cssi/SciSciGPT-SciSciNet huggingface dataset:
Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer.

We consider the problem of automatically generating textual paraphrases with modified attributes or properties, focusing on the setting without parallel data (Hu et al., 2017; Shen et al., 2017). This setting poses challenges for evaluation. We show that the metric of post-transfer classification accuracy is insufficient on its own, and propose additional metrics based on semantic preservation and fluency as well as a way to combine them into a single overall score. We contribute new loss functions and training strategies to address the different metrics. Semantic preservation is addressed by adding a cyclic consistency loss and a loss based on paraphrase pairs, while fluency is improved by integrating losses based on style-specific language models. We experiment with a Yelp sentiment dataset and a new literature dataset that we propose, using multiple models that extend prior work (Shen et al., 2017). We demonstrate that our metrics correlate well with human judgments, at both the sentence-level and system-level. Automatic and manual evaluation also show large improvements over the baseline method of Shen et al. (2017). We hope that our proposed metrics can speed up system development for new textual transfer tasks while also encouraging the community to address our three complementary aspects of transfer quality.
Haah codes on general three-manifolds

Abstract Haah codes represent a singularly interesting gapped Hamiltonian schema that has resisted a natural generalization, although recent work shows that the closely related type I fracton models are more commonplace. These type I siblings of Haah codes are better understood, and a generalized topological quantum field theory framework has been proposed. Following the same conceptual framework, we outline a program to generalize Haah codes to all 3-manifolds using Hastings’ LR stabilizer codes for finite groups.
RELA is sufficient to mediate interleukin-1 repression of androgen receptor expression and activity in an LNCaP disease progression model.

Background The androgen receptor (AR) nuclear transcription factor is a therapeutic target for prostate cancer (PCa). Unfortunately, patients can develop resistance to AR-targeted therapies and progress to lethal disease, underscoring the importance of understanding the molecular mechanisms that underlie treatment resistance. Inflammation is implicated in PCa initiation and progression and we have previously reported that the inflammatory cytokine, interleukin-1 (IL-1), represses AR messenger RNA (mRNA) levels and activity in AR-positive (AR+ ) PCa cell lines concomitant with the upregulation of prosurvival biomolecules. Thus, we contend that IL-1 can select for AR-independent, treatment-resistant PCa cells. Methods To begin to explore how IL-1 signaling leads to the repression of AR mRNA levels, we performed comprehensive pathway analysis on our RNA sequencing data from IL-1-treated LNCaP PCa cells. Our pathway analysis predicted nuclear factor kappa B (NF-κB) p65 subunit (RELA), a canonical IL-1 signal transducer, to be significantly active and potentially regulate many genes, including AR. We used small interfering RNA (siRNA) to silence the NF-κB family of transcription factor subunits, RELA, RELB, c-REL, NFKB1, or NFKB2, in IL-1-treated LNCaP, C4-2, and C4-2B PCa cell lines. C4-2 and C4-2B cell lines are castration-resistant LNCaP sublines and represent progression toward metastatic PCa disease, and we have previously shown that IL-1 represses AR mRNA levels in C4-2 and C4-2B cells. Results siRNA revealed that RELA alone is sufficient to mediate IL-1 repression of AR mRNA and AR activity. Intriguingly, while LNCaP cells are more sensitive to IL-1-mediated repression of AR than C4-2 and C4-2B cells, RELA siRNA led to a more striking derepression of AR mRNA levels and AR activity in C4-2 and C4-2B cells than in LNCaP cells. Conclusions These data indicate that there are RELA-independent mechanisms that regulate IL-1-mediated AR repression in LNCaP cells and suggest that the switch to RELA-dependent IL-1 repression of AR in C4-2 and C4-2B cells reflects changes in epigenetic and transcriptional programs that evolve during PCa disease progression.
Should accountants care about blockchain

The use of blockchain technology has increased tremendously over the last decade. Blockchain continues to evolve and new features and capabilities are developed continuously. Although the use of the technology started in cryptocurrency (specifically, Bitcoin) it has expanded to other areas that can benefit from a shared, secure, ledger. This paper investigates the potential impact of blockchain technology on the accounting profession. The paper analyzes data security and privacy considerations, technology, adoption and implementation considerations, and some considerations that relate specifically to accounting and auditing. We find that the unique needs of an accounting information system may not be a good match for blockchain as it currently exists. While we explain that blockchain may deliver many benefits, particularly in the areas of data reliability and the financial statement audit, we identify several factors which raise significant questions about whether blockchain will ever be significantly integrated in the accounting function. Chief among those concerns is the scalability of the technology at an acceptable cost. While significant investment has and will be made for further development of blockchain business applications, it is our assessment that proponents of blockchain integration in accounting have not yet made the economic case for it. We also have significant concerns about whether blockchain technology can adequately address risks associated with data security and privacy. Addressing all these issues will be a minimum requirement for gaining widespread acceptance by firms and their accountants.
KHINTCHINE'S THEOREM WITH RANDOM FRACTIONS

We prove versions of Khintchine's Theorem (1924) for approximations by rational numbers whose numerators lie in randomly chosen sets of integers, and we explore the extent to which the monotonicity assumption can be removed. Roughly speaking, we show that if the number of available fractions for each denominator grows too fast, then the monotonicity assumption cannot be removed. There are questions in this random setting which may be seen as cognates of the Duffin-Schaeffer Conjecture (1941), and are likely to be more accessible. We point out that the direct random analogue of the Duffin-Schaeffer Conjecture, like the Duffin-Schaeffer Conjecture itself, implies Catlin's Conjecture (1976). It is not obvious whether the Duffin-Schaeffer Conjecture and its random version imply one another, and it is not known whether Catlin's Conjecture implies either of them. The question of whether Catlin implies Duffin-Schaeffer has been unsettled for decades.
SNS: Analytic Receiver Analysis Software Using Electrical Scattering Matrices

SNS is a MATLAB-based software library written to aid in the design and analysis of receiver architectures. It uses electrical scattering matrices and noise wave vectors to describe receiver architectures of arbitrary topology and complexity. It differs from existing freely-available software mainly in that the scattering matrices used to describe the receiver and its components are analytic rather than numeric. This allows different types of modeling and analysis of receivers to be performed. Non-ideal behavior of receiver components can be parameterized in their scattering matrices. SNS enables the instrument designer to then derive analytic expressions for the signal and noise at the receiver outputs in terms of parameterized component imperfections, and predict their contribution to receiver systematic errors precisely. This can drive the receiver design process by, for instance, allowing the instrument designer to identify which component imperfections contribute most to receiver systematic errors, and hence place firm specifications on individual components. Using SNS to perform this analysis is preferable to traditional Jones matrix-based analysis as it includes internal reflections and is able to model noise: two effects which Jones matrix analysis is unable to describe. SNS can be used to model any receiver in which the components can be described by scattering matrices. Of particular interest to the sub-mm and terahertz frequency regime is the choice between coherent and direct detection technologies. Steady improvements in mm and sub-mm Low Noise Amplifiers (LNAs) mean that coherent receivers with LNAs as their first active element are becoming increasingly competitive, in terms of sensitivity, with bolometer-based receivers at frequencies above ∼ 100 GHz. As an example of the utility of SNS, we use it to compare two polarimeter architectures commonly used to perform measurements of the polarized Cosmic Microwave Background: differencing polarimeters, an architecture commonly used in polarization sensitive bolometer-based polarimeters; and pseudocorrelation polarimeters, an architecture commonly used in coherent, LNA-based, polarimeters. We parameterize common sources of receiver systematic errors in both architectures and compare them through their Mueller matrices, which encode how well the instruments measure the Stokes parameters of the incident radiation. These analytic Mueller matrices are used to demonstrate the different sources of systematic errors in differencing and correlation polarimeters.
Minimum Age of Information in the Internet of Things With Non-Uniform Status Packet Sizes

In this paper, a real-time Internet of Things (IoT) monitoring system is considered in which the IoT devices are scheduled to sample associated underlying physical processes and send the status updates to a common destination. In a real-world IoT, due to the possibly different dynamics of each physical process, the sizes of the status updates for different devices are often different and each status update typically requires multiple transmission slots. By taking into account such multi-time slot transmissions with non-uniform sizes of the status updates under noisy channels, the problem of joint device scheduling and status sampling is studied in order to minimize the average age of information (AoI) at the destination. This stochastic problem is formulated as an infinite horizon average cost Markov decision process (MDP). The monotonicity of the value function of the MDP is characterized and then used to show that the optimal scheduling and sampling policy is threshold-based with respect to the AoI at each device. To overcome the curse of dimensionality, a low-complexity suboptimal policy is proposed through a semi-randomized base policy and linear approximated value functions. The proposed suboptimal policy is shown to exhibit a similar structure to the optimal policy, which provides a structural base for its effective performance. A structure-aware algorithm is then developed to obtain the suboptimal policy. The analytical results are further extended to the IoT monitoring system with random status update arrivals , for which, the optimal scheduling and sampling policy is also shown to be threshold-based with the AoI at each device. Simulation results illustrate the structures of the optimal policy and show a near-optimal AoI performance resulting from the proposed suboptimal solution approach.
Estimates for the L q -mixed problem in C 1,1-domains

We consider the L q -mixed problem in domains in R n with C 1 , 1 -boundary. We assume that the boundary between the sets where we specify Neumann and Dirichlet data is Lipschitz. With these assump...
The Plasma Membrane as a Competitive Inhibitor and Positive Allosteric Modulator of KRas4B Signaling.

Abstract Mutant Ras proteins are important drivers of human cancers, yet no approved drugs act directly on this difficult target. Over the last decade, the idea has emerged that oncogenic signaling can be diminished by molecules that drive Ras into orientations in which effector-binding interfaces are occluded by the cell membrane. To support this approach to drug discovery, we characterize the orientational preferences of membrane-bound K-Ras4B in 1.45-ms aggregate time of atomistic molecular dynamics simulations. Individual simulations probe active or inactive states of Ras on membranes with or without anionic lipids. We find that the membrane orientation of Ras is relatively insensitive to its bound guanine nucleotide and activation state but depends strongly on interactions with anionic phosphatidylserine lipids. These lipids slow Ras’ translational and orientational diffusion and promote a discrete population in which small changes in orientation control Ras’ competence to bind multiple regulator and effector proteins. Our results suggest that compound-directed conversion of constitutively active mutant Ras into functionally inactive forms may be accessible via subtle perturbations of Ras’ orientational preferences at the membrane surface.
Commodity Currencies and Causality: Some High-Frequency Evidence

Abstract I investigate the link between economic fundamentals and exchange rate adjustment to commodity price fluctuations. I overcome the traditional issue of simultaneity by exploiting the September 14, 2019 drone attack on two Saudi Arabian refineries as a natural experiment. This unanticipated event caused the largest 1-day global crude oil price shock in over a decade. Using high-frequency exchange rate data for 30 countries, I link the cross-section of exchange rate movements around the event to country-specific fundamentals and currency risk factors. Crude export and import intensities were associated with appreciation (depreciation). Additionally, countries with higher policy interest rates and weaker financial positions experienced greater currency depreciation while safe haven currencies appreciated, consistent with ‘risk-off’ sentiment triggering carry trades to unwind. Estimated (pre-event) crude oil and VIX betas can also explain the cross-section of exchange rate adjustment, and these currency risk factors are tightly associated with oil-related and financial fundamentals, respectively.
Asymptotic Approximations for the Close Evaluation of Double-Layer Potentials

When using boundary integral equation methods to solve a boundary value problem, the evaluation of the solution near the boundary is challenging to compute because the layer potentials that represe...
Non-Orthogonal Multiple Access for Mobile VLC Networks with Random Receiver Orientation

We consider a downlink multiuser visible light communications (VLC) network where users randomly change their location and vertical orientation. The non-orthogonal multiple access (NOMA) strategy is adopted to serve multiple users simultaneously, and, hence, to improve spectral efficiency. We propose two novel user scheduling schemes for NOMA, which are referred to as individual and group-based. In order to further reduce the computational complexity and link overhead, novel limited-feedback schemes (on channel quality) are also proposed, which basically involve mean vertical angle (instead of its instantaneous value). Moreover, a two-bit feedback scheme is proposed for group-based user scheduling, which relies on not only distance but also vertical angle (in contrast to conventional one-bit feedback with distance only). The outage probability and sum-rate expressions are derived analytically, which show a very good match with the simulation data. Numerical results verify that the practical feedback scheme with the mean vertical angle achieves a near-optimal sum-rate performance, and the two-bit feedback significantly outperforms the one-bit feedback.
Racial/Ethnic Disparities in Hospice Utilization Among Medicare Beneficiaries Dying from Pancreatic Cancer.

We sought to define the incidence and characterize the timing of hospice utilization among racial/ethnic minority patients following pancreatectomy for pancreatic cancer. The Medicare Standard Analytic Files from 2013 to 2017 were used to identify patients with pancreatic cancer who underwent a pancreatectomy. Logistic regression was utilized to identify the association between race and patterns of hospice utilization among deceased individuals. Among the 14,495 individuals (median age 73; 52.3% female; 6.8% racial/ethnic minority) who underwent a pancreatectomy for pancreatic cancer, 47% (n = 6859) died by the end of the follow-period. Among deceased individuals, three-fourths of patients (n = 4978, 72.6%) used hospice leading up to the time of death. Racial/ethnic minority patients were less likely, however, to have used hospice services compared with white patients (racial/ethnic minorities n = 301, 67% vs. whites: n = 4677, 73%; p = 0.024). On multivariable analysis, after controlling for clinical factors, racial/ethnic minority patients remained 22% less likely than whites to initiate hospice services prior to death (OR 0.78, 95% CI 0.63–0.96). Despite overall lower use of hospice, racial/ethnic minority patients had comparable odds of late hospice utilization (i.e., within 3 days of death) versus white patients (OR 1.5, 95% CI 0.73–1.50). While most patients undergoing pancreatectomy for pancreatic cancer utilized hospice services prior to death, racial/ethnic minorities were less likely to use hospice services than whites.
Reducing the Risk of Hospitalization for Nursing Home Residents: Effects and Facility Variation From OPTIMISTIC.

Abstract Objectives The Optimizing Patient Transfers, Impacting Medical Quality, and Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) project led to significant decreases in potentially avoidable hospitalizations of long-stay nursing facility residents in external evaluation. The purpose of this study was to quantify hospitalization risk from the start of the project and describe the heterogeneity of the enrolled facilities in order to better understand the context for successful implementation. Design Pre-post analysis design of a prospective intervention within a single group. Setting and Participants A total of 4320 residents in the 19 facilities were included from admission until time to the first hospitalization. Measures Data were extracted from Minimum Data Set assessments and linked with facility-level covariates from the LTCFocus.org data set. Kaplan-Meier and Cox proportional hazards regression were used to assess risk of hospitalization during the preintervention period (2011-2012), a “ramp-up” period (2013-2014), and an intervention period (2015-2016). Results The cohort consisted of 4230 long-stay nursing facility residents. Compared with the preintervention period, residents during the intervention period had an increased probability of having no hospitalizations within 1 year, increasing from 0.51 to 0.57, which was statistically significant (P Conclusions and Implications As part of a large multisite demonstration project, OPTIMISTIC has successfully reduced hospitalizations. However, this study highlights the magnitude and extent to which results differ across facilities. Implementing the OPTIMISTIC program was associated with a 16% risk reduction after the first 18 months and continued to a final risk reduction of 26% after 5½ years. Although this model of care reduces hospitalizations overall, facility variation should be expected.
Singular Vectors on Fractals and Projections of Self-similar Measures

Singular vectors are those for which the quality of rational approximations provided by Dirichlet’s Theorem can be improved by arbitrarily small multiplicative constants. We provide an upper bound on the Hausdorff dimension of singular vectors lying on self-similar fractals in $${\mathbb {R}}^d$$ satisfying the open set condition. The bound is in terms of quantities which are closely tied to Frostman exponents of projections of the Hausdorff measure supported on the fractal. Our bound is optimal in the sense that it agrees with the exact dimension of singular vectors obtained by Cheung and Chevallier when the fractal is trivial (i.e. has non-empty interior). As a corollary, we show that if the fractal is the product of 2 copies of Cantor’s middle thirds set or the attractor of a planar homogeneous irrational IFS, then the upper bound is 2/3 the dimension of the fractal. This addresses the upper bound part of a question raised by Bugeaud, Cheung and Chevallier. We apply our method in the setting of translation flows on flat surfaces to show that the dimension of non-uniquely ergodic directions belonging to a fractal is at most 1/2 the dimension of the fractal.
Adversarial Attacks on Time Series

Time series classification models have been garnering significant importance in the research community. However, not much research has been done on generating adversarial samples for these models. These adversarial samples can become a security concern. In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models. The proposed attack on the classification model utilizes a distilled model as a surrogate that mimics the behavior of the attacked classical time series classification models. Our proposed methodology is applied onto 1-nearest neighbor dynamic time warping (1-NN DTW) and a fully convolutional network (FCN), all of which are trained on 42 University of California Riverside (UCR) datasets. In this paper, we show both models were susceptible to attacks on all 42 datasets. When compared to Fast Gradient Sign Method, the proposed attack generates a larger faction of successful adversarial black-box attacks. A simple defense mechanism is successfully devised to reduce the fraction of successful adversarial samples. Finally, we recommend future researchers that develop time series classification models to incorporating adversarial data samples into their training data sets to improve resilience on adversarial samples.
Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration.
Secure Estimation Under Causative Attacks

This paper considers the problem of secure parameter estimation when an estimation algorithm is prone to causative attacks. Causative attacks, in general, target decision-making algorithms (e.g., inference or learning algorithm) to alter their decisions in specific scenarios (e.g., distort parameter estimates for specific ranges of the parameter of interest). Such attacks influence the decisions via tampering with the mechanisms through which an algorithm acquires the statistical model of the population about which it aims to form a decision. Such attacks are viable, for instance, by contaminating the historical or training data, or by compromising an expert who provides the statistical model. In the presence of causative attacks, inference algorithms operate under a distorted statistical model for the data samples. This paper introduces a notion of secure parameter estimation and formalizes a framework under which secure estimation can be formulated and analyzed. The central premise underlying the secure estimation framework is that forming secure estimates introduces a new dimension to the estimation objective, pertaining to detecting attacks and isolating the true model. Since detection and isolation decisions themselves are imperfect, their inclusion induces an inherent coupling between the desired secure estimation objective and the auxiliary detection and isolation decisions that need to be formed in conjunction with the estimates. This paper establishes the fundamental interplay among these decisions, and characterizes the general decision rules in closed-forms for any desired estimation cost function. Furthermore, to circumvent the computational complexity associated with growing parameter dimension or attack complexity, a scalable estimation algorithm is provided, which is shown to enjoy certain optimality guarantees. Finally, the theory developed is applied to secure parameter estimation in sensor networks.
Current-voltage characteristics of polymer light-emitting diodes

Conduction in pristine conjugated polymers is by polaron hopping between sites corresponding to conjugation lengths. The strong increase of current I with voltage V observed for both emission-limited and ohmic contacts is due in large part to mobility increase as increasing field makes it more possible to overcome internal barriers, such as energy differences between sites. For emission-limited contacts an additional source of nonlinear increase of I with increasing V is greater ability to escape return to the injecting electrode due to the image force. For ohmic contacts additional nonlinearity comes from space charge effects. We are able to fit I vs. V for electron or hole conduction is some poly(p-phenylene vinylene), PPV, derivatives with ohmic contacts for reasonable values of the parameters involved.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Deep learning with diffusion basis spectrum imaging for classification of multiple sclerosis lesions.

OBJECTIVE Multiple sclerosis (MS) lesions are heterogeneous with regard to inflammation, demyelination, axonal injury, and neuronal loss. We previously developed a diffusion basis spectrum imaging (DBSI) technique to better address MS lesion heterogeneity. We hypothesized that the profiles of multiple DBSI metrics can identify lesion-defining patterns. Here we test this hypothesis by combining a deep learning algorithm using deep neural network (DNN) with DBSI and other imaging methods. METHODS Thirty-eight MS patients were scanned with diffusion-weighted imaging, magnetization transfer imaging, and standard conventional MRI sequences (cMRI). A total of 499 regions of interest were identified on standard MRI and labeled as persistent black holes (PBH), persistent gray holes (PGH), acute black holes (ABH), acute gray holes (AGH), nonblack or gray holes (NBH), and normal appearing white matter (NAWM). DBSI, diffusion tensor imaging (DTI), and magnetization transfer ratio (MTR) were applied to the 43,261 imaging voxels extracted from these ROIs. The optimized DNN with 10 fully connected hidden layers was trained using the imaging metrics of the lesion subtypes and NAWM. RESULTS Concordance, sensitivity, specificity, and accuracy were determined for the different imaging methods. DBSI-DNN derived lesion classification achieved 93.4% overall concordance with predetermined lesion types, compared with 80.2% for DTI-DNN model, 78.3% for MTR-DNN model, and 74.2% for cMRI-DNN model. DBSI-DNN also produced the highest specificity, sensitivity, and accuracy. CONCLUSIONS DBSI-DNN improves the classification of different MS lesion subtypes, which could aid clinical decision making. The efficacy and efficiency of DBSI-DNN shows great promise for clinical applications in automatic MS lesion detection and classification.
Conjunctive Representations that Integrate Stimuli, Responses, and Rules are Critical for Action Selection

People can use abstract rules to flexibly configure and select actions for specific situations, yet how exactly rules shape actions toward specific sensory and/or motor requirements remains unclear. Both research from animal models and human-level theories of action control point to the role of highly integrated, conjunctive representations, sometimes referred to as event files. These representations are thought to combine rules with other, goal-relevant sensory and motor features in a nonlinear manner and represent a necessary condition for action selection. However, so far, no methods exist to track such representations in humans during action selection with adequate temporal resolution. Here, we applied time-resolved representational similarity analysis to the spectral-temporal profiles of electroencephalography signals while participants performed a cued, rule-based action selection task. In two experiments, we found that conjunctive representations were active throughout the entire selection period and were functionally dissociable from the representation of constituent features. Specifically, the strength of conjunctions was a highly robust predictor of trial-by-trial variability in response times and was selectively related to an important behavioral indicator of conjunctive representations, the so-called partial-overlap priming pattern. These results provide direct evidence for conjunctive representations as critical precursors of action selection in humans.
Computational Methods for Predicting Sites of Functionally Important Dynamics

Understanding and controlling biological function of proteins at the atomic level is of great importance; allosteric mechanisms provide such an interface. Experimental and computational methods have been developed to search for residue mutations that produce changes in function by altering sites of correlated motion. These methods are often observational in that altered motions are achieved by random sampling without revealing the underlying mechanism(s). We present two deterministic methods founded on structure−function relationships that predict dynamic control sites (i.e., locations that experience correlated motions as a result of altered dynamics). The first method (“static”) is based on a single structure conformation (e.g., the wild type (WT)) and utilizes a graph description of atomic connectivity. The local atomic interactions are used to compute the propagation of contact paths. This description of structure connectivity reveals flexible locations that are susceptible to altered dynamics. The se...
The moral of the story.

A 20-month-old boy was brought in to the clinic because he was vomiting. Two nights before, the child had fallen down some stairs. Dr. Perri Klass writes about the worry that set in after she saw the boy and about the voices that can echo in your head after even a simple clinical decision.
Aggregation of space-time processes

Abstract In this paper we compare the relative efficiency of different methods of forecasting the aggregate of spatially correlated variables. Small sample simulations confirm the asymptotic result that improved forecasting performance can be obtained by imposing a priori constraints on the amount of spatial correlation in the system. One way to do so is to aggregate forecasts from a space-time autoregressive model (Elements of Spatial Structure, Cambridge University Press, Cambridge, 1975), which offers a solution to the ‘curse of dimensionality’ that arises when forecasting with VARs. We also show that ignoring spatial correlation, even when it is weak, leads to highly inaccurate forecasts. Finally, if the system satisfies a ‘poolability’ condition, there is a benefit in forecasting the aggregate variable directly.
On Sublevel Set Estimates and the Laplacian

Carbery proved that if $u:\mathbb {R}^{n} \rightarrow \mathbb {R}$ is a positive, strictly convex function satisfying $\det D^{2}u \geq 1$ , then we have the estimate $$ \left| \left\{x \in \mathbb{R}^{n}: u(x) \leq s \right\} \right| \lesssim_{n} s^{n/2} $$ and this is optimal. We give a short proof that also implies other results. Our main result is an estimate for the sublevel set of functions $u:[0,1]^{2} \rightarrow \mathbb {R}$ satisfying 1 ≤Δu ≤ c for some universal constant c: for any α > 0, we have $$ \left| \left\{x \in [0,1]^{2} : |u(x)| \leq \varepsilon\right\}\right| \lesssim_{c} \sqrt{\varepsilon} + \varepsilon^{\alpha - \frac12} {\int}_{[0,1]^{2}}{\frac{|\nabla u|}{|u|^{\alpha}} dx}.$$ For ‘typical’ functions, we expect the integral to be finite for α < 1. While Carbery-Christ-Wright have shown that no sublevel set estimates independently of u exist, this result shows that for ‘typical’ functions satisfying ${\Delta } u \sim 1$ , we expect the sublevel set to be $\lesssim \varepsilon ^{1/2-}$ . We do not know whether this is sharp or whether similar statements are true in higher dimensions.
The Effect of Corporate Taxes on Investment and Entrepreneurship

We present new data on effective corporate income tax rates in 85 countries in 2004. The data come from a survey, conducted jointly with PricewaterhouseCoopers, of all taxes imposed on "the same" standardized mid-size domestic firm. In a cross-section of countries, our estimates of the effective corporate tax rate have a large adverse impact on aggregate investment, FDI, and entrepreneurial activity. Corporate tax rates are correlated with investment in manufacturing but not services, as well as with the size of the informal economy. The results are robust to the inclusion of many controls.
Dressed for Success? The Effect of School Uniforms on Student Achievement and Behavior

Abstract Uniform use in public schools is rising, but we know little about how they affect students. Using a unique dataset from a large urban school district in the southwest United States, we assess how uniforms affect behavior, achievement and other outcomes. Each school in the district determines adoption independently, providing variation over schools and time. By including student and school fixed-effects we find evidence that uniform adoption improves attendance in secondary grades, while in elementary schools they generate large increases in teacher retention.
Stark broadening in hot, dense laser-produced plasmas

Broadened Lyman-$\ensuremath{\alpha}$ x-ray lines from neon x and argon XVIII radiators, which are immersed in a hot, dense deuterium or deuterium-tritium plasma, are discussed. In particular, these lines are analyzed for several temperature-density cases, characteristic of laser-produced plasmas; special attention is paid to the relative importance of ion, electron, and Doppler effects.
Virtual Reality Exposure Therapy

Virtual reality (VR) is a multimedia interactive experience in which users feel immersed within the computer generated, virtual environment. It is a powerful platform to use for therapy involving exposure to cues, particularly for persons with anxiety disorders. VR exposure (VRE) therapy has been found to be a successful tool for treating many phobias and posttraumatic stress disorder. Advantages of VRE therapy include the therapist having control over the scenarios, the ability to tailor the environments to the client’s needs, and the convenience of being able to provide a range of virtual environments without leaving the therapist’s office.
Chapter 14 – Nuclear Physics

Information about the structure of nuclei can be obtained from high-energy electron scattering experiments. The results of these experiments show that the density of nuclear matter is roughly the same at the center of all nuclei. The nuclear density increases with A, but appears to approach a limiting value of about 0.17 nucleons per fm−3 for large A. All nuclei appear to have an inner region where the density is approximately uniform and a thin surface region where the distribution of nuclear matter falls off exponentially to zero. The binding energy and mass of nuclei can be accurately described by empirical formulas. Superimposed upon the slowly varying properties of the nucleus described by the empirical formulas are deviations which are quantum mechanical in nature. The protons and neutrons in the nucleus move in a finite region of space with definite values of the angular momentum. The description of the nucleus in terms of the angular momentum of individual nucleons, which is known as the shell mode, makes it possible to understand the spin and parity of nuclei and to describe the spectra of excited states.
Taxation and the Stock Market Valuation of Capital Gains and Dividends: Theory and Empirical Results (Rev)

Abstract Dividends seem to be more heavily taxed than capital gains. Why then do corporations pay dividends rather than repurchasing shares or retaining earnings? Either corporations are not acting in the interests of shareholders, or else shareholders desire dividends sufficiently for nontax reasons to offset the tax effect. In this paper, we measure the relative valuation of dividends and capital gains in the stock market, using a variant of the capital asset pricing model. We find that dividends are not valued differently systematically from capital gains. This finding is consistent with share price maximization by firms but inconsistent with the fact that most shareholderspay a heavier tax on dividends. We also show that the relative value of dividends provides an indirect measure of a marginal Tobin's q. The measured value of dividends relative to capital gains tends to be higher during prosperous periods, as is consistent with this interpretation. We hope that this time series on a marginal Tobin's q will prove to be useful in forecasting the rate of investment.
Fixed rings of quantum generalized Weyl algebras

Generalized Weyl algebras (GWAs) appear in diverse areas of mathematics including mathematical physics, noncommutative algebra, and representation theory. We study the invariants of quantum GWAs un...
Three Pathologies of American Voting Rights Illuminated by the COVID-19 Pandemic, and How to Treat and Cure Them

The COVID-19 global pandemic, which already has claimed over 150,000 lives in the United States by the end of July 2020, has revealed cracks in American economic and social infrastructure. The pand...
To Dereplicate or Not To Dereplicate

ABSTRACT Metagenome-assembled genomes (MAGs) expand our understanding of microbial diversity, evolution, and ecology. Concerns have been raised on how sequencing, assembly, binning, and quality assessment tools may result in MAGs that do not reflect single populations in nature. Here, we reflect on another issue, i.e., how to handle highly similar MAGs assembled from independent data sets. Obtaining multiple genomic representatives for a species is highly valuable, as it allows for population genomic analyses; however, when retaining genomes of closely related populations, it complicates MAG quality assessment and abundance inferences. We show that (i) published data sets contain a large fraction of MAGs sharing >99% average nucleotide identity, (ii) different software packages and parameters used to resolve this redundancy remove very different numbers of MAGs, and (iii) the removal of closely related genomes leads to losses of population-specific auxiliary genes. Finally, we highlight some approaches that can infer strain-specific dynamics across a sample series without dereplication.
Evolutionary tracking is determined by differential selection on demographic rates and density dependence

Recent ecological forecasts predict that ~25% of species worldwide will go extinct by 2050. However, these estimates are primarily based on environmental changes alone and fail to incorporate important biological mechanisms such as genetic adaptation via evolution. Thus, environmental change can affect population dynamics in ways that classical frameworks can neither describe nor predict. Furthermore, often due to a lack of data, forecasting models commonly describe changes in population demography by summarizing changes in fecundity and survival concurrently with the intrinsic growth rate (r). This has been shown to be an oversimplification as the environment may impose selective pressure on specific demographic rates (birth and death) rather than directly on r (the difference between the birth and death rates). This differential pressure may alter population response to density, in each demographic rate, further diluting the information combined to produce r. Thus, when we consider the potential for persistence via adaptive evolution, populations with the same r can have different abilities to persist amidst environmental change. Therefore, we cannot adequately forecast population response to climate change without accounting for demography and selection on density dependence. Using a continuous-time Markov chain model to describe the stochastic dynamics of the logistic model of population growth and allow for trait evolution via mutations arising during birth events, we find persistence via evolutionary tracking more likely when environmental change alters birth rather than the death rate. Furthermore, species that evolve responses to changes in the strength of density dependence due to environmental change are less vulnerable to extinction than species that undergo selection independent of population density. By incorporating these key demographic considerations into our predictive models, we can better understand how species will respond to climate change.
Lattice dynamics and thermodynamics of bcc vanadium at high pressures

We investigate the lattice dynamics and thermodynamics of nonmagnetic bcc vanadium as a function of temperature and pressure, using the first principles linear response linear-muffin-tin-orbital method. The calculated phonon density of states (DOS) show strong temperature dependence, different from inelastic neutron scattering measurements where the phonon DOS show little change from room temperature up to 1273 K. We obtain the Helmholtz free energy including both electronic and phonon contributions and calculate various equation of state properties such as the bulk modulus and the thermal expansion coefficient. A detailed comparison has been made with available experimental measurements.
Eliminating intermediate measurements in space-bounded Quantum computation

A foundational result in the theory of quantum computation, known as the "principle of safe storage," shows that it is always possible to take a quantum circuit and produce an equivalent circuit that makes all measurements at the end of the computation. While this procedure is time efficient, meaning that it does not introduce a large overhead in the number of gates, it uses extra ancillary qubits, and so is not generally space efficient. It is quite natural to ask whether it is possible to eliminate intermediate measurements without increasing the number of ancillary qubits. We give an affirmative answer to this question by exhibiting a procedure to eliminate all intermediate measurements that is simultaneously space efficient and time efficient. In particular, this shows that the definition of a space-bounded quantum complexity class is robust to allowing or forbidding intermediate measurements. A key component of our approach, which may be of independent interest, involves showing that the well-conditioned versions of many standard linear-algebraic problems may be solved by a quantum computer in less space than seems possible by a classical computer.
Theoretical model of a finite force at the moving contact line

Abstract In theoretical analyses of the moving contact line, an infinite force along the solid wall has been reported based off the non-integrable stress along a single interface. In this investigation we demonstrate that the stress singularity is integrable and results in a finite force at the moving contact line if the contact line is treated as a one-dimensional manifold and all three interfaces that make up the moving contact line are taken into consideration. This is due to the dipole nature of the vorticity and pressure distribution around the moving contact line. Mathematically, this finite force is determined by summing all the forces that act over an infinitesimally small cylindrical control volume that encloses the entire moving contact line. With this finite force, we propose a new dynamic Young’s equation for microscopic dynamic contact angle that is a function of known parameters only, specifically the interface velocity, surface tension, and fluid viscosity. We combine our model with Cox’s model for apparent dynamic contact angle and find good agreement with published dynamic contact angle measurements.
ELSIM: End-to-end learning of reusable skills through intrinsic motivation

Taking inspiration from developmental learning, we present a novel reinforcement learning architecture which hierarchically learns and represents self-generated skills in an end-to-end way. With this architecture, an agent focuses only on task-rewarded skills while keeping the learning process of skills bottom-up. This bottom-up approach allows to learn skills that 1 - are transferable across tasks, 2 - improve exploration when rewards are sparse. To do so, we combine a previously defined mutual information objective with a novel curriculum learning algorithm, creating an unlimited and explorable tree of skills. We test our agent on simple gridworld environments to understand and visualize how the agent distinguishes between its skills. Then we show that our approach can scale on more difficult MuJoCo environments in which our agent is able to build a representation of skills which improves over a baseline both transfer learning and exploration when rewards are sparse.
Resilient Finite-Time Consensus: A Discontinuous Systems Perspective

Many algorithms have been proposed in prior literature to guarantee resilient multi-agent consensus in the presence of adversarial attacks or faults. The majority of prior work present excellent results that focus on discrete-time or discretized continuous-time systems. Fewer authors have explored applying similar resilient techniques to continuous-time systems without discretization. These prior works typically consider asymptotic convergence and make assumptions such as continuity of adversarial signals, the existence of a dwell time between switching instances for the system dynamics, or the existence of trusted agents that do not misbehave. In this paper, we expand the study of resilient continuous-time systems by removing many of these assumptions and using discontinuous systems theory to provide conditions for normally-behaving agents with nonlinear dynamics to achieve consensus in finite time despite the presence of adversarial agents.
Lower Bounds for Encrypted Multi-Maps and Searchable Encryption in the Leakage Cell Probe Model

Encrypted multi-maps (EMMs) enable clients to outsource the storage of a multi-map to a potentially untrusted server while maintaining the ability to perform operations in a privacy-preserving manner. EMMs are an important primitive as they are an integral building block for many practical applications such as searchable encryption and encrypted databases. In this work, we formally examine the tradeoffs between privacy and efficiency for EMMs.