cssi/SciSciGPT-SciSciNet huggingface dataset:
Selling information through consulting

We consider a monopoly information holder selling information to a budget-constrained decision maker, who may benefit from the seller's information. The decision maker has a utility function that depends on his action and an uncertain state of the world. The seller and the buyer each observe a private signal regarding the state of the world, which may be correlated with each other. The seller's goal is to sell her private information to the buyer and extract maximum possible revenue, subject to the buyer's budget constraints. We consider three different settings with increasing generality, i.e., the seller's signal and the buyer's signal can be independent, correlated, or follow a general distribution accessed through a black-box sampling oracle. For each setting, we design information selling mechanisms which are both optimal and simple in the sense that they can be naturally interpreted, have succinct representations, and can be efficiently computed. Notably, though the optimal mechanism exhibits slightly increasing complexity as the setting becomes more general, all our mechanisms share the same format of acting as a consultant who recommends the best action to the buyer but uses different and carefully designed payment rules for different settings. Each of our optimal mechanisms can be easily computed by solving a single polynomial-size linear program. This significantly simplifies exponential-size LPs solved by the Ellipsoid method in the previous work, which computes the optimal mechanisms in the same setting but without budget limit. Such simplification is enabled by our new characterizations of the optimal mechanism in the (more realistic) budget-constrained setting.
The effectiveness of incentives on completion rates, data quality, and nonresponse bias in a probability-based internet panel survey

Previous research has shown that increasing the size of incentives can increase response rates for probability-based, cross-sectional surveys. However, the effects of incentives on web panels have ...
Fast Record Linkage for Company Entities

Record linkage is an essential part of nearly all real-world systems that consume structured and unstructured data coming from different sources. Typically no common key is available for connecting records. Massive data integration processes often have to be completed before any data analytics and further processing can be performed. In this work we focus on company entity matching, where company name, location and industry are taken into account. Our contribution is a highly scalable, enterprise-grade end-to-end system that uses rule-based linkage algorithms in combination with a machine learning approach to account for short company names. Linkage time is greatly reduced by an efficient decomposition of the search space using MinHash. Based on real-world ground truth datasets, we show that our approach reaches a recall of 91% compared to 73% for baseline approaches, while scaling linearly with the number of nodes used in the system.
Two-curve Green's function for $2$-SLE: the interior case

A 2-$$\hbox {SLE}_\kappa $$ ($$\kappa \in (0,8)$$) is a pair of random curves $$(\eta _1,\eta _2)$$ in a simply connected domain D connecting two pairs of boundary points such that conditioning on any curve, the other is a chordal $$\hbox {SLE}_\kappa $$ curve in a complement domain. In this paper we prove that for any $$z_0\in D$$, the limit $$\lim _{r\rightarrow 0^+}r^{-\alpha _0} {\mathbb {P}}[{{\,\mathrm{dist}\,}}(z_0,\eta _j)<r,j=1,2]$$, where $$\alpha _0=\frac{(12-\kappa )(\kappa +4)}{8\kappa }$$, exists. Such limit is called a two-curve Green’s function. We find the convergence rate and the exact formula of the Green’s function in terms of a hypergeometric function up to a multiplicative constant. For $$\kappa \in (4,8)$$, we also prove the convergence of $$\lim _{r\rightarrow 0^+}r^{-\alpha _0} \mathbb {P}[{{\,\mathrm{dist}\,}}(z_0,\eta _1\cap \eta _2)<r]$$, whose limit is a constant times the previous Green’s function. To derive these results, we work on two-time-parameter stochastic processes, and use orthogonal polynomials to derive the transition density of a two-dimensional diffusion process that satisfies some system of SDE.
Parameter Modeling for Small-Scale Mobility in Indoor THz Communication

Despite such challenges as high path loss and equipment cost, THz communication is becoming one of the potentially viable means through which ultra-high data rate can be achieved. To compensate for the high path loss, we present parameter modeling for indoor THz communication. To maximize efficient and opportunistic use of resources, we analyze the potential workarounds for a single access point to satisfy most of the mobile terminals by varying such parameters as humidity, distance, frequency windows, beamwidths, antenna placement, and user mobility type. One promising parameter is antenna beamwidth, where narrower beams results in higher antenna gain. However, this can lead to ''\textit{beamwidth dilemma}" scenario, where narrower beamwidth can result in significant outages due to device mobility and orientation. In this paper, we address this challenge by presenting a mobility model that performs an extensive analysis of different human mobility scenarios, where each scenario has different data rate demands and movement patterns. We observe that for mobile users, there are optimal beamwidths that are affected by the mobility type (high mobility, constrained mobility, and low mobility) and AP placement.
David A. Boothman, 1958-2019.

Dr. David A. Boothman, our colleague, mentor and friend, unexpectedly passed away on 1 November 2019 at the age of 61. He leaves behind family, friends and colleagues in the fields of DNA repair, r...
Average-based Robustness for Continuous-Time Signal Temporal Logic

We propose a new robustness score for continuous-time Signal Temporal Logic (STL) specifications. Instead of considering only the most severe point along the evolution of the signal, we use average scores to extract more information from the signal, emphasizing robust satisfaction of all the specifications’ subformulae over their entire time interval domains. We demonstrate the advantages of this new score in falsification and control synthesis problems in systems with complex dynamics and multi-agent systems.
Minimizing geodesic nets and critical points of distance

Abstract We establish a relationship between geodesic nets and critical points of the distance function. We bound the number of balanced points for certain minimizing geodesic nets on manifolds homeomorphic to the n-sphere. This result is used to give conditions under which a minimizing geodesic flower degenerates into a simple closed geodesic.
Altered Immunity of Laboratory Mice in the Natural Environment Is Associated with Fungal Colonization

Summary Free-living mammals, such as humans and wild mice, display heightened immune activation compared with artificially maintained laboratory mice. These differences are partially attributed to microbial exposure as laboratory mice infected with pathogens exhibit immune profiles more closely resembling that of free-living animals. Here, we examine how colonization by microorganisms within the natural environment contributes to immune system maturation by releasing inbred laboratory mice into an outdoor enclosure. In addition to enhancing differentiation of T cell populations previously associated with pathogen exposure, outdoor release increased circulating granulocytes. However, these “rewilded” mice were not infected by pathogens previously implicated in immune activation. Rather, immune system changes were associated with altered microbiota composition with notable increases in intestinal fungi. Fungi isolated from rewilded mice were sufficient in increasing circulating granulocytes. These findings establish a model to investigate how the natural environment impacts immune development and show that sustained fungal exposure impacts granulocyte numbers.
CHAPTER 8 – Standard Hardware Interfaces

This chapter discusses the several parallel and serial computer interfaces. The serial interface consists of only one data line and one or more control lines. In this scheme, the data is time multiplexed. Control lines are used to indicate when the receiving end is ready to get the data along with other functions. The digital value of the data line represents a different bit at a different time. This requires a timing reference for the receiving end to decode the data accurately. When an external timing reference is used, this becomes a synchronous serial interface, with a control line carrying the required clock signal. Even though a parallel interface is inherently faster than an equivalent serial interface, it has drawbacks. Most parallel interfaces use standard digital logic voltage levels, usually TTL compatible. The parallel printer interface, sometimes called the Centronics interface, is available for nearly all personal computers and is supported by most printers.
Lead Tin Telluride (PbSnTe)

Publisher Summary This chapter focuses on lead tin telluride (PbSnTe) and provides an overview of the optical constants of Pb1-xSnxTe. There are several alloys of Pb1-xSnxTe. Pb1-xSnxTe that can be grown throughout the range Both PbTe and SnTe crystallize in the rocksalt structure, and both have interband optical transitions at the L point in the Brillouin zone. The width of the direct energy gap changes with the composition of the alloy; as x increases, the band gap first decreases and then increases. The chapter presents a list of values of n and k for Pb1-xSnxTe obtained from various references. n and k are plotted in that list for x = 0.19±0.02 over the widest possible spectral range. The chapter further reviews various studies on the dielectric properties and phonon spectra of PbSnTe and Pb1-xSnxTe.
LOESS DEPOSITS: ORIGINS AND PROPERTIES

Loess is an eolian (windblown) sediment that is an important archive of Quaternary climate changes. It may provide one of the most complete terrestrial records of interglacial–glacial cycles. Loess is unusual as a record of Quaternary climate change because it is one of the few sediments that is deposited directly from the atmosphere. Thus, it is a geologic deposit that contains a record of atmospheric circulation and can be used to reconstruct synoptic-scale paleoclimatology. Loess is also unusual in that it can be dated directly using ‘trapped electron’ or luminescence methods that require only the sediment itself. Commonly, loess deposits are not homogenous sediments, but most contain buried soils, or paleosols. It is the combination of both unaltered loess deposits and intercalated paleosols that gives this sedimentary record much of its richness as a Quaternary paleoclimate record.
Impact of Generational Commonality of Short Life Cycle Products in Manufacturing and Remanufacturing Processes

Short-life cycle products are frequently replaced and discarded despite being resource-intensive. The short life span and the low utilization rate of the end-of-life products cause severe environmental problems and waste of resources. In the case of short-life cycle products, a new generation of products is released sooner than other products, therefore there are the opportunities to have various generations of products during the remanufacturing process. The commonality between generations increases the intergenerational component compatibility, which increases the efficiency of the manufacturing and remanufacturing processes, while at the same time weakening the performance difference between generations. This paper proposes a mathematical model to investigate the effect of commonality among generations on the overall production process. Based on various given new generation product designs with different commonality, we aim to propose optimal production planning and pricing strategies to maximize the total profitability and investigate how the results vary according to the commonality strategies between product generations.
Synthesis of application-specific multiprocessor systems including memory components

Heterogeneous systems have the potential to achieve enhanced performance as well as cost-effectiveness over homogeneous systems when the application domain is known since they can match the problem structure more closely. A formal design method, SOS, has been developed which can be used to synthesize optimal heterogeneous systems for given applications. The method involves creation of a Mixed Integer-Linear Programming (MILP) model and solution of the model. In this paper, first we show how to apply the method to application-specific systems with bus-style interconnection between the processors. We further demonstrate how to extend the method to deal with memory costs explicitly. Several experiments were performed with the original as well as the extended MILP models. These results indicate that it is important to include memory costs explicitly at design time.
Flowless: Extracting Densest Subgraphs Without Flow Computations

The problem of finding dense components of a graph is a major primitive in graph mining and data analysis. The densest subgraph problem (DSP) that asks to find a subgraph with maximum average degree forms a basic primitive in dense subgraph discovery with applications ranging from community detection to unsupervised discovery of biological network modules [16]. The DSP is exactly solvable in polynomial time using maximum flows [14, 17, 22]. Due to the high computational cost of maximum flows, Charikar’s greedy approximation algorithm is usually preferred in practice due to its linear time and linear space complexity [3, 8]. It constitutes a key algorithmic idea in scalable solutions for large-scale dynamic graphs [5, 7]. However, its output density can be a factor 2 off the optimal solution. In this paper we design Greedy++, an iterative peeling algorithm that improves upon the performance of Charikar’s greedy algorithm significantly. Our iterative greedy algorithm is able to output near-optimal and optimal solutions fast by adding a few more passes to Charikar’s greedy algorithm. Furthermore Greedy++ is more robust against the structural heterogeneities (e.g., skewed degree distributions) in real-world datasets. An additional property of our algorithm is that it is able to assess quickly, without computing maximum flows, whether Charikar’s approximation quality on a given graph instance is closer to the worst case theoretical guarantee of or to optimality. We also demonstrate that our method has significant efficiency advantage over the maximum flow based exact optimal algorithm. For example, our algorithm achieves ∼ 145 × speedup on average across a variety of real-world graphs while finding subgraphs of density that are at least 90% as dense as the densest subgraph.
Profitable Informed Trading in a Simple General Equilibrium Model of Asset Pricing

Abstract We present a simple general equilibrium model of asset pricing in which profitable informed trading can occur without any "noise" added to the model. We use an equilibrium concept similar to rational expectations equilibrium, but which explicitly allows for the possibility of adverse selection. We show that models of profitable informed trading must restrict the portfolio choices of uninformed traders: in particular, they cannot buy the market portfolio. In this model, profitable informed trading lowers the welfare of all agents when compared across steady states. Journal of Economic Literature Classification Numbers: G14, D50, D60, D82.
Total difference chromatic numbers of graphs

Inspired by graceful labelings and total labelings of graphs, we introduce the idea of total difference labelings. A k-total labeling of a graph G is an assignment of k distinct labels to the edges and vertices of a graph so that adjacent vertices, incident edges, and an edge and its incident vertices receive different labels. A k-total difference labeling of a graph G is a function f from the set of edges and vertices of G to the set {1,2,…,k} that is a k-total labeling of G and for which f({u,v})=|f(u)−f(v)| for any two adjacent vertices u and v of G with incident edge {u,v}. The least positive integer k for which G has a k-total difference labeling is its total difference chromatic number, χtd(G). We determine the total difference chromatic number of paths, cycles, stars, wheels, gears and helms. We also provide bounds for total difference chromatic numbers of caterpillars, lobsters, and general trees.
Size limits for expanding light sources

Les fluctuations rapides des brillances optiques et radio observees dans les quasars, ainsi que dans les objets BL Lac, imposent des limites superieures severes sur les dimensions possibles de ces sources; contrairement a l’opinion repandue, ces limites ne sont pas notablement modifiees par l’hypothese d’une source lumineuse en expansion rapide. Le conflit entre les limites de variation des dimensions des quasars et la surface minimale pouvant emettre une energie suffisante, en supposant des distances cosmologiques, n’est pas resolu par une expansion relativiste.
Bias in Observational Studies

We consider three sources of bias affecting observational studies and ways to address them: selection bias, information bias, and confounding. We discuss these sources of bias with respect to cohort and case–control designs. Even if a study is internally valid and not subject to these sources of bias, a judgment must be made whether the results generalize to some other target population. Keywords: selection bias; information bias; confounding; recall bias; loss to follow-up; nonresponse; internal versus external validity
Repeated Measures Analyses

Repeated measures data arise when multiple measurements of response variables and/or covariates are obtained in sequence from each experimental unit and occur frequently in many disciplines. The repeated measures design provides opportunities for investigators to examine the pattern of response variable under different measurement occasions, to understand the relationship between repeated outcomes and covariates, and to discern the dependence pattern among the repeated measurements. A special feature of repeated measures data is that measurements from the same unit are usually positively correlated; thus, valid and effective analysis of repeated measures must deal with this within-subject correlation. In this article, we review some of the analysis methods for repeated measures, which include univariate summary methods, multivariate analysis of variance, parametric random-effects methods, marginal generalized estimation equation methods, and modern semiparametric and nonparametric approaches. Some simple methods for data exploration and graphical display are demonstrated using real data examples. We also briefly discuss related computational tools and statistical packages. Keywords: correlated data; generalized estimating equations; generalized linear model; longitudinal data; missing data; random-effects model
Moderators of School Intervention Outcomes for Children with Autism Spectrum Disorder

A prior cluster randomized controlled trial (RCT) compared outcomes for a comprehensive school intervention (schoolMAX) to typical educational programming (services-as-usual [SAU]) for 103 children with autism spectrum disorder (ASD) without intellectual disability. The schoolMAX intervention was superior to SAU in improving social-cognitive understanding (emotion-recognition), social/social-communication skills, and ASD-related impairment (symptoms). In the current study, a range of demographic, clinical, and school variables were tested as potential moderators of treatment outcomes from the prior RCT. Moderation effects were not evident in demographics, child IQ, language, or ASD diagnostic symptoms, or school SES. Baseline externalizing symptoms moderated the outcome of social-cognitive understanding and adaptive skills moderated the outcome of ASD-related symptoms (no other comorbid symptoms or adaptive skills ratings moderated outcomes on the three measures). Overall, findings suggest that the main effects of treatment were, with two exceptions, unaffected by third variables.
Measuring the subjective cost of listening effort using a discounting task

Purpose Objective measures of listening effort have been gaining prominence, as they provide metrics to quantify the difficulty of understanding speech under a variety of circumstances. A key challenge has been to develop paradigms that enable the complementary measurement of subjective listening effort in a quantitatively precise manner. In this study, we introduce a novel decision-making paradigm to examine age-related and individual differences in subjective effort during listening. Method Older and younger adults were presented with spoken sentences mixed with speech-shaped noise at multiple signal-to-noise ratios (SNRs). On each trial, subjects were offered the choice between completing an easier listening trial (presented at +20 dB SNR) for a smaller monetary reward and completing a harder listening trial (presented at either +4, 0, -4, -8, or -12 dB SNR) for a greater monetary reward. By varying the amount of the reward offered for the easier option, the subjective value of performing effortful listening trials at each SNR could be assessed. Results Older adults discounted the value of effortful listening to a greater degree than young adults, opting to accept less money in order to avoid more difficult SNRs. Additionally, older adults with poorer hearing and smaller working memory capacities were more likely to choose easier trials; however, in younger adults, no relationship with hearing or working memory was found. Self-reported measures of economic status did not affect these relationships. Conclusions These findings suggest that subjective listening effort depends on factors including, but not necessarily limited to, hearing and working memory. Additionally, this study demonstrates that economic decision-making paradigms can be a useful approach for assessing subjective listening effort and may prove beneficial in future research.
Oblivious Transfer with Access Control.

We present a protocol for anonymous access to a database where the different records have different access control permissions. These permissions could be attributes, roles, or rights that the user needs to have in order to access the record. Our protocol offers maximal security guarantees for both the database and the user, namely (1) only authorized users can access the record; (2) the database provider does not learn which record the user accesses; and (3) the database provider does not learn which attributes or roles the user has when she accesses the database. We prove our protocol secure in the standard model (i.e., without random oracles) under the bilinear Diffie-Hellman exponent and the strong Diffie-Hellman assumptions.
AI-Powered GUI Attack and Its Defensive Methods

Since the first Graphical User Interface (GUI) prototype was invented in the 1970s, GUI systems have been deployed into various personal computer systems and server platforms. Recently, with the development of artificial intelligence (AI) technology, malicious malware powered by AI is emerging as a potential threat to GUI systems. This type of AI-based cybersecurity attack, targeting at GUI systems, is explored in this paper. It is twofold: (1) A malware is designed to attack the existing GUI system by using AI-based object recognition techniques. (2) Its defensive methods are discovered by generating adversarial examples and other methods to alleviate the threats from the intelligent GUI attack. The results have shown that a generic GUI attack can be implemented and performed in a simple way based on current AI techniques and its countermeasures are temporary but effective to mitigate the threats of GUI attack so far.
The geometric theory of charge conservation in particle-in-cell simulations

In recent years, several gauge-symmetric particle-in-cell (PIC) methods have been developed whose simulations of particles and electromagnetic fields exactly conserve charge. While it is rightly observed that these methods’ gauge symmetry gives rise to their charge conservation, this causal relationship has generally been asserted via ad hoc derivations of the associated conservation laws. In this work, we develop a comprehensive theoretical grounding for charge conservation in gauge-symmetric Lagrangian and Hamiltonian PIC algorithms. For Lagrangian variational PIC methods, we apply Noether’s second theorem to demonstrate that gauge symmetry gives rise to a local charge conservation law as an off-shell identity. For Hamiltonian splitting methods, we show that the momentum map establishes their charge conservation laws. We define a new class of algorithms – gauge-compatible splitting methods – that exactly preserve the momentum map associated with a Hamiltonian system’s gauge symmetry – even after time discretization. This class of algorithms affords splitting schemes a decided advantage over alternative Hamiltonian integrators. We apply this general technique to design a novel, explicit, symplectic, gauge-compatible splitting PIC method, whose momentum map yields an exact local charge conservation law. Our study clarifies the appropriate initial conditions for such schemes and examines their symplectic reduction.
Range closest-pair search in higher dimensions

Abstract Range closest-pair (RCP) search is a range-search variant of the classical closest-pair problem, which aims to store a given set S of points into some space-efficient data structure such that when a query range Q is specified, the closest pair in S ∩ Q can be reported quickly. RCP search has received attention over years, but the primary focus was only on R 2 . In this paper, we study RCP search in higher dimensions. We give the first nontrivial RCP data structures for orthogonal, simplex, halfspace, and ball queries in R d for any constant d. Furthermore, we prove a conditional lower bound for orthogonal RCP search for d ≥ 3 .
TaxisPy: A Python-based software for the quantitative analysis of bacterial chemotaxis

Several species of bacteria are able to modify their swimming behavior in response to chemical attractants or repellents. Methods for the quantitative analysis of bacterial chemotaxis such as quantitative capillary assays are tedious and time-consuming. Computer-based video analysis of swimming bacteria represents a valuable method to directly assess their chemotactic response. Even though multiple studies have used this approach to elucidate various aspects of bacterial chemotaxis, to date, no computer software for such analyses is freely available. Here, we introduce TaxisPy, a Python-based software for the quantitative analysis of bacterial chemotaxis. The software comes with an intuitive graphical user interface and can be accessed easily through Docker on any operating system. Using a video of freely swimming cells as input, TaxisPy estimates the culture's average tumbling frequency over time. We demonstrate the utility of the software by assessing the effect of different concentrations of the attractant shikimate on the swimming behavior of Pseudomonas putida F1 and by capturing the adaptation process that Escherichia coli undergoes after being exposed to l-aspartate.
WEAK-IDENTIFICATION ROBUST WILD BOOTSTRAP APPLIED TO A CONSISTENT MODEL SPECIFICATION TEST

We present a new robust bootstrap method for a test when there is a nuisance parameter under the alternative, and some parameters are possibly weakly or nonidentified. We focus on a Bierens (1990, Econometrica 58, 1443–1458)-type conditional moment test of omitted nonlinearity for convenience. Existing methods include the supremum p-value which promotes a conservative test that is generally not consistent, and test statistic transforms like the supremum and average for which bootstrap methods are not valid under weak identification. We propose a new wild bootstrap method for p-value computation by targeting specific identification cases. We then combine bootstrapped p-values across polar identification cases to form an asymptotically valid p-value approximation that is robust to any identification case. Our wild bootstrap procedure does not require knowledge of the covariance structure of the bootstrapped processes, whereas Andrews and Cheng’s (2012a, Econometrica 80, 2153–2211; 2013, Journal of Econometrics 173, 36–56; 2014, Econometric Theory 30, 287–333) simulation approach generally does. Our method allows for robust bootstrap critical value computation as well. Our bootstrap method (like conventional ones) does not lead to a consistent p-value approximation for test statistic functions like the supremum and average. Therefore, we smooth over the robust bootstrapped p-value as the basis for several tests which achieve the correct asymptotic level, and are consistent, for any degree of identification. They also achieve uniform size control. A simulation study reveals possibly large empirical size distortions in nonrobust tests when weak or nonidentification arises. One of our smoothed p-value tests, however, dominates all other tests by delivering accurate empirical size and comparatively high power.
Naive Exploration is Optimal for Online LQR

We consider the problem of online adaptive control of the linear quadratic regulator, where the true system parameters are unknown. We prove new upper and lower bounds demonstrating that the optimal regret scales as $\widetilde{\Theta}({\sqrt{d_{\mathbf{u}}^2 d_{\mathbf{x}} T}})$, where $T$ is the number of time steps, $d_{\mathbf{u}}$ is the dimension of the input space, and $d_{\mathbf{x}}$ is the dimension of the system state. Notably, our lower bounds rule out the possibility of a $\mathrm{poly}(\log{}T)$-regret algorithm, which had been conjectured due to the apparent strong convexity of the problem. Our upper bound is attained by a simple variant of $\textit{certainty equivalent control}$, where the learner selects control inputs according to the optimal controller for their estimate of the system while injecting exploratory random noise. While this approach was shown to achieve $\sqrt{T}$-regret by (Mania et al. 2019), we show that if the learner continually refines their estimates of the system matrices, the method attains optimal dimension dependence as well. Central to our upper and lower bounds is a new approach for controlling perturbations of Riccati equations called the $\textit{self-bounding ODE method}$, which we use to derive suboptimality bounds for the certainty equivalent controller synthesized from estimated system dynamics. This in turn enables regret upper bounds which hold for $\textit{any stabilizable instance}$ and scale with natural control-theoretic quantities.
Warped proximal iterations for monotone inclusions

Abstract Resolvents of set-valued operators play a central role in various branches of mathematics and in particular in the design and the analysis of splitting algorithms for solving monotone inclusions. We propose a generalization of this notion, called warped resolvent, which is constructed with the help of an auxiliary operator. The properties of warped resolvents are investigated and connections are made with existing notions. Abstract weak and strong convergence principles based on warped resolvents are proposed and shown to not only provide a synthetic view of splitting algorithms but to also constitute an effective device to produce new solution methods for challenging inclusion problems.
Fast Covariance Estimation for Multivariate Sparse Functional Data.

Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor-product B-spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B-spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave-one-subject-out cross-validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer’s disease study with multiple longitudinal outcomes.
Likelihood-based Inference for Partially Observed Epidemics on Dynamic Networks

We propose a generative model and an inference scheme for epidemic processes on dynamic, adaptive contact networks. Network evolution is formulated as a link-Markovian process, which is then couple...
Prostatic peripheral zone thickness: what is normal on magnetic resonance imaging?

To report the precision of a technique of measuring the PZ thickness on T2-weighted MRI and report normal parameters in patients with normal-sized prostates. We also wanted to establish the mean and second standard deviations (2SD) above and below the mean as criteria for abnormally narrow or expanded PZ thickness. Of the initial 1566 consecutive cohort referred for evaluation for carcinoma based on elevated PSA (prostate specific antibody) or DRE (digital rectal examination), 132 separate subjects with normal-sized prostates were selected for this study. Mean age was 58.2 years (15–82). Median serum PSA was 6.2 ng/mL (range 0.3–145). Most were asymptomatic for lower urinary tract symptoms (LUTS). Inclusion criteria in this study required technically adequate T2-weighted MRI and total prostatic volume (TPV) ≤ 25 cc. Exclusion criteria included post-prostatic surgical and radiation patients, patients having had medical management or minimally invasive therapy for BPH, those being treated for prostatitis. Patients with suspected tumor expanding or obscuring measurement boundaries were also not considered. Transition zone (TZ) and peripheral zone (PZ) volumes were determined using the prolate ellipsoid model. Posterolateral measurement of the PZ was obtained at the axial level of maximal transverse diameter of the prostate on a line drawn from the outer boundary of the TZ to the inner boundary of the external prostatic capsule (EPC). The data were normally distributed. Therefore, it was analyzed using the 2-sided student t-test and Pearson product correlation statistic. Mean pooled (composite) measurement for the posterolateral PZ (PLPZ) was 10 mm (CI 9.5–10.5 mm) with SD of 2.87 mm. Means were statistically the same for the 2 observers (p = 0.75). Pearson correlation between the two observers was 0.63. In a prostate ≤ 25 cc volume , the posterolateral PZ should be no thicker than 15.8 mm and averages 10.0 mm when measured in the maximal axial plane on MRI. These norms were independent of age or use of endorectal coil. The technique measurement demonstrated clinically useful precision.
Sampling for Remote Estimation through Queues: Age of Information and Beyond

Recently, a connection between the age of information and remote estimation error was found in a sampling problem of Wiener processes: If the sampler has no knowledge of the signal being sampled, the optimal sampling strategy is to minimize the age of information; however, by exploiting causal knowledge of the signal values, it is possible to achieve a smaller estimation error. In this paper, we generalize the previous study by investigating a problem of sampling a stationary Gauss-Markov process named the Ornstein-Uhlenbeck (OU) process, where we aim to find useful insights for solving the problems of sampling more general signals. The optimal sampling problem is formulated as a constrained continuous-time Markov decision process (MDP) with an uncountable state space. We provide an exact solution to this MDP: The optimal sampling policy is a threshold policy on instantaneous estimation error and the threshold is found. Further, if the sampler has no knowledge of the OU process, the optimal sampling problem reduces to an MDP for minimizing a nonlinear age of information metric and the age-optimal sampling policy is a threshold policy on expected estimation error and the threshold is found. In both problems, the optimal sampling policies can be computed by bisection search, and the curse of dimensionality is circumvented. These results hold for (i) general service time distributions of the queueing server and (ii) sampling problems both with and without a sampling rate constraint. Numerical results are provided to compare different sampling policies.
Decentralized Control of Constrained Linear Systems via Assume-Guarantee Contracts

We consider the decentralized control of a discretetime, linear system subject to exogenous disturbances and polyhedral constraints on the state and input trajectories. The underlying system is composed of a finite collection of dynamically coupled subsystems, where each subsystem is assumed to have a dedicated local controller. The decentralization of information is expressed according to sparsity constraints on the state measurements that each local controller has access to. In this context, we investigate the design of decentralized controllers that are affinely parameterized in their measurement history. For problems with partially nested information structures, the optimization over such policy spaces is known to be convex. Convexity is not, however, guaranteed under more general (nonclassical) information structures in which the information available to one local controller can be affected by control actions that it cannot access or reconstruct. With the aim of alleviating the nonconvexity that arises in such problems, we propose an approach to decentralized control design where the information-coupling states are effectively treated as disturbances whose trajectories are constrained to take values in ellipsoidal contract sets whose location, scale, and orientation are jointly optimized with the underlying affine decentralized control policy. We establish a natural structural condition on the space of allowable contracts that facilitates the joint optimization over the control policy and the contract set via semidefinite programming.
On the Relative Efficiency of Crowdsourced Rating Mechanisms: Experimental Evidence

This paper experimentally tests the effectiveness of three crowdsourced signalling mechanisms on their ability to resolve an asymmetric information problem over product quality for a set of consume...
Model-Free Primal-Dual Methods for Network Optimization with Application to Real-Time Optimal Power Flow

This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.
From electricity to fuels: Descriptors for C1 selectivity in electrochemical CO2 reduction

Abstract Electrochemical reduction of carbon dioxide (CO2RR) over transition metals follows a complex reaction network. In this study, we combine observations from experimental literature with a theoretical analysis of energetics to rationalize that not all intermediates in the reduction of CO2 are formed through direct protonation steps. We derive a selectivity map for two-electron products (carbon monoxide (CO) and formate) on pure metal surfaces using only the CO and OH binding energies as descriptors. For the pure metals that are selective towards CO formation, the variation of the CO binding energy is sufficient to further subdivide the map into domains that predominantly form H2, CO, and more reduced products. Our analysis rationalizes experimentally observed product distributions in CO2RR across pure metal systems. Overall, we highlight the need for additional material screening descriptors for CO2R and the importance of considering competition from the elementary steps of the hydrogen evolution reaction.
SCRAMBLE: The State, Connectivity and Routing Augmentation Model for Building Logic Encryption

In this paper, we introduce SCRAMBLE, as a novel logic locking solution for sequential circuits while the access to the scan chain is restricted. The SCRAMBLE could be used to lock an FSM by hiding its state transition graph (STG) among a large number of key-controlled false transitions. Also, it could be used to lock sequential circuits (sequential datapath) by hiding the timing paths' connectivity among a large number of key-controlled false connections. Besides, the structure of SCRAMBLE allows us to engage this scheme as a new scan chain locking solution by hiding the correct scan chain sequence among a large number of the key-controlled false sequences. We demonstrate that the proposed scheme resists against both (1) the 2-stage attacks on FSM, and (2) SAT attacks integrated with unrolling as well as bounded-modelchecking. We have discussed two variants of SCRAMBLE: (I) Connectivity SCRAMBLE (SCRAMBLE-C), and (b) Logic SCRAMBLE (SCRAMBLE-L). The SCRAMBLE-C relies on the SAT-hard and key-controlled modules that are built using near non-blocking logarithmic switching networks. The SCRAMBLE-L uses input multiplexing techniques to hide a part of the FSM in a memory. In the result section, we describe the effectiveness of each variant against state-of-the-art attacks.
Exponential random graph models for little networks

Abstract Statistical models for social networks have enabled researchers to study complex social phenomena that give rise to observed patterns of relationships among social actors and to gain a rich understanding of the interdependent nature of social ties and actors. Much of this research has focused on social networks within medium to large social groups. To date, these advances in statistical models for social networks, and in particular, of Exponential-Family Random Graph Models (ERGMS), have rarely been applied to the study of small networks, despite small network data in teams, families, and personal networks being common in many fields. In this paper, we revisit the estimation of ERGMs for small networks and propose using exhaustive enumeration when possible. We developed an R package that implements the estimation of pooled ERGMs for small networks using Maximum Likelihood Estimation (MLE), called “ergmito”. Based on the results of an extensive simulation study to assess the properties of the MLE estimator, we conclude that there are several benefits of direct MLE estimation compared to approximate methods and that this creates opportunities for valuable methodological innovations that can be applied to modeling social networks with ERGMs.
Drag and Heat Transfer Effects on Hypersonic Vehicles in Close-Proximity Flight

A computational investigation of the drag and heat transfer experienced by multiple trailing (following) vehicles in hypersonic flight is presented. Linear arrays of vehicles in flight at Mach 10 a...
Factored LT and Factored Raptor Codes for Large-Scale Distributed Matrix Multiplication

We propose two coding schemes for distributed matrix multiplication in the presence of stragglers. These coding schemes are adaptations of LT codes and Raptor codes to distributed matrix multiplication and are termed Factored LT (FLT) codes and Factored Raptor (FR) codes. Empirically, we show that FLT codes have a near-optimal recovery threshold when the number of worker nodes is very large, and that FR codes have an excellent recovery threshold while the number of worker nodes is moderately large. FLT and FR codes have better recovery thresholds when compared to Product codes and they are expected to have better numerical stability when compared to Polynomial codes, while they can also be decoded with a low-complexity decoding algorithm.
Evaluating an explicit instruction teacher observation protocol through a validity argument approach

In this study, we examined the scoring and generalizability assumptions of an explicit instruction (EI) special education teacher observation protocol using many-faceted Rasch measurement (MFRM). V...
A Regulatory Program for Initiation of Wnt Signaling during Posterior Regeneration

Whole-body regeneration relies on the re-establishment of body axes for patterning of new tissue. Wnt signaling is required to correctly regenerate tissues along the primary axis in many animals. However, the causal mechanisms that first launch Wnt signaling during regeneration are poorly characterized. We use the acoel worm Hofstenia miamia to identify processes that initiate Wnt signaling during posterior regeneration and find that the ligand wnt-3 is upregulated early in posterior-facing wounds. Functional studies reveal that wnt-3 is required to regenerate posterior tissues. wnt-3 is expressed in stem cells, it is needed for their proliferation, and its function is stem cell dependent. Chromatin accessibility data reveal that wnt-3 activation requires input from the general wound response. In addition, the expression of a different Wnt ligand, wnt-1, before amputation is required for wound-induced activation of wnt-3. Our study establishes a gene regulatory network for initiating Wnt signaling in posterior tissues in a bilaterian.
A Tumbling Magnetic Microrobot System for Biomedical Applications

A microrobot system comprising an untethered tumbling magnetic microrobot, a two-degree-of-freedom rotating permanent magnet, and an ultrasound imaging system has been developed for in vitro and in vivo biomedical applications. The microrobot tumbles end-over-end in a net forward motion due to applied magnetic torque from the rotating magnet. By turning the rotational axis of the magnet, two-dimensional directional control is possible and the microrobot was steered along various trajectories, including a circular path and P-shaped path. The microrobot is capable of moving over the unstructured terrain within a murine colon in in vitro, in situ, and in vivo conditions, as well as a porcine colon in ex vivo conditions. High-frequency ultrasound imaging allows for real-time determination of the microrobot’s position while it is optically occluded by animal tissue. When coated with a fluorescein payload, the microrobot was shown to release the majority of the payload over a 1-h time period in phosphate-buffered saline. Cytotoxicity tests demonstrated that the microrobot’s constituent materials, SU-8 and polydimethylsiloxane (PDMS), did not show a statistically significant difference in toxicity to murine fibroblasts from the negative control, even when the materials were doped with magnetic neodymium microparticles. The microrobot system’s capabilities make it promising for targeted drug delivery and other in vivo biomedical applications.