cssi/SciSciGPT-SciSciNet huggingface dataset:
Quasisymmetric maps of boundaries of amenable hyperbolic groups

In this paper we show that if Y = N ×Qm is a metric space where N is a Carnot group endowed with the Carnot-Caratheodory metric then any quasisymmetric map of Y is actually bilipschitz. The key observation is that Y is the parabolic visual boundary of a mixed type locally compact amenable hyperbolic group. The same results also hold for a larger class of nilpotent Lie groups N . As part of the proof we also obtain partial quasi-isometric rigidity results for mixed type locally compact amenable hyperbolic groups. Finally we prove a rigidity result for uniform subgroups of bilipschitz maps of Y in the case of N = Rn.
Polyglot Semantic Role Labeling

Previous approaches to multilingual semantic dependency parsing treat languages independently, without exploiting the similarities between semantic structures across languages. We experiment with a new approach where we combine resources from different languages in the CoNLL 2009 shared task to build a single polyglot semantic dependency parser. Notwithstanding the absence of parallel data, and the dissimilarity in annotations between languages, our approach results in improvement in parsing performance on several languages over a monolingual baseline. Analysis of the polyglot models’ performance provides a new understanding of the similarities and differences between languages in the shared task.
Analysis and Control of Epidemics: A Survey of Spreading Processes on Complex Networks

This article reviews and presents various solved and open problems in the development, analysis, and control of epidemic models. The proper modeling and analysis of spreading processes has been a long-standing area of research among many different fields, including mathematical biology, physics, computer science, engineering, economics, and the social sciences. One of the earliest epidemic models conceived was by Daniel Bernoulli in 1760, which was motivated by studying the spread of smallpox [1]. In addition to Bernoulli, there were many different researchers also working on mathematical epidemic models around this time [2]. These initial models were quite simplistic, and the further development and study of such models dates back to the 1900s [3]-[6], where still-simple models were studied to provide insight into how various diseases can spread through a population. In recent years, there has been a resurgence of interest in these problems as the concept of "networks" becomes increasingly prevalent in modeling many different aspects of the world today. A more comprehensive review of the history of mathematical epidemiology can be found in [7] and [8].
Calculations with graded perverse-coherent sheaves

In this paper, we carry out several computations involving graded (or $\mathbb{G}_{\mathrm{m}}$-equivariant) perverse-coherent sheaves on the nilpotent cone of a reductive group in good characteristic. In the first part of the paper, we compute the weight of the $\mathbb{G}_{\mathrm{m}}$-action on certain normalized (or "canonical") simple objects, confirming an old prediction of Ostrik. In the second part of the paper, we explicitly describe all simple perverse coherent sheaves for $G = PGL_3$, in every characteristic other than 2 or 3. Applications include an explicit description of the cohomology of tilting modules for the corresponding quantum group, as well as a proof that $\mathsf{PCoh}^{\mathbb{G}_{\mathrm{m}}}(\mathcal{N})$ never admits a positive grading when the characteristic of the field is greater than 3.
Price of Privacy in the Keynesian Beauty Contest

The Keynesian Beauty Contest is a classical game in which strategic agents seek to both accurately guess the true state of the world as well as the average action of all agents. We study an augmentation of this game where agents are concerned about revealing their private information and additionally suffer a loss based on how well an observer can infer their private signals. We solve for an equilibrium of this augmented game and quantify the loss of social welfare as a result of agents acting to obscure their private information, which we call the 'price of privacy'. We analyze two versions of this this price: one from the perspective of the agents measuring their diminished ability to coordinate due to acting to obscure their information and another from the perspective of an aggregator whose statistical estimate of the true state of the world is of lower precision due to the agents adding random noise to their actions. We show that these quantities are high when agents care very strongly about protecting their personal information and low when the quality of the signals the agents receive is poor.
The stability of free fermi Hamiltonians

Recent results have shown the stability of frustration-free Hamiltonians to weak local perturbations, assuming several conditions. In this paper, we prove the stability of free fermion Hamiltonians which are gapped and local. These free fermion Hamiltonians are not necessarily frustration-free, but we are able to adapt previous work to prove stability. The key idea is to add an additional copy of the system to cancel topological obstructions. We comment on applications to quantization of Hall conductance in such systems.Recent results have shown the stability of frustration-free Hamiltonians to weak local perturbations, assuming several conditions. In this paper, we prove the stability of free fermion Hamiltonians which are gapped and local. These free fermion Hamiltonians are not necessarily frustration-free, but we are able to adapt previous work to prove stability. The key idea is to add an additional copy of the system to cancel topological obstructions. We comment on applications to quantization of Hall conductance in such systems.
On Resolutions of Cosmological Singularities in Higher-Spin Gravity

We study the resolution of certain cosmological singularity in the context of higher-spin three-dimensional gravity. We consider gravity coupled to a spin-3 field realized as Chern-Simons theory with gauge group $SL(3,\mathbb{C})$. In this context we elaborate and extend a singularity resolution scheme proposed by Krishnan and Roy. We discuss the resolution of a big-bang singularity in the case of gravity coupled to a spin-4 field realized as Chern-Simons theory with gauge group $SL(4,\mathbb{C})$. In all these cases we show the existence of gauge transformations that do not change the holonomy of the Chern-Simons gauge potential and lead to metrics without the initial singularity. We argue that such transformations always exist in the context of gravity coupled to a spin-N field when described by Chern-Simons with gauge group $SL(N,\mathbb{C})$.
Hierarchical Classification Using Binary Data

In classification problems, especially those that categorize data into a large number of classes, the classes often naturally follow a hierarchical structure. That is, some classes are likely to share similar structures and features. Those characteristics can be captured by considering a hierarchical relationship among the class labels. Motivated by a recent simple classification approach on binary data, we propose a variant that is tailored to efficient classification of hierarchical data. In certain settings, specifically, when some classes are significantly easier to identify than others, we show case computational and accuracy advantages.
A characterization of the Morse minimal set up to topological conjugacy

We establish necessary and sufficient conditions for a dynamical system to be topologically conjugate to the Morse minimal set, the shift orbit closure of the Morse sequence. Conditions for topological conjugacy to the closely related Toeplitz minimal set are also derived.
Effective equilibrium theory of nonequilibrium quantum transport

Abstract The theoretical description of strongly correlated quantum systems out of equilibrium presents several challenges and a number of open questions persist. Here, we focus on nonlinear electronic transport through an interacting quantum dot maintained at finite bias using a concept introduced by Hershfield [S. Hershfield, Phys. Rev. Lett. 70 2134 (1993)] whereby one can express such nonequilibrium quantum impurity models in terms of the system’s Lippmann–Schwinger operators. These scattering operators allow one to reformulate the nonequilibrium problem as an effective equilibrium problem associated with a modified Hamiltonian. In this paper, we provide a pedagogical analysis of the core concepts of the effective equilibrium theory. First, we demonstrate the equivalence between observables computed using the Schwinger–Keldysh framework and the effective equilibrium approach, and relate Green’s functions in the two theoretical frameworks. Second, we expound some applications of this method in the context of interacting quantum impurity models. We introduce a novel framework to treat effects of interactions perturbatively while capturing the entire dependence on the bias voltage. For the sake of concreteness, we employ the Anderson model as a prototype for this scheme. Working at the particle–hole symmetric point, we investigate the fate of the Abrikosov–Suhl resonance as a function of bias voltage and magnetic field.
Geodesic walks in polytopes

We introduce the geodesic walk for sampling Riemannian manifolds and apply it to the problem of generating uniform random points from the interior of polytopes in ℝn specified by m inequalities. The walk is a discrete-time simulation of a stochastic differential equation (SDE) on the Riemannian manifold equipped with the metric induced by the Hessian of a convex function; each step is the solution of an ordinary differential equation (ODE). The resulting sampling algorithm for polytopes mixes in O*(mn3/4) steps. This is the first walk that breaks the quadratic barrier for mixing in high dimension, improving on the previous best bound of O*(mn) by Kannan and Narayanan for the Dikin walk. We also show that each step of the geodesic walk (solving an ODE) can be implemented efficiently, thus improving the time complexity for sampling polytopes. Our analysis of the geodesic walk for general Hessian manifolds does not assume positive curvature and might be of independent interest.
The stable 4-genus of knots

We define the stable 4–genus of a knot K S , gst.K/ , to be the limiting value of g4.nK/=n , where g4 denotes the 4–genus and n goes to infinity. This induces a seminorm on the rationalized knot concordance group, CQ D C Q . Basic properties of gst are developed, as are examples focused on understanding the unit ball for gst on specified subspaces of CQ . Subspaces spanned by torus knots are used to illustrate the distinction between the smooth and topological categories. A final example is given in which Casson–Gordon invariants are used to demonstrate that gst.K/ can be a noninteger.
Local unitary representations of the braid group and their applications to quantum computing

We provide an elementary introduction to topological quantum computation based on the Jones representation of the braid group. We first cover the Burau representation and Alexander polynomial. Then we discuss the Jones representation and Jones polynomial and their application to anyonic quantum computation. Finally we outline the approximation of the Jones polynomial by a quantum computer and explicit localizations of braid group representations.
A Chip-Firing Game on the Product of Two Graphs and the Tropical Picard Group

Cartwright (2015) introduced the notion of a weak tropical complex in order to generalize the theory of divisors on graphs from Baker and Norine (2007). A weak tropical complex $\Gamma$ is a $\Delta$-complex equipped with algebraic data that allows it to be viewed as the dual complex to a certain kind of degeneration over a discrete valuation ring. Every graph has a unique tropical complex structure (which is the same structure studied by Baker and Norine) in which divisors correspond to states in the chip-firing game on that graph. Let $G$ and $H$ be graphs, and let $\Gamma$ be a triangulation of $G\times H$ obtained by adding in one diagonal of each resulting square. There is a particular weak tropical complex structure on $\Gamma$ that Cartwright conjectured was closely related to the weak tropical complex structures on $G$ and $H$. The main result of this paper is a proof of Cartwright's conjecture. In preparation, we discuss some basic properties of tropical complexes, along with some properties specific to the product-of-graphs case.
Counting SET-Free Sets

We consider the following counting problem related to the card game SET: how many k-element SET-free sets are there in an n-dimensional SET deck? Through a series of algebraic reformulations and reinterpretations, we show the answer to this question satisfies two polynomiality conditions.
Model‐free causal inference of binary experimental data

Author(s): Ding, P; Miratrix, LW | Abstract: For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. In addition to the classical method of moments, we also introduce model-free likelihood and Bayesian methods based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have some properties superior to moment-based ones such as only giving estimates in regions of feasible support. Due to the lack of identification of the causal model, we also propose a sensitivity analysis approach that allows for the characterization of the impact of the association between the potential outcomes on statistical inference.
The Sovereign-Bank Nexus in EMDEs : What Is It, Is It Rising, and What Are the Policy Implications ?

This paper explores the sovereign-bank nexus in emerging markets and developing economies: the interconnectedness between the health of the sovereign and the banking system. Data from 140 emerging markets and developing economies suggest that this nexus is rising. First, banks have increased their exposure to their sovereigns in the past decade. Second, government debt has grown, and fiscal positions have deteriorated, raising the specter of sovereign stress. Third, banking system assets and bank credit to the private sector have steadily increased, which may restrict the sovereign's capacity to contain a banking crisis. Fourth, empirical evidence from 36 emerging markets and developing economies documents the existence of the nexus and suggests that it has increased recently. However, deeper country analysis is required for a better understanding of the sovereign-bank nexus, given country idiosyncrasies, including the structure of sovereign debt and the composition of the investor base, and data lags and opacities. To minimize the adverse effects of the sovereign-bank nexus, efforts should be focused on maintaining fiscal and bank buffers, strengthening surveillance and supervision of the banking system, improving transparency and data quality of bank-sovereign linkages, better addressing the regulatory treatment of the sovereign exposures and government support of the banking sector, and carefully evaluating the policy trade-offs in the sovereign-bank nexus.
Practice induces a qualitative change in the memory representation for visuomotor learning

Recent research has shown that savings for visuomotor adaptation is attributable to retrieval of intentional, strategic compensation. This does not seem consistent with the implicit nature of memor...
DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching

Robots should understand both semantics and physics to be functional in the real world. While robot platforms provide means for interacting with the physical world they cannot autonomously acquire object-level semantics without needing human. In this paper, we investigate how to minimize human effort and intervention to teach robots perform real world tasks that incorporate semantics. We study this question in the context of visual servoing of mobile robots and propose DIViS, a Domain Invariant policy learning approach for collision free Visual Servoing. DIViS incorporates high level semantics from previously collected static human-labeled datasets and learns collision free servoing entirely in simulation and without any real robot data. However, DIViS can directly be deployed on a real robot and is capable of servoing to the user-specified object categories while avoiding collisions in the real world. DIViS is not constrained to be queried by the final view of goal but rather is robust to servo to image goals taken from initial robot view with high occlusions without this impairing its ability to maintain a collision free path. We show the generalization capability of DIViS on real mobile robots in more than 90 real world test scenarios with various unseen object goals in unstructured environments. DIViS is compared to prior approaches via real world experiments and rigorous tests in simulation. For supplementary videos, see: \href{this https URL}{this https URL}
Truly Batch Apprenticeship Learning with Deep Successor Features

We introduce a novel apprenticeship learning algorithm to learn an expert's underlying reward structure in off-policy model-free \emph{batch} settings. Unlike existing methods that require a dynamics model or additional data acquisition for on-policy evaluation, our algorithm requires only the batch data of observed expert behavior. Such settings are common in real-world tasks---health care, finance or industrial processes ---where accurate simulators do not exist or data acquisition is costly. To address challenges in batch settings, we introduce Deep Successor Feature Networks(DSFN) that estimate feature expectations in an off-policy setting and a transition-regularized imitation network that produces a near-expert initial policy and an efficient feature representation. Our algorithm achieves superior results in batch settings on both control benchmarks and a vital clinical task of sepsis management in the Intensive Care Unit.
A nonlinear Stokes-Biot model for the interaction of a non-Newtonian fluid with poroelastic media

We develop and analyze a model for the interaction of a quasi-Newtonian free fluid with a poroelastic medium. The flow in the fluid region is described by the nonlinear Stokes equations and in the poroelastic medium by the nonlinear quasi-static Biot model. Equilibrium and kinematic conditions are imposed on the interface. We establish existence and uniqueness of a solution to the weak formulation and its semidiscrete continuous-in-time finite element approximation. We present error analysis, complemented by numerical experiments.
Bidirectional Learning for Domain Adaptation of Semantic Segmentation

Domain adaptation for semantic image segmentation is very necessary since manually labeling large datasets with pixel-level labels is expensive and time consuming. Existing domain adaptation techniques either work on limited datasets, or yield not so good performance compared with supervised learning. In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation. Using the bidirectional learning, the image translation model and the segmentation adaptation model can be learned alternatively and promote to each other.Furthermore, we propose a self-supervised learning algorithm to learn a better segmentation adaptation model and in return improve the image translation model. Experiments show that our method superior to the state-of-the-art methods in domain adaptation of segmentation with a big margin. The source code is available at https://github.com/liyunsheng13/BDL
Direct speech-to-speech translation with a sequence-to-sequence model

We present an attention-based sequence-to-sequence neural network which can directly translate speech from one language into speech in another language, without relying on an intermediate text representation. The network is trained end-to-end, learning to map speech spectrograms into target spectrograms in another language, corresponding to the translated content (in a different canonical voice). We further demonstrate the ability to synthesize translated speech using the voice of the source speaker. We conduct experiments on two Spanish-to-English speech translation datasets, and find that the proposed model slightly underperforms a baseline cascade of a direct speech-to-text translation model and a text-to-speech synthesis model, demonstrating the feasibility of the approach on this very challenging task.
A fast algorithm for radiative transport in isotropic media

Abstract Constructing efficient numerical solution methods for the equation of radiative transfer (ERT) remains as a challenging task in scientific computing despite of the tremendous development on the subject in recent years. We present in this work a simple fast computational algorithm for solving the ERT in isotropic media. The algorithm we developed has two steps. In the first step, we solve a volume integral equation for the angularly-averaged ERT solution using iterative schemes such as the GMRES method. The computation in this step is accelerated with a fast multipole method (FMM). In the second step, we solve a scattering-free transport equation to recover the angular dependence of the ERT solution. The algorithm does not require the underlying medium be homogeneous. We present numerical simulations under various scenarios to demonstrate the performance of the proposed numerical algorithm for both homogeneous and heterogeneous media.
Social instability is an effective chronic stress paradigm for both male and female mice

Despite stress-associated disorders having a higher incidence rate in females, preclinical research mainly focuses on males. Chronic stress paradigms, such as chronic social defeat and chronic corticosterone (CORT) administration, were mainly designed and validated in males and subsequent attempts to use these paradigms in females has demonstrated sex differences in the behavioral and HPA axis response to stress. Here, we assessed the behavioral response to chronic CORT exposure and developed a social stress paradigm, social instability stress (SIS), which exposes adult mice to unstable social hierarchies every 3 days for 7 weeks. Sex differences in response to chronic CORT emerged, with negative valence behaviors induced in CORT treated males, not females. SIS effectively induces negative valence behaviors in the open field, light dark, and novelty suppressed feeding tests, increases immobility in the forced swim test, and activates the hypothalamus-pituitary-adrenal (HPA) axis in both males and females. Importantly, while there were effects of estrous cycle on behavior, this variability did not impact the overall effects of SIS on behavior, suggesting estrous does not need to be tracked while utilizing SIS. Furthermore, the effects of SIS on negative valence behaviors were also reversed following chronic antidepressant treatment with fluoxetine (FLX) in both males and females. SIS also reduced adult hippocampal neurogenesis in female mice, while chronic FLX treatment increased adult hippocampal neurogenesis in both males and females. Overall, these data demonstrate that the SIS paradigm is an ethologically valid approach that effectively induces chronic stress in both adult male and adult female mice.
Empirical measures of mutational effects define neutral models of regulatory evolution in Saccharomyces cerevisiae.

Understanding how phenotypes evolve requires disentangling the effects of mutation generating new variation from the effects of selection filtering it. Tests for selection frequently assume that mutation introduces phenotypic variation symmetrically around the population mean, yet few studies have tested this assumption by deeply sampling the distributions of mutational effects for particular traits. Here, we examine distributions of mutational effects for gene expression in the budding yeast Saccharomyces cerevisiae by measuring the effects of thousands of point mutations introduced randomly throughout the genome. We find that the distributions of mutational effects differ for the 10 genes surveyed and are inconsistent with normality. For example, all 10 distributions of mutational effects included more mutations with large effects than expected for normally distributed phenotypes. In addition, some genes also showed asymmetries in their distribution of mutational effects, with new mutations more likely to increase than decrease the gene’s expression or vice versa. Neutral models of regulatory evolution that take these empirically determined distributions into account suggest that neutral processes may explain more expression variation within natural populations than currently appreciated.
Unconstraining Graph-Constrained Group Testing.

In network tomography, one goal is to identify a small set of failed links in a network, by sending a few packets through the network and seeing which reach their destination. This problem can be seen as a variant of combinatorial group testing, which has been studied before under the moniker "graph-constrained group testing." The main contribution of this work is to show that for most graphs, the "constraints" imposed by the underlying network topology are no constraint at all. That is, the number of tests required to identify the failed links in "graph-constrained" group testing is near-optimal even for the corresponding group testing problem with no graph constraints. Our approach is based on a simple randomized construction of tests, to analyze our construction, we prove new results about the size of giant components in randomly sparsified graphs. Finally, we provide empirical results which suggest that our connected-subgraph tests perform better not just in theory but also in practice, and in particular perform better on a real-world network topology.
A note on dual modules and the transpose

It is a classical result in matrix algebra that any square matrix over a field can be conjugated to its transpose by a symmetric matrix. For F a non-Archimedean local field, Tupan used this to give an elementary proof that transpose inverse takes each irreducible smooth representation of $${\mathrm{GL}}_n(F)$$ to its dual. We re-prove the matrix result and related observations using module-theoretic arguments. In addition, we write down a generalization that applies to central simple algebras with an involution of the first kind. We use this generalization to extend Tupan’s method of argument to $${\mathrm{GL}}_n(D)$$ for D a quaternion division algebra over F.
Biological process activity transformation of single cell gene expression for cross-species alignment

The maintenance and transition of cellular states are controlled by biological processes. Here we present a gene set-based transformation of single cell RNA-Seq data into biological process activities that provides a robust description of cellular states. Moreover, as these activities represent species-independent descriptors, they facilitate the alignment of single cell states across different organisms.
Genetic and Environmental Influence on the Human Functional Connectome.

Detailed mapping of genetic and environmental influences on the functional connectome is a crucial step toward developing intermediate phenotypes between genes and clinical diagnoses or cognitive abilities. We analyzed resting-state functional magnetic resonance imaging data from two adult twin samples (Nos = 446 and 371) to quantify genetic and environmental influence on all pairwise functional connections between 264 brain regions (~35 000 functional connections). Nonshared environmental influence was high across the whole connectome. Approximately 14-22% of connections had nominally significant genetic influence in each sample, 4.6% were significant in both samples, and 1-2% had heritability estimates greater than 30%. Evidence of shared environmental influence was weak. Genetic influences on connections were distinct from genetic influences on a global summary measure of the connectome, network-based estimates of connectivity, and movement during the resting-state scan, as revealed by a novel connectome-wide bivariate genetic modeling procedure. The brain's genetic organization is diverse and not as one would expect based solely on structure evident in nongenetically informative data or lower resolution data. As follow-up, we make novel classifications of functional connections and examine highly localized connections with particularly strong genetic influence. This high-resolution genetic taxonomy of brain connectivity will be useful in understanding genetic influences on brain disorders.
Relativistic effects in astronomical timing measurements

In this paper the relativistic transformations that are necessary to transform the results of earth-based astronomical timing observations from the terrestrial frame in which the observations are made to the proper frame of the astronomical object being observed are derived. Two coordinate systems are considered, the first being the parametrized post-Newtonian coordinate system and the second being a coordinate system based on the use of temps dynamique barycentrique as a time coordinate. Effects due to propagation of signals through curved spacetime and due to the relativistic gravitational time dilation for the earth are derived in each system. The results are then applied to three modern accurate data types spacecraft ranging, pulsar timing, and VLBI. A new effect is found in the analysis of VLBI data, amounting to a rescaling of VLBI-determined baselines produced by the curvature of space in the neighborhood of the sun.
The Evidence for Planetary Progenitor Rotation and for the Long-Term Acceleration of the Ejected Nebular Shell

Several theories seek to explain the peculiar shapes of planetary nebulae. Those of Louise, Kirkpatrick, and Phillips and Reay rely on progenitor rotation. The velocity-radius relation for the shells of well observed planetaries do not extrapolate back through the origin, but rather fall short, suggesting that the shell acquires its velocity over a significant period of time. Kirkpatrick's theory relies heavily on long term acceleration of the nebular shell, and other theoretical studies support the idea of acceleration of the nebular shell up to the time it becomes optically thin to the ionizing radiation from the central star.
Neurlux: dynamic malware analysis without feature engineering

Malware detection plays a vital role in computer security. Modern machine learning approaches have been centered around domain knowledge for extracting malicious features. However, many potential features can be used, and it is time consuming and difficult to manually identify the best features, especially given the diverse nature of malware. In this paper, we propose Neurlux, a neural network for malware detection. Neurlux does not rely on any feature engineering, rather it learns automatically from dynamic analysis reports that detail behavioral information. Our model borrows ideas from the field of document classification, using word sequences present in the reports to predict if a report is from a malicious binary or not. We investigate the learned features of our model and show which components of the reports it tends to give the highest importance. Then, we evaluate our approach on two different datasets and report formats, showing that Neurlux improves on the state of the art and can effectively learn from the dynamic analysis reports. Furthermore, we show that our approach is portable to other malware analysis environments and generalizes to different datasets.
Simultaneous targeting of XPO1 and BCL2 as an effective treatment strategy for double-hit lymphoma

Double-hit lymphoma (DHL) is among the most aggressive and chemoresistant lymphoma subtypes. DHLs carry genomic abnormalities in MYC, BCL2, and/or BCL6 oncogenes. Due to the simultaneous overexpression of these driver oncogenes, DHLs are highly resistant to frontline therapies. Most DHLs overexpress both MYC and BCL2 driver oncogenes concurrently. We reasoned that simultaneous suppression of the two driver oncogenes would be more effective in eradicating DHLs than inactivation of single oncogene. XPO1 is a receptor for nuclear cytoplasmic transport of protein and RNA species. Recently, XPO1 inhibition was shown to downregulate MYC expression in several cancer cell lines. We therefore examined the role of XPO1 as a therapeutic target in suppressing MYC function and the potential synergistic effects of simultaneous suppression of XPO1 and BCL2 in the treatment of DHL. Here, we demonstrate that XPO1 inhibition abrogates MYC protein expression and induces massive tumor cell apoptosis. Combined use of XPO1 and BCL2 inhibitors is highly effective in eradicating DHL cells in cell culture. Notably, in a mouse model of DHL bearing primary tumor cells derived from lymphoma patients, combined treatment with XPO1 and BCL2 inhibitors blocks tumor progression, prevents brain metastasis, and extends host survival. Thus, our study confirms the simultaneous targeting of MYC and BCL2 driver oncogenes through the combined use of XPO1 and BCL2 inhibitors as a unique approach for the treatment of DHLs.
Long-term culturing of porcine nodose ganglia

Abstract Background Neuronal cell cultures are widely used in the field of neuroscience. Cell dissociation allows for the isolation of a desired cell type, yet the neuronal complexity that distinguishes the nervous system is often lost as a result. Thus, culturing neural tissues in ex vivo format provides a physiological context that more closely resembles the in vivo environment. New method We developed a simple method to culture nodose ganglia neurons from neonatal pigs long-term in ex vivo format using an in-house media formulation derived from commercially available components. Results Ganglia were cultured for six and twelve months. mRNA expression of nestin was stable across time. Vasoactive intestinal peptide and tachykinin showed statistically insignificant increases and decreases in mRNA expression, respectively. mRNA expression of glia fibrillary acidic protein decreased, whereas myelin basic protein showed no statistically significant differences, over time. Immunofluorescence studies of sectioned ganglia demonstrated neurofilament-positive cell bodies, glia fibrillary acidic protein and myelin basic protein at all time points. A significant decrease in cell nuclei density and fragmented DNA were noted. Comparison with existing method(s) There are currently no methods that describe short-term or long-term culturing of porcine nodose ganglia. Further, the media formulation we developed is new and not previously reported. Conclusions The simple procedure we developed for culturing nodose ganglia will enable both short-term and long-term investigations aimed at understanding peripheral ganglia in vitro. It is also possible that the methods described herein can be applied to other models, different developmental stages, and potentially other neural tissues.
Spherical indecomposable representations of Lie superalgebras

Abstract We present a classification of all spherical indecomposable representations of classical and exceptional Lie superalgebras. We also include information about stabilizers, symmetric algebras, and Borels for which sphericity is achieved. In one such computation, the symmetric algebra of the standard module of osp ( m | 2 n ) is computed, which in particular gives the representation-theoretic structure of polynomials on the complex supersphere.
Does Household Electrification Supercharge Economic Development

Author(s): Lee, Kenneth; Miguel, Edward; Wolfram, Catherine | Abstract: In recent years, electrification has re-emerged as a key priority in low-income countries, with a particular focus on electrifying households. Yet the microeconomic literature examining the impacts of electrifying households on economic development has produced a set of conflicting results. Does household electrification lead to measurable gains in living standards or not? Focusing on grid electrification, we discuss how the divergent conclusions across the literature can be explained by differences in methods, interventions, potential for spillovers, and populations. We then use experimental data from Lee, Miguel, and Wolfram (2019) — a field experiment that connected randomly-selected households to the grid in rural Kenya— to show that impacts can vary even across individuals in neighboring villages. Specifically, we show that households that were willing to pay more for a grid electrification may gain more from electrification compared to households that would only connect for free. We conclude that access to household electrification alone is not enough to drive meaningful gains in development outcomes. Instead, future initiatives may work better if paired with complementary inputs that allow people to do more with power.
A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models

Deep neural networks (DNN) are quickly becoming the de facto standard modeling method for many natural language generation (NLG) tasks. In order for such models to truly be useful, they must be capable of correctly generating utterances for novel meaning representations (MRs) at test time. In practice, even sophisticated DNNs with various forms of semantic control frequently fail to generate utterances faithful to the input MR. In this paper, we propose an architecture agnostic self-training method to sample novel MR/text utterance pairs to augment the original training data. Remarkably, after training on the augmented data, even simple encoder-decoder models with greedy decoding are capable of generating semantically correct utterances that are as good as state-of-the-art outputs in both automatic and human evaluations of quality.
Learning Compositional Koopman Operators for Model-Based Control

Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lays the foundation for identifying the nonlinear-to-linear coordinate transformations with data-driven methods. Recently, researchers have proposed to use deep neural networks as a more expressive class of basis functions for calculating the Koopman operators. These approaches, however, assume a fixed dimensional state space; they are therefore not applicable to scenarios with a variable number of objects. In this paper, we propose to learn compositional Koopman operators, using graph neural networks to encode the state into object-centric embeddings and using a block-wise linear transition matrix to regularize the shared structure across objects. The learned dynamics can quickly adapt to new environments of unknown physical parameters and produce control signals to achieve a specified goal. Our experiments on manipulating ropes and controlling soft robots show that the proposed method has better efficiency and generalization ability than existing baselines.
Elliptic boundary value problems for the stationary vacuum spacetimes

We develop a general method of proving the ellipticity of boundary value problems for the stationary vacuum spacetimes, showing that the stationary vacuum field equations are elliptic subjected to a geometrically natural collection of boundary conditions in the projection formalism. As an application, we prove that the moduli space of stationary vacuum spacetimes admits Banach manifold structure.
Development of a confinable gene drive system in the human disease vector Aedes aegypti

Aedes aegypti is the principal mosquito vector for many arboviruses that increasingly infect millions of people every year. With an escalating burden of infections and the relative failure of traditional control methods, the development of innovative control measures has become of paramount importance. The use of gene drives has sparked significant enthusiasm for genetic control of mosquitoes; however, no such system has been developed in Ae. aegypti. To fill this void, here we develop several CRISPR-based split gene drives for use in this vector. With cleavage rates up to 100% and transmission rates as high as 94%, mathematical models predict that these systems could spread anti-pathogen effector genes into wild populations in a safe, confinable and reversible manner appropriate for field trials and effective for controlling disease. These findings could expedite the development of effector-linked gene drives that could safely control wild populations of Ae. aegypti to combat local pathogen transmission.
Financial Self-awareness: Who Knows What They Don’t Know?

Abstract We examine the influence of financial literacy and perceptions of financial knowledge on households’ financial risk-taking. Greater literacy and self-belief in one’s literacy positively relate to equity ownership. However, self-awareness of illiteracy reduces participation by about 5%. We find that financial self-awareness is impacted by innate traits and environmental elements. Specifically, it is reduced by risk-seeking preferences and rising income but increases with income uncertainty. Overall, we demonstrate that accurate self-assessment has implications for individuals’ portfolio choices, which suggests policy implications for improving financial decision-making.
Using Importance-Performance Matrix Analysis to Evaluate the Financial Performance of American Banks During the Financial Crisis:

This research applies a technique that identifies areas of improvement that can be addressed by managerial decisions or policy activities. It extends the application of partial least squares struct...
Reverse Mortgages, Financial Inclusion, and Economic Development : Potential Benefit and Risks

This paper examines the state of reverse mortgage markets in selected countries around the world and considers the potential benefits and risks of these products from a financial inclusion and economic benefit standpoint. Despite potentially increasing demand from aging societies -- combined with limited pension income -- a series of market failures constrain supply and demand. The paper discusses a series of market failures on the supply side, such as adverse selection, moral hazard, and the costly regulation established to address these problems, leading to only a small number of providers, even in developed markets. Demand-side constraints are equally relevant, in particular high non-interest costs, abuse concerns, and the inability of reverse mortgages to cover key risks facing the elderly, particularly health and elder care. In developing countries, constraints are likely to be even higher than in advanced economies, due to high transaction costs and lack of consumer knowledge and protection. The enabling conditions for such markets to develop are outlined, along with examples of regulatory oversight. The paper concludes that these still seem to be largely products of last resort rather than well-considered purchases as part of good retirement planning.
W4IPS: A Web-based Interactive Power System Simulation Environment For Power System Security Analysis

Modern power systems are increasingly evolving Cyber-Physical Systems (CPS) that feature close interaction between Information and Communication Technology (ICT), physical and electrical devices, and human factors. The interactivity and security of CPS are the essential building blocks for the reliability, stability and economic operation of power systems. This paper presents a web-based interactive multi-user power system simulation environment and open source toolset (W4IPS) whose main features are a publish/subscribe structure, a real-time data sharing capability, role-based multi-user visualizations, distributed multi-user interactive controls, an easy to use and deploy web interface, and flexible and extensible support for communication protocols. The paper demonstrates the use of W4IPS features as an ideal platform for contingency response training and cyber security analysis, with an emphasis on interactivity and expandability. In particular, we present the use cases and the results of W4IPS in power system operation education and security analysis.
Engineering Mechanics and Materials Research in the Information Technology Age

In this paper, importance of information technology research and development for economic growth and prosperity is presented. Major missio of the research for fundamental science and engineering base is discussed. Critical points of the mechanics and materials research in the 21 st Century are proposed.
Representable Matrices: Enabling High Accuracy Analog Computation for Inference of DNNs using Memristors

Analog computing based on memristor technology is a promising solution to accelerating the inference phase of deep neural networks (DNNs). A fundamental problem is to map an arbitrary matrix to a memristor crossbar array (MCA) while maximizing the resulting computational accuracy. The state-of-the-art mapping technique is based on a heuristic that only guarantees to produce the correct output for two input vectors. In this paper, a technique that aims to produce the correct output for every input vector is proposed, which involves specifying the memristor conductance values and a scaling factor realized by the peripheral circuitry. The key insight of the paper is that the conductance matrix realized by an MCA is only required to be proportional to the target matrix. The selection of the scaling factor between the two regulates the utilization of the programmable memristor conductance range and the representability of the target matrix. Consequently, the scaling factor is set to balance precision and value range errors. Moreover, a technique of converting conductance values into state variables and vice versa is proposed to handle memristors with non-ideal device characteristics. Compared with the state-of-the-art technique, the proposed mapping results in 4X-9X smaller errors. The improvements translate into that the classification accuracy of a seven-layer convolutional neural network (CNN) on CIFAR-10 is improved from 20.5% to 71.8%.
Defending Against Machine Learning Based Inference Attacks via Adversarial Examples: Opportunities and Challenges.

As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various domains. In such an ML-equipped inference attack, an attacker has access to some data (called public data) of an individual, a software, or a system; and the attacker uses an ML classifier to automatically infer their private data. Inference attacks pose severe privacy and security threats to individuals and systems. Inference attacks are successful because private data are statistically correlated with public data, and ML classifiers can capture such statistical correlations. In this chapter, we discuss the opportunities and challenges of defending against ML-equipped inference attacks via adversarial examples. Our key observation is that attackers rely on ML classifiers in inference attacks. The adversarial machine learning community has demonstrated that ML classifiers have various vulnerabilities. Therefore, we can turn the vulnerabilities of ML into defenses against inference attacks. For example, ML classifiers are vulnerable to adversarial examples, which add carefully crafted noise to normal examples such that an ML classifier makes predictions for the examples as we desire. To defend against inference attacks, we can add carefully crafted noise into the public data to turn them into adversarial examples, such that attackers’ classifiers make incorrect predictions for the private data. However, existing methods to construct adversarial examples are insufficient because they did not consider the unique challenges and requirements for the crafted noise at defending against inference attacks. In this chapter, we take defending against inference attacks in online social networks as an example to illustrate the opportunities and challenges.