematical form of the likelihood (as proposed by Reimherr et al., 2014) but also the specific data at hand. Thus, our measure considers the location of the prior relative to the current observed data, rather than relative to the average of multiple datasets from the working model, the latter being the approach taken by Reimherr et al. (2014). Consequently, our measure can be highly variable, but we demonstrate that this is because the impact of a prior on a Bayesian analysis can intrinsically be highly variable. Our measure is called the (posterior) mean Observed Prior Effective Sample Size (mOPESS), and is a Bayes estimate of a meaningful quantity. The mOPESS well communicates the extent to which inference is determined by the prior, or framed differently, the amount of sampling effort saved due to having relevant prior information. We illustrate our ideas through a number of examples including Gaussian conjugate and non-conjugate models (continuous observations), a Beta-Binomial model (discrete observations), and a linear regression model (two unknown parameters).