cssi/SciSciGPT-SciSciNet huggingface dataset:
Boys will be Boys: Gender, Overconfidence, and Common Stock Investment

Theoretical models predict that overconedent investors trade excessively. We test this prediction by partitioning investors on gender. Psychological research demonstrates that, in areas such as enance, men are more overconedent than women. Thus, theory predicts that men will trade more excessively than women. Using account data for over 35,000 households from a large discount brokerage, we analyze the common stock investments of men and women from February 1991 through January 1997. We document that men trade 45 percent more than women. Trading reduces men’s net returns by 2.65 percentage points a year as opposed to 1.72 percentage points for women. It’s not what a man don’t know that makes him a fool, but what he does know that ain’t so. Josh Billings, nineteenth century American humorist It is difecult to reconcile the volume of trading observed in equity markets with the trading needs of rational investors. Rational investors make periodic contributions and withdrawals from their investment portfolios, rebalance their portfolios, and trade to minimize their taxes. Those possessed of superior information may trade speculatively, although rational speculative traders will generally not choose to trade with each other. It is unlikely that rational trading needs account for a turnover rate of
Regulation, Taxation, and the Development of the German Universal Banking System, 1884-1913

Previous researchers argue that the legal and regulatory environment helped shape the German financial system in the nineteenth and early-twentieth centuries, with particular emphasis on the damaging effects of the stock-exchange law of 1896. This paper finds that the stock exchange law of 1896 exerted little measurable impact on the growth and concentration of the universal banking system or on the business turnover of universal banks relative to securities markets. The paper also shows that the English commercial banking sector and the German universal banking sector underwent similar movements toward concentration between 1884 and 1920 (both accelerating after 1912), despite no corresponding regulatory changes in England–further suggesting that consolidation of universal banking resulted from factors other than the 1896 law.
Through-the-Wall Nonlinear SAR Imaging

An inverse scattering problem (ISP) for synthetic aperture radar (SAR) data in application to through-the-wall imaging is addressed. In contrast with the conventional algorithms of SAR imaging, which work with the linearized mathematical model based on the Born approximation, the fully nonlinear case is considered here. To avoid the local minima problem, the so-called “convexification” globally convergent inversion scheme is applied to approximate the distribution in the slant range (SR) plane of a function that is associated with the dielectric constant. It is demonstrated that this function estimates quite well both the value of the dielectric constant and the location of the target. The benchmark scene of this article comprises a homogeneous dielectric wall and different dielectric targets hidden behind it. The results comprise 2-D images of the SR dielectric constant of the scene of interest. Numerical results are obtained by the proposed inversion method for both the computationally simulated and experimental data. Our results show that the values, cross-range sizes, and locations of SR dielectric constants for targets hidden behind the wall are close to those of real targets. Numerical comparison with the solution of the linearized ISP provided by the Born approximation, commonly used in conventional SAR imaging, shows a significantly better accuracy of our results.
Malware Classification with GMM-HMM Models.

Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture model-HMMs (GMM-HMM), are rarely considered in the field of cybersecurity. In this paper, we use GMM-HMMs for malware classification and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropy-based sequences. For our opcode features, GMM-HMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropy-based features, GMM-HMMs generally improve significantly on the classification results that we have achieved with discrete HMMs.
Perceptions of Robotic Process Automation in Big 4 Public Accounting Firms: Do Firm Leaders and Lower-Level Employees Agree?

The use of Robotic Process Automation (RPA) is a recent innovation in the public accounting industry, and the Big 4 firms are at the forefront of its implementation. This paper examines how the adoption and use of RPA is affecting the perceived work experience of firm leaders and lower-level employees at Big 4 accounting firms. We interview 14 RPA leaders, survey 139 lower-level employees, and compare and contrast their responses. We find that the two groups generally agree that RPA is having a positive influence on the profession. Both groups believe that RPA is positively changing the work employees perform and improving employee career prospects. However, while firm leaders believe RPA will improve work satisfaction, lower-level employees report no such improvements. Our insights provide direction for the accounting profession as it increases the use of RPA and for future research studies examining related issues.
Property rights, expropriations, and business cycles in China

Abstract Real business cycles in China are different than in many other countries, including consumption being more volatile than output and uncorrelated with investment. To study whether Chinese institutions can account for these features, we augment the standard real business cycle model by private and state-owned enterprises facing time-to-build constraints, expropriations, and government expenditures. We introduce shocks to each of these activities and estimate our model with Bayesian techniques. The model matches the salient data moments quite closely, with expropriations playing a central role. In particular, shocks to expropriations account for over 70% of consumption and output volatility, and over 60% of private investment volatility. To assess whether our estimated expropriations are empirically plausible, we show that: (i) the model-generated expropriation series is positively correlated with a commonly used measure of property rights; (ii) the explanatory power of expropriations drops considerably after 2012, coinciding with the government’s anti-corruption campaign; and (iii) a placebo test estimating the model for the U.S. finds expropriations to be about one eighth of those in China, and to account for only a small share of the U.S. aggregate fluctuations.
Critical loci of convex domains in the plane

Abstract Let K be a bounded convex domain in R 2 symmetric about the origin. The critical locus of K is defined to be the (non-empty compact) set of lattices Λ in R 2 of smallest possible covolume such that Λ ∩ K = { 0 } . These are classical objects in geometry of numbers; yet all previously known examples of critical loci were either finite sets or finite unions of closed curves. In this paper we give a new construction which, in particular, furnishes examples of domains having critical locus of arbitrary Hausdorff dimension between 0 and 1.
Families of periodic orbits: Closed 1-forms and global continuability

Abstract We investigate global continuation of periodic orbits of a differential equation depending on a parameter, assuming that a closed 1-form satisfying certain properties exists. We begin by extending the global continuation theory of Alexander, Alligood, Mallet-Paret, Yorke, and others to this situation, formulating a new notion of global continuability and a new global continuation theorem tailored for this situation. In particular, we show that the existence of such a 1-form ensures that local continuability of periodic orbits implies global continuability. Using our general theory, we then develop continuation-based techniques for proving the existence of periodic orbits. In contrast to previous work, a key feature of our results is that existence of periodic orbits can be proven (i) without finding trapping regions for the dynamics and (ii) without establishing a priori upper bounds on the periods of orbits. We illustrate the theory in examples inspired by the synthetic biology literature.
Bayesian nonparametric multiway regression for clustered binomial data

We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data. Our outcome is the number of diseased sites measured over four different tooth types for each subject, with subject-specific covariates available as predictors. The outcomes are not well-characterized by simple parametric models, so we use a nonparametric approach with a binomial likelihood wherein the latent probabilities are drawn from a mixture with an arbitrary number of components, analogous to a Dirichlet Process (DP). We use a flexible probit stick-breaking formulation for the component weights that allows for covariate dependence and clustering structure in the outcomes. The parameter space for this model is large and multiway: patients $\times$ tooth types $\times$ covariates $\times$ components. We reduce its effective dimensionality, and account for the multiway structure, via low-rank assumptions. We illustrate how this can improve performance, and simplify interpretation, while still providing sufficient flexibility. We describe a general and efficient Gibbs sampling algorithm for posterior computation. The resulting fit to the PD data outperforms competitors, and is interpretable and well-calibrated. An interactive visual of the predictive model is available at this http URL , and the code is available at this https URL .
Enabling counterfactual survival analysis with balanced representations

Balanced representation learning methods have been applied successfully to counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (e.g., for equipment monitoring). When the outcome of interest is a time-to-event, special precautions for handling censored events need to be taken, as ignoring censored outcomes may lead to biased estimates. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes. Further, we formulate a nonparametric hazard ratio metric for evaluating average and individualized treatment effects. Experimental results on real-world and semi-synthetic datasets, the latter of which we introduce, demonstrate that the proposed approach significantly outperforms competitive alternatives in both survival-outcome prediction and treatment-effect estimation.
Generating Random Factored Numbers, Easily

Our goal is to generate a uniformly random number between 1 and N, along with its prime factorization. Since there are no known efficient factoring algorithms, one cannot simply generate a random number and then factor it. Eric Bach’s award-winning dissertation introduces and solves this problem. We present a four-line solution that is still efficient.
Premise-based vs conclusion-based collective choice

Imagine a group of individuals faces a yes-no type question whose answer is logically determined by multiple premises. There are two salient types of procedures to aggregate individual judgments—the “premise-based way” (PBW) and the “conclusion-based way” (CBW). We derive necessary and sufficient conditions under which two procedures are universally ordered. If (and only if) a decision problem takes a “conjunctive” form, PBW derives a positive collective judgment (i.e., “yes”) whenever CBW does. Furthermore, if we replace “conjunctive” with “disjunctive” in the previous line, PBW derives a negative collective judgment (i.e., “no”) whenever PBW does. These observations highlight the fact that these two procedures are a mathematical dual of each another. Asymptotic properties are also studied. Under classical Condorcetian assumptions, PBW ensures the probability that the voting outcome is correct converges to one as the size of a group tends to infinity, whereas this holds for CBW only if an additional condition is satisfied.
Net benefit separation and the determination curve: A probabilistic framework for cost-effectiveness estimation.

Considerations regarding clinical effectiveness and cost are essential in comparing the overall value of two treatments. There has been growing interest in methodology to integrate cost and effecti...
Built to Last: Functional and Structural Mechanisms in the Moth Olfactory Network Mitigate Effects of Neural Injury.

Most organisms suffer neuronal damage throughout their lives, which can impair performance of core behaviors. Their neural circuits need to maintain function despite injury, which in particular requires preserving key system outputs. In this work, we explore whether and how certain structural and functional neuronal network motifs act as injury mitigation mechanisms. Specifically, we examine how (i) Hebbian learning, (ii) high levels of noise, and (iii) parallel inhibitory and excitatory connections contribute to the robustness of the olfactory system in the Manduca sexta moth. We simulate injuries on a detailed computational model of the moth olfactory network calibrated to data. The injuries are modeled on focal axonal swellings, a ubiquitous form of axonal pathology observed in traumatic brain injuries and other brain disorders. Axonal swellings effectively compromise spike train propagation along the axon, reducing the effective neural firing rate delivered to downstream neurons. All three of the network motifs examined significantly mitigate the effects of injury on readout neurons, either by reducing injury’s impact on readout neuron responses or by restoring these responses to pre-injury levels. These motifs may thus be partially explained by their value as adaptive mechanisms to minimize the functional effects of neural injury. More generally, robustness to injury is a vital design principle to consider when analyzing neural systems.
Predation release of Texas horned lizards ( Phrynosoma cornutum ) living in small towns.

Texas horned lizards (Phrynosoma cornutum) have a number of ways to avoid predation, including camouflage, sharp cranial horns, flattening of the body, and the ability to squirt blood from the eyes. These characteristics and their relatively low survival rates in the wild suggest these lizards are under high predation pressure. These lizards have been declining in much of their eastern range due to increased urbanization, agriculture, and loss of prey species. However, they can be still be found in some small south Texas towns where they can reach densities that are much higher (~50 lizards/ha) than in natural areas (~4-10 lizards/ha). We hypothesized that one reason for the high densities observed in these towns may be due to reduced predation pressure. We used model Texas horned lizards to test whether predation levels were lower in two south Texas towns than on a nearby ranch. We constructed models from urethane foam, a material that is ideal for preserving marks left behind by predators. Models (n = 126) and control pieces of foam (n = 21) were left in the field for 9 days in each location in early and late summer and subsequent predation marks were categorized by predator taxa. We observed significantly more predation attempts on the models than on controls and significantly fewer attempts in town (n = 1) compared with the ranch (n = 60). On the ranch, avian predation attempts appear to be common especially when the models did not match the color of the soil. Our results suggest that human-modified environments that have suitable habitat and food resources may provide a refuge for some prey species like horned lizards from predators.
Listen, Look and Deliberate: Visual Context-Aware Speech Recognition Using Pre-Trained Text-Video Representations

In this study, we try to address the problem of leveraging visual signals to improve Automatic Speech Recognition (ASR), also known as visual context-aware ASR (VC-ASR). We explore novel VC-ASR approaches to leverage video and text representations extracted by a self-supervised pre-trained text-video embedding model. Firstly, we propose a multi-stream attention architecture to leverage signals from both audio and video modalities. This architecture consists of separate encoders for the two modalities and a single decoder that at-tends over them. We show that this architecture is better than fusing modalities at the signal level. Additionally, we also explore lever-aging the visual information in a second pass model, which has also been referred to as a ‘deliberation model’. The deliberation model accepts audio representations and text hypotheses from the first pass ASR and combines them with a visual stream for an improved visual context-aware recognition. The proposed deliberation scheme can work on top of any well trained ASR and also enabled us to leverage the pre-trained text model to ground the hypotheses with the visual features. Our experiments on HOW2 dataset show that multi-stream and deliberation architectures are very effective at the VC-ASR task. We evaluate the proposed models for two scenarios; clean audio stream and distorted audio in which we mask out some specific words in the audio. The deliberation model outperforms the multi-stream model and achieves a relative WER improvement of 6% and 8.7% for the clean and masked data, respectively, compared to an audio-only model. The deliberation model also improves re-covering the masked words by 59% relative.
The virtual element method for resistive magnetohydrodynamics

Abstract We present a virtual element method (VEM) for the numerical approximation of the electromagnetics subsystem of the resistive magnetohydrodynamics (MHD) model in two spatial dimensions. The major advantages of the virtual element method include great flexibility of polygonal meshes and automatic divergence-free constraint on the magnetic flux field. In this work, we rigorously prove the well-posedness of the method and the solenoidal nature of the discrete magnetic flux field. We also derive stability energy estimates. The design of the method includes three choices for the construction of the nodal mass matrix and criteria to more alternatives. This approach is novel in the VEM literature and allows us to preserve a commuting diagram property. We present a set of numerical experiments that independently validate theoretical results. The numerical experiments include the convergence rate study, energy estimates and verification of the divergence-free condition on the magnetic flux field. All these numerical experiments have been performed on triangular, perturbed quadrilateral and Voronoi meshes. Finally, we demonstrate the development of the VEM method on a numerical model for Hartmann flows as well as in the case of magnetic reconnection.
Multiscale Thermodynamics: Energy, Entropy, and Symmetry from Atoms to Bulk Behavior

Here, we investigate how the local properties of particles in a thermal bath may influence the thermodynamics of the bath, and consequently alter the statistical mechanics of subsystems that comprise the bath. We are guided by the theory of small-system thermodynamics, which is based on two primary postulates: that small systems can be treated self-consistently by coupling them to an ensemble of similarly small systems, and that a large ensemble of small systems forms its own thermodynamic bath. We adapt this “nanothermodynamics” to investigate how a large system may subdivide into an ensemble of smaller subsystems, causing internal heterogeneity across multiple size scales. For the semi-classical ideal gas, maximum entropy favors subdividing a large system of “atoms” into an ensemble of “regions” of variable size. The mechanism of region formation could come from quantum exchange symmetry that makes atoms in each region indistinguishable, while decoherence between regions allows atoms in separate regions to be distinguishable by their distinct locations. Combining regions reduces the total entropy, as expected when distinguishable particles become indistinguishable, and as required by a theorem in quantum mechanics for sub-additive entropy. Combining large volumes of small regions gives the usual entropy of mixing for a semi-classical ideal gas, resolving Gibbs paradox without invoking quantum symmetry for particles that may be meters apart. Other models presented here are based on Ising-like spins, which are solved analytically in one dimension. Focusing on the bonds between the spins, we find similarity in the equilibrium properties of a two-state model in the nanocanonical ensemble and a three-state model in the canonical ensemble. Thus, emergent phenomena may alter the thermal behavior of microscopic models, and the correct ensemble is necessary for fully-accurate predictions. Another result using Ising-like spins involves simulations that include a nonlinear correction to Boltzmann’s factor, which mimics the statistics of indistinguishable states by imitating the dynamics of spin exchange on intermediate lengths. These simulations exhibit 1/f-like noise at low frequencies (f), and white noise at higher f, similar to the equilibrium thermal fluctuations found in many materials.
The purine nucleoside phosphorylase pnp-1 regulates epithelial cell resistance to infection in C. elegans

Intestinal epithelial cells are subject to attack by a diverse array of microbes, including intracellular as well as extracellular pathogens. While defense in epithelial cells can be triggered by pattern recognition receptor-mediated detection of microbe-associated molecular patterns, there is much to be learned about how they sense infection via perturbations of host physiology, which often occur during infection. A recently described host defense response in the nematode C. elegans called the Intracellular Pathogen Response (IPR) can be triggered by infection with diverse natural intracellular pathogens, as well as by perturbations to protein homeostasis. From a forward genetic screen, we identified the C. elegans ortholog of purine nucleoside phosphorylase pnp-1 as a negative regulator of IPR gene expression, as well as a negative regulator of genes induced by extracellular pathogens. Accordingly, pnp-1 mutants have resistance to both intracellular and extracellular pathogens. Metabolomics analysis indicates that C. elegans pnp-1 likely has enzymatic activity similar to its human ortholog, serving to convert purine nucleosides into free bases. Classic genetic studies have shown how mutations in human purine nucleoside phosphorylase cause immunodeficiency due to T-cell dysfunction. Here we show that C. elegans pnp-1 acts in intestinal epithelial cells to regulate defense. Altogether, these results indicate that perturbations in purine metabolism are likely monitored as a cue to promote defense against epithelial infection in the nematode C. elegans.
Exploring order parameters and dynamic processes in disordered systems via variational autoencoders.

We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on atomic positions are available. This approach is predicated on the synergy of two concepts, the parsimony of physical descriptors and general rotational invariance of noncrystalline solids, and is implemented using a rotationally invariant extension of the variational autoencoder applied to semantically segmented atom-resolved data seeking the most effective reduced representation for the system that still contains the maximum amount of original information. This approach allowed us to explore the dynamic evolution of electron beam–induced processes in a silicon-doped graphene system, but it can be also applied for a much broader range of atomic scale and mesoscopic phenomena to introduce the bottom-up order parameters and explore their dynamics with time and in response to external stimuli.
Fine structure of the band 3 protein in human red cell membranes: Freeze-fracture studies

The major red cell membrane protein, band 3, is a glycoprotein which extends across the membrane from the extracellular space into the cytoplasmic compartment. It is widely held that band 3 is a component of the intramembrane particles (IMP) which can be demonstrated by freeze-fracture electron microscopy. In this study, we find that the outer surface poles of the IMP can be seen by freeze-etching after they are unmasked by proteolysis under conditions which excise the surrounding sialopeptides from the membrane. The poles appear as distinctive projections, 30--50 A in diameter, the "ES particles." The ES particles remain associated with the outer surface of the membrane following cleavage of the band 3 polypeptide by chymotrypsin or pronase. This is consistent with previous biochemical studies which have shown that the 38,000-dalton outer surface segment of band 3 is intercalated in the lipid bilayer. A granulofibrillar component at the inner surface of the membrane is provisionally identified as the 40,000-dalton inner-surface domain of band 3.
Measuring Commuting and Economic Activity inside Cities with Cell Phone Records

We show how to use commuting flows to infer the spatial distribution of income within a city. A simple workplace choice model predicts a gravity equation for commuting flows whose destination fixed effects correspond to wages. We implement this method with cell phone transaction data from Dhaka and Colombo. Model-predicted income predicts separate income data, at the workplace and residential level, and by skill group. Unlike machine learning approaches, our method does not require training data, yet achieves comparable predictive power. We show that hartals (transportation strikes) in Dhaka reduce commuting more for high model-predicted wage and high-skill commuters.
Multi-Armed Bandits With Correlated Arms

We consider a multi-armed bandit framework where the rewards obtained by pulling different arms are correlated. We develop a unified approach to leverage these reward correlations and present fundamental generalizations of classic bandit algorithms to the correlated setting. We present a unified proof technique to analyze the proposed algorithms. Rigorous analysis of C-UCB (the correlated bandit versions of Upper-confidence-bound) reveals that the algorithm end up pulling certain sub-optimal arms, termed as non-competitive , only $\mathrm {O}(1)$ times, as opposed to the $\mathrm {O}(\log T)$ pulls required by classic bandit algorithms such as UCB, TS etc. We present regret-lower bound and show that when arms are correlated through a latent random source, our algorithms obtain order-optimal regret. We validate the proposed algorithms via experiments on the MovieLens and Goodreads datasets, and show significant improvement over classical bandit algorithms.
Seam: Provably Safe Local Edits on Graphs

Algorithms that create and mutate graph data structures are challenging to implement correctly. However, verifying even basic properties of low-level implementations, such as referential integrity and memory safety, remains non-trivial. Furthermore, any extension to such a data structure multiplies the complexity of its implementation, while compounding the challenges in reasoning about correctness. We take a language design approach to this problem. We propose Seam, a language for expressing local edits to graph-like data structures, based on a relational data model, and such that data integrity can be verified automatically. We present a verification method that leverages an SMT solver, and prove it sound and precise (complete modulo termination of the SMT solver). We evaluate the verification capabilities of Seam empirically, and demonstrate its applicability to a variety of examples, most notably a new class of verification tasks derived from geometric remeshing operations used in scientific simulation and computer graphics. We describe our prototype implementation of a Seam compiler that generates low-level code, which can then be integrated into larger applications. We evaluate our compiler on a sample application, and demonstrate competitive execution time, compared to hand-written implementations.
On a class of elliptic free boundary problems with multiple solutions

We prove that a certain class of elliptic free boundary problems, which includes the Prandtl–Batchelor problem from fluid dynamics as a special case, has two distinct nontrivial solutions for large values of a parameter. The first solution is a global minimizer of the energy. The energy functional is nondifferentiable, so standard variational arguments cannot be used directly to obtain a second nontrivial solution. We obtain our second solution as the limit of mountain pass points of a sequence of $$C^1$$ -functionals approximating the energy. We use careful estimates of the corresponding energy levels to show that this limit is neither trivial nor a minimizer.
Magnesium treatment on methylation changes of transmembrane serine protease 2 (TMPRSS2).

Abstract Objectives The viral entry of SARS-CoV-2 requires host-expressed TMPRSS2 to facilitate the viral spike protein priming. This study aims to test the hypothesis that magnesium (Mg) treatment leads to DNA methylation changes in TMPRSS2. Methods This study is nested within the Personalized Prevention of Colorectal Cancer Trial, a double-blind 2 × 2 factorial randomized controlled trial, which enrolled 250 participants from Vanderbilt University Medical Center. Results We found that 12 wk of personalized Mg treatment significantly increased 5-methylcytosine methylation at cg16371860 (TSS1500, promoter) by 7.2% compared to the placebo arm (decreased by 0.1%) in those ages Conclusions Among individuals ages
TMEM106B modifies TDP-43 pathology in human ALS brain and cell-based models of TDP-43 proteinopathy

The neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration with TAR DNA-binding protein-43 (TDP-43) inclusions (FTLD-TDP) share the neuropathological hallmark of aggregates of TDP-43. However, factors governing the severity and regional distribution of TDP-43 pathology, which may account for the divergent clinical presentations of ALS and FTLD-TDP, are not well understood. Here, we investigated the influence of genotypes at TMEM106B, a locus associated with risk for FTLD-TDP, and hexanucleotide repeat expansions in C9orf72, a known genetic cause for both ALS and FTLD-TDP, on global TDP-43 pathology and regional distribution of TDP-43 pathology in 899 postmortem cases from a spectrum of neurodegenerative diseases. We found that, among the 110 ALS cases, minor (C)-allele homozygotes at the TMEM106B locus sentinel SNP rs1990622 had more TDP-43 pathology globally, as well as in select brain regions. C9orf72 expansions similarly associated with greater TDP-43 pathology in ALS. However, adjusting for C9orf72 expansion status did not affect the relationship between TMEM106B genotype and TDP-43 pathology. To elucidate the direction of causality for this association, we directly manipulated TMEM106B levels in an inducible cell system that expresses mislocalized TDP-43 protein. We found that partial knockdown of TMEM106B, to levels similar to what would be expected in rs1990622 C allele carriers, led to development of more TDP-43 cytoplasmic aggregates, which were more insoluble, in this system. Taken together, our results support a causal role for TMEM106B in modifying the development of TDP-43 proteinopathy.
scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks

Understanding cell-type-specific gene regulatory mechanisms from genetic variants to diseases remains challenging. To address this, we developed a computational pipeline, scGRNom (single-cell Gene Regulatory Network prediction from multi-omics), to predict cell-type disease genes and regulatory networks including transcription factors and regulatory elements. With applications to schizophrenia and Alzheimer's disease, we predicted disease genes and regulatory networks for excitatory and inhibitory neurons, microglia, and oligodendrocytes. Further enrichment analyses revealed cross-disease and disease-specific functions and pathways at the cell-type level. Our machine learning analysis also found that cell-type disease genes improved clinical phenotype predictions. scGRNom is a general-purpose tool available at https://github.com/daifengwanglab/scGRNom .
A Short Note on the Scaling Function Constant Problem in the Two-Dimensional Ising Model

We provide a simple derivation of the constant factor in the short-distance asymptotics of the tau-function associated with the 2-point function of the two-dimensional Ising model. This factor was first computed by Tracy (Commun Math Phys 142:297–311, 1991) via an exponential series expansion of the correlation function. Further simplifications in the analysis are due to Tracy and Widom (Commun Math Phys 190:697–721, 1998) using Fredholm determinant representations of the correlation function and Wiener–Hopf approximation results for the underlying resolvent operator. Our method relies on an action integral representation of the tau-function and asymptotic results for the underlying Painleve-III transcendent from McCoy et al. (J Math Phys 18:1058–1092, 1977).
Precis of The Exchange of Words

Much of the discussion in The Exchange of Words is oriented around two broad distinctions. From Thomas Reid I take the idea of “testifying a fact” as an example of what he calls a “social act of mi...
A simple microstructural explanation of the concavity of price impact

This article provides a simple explanation of the asymptotic concavity of the price impact of a meta-order via the microstructural properties of the market. This explanation is made more precise by a model in which the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the proposed explanation is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions, one of which can be tested, at least for large-tick stocks, using publicly available market data.
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding. As a result, these models fail to generalize to real-world out-of-distribution data. In this work, we show that the words in the NLU training set can be modeled as a long-tailed distribution. There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training. These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample. Based on this shortcut measurement, we propose a shortcut mitigation framework LGTR, to suppress the model from making overconfident predictions for samples with large shortcut degree. Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models. Experimental analysis further indicates that LGTR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.
Class groups and local indecomposability for non-CM forms

Author(s): Castella, Francesc; Wang-Erickson, Carl; Hida, Haruzo | Abstract: In the late 1990's, R. Coleman and R. Greenberg (independently) asked for a global property characterizing those $p$-ordinary cuspidal eigenforms whose associated Galois representation becomes decomposable upon restriction to a decomposition group at $p$. It is expected that such $p$-ordinary eigenforms are precisely those with complex multiplication. In this paper, we study Coleman-Greenberg's question using Galois deformation theory. In particular, for $p$-ordinary eigenforms which are congruent to one with complex multiplication, we prove that the conjectured answer follows from the $p$-indivisibility of a certain class group.
Efficient Stepping Algorithms and Implementations for Parallel Shortest Paths

The single-source shortest-path (SSSP) problem is a notoriously hard problem in the parallel context. In practice, the Δ-stepping algorithm of Meyer and Sanders has been widely adopted. However, Δ-stepping has no known worst-case bounds for general graphs, and the performance highly relies on the parameter Δ, which requires exhaustive tuning. The parallel SSSP algorithms with provable bounds, such as Radius-stepping, either have no implementations available or are much slower than Δ-stepping in practice. We propose the stepping algorithm framework that generalizes existing algorithms such as Δ-stepping and Radius-stepping. The framework allows for similar analysis and implementations for all stepping algorithms. We also propose a new abstract data type, lazy-batched priority queue (LaB-PQ ) that abstracts the semantics of the priority queue needed by the stepping algorithms. We provide two data structures for LaB-PQ, focusing on theoretical and practical efficiency, respectively. Based on the new framework and LaB-PQ, we show two new stepping algorithms, ρ-stepping and Δ^*-stepping, that are simple, with non-trivial worst-case bounds, and fast in practice. We also show improved bounds for a list of existing algorithms such as Radius-Stepping. Based on our framework, we implement three algorithms: Bellman-Ford, Δ^*-stepping, and ρ-stepping. We compare the performance with four state-of-the-art implementations. On five social and web graphs, ρ-stepping is 1.3--2.6x faster than all the existing implementations. On two road graphs, our Δ^*-stepping is at least 14% faster than existing ones, while ρ-stepping is also competitive. The almost identical implementations for stepping algorithms also allow for in-depth analyses among the stepping algorithms in practice.
The regional evapotranspiration of the Amazon

Abstract The annual cycle of evapotranspiration (ET) is an important component of the Amazon hydrological balance, which is of critical importance to the global water cycle. Understanding the changing water balance in this region is particularly important to estimate future global and regional hydroclimate change in response to projected deforestation of the rain forest in this region. Several methods have been used to estimate the annual ET cycle in the Amazon basin. These different methods, which result in a spread of annual means, ranges, and phases of the ET cycle, are evaluated here. In an attempt to reconcile the differences between them, both the data and the assumptions upon which the methods are based are scrutinized. The differences seem to originate from the geographic site where radiation and ET are simulated and/or observed and, more significantly, from the way that vegetation controls ET in the different models being used. While field campaigns conducted during the Large-Scale Biosphere Atmo...
The cost of CO2 transport and storage in global integrated assessment modeling

Abstract This paper assesses the range of CO2 transport and storage costs and evaluates their impact on economy-wide modeling results of decarbonization pathways. Much analytic work has been dedicated to evaluating the cost and performance of various CO2 capture technologies, but less attention has been paid to evaluating the cost of CO2 transport and storage. Many integrated assessment modeling studies assume a combined cost for CO2 transport and storage that is uniform in all regions, commonly estimated at $10/tCO2. Realistically, the cost of CO2 transport and storage is not fixed at $10/tCO2 and varies across geographic, geologic, and institutional settings. We surveyed the literature to identify key sources of variability in transport and storage costs and developed a method to quantify and incorporate these elements into a cost range. We find that onshore pipeline transport and storage costs vary from $4 to 45/tCO2 depending on key sources of variability including transport distance, scale (i.e. quantity of CO2 transported and stored), monitoring assumptions, reservoir geology, and transport cost variability such as pipeline capital costs. Using the MIT Economic Projection and Policy Analysis (EPPA) model, we examined the impact of variability in transport and storage costs by applying a range of uniform costs in all regions in a future where global temperature rise is limited to 2°C. We then developed three modeling cases where transport and storage costs vary regionally. In these latter cases, global cumulative CO2 captured and stored through 2100 ranges from 290 to 377 Gt CO2, compared to 425 Gt CO2 when costs are assumed to be uniformly $10/t CO2 in all regions. We conclude that the widely used assumption of $10/tCO2 for the transport and storage of CO2 is reasonable in some regions, but not in others. Moreover, CCS deployment is more sensitive to transport and storage costs in some regions than others, particularly China. More analysis is needed to further quantify CO2 transport and storage costs at a regional level.
Machine Learning-Based Anxiety Detection in Older Adults Using Wristband Sensors and Context Feature

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as electrodermal activity (EDA) and photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work are obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60–80 with mean age $$73.36 \pm 5.25$$ during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State–Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and blood volume pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor, respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy, respectively, than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.
Cryogenic Angle Measurement at NASA Langley

NASA’s National Transonic Facility is one of the few cryogenic wind-tunnel facilities in the world, where tunnel conditions may reach -250 F. Model angle of attack (AoA) at NTF is typically measured by an on-board accelerometer package that is exposed to the cryogenic tunnel conditions. These NTF AoA packages house two Q-Flex accelerometers in a compact heated environment in order to maintain constant temperature of the sensors for stability. Recently, drift in sensor output was observed in one of the NTF AoA packages. The drift is correlated with the AoA package temperature and cryogenic soak time. This paper studies the temperature and time dependent behavior of the NTF AoA packages, and reviews their design and calibration. Recommended improvements to the AoA packages are made to improve the stability of the model angle of attack measurements at cryogenic conditions.
Polarization in Geometric Opinion Dynamics

In light of increasing recent attention to political polarization, understanding how polarization can arise poses an important theoretical question. While more classical models of opinion dynamics seem poorly equipped to study this phenomenon, a recent novel approach by H\ka zl a, Jin, Mossel, and Ramnarayan (HJMR) proposes a simple geometric model of opinion evolution that provably exhibits strong polarization in specialized cases. Moreover, polarization arises quite organically in their model: in each time step, each agent updates opinions according to their correlation/response with an issue drawn at random. However, their techniques do not seem to extend beyond a set of special cases they identify, which benefit from fragile symmetry or contractiveness assumptions, leaving open how general this phenomenon really is. In this paper, we further the study of polarization in related geometric models. We show that the exact form of polarization in such models is quite nuanced: even when strong polarization does not hold, it is possible for weaker notions of polarization to nonetheless attain. We provide a concrete example where weak polarization holds, but strong polarization provably fails. However, we show that strong polarization provably holds in many variants of the HJMR model, which are also robust to a wider array of distributions of random issues---this suggests that the form of polarization introduced by HJMR is more universal than suggested by their special cases. We also show that the weaker notions connect more readily to the theory of Markov chains on general state spaces.