cssi/SciSciGPT-SciSciNet huggingface dataset:
Concerning Toponogov’s theorem and logarithmic improvement of estimates of eigenfunctions

We use Toponogov’s triangle comparison theorem from Riemannian geometry along with quantitative scale oriented variants of classical propagation of singularities arguments to obtain logarithmic improvements of the Kakeya–Nikodym norms introduced in [22] for manifolds of nonpositive sectional curvature. Using these and results from our paper [4] we are able to obtain log-improvements of $L^p (M)$ estimates for such manifolds when $2 \lt p \lt \frac{2(n+1)}{n-1}$. These in turn imply $(\log \lambda)^{\sigma_n} , \sigma_n \approx n$, improved lower bounds for $L^1$-norms of eigenfunctions of the estimates of the second author and Zelditch [28], and using a result from Hezari and the second author [18], under this curvature assumption, we are able to improve the lower bounds for the size of nodal sets of Colding and Minicozzi [12] by a factor of $(\log \lambda)^{\mu}$ for any $\mu \lt \frac{2(n+1)^2}{n-1}$, if $n \geq 3$.
Irreducible Morphisms and Locally Finite Dimensional Representations

Let \(\mathcal {A}\) be a Hom-finite additive Krull-Schmidt k-category where k is an algebraically closed field. Let \(\text {mod}\mathcal {A}\) denote the category of locally finite dimensional \(\mathcal {A}\)-modules, that is, the category of covariant functors \(\mathcal {A} \to \text {mod}k\). We prove that an irreducible monomorphism in \(\text {mod}\mathcal {A}\) has a finitely generated cokernel, and that an irreducible epimorphism in \(\text {mod}\mathcal {A}\) has a finitely co-generated kernel. Using this, we get that an almost split sequence in \(\text {mod}\mathcal {A}\) has to start with a finitely co-presented module and end with a finitely presented one. Finally, we apply our results to the study of rep(Q), the category of locally finite dimensional representations of a strongly locally finite quiver. We describe all possible shapes of the Auslander-Reiten quiver of rep(Q).
Lower bounds for finding stationary points I

We prove lower bounds on the complexity of finding $$\epsilon $$ -stationary points (points x such that $$\Vert \nabla f(x)\Vert \le \epsilon $$ ) of smooth, high-dimensional, and potentially non-convex functions f. We consider oracle-based complexity measures, where an algorithm is given access to the value and all derivatives of f at a query point x. We show that for any (potentially randomized) algorithm $$\mathsf {A}$$ , there exists a function f with Lipschitz pth order derivatives such that $$\mathsf {A}$$ requires at least $$\epsilon ^{-(p+1)/p}$$ queries to find an $$\epsilon $$ -stationary point. Our lower bounds are sharp to within constants, and they show that gradient descent, cubic-regularized Newton’s method, and generalized pth order regularization are worst-case optimal within their natural function classes.
Homotopy theory of the master equation package applied to algebra and geometry: a sketch of two interlocking programs

We interpret mathematically the pair (master equation, solution of master equation) up to equivalence, as the pair (a presentation of a free triangular dga T over a combination operad O, dga map of T into C, a dga over O) up to homotopy equivalence of dgOa maps, see Definition 1. We sketch two general applications: I to the theory of the definition and homotopy theory of infinity versions of general algebraic structures including noncompact frobenius algebras and Lie bialgebras. Here the target C would be the total Hom complex between various tensor products of another chain complex B, C = HomB, O describes combinations of operations like composition and tensor product sufficient to describe the algebraic structure and one says that B has the algebraic structure in question. II to geometric systems of moduli spaces up to deformation like the moduli of J holomorphic curves. Here C is some geometric chain complex containing the fundamental classes of the moduli spaces of the geometric problem. We also discuss analogues of homotopy groups and Postnikov systems for maps and impediments to using them related to linear terms in the master equation called anomalies.
CONFORMALLY FLAT SUBMANIFOLDS IN SPHERES AND INTEGRABLE SYSTEMS

E. Cartan proved that conformally flat hypersurfaces in $S^{n+1}$ for $n>3$ have at most two distinct principal curvatures and locally envelop a one-parameter family of $(n-1)$-spheres. We prove that the Gauss-Codazzi equation for conformally flat hypersurfaces in $S^4$ is a soliton equation, and use a dressing action from soliton theory to construct geometric Ribaucour transforms of these hypersurfaces. We describe the moduli of these hypersurfaces in $S^4$ and their loop group symmetries. We also generalise these results to conformally flat $n$-immersions in $(2n-2)$-spheres with flat and non-degenerate normal bundle.
Double-critical graph conjecture for claw-free graphs

Abstract A connected graph G with chromatic number t is double-critical if G ∖ { x , y } is ( t − 2 ) -colorable for each edge x y ∈ E ( G ) . The complete graphs are the only known examples of double-critical graphs. A long-standing conjecture of Erdős and Lovasz from 1966, which is referred to as the Double-Critical Graph Conjecture, states that there are no other double-critical graphs. That is, if a graph G with chromatic number t is double-critical, then G is the complete graph on t vertices. This has been verified for t ≤ 5 , but remains open for t ≥ 6 . In this paper, we first prove that if G is a non-complete, double-critical graph with chromatic number t ≥ 6 , then no vertex of degree t + 1 is adjacent to a vertex of degree t + 1 , t + 2 , or t + 3 in G . We then use this result to show that the Double-Critical Graph Conjecture is true for double-critical graphs G with chromatic number t ≤ 8 if G is claw-free.
Elliptic curve variants of the least quadratic nonresidue problem and Linnik’s theorem

Let E1 and E2 be ℚ¯-nonisogenous, semistable elliptic curves over ℚ, having respective conductors NE1 and NE2 and both without complex multiplication. For each prime p, denote by aEi(p) := p + 1 − #Ei(&#x1d53d;p) the trace of Frobenius. Assuming the Generalized Riemann Hypothesis (GRH) for the convolved symmetric power L-functions L(s,SymiE 1 ⊗SymjE 2) where i,j ∈{0, 1, 2}, we prove an explicit result that can be stated succinctly as follows: there exists a prime p ∤ NE1NE2 such that aE1(p)aE2(p) < 0 and p < ((32 + o(1)) ⋅log NE1NE2)2. This improves and makes explicit a result of Bucur and Kedlaya. Now, if I ⊂ [−1, 1] is a subinterval with Sato–Tate measure μ and if the symmetric power L-functions L(s,SymkE 1) are functorial and satisfy GRH for all k ≤ 8/μ, we employ similar techniques to prove an explicit result that can be stated succinctly as follows: there exists a prime p ∤ NE1 such that aE1(p)/(2p) ∈ I and p < ((21 + o(1)) ⋅ μ−2log(N E1/μ))2.
Optimal control policies for evolutionary dynamics with environmental feedback

We study a dynamical model of a population of cooperators and defectors whose actions have long-term consequences on environmental “commons” - what we term the “resource”. Cooperators contribute to restoring the resource whereas defectors degrade it. The population dynamics evolve according to a replicator equation coupled with an environmental state. Our goal is to identify methods of influencing the population with the objective of maximizing accumulation of the resource. In particular, we consider strategies that modify individual-level incentives. We then extend the model to include a public opinion state that imperfectly tracks the true environmental state, and study strategies that influence opinion. We formulate optimal control problems and solve them using numerical techniques to characterize locally optimal control policies for three problem formulations: 1) control of incentives, and control of opinions through 2) propaganda-like strategies and 3) awareness campaigns. We show numerically that the resulting controllers in all formulations achieve the objective, albeit with an unintended consequence. The resulting dynamics include cycles between low and high resource states - a regime termed an “oscillating tragedy of the commons”. This outcome may have desirable average properties, but includes risks to resource depletion. Our findings suggest the need for new approaches to controlling population-environment dynamics.
Unsupervised Risk Estimation Using Only Conditional Independence Structure

We show how to estimate a model’s test error from unlabeled data, on distributions very different from the training distribution, while assuming only that certain conditional independencies are preserved between train and test. We do not need to assume that the optimal predictor is the same between train and test, or that the true distribution lies in any parametric family. We can also efficiently compute gradients of the estimated error and hence perform unsupervised discriminative learning. Our technical tool is the method of moments, which allows us to exploit conditional independencies in the absence of a fully-specified model. Our framework encompasses a large family of losses including the log and exponential loss, and extends to structured output settings such as conditional random fields.
Onsager-corrected deep learning for sparse linear inverse problems

Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem encountered in compressive sensing, where one seeks to recover a sparse signal from a small number of noisy linear measurements. In this paper, we propose a novel neural-network architecture that decouples prediction errors across layers in the same way that the approximate message passing (AMP) algorithm decouples them across iterations: through Onsager correction. Numerical experiments suggest that our “learned AMP” network significantly improves upon Gregor and Le-Cun's “learned ISTA” network in both accuracy and complexity.1
Improved Algorithms for Scheduling Unsplittable Flows on Paths.

In this paper, we investigate offline and online algorithms for rufpp, the problem of minimizing the number of rounds required to schedule a set of unsplittable flows of non-uniform sizes on a given path with non-uniform edge capacities. rufpp is NP-hard and constant-factor approximation algorithms are known under the no bottleneck assumption (NBA), which stipulates that maximum size of a flow is at most the minimum edge capacity. We study rufpp without the NBA, and present improved online and offline algorithms. We first study offline rufpp for a restricted class of instances called $\alpha$-small, where the size of each flow is at most $\alpha$ times the capacity of its bottleneck edge, and present an $O(\log(1/(1-\alpha)))$-approximation algorithm. Our main result is an online $O(\log\log c_{\max})$-competitive algorithm for rufpp for general instances, where $c_{\max}$ is the largest edge capacities, improving upon the previous best bound of $O(\log c_{\max})$ due to Epstein et al. Our result leads to an offline $O(\min(\log n, \log m, \log\log c_{\max}))$-approximation algorithm and an online $O(\min(\log m, \log\log c_{\max}))$-competitive algorithm for rufpp, where $n$ is the number of flows and $m$ is the number of edges.
On the theorem of Davenport and generalized Dedekind sums

Abstract A symmetrized lattice of 2n points in terms of an irrational real number α is considered in the unit square, as in the theorem of Davenport. If α is a quadratic irrational, the square of the L 2 discrepancy is found to be c ( α ) log ⁡ n + O ( log ⁡ log ⁡ n ) for a computable positive constant c ( α ) . For the golden ratio φ, the value c ( φ ) log ⁡ n yields the smallest L 2 discrepancy of any sequence of explicitly constructed finite point sets in the unit square. If the partial quotients a k of α grow at most polynomially fast, the L 2 discrepancy is found in terms of a k up to an explicitly bounded error term. It is also shown that certain generalized Dedekind sums can be approximated using the same methods. For a special generalized Dedekind sum with arguments a, b an asymptotic formula in terms of the partial quotients of a b is proved.
Learning to mine aligned code and natural language pairs from stack overflow

For tasks like code synthesis from natural language, code retrieval, and code summarization, data-driven models have shown great promise. However, creating these models require parallel data between natural language (NL) and code with fine-grained alignments. Stack Overflow (SO) is a promising source to create such a data set: the questions are diverse and most of them have corresponding answers with high quality code snippets. However, existing heuristic methods (e.g., pairing the title of a post with the code in the accepted answer) are limited both in their coverage and the correctness of the NL-code pairs obtained. In this paper, we propose a novel method to mine high-quality aligned data from SO using two sets of features: hand-crafted features considering the structure of the extracted snippets, and correspondence features obtained by training a probabilistic model to capture the correlation between NL and code using neural networks. These features are fed into a classifier that determines the quality of mined NL-code pairs. Experiments using Python and Java as test beds show that the proposed method greatly expands coverage and accuracy over existing mining methods, even when using only a small number of labeled examples. Further, we find that reasonable results are achieved even when training the classifier on one language and testing on another, showing promise for scaling NL-code mining to a wide variety of programming languages beyond those for which we are able to annotate data.
STAMINA: STochastic Approximate Model-Checker for INfinite-State Analysis

Stochastic model checking is a technique for analyzing systems that possess probabilistic characteristics. However, its scalability is limited as probabilistic models of real-world applications typically have very large or infinite state space. This paper presents a new infinite state CTMC model checker, STAMINA, with improved scalability. It uses a novel state space approximation method to reduce large and possibly infinite state CTMC models to finite state representations that are amenable to existing stochastic model checkers. It is integrated with a new property-guided state expansion approach that improves the analysis accuracy. Demonstration of the tool on several benchmark examples shows promising results in terms of analysis efficiency and accuracy compared with a state-of-the-art CTMC model checker that deploys a similar approximation method.
Trade-Offs in Online Advertising: Advertising Effectiveness and Annoyance Dynamics Across the Purchase Funnel

In this study, we capture the trade-off between effective and annoying display advertising. We investigate both the enduring impact of display advertising on consumers' purchase decisions and the p...
Patient Knowledge Distillation for BERT Model Compression

Pre-trained language models such as BERT have proven to be highly effective for natural language processing (NLP) tasks. However, the high demand for computing resources in training such models hinders their application in practice. In order to alleviate this resource hunger in large-scale model training, we propose a Patient Knowledge Distillation approach to compress an original large model (teacher) into an equally-effective lightweight shallow network (student). Different from previous knowledge distillation methods, which only use the output from the last layer of the teacher network for distillation, our student model patiently learns from multiple intermediate layers of the teacher model for incremental knowledge extraction, following two strategies: (i) PKD-Last: learning from the last k layers; and (ii) PKD-Skip: learning from every k layers. These two patient distillation schemes enable the exploitation of rich information in the teacher’s hidden layers, and encourage the student model to patiently learn from and imitate the teacher through a multi-layer distillation process. Empirically, this translates into improved results on multiple NLP tasks with a significant gain in training efficiency, without sacrificing model accuracy.
Analysis of physiological noise in quantitative cardiac magnetic resonance.

Purpose To determine the impact of imaging parameters on the temporal signal-to-noise ratio (TSNR) of quantitative cardiac magnetic resonance (MR) in humans, and to determine applicability of the physiological noise covariance (PNC) model for physiological noise (PN). Methods We conducted MRI experiments in four healthy volunteers, and obtained series of short-axis cardiac images acquired with snapshot balanced steady-state free precession (bSSFP) and snapshot gradient echo (GRE) using a broad range of spatial resolutions and parallel imaging acceleration factors commonly used in quantitative cardiac MR. We measured regional SNR and TSNR in these datasets and fit the measurements to the PNC model for PN, which assumes that PN scales with signal strength. Results The relationship between SNR and TSNR in human cardiac MR without contrast preparation was well modeled by the PNC model. SNR consistently decreased as the spatial resolution (matrix size) and acceleration factor (R) increased for both GRE and bSSFP imaging. TSNR varied linearly with SNR using GRE imaging, when SNR was low (SNR 40). Conclusions The PNC model can be used to guide the choice of matrix size and acceleration factor to optimize TSNR in stable contrast cardiac MR, such as T2-prepared Blood-Oxygen-Level-Dependent (BOLD) and several variants of Arterial Spin Labeled (ASL) cardiac MR.
A Universally Optimal Multistage Accelerated Stochastic Gradient Method

We study the problem of minimizing a strongly convex, smooth function when we have noisy estimates of its gradient. We propose a novel multistage accelerated algorithm that is universally optimal in the sense that it achieves the optimal rate both in the deterministic and stochastic case and operates without knowledge of noise characteristics. The algorithm consists of stages that use a stochastic version of Nesterov's method with a specific restart and parameters selected to achieve the fastest reduction in the bias-variance terms in the convergence rate bounds.
The Herpes Simplex Virus 1 Deamidase Enhances Propagation but Is Dispensable for Retrograde Axonal Transport into the Nervous System.

ABSTRACT Upon replication in mucosal epithelia and transmission to nerve endings, capsids of herpes simplex virus 1 (HSV-1) travel retrogradely within axons to peripheral ganglia, where life-long latent infections are established. A capsid-bound tegument protein, pUL37, is an essential effector of retrograde axonal transport and also houses a deamidase activity that antagonizes innate immune signaling. In this report, we examined whether the deamidase of HSV-1 pUL37 contributes to the neuroinvasive retrograde axonal transport mechanism. We conclude that neuroinvasion is enhanced by the deamidase, but the critical contribution of pUL37 to retrograde axonal transport functions independently of this activity. IMPORTANCE Herpes simplex virus 1 invades the nervous system by entering nerve endings and sustaining long-distance retrograde axonal transport to reach neuronal nuclei in ganglia of the peripheral nervous system. The incoming viral particle carries a deamidase activity on its surface that antagonizes antiviral responses. We examined the contribution of the deamidase to the hallmark neuroinvasive property of this virus.
From twitter to GDP: Estimating economic activity from social media

Abstract Using all geo-located image tweets shared on Twitter in 2012–2013, I find that the volume of tweets is a valid proxy for estimating GDP at the country level, explaining 78 percent of cross-country variations. I also exploit the geographic granularity of social media posts to estimate and predict GDP at the sub-national level. I find that tweets alone can explain 52 percent of the variation in GDP across cities in the US. Estimates using Twitter data perform on par with the more common night-lights proxy. Furthermore, both indicators seem to capture different aspects of economic activity and thus complement each other.
Do Weather-Induced Moods Affect the Processing of Earnings News?

We investigate whether unpleasant environmental conditions affect stock market participants’ responses to information events. We draw from psychology research to develop a new prediction that weather-induced negative moods reduce market participants’ activity levels. Exploiting geographic variation in equity analysts’ locations, we find compelling evidence that analysts experiencing unpleasant weather are slower or less likely to respond to an earnings announcement relative to analysts responding to the same announcement but experiencing pleasant weather. Price association tests find evidence consistent with reduced activity due to weather-induced moods delaying equilibrium price adjustments following earnings announcements. We also use our analyst-based research design to re-examine an existing prediction that unpleasant weather induces investor pessimism, and find evidence of both analyst pessimism and reduced activity in the presence of unpleasant weather. Together, our study provides new evidence that both extends and reaffirms findings of a relation between unpleasant weather and market activities, and contributes to the broader psychology and economics literature on the impact of weather-induced mood on labor productivity. This article is protected by copyright. All rights reserved
Neural correlates of perceptual switching while listening to bistable auditory streaming stimuli.

Abstract Understanding the neural underpinning of conscious perception remains one of the primary challenges of cognitive neuroscience. Theories based mostly on studies of the visual system differ according to whether the neural activity giving rise to conscious perception occurs in modality-specific sensory cortex or in associative areas, such as the frontal and parietal cortices. Here, we search for modality-specific conscious processing in the auditory cortex using a bistable stream segregation paradigm that presents a constant stimulus without the confounding influence of physical changes to sound properties. ABA_ triplets (i.e., alternating low, A, and high, B, tones, and _ gap) with a 700 ms silent response period after every third triplet were presented repeatedly, and human participants reported nearly equivalent proportions of 1- and 2-stream percepts. The pattern of behavioral responses was consistent with previous studies of visual and auditory bistable perception. The intermittent response paradigm has the benefit of evoking spontaneous perceptual switches that can be attributed to a well-defined stimulus event, enabling precise identification of the timing of perception-related neural events with event-related potentials (ERPs). Significantly more negative ERPs were observed for 2-streams compared to 1-stream, and for switches compared to non-switches during the sustained potential (500–1000 ms post-stimulus onset). Further analyses revealed that the negativity associated with switching was independent of switch direction, suggesting that spontaneous changes in perception have a unique neural signature separate from the observation that 2-stream percepts evoke more negative ERPs than 1-stream. Source analysis of the sustained potential showed activity associated with these differences originating in anterior superior temporal gyrus, indicating involvement of the ventral auditory pathway that is important for processing auditory objects.
Disclosure and Discovery with fairness

Abstract Two standard results in the litigation literature are that an informed party will not make a costly voluntary disclosure in a screening game and that the uninformed party will not engage in costly discovery in the signaling game. Both of these results rely on the assumption that the party making the offer can extract the entire surplus of settlement from the party receiving the offer. If the recipient of the offer has a demand for fairness, then the equilibrium offer will contain some monetary surplus. However, if utility is a continuous function of the surplus contained in the offer, the standard results from the literature are unchanged. Even though the equilibrium offer contains some surplus, it still pins the recipient of the offer to her dispute level of utility. As a result, the recipient of the offer will not engage in a costly transfer of information. The standard results can be overturned if the perceived fairness of the offer is a discontinuous function of the surplus it contains. In this model offers are coded as either fair or unfair, where all unfair offers impose a fixed utility penalty on the recipient if she accepts it. In such a model, the recipient of the offer will obtain a utility surplus (relative to her dispute level of utility) if the offer is perceived as fair. If this surplus is large enough, she will be willing to initiate a costly transfer of information.
Secure Network Coding in the Setting in Which a Non-Source Node May Generate Random Keys

It is common in the study of secure multicast network coding in the presence of an eavesdropper that has access to z network links, to assume that the source node is the only node that generates random keys. In this setting, the secure multicast rate is well understood. Computing the secure multicast rate, or even the secure unicast rate, in the more general setting in which all network nodes may generate (independent) random keys is known to be as difficult as computing the (non-secure) capacity of multiple-unicast network coding instances — a well known open problem. This work treats an intermediate model of secure unicast in which only one node can generate random keys, however that node need not be the source node. The secure communication rate for this setting is characterized again with an eavesdropper that has access to z network links.
An Analysis of State Evolution for Approximate Message Passing with Side Information

A common goal in many research areas is to reconstruct an unknown signal x from noisy linear measurements. Approximate message passing (AMP) is a class of low-complexity algorithms for efficiently solving such high-dimensional regression tasks. Often, it is the case that side information (SI) is available during reconstruction. For this reason a novel algorithmic framework that incorporates SI into AMP, referred to as approximate message passing with side information (AMP-SI), has been recently introduced. An attractive feature of AMP is that when the elements of the signal are exchangeable, the entries of the measurement matrix are independent and identically distributed (i.i.d.) Gaussian, and the denoiser applies the same non-linearity at each entry, the performance of AMP can be predicted accurately by a scalar iteration referred to as state evolution (SE). However, the AMP-SI framework uses different entry-wise scalar denoisers, based on the entry-wise level of the SI, and therefore is not supported by the standard AMP theory. In this work, we provide rigorous performance guarantees for AMP-SI when the input signal and SI are drawn i.i.d. according to some joint distribution subject to finite moment constraints. Moreover, we provide numerical examples to support the theory which demonstrate empirically that the SE can predict the AMP-SI mean square error accurately.
The "Impossible Trinity" Hypothesis in an Era of Global Imbalances: Measurement and Testing

We outline new metrics for measuring the trilemma aspects: exchange rate flexibility, monetary independence, and capital account openness, taking into account recent substantial international reserve accumulation. Since 2000, the trilemma variables in emerging markets have converged towards intermediate levels, characterizing by managed flexibility, using sizable international reserves as a buffer while retaining some degree of monetary autonomy. We test the linearity of the trilemma, and find that the weighted sum of the three trilemma variables adds up to a constant. Thus, a rise in one trilemma variable should be traded-off with a drop of the weighted sum of the other two.
Socioeconomic Resilience in Sri Lanka: Natural Disaster Poverty and Wellbeing Impact Assessment

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses wellbeing losses as the main measure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular flooding events can move tens of thousands of Sri Lankans into transient poverty at once, hindering the country's recent progress on poverty eradication and shared prosperity. As metrics of disaster impacts, poverty incidence and well-being losses facilitate quantification of the benefits of interventions like rapid post-disaster support and adaptive social protection systems. Such investments efficiently reduce wellbeing losses by making exposed and vulnerable populations more resilient. Nationally and on average, the bottom income quintile suffers only 7 percent of the total asset losses but 32 percent of the total wellbeing losses. Average annual wellbeing losses due to fluvial flooding in Sri Lanka are estimated at US$119 million per year, more than double the asset losses of US$78 million. Asset losses are reported to be highly concentrated in Colombo district, and wellbeing losses are more widely distributed throughout the country. Finally, the paper applies the socioeconomic resilience framework to a cost-benefit analysis of prospective adaptive social protection systems, based on enrollment in Samurdhi, the main social support system in Sri Lanka.
Attributable Fraction and Probability of Causation

This article discusses the concepts and definitions associated with terms such as attributable fraction, attributable risk, and probability of causation. It is emphasized that there are several incompatible definitions of attributable risk. Some of these definitions apply to concepts of excess caseload and excess rate, which are estimable from epidemiologic data; others apply to concepts of etiologic fraction and probability of causation, which are not estimable without strong assumptions about biologic mechanisms of disease. Keywords: assigned shares; attributable fraction; attributable risk; attributable risk percent; causal inference; epidemiology; etiologic fraction; excess fraction; probability of causation; rate fraction
Safe Adaptive Cruise Control with Road Grade Preview and V2V Communication

We present the design of a safe Adaptive Cruise Control (ACC) which uses road grade and lead vehicle motion preview. The ACC controller is designed by using a Model Predictive Control (MPC) framework to optimize comfort, safety, energy-efficiency and speed tracking accuracy. Safety is achieved by computing a robust invariant terminal set. The paper presents a novel approach to compute such set which is less conservative than existing methods. The proposed controller ensures safe inter-vehicle spacing at all times despite changes in the road grade and uncertainty in the predicted motion of the lead vehicle. Simulation results compare the proposed controller with a controller that does not incorporate prior grade knowledge on two scenarios including car-following and autonomous intersection crossing. The results demonstrate the effectiveness of the proposed control algorithm.
Front-electrode design for efficient near-field thermophotovoltaics

In near-field TPV cells, the effects of the necessary front electrode are considered and shown to be of great importance. The electrode tradeoff between required high dc conductivity but low photonic absorption becomes detrimental for the efficiency in the very near field, as the thermal-emitter evanescent waves decay fast and are absorbed inside the electrode without penetrating sufficiently into the semiconductor. Therefore, near-field cells fail to deliver ultra-high power efficiently, as hoped. Still, efficient mid-power conversion is possible, and we compare the performance of several tunable-by-doping conducting-electrode materials. Moreover, novel phenomena arise in the near field, such as the inability to use thick transparent electrodes, while instead the feasibility of ultra-thin ‘opaque’ ones. The metallic-grid fingers exhibit an ‘anomalous’ shading loss, significantly smaller than predicted by geometry, by suppressing the thermal emission in the emitter regions across them.
Initial value diffeomorphic landmark matching and its application shape statistics

Summary form only given. In this work we study the diffeomorphic landmark matching problem central to the emerging field of computational anatomy (2002, Annual Review of Biomedical Engineering) for the study of finite dimensional face orbits. We present a new approach based on recent derivation of a conservation of momentum law for the geodesics of diffeomorphic flow. Once a template is fixed, the space of initial momentum - and Nd finite dimensional space for N d-dimensional landmarks - becomes an appropriate space for studying shape via geodesic flow since the flow at any point along the geodesic is completely determined by the momentum at the origin. We solve for the extremal paths (geodesics) of the kinetic energy by computing its variation with respect to the initial momentum and by applying a gradient descent scheme. Statistical analysis of a landmarked database of 100 faces is presented and we suggest a finite dimensional diffeomorphic active shape model.
The caudate nucleus undergoes dramatic and unique transcriptional changes in human prodromal Huntington’s disease brain

The mechanisms underlying neurodegeneration in the striatum of Huntingon’s Disease (HD) brain are currently unknown. While the striatum is massively degenerated in symptomatic individuals, which makes cellular characterization difficult, it is largely intact in asymptomatic HD gene positive (HD+) individuals. Unfortunately, as striatal tissue samples from HD+ individuals are exceedingly rare, recent focus has been on the Brodmann Area 9 (BA9), a relatively unaffected region, as a surrogate tissue. In this study, we analyze gene expression in caudate nucleus (CAU) from two HD+ individuals and compare the results with healthy and symptomatic HD brains. High-throughput mRNA sequencing (mRNA-Seq) datasets were generated from post-mortem CAU of 2 asymptomatic HD+ individuals and compared with 26 HD and 56 neurologically normal controls. Datasets were analyzed using a custom bioinformatic analysis pipeline to identify and interpret differentially expressed (DE) genes. Results were compared to publicly available brain mRNA-Seq datasets from the Genotype-Tissue Expression (GTEx) project. The analysis employed current state of the art bioinformatics tools and tailored statistical and machine learning methods. The transcriptional profiles in HD+ CAU and HD BA9 samples are highly similar. Differentially expressed (DE) genes related to the heat shock response, particularly HSPA6 and HSPA1A, are common between regions. The most perturbed pathways show extensive agreement when comparing disease with control. A random forest classifier predicts that the two HD+ CAU samples strongly resemble HD BA9 and not control BA9. Nonetheless, when genes were prioritized by their specificity to HD+ CAU, pathways spanning many biological processes emerge. Comparison of HD+ BA9 with HD BA9 identified NPAS4 and REST1/2 as potential early responders to disease and reflect the active disease process. The caudate nucleus in HD brain is dramatically affected prior to symptom onset. Gene expression patterns observed in the HD BA9 are also present in the CAU, suggesting a common response to disease. Substantial caudate-specific differences implicate many different biological pathways including metabolism, protein folding, inflammation, and neurogenic processes. While these results are at best trends due to small sample sizes, these results nonetheless provide the most detailed insight to date into the primary HD disease process.
Pain Management in Patients with Heart Failure: A Survey of Nurses’ Perception

Abstract Background Over 50% of patients with heart failure (HF) report suffering from pain and pain-related burdens; however, pain in HF patients has not been recognized or well treated. Few studies have comprehensively examined pain management in patients with HF from nurses’ perception. Aims To investigate nurses’ perception of pain management in HF patients. Methods Members of American Association of Heart Failure Nurses (AAHFN) were invited to participate in a cross-sectional online survey. Results A total of 147 nurses responded and completed the survey. The majority agreed that pain in HF patients was related to anxiety, depression, fatigue, and unplanned hospitalization, and that pain should be individually assessed and managed. More than 80% thought pain management practice in HF patients should be improved, and 78.1% were interested in getting more information and believed an online education module was the most preferable approach. Lack of pain assessment tools, drug addiction, side effect, overuse, underuse, and contraindication with other medications were the main concerns regarding opioid use in pain management in HF. The gaps in pain management in HF patients included lack of knowledge of opioid use, lack of consideration and awareness of pain, no clinical guidelines for pain assessment and treatment, and complicated pain management with multiple chronic conditions. Conclusions The impact of pain and need for individual assessment and management of pain in HF patients were perceived by nurses; improvement in pain management practice in HF patients is needed. Concerns regarding opioid use and gaps in pain management of HF patients should be addressed.
Time-Varying Risk Aversion? Evidence from Near-Miss Accidents

We present evidence consistent with time-varying risk preferences among automobile drivers. Exploiting a unique dataset of agents’ high-frequency driving behavior collected by a mobile phone applic...
Semi-Supervised Domain Adaptation via Minimax Entropy

Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target domain. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network, followed by a classification layer that computes the features' similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA. Our code is available at \url{http://cs-people.bu.edu/keisaito/research/MME.html}.
The multiple-call hand-off problem in high-capacity cellular communications systems

The author presents a preliminary analytical model that characterizes the central issues of the hand-off problem when vehicles can support multiple calls simultaneously. In such cases a cell boundary crossing by a single vehicle can generate multiple hand-off attempts. A suitable vector state representation is identified which casts the problem as a multidimensional birth-death process. An iterative method is used to find implicit hand-off parameters for systems in statistical equilibrium. Theoretical performance characteristics that show blocking, hand-off failure, and forced termination probabilities as functions of communication traffic are determined. >
High Resolution Dynamic Strain Sensor using a Polarization Maintaining Fiber Bragg Grating

Dynamic strain sensing based on fiber Bragg gratings (FBG) has found a wide range of applications in structure health monitoring and industrial process control. Conventional approaches to enhance strain resolution upon the standard configuration have shown challenges in scaling up due to much-increased system complexity. In this report, we demonstrate a new scheme based on an FBG sensor fabricated in a polarization-maintaining fiber. By using balanced detection to suppress laser intensity noise and enhance strain signal, we have demonstrated an improvement of 28 dB in signal level, 18 dB in signal-to-noise ratio, and 20× in strain resolution when compared to the results using a regular FBG sensor. Moreover, this new sensing concept does not require complicated frequency locking systems and hence allows easy scaleup.
Willing Buyer, Willing Seller: Personal Data Trade as a Service

There is an increased sensitivity by people about how companies collect information about them, and how this information is packaged, used and sold. This perceived lack of control is highlighted by the helplessness of users of various platforms in managing or halting what data is collected from/about them. In a future where users have wrested control of their data and have the autonomy to decide what information is collected, how it is used and most importantly, how much it is worth, a new market emerges. This design fiction considers possible steps prescient companies would take to meet these demands, such as providing third-party subscription platforms offering personal data trade as a service. These services would provide a means for transparent transactions that preserve an owner's control over their data; allowing them to individually make decisions about what data they avail for sale, and the amount of compensation they would accept in trade.
Policy uncertainty and bank stress testing

Abstract The accuracy of dynamic stress-test capital models remains undocumented. Three methodologies: a CLASS-style approach, Bayesian model averaging, and a Lasso specification are used to forecast the performance of 14 large US banks during the financial crisis. Individual bank models are calibrated using bank historical data while regulatory models are calibrated using representative bank data. Representative bank model forecasts differ dramatically from the forecasts from bank-specific models and from actual outcomes. The Lasso methodology is most accurate, but its superiority may be sample-specific and is only apparent ex post. The results highlight the policy uncertainty inherent in regulatory stress tests.
Operating characteristics of the rank-based inverse normal transformation for quantitative trait analysis in genome-wide association studies.

Quantitative traits analyzed in Genome-Wide Association Studies (GWAS) are often nonnormally distributed. For such traits, association tests based on standard linear regression are subject to reduced power and inflated type I error in finite samples. Applying the rank-based inverse normal transformation (INT) to nonnormally distributed traits has become common practice in GWAS. However, the different variations on INT-based association testing have not been formally defined, and guidance is lacking on when to use which approach. In this paper, we formally define and systematically compare the direct (D-INT) and indirect (I-INT) INT-based association tests. We discuss their assumptions, underlying generative models, and connections. We demonstrate that the relative powers of D-INT and I-INT depend on the underlying data generating process. Since neither approach is uniformly most powerful, we combine them into an adaptive omnibus test (O-INT). O-INT is robust to model misspecification, protects the type I error, and is well powered against a wide range of nonnormally distributed traits. Extensive simulations were conducted to examine the finite sample operating characteristics of these tests. Our results demonstrate that, for nonnormally distributed traits, INT-based tests outperform the standard untransformed association test, both in terms of power and type I error rate control. We apply the proposed methods to GWAS of spirometry traits in the UK Biobank. O-INT has been implemented in the R package RNOmni, which is available on CRAN.
A short proof of the Bernstein inequality for formal power series

Let $k$ be a field of characteristic zero, let $R$ be the ring of formal power series in $n$ variables over $k$ and let $D(R,k)$ be the ring of $k-$linear differential operators in $R$. If $M$ is a finitely generated $D(R,k)-$module then $d(M)\geq n$ where $d(M)$ is the dimension of $M$. This inequality is called the Bernstein inequality. We provide a short proof.
Structure of a rabies virus polymerase complex from electron cryo-microscopy.

Nonsegmented negative-stranded (NNS) RNA viruses, among them the virus that causes rabies (RABV), include many deadly human pathogens. The large polymerase (L) proteins of NNS RNA viruses carry all of the enzymatic functions required for viral messenger RNA (mRNA) transcription and replication: RNA polymerization, mRNA capping, and cap methylation. We describe here a complete structure of RABV L bound with its phosphoprotein cofactor (P), determined by electron cryo-microscopy at 3.3 A resolution. The complex closely resembles the vesicular stomatitis virus (VSV) L-P, the one other known full-length NNS-RNA L-protein structure, with key local differences (e.g., in L-P interactions). Like the VSV L-P structure, the RABV complex analyzed here represents a preinitiation conformation. Comparison with the likely elongation state, seen in two structures of pneumovirus L-P complexes, suggests differences between priming/initiation and elongation complexes. Analysis of internal cavities within RABV L suggests distinct template and product entry and exit pathways during transcription and replication.
Toward a robust inference method for the likelihood of low-luminosity gamma-ray bursts to be progenitors of ultrahigh-energy cosmic rays correlating with starburst galaxies

Abstract Very recently, the Pierre Auger Collaboration reported a 4.5σ correlation between the arrival directions of the highest energy cosmic rays and nearby starburst galaxies. The cosmic rays producing the anisotropy signal have been proposed to originate in low-luminosity gamma-ray bursts (llGRBs). On the basis of the well-justified assumption that at redshift z 0.3 the host metallicity is a good indicator of the llGRB production rate, we show that the association of llGRBs and the starbursts correlating with Auger data is excluded at the 90% confidence level.
A Study of a Class of Vibration-Driven Robots: Modeling, Analysis, Control and Design of the Brushbot

In this paper we present a study of a specific class of vibration-driven robots: the brushbots. In a bottom-up fashion, we start by deriving dynamic models of the brushes and we discuss the conditions under which these models can be employed to describe the motion of brushbots. Then, we present two designs of brushbots: a fully-actuated platform and a differential-drive-like one. The former is employed to experimentally validate both the developed theoretical models and the devised motion control algorithms. Finally, a coordinated-control algorithm is implemented on a swarm of differential-drive-like brushbots in order to demonstrate the design simplicity and robustness that can be achieved by employing a vibration-based locomotion strategy.