cssi/SciSciGPT-SciSciNet huggingface dataset:
Diverse Trajectory Forecasting with Determinantal Point Processes

The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles). In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions. It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode). While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data. In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories. The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples. Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation. To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP). Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories. Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space. We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.
Coverage and Rate Analysis of Downlink Cellular Vehicle-to-Everything (C-V2X) Communication

In this paper, we present the downlink coverage and rate analysis of a cellular vehicle-to-everything (C-V2X) communication network where the locations of vehicular nodes and road side units (RSUs) are modeled as Cox processes driven by a Poisson line process (PLP) and the locations of cellular macro base stations (MBSs) are modeled as a 2D Poisson point process (PPP). Assuming a fixed selection bias and maximum average received power based association, we compute the probability with which a typical receiver , an arbitrarily chosen receiving node, connects to a vehicular node or an RSU and a cellular MBS. For this setup, we derive the signal-to-interference ratio (SIR)-based coverage probability of the typical receiver. One of the key challenges in the computation of coverage probability stems from the inclusion of shadowing effects. As the standard procedure of interpreting the shadowing effects as random displacement of the location of nodes is not directly applicable to the Cox process, we propose an approximation of the spatial model inspired by the asymptotic behavior of the Cox process. Using this asymptotic characterization, we derive the coverage probability in terms of the Laplace transform of interference power distribution. Further, we compute the downlink rate coverage of the typical receiver by characterizing the load on the serving vehicular nodes or RSUs and serving MBSs. We also provide several key design insights by studying the trends in the coverage probability and rate coverage as a function of network parameters. We observe that the improvement in rate coverage obtained by increasing the density of MBSs can be equivalently achieved by tuning the selection bias appropriately without the need to deploy additional MBSs.
Evaluation of ride-sourcing search frictions and driver productivity: A spatial denoising approach

Abstract This paper considers the problem of measuring spatial and temporal variation in driver productivity on ride-sourcing trips. This variation is especially important from a driver’s perspective: if a platform’s drivers experience systematic disparities in earnings because of variation in their riders’ destinations, they may perceive the pricing model as inequitable. This perception can exacerbate search frictions if it leads drivers to avoid locations where they believe they may be assigned “unlucky” fares. To characterize any such systematic disparities in productivity, we develop an analytic framework with three key components. First, we propose a productivity metric that looks two consecutive trips ahead, thus capturing the effect on expected earnings of market conditions at drivers’ drop-off locations. Second, we develop a natural experiment by analyzing trips with a common origin but varying destinations, thus isolating purely spatial effects on productivity. Third, we apply a spatial denoising method that allows us to work with raw spatial information exhibiting high levels of noise and sparsity, without having to aggregate data into large, low-resolution spatial zones. By applying our framework to data on more than 1.4 million rides in Austin, Texas, we find significant spatial variation in ride-sourcing driver productivity and search frictions. Drivers at the same location experienced disparities in productivity after being dispatched on trips with different destinations, with origin-based surge pricing increasing these earnings disparities. Our results show that trip distance is the dominant factor in driver productivity: short trips yielded lower productivity, even when ending in areas with high demand. These findings suggest that new pricing strategies are required to minimize random disparities in driver earnings.
Duration-of-Stay Storage Assignment under Uncertainty

Storage assignment, the act of choosing what goods are placed in what locations in a warehouse, is a central problem of supply chain logistics. Past literature has shown that the optimal method to assign pallets is to arrange them in increasing duration of stay in the warehouse (the Duration-of-Stay, or DoS, method), but the methodology requires perfect prior knowledge of DoS for each pallet, which is unknown and uncertain under realistic conditions. Attempts to predict DoS have largely been unfruitful due to the multi-valuedness nature (every shipment contains multiple identical pallets with different DoS) and data sparsity induced by lack of matching historical conditions. In this paper, we introduce a new framework for storage assignment that provides a solution to the DoS prediction problem through a distributional reformulation and a novel neural network, ParallelNet. Through collaboration with a world-leading cold storage company, we show that the system is able to predict DoS with a MAPE of 29%, a decrease of ~30% compared to a CNN-LSTM model, and suffers less performance decay into the future. The framework is then integrated into a first-of-its-kind Storage Assignment system, which is being deployed in warehouses across United States, with initial results showing up to 21% in labor savings. We also release the first publicly available set of warehousing records to facilitate research into this central problem.
Low-Tubal-Rank Tensor Completion Using Alternating Minimization

The low-tubal-rank tensor model has been recently proposed for real-world multidimensional data. In this paper, we study the low-tubal-rank tensor completion problem, i.e., to recover a third-order tensor by observing a subset of its elements selected uniformly at random. We propose a fast iterative algorithm, called Tubal-AltMin , that is inspired by a similar approach for low-rank matrix completion. The unknown low-tubal-rank tensor is represented as the product of two much smaller tensors with the low-tubal-rank property being automatically incorporated, and Tubal-AltMin alternates between estimating those two tensors using tensor least squares minimization. First, we note that tensor least squares minimization is different from its matrix counterpart and nontrivial as the circular convolution operator of the low-tubal-rank tensor model is intertwined with the sub-sampling operator. Secondly, the theoretical performance guarantee is challenging since Tubal-AltMin is iterative and nonconvex. We prove that 1) Tubal-AltMin generates a best rank- $r$ approximate up to any predefined accuracy $\epsilon $ at an exponential rate, and 2) for an $n \times n \times k$ tensor $\mathcal {M}$ with tubal-rank $r \ll n$ , the required sampling complexity is $O((nr^{2}k ||\mathcal {M}||_{F}^{2} \log ^{3}~n) / \overline {\sigma }_{rk}^{2})$ , where $\overline {\sigma }_{rk}$ is the $rk$ -th singular value of the block diagonal matrix representation of $\mathcal {M}$ in the frequency domain, and the computational complexity is $O(n^{2}r^{2}k^{3} \log n \log (n/\epsilon))$ . Finally, on both synthetic data and real-world video data, evaluation results show that compared with tensor-nuclear norm minimization using alternating direction method of multipliers (TNN-ADMM), Tubal-AltMin-Simple (a simplified implementation of Tubal-AltMin) improves the recovery error by several orders of magnitude. In experiments, Tubal-AltMin-Simple is faster than TNN-ADMM by a factor of 5 for a $200 \times 200 \times 20$ tensor.
The Topological Version of Free Entropy

Notions of topological free entropy and of free capacity are introduced in the C*-algebra context. Basic properties, basic problems and connections to potential theory and random matrix theory are discussed.
The status of the UN Watercourses Convention: does it still hold water?

ABSTRACTWhen the UN General Assembly adopted the Watercourses Convention in 1997, it was heralded as a major milestone in the evolution of international water law. Yet, more than 20 years later and...
Identifying sub-phenotypes of acute kidney injury using structured and unstructured electronic health record data with memory networks

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03±17.25 years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) 1.55±0.34 mg/dL, estimated Glomerular Filtration Rate Test (eGFR) 107.65±54.98 mL/min/1.73 m2). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81±10.43 years, and was characterized by severe loss of kidney excretory function (SCr 1.96±0.49 mg/dL, eGFR 82.19±55.92 mL/min/1.73 m2). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07±11.32 years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr 1.69±0.32 mg/dL, eGFR 93.97±56.53 mL/min/1.73 m2). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.
The action dimension of Artin groups

The action dimension of a discrete group G is the minimum dimension of a contractible manifold, which admits a proper G-action. In this paper, we study the action dimension of general Artin groups. The main result is that if an Artin group with the nerve L of dimension n for $$n \ne 2$$ satisfies the $$K(\pi , 1)$$ -Conjecture and the top cohomology group of L with $${\mathbb {Z}}$$ -coefficients is trivial, then the action dimension of the Artin group is less than or equal to $$(2n + 1)$$ . For $$n = 2$$ , we need one more condition on L to get the same inequality; that is the fundamental group of L is generated by r elements where r is the rank of $$H_1(L, {\mathbb {Z}})$$ .
Early results from ISEE-1 electric field measurements

The double probe, floating potential instrumentation on ISEE-1 is producing reliable direct measurements of the ambient DC electric field at the bow shock, at the magnetopause, and throughout the magnetosheath, tail plasma sheet and plasmasphere. In the solar wind and in middle latitude regions of the magnetosphere spacecraft sheath fields obscure the ambient field under low plasma flux conditions such that valid measurements are confined to periods of moderately intense flux. Initial results show: (a) that the DC electric field is enhanced by roughly a factor of two in a narrow region at the front, increasing B, edge of the bow shock, (b) that scale lengths for large changes in E at the sub-solar magnetopause are considerably shorter than scale lengths associated with the magnetic structure of the magnetopause, and (c) that the transverse distribution of B-aligned E-fields between the outer magnetosphere and ionospheric levels must be highly complex to account for the random turbulent appearance of the magnetospheric fields and the lack of corresponding time-space variations at ionospheric levels. Spike-like, non-oscillatory, fields lasting <0.2 s are occasionally seen at the bow shock and at the magnetopause and also intermittently appear in magnetosheath and plasma sheet regions under highly variable field conditions. These suggest the existence of field phenomena occurring over dimensions comparable to the probe separation and ⩽c/ωpe (the ‘characteristic’ electron cyclotron radius) where ω pe is the electron plasma frequency.
Stable Day-to-Day Dynamics for Departure Time Choice

All existing day-to-day dynamics of departure time choice at a single bottleneck are unstable, and this has led to doubt over the existence of a stable user equilibrium in the real world. However, ...
Quasi-polynomial algorithms for submodular tree orienteering and other directed network design problems

We consider the following general network design problem on directed graphs. The input is an asymmetric metric (V, c), root r* ∈ V, monotone submodular function f : 2V → R+ and budget B. The goal is to find an r*-rooted arborescence T of cost at most B that maximizes f(T). Our main result is a very simple quasi-polynomial time [MATH HERE]-approximation algorithm for this problem, where k ≤ |V| is the number of vertices in an optimal solution. To the best of our knowledge, this is the first non-trivial approximation ratio for this problem. As a consequence we obtain an [MATH HERE]-approximation algorithm for directed (polymatroid) Steiner tree in quasi-polynomial time. We also extend our main result to a setting with additional length bounds at vertices, which leads to improved [MATH HERE]-approximation algorithms for the single-source buy-at-bulk and priority Steiner tree problems. For the usual directed Steiner tree problem, our result matches the best previous approximation ratio [15], but improves significantly on the running time: our algorithm takes nO(log1+ϵk) time whereas the previous algorithm required nO(log5k) time. For polymatroid Steiner tree and single-source buy-at-bulk, our result improves prior approximation ratios by a logarithmic factor. For directed priority Steiner tree, our result seems to be the first non-trivial approximation ratio. Under certain complexity assumptions, our approximation ratios are best possible (up to constant factors).
Canonical isometric embeddings of projective spaces into spheres

We define inductively isometric embeddings of $${\mathbb P}^n({\mathbb R})$$ and $${\mathbb P}^n({\mathbb C})$$ (with their canonical metrics conveniently scaled) into the standard unit sphere, which present the former as the restriction of the latter to the set of real points. Our argument parallels the telescopic construction of $${\mathbb P}^\infty ({\mathbb R})$$, $${\mathbb P}^\infty ({\mathbb C})$$, and $${\mathbb S}^\infty $$ in that, for each n, it extends the previous embedding to the attaching cell, which after a suitable renormalization makes it possible for the result to have image in the unit sphere.
A New Derivative-Free Linear Approximation for Solving the Network Water Flow Problem With Convergence Guarantees

Addressing challenges in urban water infrastructure systems including aging infrastructure, supply uncertainty, extreme events, and security threats, depend highly on water distribution networks modeling emphasizing the importance of realistic assumptions, modeling complexities, and scalable solutions. In this study, we propose a derivative-free, linear approximation for solving the network water flow problem (WFP). The proposed approach takes advantage of the special form of the nonlinear head loss equations and, after the transformation of variables and constraints, the WFP reduces to a linear optimization problem that can be efficiently solved by modern linear solvers. Ultimately, the proposed approach amounts to solving a series of linear optimization problems. We demonstrate the proposed approach through several case studies and show that the approach can model arbitrary network topologies and various types of valves and pumps, thus providing modeling flexibility. Under mild conditions, we show that the proposed linear approximation converges. We provide sensitivity analysis and discuss in detail the current limitations of our approach and suggest solutions to overcome these. All the codes, tested networks, and results are freely available on Github for research reproducibility.
The Metagenomic Binning Problem: Clustering Markov Sequences

The goal of metagenomics is to study the composition of microbial communities, typically using high-throughput shotgun sequencing. In the metagenomic binning problem, we observe random substrings (called contigs) from a mixture of genomes and want to cluster them according to their genome of origin. Based on the empirical observation that genomes of different bacterial species can be distinguished based on their tetranucleotide frequencies, we model this task as the problem of clustering N sequences generated by M distinct Markov processes, where $M\ll N$. Utilizing the large-deviation principle for Markov processes, we establish the information-theoretic limit for perfect binning. Specifically, we show that the length of the contigs must scale with the inverse of the Chernoff Information between the two most similar species. Our result also implies that contigs should be binned using the conditional relative entropy as a measure of distance, as opposed to the Euclidean distance often used in practice.
Ligands, receptors and transcription factors that mediate inter-cellular and intra-cellular communication during ovarian follicle development

Reliably producing a competent oocyte entails a deeper comprehension of ovarian follicle maturation, a very complex process that includes meiotic maturation of the female gamete, the oocyte, together with the mitotic divisions of the hormone-producing somatic cells. In this report, we investigate murine ovarian folliculogenesis in vivo using publicly available time-series microarrays from primordial to antral stage follicles. Manually curated protein interaction networks were employed to identify autocrine and paracrine signaling between the oocyte and the somatic cells (granulosa and theca cells) at multiple stages of follicle development. We established plausible protein-binding interactions between expressed genes that encode secreted factors and expressed genes that encode cellular receptors. Some computationally identified signaling interactions are well established, such as the paracrine signaling from the oocyte to the somatic cells through the oocyte-secreted growth factor Gdf9, while others are novel connections in term of ovarian folliculogenesis, such as the possible paracrine connection from somatic-secreted factor Ntn3 to the oocyte receptor Neo1. Additionally, we identified several of the likely transcription factors that might control the dynamic transcriptome during ovarian follicle development, noting that the YAP/TAZ signaling pathway is very active in vivo. This novel dynamic model of signaling and regulation can be employed to generate testable hypotheses regarding follicle development that could be validated experimentally, guiding the improvement of culture media to enhance in vitro ovarian follicle maturation and possibly novel therapeutic targets for reproductive diseases.
Exact solutions for the fluid impulse for incompressible and compressible flows

In this paper, we consider the Kuz’min [Phys. Lett. A 96, 88 (1983)] and Oseledets [Russ. Math. Surv. 44, 210 (1989)] formulations for the fluid impulse density for the incompressible and compressible barotropic Euler equations. We give exact solutions for the fluid impulse density and discuss their physical significance. The compressible barotropic exact solution describes a density wave riding on a steady Burgers type vortex flow with the fluid impulse velocity component in the wave-propagation direction growing monotonically with time.In this paper, we consider the Kuz’min [Phys. Lett. A 96, 88 (1983)] and Oseledets [Russ. Math. Surv. 44, 210 (1989)] formulations for the fluid impulse density for the incompressible and compressible barotropic Euler equations. We give exact solutions for the fluid impulse density and discuss their physical significance. The compressible barotropic exact solution describes a density wave riding on a steady Burgers type vortex flow with the fluid impulse velocity component in the wave-propagation direction growing monotonically with time.
Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand

For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyography (EMG) signal from residual limb muscles to infer the intended motion. However, limited signal quality, user diversity and high variability adversely affect the system robustness. Instead of solely relying on EMG signals, our work enables augmenting EMG intent inference with physical state probability through machine learning and computer vision method. To this end, we: (1) study state-of-the-art deep neural network architectures to select a performant source of knowledge transfer for the prosthetic hand, (2) use a dataset containing object images and probability distribution of grasp types as a new form of labeling where instead of using absolute values of zero and one as the conventional classification labels, our labels are a set of probabilities whose sum is 1. The proposed method generates probabilistic predictions which could be fused with EMG prediction of probabilities over grasps by using the visual information from the palm camera of a prosthetic hand. Our results demonstrate that InceptionV3 achieves highest accuracy with 0.95 angular similarity followed by 1.4 MobileNetV2 with 0.93 at \(\sim \)20% the amount of operations.
Linear and nonlinear pulsations of β Cephei stars

The best known β Cephei variable is a α Virginis (Spica) because it is a binary star (P = 4.01 days) and has a interferometer measurement of its angular diameter. In this study to see if the pulsations can be caused by mixing of hydrogen and helium in the semiconvective zone, the parameters we take are 11.5 M ⊙ , 26,300 K, 6.5 x 10 37 erg/s with an envelope composition of Y = 0.28, Z = 0.02. These data agree within the errors of observations as reported recently by Odell (1980), and the theoretical radial fundamental mode period of 0.171 day compares well with the single observed pulsation period of 0.174 day.
Mouse Navigation Strategies for Odor Source Localization.

Navigating an odor landscape is a critical behavior for the survival of many species, including mice. An ethologically relevant mouse behavior is locating food using information about odor concentration. To approximate this behavior, we used an open field odor-based spot-finding task indoors with little wind, examining navigation strategies as mice search for and approach an odor source. After mice were trained to navigate to odor sources paired with food reward, we observed behavioral changes consistent with localization 10-45 cm away from the source. These behaviors included orientation towards the source, decreased velocity, and increased exploration time. We also found that the amplitude of ‘casting,’ which we define as lateral back and forth movement of the nose, increased with proximity to the source. Based on these observations, we created a concentration-sensitive agent-based model to simulate mouse behavior. This model provided evidence for a binaral-sniffing strategy (inter-nostril comparison of concentration in a single sniff) and a serial-sniffing strategy (sampling concentration, moving in space, and then sampling again). Serial-sniffing may be accomplished at farther distances by moving the body and at closer distances by moving the head (casting). Together, these results elucidate components of behavioral strategies for odor-based navigation.
Death is on Our Side: Paleontological Data Drastically Modify Phylogenetic Hypotheses

Fossils are the only remaining evidence of the majority of species that have ever existed, providing a direct window into events in evolutionary history that shaped the diversification of life on Earth. Phylogenies underpin our ability to make sense of evolution but are routinely inferred using only data available from living organisms. Although extinct taxa have been shown to add crucial information for inferring macroevolutionary patterns and processes (such as ancestral states, paleobiogeography and diversification dynamics), the role fossils play in reconstructing phylogeny is controversial. Since the early years of phylogenetic systematics, different studies have dismissed the impact of fossils due to their incompleteness, championed their ability to overturn phylogenetic hypotheses or concluded that their behavior is indistinguishable from that of extant taxa. Based on taxon addition experiments on empirical data matrices, we show that the inclusion of paleontological data has a remarkable effect in phylogenetic inference. Incorporating fossils often (yet not always) induces stronger topological changes than increasing sampling of extant taxa. Fossils also produce unique topological rearrangements, allowing the exploration of regions of treespace that are never visited by analyses of only extant taxa. Previous studies have proposed a suite of explanations for the topological behavior of fossils, such as their retention of unique morphologies or their ability to break long branches. We develop predictive models that demonstrate that the possession of distinctive character state combinations is the primary predictor of the degree of induced topological change, and that the relative impact of taxa (fossil and extant) can be predicted to some extent before any phylogenetic analysis. Our results bolster the consensus of recent empirical studies by showing the unique role of paleontological data in phylogenetic inference, and provide the first quantitative assessment of its determinants, with broad consequences for the design of taxon sampling in both morphological and total-evidence analyses. [phylogeny, morphology, fossils, parsimony, Bayesian inference.].
Functional summaries of persistence diagrams

One of the primary areas of interest in applied algebraic topology is persistent homology, and, more specifically, the persistence diagram. Persistence diagrams have also become objects of interest in topological data analysis. However, persistence diagrams do not naturally lend themselves to statistical goals, such as inferring certain population characteristics, because their complicated structure makes common algebraic operations—such as addition, division, and multiplication—challenging (e.g., the mean might not be unique). To bypass these issues, several functional summaries of persistence diagrams have been proposed in the literature (e.g. landscape and silhouette functions). The problem of analyzing a set of persistence diagrams then becomes the problem of analyzing a set of functions, which is a topic that has been studied for decades in statistics. First, we review the various functional summaries in the literature and propose a unified framework for the functional summaries. Then, we generalize the definition of persistence landscape functions, establish several theoretical properties of the persistence functional summaries, and demonstrate and discuss their performance in the context of classification using simulated prostate cancer histology data, and two-sample hypothesis tests comparing human and monkey fibrin images, after developing a simulation study using a new data generator we call the Pickup Sticks Simulator (STIX).
The Impact of Incentive Regulation on Service Quality in Telecommunications in the United States

Incentive regulation in the form of price caps was adopted for interstate access servicein 1991 as a way to provide local exchange carriers with an incentive to improve theirproductive efficiency. An issue that has arisen with the adoption of price cap regulationis whether deterioration in service quality for interstate access service has been an unintended consequence. The analysis in this paper uses several different measures ofservice quality, including the average installation interval, the percent of commitmentsmet, total trouble reports, and the average repair interval for both switched access service and special access service, to investigate empirically whether there has been a decline in service quality between 1991 and 2000. The results are conclusive. Overall service quality has fallen. To rectify the situation, a proposal is offered to adjust the price cap index to penalize LECs who fail to provide an acceptable level of aggregate service quality.
Absence of $D^4 R^4$ in M-Theory From ABJM

Supersymmetry allows a D4R4 interaction in M-theory, but such an interaction is inconsistent with string theory dualities and so is known to be absent. We provide a novel proof of the absence of the D4R4 M-theory interaction by calculating 4-point scattering amplitudes of 11d supergravitons from ABJM theory. This calculation extends a previous calculation performed to the order corresponding to the R4 interaction. The new ingre- dient in this extension is the interpretation of the fourth derivative of the mass deformed S3 partition function of ABJM theory, which can be determined using supersymmetric localization, as a constraint on the Mellin amplitude associated with the stress tensor mul- tiplet 4-point function. As part of this computation, we relate the 4-point function of the superconformal primary of the stress tensor multiplet of any 3d $$ \mathcal{N} $$ = 8 SCFT to some of the 4-point functions of its superconformal descendants. We also provide a concise formula for a general integrated 4-point function on Sd for any d.
Anxiety Impedes Adaptive Social Learning Under Uncertainty

Very little is known about how individuals learn under uncertainty when other people are involved. We propose that humans are particularly tuned to social uncertainty, which is especially noisy and ambiguous. Individuals exhibiting less tolerance for uncertainty, such as those with anxiety, may have greater difficulty learning in uncertain social contexts and therefore provide an ideal test population to probe learning dynamics under uncertainty. Using a dynamic trust game and a matched nonsocial task, we found that healthy subjects (n = 257) were particularly good at learning under negative social uncertainty, swiftly figuring out when to stop investing in an exploitative social partner. In contrast, subjects with anxiety (n = 97) overinvested in exploitative partners. Computational modeling attributed this pattern to a selective reduction in learning from negative social events and a failure to enhance learning as uncertainty rises-two mechanisms that likely facilitate adaptive social choice.
A Model of Money and Credit, with Application to the Credit Card Debt Puzzle

Many individuals simultaneously have significant credit card debt and money in the bank. The credit card debt puzzle is: given high interest rates on credit cards and low rates on bank accounts, why not pay down the debt? While economists have gone to elaborate lengths to explain this observation, we argue it is a special case of the rate of return dominance puzzle from monetary economics. We analyze the issue by extending standard monetary theory to incorporate consumer debt. This seems interesting in its own right, since developing models where money and credit coexist is a long-standing challenge, and it helps put into context recent discussions of consumer debt.
Predicting predictability: The influence of student and institutional characteristics on the prediction of grades

This study examined the statistical and institutional influences on the prediction of first-year college grades, using data from College Board validity studies and the College Handbook. The criterion was the size of the multiple correlation between academic predictors and first-year college grades. The independent variables were the statistical data of the validity study and college characteristics. In general, the extent of the variation of the academic ability of the students was positively related to the size of the multiple correlation, and the heterogeneity of the programs and experience of college negatively related. Further analyses investigated the characteristics that were associated with the greater or lesser efficiency of the predictors (SAT Verbal and Mathematical and high school grades.)
Psychological health in adults with Morquio syndrome

Mucopolysaccharidosis type IV (MPSIV), also known as Morquio syndrome, is a progressive genetic condition which predominantly affects skeletal development. Research thus far has focused on physical manifestations, with little attention to psychological characteristics. As a first step in determining the natural occurrence of psychological symptoms in this population, we administered Achenbach measures of psychological functioning (ASEBA ASR and OASR), quality of life (SF-36), and pain severity (BPI) questionnaires to 20 adults with Morquio syndrome. 11/20 subjects (55%) scored within the symptomatic range on at least one or more ASEBA problem scales. These subjects also had higher pain severity scores (p = 0.051) and pain interference scores (p = 0.03) on the BPI. However, subjects with psychological symptoms did not differ significantly on QOL measures from those without psychological symptoms. Overall, subjects scored below the US mean only in physical health QOL (p < 0.001) on the SF-36, not mental health QOL. Implications of this study include the need for greater attention to psychological health in persons with Morquio syndrome, including regular assessment for psychological symptoms in addition to the quality of life measures typically used, as the latter may miss important information. Greater attention to psychological symptoms may help maximize overall health in adults with Morquio syndrome. Comparison with psychological studies on other lysosomal storage diseases suggests these results may be disease specific, rather than the result of living with chronic pain or having an LSD in general.
On Almost k-Covers of Hypercubes

In this paper, we consider the following problem: what is the minimum number of affine hyperplanes in ℝn, such that all the vertices of $$\overrightarrow 0$$ are covered at least k times, and $${\left\{{0,1} \right\}^n}\backslash \left\{{\overrightarrow 0} \right\}$$ is uncovered? The k = 1 case is the well-known Alon-Furedi theorem which says a minimum of n affine hyperplanes is required, which follows from the Combinatorial Nullstellensatz. We develop an analogue of the Lubell-Yamamoto-Meshalkin inequality for subset sums, and completely solve the fractional version of this problem, which also provides an asymptotic answer to the integral version for fixed n and k → ∞. We also use a Punctured Combinatorial Nullstellensatz developed by Ball and Serra, to show that a minimum of n + 3 affine hyperplanes is needed for k = 3, and pose a conjecture for arbitrary k and large n.
The Cross Section of Analyst Recommendations

We analyze the price reaction to analysts' revisions by testing the Griffin and Tversky (1992) hypothesis that agents place emphasis on the strength of the signal (the dramatic nature of the event) and may de-emphasize the weight (the ability of the analyst making the recommendation). Two attributes, namely, years of experience and the reputation of the analysts' brokerage houses form proxies for analyst ability (or weight) that we validate by documenting that revisions by high ability analysts outperform those by low ability ones. We find evidence of return persistence following small (low strength) revisions by high ability analysts and the opposite return pattern following large (high strength) revisions of low ability analysts, consistent with the arguments of Griffin and Tversky (1992). Our study provides an empirical link between evidence on individual decision making and stock market returns, and also helps promote an understanding of the analyst industry as well as its interaction with the investing population.
Axiomatic Equilibrium Selection for Generic Two-Player Games

For a finite game with perfect recall, a refinement of its set of Nash equilibria selects closed connected subsets, called solutions. Assume that each solution's equilibria use undominated strategies and some of its equilibria are quasi-perfect, and that all solutions are immune to presentation effects; namely, if the game is embedded in a larger game with more pure strategies and more players such that the original players' feasible mixed strategies and expected payoffs are preserved regardless of what other players do, then the larger game's solutions project to the original game's solutions. Then, for a game with two players and generic payoffs, each solution is an essential component of the set of equilibria that use undominated strategies, and thus a stable set of equilibria as defined by Mertens (1989).
Synthetic biomaterials for pelvic floor reconstruction

Pelvic organ prolapse and stress urinary incontinence increase with age. The increasing proportion of the aging female population is likely to result in a demand for care of pelvic floor prolapse and incontinence. Experimental evidence of altered connective tissue metabolism may predispose to pelvic floor dysfunction, supporting the use of biomaterials, such as synthetic mesh, to correct pelvic fascial defects. Re-establishing pelvic support and continence calls for a biomaterial to be inert, flexible, and durable and to simultaneously minimize infection and erosion risk. Mesh as a biomaterial has evolved considerably throughout the past half century to the current line that combines ease of use, achieves good outcomes, and minimizes risk. This article explores the biochemical basis for pelvic floor attenuation and reviews various pelvic reconstructive mesh materials, their successes, failures, complications, and management.
Smoothing sudden stops

Abstract Emerging economies are often exposed to sudden shortages of international financial resources. Yet domestic agents do not seem to take preventive measures against these sudden stops. We highlight the central role played by the limited development of ex ante (insurance) and ex post (spot) domestic financial markets in generating this collective undervaluation of international resources. We study several policies to counteract the external underinsurance. We do this by solving for the optimal mechanism given the constraints imposed by limited financial development, and then considering the main financial policies—in terms of the model and practical relevance—that implement this solution.
An Application of Random Walk Resampling to Phylogenetic HMM Inference and Learning

Statistical resampling methods are widely used for confidence interval placement and as a data perturbation technique for statistical inference and learning. An important assumption of popular resampling methods such as the standard bootstrap is that input observations are identically and independently distributed (i.i.d.). However, within the area of computational biology and bioinformatics, many different factors can contribute to intra-sequence dependence, such as recombination and other evolutionary processes governing sequence evolution. The SEquential RESampling (“SERES”) framework was previously proposed to relax the simplifying assumption of i.i.d. input observations. SERES resampling takes the form of random walks on an input of either aligned or unaligned biomolecular sequences. This study introduces the first application of SERES random walks on aligned sequence inputs and is also the first to demonstrate the utility of SERES as a data perturbation technique to yield improved statistical estimates. We focus on the classical problem of recombination-aware local genealogical inference. We show in a simulation study that coupling SERES resampling and re-estimation with recHMM, a hidden Markov model-based method, produces local genealogical inferences with consistent and often large improvements in terms of topological accuracy. We further evaluate method performance using empirical HIV genome sequence datasets.
Scan Statistics for Disease Surveillance

Scan statistics provide the researcher with a measure of unusualness (statistical significance) associated with a clustering of events in time, as would occur when observing an unexpectedly large number of events in a short time period. The statistic can also be used to detect seasonal clustering, and as a method to detect geographical, time, or space–time clustering. The scan statistic can also be generalized and then applied to the analysis of DNA sequence data focusing on detecting a certain pattern. Keywords: Bonferroni bounds; clustering; DNA sequencing; Poisson process; seasonal time series; space–time clustering; temporal clustering
HEDGE FUNDS WITH STYLE

The popular perception is that hedge funds follow a reasonably well-defined market-neutral investment style. Investigation of the monthly return history of hedge funds over 1989–2000, however, finds there are in fact distinct styles of management that account for about 20% of the cross-sectional variability in performance. This result is consistent across the years in the sample and robust as to the way investment style is determined. Appropriate style analysis and style management are crucial to success for investors looking to invest in hedge funds.
Symplectic invariants of semitoric systems and the inverse problem for quantum systems

Abstract Simple semitoric systems were classified about ten years ago in terms of a collection of invariants, essentially given by a convex polygon with some marked points corresponding to focus-focus singularities. Each marked point is endowed with labels which are symplectic invariants of the system. We will review the construction of these invariants, and explain how they have been generalized or applied in different contexts. One of these applications concerns quantum integrable systems and the corresponding inverse problem, which asks how much information of the associated classical system can be found in the spectrum. An approach to this problem has been to try to compute invariants in the spectrum. We will explain how this has been recently achieved for some of the invariants of semitoric systems, and discuss an open question in this direction.
Sound and fury: McCloskey and significance testing in economics

For more than 20 years, Deidre McCloskey has campaigned to convince the economics profession that it is hopelessly confused about statistical significance. She argues that many practices associated with significance testing are bad science and that most economists routinely employ these bad practices: ‘Though to a child they look like science, with all that really hard math, no science is being done in these and 96 percent of the best empirical economics …’ (McCloskey 1999). McCloskey’s charges are analyzed and rejected. That statistical significance is not economic significance is a jejune and uncontroversial claim, and there is no convincing evidence that economists systematically mistake the two. Other elements of McCloskey’s analysis of statistical significance are shown to be illfounded, and her criticisms of practices of economists are found to be based in inaccurate readings and tendentious interpretations of those economists’ work. Properly used, significance tests are a valuable tool for assessing signal strength, for assisting in model specification, and for determining causal structure.
Pricing and Liquidity in Decentralized Asset Markets

I develop a search-and-bargaining model of liquidity provision in over-the-counter markets where investors differ in their search intensities. A distinguishing characteristic of my model is its tractability: it allows for heterogeneity, unrestricted asset positions, and fully decentralized trade. I find that investors with higher search intensities (i.e., fast investors) are less averse to holding inventories and more attracted to cash earnings, which makes the model corroborate a number of stylized facts that do not emerge from existing models: (i) fast investors provide intermediation by charging a speed premium, and (ii) fast investors hold larger and more volatile inventories. Then, I use the model to study the effect of trading frictions on the supply and price of liquidity. The results have policy implications concerning the Volcker rule.
21 – Radiation protection

Publisher Summary This chapter describes the protection of biological entities from hazard of radiation exposure. Radiation protection of living organisms requires control of sources, barriers between source and living being, or removal of the target entity. Calculations are required to evaluate external hazard. These calculations include material, and time; the inverse square geometric spreading effect; and the exponential attenuation in shielding materials. The considerations also include the time that a person remains within a radiation zone, to reduce the dose received. Internal hazard depends on many physical and biological factors. Maximum permissible concentrations of radioisotopes in air and water can be deduced from the properties of the emitter and the dose limits. Application of the principle of “as low as is reasonably achievable,” (ALARA) is designed to reduce exposure to levels that are as low as reasonably achievable. There are many biological pathways that transport radioactive materials. New dose limit rules are based on the total effects of radiation external and internal on all parts of the body.
Chapter 8 – Concurrent Events

The event calculus allows several events to occur at the same time. In this case, we say that the event occurrences are concurrent or simultaneous. We first consider some concurrent event occurrences that we might want to prohibit and methods for prohibiting them. Then, we discuss concurrent event occurrences that have cumulative and canceling effects and how to handle them.