cssi/SciSciGPT-SciSciNet huggingface dataset:
Robust Normalization of Luciferase Reporter Data

Transient Luciferase reporter assays are widely used in the study of gene regulation and intracellular cell signaling. In order to control for sample-to-sample variation in luminescence arising from variability in transfection efficiency and other sources, an internal control reporter is co-transfected with the experimental reporter. The luminescence of the experimental reporter is normalized against the control by taking the ratio of the two. Here we show that this method of normalization, “ratiometric”, performs poorly when the transfection efficiency is low and leads to biased estimates of relative activity. We propose an alternative methodology based on linear regression that is much better suited for the normalization of reporter data, especially when transfection efficiency is low. We compare the ratiometric method against three regression methods on both simulated and empirical data. Our results suggest that robust errors-in-variables (REIV) regression performs the best in normalizing Luciferase reporter data. We have made the R code for Luciferase data normalization using REIV available on GitHub.
IPr⁎-recurrence and nilsystems

Abstract By a result due to Furstenberg, a homeomorphism T of a compact space is distal if and only if it possesses the property of IP ⁎ -recurrence, meaning that for any x 0 ∈ X , for any open neighborhood U of x 0 , and for any sequence ( n i ) in Z , the set R U ( x 0 ) = { n ∈ Z : T n x 0 ∈ U } has a non-trivial intersection with the set of finite sums { n i 1 + n i 2 + ⋯ + n i s : i 1 i 2 … i s , s ∈ N } . We show that translations on compact nilmanifolds (which are known to be distal) are characterized by a stronger property of IP r ⁎ -recurrence, which asserts that for any x 0 ∈ X and any neighborhood U of x 0 there exists r ∈ N such that for any r-element sequence n 1 , … , n r in Z the set R U ( x 0 ) has a non-trivial intersection with the set { n i 1 + n i 2 + ⋯ + n i s : i 1 i 2 … i s , s ≤ r } . We also show that the property of IP r ⁎ -recurrence is equivalent to an ostensibly much stronger property of polynomial IP r ⁎ -recurrence. (This should be juxtaposed with the fact that for general distal transformations the polynomial IP ⁎ -recurrence is strictly stronger than the IP ⁎ -recurrence.)
On the approximation resistance of balanced linear threshold functions

In this paper, we show that there exists a balanced linear threshold function (LTF) which is unique games hard to approximate, refuting a conjecture of Austrin, Benabbas, and Magen. We also show that the almost monarchy predicate P(x) = sign((k−4)x1 + ∑i=2kxi) is approximable for sufficiently large k.
Sets with few differences in abelian groups

Let $(G, +)$ be an abelian group. In 2004, Eliahou and Kervaire found an explicit formula for the smallest possible cardinality of the sumset $A+A$, where $A \subseteq G$ has fixed cardinality $r$. We consider instead the smallest possible cardinality of the difference set $A-A$, which is always greater than or equal to the smallest possible cardinality of $A+A$ and can be strictly greater. We conjecture a formula for this quantity and prove the conjecture in the case that $G$ is an elementary abelian $p$-group. This resolves a conjecture of Bajnok and Matzke on signed sumsets.
Deep-LK for Efficient Adaptive Object Tracking

In this paper, we present a new approach for efficient regression-based object tracking. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework [1]. We make the following contributions. First, we demonstrate that there is a theoretical relationship between Siamese regression networks like GOTURN and the classical Inverse Compositional Lucas & Kanade (IC-LK) algorithm. Further, we demonstrate that unlike GOTURN, IC-LK adapts its regressor to the appearance of the current tracked frame. We argue that the lack of such property in GOTURN attributes to its poor performance on unseen objects and/or viewpoints. Second, we propose a novel framework for object tracking inspired by the IC-LK framework, which we refer to as Deep-LK. Finally, we show impressive results demonstrating that Deep-LK substantially outperforms GOTURN and demonstrate comparable tracking performance against current state-of-the-art deep trackers on high frame-rate sequences whilst being an order of magnitude (100 FPS) computationally efficient.
On the Number of Similar Instances of a Pattern in a Finite Set

New bounds on the number of similar or directly similar copies of a pattern within a finite subset of the line or the plane are proved. The number of equilateral triangles whose vertices all lie within an $n$-point subset of the plane is shown to be no more than $\lfloor{(4 n-1)(n-1)/18}\rfloor$. The number of $k$-term arithmetic progressions that lie within an $n$-point subset of the line is shown to be at most $(n-r)(n+r-k+1)/(2 k-2)$, where $r$ is the remainder when $n$ is divided by $k-1$. This upper bound is achieved when the $n$ points themselves form an arithmetic progression, but for some values of $k$ and $n$, it can also be achieved for other configurations of the $n$ points, and a full classification of such optimal configurations is given. These results are achieved using a new general method based on ordering relations.
An exactly solvable quantum four-body problem associated with the symmetries of an octacube

In this article, we show that eigenenergies and eigenstates of a system consisting of four one-dimensional hard-core particles with masses 6m, 2m, m, and 3m in a hard-wall box can be found exactly using Bethe Ansatz. The Ansatz is based on the exceptional affine reflection group associated with the symmetries and tiling properties of an octacube—a Platonic solid unique to four-dimensions, with no three-dimensional analogues. We also uncover the Liouville integrability structure of our problem: the four integrals of motion in involution are identified as invariant polynomials of the finite reflection group F4, taken as functions of the components of momenta.
Emergence of reflectionless scattering from linearizations of integrable PDEs around solitons

We present four examples of integrable partial differential equations (PDEs) of mathematical physics that—when linearized around a stationary soliton—exhibit scattering without reflection at all energies. Starting from the most well-known and the most empirically relevant phenomenon of the transparency of one-dimensional bright bosonic solitons to Bogoliubov excitations, we proceed to the sine-Gordon, Korteweg–de Vries, and Liouville’s equation whose stationary solitons also support our assertion. The proposed connection between integrability and reflectionless scattering seems to span at least two distinct paradigms of integrability: S-integrability in the first three cases, and C-integrability in the last one. We argue that the transparency of linearized integrable PDEs is necessary to ensure that they can support the transparency of stationary solitons in the original integrable PDEs. As contrasting cases, the analysis is further extended to cover two non-integrable systems: a sawtooth-Gordon and a model.
Least Squares Shadowing for Sensitivity Analysis of Turbulent Fluid Flows

Computational methods for sensitivity analysis are invaluable tools for aerodynamics research and engineering design. However, traditional sensitivity analysis methods break down when applied to long-time averaged quantities in turbulent fluid flow fields, specifically those obtained using high-fidelity turbulence simulations. This is because of a number of dynamical properties of turbulent and chaotic fluid flows, most importantly high sensitivity of the initial value problem, popularly known as the “butterfly effect”. The recently developed least squares shadowing (LSS) method avoids the issues encountered by traditional sensitivity analysis methods by approximating the “shadow trajectory” in phase space, avoiding the high sensitivity of the initial value problem. The following paper discusses how the least squares problem associated with LSS is solved. Two methods are presented and are demonstrated on a simulation of homogeneous isotropic turbulence and the Kuramoto-Sivashinsky (KS) equation, a 4th order chaotic partial differential equation. We find that while LSS computes fairly accurate gradients, faster, more efficient linear solvers are needed to apply both LSS methods presented in this paper to larger simulations.
The Strategic LQG System: A Dynamic Stochastic VCG Framework for Optimal Coordination

The classic Vickrey-Clarke-Groves (VCG) mechanism ensures incentive compatibility, i.e., that truth-telling of all agents is a dominant strategy, for a static one-shot game. However, in a dynamic environment that unfolds over time, the agents' intertemporal payoffs depend on the expected future controls and payments, and a direct extension of the VCG mechanism is not sufficient to guarantee incentive compatibility. In fact, it does not appear to be feasible to construct mechanisms that ensure the dominance of dynamic truth-telling for agents comprised of general stochastic dynamic systems. The contribution of this paper is to show that such a dynamic stochastic extension does exist for the special case of Linear-Quadratic-Gaussian (LQG) agents with a careful construction of a sequence of layered payments over time. We propose a layered version of a modified VCG mechanism for payments that decouples the intertemporal effect of current bids on future payoffs, and prove that truth-telling of dynamic states forms a dominant strategy if system parameters are known and agents are rational. An important example of a problem needing such optimal dynamic coordination of stochastic agents arises in power systems where an Independent System Operator (ISO) has to ensure balance of generation and consumption at all time instants, while ensuring social optimality (maximization of the sum of the utilities of all agents). Addressing strategic behavior is critical as the price-taking assumption on market participants may not hold in an electricity market. Agents, can lie or otherwise game the bidding system. The challenge is to determine a bidding scheme between all agents and the ISO that maximizes social welfare, while taking into account the stochastic dynamic models of agents, since renewable energy resources such as solar/wind are stochastic and dynamic in nature, as are consumptions by loads which are influenced by factors such as local temperatures and thermal inertias of facilities.
Neural Lattice Language Models

In this work, we propose a new language modeling paradigm that has the ability to perform both prediction and moderation of information flow at multiple granularities: neural lattice language model...
Ricci Flat Kähler Metrics with Edge Singularities

Let (M, ω0) be a compact Kahler manifold of complex dimension n, and Σ be a smooth complex hypersurface in M. Moreover, let Λ denote the holomorphic line bundle associated to Σ , and let h be a bundle metric on Λ. We are interested in Kahler metrics on M \Σ which have edge singularities along Σ . To construct an example of such a metric, let us fix a smooth Kahler metric ω0 on M; a holomorphic section s of Λ satisfying Σ = {s = 0}; and a real number β ∈ (0, 2 ). If λ> 0 is sufficiently small, then the (1,1)-form ω=ω0 + λ √−1∂∂(|s|2β h )
Water from Two Rocks: Maximizing the Mutual Information

We build a natural connection between the learning problem, co-training, and forecast elicitation without verification (related to peer-prediction) and address them simultaneously using the same information theoretic approach. In co-training/multiview learning, the goal is to aggregate two views of data into a prediction for a latent label. We show how to optimally combine two views of data by reducing the problem to an optimization problem. Our work gives a unified and rigorous approach to the general setting. In forecast elicitation without verification we seek to design a mechanism that elicits high quality forecasts from agents in the setting where the mechanism does not have access to the ground truth. By assuming the agents' information is independent conditioning on the outcome, we propose mechanisms where truth-telling is a strict equilibrium for both the single-task and multi-task settings. Our multi-task mechanism additionally has the property that the truth-telling equilibrium pays better than any other strategy profile and strictly better than any other "non-permutation" strategy profile.
Distributed Rumor Blocking With Multiple Positive Cascades

Misinformation and rumor can spread rapidly and widely through online social networks and therefore rumor controlling has become a critical issue. It is assumed in the existing works that there is a single authority whose goal is to minimize the spread of rumor by generating a positive cascade. In this paper, we study a more realistic scenario when there is multiple positive cascades generated by different agents. For the multiple-cascade diffusion, we propose the peer-to-peer independent cascade model for private social communications. The main contribution of this paper is an analysis of the rumor blocking effect (i.e., the number of the users activated by rumor) when the agents noncooperatively generate the positive cascades. We show that the rumor blocking effect provided by the Nash equilibrium will not be arbitrarily worse even if the positive cascades are generated noncooperatively. In addition, we give a discussion on how the cascade priority and activation order affect the rumor blocking problem. We experimentally examine the Nash equilibrium of the proposed games by simulations done on real social network structures.
Generalizing Multiple Access Wiretap and Wiretap II Channel Models: Achievable Rates and Cost of Strong Secrecy

In this paper, new two-user multiple access wiretap channel models are studied. First, the multiple access wiretap channel II with a discrete memoryless main channel under different wiretapping scenarios is introduced. The wiretapper, as in the classical wiretap channel II model, chooses a fixed-size subset of the channel uses, in which it obtains noise-free observations of one of the codewords: a deterministic function, e.g., superposition, of the two codewords or each of the two codewords. A fourth wiretapping scenario is considered, in which the wiretapper, in each position it chooses, decides to observe either one of the codewords or both codewords, with an overall budget on the number of its noiselessly observed symbols. These, thus, extend the recently examined wiretap channel II with a noisy main channel to a multiple access setting with a variety of attack models for the wiretapper. Next, the proposed multiple access wiretap channel II models are further generalized to the case when the wiretapper observes the outputs of a discrete memoryless channel, instead of erasures, outside the subset of noiseless observations. Achievable strong secrecy rate regions for all the proposed models are derived. Achievability is established by solving dual multi-terminal secret key agreement problems in the source model and converting the solution to the original channel models using probability distribution approximation arguments. The derived achievable rate regions quantify the secrecy cost due to the additional capabilities of the wiretapper with respect to the previous multiple access wiretap models.
On the equivalence between low-rank matrix completion and tensor rank

The rank minimization problem (RMP) asks to find a matrix of lowest rank inside a linear variety of the space of matrices. The low-rank matrix completion (LRMC) problem asks to complete a partially filled matrix such that the resulting matrix has smallest possible rank. The tensor rank problem asks to determine the rank of a tensor. We show that these three problems are equivalent: each one of the problems can be reduced to the other two.
Join irreducible semigroups

We begin a systematic study of finite semigroups that generate join irreducible members of the lattice of pseudovarieties of finite semigroups, which are important for the spectral theory of this l...
Some stable homology calculations and Occam's razor for Hodge structures

Abstract Motivated by motivic zeta function calculations, Vakil and Wood in [10] made several conjectures regarding the topology of subspaces of symmetric products. The purpose of this note is to prove two of these conjectures and disprove a strengthening of one of them.
Random Multiplicative Functions in Short Intervals

We consider random multiplicative functions taking the values $\pm 1$. Using Stein's method for normal approximation, we prove a central limit theorem for the sum of such multiplicative functions in appropriate short intervals.
Multimodal Event Detection in Twitter Hashtag Networks

Event detection in a multimodal Twitter dataset is considered. We treat the hashtags in the dataset as instances with two modes: text and geolocation features. The text feature consists of a bag-of-words representation. The geolocation feature consists of geotags (i.e., geographical coordinates) of the tweets. Fusing the multimodal data we aim to detect, in terms of topic and geolocation, the interesting events and the associated hashtags. To this end, a generative latent variable model is assumed, and a generalized expectation-maximization (EM) algorithm is derived to learn the model parameters. The proposed method is computationally efficient, and lends itself to big datasets. Experimental results on a Twitter dataset from August 2014 show the efficacy of the proposed method.
Projecting Trouble: Light Based Adversarial Attacks on Deep Learning Classifiers

This work demonstrates a physical attack on a deep learning image classification system using projected light onto a physical scene. Prior work is dominated by techniques for creating adversarial examples which directly manipulate the digital input of the classifier. Such an attack is limited to scenarios where the adversary can directly update the inputs to the classifier. This could happen by intercepting and modifying the inputs to an online API such as Clarifai or Cloud Vision. Such limitations have led to a vein of research around physical attacks where objects are constructed to be inherently adversarial or adversarial modifications are added to cause misclassification. Our work differs from other physical attacks in that we can cause misclassification dynamically without altering physical objects in a permanent way. We construct an experimental setup which includes a light projection source, an object for classification, and a camera to capture the scene. Experiments are conducted against 2D and 3D objects from CIFAR-10. Initial tests show projected light patterns selected via differential evolution could degrade classification from 98% to 22% and 89% to 43% probability for 2D and 3D targets respectively. Subsequent experiments explore sensitivity to physical setup and compare two additional baseline conditions for all 10 CIFAR classes. Some physical targets are more susceptible to perturbation. Simple attacks show near equivalent success, and 6 of the 10 classes were disrupted by light.
Compositional Analysis of Hybrid Systems Defined Over Finite Alphabets.

Abstract We consider the stability and the input-output analysis problems of a class of large-scale hybrid systems composed of continuous dynamics coupled with discrete dynamics defined over finite alphabets, e.g., deterministic finite state machines (DFSMs). This class of hybrid systems can be used to model physical systems controlled by software. For such classes of systems, we use a method based on dissipativity theory for compositional analysis that allows us to study stability, passivity and input-output norms. We show that the certificates of the method based on dissipativity theory can be computed by solving a set of semi-definite programs. Nonetheless, the formulation based on semi-definite programs become computationally intractable for relatively large number of discrete and continuous states. We demonstrate that, for systems with large number of states consisting of an interconnection of smaller hybrid systems, accelerated alternating method of multipliers can be used to carry out the computations in a scalable and distributed manner. The proposed methodology is illustrated by an example of a system with 60 continuous states and 18 discrete states.
Inferring mechanisms for global constitutional progress

Constitutions help define domestic political orders, but are known to be influenced by international mechanisms that are normative, temporal and network based. Here we introduce the concept of the ‘provision space’—the set of all legal provisions existing across the world’s constitutions, which grows over time. We make use of techniques from network science and information retrieval to quantify and compare temporal and network effects on constitutional change, which have been the focus of previous work. Furthermore, we propose that hierarchical effects—a set of mechanisms by which the adoption of certain constitutional provisions leads to or facilitates the adoption of additional provisions—are also crucial. These hierarchical mechanisms appear to play an important role in the emergence of new political rights, and may therefore provide a useful roadmap for advocates of those rights. Rutherford et al. analyse temporal, network and hierarchical effects to uncover, understand and quantify competing mechanisms of constitutional change worldwide.
Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms.

Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity stems from the updating schedule. Standard updating schedules include the synchronous update, where all the nodes are updated at the same time, and the asynchronous update where a random node is updated at each time step. The former produces a deterministic dynamics while the latter a stochastic dynamics. A more general stochastic setting considers propensity parameters for updating each node. Stochastic Discrete Dynamical Systems (SDDS) are a modeling framework that considers two propensity parameters for updating each node and uses one when the update has a positive impact on the variable, that is, when the update causes the variable to increase its value, and uses the other when the update has a negative impact, that is, when the update causes it to decrease its value. This framework offers additional features for simulations but also adds a complexity in parameter estimation of the propensities. This paper presents a method for estimating the propensity parameters for SDDS. The method is based on adding noise to the system using the Google PageRank approach to make the system ergodic and thus guaranteeing the existence of a stationary distribution. Then with the use of a genetic algorithm, the propensity parameters are estimated. Approximation techniques that make the search algorithms efficient are also presented and Matlab/Octave code to test the algorithms are available at~\href{http://www.ms.uky.edu/~dmu228/GeneticAlg/Code.html}{http://www.ms.uky.edu/$\sim$dmu228/GeneticAlg/Code.html}.
Matrix Factorization-based clustering of image features for bandwidth-constrained information retrieval

We consider the problem of accurately and efficiently querying a remote server to retrieve information about images captured by a mobile device. In addition to reduced transmission overhead and computational complexity, the retrieval protocol should be robust to variations in the image acquisition process, such as translation, rotation, scaling, and sensor-related differences. We propose to extract scale-invariant image features and then perform clustering to reduce the number of features needed for image matching. Principal Component Analysis (PCA) and Non-negative Matrix Factorization (NMF) are investigated as candidate clustering approaches. The image matching complexity at the database server is quadratic in the (small) number of clusters, not in the (very large) number of image features. We employ an image-dependent information content metric to approximate the model order, i.e., the number of clusters, needed for accurate matching, which is preferable to setting the model order using trial and error. We show how to combine the hypotheses provided by PCA and NMF factor loadings, thereby obtaining more accurate retrieval than using either approach alone. In experiments on a database of urban images, we obtain a top-1 retrieval accuracy of 89% and a top-3 accuracy of 92.5%.
Data-Driven Clustering via Parameterized Lloyd's Families

Algorithms for clustering points in metric spaces is a long-studied area of research. Clustering has seen a multitude of work both theoretically, in understanding the approximation guarantees possible for many objective functions such as k-median and k-means clustering, and experimentally, in finding the fastest algorithms and seeding procedures for Lloyd's algorithm. The performance of a given clustering algorithm depends on the specific application at hand, and this may not be known up front. For example, a "typical instance" may vary depending on the application, and different clustering heuristics perform differently depending on the instance. In this paper, we define an infinite family of algorithms generalizing Lloyd's algorithm, with one parameter controlling the the initialization procedure, and another parameter controlling the local search procedure. This family of algorithms includes the celebrated k-means++ algorithm, as well as the classic farthest-first traversal algorithm. We design efficient learning algorithms which receive samples from an application-specific distribution over clustering instances and learn a near-optimal clustering algorithm from the class. We show the best parameters vary significantly across datasets such as MNIST, CIFAR, and mixtures of Gaussians. Our learned algorithms never perform worse than k-means++, and on some datasets we see significant improvements.
What Do Deep CNNs Learn About Objects

Deep convolutional neural networks learn extremely powerful image representations, yet most of that power is hidden in the millions of deep-layer parameters. What exactly do these parameters represent? Recent work has started to analyse CNN representations, finding that, e.g., they are invariant to some 2D transformations Fischer et al. (2014), but are confused by particular types of image noise Nguyen et al. (2014). In this work, we delve deeper and ask: how invariant are CNNs to object-class variations caused by 3D shape, pose, and photorealism?
On Self Modulation for Generative Adversarial Networks

Training Generative Adversarial Networks (GANs) is notoriously challenging. We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data. In a large-scale empirical study we observe a relative decrease of $5\%-35\%$ in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in $124/144$ ($86\%$) of the studied settings. Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN.
Towards reproducible research: automatic classification of empirical requirements engineering papers

Research must be reproducible in order to make an impact on science and to contribute to the body of knowledge in our field. Yet studies have shown that 70% of research from academic labs cannot be reproduced. In software engineering, and more specifically requirements engineering (RE), reproducible research is rare, with datasets not always available or methods not fully described. This lack of reproducible research hinders progress, with researchers having to replicate an experiment from scratch. A researcher starting out in RE has to sift through conference papers, finding ones that are empirical, then must look through the data available from the empirical paper (if any) to make a preliminary determination if the paper can be reproduced. This paper addresses two parts of that problem, identifying RE papers and identifying empirical papers within the RE papers. Recent RE and empirical conference papers were used to learn features and to build an automatic classifier to identify RE and empirical papers. We introduce the Empirical Requirements Research Classifier (ERRC) method, which uses natural language processing and machine learning to perform supervised classification of conference papers. We compare our method to a baseline keyword-based approach. To evaluate our approach, we examine sets of papers from the IEEE Requirements Engineering conference and the IEEE International Symposium on Software Testing and Analysis. We found that the ERRC method performed better than the baseline method in all but a few cases.
Learning Ising Models with Independent Failures.

We give the first efficient algorithm for learning the structure of an Ising model that tolerates independent failures; that is, each entry of the observed sample is missing with some unknown probability p. Our algorithm matches the essentially optimal runtime and sample complexity bounds of recent work for learning Ising models due to Klivans and Meka (2017). We devise a novel unbiased estimator for the gradient of the Interaction Screening Objective (ISO) due to Vuffray et al. (2016) and apply a stochastic multiplicative gradient descent algorithm to minimize this objective. Solutions to this minimization recover the neighborhood information of the underlying Ising model on a node by node basis.
Generalization of LRU Cache Replacement Policy with Applications to Video Streaming

Caching plays a crucial role in networking systems to reduce the load on the network and is commonly employed by content delivery networks (CDNs) to improve performance. One of the commonly used mechanisms, Least Recently Used (LRU), works well for identical file sizes. However, for asymmetric file sizes, the performance deteriorates. This article proposes an adaptation to the LRU strategy, called gLRU, where the file is sub-divided into equal-sized chunks. In this strategy, a chunk of the newly requested file is added in the cache, and a chunk of the least-recently-used file is removed from the cache. Even though approximate analysis for the hit rate has been studied for LRU, the analysis does not extend to gLRU, since the metric of interest is no longer the hit rate as the cache has partial files. This article provides a novel approximation analysis for this policy where the cache may have partial file contents. The approximation approach is validated by simulations. Further, gLRU outperforms the LRU strategy for a Zipf file popularity distribution and censored Pareto file size distribution for the file download times. Video streaming applications can further use the partial cache contents to help the stall duration significantly, and the numerical results indicate significant improvements (32%) in stall duration using the gLRU strategy as compared to the LRU strategy. Furthermore, the gLRU replacement policy compares favorably to two other cache replacement policies when simulated on MSR Cambridge Traces obtained from the SNIA IOTTA repository.
Indirect cost of traumatic brachial plexus injuries in the United States

Background:Traumatic brachial plexus injuries (BPIs) disproportionately affect young, able-bodied individuals. Beyond direct costs associated with medical treatment, there are far-reaching indirect costs related to disability and lost productivity. Our objective was to estimate per-patient indirect
Coordination and Trajectory Prediction for Vehicle Interactions via Bayesian Generative Modeling

Coordination recognition and subtle pattern prediction of future trajectories play a significant role when modeling interactive behaviors of multiple agents. Due to the essential property of uncertainty in the future evolution, deterministic predictors are not sufficiently safe and robust. In order to tackle the task of probabilistic prediction for multiple, interactive entities, we propose a coordination and trajectory prediction system (CTPS), which has a hierarchical structure including a macro-level coordination recognition module and a micro-level subtle pattern prediction module which solves a probabilistic generation task. We illustrate two types of representation of the coordination variable: categorized and real-valued, and compare their effects and advantages based on empirical studies. We also bring the ideas of Bayesian deep learning into deep generative models to generate diversified prediction hypotheses. The proposed system is tested on multi-ple driving datasets in various traffic scenarios, which achieves better performance than baseline approaches in terms of a set of evaluation metrics. The results also show that using categorized coordination can better capture multi-modality and generate more diversified samples than the real-valued coordination, while the latter can generate prediction hypotheses with smaller errors with a sacrifice of sample diversity. Moreover, employing neural networks with weight uncertainty is able to generate samples with larger variance and diversity.
User Experience in the Threshold Matrix of Public Space Design Intervention in a Complex Environment

Designers seek to create environments that offer their inhabitants comfortable, desirable, meaningful or other qualified positive forms of experience. In some successful models, solutions involve a synthesis of space and person, such that they are mutually constitutive. Often as well, models involve an understanding of space, person and their union as dynamic. Models of space design which recognize such a dynamic relationship are said to view space as relational. In this paper, we claim space as relational. We also present the case that emergent in actualizing the relational idiom is the idea of threshold. Threshold is many things and complex. It symbolizes crossing but also pause, end but also beginning, a brief moment but also extended duration. In the paper, we explore the concept of threshold within physical, perceptual and rational frames. We attempt to foster a rich awareness of threshold, link it to a fertile, generative picture of public space, connect it to enrichment of inhabitants' experience of the city and articulate roles for the designer. City and cultural spaces have pleasures embedded in the idea of threshold. Through analysis and intervention the designer can tap into those pleasures for the benefit of the
Scaling Relations for Terrestrial Exoplanet Atmospheres from Baroclinic Criticality

The macroturbulent atmospheric circulation of Earth-like planets mediates their equator-to-pole heat transport. For fast-rotating terrestrial planets, baroclinic instabilities in the mid-latitudes lead to turbulent eddies that act to transport heat poleward. In this work, we derive a scaling theory for the equator-to-pole temperature contrast and bulk lapse rate of terrestrial exoplanet atmospheres. This theory is built on the work of Jansen & Ferrari (2013), and determines how unstable the atmosphere is to baroclinic instability (the baroclinic "criticality") through a balance between the baroclinic eddy heat flux and radiative heating/cooling. We compare our scaling theory to General Circulation Model (GCM) simulations and find that the theoretical predictions for equator-to-pole temperature contrast and bulk lapse rate broadly agree with GCM experiments with varying rotation rate and surface pressure throughout the baroclincally unstable regime. Our theoretical results show that baroclinic instabilities are a strong control of heat transport in the atmospheres of Earth-like exoplanets, and our scalings can be used to estimate the equator-to-pole temperature contrast and bulk lapse rate of terrestrial exoplanets. These scalings can be tested by spectroscopic retrievals and full-phase light curves of terrestrial exoplanets with future space telescopes.
Hidden Markov chain identification

An identification scheme is developed for hidden Markov models (HMM). Unlike the realization problem, where one starts from exact probabilities, the identification problem makes a statistical inference from the pathwise output sequences. The basic principle in the identification of Markovian finite state systems from nonnumeric inputs and outputs is its lifting to a numerical representation via the vector valued indicator function. This allows the subsequent use of subspace methods which generate the relevant statistics for identification.
Manifold learning using geodesic entropic graphs

Summary form only given. In the manifold learning problem one seeks to discover a smooth low dimensional surface, i.e., a manifold embedded in a higher dimensional linear vector space, based on a set of measured sample points on the surface. In this paper we consider the closely related problem of estimating the manifold's intrinsic dimension and the intrinsic entropy of the sample points. Specifically, we view the sample points as realizations of an unknown multivariate density supported on an unknown smooth manifold. We present a novel geometrical probability approach, called the geodesic entropic graph (GET) method, to obtaining asymptotically consistent estimates of the manifold dimension and the Renyi /spl alpha/-entropy of the sample density on the manifold. The GET approach is striking in its simplicity and does not require reconstructing the manifold or estimating the multivariate density of the samples. The GET method simply constructs an entropic graph, e.g., a minimal spanning tree (MST) or k-nearest neighbor graph (k-NNG), to estimate the geodesic neighborhoods connecting points on the manifold. The growth rate of the length functional of the entropic graph is then used to simultaneously estimate manifold dimension and sample entropy.
Iterative motion-compensation reconstruction ultra-short TE (iMoCo UTE) for high-resolution free-breathing pulmonary MRI.

Purpose To develop a high-scanning efficiency, motion-corrected imaging strategy for free-breathing pulmonary MRI by combining an iterative motion-compensation reconstruction with a ultrashort echo time (UTE) acquisition called iMoCo UTE. Methods An optimized golden-angle ordering radial UTE sequence was used to continuously acquire data for 5 minutes. All readouts were grouped to different respiratory motion states based on self-navigator signals, and then motion-resolved data was reconstructed by XD golden-angle radial sparse parallel reconstruction. One state from the motion-resolved images was selected as a reference, and then motion fields from the other states to the reference were derived via nonrigid registration. Finally, all motion-resolved data and motion fields were reconstructed by using an iterative motion-compensation (MoCo) reconstruction with a total generalized variation sparse constraint. Results The iMoCo UTE strategy was evaluated in volunteers and nonsedated pediatric patient (4-6 years old) studies. Images reconstructed with iMoCo UTE provided sharper anatomical lung structures and higher apparent SNR and contrast-to-noise ratio compared to using other motion-correction strategies, such as soft-gating, motion-resolved reconstruction, and nonrigid MoCo. iMoCo UTE also showed promising results in an infant study. Conclusion The proposed iMoCo UTE combines self-navigation, motion modeling, and a compressed sensing reconstruction to increase scan efficiency and SNR and to reduce respiratory motion in lung MRI. This proposed strategy shows improvements in free-breathing lung MRI scans, especially in very challenging application situations such as pediatric MRI studies.
Coordination of multiple joints increases bilateral connectivity with ipsilateral sensorimotor cortices.

Abstract Although most activities of daily life require simultaneous coordination of both proximal and distal joints, motor preparation during such movements has not been well studied. Previous results for motor preparation have focused on hand/finger movements. For simple hand/finger movements, results have found that such movements typically evoke activity primarily in the contralateral motor cortices. However, increasing the complexity of the finger movements, such as during a distal sequential finger-pressing task, leads to additional recruitment of ipsilateral resources. It has been suggested that this involvement of the ipsilateral hemisphere is critical for temporal coordination of distal joints. The goal of the current study was to examine whether increasing simultaneous coordination of multiple joints (both proximal and distal) leads to a similar increase in coupling with ipsilateral sensorimotor cortices during motor preparation compared to a simple distal movement such as hand opening. To test this possibility, 12 healthy individuals participated in a high-density EEG experiment in which they performed either hand opening or simultaneous hand opening while lifting at the shoulder on a robotic device. We quantified within- and cross-frequency cortical coupling across the sensorimotor cortex for the two tasks using dynamic causal modeling. Both hand opening and simultaneous hand opening while lifting at the shoulder elicited coupling from secondary motor areas to primary motor cortex within the contralateral hemisphere exclusively in the beta band, as well as from ipsilateral primary motor cortex. However, increasing the task complexity by combining hand opening while lifting at the shoulder also led to an increase in cross-frequency coupling within the ipsilateral hemisphere including theta, beta, and gamma frequencies, as well as a change in the coupling frequency of the interhemispheric coupling between the primary motor and premotor cortices. These findings demonstrate that increasing the demand of joint coordination between proximal and distal joints leads to increases in communication with the ipsilateral hemisphere as previously observed in distal sequential finger tasks.
Asymptotic filtered colimits

Abstract If one has a collection of large scale spaces { ( X s , LSS s ) } s ∈ S with certain compatibility conditions one may define a large scale space on X = ⋃ s ∈ S X s in a way where every function on X is large scale continuous if and only if the function restricted to every X s is large scale continuous. This large scale structure is called the asymptotic filtered colimit of { ( X s , LSS s ) } s ∈ S . In this paper, we explore a wide variety of coarse invariants that are preserved between { ( X s , LSS s ) } s ∈ S and the asymptotic filtered colimit ( X , LSS ) . These invariants include finite asymptotic dimension, exactness, property A, and being coarsely embeddable into a separable Hilbert space. We also put forth some questions and show some examples of filtered colimits that give an insight into how to construct filtered colimits and what may not be preserved as well.
Robustness to capitalization errors in named entity recognition

Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise.Existing methods to improve robustness to the noise completely discard given orthographic information, which significantly degrades their performance on well-formed text. We propose a simple alternative approach based on data augmentation, which allows the model to learn to utilize or ignore orthographic information depending on its usefulness in the context. It achieves competitive robustness to capitalization errors while making negligible compromise to its performance on well-formed text and significantly improving generalization power on noisy user-generated text. Our experiments clearly and consistently validate our claim across different types of machine learning models, languages, and dataset sizes.
Fixed Points and Excedances in Restricted Permutations

Using an unprecedented technique involving diagonals of non-rational generating functions, we prove that among the permutations of length $n$ with $i$ fixed points and $j$ excedances, the number of 321-avoiding ones equals the number of 132-avoiding ones, for any given $i,j$. Our theorem generalizes a result of Robertson, Saracino and Zeilberger. Even though bijective proofs have later been found by the author jointly with Pak and with Deutsch, this paper contains the original analytic proof that was presented at FPSAC 2003.
CLN2INV: Learning Loop Invariants with Continuous Logic Networks

Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs. Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops. In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces. Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT) for loop invariants from program execution traces. We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently. We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset. CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset. Moreover, CLN2INV takes only 1.1 second on average for each problem, which is 40 times faster than existing approaches. We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset.
Survival benefit of radiation in high-risk, early-stage endometrioid carcinoma.

Objective: To better delineate optimal management of high-risk, early-stage endometrial cancer, as national guidelines permit substantial practice variations. Methods: Patients with International Federation of Gynecology and Obstetrics (FIGO) stage IB grade 3 and stage II endometrioid carcinoma who underwent at least total hysterectomy were identified in SEER-Medicare. Adjuvant treatments were brachytherapy (BT), external beam radiation therapy (EBRT), and chemotherapy. Death from endometrial cancer (cancer-specific mortality [CSM]) and local recurrence were analyzed using Gray''s test and Fine-Gray regression. Results: In total, 1,095 patients were identified: 52% received BT, 56% received EBRT, 16% received chemotherapy, and 29% received no adjuvant treatment. Survival outcomes were significantly worse for stage IB grade 3 and stage II grade 3 relative to stage II grades 1-2 (5-year CSM: 18% and 23% vs. 10%; p<0.001 and p=0.003, respectively), while there was no difference between stage IB grade 3 and stage II grade 3 (p=0.618). BT had a local control benefit across all patients (p<0.001) that translated into a survival benefit in stage IB grade 3 (adjusted hazard ratio [HR] for CSM=0.47, p=0.003). EBRT had a survival benefit in stage II grade 3 (adjusted HR for CSM=0.36; p=0.031), as did lymph node dissection (p=0.015). Chemotherapy was not significantly correlated with CSM. Conclusions: High-risk, early-stage endometrioid carcinoma is a heterogeneous population. BT was associated with a survival benefit in stage IB grade 3, whereas regional treatment with EBRT and lymphadenectomy was associated with a survival benefit in stage II grade 3.