cssi/SciSciGPT-SciSciNet huggingface dataset:
Momentum-space engineering of gaseous Bose-Einstein condensates

Bose-Einstein condensate momentum distributions can be shaped by standing-wave laser pulse sequences. We find that several momentum distributions, important in atom-interferometry applications, can be engineered with high fidelity with two or three pulses.
Model-free control for distributed stream data processing using deep reinforcement learning

In this paper, we focus on general-purpose Distributed Stream Data Processing Systems (DSDPSs), which deal with processing of unbounded streams of continuous data at scale distributedly in real or near-real time. A fundamental problem in a DSDPS is the scheduling problem (i.e., assigning workload to workers/machines) with the objective of minimizing average end-to-end tuple processing time. A widely-used solution is to distribute workload evenly over machines in the cluster in a round-robin manner, which is obviously not efficient due to lack of consideration for communication delay. Model-based approaches (such as queueing theory) do not work well either due to the high complexity of the system environment.We aim to develop a novel model-free approach that can learn to well control a DSDPS from its experience rather than accurate and mathematically solvable system models, just as a human learns a skill (such as cooking, driving, swimming, etc). Specifically, we, for the first time, propose to leverage emerging Deep Reinforcement Learning (DRL) for enabling model-free control in DSDPSs; and present design, implementation and evaluation of a novel and highly effective DRL-based control framework, which minimizes average end-to-end tuple processing time by jointly learning the system environment via collecting very limited runtime statistics data and making decisions under the guidance of powerful Deep Neural Networks (DNNs). To validate and evaluate the proposed framework, we implemented it based on a widely-used DSDPS, Apache Storm, and tested it with three representative applications: continuous queries, log stream processing and word count (stream version). Extensive experimental results show 1) Compared to Storm's default scheduler and the state-of-the-art model-based method, the proposed framework reduces average tuple processing by 33.5% and 14.0% respectively on average. 2) The proposed framework can quickly reach a good scheduling solution during online learning, which justifies its practicability for online control in DSDPSs.
Enhancing Physical Layer Security for NOMA Transmission in mmWave Drone Networks

Physical layer security (PLS) is critically important for emerging wireless communication networks to maintain the confidentiality of the information of legitimate users. In this paper, we investigate enhancing PLS in an unmanned aerial vehicle (UAV) based communication network where a UAV acting as an aerial base station (BS) provides coverage in a densely packed user area (such as a stadium or a concert area). In particular, non-orthogonal multiple access (NOMA) together with highly-directional multi-antenna transmission techniques in mmWave frequency bands are utilized for improving spectral efficiency. In order to achieve PLS against potential eavesdropper attacks, we introduce a protected zone around the user region. However, limited resource availability refrain protected zone being extended to cover the entire eavesdropper region. Hence, we propose an approach to optimize the protected zone shape (for fixed area) at each UAV-BS hovering altitude. The associated secrecy performance is evaluated considering the secrecy outage and sum secrecy rates. Numerical results reveal the importance of protected zone shape optimization at each altitude to maximize NOMA secrecy rates.
Exact tensor completion with sum-of-squares

We obtain the first polynomial-time algorithm for exact tensor completion that improves over the bound implied by reduction to matrix completion. The algorithm recovers an unknown 3-tensor with $r$ incoherent, orthogonal components in $\mathbb R^n$ from $r\cdot \tilde O(n^{1.5})$ randomly observed entries of the tensor. This bound improves over the previous best one of $r\cdot \tilde O(n^{2})$ by reduction to exact matrix completion. Our bound also matches the best known results for the easier problem of approximate tensor completion (Barak & Moitra, 2015). Our algorithm and analysis extends seminal results for exact matrix completion (Candes & Recht, 2009) to the tensor setting via the sum-of-squares method. The main technical challenge is to show that a small number of randomly chosen monomials are enough to construct a degree-3 polynomial with precisely planted orthogonal global optima over the sphere and that this fact can be certified within the sum-of-squares proof system.
Learning hierarchical object maps of non-stationary environments with mobile robots

Building models, or maps, of robot environments is a highly active research area; however, most existing techniques construct unstructured maps and assume static environments. In this paper, we present an algorithm for learning object models of non-stationary objects found in office-type environments. Our algorithm exploits the fact that many objects found in office environments look alike (e.g., chairs, recycling bins). It does so through a two-level hierarchical representation, which links individual objects with generic shape templates of object classes. We derive an approximate EM algorithm for learning shape pararneters at both levels of the hierarchy, using local occupancy grid maps for representing shape. Additionally, we develop a Bayesian model selection algorithm that enables the robot to estimate the total number of objects and object templates in the environment. Experimental results using a real robot equipped with a laser range finder indicate that our approach performs well at learning object-based maps of simple office environments. The approach outperforms a previously developed non-hierarchical algorithm that models objects jects but lacks class templates.
Imaging of buried objects from multi-frequency experimental data using a globally convergent inversion method

This paper is concerned with the numerical solution to a 3D coefficient inverse problem for buried objects with multi-frequency experimental data. The measured data, which are associated with a single direction of an incident plane wave, are backscatter data for targets buried in a sandbox. These raw scattering data were collected using a microwave scattering facility at the University of North Carolina at Charlotte. We develop a data preprocessing procedure and exploit a newly developed globally convergent inversion method for solving the inverse problem with these preprocessed data. It is shown that dielectric constants of the buried targets as well as their locations are reconstructed with a very good accuracy. We also prove a new analytical result which rigorously justifies an important step of the so-called "data propagation" procedure.
CATEGORIES IN CONTROL

Control theory uses 'signal-ow diagrams' to describe processes where real- valued functions of time are added, multiplied by scalars, dierentiated and integrated, duplicated and deleted. These diagrams can be seen as string diagrams for the sym- metric monoidal category FinVectk of nite-dimensional vector spaces over the eld of rational functions k = R(s), where the variable s acts as dierentiation and the monoidal structure is direct sum rather than the usual tensor product of vector spaces. For any eld k we give a presentation of FinVectk in terms of the generators used in signal- ow diagrams. A broader class of signal-ow diagrams also includes 'caps' and 'cups' to model feedback. We show these diagrams can be seen as string diagrams for the symmetric monoidal category FinRelk, where objects are still nite-dimensional vector spaces but the morphisms are linear relations. We also give a presentation for FinRelk. The relations say, among other things, that the 1-dimensional vector space k has two special commutative y-Frobenius structures, such that the multiplication and unit of either one and the comultiplication and counit of the other t together to form a bi- monoid. This sort of structure, but with tensor product replacing direct sum, is familiar from the 'ZX-calculus' obeyed by a nite-dimensional Hilbert space with two mutually unbiased bases.
Universal convexification via risk-aversion

We develop a framework for convexifying a general class of optimization problems. We analyze the suboptimality of the solution to the convexified problem relative to the original nonconvex problem, and prove additive approximation guarantees under some assumptions. In simple settings, the convexification procedure can be applied directly and standard optimization methods can be used. In the general case we rely on stochastic gradient algorithms, whose convergence rate can be bounded using the convexity of the underlying optimization problem. We then extend the framework to a general class of discrete-time dynamical systems where our convexification approach falls under the paradigm of risk-sensitive Markov Decision Processes. We derive the first model-based and model-free policy gradient optimization algorithms with guaranteed convergence to the optimal solution. We also present numerical results in different machine learning applications.
Oscillation Properties of Scalar Conservation Laws

We obtain several new regularity results for solutions of scalar conservation laws satisfying the genuine nonlinearity condition. We prove that the solutions are continuous outside of the jump set, which is codimension one rectifiable. We show that the entropy dissipation vanishes away from the closure of the jump set. We prove that the solution decays algebraically in $L^\infty$ as $t \to \infty$ and we compute the presumably optimal decay rate. All these results are based on a local oscillation estimate which is obtained properly adapting some ideas of De Giorgi from the context of elliptic equations.
Exact, graded, immersed Lagrangians and Floer theory

We develop Lagrangian Floer Theory for exact, graded, immersed Lagrangians with clean self-intersection using Seidel's setup. A positivity assumption on the index of the self intersection points is imposed to rule out certain (but not all) disc bubbles. This allows the Lagrangians to be included in the exact Fukaya category. We also study quasi-isomorphism of Lagrangians under certain exact deformations which are not Hamiltonian.
Display of probability densities for data from a continuous distribution

Abstract Based on cumulative distribution functions, Fourier series expansion and Kolmogorov tests, we present a simple method to display probability densities for data drawn from a continuous distribution. It is often more e_cient thanusing histograms.
Adaptive MPC for Iterative Tasks

This paper proposes an Adaptive Learning Model Predictive Control strategy for uncertain constrained linear systems performing iterative tasks. The additive uncertainty is modeled as the sum of a bounded process noise and an unknown constant offset. As new data becomes available, the proposed algorithm iteratively adapts the believed domain of the unknown offset after each iteration. An MPC strategy robust to all feasible offsets is employed in order to guarantee recursive feasibility. We show that the adaptation of the feasible offset domain reduces conservatism of the proposed strategy compared to classical robust MPC strategies. As a result, the controller performance improves. Performance is measured in terms of following trajectories with lower associated costs at each iteration. Numerical simulations highlight the main advantages of the proposed approach.
Commutators and linear spans of projections in certain finite C*-algebras

Assume that A is a unital separable simple C*-algebra with real rank zero, stable rank one, strict comparison of projections, and that its tracial simplex T(A) has a finite number of extremal points. We prove that every self-adjoint element a in A with τ(a)=0 for all τ∈T(A) is the sum of two commutators in A and that every positive element of A is a linear combination of projections with positive coefficients. Assume that A is as above but σ-unital and not necessarily unital. Then an element (resp. a positive element) a of A is a linear combination (resp. a linear combination with positive coefficients) of projections if and only if τ¯(Ra)<∞ for every τ∈T(A), where τ¯ denotes the extension of τ to a tracial weight on A⁎⁎ and Ra∈A⁎⁎ denotes the range projection of a. Assume that A is unital and as above but T(A) has infinitely many extremal points. Then A is not the linear span of its projections. This result settles two open problems of Marcoux in [32].
Fourier coefficients of theta functions at cusps other than infinity

In this paper we study the Fourier coefficients of theta functions attached to Dirichlet characters at cusps other than infinity. The method is based on expressing them in terms of explicit elements of the adelic Schwartz space and studying the action of the adelic metaplectic group on these elements. We derive explicit formulae for the Fourier coefficients at all cusps. For the sake of simplicity, some restrictions are placed on the Dirichlet characters considered.
Extremes of q-Ornstein–Uhlenbeck processes

Abstract Two limit theorems are established on the extremes of a family of stationary Markov processes, known as q -Ornstein–Uhlenbeck processes with q ∈ ( − 1 , 1 ) . Both results are crucially based on the weak convergence of the tangent process at the lower boundary of the domain of the process, a positive self-similar Markov process little investigated so far in the literature. The first result is the asymptotic excursion probability established by the double-sum method, with an explicit formula for the Pickands constant in this context. The second result is a Brown–Resnick-type limit theorem on the minimum process of i.i.d. copies of the q -Ornstein–Uhlenbeck process: with appropriate scalings in both time and magnitude, a new semi-min-stable process arises in the limit.
Video Streaming in Distributed Erasure-Coded Storage Systems: Stall Duration Analysis

The demand for global video has been burgeoning across industries. With the expansion and improvement of video-streaming services, cloud-based video is evolving into a necessary feature of any successful business for reaching internal and external audiences. This paper considers video streaming over distributed systems where the video segments are encoded using an erasure code for better reliability, thus being the first work to our best knowledge that considers video streaming over erasure-coded distributed cloud systems. The download time of each coded chunk of each video segment is characterized, and the ordered statistics over the choice of the erasure-coded chunks is used to obtain the playback time of different video segments. Using the playback times, bounds on the moment generating function on the stall duration are used to bound the mean stall duration. Moment generating function-based bounds on the ordered statistics are also used to bound the stall duration tail probability, which determines the probability that the stall time is greater than a pre-defined number. These two metrics, mean stall duration and the stall duration tail probability, are important quality of experience (QoE) measures for the end users. Based on these metrics, we formulate an optimization problem to jointly minimize the convex combination of both the QoE metrics averaged over all requests over the placement and access of the video content. The non-convex problem is solved using an efficient iterative algorithm. Numerical results show a significant improvement in QoE metrics for cloud-based video compared to the considered baselines.
The topology of Double Field Theory

We describe the doubled space of Double Field Theory as a group manifold G with an arbitrary generalized metric. Local information from the latter is not relevant to our discussion and so G only captures the topology of the doubled space. Strong Constraint solutions are maximal isotropic submanifold M in G. We construct them and their Generalized Geometry in Double Field Theory on Group Manifolds. In general, G admits different physical subspace M which are Poisson-Lie T-dual to each other. By studying two examples, we reproduce the topology changes induced by T-duality with non-trivial H-flux which were discussed by Bouwknegt, Evslin and Mathai [1].
Real Analysis in Reverse

Many of the theorems of real analysis, against the background of the ordered field axioms, are equivalent to Dedekind completeness, and hence can serve as completeness axioms for the reals. In the ...
Knot Cabling and the Degree of the Colored Jones Polynomial

We study the behavior of the degree of the colored Jones polynomial and the boundary slopes of knots under the operation of cabling. We show that, under certain hypothesis on this degree, if a knot K satises Garoufalidis' Slope Conjecture then a (p;q)-cable of K satises the conjecture, provided that p=q is not a Jones slope of K. As an application we verify the Slope Conjecture for iterated cables of adequate knots and for iterated torus knots. We also state a conjecture suggesting a topological interpretation the linear terms of the degree of the colored Jones polynomial, and and we prove it for the following classes of knots: iterated torus knots and iterated cables of adequate knots, pretzel knots of type ( 2; 3;p) and iterated cables of several knots with up to nine crossings. 2010 Mathematics Classication: Primary 57N10. Secondary 57M25.
Bitcoin-NG: a scalable blockchain protocol

Cryptocurrencies, based on and led by Bitcoin, have shown promise as infrastructure for pseudonymous online payments, cheap remittance, trustless digital asset exchange, and smart contracts. However, Bitcoinderived blockchain protocols have inherent scalability limits that trade off between throughput and latency, which withhold the realization of this potential. This paper presents Bitcoin-NG (Next Generation), a new blockchain protocol designed to scale. Bitcoin-NG is a Byzantine fault tolerant blockchain protocol that is robust to extreme churn and shares the same trust model as Bitcoin. In addition to Bitcoin-NG, we introduce several novel metrics of interest in quantifying the security and efficiency of Bitcoin-like blockchain protocols. We implement Bitcoin-NG and perform large-scale experiments at 15% the size of the operational Bitcoin system, using unchanged clients of both protocols. These experiments demonstrate that Bitcoin-NG scales optimally, with bandwidth limited only by the capacity of the individual nodes and latency limited only by the propagation time of the network.
Higher derivatives and the inverse derivative of a tensor-valued function of a tensor

The n th derivative of a tensor-valued function of a tensor is defined by a finite number of coefficients each with closed form expression.
Tree Tribes and Lower Bounds for Switching Lemmas

Let f be a Boolean function on n variables, rho a random p-restriction that independently keeps each variable unset (or free) with probability p and otherwise uniformly sets it to 0 or 1, and DT_{depth}(f) denote the depth of the smallest depth decision tree for f. Let R_d(f|rho) be the resilience of f to rho for depth d, defined as R_d(f|rho)=Pr_{rho = d]. If d >> pn, all functions have resilience close to 0 since less than d variables would remain unset with high probability. For d << pn, most functions f on n variables have resilience close to 1, and some functions, like AND and OR, have resilience close to 0. Hastad's Switching Lemma states that for t-DNFs, the resilience R_d(f|rho) is upper bounded by (5pt)^d, and from known upper bounds on the size of constant depth circuits computing the parity function, it follows that there exist t-DNFs whose resilience is close to the bound obtained by Hastad. However, the exact bounds for such maximally resilient DNFs or their structure is unclear, and moreover, the argument is non-constructive. In this work, we give an explicit construction of functions called Tree Tribes parameterized by an integer t and denoted Xi_t (on n variables), such that R_d(Xi_t|rho) =(c_0 p2^t)^d, for 0 <= p <= c_p 2^-t and 0 <= d <= c_d * (log n)/(2^t * t log t) (where c_0,c_p,c_d are universal constants). As a result, for sufficiently large n and small d, this gives a hierarchy of functions with strictly increasing resilience, and covers the entire region between the two extremes where functions have resilience (close to) 0 or 1.
STEADY-STATE SOLUTIONS IN AN ALGEBRA OF GENERALIZED FUNCTIONS:

Formulas for the solutions of initial value problems for ordinary differential equations with singular (n) -like driving terms are derived in the framework of an algebra of generalized functions (of Colom- beau type) over aeld of generalized scalars. Some of the solutions might have physical meaning - such as of the electrical current after lightning or under superconductivit - but do not have counterparts in the theory of Schwartz distributions. What is somewhat unusual (compared with other similar works) is the involvement of innitely large constants, such as (0), in some of the formulas for the solutions.
Fully commutative elements of type D and homogeneous representations of KLR-algebras

In this paper, we decompose the set of fully commutative elements into natural subsets when the Coxeter group is of type Dn, and study combinatorics of these subsets, revealing hidden structures. (We do not consider type An first, since a similar decomposition for type An is trivial.) As an application, we classify and enumerate the homogeneous representations of the Khovanov–Lauda– Rouquier algebras of type Dn.
Jackknife multiplier bootstrap: finite sample approximations to the U -process supremum with applications

This paper is concerned with finite sample approximations to the supremum of a non-degenerate U-process of a general order indexed by a function class. We are primarily interested in situations where the function class as well as the underlying distribution change with the sample size, and the U-process itself is not weakly convergent as a process. Such situations arise in a variety of modern statistical problems. We first consider Gaussian approximations, namely, approximate the U-process supremum by the supremum of a Gaussian process, and derive coupling and Kolmogorov distance bounds. Such Gaussian approximations are, however, not often directly applicable in statistical problems since the covariance function of the approximating Gaussian process is unknown. This motivates us to study bootstrap-type approximations to the U-process supremum. We propose a novel jackknife multiplier bootstrap (JMB) tailored to the U-process, and derive coupling and Kolmogorov distance bounds for the proposed JMB method. All these results are non-asymptotic, and established under fairly general conditions on function classes and underlying distributions. Key technical tools in the proofs are new local maximal inequalities for U-processes, which may be useful in other problems. We also discuss applications of the general approximation results to testing for qualitative features of nonparametric functions based on generalized local U-processes.
Wasserstein Fair Classification

We propose an approach to fair classification that enforces independence between the classifier outputs and sensitive information by minimizing Wasserstein-1 distances. The approach has desirable theoretical properties and is robust to specific choices of the threshold used to obtain class predictions from model outputs. We introduce different methods that enable hiding sensitive information at test time or have a simple and fast implementation. We show empirical performance against different fairness baselines on several benchmark fairness datasets.
Taskonomy: Disentangling Task Transfer Learning.

Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and probing this taxonomical structure including a solver that users can employ to devise efficient supervision policies for their use cases.
A Geometric Analysis of Power System Loadability Regions

Understanding the feasible power flow region is of central importance to power system analysis. In this paper, we propose a geometric view of the power system loadability problem. By using rectangular coordinates for complex voltages, we provide an integrated geometric understanding of active and reactive power flow equations on loadability boundaries. Based on such an understanding, we develop a linear programming framework to 1) verify if an operating point is on the loadability boundary, 2) compute the margin of an operating point to the loadability boundary, and 3) calculate a loadability boundary point of any direction. The proposed method is computationally more efficient than existing methods since it does not require solving nonlinear optimization problems or calculating the eigenvalues of the power flow Jacobian. Standard IEEE test cases demonstrate the capability of the new method compared to the current state-of-the-art methods. Understanding the feasible power flow region is of central importance to power system analysis. This paper proposes a geometric view of the power system loadability problem. By using rectangular coordinates for complex voltages, this paper provides an integrated geometric understanding of active and reactive power flow equations on loadability boundaries. Based on such an understanding, this paper develops a linear programming framework to 1) verify if an operating point is on the loadability boundary, 2) compute the margin of an operating point to the loadability boundary, and 3) calculate a loadability boundary point of any direction. The proposed method is computationally more efficient than existing methods since it does not require solving nonlinear optimization problems or calculating the eigenvalues of the power flow Jacobian. Standard IEEE test cases demonstrate the capability of the new method compared to the current state-of-the-art methods.
Use of RNA and DNA to Identify Mechanisms of Bacterial Community Homogenization.

Biotic homogenization, i.e. the increase in community similarity through time or space, is a commonly observed response following conversion of native ecosystems to agriculture, but our understanding of the ecological mechanisms underlying this process is limited for bacterial communities. Identifying mechanisms of bacterial community homogenization following rapid environmental change may be complicated by the fact only a minority of taxa is active at any time. Here we used RNA- and DNA-based metabarcoding to distinguish putatively active taxa in the bacterial community from inactive taxa. We asked how soil bacterial communities respond to land use change following a rapid transition from rainforest to agriculture in the Congo Basin using a chronosequence that spans from roughly 1 week following slash-and-burn to an active plantation roughly 1.5 years post-conversion. Our results indicate that the magnitude of community homogenization is larger in the RNA-inferred community than the DNA-inferred perspective. We show that as the soil environment changes, the RNA-inferred community structure tracks environmental variation and loses spatial structure. The DNA-inferred community does not respond to environmental variability to the same degree, and is instead homogenized by a subset of taxa that is shared between forest and conversion sites. Our results suggest that complementing DNA-based surveys with RNA can provide insights into the way bacterial communities respond to environmental change.
A Hybrid Control Design for Autonomous Vehicles at Uncontrolled Crosswalks

As autonomous vehicles (AVs) inch closer to reality, a central requirement for acceptance will be earning the trust of humans in everyday driving situations. In particular, the interaction between AVs and pedestrians is of high importance, as every human is a pedestrian at some point of the day. This paper considers the interaction of a pedestrian and an autonomous vehicle at a mid-block, unsignalized intersection where there is ambiguity over when the pedestrian should cross and when and how the vehicle should yield. By modeling pedestrian behavior through the concept of gap acceptance, the authors show that a hybrid controller with just four distinct modes allows an autonomous vehicle to successfully interact with a pedestrian across a continuous spectrum of possible crosswalk entry behaviors. The controller is validated through extensive simulation and compared to an alternate POMDP solution and experimental results are provided on a research vehicle for a virtual pedestrian.
Universal Approximation of Input-Output Maps by Temporal Convolutional Nets

There has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to convolutional network architectures due to computational advantages in training and operation while still achieving competitive performance. For systems having limited long-term temporal dependencies, the approximation capability of recurrent networks is essentially equivalent to that of temporal convolutional nets (TCNs). We prove that TCNs can approximate a large class of input-output maps having approximately finite memory to arbitrary error tolerance. Furthermore, we derive quantitative approximation rates for deep ReLU TCNs in terms of the width and depth of the network and modulus of continuity of the original input-output map, and apply these results to input-output maps of systems that admit finite-dimensional state-space realizations (i.e., recurrent models).
Bootstrapping spectral statistics in high dimensions

Author(s): Lopes, ME; Blandino, A; Aue, A | Abstract: Statistics derived from the eigenvalues of sample covariance matrices are called spectral statistics, and they play a central role in multivariate testing. Although bootstrap methods are an established approach to approximating the laws of spectral statistics in low-dimensional problems, such methods are relatively unexplored in the high-dimensional setting. The aim of this article is to focus on linear spectral statistics as a class of prototypes for developing a new bootstrap in high dimensions, a method we refer to as the spectral bootstrap. In essence, the proposed method originates from the parametric bootstrap and is motivated by the fact that in high dimensions it is difficult to obtain a nonparametric approximation to the full data-generating distribution. From a practical standpoint, the method is easy to use and allows the user to circumvent the difficulties of complex asymptotic formulas for linear spectral statistics. In addition to proving the consistency of the proposed method, we present encouraging empirical results in a variety of settings. Lastly, and perhaps most interestingly, we show through simulations that the method can be applied successfully to statistics outside the class of linear spectral statistics, such as the largest sample eigenvalue and others.
Analysis of Glutamine Deamidation: Products, Pathways, and Kinetics

Spontaneous chemical modifications play an important role in human disease and aging at the molecular level. Deamidation and isomerization are known to be among the most prevalent chemical modifications in long-lived human proteins and are implicated in a growing list of human pathologies, but the relatively minor chemical change associated with these processes has presented a long standing analytical challenge. Although the adoption of high-resolution mass spectrometry has greatly aided the identification of deamidation sites in proteomic studies, isomerization (and the isomeric products of deamidation) remain exceptionally challenging to characterize. Herein, we present a liquid chromatography/mass spectrometry-based approach for rapidly characterizing the isomeric products of Gln deamidation using diagnostic fragments that are abundantly produced and capable of unambiguously identifying both Glu and isoGlu. Importantly, the informative fragment ions are produced through orthogonal fragmentation pathways, thereby enabling the simultaneous detection of both isomeric forms while retaining compatibility with shotgun proteomics. Furthermore, the diagnostic fragments associated with isoGlu pinpoint the location of the modified residue. The utility of this technique is demonstrated by characterizing the isomeric products generated during in vitro aging of a series of glutamine-containing peptides. Sequence-dependent product profiles are obtained, and the abundance of deamidation-linked racemization is examined. Finally, comparisons are made between Gln deamidation, which is relatively poorly understood, and asparagine deamidation, which has been more thoroughly studied.
Primary Care Physician Practice Styles and Patient Care: Evidence from Physician Exits in Medicare

Primary care physicians (PCPs) provide frontline health care to patients in the U.S.; however, it is unclear how their practice styles affect patient care. In this paper, we estimate the long-lasting effects of PCP practice styles on patient health care utilization by focusing on Medicare patients affected by PCP relocations or retirements, which we refer to as "exits." Observing where patients receive care after these exits, we estimate event studies to compare patients who switch to PCPs with different practice style intensities. We find that PCPs have large effects on a range of aggregate utilization measures, including physician and outpatient spending and the number of diagnosed conditions. Moreover, we find that PCPs have large effects on the quality of care that patients receive, and that all of these effects persist for several years. Our results suggest that switching to higher-quality PCPs could significantly affect patients' longer-run health outcomes.
Microbial biosynthesis of lactate esters.

Green organic solvents such as lactate esters have broad industrial applications and favorable environmental profiles. Thus, manufacturing and use of these biodegradable solvents from renewable feedstocks help benefit the environment. However, to date, the direct microbial biosynthesis of lactate esters from fermentable sugars has not yet been demonstrated. In this study, we present a microbial conversion platform for direct biosynthesis of lactate esters from fermentable sugars. First, we designed a pyruvate-to-lactate ester module, consisting of a lactate dehydrogenase (ldhA) to convert pyruvate to lactate, a propionate CoA-transferase (pct) to convert lactate to lactyl-CoA, and an alcohol acyltransferase (AAT) to condense lactyl-CoA and alcohol(s) to make lactate ester(s). By generating a library of five pyruvate-to-lactate ester modules with divergent AATs, we screened for the best module(s) capable of producing a wide range of linear, branched, and aromatic lactate esters with an external alcohol supply. By co-introducing a pyruvate-to-lactate ester module and an alcohol (i.e., ethanol, isobutanol) module into a modular Escherichia coli (chassis) cell, we demonstrated for the first time the microbial biosynthesis of ethyl and isobutyl lactate esters directly from glucose. In an attempt to enhance ethyl lactate production as a proof-of-study, we re-modularized the pathway into (1) the upstream module to generate the ethanol and lactate precursors and (2) the downstream module to generate lactyl-CoA and condense it with ethanol to produce the target ethyl lactate. By manipulating the metabolic fluxes of the upstream and downstream modules through plasmid copy numbers, promoters, ribosome binding sites, and environmental perturbation, we were able to probe and alleviate the metabolic bottlenecks by improving ethyl lactate production by 4.96-fold. We found that AAT is the most rate-limiting step in biosynthesis of lactate esters likely due to its low activity and specificity toward the non-natural substrate lactyl-CoA and alcohols. We have successfully established the biosynthesis pathway of lactate esters from fermentable sugars and demonstrated for the first time the direct fermentative production of lactate esters from glucose using an E. coli modular cell. This study defines a cornerstone for the microbial production of lactate esters as green solvents from renewable resources with novel industrial applications.
Using Quantization to Deploy Heterogeneous Nodes in Two-Tier Wireless Sensor Networks

We study a heterogeneous two-tier wireless sensor network in which N heterogeneous access points (APs) collect sensing data from densely distributed sensors and then forward the data to M heterogeneous fusion centers (FCs). This heterogeneous node deployment problem is modeled as a quantization problem with distortion defined as the total power consumption of the network. The necessary conditions of the optimal AP and FC node deployment are explored in this paper. We provide a variation of Voronoi diagrams as the optimal cell partition for this network, and show that each AP should be placed between its connected FC and the geometric center of its cell partition. In addition, we propose a heterogeneous two-tier Lloyd-like algorithm to optimize the node deployment. Simulation results show that our proposed algorithm outperforms the existing methods like Minimum Energy Routing, Agglomerative Clustering, and Divisive Clustering, on average.
High School Course Access and Postsecondary STEM Enrollment and Attainment

We study the effects of access to high school math and science courses on postsecondary science, technology, engineering, and mathematics (STEM) enrollment and degree attainment using administrativ...
Fabrication of High Quality Quantum Emitters in Diamond Nanostructures

As the field of solid-state quantum engineering matures, it is increasingly necessary to produce quantum emitters with narrow optical transitions and long spin coherence times aligned to nanophotonic structures [1]. The nitrogen-vacancy (NV) defect center in diamond is a well-suited candidate owing to a spin ground state with a long coherence time [2], nearby nuclear spins for quantum memories [3], and spin-selective optical transitions allowing for efficient optical spin initialization and readout [4].
How Does Orofacial Trauma in Children Affect the Developing Dentition? Long-term Treatment and Associated Complications.

Abstract Each year, millions of children are injured and live with the consequences of those injuries. Through infancy and childhood, orofacial trauma caused by falls or being struck by or against objects occurs in children. The long-term implications on the developing permanent teeth are little known, even when the oral region is the second most frequently injured body area in children under 6 years of age. During this period, the developing permanent teeth may be directly involved after trauma, causing mild to severe hypoplasia, displacement, damage to the tooth germ, or an extended range of morphofunctional disturbances. In some cases, the effects of oral and dental injuries caused by trauma appear later with the eruption of the permanent incisors when ectopic eruption, malalignments, and other developmental disturbances become visible. Therefore, long-term follow up of the patient in order to diagnose and treat associated complications becomes essential. Critical points for facing the consequences of orofacial trauma on the developing dentition are to recognize the impact of orofacial trauma in young children and the dentist’s role in providing anticipatory guidance to parents and health care professionals, differentiate between mild and severe disturbances affecting the developing permanent teeth after oral injuries in early childhood, recognize the importance of follow-up controls, and recognize the importance of early referral to a pediatric dentist and orthodontist for diagnosis and treatment planning.
Will the Supreme Court Rein in “Excessive Fines” and Forfeitures? Don’t Rely on Timbs v. Indiana

The Supreme Court’s decision in Timbs v. Indiana, 139 S. Ct. 682 (2019), last term buoyed the hopes of those who saw it as a powerful signal to states and municipalities to rein in excessive fines, fees, and forfeitures. Yet, the Court seems disinclined to fill the term “proportionality” with robust meaning or wrestle with Eighth Amendment challenges to fines and fees. Those steps would be required for the Excessive Fines Clause to function as an effective backstop against revenue-raising and increasingly abusive local and state practices. In the end, state courts and state legislative changes may more likely address effectively the essential question of what is excessive and restrain criminal justice actors from imposing ever heavier financial burdens on those caught up in the system. This article first sets out the Supreme Court’s decision in Timbs in light of the incorporation debate and prior case law in the area. Next it turns to the underlying but unaddressed contours of “excessive” in the context of fines and forfeitures. The article then provides a broader look at forfeiture, including the interplay between state and federal law enforcement in the area. Finally, the piece addresses state and local fines and fees, which will now be subject to Eighth Amendment analysis. The Court, however, rejected the first opportunity to take up such a challenge. At least for now, litigants may be more successful in reining in abusive fines and forfeitures in state legislatures and state courts.
D.H. Lehmer’s Tridiagonal Determinant: An Étude in (Andrews-Inspired) Experimental Mathematics

We use George Andrews’ “reverse-engineering” method to reprove, using experimental mathematics, a conjecture of D.H. Lehmer.
Spike-Based Winner-Take-All Computation: Fundamental Limits and Order-Optimal Circuits.

Winner-take-all (WTA) refers to the neural operation that selects a (typically small) group of neurons from a large neuron pool. It is conjectured to underlie many of the brain's fundamental comput...
Exercise-induced hypoxemia predicts heart failure hospitalization and death in patients supported with left ventricular assist devices.

Following implantation of continuous-flow left ventricular assist devices, mechanical off-loading results in improved resting hemodynamics; however, peak exercise capacity generally does not increa...
Asymptotics for the Ginzburg-Landau equation on manifolds with boundary under homogeneous Neumann condition

Abstract On a compact manifold M ‾ n ( n ≥ 3 ) with boundary, we study the asymptotic behavior as ϵ tends to zero of solutions u ϵ : M → C to the equation Δ u ϵ + ϵ − 2 ( 1 − | u ϵ | 2 ) u ϵ = 0 with the boundary condition ∂ ν u ϵ = 0 on ∂M. Assuming an energy upper bound on the solutions and a convexity condition on ∂M, we show that along a subsequence, the energy of { u ϵ } breaks into two parts: one captured by a harmonic 1-form ψ on M, and the other concentrating on the support of a rectifiable ( n − 2 ) -varifold V which is stationary with respect to deformations preserving ∂M. Examples are given which shows that V could vanish altogether, or be non-zero but supported only on ∂M.
Deep Transfer Reinforcement Learning for Text Summarization.

Deep neural networks are data hungry models and thus face difficulties when attempting to train on small text datasets. Transfer learning is a potential solution but their effectiveness in the text domain is not as explored as in areas such as image analysis. In this paper, we study the problem of transfer learning for text summarization and discuss why existing state-of-the-art models fail to generalize well on other (unseen) datasets. We propose a reinforcement learning framework based on a self-critic policy gradient approach which achieves good generalization and state-of-the-art results on a variety of datasets. Through an extensive set of experiments, we also show the ability of our proposed framework to fine-tune the text summarization model using only a few training samples. To the best of our knowledge, this is the first work that studies transfer learning in text summarization and provides a generic solution that works well on unseen data.
Targeting the COX1/2-Driven thromboxane A2 pathway suppresses Barrett's esophagus and esophageal adenocarcinoma development

Abstract Background Barrett's esophagus (BE), a complication of gastroesophageal reflux disease (GERD), predisposes patients to esophageal adenocarcinoma (EAC). Reliable biomarkers for early detection and discovery of potential drug targets are urgently needed for improved BE and EAC patient outcomes. Methods Patient biopsy samples were evaluated for COX1/2, and thromboxane A2 synthase (TBXAS) expression. Circulating prostaglandins biosynthesis was determined using enzyme immunoassay kits. Anchorage-independent cell growth assay, crystal violet staining assay, and xenograft experiments were conducted to assess BE and EAC cell growth. A surgical mouse model of reflux (i.e., esophagoduodenostomy) was established and samples were analyzed using an enzyme immunoassay kit, immunohistochemistry, immunoblotting, or RT-PCR. Esophageal biopsy samples (pre- and post-intervention) were obtained from a randomized clinical trial in which participants were administered esomeprazole (40 mg) twice daily in combination with an acetylsalicylic acid (ASA) placebo or 81 or 325 mg ASA for 28 days. Esophageal biopsy specimens before and after the intervention period were analyzed. Findings COX2 and TBXAS are highly expressed in BE and EAC patients accompanied by a pronounced elevation of circulating TXA2 levels. ASA suppressed BE and EAC growth by targeting the TXA2 pathway. Additionally, biopsies from 49 patients (with similar baseline characteristics) showed that ASA substantially decreased serum TXA2 levels, resulting in reduced inflammation. Interpretation This study establishes the importance of the COX1/2-driven TXA2 pathway in BE and EAC pathophysiology and lays the groundwork for introducing a TXA2-targeting strategy for EAC prevention and early detection. Funding Hormel Foundation , Exact Sciences, Pentax Medical, Intromedic and National Cancer.