cssi/SciSciGPT-SciSciNet huggingface dataset:
An Uncertain Future for Section 5 of the Voting Rights Act: The Need for a Revised Bailout System

In Northwest Austin Municipal Utility District Number One v. Holder (“NAMUDNO”), 129 S. Ct. 2504 (2009), the Supreme Court declined to decide one of the 2008 Term’s most prominent issues: the constitutionality of the 2006 renewal of Section 5 of the Voting Rights Act. Instead, the Court adopted an unexpected statutory construction permitting the plaintiff to seek an exemption called “bailout” from continued coverage under this provision. Even though the Court avoided directly ruling on its constitutionality, NAMUDNO left little doubt that Section 5 remains on uncertain constitutional ground. A revised bailout system is likely the best approach for placing Section 5 on a more solid footing. To date, however, bailout has been little used; despite predictions made during the previous renewal of Section 5 in 1982, only a handful of the thousands of covered jurisdictions have sought and successfully obtained bailout. This article suggests that Congress should consider two major changes to the existing bailout system. First, Congress should implement an “automatic” bailout that would unilaterally remove from coverage all jurisdictions that have not violated the major provisions of the Voting Rights Act since the 1982 renewal. Second, the current requirements for obtaining bailout—which this Article calls “optional” bailout—should be revised to make it easier for jurisdictions to determine whether they are eligible. Adopting these changes will more narrowly tailor Section 5 to apply to jurisdictions with a recent history of discrimination in voting and thus make it more likely to survive constitutional scrutiny the next time the issue is before the Court. * Visiting Assistant Professor, Chicago-Kent College of Law. J.D., University of Pennsylvania Law School, 2004; B.A., Swarthmore College, 2000. I thank Gerry Hebert, Peyton McCrary, Carolyn Shapiro, and Rick Valelly, as well as the other participants of the Voting 45 Years After the Voting Rights Act Symposium, for their helpful comments and suggestions.
Assessing U.S. State Susceptibility to Environmental Regulatory Competition

In this article, I examine the environmental race-to-the-bottom argument by studying whether state susceptibility to interstate economic competition helps explain which U.S. states engage in environmental regulatory competition. Specifically, I create a susceptibility index using four state economic attributes: overall growth, unemployment, manufacturing growth, and manufacturing employment. Studying state enforcement of federal environmental programs, I find little evidence that states, which are theoretically more susceptible to interstate economic competition are more likely to respond strategically to the regulatory behavior of economic competitor states. These results cast doubt on the idea that the environmental regulatory competition predicted by race-to-the-bottom theory is mediated by intrastate economic conditions.
A Comparison of Regression Spline Smoothing Procedures

Regression spline smoothing involves modelling a regression function as a piecewise polynomial with a high number of pieces relative to the sample size. Because the number of possible models is so large, efficient strategies for choosing among them are required. In this paper we review approaches to this problem and compare them through a simulation study. For simplicity and conciseness we restrict attention to the univariate smoothing setting with Gaussian noise and the truncated polynomial regression spline basis.
Adaptive testing on a regression function at a point

We consider the problem of inference on a regression function at a point when the entire function satisfies a sign or shape restriction under the null. We propose a test that achieves the optimal minimax rate adaptively over a range of Holder classes, up to a $\log\log n$ term, which we show to be necessary for adaptation. We apply the results to adaptive one-sided tests for the regression discontinuity parameter under a monotonicity restriction, the value of a monotone regression function at the boundary and the proportion of true null hypotheses in a multiple testing problem.
Promotional Chat on the Internet

Chat rooms, recommendation sites, and customer review sections allow consumers to overcome geographic boundaries and to communicate based on mutual interests. However, marketers also have incentives to supply promotional chat or reviews in order to influence the consumers' evaluation of their products. Moreover, firms can disguise their promotion as consumer recommendations due to the anonymity afforded by online communities. We explore this new setting where advertising and word of mouth become perfect substitutes because they appear indistinguishable to the consumer. Specifically, we investigate here whether word of mouth remains credible and whether firms choose to devote more resources promoting their inferior or superior products. We develop a game theoretic model in which two products are differentiated in their value to the consumer. Unlike the firms, the consumers are uncertain about the products' quality. The consumers read messages online that help them decide on the identity of the superior product. We find a unique equilibrium where online word of mouth is persuasive despite the promotional chat activity by competing firms. In this equilibrium, firms spend more resources promoting inferior products, in striking contrast to existing advertising literature. In addition, we discuss consumer welfare implications and how other marketing strategies might interact with promotional chat.
Lending booms, sharp reversals and real exchange rate dynamics

Abstract Emerging markets in the 1990s experienced periods of booms followed by collapses in gross domestic product, consumption, traded and non-traded sector output and real exchange rate movements alongside unprecedented movements in foreign investor participation in these economies. An important feature of these episodes is the asymmetry in the pattern of booms and collapses. We introduce a natural search friction into the foreign investment decision in a small open economy and demonstrate that this can generate the asymmetry observed in the data. The magnitude of the reversals predicted by the model can be quantitatively large and empirically relevant.
Insider trading restrictions and top executive compensation

The use of equity incentives is significantly greater in countries with stronger insider trading restrictions, and these higher incentives are associated with higher total pay. These findings are robust to alternative definitions of insider trading restrictions and enforcement, and to panel regressions with country fixed effects. We also find significant increases in top executive pay and the use of equity-based incentives in the period immediately following the initial enforcement of insider trading laws. We conclude that insider trading laws are one channel through which cross-country differences in pay practices can be explained.
Putting university research in context: Assessing alternative measures of production and diffusion at Stanford

Abstract Scholars widely acknowledge that university research is critical to innovation and entrepreneurship. Much of the literature on university research, however, evokes a linear model from “science to products” and focuses, therefore, upon a limited set of indicators such as patents and licenses. Such a perspective runs the danger of missing the myriad ways in which science and commerce are intertwined and the myriad ways in which these activities might be assessed. In this paper, I address the question of how different measures reflect different perspectives and biases by investigating the production and diffusion of research associated with one of Stanford University's most prolific interdisciplinary centers, the Center for Computer Research in Music and Acoustics (CCRMA). I draw upon a unique data set that captures activities and engagement surrounding CCRMA for its 30-year history through a wide variety of measures, ranging from publication citations to industrial affiliates to personnel mobility. Employing the analytic categories of “description” and “enactment,” and distinguishing between “indicators” and “pathways,” I show how different measures reflect different activities and learning processes, and how they dramatically alter perceptions of active individuals, organizational reach, and timing and sequencing of activities. Building on these findings, I present a more complete model of university research production and diffusion, I discuss how alternative measures challenge certain assumptions in the literature, and I suggest concrete policy initiatives to improve our measurement and assessment of university research.
Scheduling Precedence-Constrained Jobs on Related Machines with Communication Delay

We consider the problem of scheduling precedence-constrained jobs on uniformly-related machines in the presence of an arbitrary, fixed communication delay. Communication delay is the amount of time that must pass between the completion of a job on one machine and the start of any successor of that job on a different machine. We consider a model that allows job duplication, i.e. processing of the same job on multiple machines, which, as we show, can reduce the length of a schedule (i.e., its makespan) by a logarithmic factor. Our main result is an approximation algorithm for makespan with approximation ratio polylogarithmic in the number of machines and the length of the communication delay, assuming the minimum makespan is at least the delay. Our algorithm is based on rounding a linear programming relaxation for the problem, which includes carefully designed constraints capturing the interaction among communication delay, precedence requirements, varying speeds, and job duplication. To derive a schedule from a solution to the linear program, we balance the benefits of duplication in satisfying precedence constraints early against its drawbacks in increasing overall system load. Our result builds on two previous lines of work, one with communication delay but identical machines (Lepere, Rapine 2002), and the other with uniformly-related machines but no communication delay (Chudak, Shmoys 1999). We next show that the integrality gap of our mathematical program is polylogarithmic in the communication delay. Our gap construction employs expander graphs and exploits a property of robust expansion and its generalization to paths of longer length, which may be of independent interest. Finally, we quantify the advantage of duplication in scheduling with communication delay. We show that the best schedule without duplication can have a larger makespan than the optimal with duplication by a logarithmic factor. Nevertheless, we present a polynomial time algorithm to transform any schedule to a schedule without duplication at the cost of an increase in makespan polylogarithmic in the number of jobs and machines. Together with our makespan approximation algorithm for schedules allowing duplication, this also yields a polylogarithmic-approximation algorithm for the setting where duplication is not allowed.
Incentive-Compatible Learning of Reserve Prices for Repeated Auctions

How can an auctioneer optimize revenue by learning the reserve prices from the bids in the previous auctions? How should the long-term incentives and strategic behavior of the bidders be taken into...
Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis

COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significant rise in cyberbullying. In this paper, we have examined the hypothesis of whether cyberbullying and reporting of such incidents have increased in recent times. To evaluate the speculations, we collected cyberbullying related public tweets (N = 454, 046) posted between January 1st, 2020 - June 7th, 2020. A simple visual frequentist analysis ignores serial correlation and does not depict changepoints as such. To address correlation and a relatively small number of time points, Bayesian estimation of the trends is proposed for the collected data via an autoregressive Poisson model. We show that this new Bayesian method detailed in this paper can clearly show the upward trend on cyberbullying-related tweets since mid-March 2020. However, this evidence itself does not signify a rise in cyberbullying but shows a correlation of the crisis with the discussion of such incidents by individuals. Our work emphasizes a critical issue of cyberbullying and how a global crisis impacts social media abuse and provides a trend analysis model that can be utilized for social media data analysis in general.
Ozone depletion-induced climate change following a 50 pc supernova

The authors explore the influence of a supernova explosion at about 50 parsecs and find that it would trigger a small increase in global average temperature, with minor changes in atmospheric transport and precipitation patterns.
Functoriality for higher rho invariants of elliptic operators

Abstract Let N be a closed spin manifold with positive scalar curvature and D N the Dirac operator on N. Let M 1 and M 2 be two Galois covers of N such that M 2 is a quotient of M 1 . Then the quotient map from M 1 to M 2 naturally induces maps between the geometric C ⁎ -algebras associated to the two manifolds. We prove, by a finite-propagation argument, that the maximal higher rho invariants of the lifts of D N to M 1 and M 2 behave functorially with respect to the above quotient map. This can be applied to the computation of higher rho invariants, along with other related invariants.
Can Transparency and Accountability Programs Improve Health? Experimental Evidence from Indonesia and Tanzania

Abstract We assess the impact of a transparency and accountability program designed to improve maternal and newborn health (MNH) outcomes in Indonesia and Tanzania. Co-designed with local partner organizations to be community-led and non-prescriptive, the program sought to encourage community participation to address local barriers in access to high quality care for pregnant women and infants. We evaluate the impact of this program through randomized controlled trials (RCTs), involving 100 treatment and 100 control communities in each country. We find that on average, this program did not have a statistically significant impact on the use or content of maternal and newborn health services, nor on perceptions of civic efficacy or civic participation among recent mothers in the communities where it was offered. These findings hold in both countries and in a set of prespecified subgroups. To identify reasons for the lack of impacts, we use a mixed-method approach combining interviews, observations, surveys, focus groups, and ethnographic studies that together provide an in-depth assessment of the complex causal paths linking participation in the program to improvements in MNH outcomes. Although participation in program meetings was substantial and sustained in most communities, and most attempted at least some of what they had planned, only a minority achieved tangible improvements, and fewer still saw more than one such success. In our assessment, the main explanation for the lack of impact is that few communities were able to traverse the complex causal paths from planning actions to accomplishing tangible improvements in their access to quality health care.
Hybrid III-V diamond photonic platform for quantum nodes based on neutral silicon vacancy centers in diamond

Integrating atomic quantum memories based on color centers in diamond with on-chip photonic devices would enable entanglement distribution over long distances. However, efforts towards integration have been challenging because color centers can be highly sensitive to their environment, and their properties degrade in nanofabricated structures. Here, we describe a heterogeneously integrated, on-chip, III-V diamond platform designed for neutral silicon vacancy (SiV0) centers in diamond that circumvents the need for etching the diamond substrate. Through evanescent coupling to SiV0 centers near the surface of diamond, the platform will enable Purcell enhancement of SiV0 emission and efficient frequency conversion to the telecommunication C-band. The proposed structures can be realized with readily available fabrication techniques.
Twisted Alexander modules of hyperplane arrangement complements

We study torsion properties of the twisted Alexander modules of the affine complement M of a complex essential hyperplane arrangement, as well as those of punctured stratified tubular neighborhoods of complex essential hyperplane arrangements. We investigate divisibility properties between the twisted Alexander polynomials of the two spaces, compute the (first) twisted Alexander polynomial of a punctured stratified tubular neighborhood of an essential line arrangement, and study the possible roots of the twisted Alexander polynomials of both the complement and the punctured stratified tubular neighborhood of an essential hyperplane arrangement in higher dimensions. We apply our results to distinguish non-homeomorphic homotopy equivalent arrangement complements. We also relate the twisted Alexander polynomials of M with the corresponding twisted homology jump loci.
Efficient estimation of smooth functionals in Gaussian shift models

We study a problem of estimation of smooth functionals of parameter θ of Gaussian shift model X=θ+ξ,θ∈E, where E is a separable Banach space and X is an observation of unknown vector θ in Gaussian noise ξ with zero mean and known covariance operator Σ. In particular, we develop estimators T(X) of f(θ) for functionals f:E↦R of Holder smoothness s>0 such that sup‖θ‖≤1Eθ(T(X)−f(θ))2≲(‖Σ‖∨(E‖ξ‖2)s)∧1, where ‖Σ‖ is the operator norm of Σ, and show that this mean squared error rate is minimax optimal at least in the case of standard finite-dimensional Gaussian shift model (E=Rd equipped with the canonical Euclidean norm, ξ=σZ, Z∼N(0;Id)). Moreover, we determine a sharp threshold on the smoothness s of functional f such that, for all s above the threshold, f(θ) can be estimated efficiently with a mean squared error rate of the order ‖Σ‖ in a “small noise” setting (that is, when E‖ξ‖2 is small). The construction of efficient estimators is crucially based on a “bootstrap chain” method of bias reduction. The results could be applied to a variety of special high-dimensional and infinite-dimensional Gaussian models (for vector, matrix and functional data).
C-for-metal: high performance SIMD programming on intel GPUs

The SIMT execution model is commonly used for general GPU development. CUDA and OpenCL developers write scalar code that is implicitly parallelized by compiler and hardware. On Intel GPUs, however, this abstraction has profound performance implications as the underlying ISA is SIMD and important hardware capabilities cannot be fully utilized. To close this performance gap we introduce C- For- Metal (CM), an explicit SIMD programming framework designed to deliver close-to-the-metal performance on Intel GPUs. The CM programming language and its vector/matrix types provide an intuitive interface to exploit the underlying hardware features, allowing fine-grained register management, SIMD size control and cross-lane data sharing. Experimental results show that CM applications from different domains outperform the best-known SIMT-based OpenCL implementations, achieving up to 2.7x speedup on the latest Intel GPU.
Remittances, Ethnic Diversity, and Entrepreneurship in Developing Countries

This paper examines the moderating influence of home-country ethnic diversity in the relationship between migrant remittances and new business creation in developing countries. By employing the theories of transaction cost, social network, social identity, and trust, we argue that ethnic diversity is negatively associated with new business creation; nevertheless, it strengthens the positive association between migrant remittances and new business creation in developing countries. We test our hypotheses on 64 developing countries over an 11-year period (2006–2016). This paper contributes to entrepreneurship literature by emphasizing the importance of home-country ethnic diversity in channeling migrants’ remittances to new business creation in developing countries. Plain English Summary Ethnic diversity leads to a stronger positive link between remittances and new business creation in developing countries. We show that higher remittance-receiving countries create a higher number of new firms on average. Furthermore, this positive relationship is stronger in countries with ethnically diverse societies. Our results underline the importance of the ethnic structure of the country for entrepreneurship and economic development.
You are Suffocating Me! Firm-Level Evidence on Crowding Out

Literature on whether government spending crowds out or crowds in the private sector is large, but still without an unambiguous conclusion. Using firm-level data from Ukraine, this paper provides a granular empirical investigation to disentangle the impact of state-owned enterprises (SOEs) on private firm investment in Ukraine-a large transition economy. Controlling for firm characteristics and systematic differences across sectors, the results indicate that the SOE concentration in a given sector has a statistically significant negative effect on private fixed capital formation, and that the impact of SOEs is stronger in those industries in which SOEs have a more dominant presence. These findings imply that private firms operating in sectors with a high level of SOE concentration invest systematically less than businesses that are not competing directly with SOEs.
Multi-Layer Interference Alignment and GDoF of the K-User Asymmetric Interference Channel

In wireless networks, link strengths are often affected by some topological factors such as propagation path loss, shadowing and inter-cell interference. Thus, different users in the network might experience different link strengths. In this work we consider a $K$ -user asymmetric interference channel, where the channel gains of the links connected to Receiver $k$ are scaled with $\sqrt {P^{\alpha _{k}}}$ , $k=1,2, \cdots, K$ , for $0 . For this setting, we show that the optimal sum generalized degrees-of-freedom (GDoF) is characterized as $d_{\text {sum}}= \frac { \sum _{k=1}^{K} \alpha _{k} + \alpha _{K} -\alpha _{K-1}}{2}$ which matches the existing result $d_{\text {sum}}= \frac {K}{2}$ when $\alpha _{1} = \alpha _{2} = \cdots = \alpha _{K} =1$ . The achievability is based on multi-layer interference alignment, where different interference alignment sub-schemes are designed in different layers associated with specific power levels, and successive decoding is applied at the receivers. While the converse for the symmetric case only requires bounding the sum degrees-of-freedom (DoF) for selected two users, the converse for this asymmetric case involves bounding the weighted sum GDoF for selected $J+2$ users, with corresponding weights $(2^{J}, 2^{J-1}, \cdots, 2^{2}, 2^{1})$ , a geometric sequence with common ratio 2, for the first $J$ users and with corresponding weights (1, 1) for the last two users, for $J \in \left\{{1,2, \cdots, \lceil \log \frac {K}{2} \rceil }\right\}$ .
Abstract 1012: Selective estrogen receptor downregulators and immune checkpoint inhibitors in breast cancer immunotherapy

Breast cancers (BC) with expression of estrogen receptor-alpha (ERα) occur in more than 70% of newly-diagnosed patients in the U.S. Endocrine therapy with antiestrogens or aromatase inhibitors is an important intervention for BCs that express ERα, and it remains one of the most effective targeted treatments. However, substantial numbers of patients with localized disease, and almost all patients with metastatic BC, become resistant to current endocrine therapies. ERα is present in most resistant BCs, and in many of these its activity continues to regulate BC growth. Fulvestrant represents a class of ERα antagonists that elicit selective ER downregulation (SERDs), an action that helps overcome several resistance mechanisms. Unfortunately, full antitumor efficacy of fulvestrant is limited by its poor bioavailability in clinic. We have designed and tested a new generation of steroid-like SERDs. Using ERα-positive BC cells in vitro, we find that these compounds suppress ERα protein levels with efficacy similar to fulvestrant. Moreover, these new SERDs markedly inhibit ERα-positive BC cell proliferation in vitro even in the presence of estradiol-17β. In vivo, SERD-128 significantly inhibits tumor growth in MCF-7 xenograft models in a dose-dependent manner (P Citation Format: Diana C. Marquez-Garban, Gang Deng, Begonya Comin-Anduix, Alejandro J. Garcia, Emelyine Diers, Gaoyuan Ma, Nalo Hamilton, Michael E. Jung, Richard J. Pietras. Selective estrogen receptor downregulators and immune checkpoint inhibitors in breast cancer immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1012.
Teaching recurrent neural networks to infer global temporal structure from local examples

The ability to store and manipulate information is a hallmark of computational systems. Whereas computers are carefully engineered to represent and perform mathematical operations on structured data, neurobiological systems adapt to perform analogous functions without needing to be explicitly engineered. Recent efforts have made progress in modelling the representation and recall of information in neural systems. However, precisely how neural systems learn to modify these representations remains far from understood. Here, we demonstrate that a recurrent neural network (RNN) can learn to modify its representation of complex information using only examples, and we explain the associated learning mechanism with new theory. Specifically, we drive an RNN with examples of translated, linearly transformed or pre-bifurcated time series from a chaotic Lorenz system, alongside an additional control signal that changes value for each example. By training the network to replicate the Lorenz inputs, it learns to autonomously evolve about a Lorenz-shaped manifold. Additionally, it learns to continuously interpolate and extrapolate the translation, transformation and bifurcation of this representation far beyond the training data by changing the control signal. Furthermore, we demonstrate that RNNs can infer the bifurcation structure of normal forms and period doubling routes to chaos, and extrapolate non-dynamical, kinematic trajectories. Finally, we provide a mechanism for how these computations are learned, and replicate our main results using a Wilson–Cowan reservoir. Together, our results provide a simple but powerful mechanism by which an RNN can learn to manipulate internal representations of complex information, enabling the principled study and precise design of RNNs. Recurrent neural networks (RNNs) can learn to process temporal information, such as speech or movement. New work makes such approaches more powerful and flexible by describing theory and experiments demonstrating that RNNs can learn from a few examples to generalize and predict complex dynamics including chaotic behaviour.
Brd4-bound enhancers drive cell-intrinsic sex differences in glioblastoma

Sex can be an important determinant of cancer phenotype, and exploring sex-biased tumor biology holds promise for identifying novel therapeutic targets and new approaches to cancer treatment. In an established isogenic murine model of glioblastoma (GBM), we discovered correlated transcriptome-wide sex differences in gene expression, H3K27ac marks, large Brd4-bound enhancer usage, and Brd4 localization to Myc and p53 genomic binding sites. These sex-biased gene expression patterns were also evident in human glioblastoma stem cells (GSCs). These observations led us to hypothesize that Brd4-bound enhancers might underlie sex differences in stem cell function and tumorigenicity in GBM. We found that male and female GBM cells exhibited sex-specific responses to pharmacological or genetic inhibition of Brd4. Brd4 knockdown or pharmacologic inhibition decreased male GBM cell clonogenicity and in vivo tumorigenesis while increasing both in female GBM cells. These results were validated in male and female patient-derived GBM cell lines. Furthermore, analysis of the Cancer Therapeutic Response Portal of human GBM samples segregated by sex revealed that male GBM cells are significantly more sensitive to BET (bromodomain and extraterminal) inhibitors than are female cells. Thus, Brd4 activity is revealed to drive sex differences in stem cell and tumorigenic phenotypes, which can be abrogated by sex-specific responses to BET inhibition. This has important implications for the clinical evaluation and use of BET inhibitors.
Automated in vivo Tracking of Cortical Oligodendrocytes

Oligodendrocytes exert a profound influence on neural circuits by accelerating action potential conduction, altering excitability, and providing metabolic support. As oligodendrogenesis continues in the adult brain and is essential for myelin repair, uncovering the factors that control their dynamics is necessary to understand the consequences of adaptive myelination and develop new strategies to enhance remyelination in diseases such as multiple sclerosis. Unfortunately, few methods exist for analysis of oligodendrocyte dynamics, and even fewer are suitable for in vivo investigation. Here, we describe the development of a fully automated cell tracking pipeline using convolutional neural networks (Oligo-Track) that provides rapid volumetric segmentation and tracking of thousands of cells over weeks in vivo. This system reliably replicated human analysis, outperformed traditional analytic approaches, and extracted injury and repair dynamics at multiple cortical depths, establishing that oligodendrogenesis after cuprizone-mediated demyelination is suppressed in deeper cortical layers. Volumetric data provided by this analysis revealed that oligodendrocyte soma size progressively decreases after their generation, and declines further prior to death, providing a means to predict cell age and eventual cell death from individual time points. This new CNN-based analysis pipeline offers a rapid, robust method to quantitatively analyze oligodendrocyte dynamics in vivo, which will aid in understanding how changes in these myelinating cells influence circuit function and recovery from injury and disease.
Granger Causality Based Hierarchical Time Series Clustering for State Estimation

Abstract Clustering is an unsupervised learning technique that is useful when working with a large volume of unlabeled data. Complex dynamical systems in real life often entail data streaming from a large number of sources. Although it is desirable to use all source variables to form accurate state estimates, it is often impractical due to large computational power requirements, and sufficiently robust algorithms to handle these cases are not common. We propose a hierarchical time series clustering technique based on symbolic dynamic filtering and Granger causality, which serves as a dimensionality reduction and noise-rejection tool. Our process forms a hierarchy of variables in the multivariate time series with clustering of relevant variables at each level, thus separating out noise and less relevant variables. A new distance metric based on Granger causality is proposed and used for the time series clustering, as well as validated on empirical data sets. Experimental results from occupancy detection and building temperature estimation tasks shows fidelity to the empirical data sets while maintaining state-prediction accuracy with substantially reduced data dimensionality.
Tax induces the recruitment of NF-kB to unintegrated HIV-1 DNA to rescue viral gene expression and replication

We have previously reported that the normally essential step of integration of the HIV-1 proviral DNA intermediate into the host cell genome becomes dispensable in T cells that express the Human T cell leukemia virus 1 (HTLV-1) Tax protein, a known activator of cellular NF-kB. The rescue of integrase (IN) deficient HIV-1 replication by Tax results from the strong activation of transcription from the long terminal repeat (LTR) promoter on episomal HIV-1 DNA, an effect that is closely correlated with the recruitment of activating epigenetic marks, such as H3Ac, and depletion of repressive epigenetic marks, such as H3K9me3, from chromatinized unintegrated proviruses. In addition, activation of transcription from unintegrated HIV-1 DNA coincides with the recruitment of NF-kB to the two NF-kB binding sites found in the HIV-1 LTR enhancer. Here we report that the recruitment of NF-kB to unintegrated viral DNA precedes, and is a prerequisite for, Tax-induced changes in epigenetic marks, so that an IN- HIV-1 mutant lacking both LTR NF-kB sites is entirely non-responsive to Tax and fails to undergo the epigenetic changes listed above. Interestingly, we found that induction of Tax expression at 24 hours post-infection, when unintegrated HIV-1 DNA is already fully repressed by inhibitory chromatin modifications, is able to effectively reverse the epigenetic silencing of that DNA and rescue viral gene expression. Finally, we report that heterologous promoters introduced into IN-deficient HIV-1-based vectors are transcriptionally active even in the absence of Tax and do not increase their activity when the HIV-1 promoter and enhancer, located in the LTR U3 region,are deleted, as has been recently proposed.ImportanceIntegrase-deficient expression vectors based on HIV-1 are becoming increasingly popular as tools for gene therapy in vivo due to their inability to cause insertional mutagenesis. However, many IN- lentiviral vectors are able to achieve only low levels of gene expression and methods to increase this low level have not been extensively explored. Here we analyze how the HTLV-1 Tax protein is able to rescue the replication of IN- HIV-1 in T cells and describe IN- lentiviral vectors, lacking any inserted origin of replication, that are able to express a heterologous gene effectively.
Improving deep learning-based protein distance prediction in CASP14.

MOTIVATION Accurate prediction of residue-residue distances is important for protein structure prediction. We developed several protein distance predictors based on a deep learning distance prediction method and blindly tested them in the 14th Critical Assessment of Protein Structure Prediction (CASP14). The prediction method uses deep residual neural networks with the channel-wise attention mechanism to classify the distance between every two residues into multiple distance intervals. The input features for the deep learning method include co-evolutionary features as well as other sequence-based features derived from multiple sequence alignments (MSAs). Three alignment methods are used with multiple protein sequence/profile databases to generate MSAs for input feature generation. Based on different configurations and training strategies of the deep learning method, five MULTICOM distance predictors were created to participate in the CASP14 experiment. RESULTS Benchmarked on 37 hard CASP14 domains, the best performing MULTICOM predictor is ranked 5th out of 30 automated CASP14 distance prediction servers in terms of precision of top L/5 long-range contact predictions (i.e. classifying distances between two residues into two categories: in contact (< 8 Angstrom) and not in contact otherwise) and performs better than the best CASP13 distance prediction method. The best performing MULTICOM predictor is also ranked 6th among automated server predictors in classifying inter-residue distances into 10 distance intervals defined by CASP14 according to the precision of distance classification. The results show that the quality and depth of MSAs depend on alignment methods and sequence databases and have a significant impact on the accuracy of distance prediction. Using larger training datasets and multiple complementary features improves prediction accuracy. However, the number of effective sequences in MSAs is only a weak indicator of the quality of MSAs and the accuracy of predicted distance maps. In contrast, there is a strong correlation between the accuracy of contact/distance predictions and the average probability of the predicted contacts, which can therefore be more effectively used to estimate the confidence of distance predictions and select predicted distance maps. AVAILABILITY The software package, source code, and data of DeepDist2 are freely available at https://github.com/multicom-toolbox/deepdist and https://zenodo.org/record/4712084#.YIIM13VKhQM.
Dyson's classification and real division superalgebras

It is well-known that unitary irreducible representations of groups can be usefully classified in a 3-fold classification scheme: Real, Complex, Quaternionic. In 1962 Freeman Dyson pointed out that there is an analogous 10-fold classification of irreducible representations of groups involving both unitary and antiunitary operators. More recently, it was realized that there is also a 10-fold classification scheme involving superdivision algebras. Here we give a careful proof of the equivalence of these two 10-fold ways.
Sparse Graph Based Sketching for Fast Numerical Linear Algebra

In recent years, a variety of randomized constructions of sketching matrices have been devised, that have been used in fast algorithms for numerical linear algebra problems, such as least squares regression, low-rank approximation, and the approximation of leverage scores. A key property of sketching matrices is that of subspace embedding. In this paper, we study sketching matrices that are obtained from bipartite graphs that are sparse, i.e., have left degree s that is small. In particular, we explore two popular classes of sparse graphs, namely, expander graphs and magical graphs. For a given subspace ${\mathcal{U}} \subseteq {{\mathbb{R}}^n}$ of dimension k, we show that the magical graph with left degree s = 2 yields a (1 ± ϵ) l 2 -subspace embedding for ${\mathcal{U}}$, if the number of right vertices (the sketch size) $m = {\mathcal{O}}\left( {{k^2}/{\varepsilon ^2}} \right)$. The expander graph with $s = {\mathcal{O}}(\log k/\varepsilon )$ yields a subspace embedding for $m = {\mathcal{O}}\left( {k\log k/{\varepsilon ^2}} \right)$. We also discuss the construction of sparse sketching matrices with reduced randomness using expanders based on error-correcting codes. Empirical results on various synthetic and real datasets show that these sparse graph sketching matrices work very well in practice.
Fractionally log-concave and sector-stable polynomials: counting planar matchings and more

We show fully polynomial time randomized approximation schemes (FPRAS) for counting matchings of a given size, or more generally sampling/counting monomer-dimer systems in planar, not-necessarily-bipartite, graphs. While perfect matchings on planar graphs can be counted exactly in polynomial time, counting non-perfect matchings was shown by Jerrum (J Stat Phys 1987) to be #P-hard, who also raised the question of whether efficient approximate counting is possible. We answer this affirmatively by showing that the multi-site Glauber dynamics on the set of monomers in a monomer-dimer system always mixes rapidly, and that this dynamics can be implemented efficiently on downward-closed families of graphs where counting perfect matchings is tractable. As further applications of our results, we show how to sample efficiently using multi-site Glauber dynamics from partition-constrained strongly Rayleigh distributions, and nonsymmetric determinantal point processes. In order to analyze mixing properties of the multi-site Glauber dynamics, we establish two notions for generating polynomials of discrete set-valued distributions: sector-stability and fractional log-concavity. These notions generalize well-studied properties like real-stability and log-concavity, but unlike them robustly degrade under useful transformations applied to the distribution. We relate these notions to pairwise correlations in the underlying distribution and the notion of spectral independence introduced by Anari et al. (FOCS 2020), providing a new tool for establishing spectral independence based on geometry of polynomials. As a byproduct of our techniques, we show that polynomials avoiding roots in a sector of the complex plane must satisfy what we call fractional log-concavity; this generalizes a classic result established by Garding (J Math Mech 1959) who showed homogeneous polynomials that have no roots in a half-plane must be log-concave over the positive orthant.
DMPC: A data-and model-driven approach to predictive control

Abstract This work presents DMPC (Data-and Model-Driven Predictive Control) to solve control problems in which some of the constraints or parts of the objective function are known, while others are entirely unknown to the controller. It is assumed that there is an exogenous “black box” system, e.g. a machine learning technique , that predicts the value of the unknown functions for a given trajectory. DMPC (1) provides an approach to merge both the model-based and black-box systems; (2) can cope with very little data and is sample efficient, building its solutions based on recently generated trajectories; and (3) improves its cost in each iteration until converging to an optimal trajectory, typically needing only a few trials even for nonlinear dynamics and objectives. Theoretical analysis of the algorithm is presented, proving that the quality of the trajectory does not worsen with each new iteration. We apply the DMPC algorithm to the motion planning of an autonomous vehicle with nonlinear dynamics.
Probing Small-Scale Power Spectra with Pulsar Timing Arrays

Models of Dark Matter (DM) can leave unique imprints on the Universe’s small scale structure by boosting density perturbations on small scales. We study the capability of Pulsar Timing Arrays to search for, and constrain, subhalos from such models. The models of DM we consider are ordinary adiabatic perturbations in ΛCDM, QCD axion miniclusters, models with early matter domination, and vector DM produced during inflation. We show that ΛCDM, largely due to tidal stripping effects in the Milky Way, is out of reach for PTAs. Axion miniclusters may be within reach, although this depends crucially on whether the axion relic density is dominated by the misalignment or string contribution. Models where there is matter domination with a reheat temperature below 1 GeV may be observed with future PTAs. Lastly, vector DM produced during inflation can be detected if it is lighter than 10−16 GeV. We also make publicly available a Python Monte Carlo tool for generating the PTA time delay signal from any model of DM substructure.
Temperature and Velocity Measurements in Premixed Turbulent Flames

Turbulent flame speed data for premixed flames of methane-air, propane-air and ethylene-air mixtures stabilized in grid turbulence are reported and discussed. It is shown that turbulence effects on flame speed cannot be fully correlated by the turbulence length scale and r.m.s. velocity in the cold flow. Rather there appear to be significant flame-flow-turbulence interactions affecting both turbulence level in the reaction zone and measured flame speeds. Results of detailed velocity measurements, including autocorrelations, by laser velocimetry are used to elucidate the nature of these interactions. It is concluded that flame speed experiments must be designed and conducted to provide sufficient information (e.g., boundary conditions) to allow for reconstruction of the flow field and these interactions by modelers if the data are to be of value in turbulent combustion model development and evaluation.
Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing

Extensive work has argued in favour of paying crowd workers a wage that is at least equivalent to the U.S. federal minimum wage. Meanwhile, research on collecting high quality annotations suggests using a qualification that requires workers to have previously completed a certain number of tasks. If most requesters who pay fairly require workers to have completed a large number of tasks already then workers need to complete a substantial amount of poorly paid work before they can earn a fair wage. Through analysis of worker discussions and guidance for researchers, we estimate that workers spend approximately 2.25 months of full time effort on poorly paid tasks in order to get the qualifications needed for better paid tasks. We discuss alternatives to this qualification and conduct a study of the correlation between qualifications and work quality on two NLP tasks. We find that it is possible to reduce the burden on workers while still collecting high quality data.
Master Limited Partnership Research in Accounting, Economics, and Finance

I review the research on master limited partnerships (“MLPs”) in the accounting, economics, and finance literature. I begin by outlining the scope of the review and providing a brief background on the structure, taxation, and governance of master limited partnerships. Next, I describe the various sources from which MLP data is derived. I then review the research, aggregating it into four broad categories: (1) taxes and organizational form; (2) taxes, capital structure, and payout policy; (3) valuation; and (4) governance research. Within each section, I present possible avenues for future research in accounting, economics, and finance.
Dichotomy Result on 3-Regular Bipartite Non-negative Functions

We prove a complexity dichotomy theorem for a class of Holant problems on 3-regular bipartite graphs. Given an arbitrary nonnegative weighted symmetric constraint function \(f = [x_0, x_1, x_2, x_3]\), we prove that the bipartite Holant problem \({\text {Holant}}\left( f\mid (=_3)\right) \) is either computable in polynomial time or #P-hard. The dichotomy criterion on f is explicit.
Disentangling Online Chats with DAG-structured LSTMs.

Many modern messaging systems allow fast and synchronous textual communication among many users. The resulting sequence of messages hides a more complicated structure in which independent sub-conversations are interwoven with one another. This poses a challenge for any task aiming to understand the content of the chat logs or gather information from them. The ability to disentangle these conversations is then tantamount to the success of many downstream tasks such as summarization and question answering. Structured information accompanying the text such as user turn, user mentions, timestamps, is used as a cue by the participants themselves who need to follow the conversation and has been shown to be important for disentanglement. DAG-LSTMs, a generalization of Tree-LSTMs that can handle directed acyclic dependencies, are a natural way to incorporate such information and its non-sequential nature. In this paper, we apply DAG-LSTMs to the conversation disentanglement task. We perform our experiments on the Ubuntu IRC dataset. We show that the novel model we propose achieves state of the art status on the task of recovering reply-to relations and it is competitive on other disentanglement metrics.