cssi/SciSciGPT-SciSciNet huggingface dataset:
Why regularized auto-encoders learn sparse representation?

Sparse distributed representation is the key to learning useful features in deep learning algorithms, because not only it is an efficient mode of data representation, but also - more importantly - it captures the generation process of most real world data. While a number of regularized autoencoders (AE) enforce sparsity explicitly in their learned representation and others don't, there has been little formal analysis on what encourages sparsity in these models in general. Our objective is to formally study this general problem for regularized auto-encoders. We provide sufficient conditions on both regularization and activation functions that encourage sparsity. We show that multiple popular models (de-noising and contractive auto encoders, e.g.) and activations (rectified linear and sigmoid, e.g.) satisfy these conditions; thus, our conditions help explain sparsity in their learned representation. Thus our theoretical and empirical analysis together shed light on the properties of regularization/activation that are conductive to sparsity and unify a number of existing auto-encoder models and activation functions under the same analytical framework.
Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering

We address the problem of Visual Question Answering (VQA), which requires joint image and language understanding to answer a question about a given photograph. Recent approaches have applied deep image captioning methods based on convolutional-recurrent networks to this problem, but have failed to model spatial inference. To remedy this, we propose a model we call the Spatial Memory Network and apply it to the VQA task. Memory networks are recurrent neural networks with an explicit attention mechanism that selects certain parts of the information stored in memory. Our Spatial Memory Network stores neuron activations from different spatial regions of the image in its memory, and uses attention to choose regions relevant for computing the answer. We propose a novel question-guided spatial attention architecture that looks for regions relevant to either individual words or the entire question, repeating the process over multiple recurrent steps, or “hops”. To better understand the inference process learned by the network, we design synthetic questions that specifically require spatial inference and visualize the network’s attention. We evaluate our model on two available visual question answering datasets and obtain improved results.
Linear size distance preservers

The famous shortest path tree lemma states that, for any node s in a graph G = (V, E), there is a subgraph on O(n) edges that preserves all distances between node pairs in the set {s} × V. A very basic question in distance sketching research, with applications to other problems in the field, is to categorize when else graphs admit sparse subgraphs that preserve distances between a set P of p node pairs, where P has some different structure than {s} × V or possibly no guaranteed structure at all. Trivial lower bounds of a path or a clique show that such a subgraph will need Ω(n + p) edges in the worst case. The question is then to determine when these trivial lower bounds are sharp; that is, when do graphs have linear size distance preservers on O(n + p) edges?In this paper, we make the first new progress on this fundamental question in over ten years. We show:1. All G, P has a distance preserver on O(n) edges whenever p = O(n1/3), even if G is directed and/or weighted. These are the first nontrivial preservers of size O(n) known for directed graphs.2. All G, P has a distance preserver on O(p) edges whenever [EQUATION], and G is undirected and unweighted. Here, RS(n) is the Ruzsa-Szemeredi function from combinatoric graph theory. These are the first nontrivial preservers of size O(p) known in any setting.3. To preserve distances within a subset of s nodes in a graph, ω(s2) edges are sometimes needed when [EQUATION] even if G is undirected and unweighted. For weighted graphs, the range of this lower bound improves to s = o(n2/3). This result reflects a polynomial improvement over lower bounds given by Coppersmith and Elkin (SODA '05).
Efficient Nonlinear Transforms for Lossy Image Compression

We assess the performance of two techniques in the context of nonlinear transform coding with artificial neural networks, Sadam and GDN. Both techniques have been success- fully used in state-of-the-art image compression methods, but their performance has not been individually assessed to this point. Together, the techniques stabilize the training procedure of nonlinear image transforms and increase their capacity to approximate the (unknown) rate-distortion optimal transform functions. Besides comparing their performance to established alternatives, we detail the implementation of both methods and provide open-source code along with the paper.
On a uniform bound for the number of exceptional linear subvarieties in the dynamical Mordell-Lang conjecture

Let F : P^n --> P^n be a morphism of degree d > 1 defined over C. The dynamical Mordell--Lang conjecture says that the intersection of an orbit O_F(P) and a subvariety X of P^n is usually finite. We consider the number of linear subvarieties L in P^n such that the intersection of O_F(P)and L is "larger than expected." When F is the d'th-power map and the coordinates of P are multiplicatively independent, we prove that there are only finitely many linear subvarieties that are "super-spanned" by O_F(P), and further that the number of such subvarieties is bounded by a function of n, independent of the point P or the degree d. More generally, we show that there exists a finite subset S, whose cardinality is bounded in terms of n, such that any n+1 points in O_F(P)-S are in linear general position in P^n.
Improving the Stability of the Knockoff Procedure: Multiple Simultaneous Knockoffs and Entropy Maximization

The Model-X knockoff procedure has recently emerged as a powerful approach for feature selection with statistical guarantees. The advantage of knockoffs is that if we have a good model of the features X, then we can identify salient features without knowing anything about how the outcome Y depends on X. An important drawback of knockoffs is its instability: running the procedure twice can result in very different selected features, potentially leading to different conclusions. Addressing this instability is critical for obtaining reproducible and robust results. Here we present a generalization of the knockoff procedure that we call simultaneous multi-knockoffs. We show that multi-knockoffs guarantee false discovery rate (FDR) control, and are substantially more stable and powerful compared to the standard (single) knockoffs. Moreover we propose a new algorithm based on entropy maximization for generating Gaussian multi-knockoffs. We validate the improved stability and power of multi-knockoffs in systematic experiments. We also illustrate how multi-knockoffs can improve the accuracy of detecting genetic mutations that are causally linked to phenotypes.
Gaussian Quadrature for Kernel Features.

Kernel methods have recently attracted resurgent interest, showing performance competitive with deep neural networks in tasks such as speech recognition. The random Fourier features map is a technique commonly used to scale up kernel machines, but employing the randomized feature map means that O(e-2) samples are required to achieve an approximation error of at most e. We investigate some alternative schemes for constructing feature maps that are deterministic, rather than random, by approximating the kernel in the frequency domain using Gaussian quadrature. We show that deterministic feature maps can be constructed, for any γ > 0, to achieve error e with O(eγ + e-1/γ) samples as e goes to 0. Our method works particularly well with sparse ANOVA kernels, which are inspired by the convolutional layer of CNNs. We validate our methods on datasets in different domains, such as MNIST and TIMIT, showing that deterministic features are faster to generate and achieve accuracy comparable to the state-of-the-art kernel methods based on random Fourier features.
False data injection attacks on phasor measurements that bypass low-rank decomposition

This paper studies the vulnerability of phasor measurement units (PMUs) to false data injection (FDI) attacks. Prior work demonstrated that unobservable FDI attacks that can bypass traditional bad data detectors based on measurement residuals can be identified by detector based on low-rank decomposition (LD). In this work, a class of more sophisticated FDI attacks that captures the temporal correlation of PMU data is introduced. Such attacks are designed with a convex optimization problem and can always bypass the LD detector. The vulnerability of this attack model is illustrated on both the IEEE 24-bus RTS and the IEEE 118-bus systems.
Hardware based spatio-temporal neural processing backend for imaging sensors: towards a smart camera

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras.
The Chrial Magnetic Wave and Strong Field Effects in Heavy Ion Collisions

A number of recent progresses in the study of strong field effects in heavy ion collisions are discussed here: 1) the Chiral Magnetic Wave (CMW) and its experimental manifestation via splitting of positive/negative pions' elliptic flow; 2) the event-by-event azimuthal fluctuations of strong EM fields and its correlations with matter geometry; 3) a new mechanism for generating axial current in external electric field, the Chiral Electric Separation Effect (CESE).
MovePattern: Interactive Framework to Provide Scalable Visualization of Movement Patterns

The rapid growth of movement data sources such as GPS traces, traffic networks and social media have provided analysts with the opportunity to explore collective patterns of geographical movements in a nearly real-time fashion. A fast and interactive visualization framework can help analysts to understand these massive and dynamically changing datasets. However, previous studies on movement visualization either ignore the unique properties of geographical movement or are unable to handle today's massive data. In this paper, we develop MovePattern, a novel framework to 1) efficiently construct a concise multi-level view of movements using a scalable and spatially-aware MapReduce-based approach and 2) present a fast and highly interactive web-based environment which engages vector-based visualization to include on-the-fly customization and the ability to enhance analytical functions by storing metadata for both places and movements. We evaluate the framework using the movements of Twitter users captured from geo-tagged tweets. The experiments confirmed that our framework is able to aggregate close to 180 million movements in a few minutes. In addition, we run series of stress tests on the front-end of the framework to ensure that simultaneous user queries do not lead to long latency in the user response.
Quasidiagonal representations of nilpotent groups

We show that every unitary representation of a discrete solvable virtually nilpotent group G is quasidiagonal. Roughly speaking, this says that every unitary representation of G approximately decomposes as a direct sum of finite dimensional approximate representations. In operator algebraic terms we show that C⁎(G) is strongly quasidiagonal.
Not too delayed CSIT achieves the optimal degrees of freedom

Channel state information at the transmitter (CSIT) aids interference management in many communication systems. Due to channel state information (CSI) feedback delay and time-variation in the wireless channel, perfect CSIT is not realistic. In this paper, the CSI feedback delay-DoF gain trade-off is characterized for the multi-user vector broadcast channel. A major insight is that it is possible to achieve the optimal degrees of freedom (DoF) gain if the delay is less than a certain fraction of the channel coherence time. This precisely characterizes the intuition that a small delay should be negligeable. To show this, a new transmission method called space-time interference alignment is proposed, which actively exploits both the current and past CSI.
To Skip or to Switch? Minimizing Age of Information Under Link Capacity Constraint

Consider a scenario where a source continuously monitors an object and sends time-stamped status updates to a destination through a rate-limited link. In order to measure the “freshness” of the status information available at the destination, we adopt the metric called Age of Information (AoI). We assume all updates are of the same size, and arrive randomly at the source according to a Bernoulli process. Due to the link capacity constraint, it takes d(d< 2) time slots for the source to complete the transmission of an update. Therefore, when a new update arrives at the source during the transmission of another update, the source needs to decide whether to skip the new arrival or to switch to it, in order to minimize the expected average AoI at the destination. We prove that within a broadly defined class of online policies, the optimal policy should be a renewal policy, and has a sequential switching property. We then show that the optimal decision of the source in any time slot has a multiple-threshold structure, and only depends on the age of the update being transmitted and the AoI in the system. The thresholds are then numerically identified by formulating the problem as a Markov Decision Process (MDP).
Bi-Lipschitz extension from boundaries of certain hyperbolic spaces

Tukia and Vaisala showed that every quasi-conformal map of \({\mathbb{R}^n}\) extends to a quasi-conformal self-map of \({\mathbb{R}^{n+1}}\). The restriction of the extended map to the upper half-space \({\mathbb{R}^n \times \mathbb{R}_+}\) is, in fact, bi-Lipschitz with respect to the hyperbolic metric. More generally, every simply connected homogeneous negatively curved manifold decomposes as \({M = N \rtimes \mathbb{R}_+}\) where N is a nilpotent group with a metric on which \({\mathbb{R}_+}\) acts by dilations. We show that under some assumptions on N, every quasi-symmetry of N extends to a bi-Lipschitz map of M. The result applies to a wide class of manifolds M including non-compact rank one symmetric spaces and certain manifolds that do not admit co-compact group actions. Although M must be Gromov hyperbolic, its curvature need not be strictly negative.
Embedded self-similar shrinkers of genus $0$

We conrm a well-known conjecture that the round sphere is the only compact, embedded self-similar shrinking solution of mean curvature ow in R 3 with genus 0. More generally, we show that the only properly embedded self-similar shrinkers in R 3 with vanishing intersection form are the sphere, the cylinder, and the plane. This answers two questions posed by T. Ilmanen.
Scalable Greedy Feature Selection via Weak Submodularity.

Greedy algorithms are widely used for problems in machine learning such as feature selection and set function optimization. Unfortunately, for large datasets, the running time of even greedy algorithms can be quite high. This is because for each greedy step we need to refit a model or calculate a function using the previously selected choices and the new candidate. Two algorithms that are faster approximations to the greedy forward selection were introduced recently ([Mirzasoleiman et al. 2013, 2015]). They achieve better performance by exploiting distributed computation and stochastic evaluation respectively. Both algorithms have provable performance guarantees for submodular functions. In this paper we show that divergent from previously held opinion, submodularity is not required to obtain approximation guarantees for these two algorithms. Specifically, we show that a generalized concept of weak submodularity suffices to give multiplicative approximation guarantees. Our result extends the applicability of these algorithms to a larger class of functions. Furthermore, we show that a bounded submodularity ratio can be used to provide data dependent bounds that can sometimes be tighter also for submodular functions. We empirically validate our work by showing superior performance of fast greedy approximations versus several established baselines on artificial and real datasets.
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator

Reinforcement learning (RL) has been successfully used to solve many continuous control tasks. Despite its impressive results however, fundamental questions regarding the sample complexity of RL on continuous problems remain open. We study the performance of RL in this setting by considering the behavior of the Least-Squares Temporal Difference (LSTD) estimator on the classic Linear Quadratic Regulator (LQR) problem from optimal control. We give the first finite-time analysis of the number of samples needed to estimate the value function for a fixed static state-feedback policy to within $\varepsilon$-relative error. In the process of deriving our result, we give a general characterization for when the minimum eigenvalue of the empirical covariance matrix formed along the sample path of a fast-mixing stochastic process concentrates above zero, extending a result by Koltchinskii and Mendelson in the independent covariates setting. Finally, we provide experimental evidence indicating that our analysis correctly captures the qualitative behavior of LSTD on several LQR instances.
The geometry of k-free hyperbolic 3-manifolds

We investigate the geometry of closed, orientable, hyperbolic 3-manifolds whose fundamental groups are k-free for a given integer k ≥ 3. We show that any such manifold M contains a point P with the...
The Hilbert Series of the Ring Associated to an Almost Alternating Matrix

We give an explicit formula for the Hilbert Series of an algebra defined by a linearly presented, standard graded, residual intersection of a grade three Gorenstein ideal.
Variants of normality for Noetherian schemes

This note presents a uniform treatment of normality and three of its variants---topological, weak and seminormality---for Noetherian schemes. The key is to define these notions for pairs $(Z, X)$ consisting of a (not necessarily reduced) scheme $X$ and a closed, nowhere dense subscheme $Z$. An advantage of the new definitions is that, unlike the usual absolute ones, they are preserved by completions. This shortens some of the proofs and leads to more general results. Version 2: small changes.
Unraveling binary evolution from gravitational-wave signals and source statistics

The next generation of ground-based gravitational-wave detectors are likely to observe gravitational waves from the coalescences of compact-objects binaries. We describe the state of the art for predictions of the rate of compact-binary coalescences and report on initial efforts to develop a framework for converting gravitational-wave observations into improved constraints on astrophysical parameters.
POMDP Model Learning for Human Robot Collaboration

Recent years have seen human-robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing work in HRC focused on either low-level human-aware motion planning or HRC interface design, we are particularly interested in a formal design of HRC with respect to high-level complex missions, where it is of critical importance to obtain an accurate and tractable human model. Instead of assuming the human model is given, we ask whether it is reasonable to learn human models from observed data, such as the gesture, eye movements, head motions. We adopt a partially observable Markov decision process (POMDP) model in this work, as mounting evidence has suggested Markovian properties of human behaviors from psychology studies. In addition, POMDP provides a general modeling framework for sequential decision making where states are hidden and actions have stochastic outcomes. Distinct from the majority of POMDP model learning literature, we do not assume that the state, the transition structure or the bound of the number of states are given. Instead, we use a Bayesian non-parametric learning approach to decide the potential human states from data. Then we adopt an approach inspired by probably approximately correct (PAC) learning to obtain not only an estimation of the transition probability but also a confidence interval associated with the estimation. The performance of applying the control policy derived from the estimated model is guaranteed to be sufficiently close to the true model. Finally, data collected from a driver-assistance test-bed are used to illustrate the effectiveness of the proposed learning method.
Orientation sensitive Terahertz resonances observed in protein crystals

A method is presented for measuring anisotropic THz response for small crystals, Crystal Anisotropy Terahertz Microscopy (CATM). Sucrose CATM measurements find the expected anisotropic phonon resonances. CATM measurements of protein crystals find the expected broadband water absorption is suppressed and strong orientation and hydration dependent resonant features.
Weak subconvexity for central values of $L$-functions

We describe a general method to obtain weak subconvexity bounds for many classes of L-functions. We give several examples of our bound, and our work has applications to a conjecture of Rudnick and Sarnak for the mass equidistribution of Hecke eigenforms.
New computational guarantees for solving convex optimization problems with first order methods, via a function growth condition measure

Motivated by recent work of Renegar (A framework for applying subgradient methods to conic optimization problems, arXiv:1503.02611v2 , 2015) we present new computational methods and associated computational guarantees for solving convex optimization problems using first-order methods. Our problem of interest is the general convex optimization problem $$f^* = \min _{x \in Q} f(x)$$ , where we presume knowledge of a strict lower bound $$f_{\mathrm{slb}}< f^*$$ . [Indeed, $$f_{\mathrm{slb}}$$ is naturally known when optimizing many loss functions in statistics and machine learning (least-squares, logistic loss, exponential loss, total variation loss, etc.) as well as in Renegar’s transformed version of the standard conic optimization problem arXiv:1503.02611v2 , 2015; in all these cases one has $$f_{\mathrm{slb}}= 0 < f^*$$ .] We introduce a new functional measure called the growth constant G for $$f(\cdot )$$ , that measures how quickly the level sets of $$f(\cdot )$$ grow relative to the function value, and that plays a fundamental role in the complexity analysis. When $$f(\cdot )$$ is non-smooth, we present new computational guarantees for the Subgradient Descent Method and for smoothing methods, that can improve existing computational guarantees in several ways, most notably when the initial iterate $$x^0$$ is far from the optimal solution set. When $$f(\cdot )$$ is smooth, we present a scheme for periodically restarting the Accelerated Gradient Method that can also improve existing computational guarantees when $$x^0$$ is far from the optimal solution set, and in the presence of added structure we present a scheme using parametrically increased smoothing that further improves the associated computational guarantees.
Positive configurations of flags in a building and limits of positive representations

Parreau compactified the Hitchin component of a closed surface S of negative Euler characteristic in such a way that a boundary point corresponds to the projectivized length spectrum of an action of $$\pi _1(S)$$ on an $${\mathbb {R}}$$ -Euclidean building. In this paper, we use the positivity properties of Hitchin representations introduced by Fock and Goncharov to explicitly describe the geometry of a preferred collection of apartments in the limiting building.
Faster Exact and Approximate Algorithms for k-Cut

In the k-cut problem, we are given an edge-weighted graph G and an integer k, and have to remove a set of edges with minimum total weight so that G has at least k connected components. The current best algorithms are an O(n^(2-o(1))k) randomized algorithm due to Karger and Stein, and an O(n^2k) deterministic algorithm due to Thorup. Moreover, several 2-approximation algorithms are known for the problem (due to Saran and Vazirani, Naor and Rabani, and Ravi and Sinha). It has remained an open problem to (a) improve the runtime of exact algorithms, and (b) to get better approximation algorithms. In this paper we show an O(k^O(k) n^(2Ω/3 + o(1))k)-time algorithm for k-cut. Moreover, we show an (1+e)-approximation algorithm that runs in time O((k/e)^O(k) n^k + O(1)), and a 1.81-approximation in fixed-parameter time O(2^O(k^2) poly(n)).
A generalization of Wantzel's Theorem, m-sectable angles, and the density of certain Chebyshev-polynomial images

Abstract The eponymous theorem of P.L. Wantzel [5] presents a necessary and sufficient criterion for angle trisectability in terms of the third Chebyshev polynomial T 3 , thus making it easy to prove that there exist non-trisectable angles. We generalize this theorem to the case of all Chebyshev polynomials T m (Corollary 1.4.1). We also study the set m-Sect consisting of all cosines of m -sectable angles (see Section 1), showing that, when m is not a power of two, m-Sect contains only algebraic numbers (Theorem 1.1). We then introduce a notion of density based on the diophantine-geometric concept of height of an algebraic number and obtain a result on the density of certain polynomial images. Using this in conjunction with the Generalized Wantzel Theorem, we obtain our main result: for every real algebraic number field K , the set m-Sect ∩ K has density zero in [ − 1 , 1 ] ∩ K when m is not a power of two (Corollary 1.5.1).
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Spatial pyramid pooling module or encode-decoder structure are used in deep neural networks for semantic segmentation task. The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information. In this work, we propose to combine the advantages from both methods. Specifically, our proposed model, DeepLabv3+, extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries. We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. We demonstrate the effectiveness of the proposed model on PASCAL VOC 2012 and Cityscapes datasets, achieving the test set performance of 89% and 82.1% without any post-processing. Our paper is accompanied with a publicly available reference implementation of the proposed models in Tensorflow at https://github.com/tensorflow/models/tree/master/research/deeplab.
3DN: 3D Deformation Network

Applications in virtual and augmented reality create a demand for rapid creation and easy access to large sets of 3D models. An effective way to address this demand is to edit or deform existing 3D models based on a reference, e.g., a 2D image which is very easy to acquire. Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target. Our method infers per-vertex offset displacements while keeping the mesh connectivity of the source model fixed. We present a training strategy which uses a novel differentiable operation, mesh sampling operator, to generalize our method across source and target models with varying mesh densities. Mesh sampling operator can be seamlessly integrated into the network to handle meshes with different topologies. Qualitative and quantitative results show that our method generates higher quality results compared to the state-of-the art learning-based methods for 3D shape generation.
Subsets of Euclidean space with nearly maximal Gowers norms

A set E ⊂ ℝd whose indicator function 1E has maximal Gowers norm, among all sets of equal measure, is an ellipsoid up to Lebesgue null sets. If 1E has nearly maximal Gowers norm then E nearly coincides with some ellipsoid, in the sense that their symmetric difference has small Lebesgue measure.
Breast Density Classification with Deep Convolutional Neural Networks

Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explored the limits of this task with a data set coming from over 200,000 breast cancer screening exams. We used this data to train and evaluate a strong convolutional neural network classifier. In a reader study, we found that our model can perform this task comparably to a human expert.
Nonlinear System Identification using Neural Networks and Trajectory-based Optimization

In this paper, we study the identification of two challenging benchmark problems using neural networks. Two different global optimization approaches are used to train a recurrent neural network to identify two challenging nonlinear models, the cascaded tanks and the Bouc-Wen system. The first approach, quotient gradient system (QGS), uses the trajectories of the nonlinear dynamical system to find the local minima of the optimization problem. The second approach, dynamical trajectory based methodology, uses two different nonlinear dynamical systems to find the connected components of the feasible region and then searches the regions for local minima of the optimization problem. Simulation results show that both approaches effectively identify the model of the cascade tanks and the Bouc-Wen model.
A microfluidic device for label-free isolation of tumor cell clusters from unprocessed blood samples.

Primary cancers disseminate both single circulating tumor cells (CTCs) and CTC “clusters,” the latter of which have been shown to demonstrate greater metastatic propensity and adverse impact on prognosis. Many devices developed to isolate single CTCs also capture CTC clusters, but there is translational potential for a platform specifically designed to isolate CTC clusters. Herein, we introduce our microfluidic device for isolating CTC clusters (“Microfluidic Isolation of CTC Clusters” or MICC), which is equipped with ∼10 000 trap chambers that isolate tumor cell clusters based on their large sizes and dynamic force balance against a pillar obstacle in the trap chamber. Whole blood is injected, followed by a wash step to remove blood cells and a final backflush to release intact clusters for downstream analysis. Using clusters from tumor cell-line and confocal microscopy, we verified the ability of the MICC platform to specifically capture tumor cell clusters in the trap chambers. Our flow rate optimization experiments identified 25 μl/min for blood injection, 100 μl/min as wash flow rate, and 300 μl/min as the release flow rate – indicating that 1 ml of whole blood can be processed in less than an hour. Under these optimal flow conditions, we assessed the MICC platform’s capture and release performance using blood samples spiked with different concentrations of clusters, revealing a capture efficiency of 66%–87% and release efficiency of 76%–90%. The results from our study suggest that the MICC platform has the potential to isolate CTC clusters from cancer patient blood, enabling it for clinical applications in cancer management.
The complex-time Segal–Bargmann transform

Abstract We introduce a new form of the Segal–Bargmann transform for a connected Lie group K of compact type. We show that the heat kernel ( ρ t ( x ) ) t > 0 , x ∈ K has a space-time analytic continuation to a holomorphic function ( ρ C ( τ , z ) ) Re τ > 0 , z ∈ K C , where K C is the complexification of K. The new transform is defined by the integral ( B τ f ) ( z ) = ∫ K ρ C ( τ , z k − 1 ) f ( k ) d k , z ∈ K C . If s > 0 and τ ∈ D ( s , s ) (the disk of radius s centered at s), this integral defines a holomorphic function on K C for each f ∈ L 2 ( K , ρ s ) . We construct a heat kernel density μ s , τ on K C such that, for all s , τ as above, B s , τ : = B τ | L 2 ( K , ρ s ) is an isometric isomorphism from L 2 ( K , ρ s ) onto the space of holomorphic functions in L 2 ( K C , μ s , τ ) . When τ = t = s , the transform B t , t coincides with the one introduced by the second author for compact groups and extended by the first author to groups of compact type. When τ = t ∈ ( 0 , 2 s ) , the transform B s , t coincides with the one introduced by the first two authors.
An Online Pricing Mechanism for Electric Vehicle Parking Assignment and Charge Scheduling

In this paper, we design a pricing framework for online electric vehicle (EV) parking assignment and charge scheduling. Here, users with electric vehicles want to park and charge at electric-vehicle-supply-equipment (EVSEs) at different locations and arrive/depart throughout the day. The goal is to assign and schedule users to the available EVSEs while maximizing user utility and minimizing operational costs. Our formulation can accommodate multiple locations, limited resources, operational costs, as well as variable arrival patterns. With this formulation, the parking facility management can optimize for behind-the-meter solar integration and reduce costs due to procuring electricity from the grid. We use an online pricing mechanism to approximate the EVSE reservation problem's solution and we analyze the performance compared to the offline solution. Our numerical simulation validates the performance of the EVSE reservation system in a downtown area with multiple parking locations equipped with EVSEs.
Self-assembling manifolds in single-cell RNA sequencing data

Single-cell RNA sequencing has spurred the development of computational methods that enable researchers to classify cell types, delineate developmental trajectories, and measure molecular responses to external perturbations. Many of these technologies rely on their ability to detect genes whose cell-to-cell variations arise from the biological processes of interest rather than transcriptional or technical noise. However, for datasets in which the biologically relevant differences between cells are subtle, identifying these genes is challenging. We present the self-assembling manifold (SAM) algorithm, an iterative soft feature selection strategy to quantify gene relevance and improve dimensionality reduction. We demonstrate its advantages over other state-of-the-art methods with experimental validation in identifying novel stem cell populations of Schistosoma mansoni, a prevalent parasite that infects hundreds of millions of people. Extending our analysis to a total of 56 datasets, we show that SAM is generalizable and consistently outperforms other methods in a variety of biological and quantitative benchmarks.
A training sequence based transmit diversity technique for frequency selective fading channels

We propose a training sequence based time-reversed space-time block coding (TR-STBC) system for single carrier transmission through frequency selective fading channels. This system can achieve both multipath gain and diversity gain. It also offers an alternative to estimate the channels through training sequences padded between symbol blocks. The performance of the proposed communication scheme is evaluated theoretically as well as through simulations.
Plasticity of cell migration resulting from mechanochemical coupling

Eukaryotic cells can migrate using different modes, ranging from amoeboid-like, during which actin filled protrusions come and go, to keratocyte-like, characterized by a stable morphology and persistent motion. How cells can switch between these modes is not well understood but waves of signaling events are thought to play an important role in these transitions. Here we present a simple two-component biochemical reaction-diffusion model based on relaxation oscillators and couple this to a model for the mechanics of cell deformations. Different migration modes, including amoeboid-like and keratocyte-like, naturally emerge through transitions determined by interactions between biochemical traveling waves, cell mechanics and morphology. The model predictions are explicitly verified by systematically reducing the protrusive force of the actin network in experiments using Dictyostelium discoideum cells. Our results indicate the importance of coupling signaling events to cell mechanics and morphology and may be applicable in a wide variety of cell motility systems.
Metabolic Modeling Elucidates the Transactions in the Rumen Microbiome and the Shifts Upon Virome Interactions

The complex microbial ecosystem within the bovine rumen plays a crucial role in host nutrition, health, and environmental impact. However, little is known about the interactions between the functional entities within the system, which dictates the community structure and functional dynamics and host physiology. With the advancements in high-throughput sequencing and mathematical modeling, in silico genome-scale metabolic analysis promises to expand our understanding of the metabolic interplay in the community. In an attempt to understand the interactions between microbial species and the phages inside rumen, a genome-scale metabolic modeling approach was utilized by using key members in the rumen microbiome (a bacteroidete, a firmicute, and an archaeon) and the viral phages associated with them. Individual microbial host models were integrated into a community model using multi-level mathematical frameworks. An elaborate and heuristics-based computational procedure was employed to predict previously unknown interactions involving the transfer of fatty acids, vitamins, coenzymes, amino acids, and sugars among the community members. While some of these interactions could be inferred by the available multi-omic datasets, our proposed method provides a systemic understanding of why the interactions occur and how these affect the dynamics in a complex microbial ecosystem. To elucidate the functional role of the virome on the microbiome, local alignment search was used to identify the metabolic functions of the viruses associated with the hosts. The incorporation of these functions demonstrated the role of viral auxiliary metabolic genes in relaxing the metabolic bottlenecks in the microbial hosts and complementing the inter-species interactions. Finally, a comparative statistical analysis of different biologically significant community fitness criteria identified the variation in flux space and robustness of metabolic capacities of the community members. Our elucidation of metabolite exchange among the three members of the rumen microbiome shows how their genomic differences and interactions with the viral strains shape up a highly sophisticated metabolic interplay and explains how such interactions across kingdoms can cause metabolic and compositional shifts in the community and affect the health, nutrition, and pathophysiology of the ruminant animal.
Evaluation of a polyurethane-reinforced hydrogel patch in a rat right ventricle wall replacement model

Congenital heart defects affect about 1% births in the United States. Many of the defects are treated with surgically implanted patches made from inactive materials or fixed pericardium that do not grow with the patients, leading to an increased risk of arrhythmia, sudden cardiac death, and heart failure. This study investigated an angiogenic poly(ethylene glycol) fibrin-based hydrogel reinforced with an electrospun biodegradable poly(ether ester urethane) urea (BPUR) mesh layer that was designed to encourage cell invasion, angiogenesis, and regenerative remodeling in the repair of an artificial defect created onto the rat right ventricle wall. Electrocardiogram signals were analyzed, heart function was measured, and fibrosis, macrophage infiltration, muscularization, vascularization, and defect size were evaluated at 4- and 8-weeks post-surgery. Compared with rats with fixed pericardium patches, rats with BPUR-reinforced hydrogel patches had fewer arrhythmias and greater right ventricular ejection fraction and cardiac output, as well as greater left ventricular ejection fraction, fractional shorting, stroke work and cardiac output. Histology and immunofluorescence staining showed less fibrosis and less patch material remaining in rats with BPUR-reinforced hydrogel patches at 4- and 8-weeks. Rats with BPUR-reinforced hydrogel patches also had a greater volume of granular tissue, a greater volume of muscularized tissue, more blood vessels, and a greater number of leukocytes, pan-macrophages, and M2 macrophages at 8 weeks. Overall, this study demonstrated that the engineered BPUR-reinforced hydrogel patch initiated greater regenerative vascular and muscular remodeling with a limited fibrotic response, resulting in fewer incidences of arrhythmia and improved heart function compared with fixed pericardium patches when applied to heal the defects created on the rat right ventricle wall. STATEMENT OF SIGNIFICANCE: The study tested a polyurethane-reinforced hydrogel patch in a rat right ventricle wall replacement model. Compared with fixed pericardium patches, these reinforced hydrogel patches initiated greater regenerative vascular and muscular remodeling with a reduced fibrotic response, resulting in fewer incidences of arrhythmia and improved heart function at 4- and 8-weeks post surgery. Overall, the new BPUR-reinforced hydrogel patches resulted in better heart function when replacing contractile myocardium than fixed pericardium patches.
Induction of Non-monotonic Logic Programs To Explain Statistical Learning Models.

We present a fast and scalable algorithm to induce non-monotonic logic programs from statistical learning models. We reduce the problem of search for best clauses to instances of the High-Utility Itemset Mining (HUIM) problem. In the HUIM problem, feature values and their importance are treated as transactions and utilities respectively. We make use of TreeExplainer, a fast and scalable implementation of the Explainable AI tool SHAP, to extract locally important features and their weights from ensemble tree models. Our experiments with UCI standard benchmarks suggest a significant improvement in terms of classification evaluation metrics and running time of the training algorithm compared to ALEPH, a state-of-the-art Inductive Logic Programming (ILP) system.
Neurodevelopmental shifts in learned value transfer on cognitive control during adolescence

Abstract Value-associated cues in the environment often enhance subsequent goal-directed behaviors in adults, a phenomenon supported by the integration of motivational and cognitive neural systems. Given that the interactions among these systems change throughout adolescence, we tested when the beneficial effects of value associations on subsequent cognitive control performance emerge during adolescence. Participants (N = 81) aged 13–20 completed a reinforcement learning task with four cue-incentive pairings that could yield high gain, low gain, high loss, or low loss outcomes. Next, participants completed a Go/NoGo task during fMRI where the NoGo targets comprised the previously learned cues, which tested how prior value associations influence cognitive control performance. Improved accuracy for previously learned high gain relative to low gain cues emerged with age. Older adolescents exhibited enhanced recruitment of the dorsal striatum and ventrolateral prefrontal cortex during cognitive control execution to previously learned high gain relative to low gain cues. Older adolescents also expressed increased coupling between the dorsal striatum and dorsolateral prefrontal cortex for high gain cues, whereas younger adolescents expressed increased coupling between the striatum and ventromedial prefrontal cortex. These findings reveal that learned high value cue-incentive associations enhance cognitive control in late adolescence in parallel with value-selective recruitment of corticostriatal systems.
Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference

Homomorphic Encryption (HE) is a cryptosystem which supports computation on encrypted data. Lo pez-Alt et al. (STOC 2012) proposed a generalized notion of HE, called Multi-Key Homomorphic Encryption (MKHE), which is capable of performing arithmetic operations on ciphertexts encrypted under different keys. In this paper, we present multi-key variants of two HE schemes with packed ciphertexts. We present new relinearization algorithms which are simpler and faster than previous method by Chen et al. (TCC 2017). We then generalize the bootstrapping techniques for HE to obtain multi-key fully homomorphic encryption schemes. We provide a proof-of-concept implementation of both MKHE schemes using Microsoft SEAL. For example, when the dimension of base ring is 8192, homomorphic multiplication between multi-key BFV (resp. CKKS) ciphertexts associated with four parties followed by a relinearization takes about 116 (resp. 67) milliseconds. Our MKHE schemes have a wide range of applications in secure computation between multiple data providers. As a benchmark, we homomorphically classify an image using a pre-trained neural network model, where input data and model are encrypted under different keys. Our implementation takes about 1.8 seconds to evaluate one convolutional layer followed by two fully connected layers on an encrypted image from the MNIST dataset.
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media.

Stress is a nigh-universal human experience, particularly in the online world. While stress can be a motivator, too much stress is associated with many negative health outcomes, making its identification useful across a range of domains. However, existing computational research typically only studies stress in domains such as speech, or in short genres such as Twitter. We present Dreaddit, a new text corpus of lengthy multi-domain social media data for the identification of stress. Our dataset consists of 190K posts from five different categories of Reddit communities; we additionally label 3.5K total segments taken from 3K posts using Amazon Mechanical Turk. We present preliminary supervised learning methods for identifying stress, both neural and traditional, and analyze the complexity and diversity of the data and characteristics of each category.