cssi/SciSciGPT-SciSciNet huggingface dataset:
Single-Nucleotide-Resolution Computing and Memory in Living Cells

The ability to process and store information in living cells is essential for developing next-generation therapeutics and studying biology in situ. However, existing strategies have limited recording capacity and are challenging to scale. To overcome these limitations, we developed DOMINO, a robust and scalable platform for encoding logic and memory in bacterial and eukaryotic cells. Using an efficient single-nucleotide-resolution Read-Write head for DNA manipulation, DOMINO converts the living cells' DNA into an addressable, readable, and writable medium for computation and storage. DOMINO operators enable analog and digital molecular recording for long-term monitoring of signaling dynamics and cellular events. Furthermore, multiple operators can be layered and interconnected to encode order-independent, sequential, and temporal logic, allowing recording and control over the combination, order, and timing of molecular events in cells. We envision that DOMINO will lay the foundation for building robust and sophisticated computation-and-memory gene circuits for numerous biotechnological and biomedical applications.
On NET maps: Examples and nonexistence results

A Thurston map is called nearly Euclidean if its local degree at each critical point is 2 and it has exactly four postcritical points. Nearly Euclidean Thurston (NET) maps are simple generalizations of rational Lattes maps. We investigate when such a map has the property that the associated pullback map on Teichmuller space is constant. We also show that no Thurston map of degree 2 has constant pullback map
Mapping cell migrations and fates in a gastruloid model to the human primitive streak

Although fate maps of early embryos exist for nearly all model organisms, a fate map of the gastrulating human embryo remains elusive. Here, we use human gastruloids to piece together a rudimentary fate map for the human primitive streak (PS). This is possible because differing levels of BMP, WNT and NODAL lead to self-organization of gastruloids into homogenous subpopulations of endoderm and mesoderm, and comparative analysis of these gastruloids, together with the fate map of the mouse embryo, allows the organization of these subpopulations along an anterior-posterior axis. We also developed a novel cell tracking technique that detected robust fate-dependent cell migrations in our gastruloids comparable with those found in the mouse embryo. Taken together, our fate map and recording of cell migrations provides a first coarse view of what the human PS may resemble in vivo.
Certainty Equivalence is Efficient for Linear Quadratic Control

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ) control problems with unknown transition dynamics. We show that for both the fully and partially observed settings, the sub-optimality gap between the cost incurred by playing the certainty equivalent controller on the true system and the cost incurred by using the optimal LQ controller enjoys a fast statistical rate, scaling as the square of the parameter error. To the best of our knowledge, our result is the first sub-optimality guarantee in the partially observed Linear Quadratic Gaussian (LQG) setting. Furthermore, in the fully observed Linear Quadratic Regulator (LQR), our result improves upon recent work by Dean et al., who present an algorithm achieving a sub-optimality gap linear in the parameter error. A key part of our analysis relies on perturbation bounds for discrete Riccati equations. We provide two new perturbation bounds, one that expands on an existing result from Konstantinov, and another based on a new elementary proof strategy.
A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions

The field of machine programming (MP), the automation of the development of software, is making notable research advances. This is, in part, due to the emergence of a wide range of novel techniques in machine learning. In this paper, we apply MP to the automation of software performance regression testing. A performance regression is a software performance degradation caused by a code change. We present AutoPerf – a novel approach to automate regression testing that utilizes three core techniques: (i) zero-positive learning, (ii) autoencoders, and (iii) hardware telemetry. We demonstrate AutoPerf’s generality and efficacy against 3 types of performance regressions across 10 real performance bugs in 7 benchmark and open-source programs. On average, AutoPerf exhibits 4% profiling overhead and accurately diagnoses more performance bugs than prior state-of-the-art approaches. Thus far, AutoPerf has produced no false negatives.
Behavior Planning of Autonomous Cars with Social Perception

Autonomous cars have to navigate in dynamic environment which can be full of uncertainties. The uncertainties can come either from sensor limitations such as occlusions and limited sensor range, or from probabilistic prediction of other road participants, or from unknown social behavior in a new area. To safely and efficiently drive in the presence of these uncertainties, the decision-making and planning modules of autonomous cars should intelligently utilize all available information and appropriately tackle the uncertainties so that proper driving strategies can be generated. In this paper, we propose a social perception scheme which treats all road participants as distributed sensors in a sensor network. By observing the individual behaviors as well as the group behaviors, uncertainties of the three types can be updated uniformly in a belief space. The updated beliefs from the social perception are then explicitly incorporated into a probabilistic planning framework based on Model Predictive Control (MPC). The cost function of the MPC is learned via inverse reinforcement learning (IRL). Such an integrated probabilistic planning module with socially enhanced perception enables the autonomous vehicles to generate behaviors which are defensive but not overly conservative, and socially compatible. The effectiveness of the proposed framework is verified in simulation on an representative scenario with sensor occlusions.
Directed Formation Control of n Planar Agents with Distance and Area Constraints

In this paper, we take a first step towards generalizing a recently proposed method for dealing with the problem of convergence to incorrect equilibrium points of distance-based formation controllers. Specifically, we introduce a distance and area-based scheme for the formation control of n-agent systems in two dimensions using directed graphs and the single-integrator model. We show that under certain conditions on the edge lengths of the triangulated desired formation, the control ensures almost-global convergence to the correct formation.
IRF: interactive recommendation through dialogue

Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users' preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
On the Benefits of Waiting in Status Update Systems

This paper explores the potential of waiting before packet transmission in improving the Age of Information (AoI) in status update systems. We consider a non-preemptive queue with Poisson arrivals and independent general service distribution, which we term $M/GI/1/2\ast$ . There is a single unit buffer that captures the latest arriving status update packet and discards the previous one. As a means to improve AoI performance, we allow “server waiting” in this queuing model. Depending on idle or busy system state, the server waits for a deterministic time before starting service of the packet so as to capture a potential newer arrival. Different from most existing works, we analyze AoI evolution by indexing the incoming packets, which is enabled by an alternative method of partitioning the area under the evolution of instantaneous AoI to calculate its time average. We obtain average AoI expressions for the $M/GI/1/2\ast$ discipline with waiting for two distributions. Our numerical results demonstrate that waiting before service can bring significant improvement in average age – more than 75% in some cases – particularly, for heavy-tailed service distributions.
Speech enhancement with noise parameter estimated by a sequential Monte Carlo method

We present a speech enhancement scheme that is based on sequential time-varying noise parameter estimation and time-varying linear filter. The time-varying noise parameter is estimated within a Bayesian framework by a sequential Monte Carlo method. The method approximates posterior probabilities of speech and noise parameters by a set of samples and estimates the time-varying noise parameters by minimum mean square error estimation over these samples. The time-varying filter can make use of the masking properties of human auditory systems. The proposed speech enhancement scheme can work in non-stationary noise. Experiments were conducted in various non-stationary noise situations, and results showed that the method could have improved performances as compared to some alternative methods.
Chiral modular bootstrap

Using modular bootstrap we show the lightest primary fields of a unitary compact two-dimensional conformal field theory (with c, c > 1) has a conformal weight h1 ≤ c 12 + &#x1d4aa;(1). This implies that t...
Functorial invariants of trees and their cones

We study the category whose objects are trees (with or without roots) and whose morphisms are contractions. We show that the corresponding contravariant module categories are locally Noetherian, and we study two natural families of modules over these categories. The first takes a tree to a graded piece of the homology of its unordered configuration space, or to the homology of the unordered configuration space of its cone. The second takes a tree to a graded piece of the intersection homology of the reciprocal plane of its cone, which is a vector space whose dimension is given by a Kazhdan–Lusztig coefficient. We prove finite generation results for each of these modules, which allow us to draw conclusions about the growth of Betti numbers of configuration spaces and of Kazhdan–Lusztig coefficients of graphical matroids.
3D printing of conjugated polymers

Three‐dimensional (3D) printing brings exciting prospects to the realm of conjugated polymers (CPs) and organic electronics through vastly enhanced design flexibility, structural complexity, and environmental sustainability. However, the use of 3D printing for CPs is still in its infancy and remains full of challenges. In this review, we highlight recent studies that demonstrate proof‐of‐concept strategies to mitigate some of these problems. Two general additive manufacturing approaches are featured: direct ink writing and vat photopolymerization. We conclude with an outlook for this thriving field of research and draw attention to the new possibilities that 3D printing can bring to CPs. © 2019 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2019, 57, 1592–1605
An Approximation Algorithm for Active Friending in Online Social Networks

Guiding users to actively expanding their online social circles is one of the primary strategies for enhancing user participation and growing online social networks. In this paper, we study the active friending problem which aims at providing users with the strategy for methodically sending invitations to successfully build a friendship with target users. We consider the prominent linear threshold model for the friending process and formulate the active friending problem as an optimization problem. The key observation is the relationship between the active friending problem and the minimum subset cover problem, based on which we present the first randomized algorithm with a data-independent approximation ratio and a controllable success probability for general graphs. The performance of the proposed algorithm is theoretically analyzed and supported by encouraging simulation results done on extensive datasets.
Lyubeznik numbers of irreducible projective varieties depend on the embedding

We construct irreducible complex projective varieties such that the Lyubeznik numbers of their affine cones depend on the choices of projective embeddings. The main ingredient is the recent work of Reichelt-Saito-Walther, where the Lyubeznik numbers are reinterpreted using perverse sheaves and reducible projective varieties with dependent Lyubeznik numbers are constructed.
The joint spillover index

Abstract We propose an alternative measure of system-wide connectedness to the popular generalized spillover index, based on generalized forecast error variance decompositions, of Diebold and Yilmaz (2012, 2014). Our measure relies on joint conditional forecasts to decompose variance, as opposed to the popular method's reliance on single-variable conditioning sets, and is a more precise measure of aggregate spillovers. We show in an application to US industry sector stock returns that the difference between the two measures can be substantial.
Strongly Uncontrollable Network Topologies

In this article, we present a class of network topologies under which the Laplacian consensus dynamics exhibit undesirable controllability properties under a broadcast control signal. Specifically, the networks we characterize are uncontrollable for any subset of the nodes chosen as control inputs and that emit a common control signal. We provide a sufficient condition for a network to contain this strong uncontrollability property and describe network perturbations that leave the uncontrollability property invariant. As a by-product, we identify nontrivial network topologies that require the control of approximately half the nodes in the network as a necessary condition for controllability.
Maxed Out? The Effect of Larger Student Loan Limits on Borrowing and Education Outcomes

Despite large and growing student loan balances, there is relatively little evidence on the effects of access to student loans on borrowing and educational outcomes. We examine the effect of access to credit by using policy variation in the maximum federal student loan amounts available to U.S. college students. In particular, first-, second-, and third-year students have access to different amounts of federal student loans. Using a regression discontinuity and administrative data from a state higher education system, we find that access to higher loan limits increases borrowing for at least 26 percent of borrowers. Despite this increase in borrowing, we find no evidence that eligibility for additional loans affects student GPA, persistence, or graduation.
Quantitative thermal probing of devices at sub-100 nm resolution

Localized Joule heating in submicron features affects reliability of VLSI devices. This paper reports the use of batch-fabricated probes for scanning thermal microscopy (SThM) to characterize self-heating in miniaturized devices. The spatial resolution of the SThM technique is found to be about 70 nm. Existence of a liquid film bridging the tip and sample during scanning is verified and the thermal contact conductance of the liquid bridge is found to be significant. The thermal design of the probe was optimized in previous work and its thermal performance is now characterized. We apply the SThM technique for mapping temperature distribution on VLSI via structures under DC current heating. Excellent agreement was found between the results obtained from the SThM technique and that from a resistive thermometry method. This paper also demonstrates a novel phase imaging technique for locating subsurface hot spots. The subsurface imaging technique has the potential to be used for detecting defects in multilevel interconnects.
3D-LaneNet: End-to-End 3D Multiple Lane Detection

We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with on-board sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. The intra-network IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-per-column output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly handles complex situations such as lane merges and splits. Results are shown on two new 3D lane datasets, a synthetic and a real one. For comparison with existing methods, we test our approach on the image-only tuSimple lane detection benchmark, achieving performance competitive with state-of-the-art.
Downlink Analysis for the Typical Cell in Poisson Cellular Networks

Owing to its unparalleled tractability, the Poisson point process (PPP) has emerged as a popular model for the analysis of cellular networks. Considering a stationary point process of users, which is independent of the base station (BS) point process, it is well known that the typical user does not lie in the typical cell and thus may not truly represent the typical cell performance. Inspired by this observation, we present a construction that allows a direct characterization of the downlink performance of the typical cell. For this, we present an exact downlink analysis for the 1-D case and a remarkably accurate approximation for the 2-D case. Several useful insights about the differences and similarities in the two viewpoints (typical user vs. typical cell) are also provided.
History-dependent perturbation response in limb muscle.

ABSTRACT Muscle mediates movement but movement is typically unsteady and perturbed. Muscle is known to behave non-linearly and with history-dependent properties during steady locomotion, but the importance of history dependence in mediating muscle function during perturbations remains less clear. To explore the capacity of muscles to mitigate perturbations during locomotion, we constructed a series of perturbations that varied only in kinematic history, keeping instantaneous position, velocity and time from stimulation constant. We found that the response of muscle to a perturbation is profoundly history dependent, varying 4-fold as baseline frequency changes, and dissipating energy equivalent to ∼6 times the kinetic energy of all the limbs in 5 ms (nearly 2400 W kg−1). Muscle energy dissipation during a perturbation is predicted primarily by the force at the onset of the perturbation. This relationship holds across different frequencies and timings of stimulation. This history dependence behaves like a viscoelastic memory producing perturbation responses that vary with the frequency of the underlying movement.
Rank growth of elliptic curves in nonabelian extensions

Given an elliptic curve $E/\mathbb{Q}$, it is a conjecture of Goldfeld that asymptotically half of its quadratic twists will have rank zero and half will have rank one. Nevertheless, higher rank twists do occur: subject to the parity conjecture, Gouv\^ea and Mazur constructed $X^{1/2-\epsilon}$ twists by discriminants up to $X$ with rank at least two. For any $d\geq 3$, we build on their work to consider twists by degree $d$ $S_d$-extensions of $\mathbb{Q}$ with discriminant up to $X$. We prove that there are at least $X^{c_d-\epsilon}$ such twists with positive rank, where $c_d$ is a positive constant that tends to $1/4$ as $d\to\infty$. Moreover, subject to a suitable parity conjecture, we obtain the same result for twists with rank at least two.
Position-theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate.

Spiking activity of place cells in the hippocampus encodes the animal’s position as it moves through an environment. Within a cell’s place field, both the firing rate and the phase of spiking in the local theta oscillation contain spatial information. We propose a position–theta-phase (PTP) model that captures the simultaneous expression of the firing-rate code and theta-phase code in place cell spiking. This model parametrically characterizes place fields to compare across cells, time, and conditions; generates realistic place cell simulation data; and conceptualizes a framework for principled hypothesis testing to identify additional features of place cell activity. We use the PTP model to assess the effect of running speed in place cell data recorded from rats running on linear tracks. For the majority of place fields, we do not find evidence for speed modulation of the firing rate. For a small subset of place fields, we find firing rates significantly increase or decrease with speed. We use the PTP model to compare candidate mechanisms of speed modulation in significantly modulated fields and determine that speed acts as a gain control on the magnitude of firing rate. Our model provides a tool that connects rigorous analysis with a computational framework for understanding place cell activity.
Optimal trading of a basket of futures contracts

We study the problem of dynamically trading multiple futures contracts with different underlying assets. To capture the joint dynamics of stochastic bases for all traded futures, we propose a new model involving a multi-dimensional scaled Brownian bridge that is stopped before price convergence. This leads to the analysis of the corresponding Hamilton–Jacobi–Bellman equations, whose solutions are derived in semi-explicit form. The resulting optimal trading strategy is a long-short policy that accounts for whether the futures are in contango or backwardation. Our model also allows us to quantify and compare the values of trading in the futures markets when the underlying assets are traded or not. Numerical examples are provided to illustrate the optimal strategies and the effects of model parameters.
Components from the Human c-myb Transcriptional Regulation System Reactivate Epigenetically Repressed Transgenes.

A persistent challenge for mammalian cell engineering is the undesirable epigenetic silencing of transgenes. Foreign DNA can be incorporated into closed chromatin before and after it has been integrated into a host cell’s genome. To identify elements that mitigate epigenetic silencing, we tested components from the c-myb and NF-kB transcriptional regulation systems in transiently transfected DNA and at chromosomally integrated transgenes in PC-3 and HEK 293 cells. DNA binding sites for MYB (c-myb) placed upstream of a minimal promoter enhanced expression from transiently transfected plasmid DNA. We targeted p65 and MYB fusion proteins to a chromosomal transgene, UAS-Tk-luciferase, that was silenced by ectopic Polycomb chromatin complexes. Transient expression of Gal4-MYB induced an activated state that resisted complete re-silencing. We used custom guide RNAs and dCas9-MYB to target MYB to different positions relative to the promoter and observed that transgene activation within ectopic Polycomb chromatin required proximity of dCas9-MYB to the transcriptional start site. Our report demonstrates the use of MYB in the context of the CRISPR-activation system, showing that DNA elements and fusion proteins derived from c-myb can mitigate epigenetic silencing to improve transgene expression in engineered cell lines.
Sharp Constants of Approximation Theory. I. Multivariate Bernstein–Nikolskii Type Inequalities

Given a centrally symmetric convex body $$V\subset {{\mathbb {R}}}^m$$, let $${{\mathcal {T}}}_{aV}$$ be the set of all trigonometric polynomials with the spectrum in $$aV,\,a>0$$, and let $$B_V$$ be the set of all entire functions of exponential type with the spectrum in V. We discuss limit relations between the sharp constants in the multivariate Bernstein–Nikolskii inequalities defined by $$\begin{aligned}&P_{p,q,D_N,a,V}:=a^{-N-m/p+m/q}\sup _{T\in {{\mathcal {T}}}_{aV}\setminus \{0\}}\frac{\Vert D_N(T)\Vert _{L_q\left( Q_{\pi }\right) }}{\Vert T\Vert _{L_p\left( Q_{\pi }\right) }},\\&E_{p,q,D_N,V}:= \sup _{f\in (B_V\cap L_p({{\mathbb {R}}}^m))\setminus \{0\}}\frac{\Vert D_N(f)\Vert _{L_q({{\mathbb {R}}}^m)}}{\Vert f\Vert _{L_p({{\mathbb {R}}}^m)}}, \end{aligned}$$where $$0<p\le q\le {\infty },\,Q_{\pi }=\{x\in {{\mathbb {R}}}^m: \vert x_j\vert \le \pi ,\,1\le j\le m\},$$ and $$ D_N=\sum _{\vert {\alpha }\vert =N}b_{\alpha }D^{\alpha }$$ is a differential operator with constant coefficients. We prove that $$\begin{aligned} E_{p,q,D_N,V}\le \liminf _{a\rightarrow {\infty }}P_{p,q,D_N,a,V},\qquad E_{p,{\infty },D_N,V}= \lim _{a\rightarrow {\infty }}P_{p,{\infty },D_N,a,V}. \end{aligned}$$
Constant Sub-second Cycling between Representations of Possible Futures in the Hippocampus.

Cognitive faculties such as imagination, planning, and decision-making entail the ability to represent hypothetical experience. Crucially, animal behavior in natural settings implies that the brain can represent hypothetical future experience not only quickly but also constantly over time, as external events continually unfold. To determine how this is possible, we recorded neural activity in the hippocampus of rats navigating a maze with multiple spatial paths. We found neural activity encoding two possible future scenarios (two upcoming maze paths) in constant alternation at 8 Hz: one scenario per ∼125-ms cycle. Further, we found that the underlying dynamics of cycling (both inter- and intra-cycle dynamics) generalized across qualitatively different representational correlates (location and direction). Notably, cycling occurred across moving behaviors, including during running. These findings identify a general dynamic process capable of quickly and continually representing hypothetical experience, including that of multiple possible futures.
DFT modelling of explicit solid–solid interfaces in batteries: methods and challenges

Density Functional Theory (DFT) calculations of electrode material properties in high energy density storage devices like lithium batteries have been standard practice for decades. In contrast, DFT modelling of explicit interfaces in batteries arguably lacks universally adopted methodology and needs further conceptual development. In this paper, we focus on solid-solid interfaces, which are ubiquitous not just in all-solid state batteries; liquid-electrolyte-based batteries often rely on thin, solid passivating films on electrode surfaces to function. We use metal anode calculations to illustrate that explicit interface models are critical for elucidating contact potentials, electric fields at interfaces, and kinetic stability with respect to parasitic reactions. The examples emphasize three key challenges: (1) the "dirty" nature of most battery electrode surfaces; (2) voltage calibration and control; and (3) the fact that interfacial structures are governed by kinetics, not thermodynamics. To meet these challenges, developing new computational techniques and importing insights from other electrochemical disciplines will be beneficial.
Distributed MST: A Smoothed Analysis

We study smoothed analysis of distributed graph algorithms, focusing on the fundamental minimum spanning tree (MST) problem. With the goal of studying the time complexity of distributed MST as a function of the "perturbation" of the input graph, we posit a smoothing model that is parameterized by a smoothing parameter 0 ≤ ϵ(n) ≤ 1 which controls the amount of random edges that can be added to an input graph G per round. Informally, ϵ(n) is the probability (typically a small function of n, e.g., n--¼) that a random edge can be added to a node per round. The added random edges, once they are added, can be used (only) for communication. We show upper and lower bounds on the time complexity of distributed MST in the above smoothing model. We present a distributed algorithm that, with high probability, 1 computes an MST and runs in O(min{1/√ϵ(n)2O(√log n), D+ √n}) rounds2 where ϵ is the smoothing parameter, D is the network diameter and n is the network size. To complement our upper bound, we also show a lower bound of Ω(min{1/√ϵ(n), D + √n}). We note that the upper and lower bounds essentially match except for a multiplicative 2O(√log n) polylog(n) factor. Our work can be considered as a first step in understanding the smoothed complexity of distributed graph algorithms.
MUSCLE MITOCHONDRIAL CAPACITY AND ENDURANCE IN ADULTS WITH TYPE 1 DIABETES.

The impact of type 1 diabetes (T1D) on muscle endurance and oxidative capacity is currently unknown. Purpose: Measure muscle endurance and oxidative capacity of adults with T1D compared to controls. Methods: A cross-sectional study design with a control group was used. Subjects (19-37 years old) with T1D (n=17) and controls (n=17) were assessed with hemoglobin A1c (HbA1c) and casual glucose. Muscle endurance was measured with an accelerometer at stimulation frequencies of 2, 4, and 6 Hz for a total of nine minutes. Mitochondrial capacity was measured using near-infrared spectroscopy after exercise as the rate constant of the rate of recovery of oxygen consumption. Results: T1D and control groups were similar in age, sex, height, and race. The T1D group had slightly higher BMI values and adipose tissue thickness over the forearm muscles. Casual glucose was 150+70 mg/dL for T1D and 98+16 mg/dL for controls (P=0.006). HbA1c of T1D subjects was 7.1+0.9% and 5.0+0.4% for controls (P<0.01). Endurance indexes at 2, 4, and 6 Hz were 94.5+5.2%, 81.8+8.4%, and 68.6+13.5% for T1D and 94.6+4.1%, 85.9+6.3%, and 68.7+15.4% for controls (p = 0.97, 0.12, 0.99, respectively). There were no differences between groups in mitochondrial capacity (T1D= 1.9+0.5 min-1 and control=1.8+0.4 min-1, P=0.29) or reperfusion rate (T1D= 8.8+2.8s and control=10.3+3.0s, P=0.88). There were no significant correlations between HbA1c and either muscle endurance, mitochondrial capacity or reperfusion rate. Conclusions: Adults with T1D did not have reduced oxidative capacity, muscle endurance or muscle reperfusion rates compared to controls. HbA1c also did not correlate with muscle endurance, mitochondrial capacity or reperfusion rates. Future studies should extend these measurements to older people or people with poorly-controlled T1D.
Obesity and time to conception

Objective: To examine the association between body mass index and time to conception. Methods: Secondary data analysis of The Asthma in Pregnancy Study, a Prospective cohort of 2205 women assembled from April 1997–June 2000. Results: Time to conception in women not on contraception, by BMI category, did not show a significant difference among the groups, p = 0.81 for test of equality over the strata. Conclusions: No association between time to conception and BMI was demonstrated in these data.
Naive Bayes with Correlation Factor for Text Classification Problem

Naive Bayes estimator is widely used in text classification problems. However, it doesn't perform well with small-size training dataset. We propose a new method based on Naive Bayes estimator to solve this problem. A correlation factor is introduced to incorporate the correlation among different classes. Experimental results show that our estimator achieves a better accuracy compared with traditional Naive Bayes in real world data.
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity

With the recent proliferation of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via notifications on mobile devices and designed to help users prevent negative health outcomes and to promote the adoption and maintenance of healthy behaviors. A JITAI involves a sequence of decision rules (i.e., treatment policies) that take the user's current context as input and specify whether and what type of intervention should be provided at the moment. In this work, we describe a reinforcement learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as data is being collected from the user. This work is motivated by our collaboration on designing an RL algorithm for HeartSteps V2 based on data collected HeartSteps V1. HeartSteps is a physical activity mobile health application. The RL algorithm developed in this work is being used in HeartSteps V2 to decide, five times per day, whether to deliver a context-tailored activity suggestion.
Bypassing the Axion Paradigm through Fractional Field Theory

A long-standing challenge of the Standard Model (SM) is to convincingly explain the physical mechanism behind the axial anomaly of Quantum Chromodynamics (QCD). While instantons are routinely invoked as solution to this anomaly, they create new puzzles – the strong CP problem and the postulated existence of axions. Here we suggest that fractional field theory offers an alternative resolution of the axial anomaly, shedding new light on the strong CP problem and bypassing the axion hypothesis.
A reverse Sidorenko inequality

Let H be a graph allowing loops as well as vertex and edge weights. We prove that, for every triangle-free graph G without isolated vertices, the weighted number of graph homomorphisms $$\hom (G, H)$$ satisfies the inequality $$\begin{aligned} \hom (G, H ) \le \prod _{uv \in E(G)} \hom (K_{d_u,d_v}, H )^{1/(d_ud_v)}, \end{aligned}$$ where $$d_u$$ denotes the degree of vertex u in G. In particular, one has $$\begin{aligned} \hom (G, H )^{1/|E(G)|} \le \hom (K_{d,d}, H )^{1/d^2} \end{aligned}$$ for every d-regular triangle-free G. The triangle-free hypothesis on G is best possible. More generally, we prove a graphical Brascamp–Lieb type inequality, where every edge of G is assigned some two-variable function. These inequalities imply tight upper bounds on the partition function of various statistical models such as the Ising and Potts models, which includes independent sets and graph colorings. For graph colorings, corresponding to $$H = K_q$$ , we show that the triangle-free hypothesis on G may be dropped; this is also valid if some of the vertices of $$K_q$$ are looped. A corollary is that among d-regular graphs, $$G = K_{d,d}$$ maximizes the quantity $$c_q(G)^{1/|V(G)|}$$ for every q and d, where $$c_q(G)$$ counts proper q-colorings of G.Finally, we show that if the edge-weight matrix of H is positive semidefinite, then $$\begin{aligned} \hom (G, H) \le \prod _{v \in V(G)} \hom (K_{d_v+1}, H )^{1/(d_v+1)}. \end{aligned}$$ This implies that among d-regular graphs, $$G = K_{d+1}$$ maximizes $$\hom (G, H)^{1/|V(G)|}$$ . For 2-spin Ising models, our results give a complete characterization of extremal graphs: complete bipartite graphs maximize the partition function of 2-spin antiferromagnetic models and cliques maximize the partition function of ferromagnetic models. These results settle a number of conjectures by Galvin–Tetali, Galvin, and Cohen–Csikvari–Perkins–Tetali, and provide an alternate proof to a conjecture by Kahn.
Compact-Range RCS Measurements and Modeling of Small Drones at 15 GHz and 25 GHz

The knowledge of the radar signature of aerial targets, such as drones, is critical in designing an effective radar detection system. It is a challenging task to measure the radar cross-section (RCS) of small drones. This paper describes a compact-range approach for measuring the RCS of small drones at 15 GHz and 25 GHz. The measurement results show that the average RCS of the three small drones varies with the radar frequency with higher reflections observed around certain directions. Moreover, the results show that for each drone, the RCS at 25 GHz is higher than the RCS at 15 GHz. Besides, information-theoretical based model selection for the RCS data is carried using the Akaike information criterion (AIC). We find that the generalized extreme value distribution is a good fit for modeling the RCS of small drones.
Estimating Tumor Vascular Permeability of Nanoparticles Using an Accessible Diffusive Flux Model

Understanding the complex interplay of factors affecting nanoparticle accumulation in solid tumors is a challenge that must be surmounted to develop effective cancer nanomedicine. Among other unique microenvironment properties, tumor vascular permeability is an important feature of leaky tumor vessels which enables nanoparticles to extravasate. However, permeability has thus far been measured by intravital microscopy on optical window tumors, which has many limitations of its own. Additionally, mathematical models of particle tumor transport are often too complicated to be accessible to most researchers. Here, we present a more simplified and accessible mathematical model based on diffusive flux, which uses particle tumor accumulation and plasma pharmacokinetics to yield effective permeability, Peff. This model, called diffusive flux modeling (DFM), allows effects from multiple parameters to be decoupled and is also the first demonstration, to the best our knowledge, of extracting Peff values from bulk biodistribution results (e.g., routine positron emission tomography studies). The DFM equation was used to explain in vivo results of sub-20 nm nanocarriers called three-helix-micelles (3HM), particularly 3HM's selective accumulation in different tumor models. When DFM was applied to multiple published biodistribution data, a semiquantitative comparison of various tumor models, particle size, and active targeting strategies could be made. The analysis clearly pointed out the importance of balancing multiple characteristics of nanoparticles to ensure successful treatment outcome and highlights the usefulness of this simple model for initial particle design, selection, and subsequent optimization.
MATLAB Implementation of C1 Finite Elements: Bogner-Fox-Schmit Rectangle

Rahman and Valdman (2013) introduced a new vectorized way to assemble finite element matrices. We utilize underlying vectorization concepts and extend MATLAB codes to implementation of Bogner-Fox-Schmit C1 rectangular elements in 2D. Our focus is on the detailed construction of elements and simple computer demonstrations including energies evaluations and their visualizations.
Separating an Outlier from a Change

We study the quickest change detection problem with an unknown post-change distribution. In this scenario, the unknown change in the distribution of observations may occur in many ways without much structure, while, before change, an outlier (a false alarm event) is highly structured, following a particular sample path. We first characterize these likely events for the deviation of finite strings and propose a method to test the deviation, relative to the most likely way for it to occur as an outlier. Our method works along with other change detection schemes to substantially reduce the false positive rates associated with the plain scheme used without the heavy computation associated with the generalized likelihood ratio test. Finally, we apply our method on economic market indicators and climate data. Our method successfully captures the regime shifts during times of historical significance and identifies the current climate change phenomenon to be a highly likely regime shift.
Space–Time Surface Plasmon Polaritons: A New Propagation-Invariant Surface Wave Packet

We introduce the unique class of propagation-invariant surface plasmon polaritons (SPPs) representing pulsed surface wave packets propagating along unpatterned metal-dielectric interfaces and are l...
Identifying Price Bubble Periods in the Energy Sector

Abstract In this paper we test for the existence of single and multiple episodes of explosive behavior in three energy sector indices (crude oil, heating oil, and natural gas) and five energy sector spot prices (West Texas Intermediate (WTI), Brent, heating oil, natural gas, and jet fuel). The results from the Supremum Augmented Dickey-Fuller (SADF) and the Generalized SADF tests provide strong statistical evidence of explosive behavior in all of our energy series. A simple theoretical framework of commodity pricing allows us to understand the assumptions to interpret explosive behavior as bubbles. By constructing implied convenience yields using futures prices we test the key assumption and we are able to identify the beginning and the end of bubble periods for the WTI, Brent, heating oil, and natural gas spot prices.
9 – Isotope separators

Publisher Summary This chapter describes several methods by which isotopes of uranium and other elements are separated. The separation of isotopes requires a physical process that depends on mass.In the electromagnetic method, as used in a mass spectrograph, ions to be separated travel in circles of different radii. In the gaseous diffusion process, light molecules of a gas diffuse through a membrane more readily than do heavy molecules. The amount of enrichment in gaseous diffusion depends on the square root of the ratio of the masses. By the use of material balance equations, the amount of feed can be computed, and by the use of tables of separate work, costs of enriching uranium for reactor fuel can be found. An alternative separation device is the gas centrifuge, in which gases diffuse against the centrifugal forces produced by high speeds of rotation. Laser isotope separation involves the selective excitation of uranium atoms by lasers to produce chemical reactions.
Tomita–Takesaki Modular Theory

We provide an brief overview of Tomita-Takesaki modular theory and some of its applications to mathematical physics. This is an article commissioned by the Encyclopedia of Mathematical Physics, edited by J.-P. Francoise, G. Naber and T.S. Tsun, to be published by the Elsevier publishing house.
Matching People and Organizations: Selection and Socialization in Public Accounting Firms

Understanding and predicting behavior in organizations requires a consideration of person and situation factors, and how these factors interact. This paper develops and longitudinally tests a model...
On Optimal Bilinear Quadrilateral Meshes

The novelty of this work is in presenting interesting error properties of two types of asymptotically ‘optimal’ quadrilateral meshes for bilinear approximation. The first type of mesh has an error equidistributing property, where the maximum interpolation error is asymptotically the same over all elements. The second type has faster than expected ‘super-convergence’ property for certain saddle-shaped data functions. The ‘super-convergent’ mesh may be an order of magnitude more accurate than the error equidistributing mesh. Both types of mesh are generated by a coordinate transformation of a regular mesh of squares. The coordinate transformation is derived by interpreting the Hessian matrix of a data function as a metric tensor. The insights in this work may have application in mesh design near known corner or point singularities.
Kalman filtering for relative spacecraft attitude and position estimation

In this paper a novel approach is developed for relative state estimation of spacecraft flying in formation. The approach uses information from an optical sensor to provide multiple line-of-sight vectors from one spacecraft to another. The line-of-sight measurements are coupled with gyro measurements and dynamical models in an extended Kalman filter to determine relative attitude, position, and gyro biases. The quaternion is used to describe the relative kinematics, whereas general relative orbital equations are used to describe the positional dynamics. Three different attitude formulations are presented. The first estimates the relative attitude and individual gyro biases for the chief and deputy spacecraft. The second estimates the relative attitude, and the relative velocity bias and the deputy gyro bias. The third estimates the relative attitude, and the relative velocity bias and the chief gyro bias. Simulation results indicate that the combined sensor/estimator approach provides accurate relative attitude and position estimates.
On the quantification and efficient propagation of imprecise probabilities with copula dependence

Abstract This paper addresses the problem of quantification and propagation of uncertainties associated with dependence modeling when data for characterizing probability models are limited. Practically, the system inputs are often assumed to be mutually independent or correlated by a multivariate Gaussian distribution. However, this subjective assumption may introduce bias in the response estimate if the real dependence structure deviates from this assumption. In this work, we overcome this limitation by introducing a flexible copula dependence model to capture complex dependencies. A hierarchical Bayesian multimodel approach is proposed to quantify uncertainty in dependence model-form and model parameters that result from small data sets. This approach begins by identifying, through Bayesian multimodel inference, a set of candidate marginal models and their corresponding model probabilities, and then estimating the uncertainty in the copula-based dependence structure, which is conditional on the marginals and their parameters. The overall uncertainties integrating marginals and copulas are probabilistically represented by an ensemble of multivariate candidate densities. A novel importance sampling reweighting approach is proposed to efficiently propagate the overall uncertainties through a computational model. Through an example studying the influence of constituent properties on the out-of-plane properties of transversely isotropic E-glass fiber composites, we show that the composite property with copula-based dependence model converges to the true estimate as data set size increases, while an independence or arbitrary Gaussian correlation assumption leads to a biased estimate.
Model-driven generation of artificial yeast promoters

Promoters play a central role in controlling gene regulation; however, a small set of promoters is used for most genetic construct design in the yeast Saccharomyces cerevisiae. Generating and utilizing models that accurately predict protein expression from promoter sequences would enable rapid generation of useful promoters and facilitate synthetic biology efforts in this model organism. We measure the gene expression activity of over 675,000 sequences in a constitutive promoter library and over 327,000 sequences in an inducible promoter library. Training an ensemble of convolutional neural networks jointly on the two data sets enables very high (R2 > 0.79) predictive accuracies on multiple sequence-activity prediction tasks. We describe model-guided design strategies that yield large, sequence-diverse sets of promoters exhibiting activities higher than those represented in training data and similar to current best-in-class sequences. Our results show the value of model-guided design as an approach for generating useful DNA parts. A small set of promoters is used for most genetic construct design in S. cerevisiae. Here, the authors develop a predictive model of promoter activity trained on a data set of over one million sequences and use it to design large sets of high-activity promoters.