cssi/SciSciGPT-SciSciNet huggingface dataset:
On Shige Peng’s central limit theorem

Abstract We give error estimates in Peng’s central limit theorem for not necessarily nondegenerate case. The exposition uses the language of the classical probability theory instead of the language of the theory of sublinear expectations. We only consider the one-dimensional case. The higher dimensional extension is left to the interested reader.
Dynamic Multi-Level Multi-Task Learning for Sentence Simplification

Sentence simplification aims to improve readability and understandability, based on several operations such as splitting, deletion, and paraphrasing. However, a valid simplified sentence should also be logically entailed by its input sentence. In this work, we first present a strong pointer-copy mechanism based sequence-to-sequence sentence simplification model, and then improve its entailment and paraphrasing capabilities via multi-task learning with related auxiliary tasks of entailment and paraphrase generation. Moreover, we propose a novel ‘multi-level’ layered soft sharing approach where each auxiliary task shares different (higher versus lower) level layers of the sentence simplification model, depending on the task’s semantic versus lexico-syntactic nature. We also introduce a novel multi-armed bandit based training approach that dynamically learns how to effectively switch across tasks during multi-task learning. Experiments on multiple popular datasets demonstrate that our model outperforms competitive simplification systems in SARI and FKGL automatic metrics, and human evaluation. Further, we present several ablation analyses on alternative layer sharing methods, soft versus hard sharing, dynamic multi-armed bandit sampling approaches, and our model’s learned entailment and paraphrasing skills.
Universal Cycles for Weak Orders

Universal cycles are generalizations of de Bruijn cycles and Gray codes that were introduced originally by Chung, Diaconis, and Graham in 1992. They have been developed by many authors since, for various combinatorial objects such as strings, subsets, permutations, partitions, vector spaces, and designs. One generalization of universal cycles, which require almost complete overlap of consecutive words, is $s$-overlap cycles, which relax such a constraint. In this paper we study weak orders, which are relations that are transitive and complete. We prove the existence of universal and $s$-overlap cycles for weak orders, as well as for fixed height and/or weight weak orders, and apply the results to cycles for ordered partitions.
Using multiple measurements of tissue to estimate subject- and cell-type-specific gene expression.

Motivation Patterns of gene expression, quantified at the level of tissue or cells, can inform on etiology of disease. There are now rich resources for tissue-level (bulk) gene expression data, which have been collected from thousands of subjects, and resources involving single-cell RNA-sequencing (scRNA-seq) data are expanding rapidly. The latter yields cell type information, although the data can be noisy and typically are derived from a small number of subjects. Results Complementing these approaches, we develop a method to estimate subject- and cell-type-specific (CTS) gene expression from tissue using an empirical Bayes method that borrows information across multiple measurements of the same tissue per subject (e.g. multiple regions of the brain). Analyzing expression data from multiple brain regions from the Genotype-Tissue Expression project (GTEx) reveals CTS expression, which then permits downstream analyses, such as identification of CTS expression Quantitative Trait Loci (eQTL). Availability and implementation We implement this method as an R package MIND, hosted on https://github.com/randel/MIND. Supplementary information Supplementary data are available at Bioinformatics online.
Wasserstein Index Generation Model: Automatic generation of time-series index with application to Economic Policy Uncertainty

Abstract I propose a novel method, the Wasserstein Index Generation model (WIG), to generate a public sentiment index automatically. To test the model’s effectiveness, an application to generate Economic Policy Uncertainty (EPU) index is showcased.
Fabric Soft Poly-Limbs for Physical Assistance of Daily Living Tasks

This paper presents the design and development of a highly articulated, continuum, wearable, fabric-based Soft Poly-Limb (fSPL). This fabric soft arm acts as an additional limb that provides the wearer with mobile manipulation assistance through the use of soft actuators made with high-strength inflatable fabrics. In this work, a set of systematic design rules is presented for the creation of highly compliant soft robotic limbs through an understanding of the fabric based components behavior as a function of input pressure. These design rules are generated by investigating a range of parameters through computational finite-element method (FEM) models focusing on the fSPL’s articulation capabilities and payload capacity in 3D space. The theoretical motion and payload outputs of the fSPL and its components are experimentally validated as well as additional evaluations verify its capability to safely carry loads 10.1x its body weight, by wrapping around the object. Finally, we demonstrate how the fully collapsible fSPL can comfortably be stored in a soft-waist belt and interact with the wearer through spatial mobility and preliminary pick-and-place control experiments.
Reconciling Similar Sets of Data

In this paper, we consider the problem of synchronizing two sets of data where the size of the symmetric difference between the sets is small and, in addition, the elements in the symmetric difference are related through the Hamming distance metric. Upper and lower bounds are derived on the minimum amount of information exchange. Furthermore, explicit encoding and decoding algorithms are provided for many cases.
Make Love, Not War: Do Single Young Men Cause Political Violence?

The theory that single young men drive terrorism and civil war has been repeated in academic articles, diplomatic dispatches and the popular press. This paper tests the theory and finds that while the proportion of young men has a statistically-significant impact on political violence, whether or not these men are married has no additional impact. The result may initially appear to contradict the individual-level evidence that shows that the overwhelming majority of political violence is committed by young, unmarried men. However, if being a terrorist or a guerrilla caused men to be single, then this would explain why most terrorists and guerrillas are single. Moreover, given that terrorists and guerrillas make up only a small fraction of the population, if participating in political violence caused them to be single then this would have a negligible impact on a country’s proportion of single young men. As a result, the effect of young men would be the same as that of single young men. This theory would therefore explain both the individual-level evidence that those who participate in violence are single as well as the country-level regression results that the effect of single young men is identical to that of young men.
Evaluating Pruning Methods in Gene Network Inference

One challenge in gene network inference is distinguishing between direct and indirect regulation. Some algorithms, including ARACNE and Phixer, approach this problem by using pruning methods to eliminate redundant edges in an attempt to explain the observed data with the simplest possible network structure. However, we hypothesize that there may be a cost in accuracy to simplifying the predicted networks in this way, especially due to the prevalence of redundant connections, such as feed forward loops, in gene networks. In this paper, we evaluate the pruning methods of ARACNE and Phixer, and score their accuracy using receiver operating characteristic curves and precision-recall curves. Our results suggest that while pruning can be useful in some situations, it may have a negative effect on overall accuracy that has not been previously studied. Researchers should be aware of both the advantages and disadvantages of pruning when inferring networks, in order to choose the best inference strategy for their experimental context.
A Framework for Automated Collaborative Fault Detection in Large-Scale Vehicle Networks

This research presents a novel framework for automated fault detection in cyber-physical systems, with specific focus on large-scale vehicle networks. Agents in a network develop system identification models of themselves which are sent to a local or global authority. The authority excites the system models and generates a fixed-size vector for each one using an echo state network coupled with an autoencoder. The resultant vectors are grouped using standard clustering algorithms, with each group representing similar system model responses. A human expert labels each group once, so that any new group members can be can be associated with the group label. The largest group is assumed to be operating nominally, with all other groups representing a fault or off-nominal operation. We apply our framework to a detailed vehicle cooling system model to demonstrate its efficacy.
Missing not at random in matrix completion: The effectiveness of estimating missingness probabilities under a low nuclear norm assumption

Matrix completion is often applied to data with entries missing not at random (MNAR). For example, consider a recommendation system where users tend to only reveal ratings for items they like. In this case, a matrix completion method that relies on entries being revealed at uniformly sampled row and column indices can yield overly optimistic predictions of unseen user ratings. Recently, various papers have shown that we can reduce this bias in MNAR matrix completion if we know the probabilities of different matrix entries being missing. These probabilities are typically modeled using logistic regression or naive Bayes, which make strong assumptions and lack guarantees on the accuracy of the estimated probabilities. In this paper, we suggest a simple approach to estimating these probabilities that avoids these shortcomings. Our approach follows from the observation that missingness patterns in real data often exhibit low nuclear norm structure. We can then estimate the missingness probabilities by feeding the (always fully-observed) binary matrix specifying which entries are revealed to an existing nuclear-norm-constrained matrix completion algorithm by Davenport et al. [2014]. Thus, we tackle MNAR matrix completion by solving a different matrix completion problem first that recovers missingness probabilities. We establish finite-sample error bounds for how accurate these probability estimates are and how well these estimates debias standard matrix completion losses for the original matrix to be completed. Our experiments show that the proposed debiasing strategy can improve a variety of existing matrix completion algorithms, and achieves downstream matrix completion accuracy at least as good as logistic regression and naive Bayes debiasing baselines that require additional auxiliary information.
JacquardToolkit: enabling interactions with the Levi's Jacquard jacket

JacquardTooIkit enables full access to the Levi's Jacquard Jacket and the ability to create gestures such as our new "Force Touch" gesture. We conduct a 25 participant user study testing all available gestures for accuracy and intuitiveness. "Brush In" and "Brush Out" are rated highly intuitive and have high recognition accuracy.
Faster Matroid Intersection

In this paper we consider the classic matroid intersection problem: given two matroids M_1=(V, I_1) and M_2=(V, I_2) defined over a common ground set V, compute a set S∊I_1 ∩ I_2 of largest possible cardinality, denoted by r. We consider this problem both in the setting where each M_i is accessed through an independence oracle, i.e. a routine which returns whether or not a set S ∊ I_i in T_ind time, and the setting where each M_i is accessed through a rank oracle, i.e. a routine which returns the size of the largest independent subset of S in M_i in T_rank time. In each setting we provide faster exact and approximate algorithms. Given an independence oracle, we provide an exact O(nr log r⋅T_ind) time algorithm. This improves upon previous best known running times of O(nr^1.5⋅T_ind) due to Cunningham in 1986 and O(n^2⋅T_ind+n^3) due to Lee, Sidford, and Wong in 2015. We also provide two algorithms which compute a (1-e)-approximate solution to matroid intersection running in times O(n^1.5/e^1.5⋅T_ind) and O((n^2r^-1e^-2+r^1.5e^-4.5)⋅T_ind), respectively. These results improve upon the O(nr/e⋅T_ind) -time algorithm of Cunningham (noted recently by Chekuri and Quanrud). Given a rank oracle, we provide algorithms with even better dependence on n and r. We provide an O(n√rlog n⋅T_rank) -time exact algorithm and an O(ne^-1 log n⋅T_rank) -time algorithm which obtains a (1-e) -approximation to the matroid intersection problem. The former result improves over the O(nr⋅T_rank+n^3) -time algorithm by Lee, Sidford, and Wong. The rank oracle is of particular interest as the matroid intersection problem with this oracle is a special case (via Edmond's minimax characterization of matroid intersection) of the submodular function minimization (SFM) problem with an evaluation oracle, and understanding SFM query complexity is an outstanding open question.
Caregiver Comfort in Adolescents Independently Completing Screening Tablet-Based Questionnaires at Primary Care Visits.

Abstract Purpose The objective of this study was to assess caregiver comfort regarding adolescent completion of computerized health screening questionnaires created for adolescents. Methods We conducted a mixed-method, cross-sectional survey of caregivers of adolescent patients (n = 104) aged 12–18 years who had a medical visit between June 2017 and August 2017. Topics assessed included who completed the questionnaire, caregiver comfort and concern regarding questionnaire data, and caregiver reasons for involvement in completing the questionnaire. A one-way analysis of variance was used to compare the age of the adolescent and caregiver involvement in the questionnaire. Results The majority of adolescents (64%) reported independent completion of the questionnaire. Thirteen percent of caregivers completed the questionnaire with no involvement of the adolescent and 23% reported that caregivers and adolescents completed the questionnaire in tandem. The majority of caregivers (84%) were comfortable with adolescents completing the questionnaire. A variety of reasons were identified for caregivers completing the questionnaire (time constraints, 22%; adolescent requested caregiver help, 19%; caregiver desired to answer questions, 14%; caregiver did not realize that the questionnaire was intended for the adolescent, 11%; caregiver believed that the adolescent was too young to respond alone, 11%). Caregiver comfort with adolescent completing the questionnaire increase with age. Conclusion We found the reason most caregivers gave for completing the questionnaires were related to clinic processes (e.g. time constraints). Caregivers were more likely to complete the questionnaire with younger adolescents. Thus, pediatricians should consider how to best prepare families for initial questionnaires in primary care.
Multilingual Speech Recognition with Corpus Relatedness Sampling.

Multilingual acoustic models have been successfully applied to low-resource speech recognition. Most existing works have combined many small corpora together and pretrained a multilingual model by sampling from each corpus uniformly. The model is eventually fine-tuned on each target corpus. This approach, however, fails to exploit the relatedness and similarity among corpora in the training set. For example, the target corpus might benefit more from a corpus in the same domain or a corpus from a close language. In this work, we propose a simple but useful sampling strategy to take advantage of this relatedness. We first compute the corpus-level embeddings and estimate the similarity between each corpus. Next, we start training the multilingual model with uniform-sampling from each corpus at first, then we gradually increase the probability to sample from related corpora based on its similarity with the target corpus. Finally, the model would be fine-tuned automatically on the target corpus. Our sampling strategy outperforms the baseline multilingual model on 16 low-resource tasks. Additionally, we demonstrate that our corpus embeddings capture the language and domain information of each corpus.
The role of theory in the laboratory control of quantum dynamics phenomena

Interest in the control of quantum dynamics phenomena has grown in recent years, with laboratory studies showing increasing successes. The role of theory in the control of quantum phenomena encompasses the design of laser controls, the development of algorithms to guide the laboratory studies, and the means to analyze the ensuing dynamics observations. Laboratory laser control instrumentation has the special capability of performing massive numbers of experiments in a short period of time, to rapidly search for controls that meet the objectives. This unique laboratory feature needs to be factored in when considering how to best utilize theoretical analyses. The present paper reviews the role that theory is playing, as well as suggests some future avenues for theory in the laser control of quantum phenomena.
On roots of Wiener polynomials of trees

Abstract The Wiener polynomial of a connected graph G is the polynomial W ( G ; x ) = ∑ i = 1 D ( G ) d i ( G ) x i where D ( G ) is the diameter of G , and d i ( G ) is the number of pairs of vertices of G at distance i from each other. We examine the roots of Wiener polynomials of trees. We prove that the collection of real Wiener roots of trees is dense in ( − ∞ , 0 ] , and the collection of complex Wiener roots of trees is dense in ℂ . We also prove that the maximum modulus among all Wiener roots of trees of order n ≥ 31 is between 2 n − 16 and 2 n − 15 , and we determine the unique tree that achieves the maximum for n ≥ 31 . Finally, we find trees of arbitrarily large diameter whose Wiener roots are all real.
Reconfiguring Motor Circuits for a Joint Manual and BCI Task

Designing brain-computer interfaces (BCIs) that can be used in conjunction with ongoing motor behavior requires an understanding of how neural activity co-opted for brain control interacts with existing neural circuits. For example, BCIs may be used to regain lost motor function after stroke. This requires that neural activity controlling unaffected limbs is dissociated from activity controlling the BCI. In this study we investigated how primary motor cortex accomplishes simultaneous BCI control and motor control in a task that explicitly required both activities to be driven from the same brain region (i.e. a dual-control task). Single-unit activity was recorded from intracortical, multi-electrode arrays while a non-human primate performed this dual-control task. Compared to activity observed during naturalistic motor control, we found that both units used to drive the BCI directly (control units) and units that did not directly control the BCI (non-control units) significantly changed their tuning to wrist torque. Using a measure of effective connectivity, we observed that control units decrease their connectivity. Through an analysis of variance we found that the intrinsic variability of the control units has a significant effect on task proficiency. When this variance is accounted for, motor cortical activity is flexible enough to perform novel BCI tasks that require active decoupling of natural associations to wrist motion. This study provides insight into the neural activity that enables a dual-control brain-computer interface.
Polarimetric time-frequency music in coherent signal environment

The spatial time-frequency distributions (STFDs) have been developed and successfully applied to high-resolution direction-of-arrival (DOA) estimations and blind recovery of the source waveforms. In [Y. Zhang et al., Aug. 2003], the spatial polarimetric time-frequency distribution (SPTFD) was introduced as a platform for space-time processing for nonstationary source signals with different polarization properties. Based on SPTFD, polarimetric time-frequency MUSIC (PTF-MUSIC) has been proposed for DOA estimation of non-coherent signals. In this paper, the use of the PTF-MUSIC is extended to the coherent signal environment using spatial and polarization averaging. Simulation results are presented to show the effectiveness of the aforementioned decorrelation techniques.
Weakly Supervised Deep Image Hashing Through Tag Embeddings

Many approaches to semantic image hashing have been formulated as supervised learning problems that utilize images and label information to learn the binary hash codes. However, large-scale labelled image data is expensive to obtain, thus imposing a restriction on the usage of such algorithms. On the other hand, unlabelled image data is abundant due to the existence of many Web image repositories. Such Web images may often come with images tags that contains useful information, although raw tags in general do not readily lead to semantic labels. Motivated by this scenario, we formulate the problem of semantic image hashing as a weakly-supervised learning problem. We utilize the information contained in the user-generated tags associated with the images to learn the hash codes. More specifically, we extract the word2vec semantic embeddings of the tags and use the information contained in them for constraining the learning. Accordingly, we name our model Weakly Supervised Deep Hashing using Tag Embeddings (WDHT). WDHT is tested for the task of semantic image retrieval and is compared against several state-of-art models. Results show that our approach sets a new state-of-art in the area of weekly supervised image hashing.
Optimizing cancer care for hepatocellular carcinoma at a safety-net hospital: The value of a multidisciplinary disease management team.

BACKGROUND Hepatitis C (HCV) is the primary etiology of hepatocellular carcinoma (HCC) in the US multidisciplinary disease management teams (DMT) that optimize oncologic care. The impact of DMT for HCC in safety-net hospitals is unknown. METHODS Patients diagnosed with HCC from 2009 to 2016 at Grady Memorial Hospital (GMH) were included. The primary aim was to evaluate referrals to care, receipt of therapy, and overall survival (OS) after DMT formation. Screening patterns of HCV patients for HCC were also examined. RESULTS Of 204 HCC patients, median age was 58 years, with 81% male, 83% black. 46% presented with stage 4 disease, 53% had treatment with median OS 9.8 months. DMT formation was associated with increased referrals to surgery (49% vs 30%; P = .02), liver-directed therapy (58% vs 31%; P = .001), and radiation (13% vs 3%; P = .019). Patients were also more likely to get treatment (59% vs 41%; P = .026), with improved median OS (30.7 vs 4.9 months; P < .001). DMT did not alter HCV screening for HCC (23%). HCV patients screened for HCC had earlier stage disease (P = .001). CONCLUSION Implementation of a DMT at GMH is associated with increased HCC patients referred for/receiving treatment, as well as improved survival. Few patients with HCV at risk for HCC are screened, despite DMT. Future efforts should aim to establish screening programs for HCV patients at risk for HCC.
Regional convergence and structural change in US housing markets

If house prices are convergent at the national level, monetary policy is easier to implement, and labour has an easier time achieving mobility across regions. Accordingly there have been several st...
Filtering of Randomly Sampled Time-Stamped Measurements

This paper concerns the filtering of measurements that are taken by networked sensors at nonuniform intervals but that are accurately time stamped. Traditional digital filtering methods are difficult or impossible to use due to nonuniform sampling. Two filtering methods are described. Both are based on making an assumption about the signal behavior between measurements, such as the signal being constant between measurements. In the first method, a filter is formulated as an ordinary differential equation that is incrementally solved as measurements arrive. Such filtering is general; nonlinear and nontime invariant filters may be constructed. In the second method, signal convolution with a continuous-time finite impulse response filter is efficiently performed using a spline representation for the filter response. Such filters are ldquoFIR likerdquo in the sense that they have frequency-domain performance similar to FIR filters and have only slightly worse asymptotic computation time and memory requirements compared to FIR filters, yet have the advantage of being able to deal with nonuniformly sampled measurements. Examples of the operation of both sorts of filters are shown on actual measured data.
A variational approach to coarse graining of equilibrium and non-equilibrium atomistic description at finite temperature

The aim of this paper is the development of equilibrium and non-equilibrium extensions of the quasicontinuum (QC) method. We first use variational mean-field theory and the maximum-entropy (max-ent) formalism for deriving approximate probability distribution and partition functions for the system. The resulting probability distribution depends locally on atomic temperatures defined for every atom and the corresponding thermodynamic potentials are explicit and local in nature. The method requires an interatomic potential as the sole empirical input. Numerical validation is performed by simulating thermal equilibrium properties of selected materials using the Lennard–Jones (LJ) pair potential and the embedded-atom method (EAM) potential and comparing with molecular dynamics results as well as experimental data. The max-ent variational approach is then taken as a basis for developing a three-dimensional non-equilibrium finite-temperature extension of the QC method. This extension is accomplished by coupling the local temperature-dependent free energy furnished by the max-ent approximation scheme to the heat equation in a joint thermo-mechanical variational setting. Results for finite-temperature nanoindentation tests demonstrate the ability of the method to capture non-equilibrium transport properties and differentiate between slow and fast indentation.
Implicitly Modeling Frequency Control within Power Flow

In this paper, we extend a circuit-based, current-voltage power flow formulation to include frequency deviations and implicitly model generator primary and secondary control actions as a function of their temporal dependence. This includes extending the slack bus generator model(s) to better represent its true behavior with frequency controls. These implicit models obviate the need for outer iteration loops and improve the robustness of the simulation convergence when frequency deviations are considered. The simulation framework is highly scalable and is demonstrated on 81k+ bus systems.
Correction of respiratory artifacts in MRI head motion estimates

Head motion represents one of the greatest technical obstacles in magnetic resonance imaging (MRI) of the human brain. Accurate detection of artifacts induced by head motion requires precise estimation of movement. However, head motion estimates may be corrupted by artifacts due to magnetic main field fluctuations generated by body motion. In the current report, we examine head motion estimation in multiband resting state functional connectivity MRI (rs-fcMRI) data from the Adolescent Brain and Cognitive Development (ABCD) Study and comparison 'single-shot' datasets. We show that respirations contaminate movement estimates in functional MRI and that respiration generates apparent head motion not associated with functional MRI quality reductions. We have developed a novel approach using a band-stop filter that accurately removes these respiratory effects from motion estimates. Subsequently, we demonstrate that utilizing a band-stop filter improves post-processing fMRI data quality. Lastly, we demonstrate the real-time implementation of motion estimate filtering in our FIRMM (Framewise Integrated Real-Time MRI Monitoring) software package.
An Exponential Learning Rate Schedule for Deep Learning

Intriguing empirical evidence exists that deep learning can work well with exotic schedules for varying the learning rate. This paper suggests that the phenomenon may be due to Batch Normalization or BN(Ioffe & Szegedy, 2015), which is ubiq- uitous and provides benefits in optimization and generalization across all standard architectures. The following new results are shown about BN with weight decay and momentum (in other words, the typical use case which was not considered in earlier theoretical analyses of stand-alone BN (Ioffe & Szegedy, 2015; Santurkar et al., 2018; Arora et al., 2018) • Training can be done using SGD with momentum and an exponentially in- creasing learning rate schedule, i.e., learning rate increases by some (1 + α) factor in every epoch for some α > 0. (Precise statement in the paper.) To the best of our knowledge this is the first time such a rate schedule has been successfully used, let alone for highly successful architectures. As ex- pected, such training rapidly blows up network weights, but the net stays well-behaved due to normalization. • Mathematical explanation of the success of the above rate schedule: a rigor- ous proof that it is equivalent to the standard setting of BN + SGD + Standard Rate Tuning + Weight Decay + Momentum. This equivalence holds for other normalization layers as well, Group Normalization(Wu & He, 2018), Layer Normalization(Ba et al., 2016), Instance Norm(Ulyanov et al., 2016), etc. • A worked-out toy example illustrating the above linkage of hyper- parameters. Using either weight decay or BN alone reaches global minimum, but convergence fails when both are used.
A Ring-Theorist's Description of Fedosov Quantization

We present a formal, algebraic treatment of Fedosov's argument that the coordinate algebra of a symplectic manifold has a deformation quantization. His remarkable formulas are established in the context of affine symplectic algebras.
Is Obesity a Disease?-The Evolving Concepts, Cancer Paradox and Association With Improved Cancer Immunotherapy Efficacy

Obesity represents a growing health problem in the United States and worldwide, with a prevalence that has doubled since 1980 (1). In the United States, 70% of adults and 37% of adolescents are overweight or obese. The chronic health implications of obesity make it a public health issue with significant economic ramifications. By promoting an altered whole-body physiology, obesity increases the risk for chronic diseases including diabetes, cardiovascular disease, and cancer (2).
The intrinsic scale of networks is small

We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging from structured (e.g. road networks) to ad-hoc and unstructured (e.g. crowd sourced information networks), to biological. We find: (a) The intrinsic scale is surprisingly small (7-20 vertices), even though the networks are many orders of magnitude larger. (b) The intrinsic scale quantifies "structure" in a network - networks which are explicitly constructed for specific tasks have smaller intrinsic scale. (c) The structure at different scales can be fragile (easy to disrupt) or robust.
At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?

In paired experiments, units are matched into pairs, and one unit of each pair is randomly assigned to treatment. To estimate the treatment effect, researchers often regress their outcome on a treatment indicator and pair fixed effects, clustering standard errors at the unit-of-randomization level. We show that the variance estimator in this regression may be severely downward biased: under constant treatment effect, its expectation equals 1/2 of the true variance. Instead, we show that researchers should cluster their standard errors at the pair level. Using simulations, we show that those results extend to stratified experiments with few units per strata.
A new numerical approach to the solution of the 2-D Helmholtz equation with optimal accuracy on irregular domains and Cartesian meshes

A new numerical approach for the time independent Helmholtz equation on irregular domains has been developed. Trivial Cartesian meshes and simple 9-point stencil equations with unknown coefficients are used for 2-D irregular domains. The calculation of the coefficients of the stencil equations is based on the minimization of the local truncation error of the stencil equations and yields the optimal order of accuracy. At similar 9-point stencils, the accuracy of the new approach is two orders higher for the Dirichlet boundary conditions and one order higher for the Neumann boundary conditions than that for the linear finite elements. The numerical results for irregular domains also show that at the same number of degrees of freedom, the new approach is even much more accurate than the quadratic and cubic finite elements with much wider stencils. The new approach can be equally applied to the Helmholtz and screened Poisson equations.
Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data.

Neurons carry information in the form of electrical signals. Each of these signals is too weak to detect on its own. But the combined signals from large groups of neurons can be detected using techniques called EEG and MEG. Sensors on or near the scalp detect changes in the electrical activity of groups of neurons from one millisecond to the next. These recordings can also reveal changes in brain activity due to disease. But how do EEG/MEG signals relate to the activity of neural circuits? While neuroscientists can rarely record electrical activity from inside the human brain, it is much easier to do so in other animals. Computer models can then compare these recordings from animals to the signals in human EEG/MEG to infer how the activity of neural circuits is changing. But building and interpreting these models requires advanced skills in mathematics and programming, which not all researchers possess. Neymotin et al. have therefore developed a user-friendly software platform that can help translate human EEG/MEG recordings into circuit-level activity. Known as the Human Neocortical Neurosolver, or HNN for short, the open-source tool enables users to develop and test hypotheses on the neural origin of EEG/MEG signals. The model simulates the electrical activity of cells in the outer layers of the human brain, the neocortex. By feeding human EEG/MEG data into the model, researchers can predict patterns of circuit-level activity that might have given rise to the EEG/MEG data. The HNN software includes tutorials and example datasets for commonly measured signals, including brain rhythms. It is free to use and can be installed on all major computer platforms or run online. HNN will help researchers and clinicians who wish to identify the neural origins of EEG/MEG signals in the healthy or diseased brain. Likewise, it will be useful to researchers studying brain activity in animals, who want to know how their findings might relate to human EEG/MEG signals. As HNN is suitable for users without training in computational neuroscience, it offers an accessible tool for discoveries in translational neuroscience.
Chipper: Open-source software for semi-automated segmentation and analysis of birdsong and other natural sounds

O_LIAudio recording devices have changed significantly over the last 50 years, making large datasets of recordings of natural sounds, such as birdsong, easier to obtain. This increase in digital recordings necessitates an increase in high-throughput methods of analysis for researchers. Specifically, there is a need in the community for open-source methods that are tailored to recordings of varying qualities and from multiple species collected in nature.nC_LIO_LIWe developed Chipper, a Python-based software to semi-automate both the segmentation of acoustic signals and the subsequent analysis of their frequencies and durations. For avian recordings, we provide widgets to best determine appropriate thresholds for noise and syllable similarity, which aid in calculating note measurements and determining syntax. In addition, we generated a set of synthetic songs with various levels of background noise to test Chippers accuracy, repeatability, and reproducibility.nC_LIO_LIChipper provides an effective way to quickly generate reproducible estimates of birdsong features. The cross-platform graphical user interface allows the user to adjust parameters and visualize the resulting spectrogram and signal segmentation, providing a simplified method for analyzing field recordings.nC_LIO_LIChipper streamlines the processing of audio recordings with multiple user-friendly tools and is optimized for multiple species and varying recording qualities. Ultimately, Chipper supports the use of citizen-science data and increases the feasibility of large-scale multi-species birdsong studies.nC_LI
Detecting exotic spheres in low dimensions using coker J

Building off of the work of Kervaire and Milnor, and Hill, Hopkins, and Ravenel, Xu and Wang showed that the only odd dimensions n for which S^n has a unique differentiable structure are 1, 3, 5, and 61. We show that the only even dimensions below 140 for which S^n has a unique differentiable structure are 2, 6, 12, 56, and perhaps 4.
On Transformations of A-Hypergeometric Functions

We propose a systematic study of transformations of $A$-hypergeometric functions. Our approach is to apply changes of variables corresponding to automorphisms of toric rings, to Euler-type integral representations of $A$-hypergeometric functions. We show that all linear $A$-hypergeometric transformations arise from symmetries of the corresponding polytope. As an application of the techniques developed here, we show that the Appell function $F_4$ does not admit a certain kind of Euler-type integral representation.
Mechanophenotyping of 3D multicellular clusters using displacement arrays of rendered tractions

Epithelial tissues mechanically deform the surrounding extracellular matrix during embryonic development, wound repair, and tumor invasion. Ex vivo measurements of such multicellular tractions within three-dimensional (3D) biomaterials could elucidate collective dissemination during disease progression and enable preclinical testing of targeted antimigration therapies. However, past 3D traction measurements have been low throughput due to the challenges of imaging and analyzing information-rich 3D material deformations. Here, we demonstrate a method to profile multicellular clusters in a 96-well-plate format based on spatially heterogeneous contractile, protrusive, and circumferential tractions. As a case study, we profile multicellular clusters across varying states of the epithelial–mesenchymal transition, revealing a successive loss of protrusive and circumferential tractions, as well as the formation of localized contractile tractions with elongated cluster morphologies. These cluster phenotypes were biochemically perturbed by using drugs, biasing toward traction signatures of different epithelial or mesenchymal states. This higher-throughput analysis is promising to systematically interrogate and perturb aberrant mechanobiology, which could be utilized with human-patient samples to guide personalized therapies.
Spectres of Seppo: the afterlives of Finland's nuclear waste experts

This article examines how Finland’s nuclear waste repository safety assessment experts summoned memories of Seppo: a deceased colleague whose ‘spectre’ was said to still ‘haunt’ their workplace. First, it tracks how Seppo appeared in predecessor parables: cautionary tales told about his death, which conveyed value judgements about how experts ought to act, engage, and aspire. Second, it explores how Seppo’s long-time ‘right-hand man’ Gustav still felt haunted by his colleague’s affective intensities, scientific vision, and sharp tongue. This led Gustav to prod his workmates to reconsider how they modelled Finland’s ecosystems many millennia into the future. Studying this ethnographically revealed how traces of Seppo’s past life infused living experts with emotions, opened them to alternative futures, and spurred them to rethink their professional values. This article concludes by introducing expert afterlives as a temporal, epistemic, and affective nexus that can shape how technocratic projects are organized and how expert knowledge is made.
Parallel and Distributed Algorithms for the Housing Allocation Problem.

We give parallel and distributed algorithms for the housing allocation problem. In this problem, there is a set of agents and a set of houses. Each agent has a strict preference list for a subset of houses. We need to find a matching such that some criterion is optimized. One such criterion is Pareto Optimality. A matching is Pareto optimal if no coalition of agents can be strictly better off by exchanging houses among themselves. We also study the housing market problem, a variant of the housing allocation problem, where each agent initially owns a house. In addition to Pareto optimality, we are also interested in finding the core of a housing market. A matching is in the core if there is no coalition of agents that can be better off by breaking away from other agents and switching houses only among themselves. In the first part of this work, we show that computing a Pareto optimal matching of a house allocation is in {\bf CC} and computing the core of a housing market is {\bf CC}-hard. Given a matching, we also show that verifying whether it is in the core can be done in {\bf NC}. We then give an algorithm to show that computing a maximum Pareto optimal matching for the housing allocation problem is in {\bf RNC}^2 and quasi-{\bf NC}^2. In the second part of this work, we present a distributed version of the top trading cycle algorithm for finding the core of a housing market. To that end, we first present two algorithms for finding all the disjoint cycles in a functional graph: a Las Vegas algorithm which terminates in $O(\log l)$ rounds with high probability, where $l$ is the length of the longest cycle, and a deterministic algorithm which terminates in $O(\log^* n \log l)$ rounds, where $n$ is the number of nodes in the graph. Both algorithms work in the synchronous distributed model and use messages of size $O(\log n)$.
Racial variation in the Clinical and Economic burden of Skeletal-related events among elderly Men with stage iv metastatic Prostate Cancer

Prostate cancer (PCa) outcomes vary widely among African American (AA) and non-Hispanic White (NHW) men. The authors investigated racial variation in the incidence of skeletal-related events (SREs) and SRE-related healthcare costs among AA and NHW men, a topic that has received limited attention in the literature. AA and NHW men diagnosed with metastatic PCa were identified from the linked Surveillance, Epidemiology and End Results–Medicare dataset. The sample included 6455 men with metastatic PCa, including 5420 NHW men and 1035 AA men. Approximately 16% experienced SREs during follow-up. AA men were less likely to experience SREs compared with NHW men, controlling for individual characteristics (adjusted odds ratio: 0.79; 95% CI: 0.66– 0.94). The SRE-specific costs were US$35,725 (US$22,190–US$49,260) among AA men and US$25,896 (US$21,669–US$30,123) among NHW men. Although AA men were less likely to experience SREs, there were substantial costs attributable to the treatment of SREs among AA men.
A prime-characteristic analogue of a theorem of Hartshorne-Polini

Abstract Let R be an F-finite Noetherian regular ring containing an algebraically closed field k of positive characteristic, and let M be an F -finite F -module over R in the sense of Lyubeznik (for example, any local cohomology module of R). We prove that the F p -dimension of the space of F -module morphisms M → E ( R / m ) (where m is any maximal ideal of R and E ( R / m ) is the R-injective hull of R / m ) is equal to the k-dimension of the Frobenius stable part of Hom R ( M , E ( R / m ) ) . This is a positive-characteristic analogue of a recent result of Hartshorne and Polini for holonomic D -modules in characteristic zero.
The scope of sequential screening with ex post participation constraints

Abstract We study the classic sequential screening problem in the presence of ex post participation constraints. We establish necessary and sufficient conditions that determine when the optimal selling mechanism is either static or sequential. In the static contract, the buyers are not screened with respect to their interim type and the object is sold at a posted price. In the sequential contract, the buyers are screened with respect to their interim type and a menu of quantities is offered. We completely characterize the optimal sequential contract with binary interim types and a continuum of ex post values. Importantly, the optimal sequential contract randomizes the allocation of the low-type buyer and awards a deterministic allocation to the high type buyer. Finally, we provide additional results for the case of multiple interim types.
Computational discovery of promising new n-type dopable ABX Zintl thermoelectric materials

Computational prediction of good thermoelectric (TE) performance in several n-type doped Zintl phases, combined with successful experimental realization, has sparked interest in discovering new n-type dopable members of this family of materials. However, most known Zintls are typically only p-type dopable; prior successes in finding n-type Zintl phases have been largely serendipitous. Here, we go beyond previously synthesized Zintl phases and perform chemical substitutions in known n-type dopable ABX Zintl phases to discover new ones. We use first-principles calculations to predict their stability, potential for TE performance as well as their n-type dopability. Using this approach, we find 17 new ABX Zintl phases in the KSnSb structure type that are predicted to be stable. Several of these newly predicted phases (KSnBi, RbSnBi, NaGeP) are found to exhibit promising n-type TE performance and are n-type dopable. We propose these compounds for further experimental studies, especially KSnBi and RbSnBi, which are both predicted to be good TE materials with high electron concentrations due to self-doping by native defects, when grown under alkali-rich conditions.
Discovering San Antón de Carlos: The Sixteenth-Century Spanish Buildings and Fortifications of Mound Key, Capital of the Calusa

In 1566, Pedro Menendez de Aviles arrived at the capital of the Calusa kingdom. During that same year Menendez issued the order to construct Fort San Anton de Carlos, which was occupied until 1569. This fort was also the location of one of the first Jesuit missions (1567) in what is now the United States. We now can confirm what archaeologists and historians have long suspected: the location of the fort and the capital of the Calusa was Mound Key (8LL2), located in Estero Bay in southwestern Florida. In this article, we present the first archaeological evidence of structures and fortifications associated with the 16th-century Spanish fort and mission of San Anton de Carlos. We conducted this work, which includes both remote sensing and excavation, in an effort to better document the history of the Calusa capital up to and including the colonial period.
Rapid Indirect Trajectory Optimization on Highly Parallel Computing Architectures

A graphics-processing-units-accelerated indirect trajectory optimization methodology that uses the multiple shooting method and continuation is developed using the CUDA platform. The algorithm is d...